Exploring the Interplay between Job Stress,Work Performance,and Attitudes toward Professional Psychological Help among Employees

Objective:This study explores the interplay between job stress,job-related factors,work performance,and attitudes toward seeking professional psychological help among Vietnamese employees.Methods:A total of 374 employees in Vietnam were surveyed using random sampling and an online questionnaire from November 07 to November 28,2023.Demographic data and self-reported from three scales:The New Job Stress Scale(NJSS),Work Performance(WP),and Attitudes Toward Seeking Professional Psychological Help(ATSPPH_SF)were collected.Results:Significant variations were found across several variables,including forms of work,operating hours,education levels,monthly income,numbers of daily working hours,and the presence of a psychological counseling department within the company.Our analysis has highlighted direct relationships between key latent variables.Employees who were more open to seeking professional help tended to report higher levels of job stress.The negative relationship was found between job stress and attitudes toward seeking professional help.Additionally,work effort was positively associated with work quality.Moderation analyses revealed the influence of co-worker support on role expectation conflict and work effort,role expectation conflict and work-life balance,as well as interactions between role expectation conflict and attitudes needed toward seeking professional help.Mediation analyses showed that work effort mediated relationships between openness to seeking professional help,co-worker support,work-life balance,role expectation conflict,and work quality.Attitudes toward seeking professional help also mediated relationships between work-life balance,job stress,and work quality.Conclusion:The study highlights the complex dynamics surrounding job stress,job-related factors,work performance,and attitudes toward seeking professional psychological help among Vietnamese employees.It highlights the importance of addressing help-seeking barriers,promoting work engagement,and fostering healthy work-life balance for employee well-being and productivity.Further research across diverse contexts and interventions is needed.

Characteristics of Multi-Objective Linear Programming Problem and Multi-Objective Linear Fractional Programming Problem Taking Maximum Value of Multi-Objective Functions

In this paper, a new statistical averaging technique is proposed for finding an optimal solution to a multi-objective linear fractional programming problem (MOLFPP) and multi-objective linear programming problem (MOLPP) by using new arithmetic averaging method and new geometric averaging method. It is significantly noticeable same characteristics among all the technique while taking maximum or minimum among all optimized values for multi-objective functions using simplex algorithm. The characteristics provided from the problems are verified by the numerical examples.

Analysis of Extreme Temperature Variability in Rwanda

The temperature is one of the most important factors in weather and climate forecasting.Studying its behaviour is crucial to understanding climate variability,which could vary spatially and temporally at local,regional,and global scales.Several recent studies on air temperature findings show that the Earth’s near surface air temperature increased between 0.6℃ and 0.8℃ throughout the twentieth century.Using temperature records from ten meteorological stations,this study examined climate variability in Rwanda from the 1930s to 2014.The air temperature data were collected from Meteo Rwanda.Before making the analysis,the authors used software,such as Excel 2007 and INSTAT to control the quality of the raw data.The analysis of maxima and minima indicated that the trends of maximum air temperature were positive and significant at height meteorological stations,whereas the trends for minimum air temperature were found to be at 10 meteorological stations.For all parameters analysed,Kigali Airport meteorological station indicated the higher significance of the trends.The majority of meteorological stations showed an increase in both hot days and nights,confirming Rwanda’s warming over time.The analysis of average seasonal air temperature showed almost similar trends even though not all were significant.This similarity in trends could be attributed to the fact that Rwanda’s short and long dry seasons coincide with rainy seasons.

Spatio-temporal variation of water conservation and its impact factors on the southern slope of Qilian Mountains

