ABMRF:An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu

This study explores the area of Author Profiling(AP)and its importance in several industries,including forensics,security,marketing,and education.A key component of AP is the extraction of useful information from text,with an emphasis on the writers’ages and genders.To improve the accuracy of AP tasks,the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1(ABM1)and Random Forest(RF).The work uses an extensive technique that involves textmessage dataset pretreatment,model training,and assessment.To evaluate the effectiveness of several machine learning(ML)algorithms in classifying age and gender,including Composite Hypercube on Random Projection(CHIRP),Decision Trees(J48),Na飗e Bayes(NB),K Nearest Neighbor,AdaboostM1,NB-Updatable,RF,andABMRF,they are compared.The findings demonstrate thatABMRFregularly beats the competition,with a gender classification accuracy of 71.14%and an age classification accuracy of 54.29%,respectively.Additional metrics like precision,recall,F-measure,Matthews Correlation Coefficient(MCC),and accuracy support ABMRF’s outstanding performance in age and gender profiling tasks.This study demonstrates the usefulness of ABMRF as an ensemble model for author profiling and highlights its possible uses in marketing,law enforcement,and education.The results emphasize the effectiveness of ensemble approaches in enhancing author profiling task accuracy,particularly when it comes to age and gender identification.

A comparative study of polymer nanocomposites containing multi-walled carbon nanotubes and graphene nanoplatelets

Featuring exceptional mechanical and functional performance, MWCNTs and graphene(nano)platelets(GNPs or Gn Ps;each platelet below 10 nm in thickness) have been increasingly used for the development of polymer nanocomposites. Since MWCNTs are now cost-effective at US$30 per kg for industrial applications, this work starts by briefly reviewing the disentanglement and surface modification of MWCNTs as well as the properties of the resulting polymer nanocomposites. GNPs can be made through the thermal treatment of graphite intercalation compounds followed by ultrasonication;GNPs would have lower cost yet higher electrical conductivity over 1,400 S cmthan MWCNTs. Through proper surface modification and compounding techniques, both types of fillers can reinforce or toughen polymers and simultaneously add anti-static performance. A high ratio of MWCNTs to GNPs would increase the synergy for polymers. Green, solvent-free systhesis methods are desired for polymer nanocomposites. Perspectives on the limitations, current challenges and future prospects are provided.

An integrated laboratory experiment of realistic diagenesis,perforation and sand production using a large artificial sandstone specimen

Sand production is,a challenging issue in petroleum industry,mainly associated with weak unconsolidated formations.A novel testing procedure and a new apparatus were developed to conduct an integrated experiment of diagenesis,perforation and sand production on a single large cylindrical artificial sandstone specimen,where solid and fluid pressures can be independently controlled such that realistic reservoir historical conditions can be well simulated in the laboratory.Fluid injection can be performed in both radial and vertical directions,where both single-and two-phase flows can be implemented for study of sand production behaviors at different reservoir’s maturity stages.The equipment consists of an intensive instrumentation system to monitor pressures,displacements and material states continuously.The produced sand particles were filtered and monitored in real-time for the study of time-dependent phenomena.The experimental results showed similar patterns to that observed in the field and provided valuable insight for the development of prediction methods for sand production of similar materials.

Concurrent hetero-/homo-geneous electrocatalysts to bi-phasically mediate sulfur species for lithium-sulfur batteries

Expediting redox kinetics of sulfur species on conductive scaffolds with limited charge accessible surface is considered as an imperative approach to realize energy-dense and power-intensive lithium-sulfur(Li-S)batteries.In this work,the concept of concurrent hetero-/homo-geneous electrocatalysts is proposed to simultaneously mediate liquid-solid conversion of lithium polysulfides(LiPSs)and solid lithium disulfide/sulfide(Li_(2)S_(2)/Li_(2)S)propagation,the latter of which suffers from sluggish reduction kinetics due to buried conductive scaffold surface by extensive deposition of Li_(2)S_(2)/Li_(2)S.The selected model material to verify this concept is a two-in-one catalyst:carbon nanotube(CNT)scaffold supported iron-cobalt(Fe-Co)alloy nanoparticles and partially carbonized selenium(C-Se)component.The Fe-Co alloy serves as a heterogeneous electrocatalyst to seed Li_(2)S_(2)/Li_(2)S through sulphifilic active sites,while the C-Se sustainably releases soluble lithium polyselenides and functions as a homogeneous electrocatalyst to propagate Li_(2)S_(2)/Li_(2)S via solution pathways.Such bi-phasic mediation of the sulfur species benefits reduction kinetics of LiPS conversion,especially for the massive Li_(2)S_(2)/Li_(2)S growth scenario by affording an additional solution directed route in case of conductive surface being largely buried.This strategy endows the Li-S batteries with improved cycling stability(836 mA h g^(-1)after 180 cycles),rate capability(547 mA h g^(-1)at 4 C)and high sulfur loading superiority(2.96 mA h cm^(-2)at 2.4 mg cm^(-2)).This work hopes to enlighten the employment of bi-phasic electrocatalysts to dictate liquid-solid transformation of intermediates for conversion chemistry batteries.

