Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, the...Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.展开更多
The Bleve is an explosion involving both the rapid vaporization of liquid and the rapid expansion of vapor in a vessel.The loss of containment results in a large fireball if the stored chemical is flammable.In order t...The Bleve is an explosion involving both the rapid vaporization of liquid and the rapid expansion of vapor in a vessel.The loss of containment results in a large fireball if the stored chemical is flammable.In order to predict the damage generated by a Bleve,several authors propose analytical or semi-empirical correlations,which consist in predicting the diameter and the lifetime of the fireballs according to the quantity of fuel.These models are based on previous experience,which makes their validity arbitrary in relation to the initial conditions and the nature of the product concerned.The article delves into uncertainty analysis associated with analytical and semi-empirical models of the BLEVE fireball.It could explore how uncertainties in input data,and the choice of a more or less inappropriate model,propagate into the model results.Statistical techniques such as global sensitivity analysis or uncertainty analysis are employed to quantify these uncertainties.In this paper,an attempt is made to evaluate and select reasonable models available in the literature for characterizing fireballs and their consequences.Correlations were analyzed using statistical methods and BLEVE data(experimental and estimated data by correlation)to determine the residual sum of squares(RSS)and average absolute deviation(AAD).Analysis revealed that the Center for Chemical Process Safety(CCPS),the TNO(Netherlands Organization for Applied Scientific Research),and the Gayle model revealed a high degree of satisfaction between the experimental and estimated data through correlation.展开更多
Future constructions in the context of the industrial wastelands reuse may be exposed to Vapor Intrusion(VI).VI can be evaluated by combining in-situ measures and analytical models to evaluate exposure risk in future ...Future constructions in the context of the industrial wastelands reuse may be exposed to Vapor Intrusion(VI).VI can be evaluated by combining in-situ measures and analytical models to evaluate exposure risk in future indoor environments.However,the assumptions in the existing models may reduce their accuracy when they do not meet the characteristics of real situations.Wrong estimations of indoor concentration levels may lead to inappropriate solutions against VI.In this context,new semi-empirical models(SEM)are proposed in order to better specify pollution scenarios and thus increase the accuracy of VI estimations.This development is based on a parametric study(numerical CFD)and a dimensionless analysis combined to existing VI models that consider a continuous source distribution in the soil.These expressions allow to better take into account the source position in the soil(i.e.depth and lateral source/building separation),soil properties(air permeability,diffusion coefficient of the pollutant,…)and building features(building foundation,indoor pressure,air exchange rate,…)in the estimation of indoor concentration levels.The obtained results with the proposed SEM were compared with a numerical CFD model and available experimental data,showing good accuracy in the estimation of VI.Given the advantages of these new models,they can provide better precision in the health risk assessments associated with VI.Furthermore,these expressions can be easily integrated into building ventilation codes allowing to consider air exchange rate and indoor pressure variations over time.展开更多
Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,...Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.展开更多
Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism rem...Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.展开更多
This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models ...This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models for training and forecasting. Model performance is evaluated using MSE, AIC, and BIC. The models are further applied to neonatal mortality data from Saudi Arabia to assess their predictive capabilities. The results indicate that the NNAR model outperforms ARIMA in both training and forecasting.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-spec...Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical r...The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.展开更多
Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein functio...Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.展开更多