The ecology of Qilian Mountains has been seriously threatened by uncontrolled grazing and wasteland reclamation. This study examined the ecological changes on the southern slope of Qilian Mountains in China from the perspective of water conservation by classifying different clusters of water conservation functional areas to efficiently use limited human resources to tackle the water conservation protection problem. In this study, we used Integrate Valuation of Ecosystem Services and Tradeoffs(InVEST) model to estimate water conservation and analyzed the factors that influence the function. The results of this study include:(1) from 2000 to 2015, the water conservation of the southern slope of Qilian Mountains generally showed an increasing trend, and the total water conservation in 2015 increased by 42.18% compared with that in 2000.(2) Rainfall, fractional vegetation cover(FVC), and evapotranspiration have the most significant influence on the water conservation of the study area. Among them, water conservation is positively correlated with rainfall and FVC(P<0.05) and negatively correlated with evapotranspiration(P<0.05).(3) The importance level of water conservation functional areas gradually increases from northwest to southeast, and the region surrounding Menyuan Hui Autonomous County in the southeast of the southern slope of Qilian Mountains is the core water conservation functional area. And(4) the study area was divided into five clusters(Cluster Ⅰ–Cluster Ⅴ) of water conservation, with the areas of Clusters Ⅰ through Ⅴ accounting for 0.58%, 13.74%, 41.23%, 32.43%, and 12.01% of the whole study area, respectively.

Vehicle Detection and Tracking in UAV Imagery via YOLOv3 and Kalman Filter

Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challenges and the large variety of applications.This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it.It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile.The goal of this study is to develop a method that first extracts the region of interest(ROI),then finds and tracks the items of interest.The suggested system is divided into six stages.The photos from the obtained dataset are appropriately georeferenced to their actual locations in the first phase,after which they are all co-registered.The ROI,or road and its objects,are retrieved using the GrabCut method in the second phase.The third phase entails data preparation.The segmented images’noise is eliminated using Gaussian blur,after which the images are changed to grayscale and forwarded to the following stage for additional morphological procedures.The YOLOv3 algorithm is used in the fourth step to find any automobiles in the photos.Following that,the Kalman filter and centroid tracking are used to perform the tracking of the detected cars.The Lucas-Kanade method is then used to perform the trajectory analysis on the vehicles.The suggested model is put to the test and assessed using the Vehicle Aerial Imaging from Drone(VAID)dataset.For detection and tracking,the model was able to attain accuracy levels of 96.7%and 91.6%,respectively.

The role of land as the central piece to sustainable food systems:Lessons learned from Portugal national food-related policies

This paper discusses to what extent land is considered in food-related policies from a systemic perspective.Based on existing literature and international agendas priorities,we argue that access to and preservation of land for food along the food chain play a major role in sustainable food systems.To further explore this argument,we address the following question:Where does land stand in sectorial and national food-related policies?The mul-tiple dimensions and implications of food systems and the international agenda’s priorities namely the“Right to adequate Food”,“Food Sovereignty”and the“Right to Land”,were used as our conceptual framework.To work out how land is being considered in food sectorial policies,both quantitative and qualitative methods were applied to examine ten Portuguese national food-related policies.Results are showing a lack of consideration for land access and land preservation,needed all along the food chain to build a national sustainable food system.On the other hand,there is a gap between current grassroots priorities international agendas and food-related national policies.Such a situation strongly suggests that building a sustainable food system needs a clear food territorial perspective,still neglected,and shifting from sectorial policies towards a more integrated food system approach,that will include land as a central piece.

The BRICS Council for Exercise and Sport Science(BRICSCESS)--A new era has dawned

BRICS is the acronym for an international association of 5 major emerging national economies： Brazil, Russia, India, China, and South Africa.According to the World Economic Forum, the BRICS economies are the most competitive among all cotmtries and are distinguished by their significant regional and global influence. The health and wellness of the people within these countries is a primary agenda item of these governments for connecting their fast economic growth and the need to provide a better quality of life. As of 2015, BRICS countries represent 〉3.6 billion people or one-half of the world population. All 5 countries are members of the top 25 countries of the world by population and 4 of these cotmtries make the top 10 on this list.

Fecal microbiota transplantation in patients with metabolic syndrome and obesity:A randomized controlled trial