Precise and accurate rainfall simulation is essential for Tanzania, where complex topography and diverse climatic influences result in variable precipitation patterns. In this study, the 31st October 2023 to 02nd Nove...Precise and accurate rainfall simulation is essential for Tanzania, where complex topography and diverse climatic influences result in variable precipitation patterns. In this study, the 31st October 2023 to 02nd November 2023 daily observation rainfall was used to assess the performance of 5 land surface models (LSMs) and 7 microphysics schemes (MPs) using the Weather Research and Forecasting (WRF) model. The 35 different simulations were then evaluated using the observation data from the ground stations (OBS) and the gridded satellite (CHIRPS) dataset. It was found that the WSM6 scheme performed better than other MPs even though the performance of the LSMs was dependent on the observation data used. The CLM4 performed better than others when the simulations were compared with OBS whereas the 5 Layer Slab produced the lowest mean absolute error (MAE) and root mean square error (RMSE) values while the Noah-MP and RUC schemes produced the lowest average values of RMSE and MAE respectively when the CHIRPS dataset was used. The difference in performance of land surface models when compared to different sets of observation data was attributed to the fact that each observation dataset had a different number of points over the same area, influencing their performances. Furthermore, it was revealed that the CLM4-WSM6 combination performed better than others in the simulation of this event when it was compared against OBS while the 5 Layer Slab-WSM6 combination performed well when the CHIRPS dataset was used for comparison. This research highlights the critical role of the selection of land surface models and microphysics schemes in forecasting extreme rainfall events and underscores the importance of integrating different observational data for model validation. These findings contribute to improving predictive capabilities for extreme rainfall events in similar climatic regions.展开更多
Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the perform...Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the performance of traditional linear time series models, namely Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, and Moving Average (MA) against neural network architectures. The primary goal is to evaluate the effectiveness of these models in predicting healthcare outcomes using patient records, specifically the Cancerpatient.xlsx dataset, which tracks variables such as patient age, symptoms, genetic risk factors, and environmental exposures over time. The proposed strategy involves training each model on historical patient data to predict age progression and other related health indicators, with performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Our findings reveal that neural networks consistently outperform ARIMA and SARIMA by capturing non-linear patterns and complex temporal dependencies within the dataset, resulting in lower forecasting errors. This research highlights the potential of neural networks to enhance predictive accuracy in healthcare applications, supporting better resource allocation, patient monitoring, and long-term health outcome predictions.展开更多
Frozen shoulder(FS),also known as adhesive capsulitis,is a condition that causes contraction and stiffness of the shoulder joint capsule.The main symptoms are per-sistent shoulder pain and a limited range of motion in...Frozen shoulder(FS),also known as adhesive capsulitis,is a condition that causes contraction and stiffness of the shoulder joint capsule.The main symptoms are per-sistent shoulder pain and a limited range of motion in all directions.These symp-toms and poor prognosis affect people's physical health and quality of life.Currently,the specific mechanisms of FS remain unclear,and there is variability in treatment methods and their efficacy.Additionally,the early symptoms of FS are difficult to distinguish from those of other shoulder diseases,complicating early diagnosis and treatment.Therefore,it is necessary to develop and utilize animal models to under-stand the pathogenesis of FS and to explore treatment strategies,providing insights into the prevention and treatment of human FS.This paper reviews the rat models available for FS research,including external immobilization models,surgical internal immobilization models,injection modeling models,and endocrine modeling models.It introduces the basic procedures for these models and compares and analyzes the advantages,disadvantages,and applicability of each modeling method.Finally,our paper summarizes the common methods for evaluating FS rat models.展开更多
The kinetic characteristics of plasma-assisted oxidative pyrolysis of ammonia are studied by using the global/fluid models hybrid solution method.Firstly,the stable products of plasma-assisted oxidative pyrolysis of a...The kinetic characteristics of plasma-assisted oxidative pyrolysis of ammonia are studied by using the global/fluid models hybrid solution method.Firstly,the stable products of plasma-assisted oxidative pyrolysis of ammonia are measured.The results show that the consumption of NH_(3)/O_(2)and the production of N_(2)/H_(2)change linearly with the increase of voltage,which indicates the decoupling of nonequilibrium molecular excitation and oxidative pyrolysis of ammonia at low temperatures.Secondly,the detailed reaction kinetics mechanism of ammonia oxidative pyrolysis stimulated by a nanosecond pulse voltage at low pressure and room temperature is established.Based on the reaction path analysis,the simplified mechanism is obtained.The detailed and simplified mechanism simulation results are compared with experimental data to verify the accuracy of the simplified mechanism.Finally,based on the simplified mechanism,the fluid model of ammonia oxidative pyrolysis stimulated by the nanosecond pulse plasma is established to study the pre-sheath/sheath behavior and the resultant consumption and formation of key species.The results show that the generation,development,and propagation of the pre-sheath have a great influence on the formation and consumption of species.The consumption of NH_(3)by the cathode pre-sheath is greater than that by the anode pre-sheath,but the opposite is true for OH and O(1S).However,within the sheath,almost all reactions do not occur.Further,by changing the parameters of nanosecond pulse power supply voltage,it is found that the electron number density,electron current density,and applied peak voltages are not the direct reasons for the structural changes of the sheath and pre-sheath.Furthermore,the discharge interval has little effect on the sheath structure and gas mixture breakdown.The research results of this paper not only help to understand the kinetic promotion of non-equilibrium excitation in the process of oxidative pyrolysis but also help to explore the influence of transport and chemical reaction kinetics on the oxidative pyrolysis of ammonia.展开更多
Many autoimmune diseases exhibit an alternating pattern of relapses and remissions in which the apparent self-tolerance phase is interrupted by periodic autoimmune episodes. In this paper, we introduce a class of term...Many autoimmune diseases exhibit an alternating pattern of relapses and remissions in which the apparent self-tolerance phase is interrupted by periodic autoimmune episodes. In this paper, we introduce a class of terminally differentiated effector T cells to an existing model of autoimmune disease and investigate the stability and Hopf branching phenomenon in a model of multiple sclerosis with a saturable functional response. First, we explore the local asymptotic stability of the equilibrium point and propose conditions for the existence of Hopf branching. Finally, with the help of canonical type theory and the central manifold theorem, we analyze the direction of Hopf branching and the stability of branching periodic solutions.展开更多
Osyris lanceolata is heavily and illegally exploited in East Africa for its essential oils, yet little is known about its population status and ecological requirements. This study examined its population structure and...Osyris lanceolata is heavily and illegally exploited in East Africa for its essential oils, yet little is known about its population status and ecological requirements. This study examined its population structure and environmental factors influencing its distribution in the semi-arid Karamoja sub-region, Uganda. We surveyed 388 plots (5 m radius) at different altitudes, recording life stages, stem diameters, and regeneration patterns, and analyzed soil samples. Multivariate analyses, including Canonical Correspondence Analysis (CCA), Detrended Correspondence Analysis (DCA), Non-metric Multidimensional Scaling (NMDS), and Multiple Regression Modeling (MRM), identified key environmental factors affecting its distribution. Findings show that O. lanceolata populations in Moroto, Nakapiripirit, and Amudat districts are severely degraded due to overexploitation. The species is primarily regenerating through coppicing rather than seedlings, with an exploitation intensity of 56.6%. Population densities are low, distribution is irregular, and sustainable harvesting is not viable. Soil properties, particularly Ca2+, N, P, K+, Na+, and organic matter, significantly influence its abundance. Conservation efforts should focus on identifying suitable provenances for genetic preservation and plantation establishment. Areas with at least 9 trees per hectare in Moroto, Nakapiripirit, and Amudat could serve as potential sites for ex-situ plantations. Further research should explore how biotic interactions, genetic diversity, and morphology affect oil yield and quality to support restoration, breeding, and domestication initiatives.展开更多
Multiple autoimmune diseases often exhibit a cyclic pattern of relapse and remission, with significant periods of loss of self-tolerance being interrupted by recurrent autoimmune events. In this article, we explore a ...Multiple autoimmune diseases often exhibit a cyclic pattern of relapse and remission, with significant periods of loss of self-tolerance being interrupted by recurrent autoimmune events. In this article, we explore a specific type of terminally differentiated regulatory T cell HLA−DR+TRegcells, and their application in existing autoimmune disease models. We also conduct an in-depth study on a multiple sclerosis model. This model incorporates a Holling-II type functional response mechanism. The focus of the study is to analyze whether the equilibrium points of the system have local asymptotic stability and determine the conditions for the existence of Hopf bifurcation. Furthermore, the direction of Hopf bifurcations and the stability of its periodic solutions can be analyzed through normal form theory and center manifold theorem.展开更多
The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic developm...The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
文摘Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.