BACKGROUND Metabolic syndrome is a multifactorial disease,and the gut microbiota may play a role in its pathogenesis.Obesity,especially abdominal obesity,is associated with insulin resistance,often increasing the risk of type two diabetes mellitus,vascular endothelial dysfunction,an abnormal lipid profile,hypertension,and vascular inflammation,all of which promote the development of atherosclerotic cardiovascular disease.AIM To evaluate the outcomes of fecal microbiota transplantation(FMT)in patients with metabolic syndrome.METHODS This was a randomized,single-blind placebo-controlled trial comparing FMT and a sham procedure in patients with metabolic syndrome.We selected 32 female patients,who were divided into eight groups of four patients each.All of the patients were submitted to upper gastrointestinal endoscopy.In each group,two patients were randomly allocated to undergo FMT,and the other two patients received saline infusion.The patients were followed for one year after the procedures,during which time anthropometric,bioimpedance,and biochemical data were collected.The patients also had periodic consultations with a nutritionist and an endocrinologist.The primary end point was a change in the gut microbiota.RESULTS There was evidence of a postprocedural change in microbiota composition in the patients who underwent FMT in relation to that observed in those who underwent the sham procedure.However,we found no difference between the two groups in terms of the clinical parameters evaluated.CONCLUSION There were no significant differences in biochemical or anthropometric parameters,between the two groups evaluated.Nevertheless,there were significant postprocedural differences in the microbiota composition between the placebo group.To date,clinical outcomes related to FMT remain uncertain.

Integrating stable isotopes and factor analysis to delineate the groundwater provenance and pollution sources in the northwestern part of the Amman-Al Zarqa Basin, Jordan

Globally,groundwater contamination by nitrate is one of the most widespread environmental problems,particularly in arid and semiarid areas,which are characterized by low amounts of rainfall and groundwater recharge.The stable isotope composition of groundwater(δ2H-H2O andδ18O-H2O)and dissolved nitrate(δ15N-NO3–andδ18O-NO3–)and factor analysis(FA)were applied to explore groundwater provenance,pollution,and chemistry evolution in the northwestern part of the Amman-Al Zarqa Basin,Jordan.In this study,we collected 23 samples from the Lower Ajloun aquifer in 2021,including 1 sample from a groundwater well and 22 samples from springs.These samples were tested for electrical conductivity,total dissolved solids,pH,temperature,dissolved oxygen,the concentration of major ions(Ca2+,Mg2+,Na+,K+,HCO3–,Cl–,SO42–,and NO3–),and the stable isotope composition of groundwater and dissolved nitrate.The results revealed that groundwater in the study area is mainly Ca–Mg–HCO3 type and can be classified as fresh water,hard water,and very hard water.The range and average concentration of NO3–were 3.5–230.8 and 50.9 mg/L,respectively.Approximately 33%of the sampling points showed NO3–levels above the maximum allowable concentration of 50.0 mg/L set by the World Health Organization(WHO)guidelines for drinking water quality.The values ofδ18O-H2O andδ2H-H2O showed that groundwater in the study area is part of the current water cycle,originating in the Mediterranean Sea,with significant evaporation,orographic,and amount effects.The values of the stable isotope composition of NO3–corresponded toδ15N-NO3–andδ18O-NO3–values produced by the nitrification process of manure or septic waste and soil NH4+.The FA performed on the hydrochemical parameters and isotope data resulted in three main factors,with Factor 1,Factor 2,and Factor 3,accounting for 50%,21%,and 11%of the total variance,respectively.Factor 1 was considered human-induced factor,named"pollution factor",whereas Factor 2,named"conservative fingerprint factor",and Factor 3,named"hardness factor",were considered natural factors.This study will help local researchers manage groundwater sustainably in the study area and other similar arid and semiarid areas in the world.

New Configurations of the Fuzzy Fractional Differential Boussinesq Model with Application in Ocean Engineering and Their Analysis in Statistical Theory

The fractional-order Boussinesq equations(FBSQe)are investigated in this work to see if they can effectively improve the situation where the shallow water equation cannot directly handle the dispersion wave.The fuzzy forms of analytical FBSQe solutions are first derived using the Adomian decomposition method.It also occurs on the sea floor as opposed to at the functionality.A set of dynamical partial differential equations(PDEs)in this article exemplify an unconfined aquifer flow implication.Thismethodology can accurately simulate climatological intrinsic waves,so the ripples are spread across a large demographic zone.The Aboodh transform merged with the mechanism of Adomian decomposition is implemented to obtain the fuzzified FBSQe in R,R^(n) and(2nth)-order involving generalized Hukuhara differentiability.According to the system parameter,we classify the qualitative features of the Aboodh transform in the fuzzified Caputo and Atangana-Baleanu-Caputo fractional derivative formulations,which are addressed in detail.The illustrations depict a comparison analysis between the both fractional operators under gH-differentiability,as well as the appropriate attributes for the fractional-order and unpredictability factorsσ∈[0,1].A statistical experiment is conducted between the findings of both fractional derivatives to prevent changing the hypothesis after the results are known.Based on the suggested analyses,hydrodynamic technicians,as irrigation or aquifer quality experts,may be capable of obtaining an appropriate storage intensity amount,including an unpredictability threshold.