文摘The Bleve is an explosion involving both the rapid vaporization of liquid and the rapid expansion of vapor in a vessel.The loss of containment results in a large fireball if the stored chemical is flammable.In order to predict the damage generated by a Bleve,several authors propose analytical or semi-empirical correlations,which consist in predicting the diameter and the lifetime of the fireballs according to the quantity of fuel.These models are based on previous experience,which makes their validity arbitrary in relation to the initial conditions and the nature of the product concerned.The article delves into uncertainty analysis associated with analytical and semi-empirical models of the BLEVE fireball.It could explore how uncertainties in input data,and the choice of a more or less inappropriate model,propagate into the model results.Statistical techniques such as global sensitivity analysis or uncertainty analysis are employed to quantify these uncertainties.In this paper,an attempt is made to evaluate and select reasonable models available in the literature for characterizing fireballs and their consequences.Correlations were analyzed using statistical methods and BLEVE data(experimental and estimated data by correlation)to determine the residual sum of squares(RSS)and average absolute deviation(AAD).Analysis revealed that the Center for Chemical Process Safety(CCPS),the TNO(Netherlands Organization for Applied Scientific Research),and the Gayle model revealed a high degree of satisfaction between the experimental and estimated data through correlation.
文摘Future constructions in the context of the industrial wastelands reuse may be exposed to Vapor Intrusion(VI).VI can be evaluated by combining in-situ measures and analytical models to evaluate exposure risk in future indoor environments.However,the assumptions in the existing models may reduce their accuracy when they do not meet the characteristics of real situations.Wrong estimations of indoor concentration levels may lead to inappropriate solutions against VI.In this context,new semi-empirical models(SEM)are proposed in order to better specify pollution scenarios and thus increase the accuracy of VI estimations.This development is based on a parametric study(numerical CFD)and a dimensionless analysis combined to existing VI models that consider a continuous source distribution in the soil.These expressions allow to better take into account the source position in the soil(i.e.depth and lateral source/building separation),soil properties(air permeability,diffusion coefficient of the pollutant,…)and building features(building foundation,indoor pressure,air exchange rate,…)in the estimation of indoor concentration levels.The obtained results with the proposed SEM were compared with a numerical CFD model and available experimental data,showing good accuracy in the estimation of VI.Given the advantages of these new models,they can provide better precision in the health risk assessments associated with VI.Furthermore,these expressions can be easily integrated into building ventilation codes allowing to consider air exchange rate and indoor pressure variations over time.
基金supported by the Project of Stable Support for Youth Team in Basic Research Field,CAS(grant No.YSBR-018)the National Natural Science Foundation of China(grant Nos.42188101,42130204)+4 种基金the B-type Strategic Priority Program of CAS(grant no.XDB41000000)the National Natural Science Foundation of China(NSFC)Distinguished Overseas Young Talents Program,Innovation Program for Quantum Science and Technology(2021ZD0300301)the Open Research Project of Large Research Infrastructures of CAS-“Study on the interaction between low/mid-latitude atmosphere and ionosphere based on the Chinese Meridian Project”.The project was supported also by the National Key Laboratory of Deep Space Exploration(Grant No.NKLDSE2023A002)the Open Fund of Anhui Provincial Key Laboratory of Intelligent Underground Detection(Grant No.APKLIUD23KF01)the China National Space Administration(CNSA)pre-research Project on Civil Aerospace Technologies No.D010305,D010301.
文摘Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.
文摘Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.
文摘This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models for training and forecasting. Model performance is evaluated using MSE, AIC, and BIC. The models are further applied to neonatal mortality data from Saudi Arabia to assess their predictive capabilities. The results indicate that the NNAR model outperforms ARIMA in both training and forecasting.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
文摘Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
文摘The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.
基金supported by Warren Alpert Foundation and Houston Methodist Academic Institute Laboratory Operating Fund(to HLC).
文摘Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.
文摘Precise and accurate rainfall simulation is essential for Tanzania, where complex topography and diverse climatic influences result in variable precipitation patterns. In this study, the 31st October 2023 to 02nd November 2023 daily observation rainfall was used to assess the performance of 5 land surface models (LSMs) and 7 microphysics schemes (MPs) using the Weather Research and Forecasting (WRF) model. The 35 different simulations were then evaluated using the observation data from the ground stations (OBS) and the gridded satellite (CHIRPS) dataset. It was found that the WSM6 scheme performed better than other MPs even though the performance of the LSMs was dependent on the observation data used. The CLM4 performed better than others when the simulations were compared with OBS whereas the 5 Layer Slab produced the lowest mean absolute error (MAE) and root mean square error (RMSE) values while the Noah-MP and RUC schemes produced the lowest average values of RMSE and MAE respectively when the CHIRPS dataset was used. The difference in performance of land surface models when compared to different sets of observation data was attributed to the fact that each observation dataset had a different number of points over the same area, influencing their performances. Furthermore, it was revealed that the CLM4-WSM6 combination performed better than others in the simulation of this event when it was compared against OBS while the 5 Layer Slab-WSM6 combination performed well when the CHIRPS dataset was used for comparison. This research highlights the critical role of the selection of land surface models and microphysics schemes in forecasting extreme rainfall events and underscores the importance of integrating different observational data for model validation. These findings contribute to improving predictive capabilities for extreme rainfall events in similar climatic regions.
文摘Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the performance of traditional linear time series models, namely Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, and Moving Average (MA) against neural network architectures. The primary goal is to evaluate the effectiveness of these models in predicting healthcare outcomes using patient records, specifically the Cancerpatient.xlsx dataset, which tracks variables such as patient age, symptoms, genetic risk factors, and environmental exposures over time. The proposed strategy involves training each model on historical patient data to predict age progression and other related health indicators, with performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Our findings reveal that neural networks consistently outperform ARIMA and SARIMA by capturing non-linear patterns and complex temporal dependencies within the dataset, resulting in lower forecasting errors. This research highlights the potential of neural networks to enhance predictive accuracy in healthcare applications, supporting better resource allocation, patient monitoring, and long-term health outcome predictions.
基金National Key R&D Program of China,Grant/Award Number:2021YFC2502100,2023YFC3603404 and 2019YFA0111900The National Natural Science Foundation of China,Grant/Award Number:82072506,82272611 and 92268115+7 种基金The Hunan Provincial Science Fund for Distinguished Young Scholars,Grant/Award Number:2024JJ2089The Hunan Young Talents of Science and Technology,Grant/Award Number:2021RC3025The Provincial Clinical Medical Technology Innovation Project of Hunan,Grant/Award Number:2023SK2024 and 2020SK53709The Provincial Natural Science Foundation of Hunan,Grant/Award Number:2020JJ3060The National Natural Science Foundation of Hunan Province,Grant/Award Number:2023JJ30949The National Clinical Research Center for Geriatric Disorders,Xiangya Hospital,Grant/Award Number:2021KFJJ02 and 2021LNJJ05The Hunan Provincial Innovation Foundation for Postgraduate,Grant/Award Number:CX20230308 and CX20230312The Independent Exploration and Innovation Project for Postgraduate Students of Central South University,Grant/Award Number:2024ZZTS0163。
文摘Frozen shoulder(FS),also known as adhesive capsulitis,is a condition that causes contraction and stiffness of the shoulder joint capsule.The main symptoms are per-sistent shoulder pain and a limited range of motion in all directions.These symp-toms and poor prognosis affect people's physical health and quality of life.Currently,the specific mechanisms of FS remain unclear,and there is variability in treatment methods and their efficacy.Additionally,the early symptoms of FS are difficult to distinguish from those of other shoulder diseases,complicating early diagnosis and treatment.Therefore,it is necessary to develop and utilize animal models to under-stand the pathogenesis of FS and to explore treatment strategies,providing insights into the prevention and treatment of human FS.This paper reviews the rat models available for FS research,including external immobilization models,surgical internal immobilization models,injection modeling models,and endocrine modeling models.It introduces the basic procedures for these models and compares and analyzes the advantages,disadvantages,and applicability of each modeling method.Finally,our paper summarizes the common methods for evaluating FS rat models.