Spatial niche segregation between bird species in the Białowieża primeval forest(NE Poland)

Drivers of bird niche separation in temperate primeval forests have not yet been determined,due to the low number of intact forest sites.Here,we analyzed the general patterns of niche usage on trees by birds’assemblages.We tested how tree species affect the sharing of space in an assemblage of birds and their level of specialization.We conducted our study in the Białowieża primeval forest(NE Poland).During two breeding seasons,we observed bird positions on trees,divided into three vertical and six horizontal zones in 768 observation points.Based on the interaction networks framework we calculated the Kullback-Leibler distance d′as a metric of species specialization and the resource range RR index,which estimates the species’specificity based on the fraction of space utilized with a non-zero performance.The d′index was the highest in the lower understory for all parts of the tree on the basis of the position from the trunk.The RR index was the highest in the lower understory for all parts based on the position from the trunk.Regarding the tree species,the lowest d′index was observed in all tree species together,and hornbeam,whereas the highest in oak.At the tree species level,the RR index was the lowest for hornbeam.We show tree species importance in forest space sharing in the case of bird assemblages.Hornbeam,alder,and spruce are universal species that were used by birds almost entirely.In contrast,oak,lime,and maple were only partially used.Tree species with less complex structures could shape bird assemblages,as they could offer birds much fewer niches to realize.Therefore,we showed that high specialization and niche separation are drivers of high bird diversity.Consequently maintaining a diverse stand structure is crucial for biodiversity conservation.

An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

Intrusion Detection System(IDS)is a network security mechanism that analyses all users’and applications’traffic and detectsmalicious activities in real-time.The existing IDSmethods suffer fromlower accuracy and lack the required level of security to prevent sophisticated attacks.This problem can result in the system being vulnerable to attacks,which can lead to the loss of sensitive data and potential system failure.Therefore,this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification(LTH-CNN).Here,the Correlation Coefficient based Mayfly Optimization(CC-MA)algorithm is used to extract the input characteristics for the IDS from the input data.Then,the optimized features are utilized by the LTH-CNN,which returns the attacked and non-attacked data.After that,the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security phases.To prevent the system from cyber-attack,the Source and Destination IP address is converted into a complex binary format named 1’s Complement Reverse Shift Right(CRSR),where,in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography(PXORP-ECC)Algorithm to improve the data security.TheNetwork Security Laboratory-Knowledge Discovery inDatabases(NSLKDD)dataset and real-time sensor are used to train and evaluate the proposed LTH-CNN.The suggested model is evaluated based on accuracy,sensitivity,and specificity,which outperformed the existing IDS methods,according to the results of the experiments.

Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of dopamine.Investigators have identified the voice as the underlying symptom of PD.Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection.Machine learning(ML)models have recently helped to solve problems in the classification of chronic diseases.This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system.It includes PD classification models of Random forest,decision Tree,neural network,logistic regression and support vector machine.The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques.Random forest kerb feature selection(RFKFS)selects only 17 features from 754 attributes.The proposed technique uses validation metrics to assess the performance of ML models.The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56%and a precision of 88.02%,a sensitivity of 98.26%,a specificity of 96.06%.The respective validation score has an Non polynomial vector(NPV)of 99.47%,a Geometric Mean(GM)of 97.15%,a Youden’s index(YI)of 94.32%,and a Matthews’s correlation method(MCC)90.84%.The proposed model is also more robust than other models.It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.

Prediction of Uncertainty Estimation and Confidence Calibration Using Fully Convolutional Neural Network

Convolution neural networks(CNNs)have proven to be effective clinical imagingmethods.This study highlighted some of the key issues within these systems.It is difficult to train these systems in a limited clinical image databases,and many publications present strategies including such learning algorithm.Furthermore,these patterns are known formaking a highly reliable prognosis.In addition,normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training.Furthermore,these systems are improperly regulated,resulting in more confident ratings for correct and incorrect classification,which are inaccurate and difficult to understand.This study examines the risk assessment of Fully Convolutional Neural Networks(FCNNs)for clinical image segmentation.Essential contributions have been made to this planned work:1)dice loss and cross-entropy loss are compared on the basis of segment quality and uncertain assessment of FCNNs;2)proposal for a group model for assurance measurement of full convolutional neural networks trained with dice loss and group normalization;And 3)the ability of the measured FCNs to evaluate the segment quality of the structures and to identify test examples outside the distribution.To evaluate the study’s contributions,it conducted a series of tests in three clinical image division applications such as heart,brain and prostate.The findings of the study provide significant insights into the predictive ambiguity assessment and a practical strategies for outside-distribution identification and reliable measurement in the clinical image segmentation.The approaches presented in this research significantly enhance the reliability and accuracy rating of CNNbased clinical imaging methods.

A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D cameras.They enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given scenarios.Depth cues can improve the quality of object detection and recognition.The main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D datasets.The proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model(mWMM).mWMM is challenging to handle when the quality of the image is not good.Hence,the proposed RGB-D-based system uses depth cues for segmentation with the help of mWMM.Then it extracts multiple features from the segmented images.The selected features are fed to the Artificial Neural Network(ANN)and Convolutional Neural Network(CNN)for detecting objects.We achieved 92.13%of mean accuracy over NYUv1 dataset and 90.00%of mean accuracy for the Redweb_v1 dataset.Finally,their results are compared and the proposed model with CNN outperforms other state-of-the-art methods.The proposed architecture can be used in autonomous cars,traffic monitoring,and sports scenes.

Green synthesis,characterization,acaricidal,larvacidal,and repellent activities of copper nanoparticles of Astragalus sinicus against Hyalomma anatolicum

Objective:To green synthesize and characterize copper nanoparticles(Cu NPs)using Astragalus sinicus,as well as evaluate the acaricidal,larvacidal,and repellent activities of Cu NPs against Hyalomma anatolicum(H.anatolicum),one of the most prevalent ticks infesting cattle in Saudi Arabia.Methods:Cu NPs were green synthesized by adding the Astragalus sinicus extract to a copper sulfate solution.The acaricidal,larvicidal,and repellent activities of Cu NPs against H.anatolicum were assessed via the adult immersion test,the larval packet test,and the vertical movement behavior of tick larvae,respectively.The effects of Cu NPs on acetylcholinesterase as well as oxidative enzyme activities were examined.Results:The green synthesized Cu NPs displayed a spherical form with a size range of 15-75 nm.After exposure of adult H.anatolicum to different concentrations of Cu NPs,the viability rate of adult H.anatolicum and the mean number,weight,and hatchability of eggs were noticeably reduced,in comparison to the control group(P<0.001).In addition,the viability rate of larvae considerably declined(P<0.001)with the LC_(50)and LC_(90)values of 11.30 and 20.34μg/m L,respectively.The maximum repellent activity of Cu NPs was observed at 50,100,and 200μg/m L with complete repellent activity after 60,120,and 180 min of exposure,respectively.Cu NPs,mainly at 1/2 LC_(50)and LC_(50)concentrations,markedly suppressed the acetylcholinesterase activity of the larval stage of H.anatolicum(P<0.001).Moreover,Cu NPs,mainly at LC_(50)dose,significantly elevated malondialdehyde level while declining glutathione-S-transferase level in H.anatolicum larvae(P<0.001).Conclusions:Cu NPs show potent acaricidal,larvicidal,and repellent activities against adults and larvae of H.anatolicum.However,further studies must be performed to clarify the precise mechanisms and the efficacy of Cu NPs in practical use.

Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

Pedestrian detection and tracking are vital elements of today’s surveillance systems,which make daily life safe for humans.Thus,human detection and visualization have become essential inventions in the field of computer vision.Hence,developing a surveillance system with multiple object recognition and tracking,especially in low light and night-time,is still challenging.Therefore,we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night.In particular,we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared(IR)images using machine learning and tracking them using particle filters.Moreover,a random forest classifier is adopted for image segmentation to identify pedestrians in an image.The result of detection is investigated by particle filter to solve pedestrian tracking.Through the extensive experiment,our system shows 93%segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes.Moreover,the system achieved a detection accuracy of 90%usingmultiple templatematching techniques and 81%accuracy for pedestrian tracking.Furthermore,our system can identify that the detected object is a human.Hence,our system provided the best results compared to the state-ofart systems,which proves the effectiveness of the techniques used for image segmentation,classification,and tracking.The presented method is applicable for human detection/tracking,crowd analysis,and monitoring pedestrians in IR video surveillance.

Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

Road congestion,air pollution,and accident rates have all increased as a result of rising traffic density andworldwide population growth.Over the past ten years,the total number of automobiles has increased significantly over the world.In this paper,a novel method for intelligent traffic surveillance is presented.The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes.To improve the results,mean-shift clustering was applied to the segmented images.Afterward,the pixels given the label for the vehicle were extracted and blob detection was applied to mark each vehicle.For the validation of each detection,a vehicle verification method based on the structural similarity index is proposed.The tracking of vehicles across the image frames is done using the Identifier(ID)assignment technique and particle filter.Also,vehicle counting in each frame along with trajectory estimation was done for each object.Our proposed system demonstrated a remarkable vehicle detection rate of 0.83 over Vehicle Aerial Imaging from Drone(VAID),0.86 over AU-AIR,and 0.75 over the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)dataset during the experimental evaluation.The proposed system can be used for several purposes,such as vehicle identification in traffic,traffic density estimation at intersections,and traffic congestion sensing on a road.

Investigation of the sodium storage mechanism of iron fluoride hydrate cathodes using X-ray absorption spectroscopy and mossbauer spectroscopy

Elucidation of a reaction mechanism is the most critical aspect for designing electrodes for highperformance secondary batteries.Herein,we investigate the sodium insertion/extraction into an iron fluoride hydrate(FeF_(3)·0.5H_(2)O)electrode for sodium-ion batteries(SIBs).The electrode material is prepared by employing an ionic liquid 1-butyl-3-methylimidazolium-tetrafluoroborate,which serves as a reaction medium and precursor for F^(-)ions.The crystal structure of FeF_(3)·0.5H_(2)O is observed as pyrochlore type with large open 3-D tunnels and a unit cell volume of 1129A^(3).The morphology of FeF_(3)·0.5H_(2)O is spherical shape with a mesoporous structure.The microstructure analysis reveals primary particle size of around 10 nm.The FeF_(3)·0.5H_(2)O cathode exhibits stable discharge capacities of 158,210,and 284 mA h g^(-1) in three different potential ranges of 1.5-4.5,1.2-4.5,and 1.0-4.5 V,respectively at 0.05 C rate.The specific capacities remained stable in over 50 cycles in all three potential ranges,while the rate capability was best in the potential range of 1.5-4.5 V.The electrochemical sodium storage mechanism is studied using X-ray absorption spectroscopy,indicating higher conversion at a more discharged state.Ex-situ M?ssbauer spectroscopy strengthens the results for reversible reduction/oxidation of Fe.These results will be favorable to establish high-performance cathode materials with selective voltage window for SIBs.

Taxonomic Studies of Weed Communities Growing in Date Palm and Christ’s Thorn Jujube Farms in Ad-Dawadimi, KSA

The problem of food shortage is one of the most important problems facing many countries in the world. Various factors affect the decline in crop productivity. Where weeds are the most important reasons that cause a huge loss in crop productivity. Studying agricultural ecosystems, knowing their components and explaining the relationship between all of their components, helps a lot in achieving the highest productivity of different crops in addition to benefiting from some types of weeds, as well as, identifying appropriate methods to control the growth of weeds. In this study, 60 species were listed. The most frequent plant families were Asteraceae, Poaceae and Zygophyllaceae. Annuals were the most common life span, as well as, therophytes were the most frequent life form. The most frequent floristic categories were Saharo-Sindian-Sudano-Zambezian and Saharo-Sindian regions. The most famous indicators of biodiversity (species richness, species evenness and species diversity) have been calculated. For managing and classifying data PC-ORD program (TWINSPAN and DCA analyses) was used.