基金Fundamental Research Funds for the Central Universities(M23JBZY00050)National Natural Science Foundation of China(22278032)。
文摘The kinetic characteristics of plasma-assisted oxidative pyrolysis of ammonia are studied by using the global/fluid models hybrid solution method.Firstly,the stable products of plasma-assisted oxidative pyrolysis of ammonia are measured.The results show that the consumption of NH_(3)/O_(2)and the production of N_(2)/H_(2)change linearly with the increase of voltage,which indicates the decoupling of nonequilibrium molecular excitation and oxidative pyrolysis of ammonia at low temperatures.Secondly,the detailed reaction kinetics mechanism of ammonia oxidative pyrolysis stimulated by a nanosecond pulse voltage at low pressure and room temperature is established.Based on the reaction path analysis,the simplified mechanism is obtained.The detailed and simplified mechanism simulation results are compared with experimental data to verify the accuracy of the simplified mechanism.Finally,based on the simplified mechanism,the fluid model of ammonia oxidative pyrolysis stimulated by the nanosecond pulse plasma is established to study the pre-sheath/sheath behavior and the resultant consumption and formation of key species.The results show that the generation,development,and propagation of the pre-sheath have a great influence on the formation and consumption of species.The consumption of NH_(3)by the cathode pre-sheath is greater than that by the anode pre-sheath,but the opposite is true for OH and O(1S).However,within the sheath,almost all reactions do not occur.Further,by changing the parameters of nanosecond pulse power supply voltage,it is found that the electron number density,electron current density,and applied peak voltages are not the direct reasons for the structural changes of the sheath and pre-sheath.Furthermore,the discharge interval has little effect on the sheath structure and gas mixture breakdown.The research results of this paper not only help to understand the kinetic promotion of non-equilibrium excitation in the process of oxidative pyrolysis but also help to explore the influence of transport and chemical reaction kinetics on the oxidative pyrolysis of ammonia.
文摘Many autoimmune diseases exhibit an alternating pattern of relapses and remissions in which the apparent self-tolerance phase is interrupted by periodic autoimmune episodes. In this paper, we introduce a class of terminally differentiated effector T cells to an existing model of autoimmune disease and investigate the stability and Hopf branching phenomenon in a model of multiple sclerosis with a saturable functional response. First, we explore the local asymptotic stability of the equilibrium point and propose conditions for the existence of Hopf branching. Finally, with the help of canonical type theory and the central manifold theorem, we analyze the direction of Hopf branching and the stability of branching periodic solutions.
文摘Osyris lanceolata is heavily and illegally exploited in East Africa for its essential oils, yet little is known about its population status and ecological requirements. This study examined its population structure and environmental factors influencing its distribution in the semi-arid Karamoja sub-region, Uganda. We surveyed 388 plots (5 m radius) at different altitudes, recording life stages, stem diameters, and regeneration patterns, and analyzed soil samples. Multivariate analyses, including Canonical Correspondence Analysis (CCA), Detrended Correspondence Analysis (DCA), Non-metric Multidimensional Scaling (NMDS), and Multiple Regression Modeling (MRM), identified key environmental factors affecting its distribution. Findings show that O. lanceolata populations in Moroto, Nakapiripirit, and Amudat districts are severely degraded due to overexploitation. The species is primarily regenerating through coppicing rather than seedlings, with an exploitation intensity of 56.6%. Population densities are low, distribution is irregular, and sustainable harvesting is not viable. Soil properties, particularly Ca2+, N, P, K+, Na+, and organic matter, significantly influence its abundance. Conservation efforts should focus on identifying suitable provenances for genetic preservation and plantation establishment. Areas with at least 9 trees per hectare in Moroto, Nakapiripirit, and Amudat could serve as potential sites for ex-situ plantations. Further research should explore how biotic interactions, genetic diversity, and morphology affect oil yield and quality to support restoration, breeding, and domestication initiatives.
文摘Multiple autoimmune diseases often exhibit a cyclic pattern of relapse and remission, with significant periods of loss of self-tolerance being interrupted by recurrent autoimmune events. In this article, we explore a specific type of terminally differentiated regulatory T cell HLA−DR+TRegcells, and their application in existing autoimmune disease models. We also conduct an in-depth study on a multiple sclerosis model. This model incorporates a Holling-II type functional response mechanism. The focus of the study is to analyze whether the equilibrium points of the system have local asymptotic stability and determine the conditions for the existence of Hopf bifurcation. Furthermore, the direction of Hopf bifurcations and the stability of its periodic solutions can be analyzed through normal form theory and center manifold theorem.
文摘The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