The roles of black phosphorus in performance enhancement of halide perovskite solar cells

Hybrid organic-inorganic perovskite solar cells(PSCs) are considered to be the most promising thirdgeneration photovoltaic(PV) technology with the most rapid rate of increase in the power conversion efficiency(PCE). To date, their PCE values are comparable to the established photovoltaic technologies such as crystalline silicon. Intensive research activities associated with PSCs have been being performed,since 2009, aiming to further boost the device performance in terms of efficiency and stability via different strategies in order to accelerate the progress of commercialization. The emerging 2 D black phosphorus(BP) is a novel class of semiconducting material owing to its unique characteristics, allowing them to become attractive materials for applications in a variety of optical and electronic devices, which have been comprehensively reviewed in the literature. However, comprehensive reviews focusing on the application of BP in PSCs are scarce in the community. This review discusses the research works with the incorporation of BP as a functional material in PSCs. The methodology as well as the effects of employing BP in different regions of PSCs are summarized. Further challenges and potential research directions are also highlighted.

Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

Flue gas analysis for biomass and coal co‑fring in fuidized bed:process simulation and validation

Coal-conversion technologies,although used ubiquitously,are often discredited due to high pollutant emissions,thereby emphasizing a dire need to optimize the combustion process.The co-fring of coal/biomass in a fuidized bed reactor has been an efcient way to optimize the pollutants emission.Herein,a new model has been designed in Aspen Plus®to simultaneously include detailed reaction kinetics,volatile compositions,tar combustion,and hydrodynamics of the reactor.Validation of the process model was done with variations in the fuel including high-sulfur Spanish lignite,high-ash Ekibastuz coal,wood pellets,and locally collected municipal solid waste(MSW)and the temperature ranging from 1073 to 1223 K.The composition of the exhaust gases,namely,CO/CO_(2)/NO/SO_(2)were determined from the model to be within 2%of the experimental observations.Co-combustion of local MSW with Ekibastuz coal had fue gas composition ranging from 1000 to 5000 ppm of CO,16.2%–17.2%of CO_(2),200–550 ppm of NO,and 130–210 ppm of SO_(2).A sensitivity analysis on co-fring of local biomass and Ekibastuz coal demonstrated the optimal operating temperature for fuidized bed reactor at 1148 K with the recommended biomass-to-coal ratio is 1/4,leading to minimum emissions of CO,NO,and SO_(2).

Effect of ambient polycyclic aromatic hydrocarbons and nicotine on the structure of Aβ_(42) protein

Recent studies have correlated the chronic impact of ambient environmental pollutants like polycyclic aromatic hydrocarbons(PAHs)with the progression of neurodegenerative disorders,either by using statistical data from various cities,or via tracking biomarkers during in-vivo experiments.Among different neurodegenerative disorders,PAHs are known to cause increased risk for Alzheimer’s disease,related to the development of amyloid beta(Aβ)peptide oligomers.However,the complex molecular interactions between peptide monomers and organic pollutants remains obscured.In this work,we performed an atomistic molecular dynamics study via GROMACS to investigate the structure of Aβ_(42) peptide monomer in the presence of benzo[a]pyrene,nicotine,and phenanthrene.Interestingly the results revealed strong hydrophobic,and hydrogen-bond based interactions between Aβpeptides and these environmental pollutants that resulted in the formation of stable intermolecular clusters.The strong interactions affected the secondary structure of the Aβ_(42) peptide in the presence of the organic pollutants,with almost 50%decrease in theα-helix and 2%–10%increase in theβ-sheets of the peptide.Overall,the undergoing changes in the secondary structure of the peptide monomer in the presence of the pollutants under the study indicates an enhanced formation of Aβpeptide oligomers,and consequent progression of Alzheimer’s disease.

Characteristics of unsaturated soil slope covered with capillary barrier system and deep-rooted grass under different rainfall patterns Check for updates

Rainfall-induced slope failures commonly occur in residual soil slopes.Slope failures are triggered by the reduction in soil strength.This is attributed to the decrease in soil suction due to rainwater infiltration.Slope covers like capillary barrier system and vegetative cover are effective methods that can be used to prevent rainfall-induced slope failures.The capillary barrier system is able to limit the rainwater infiltration,and vegetation can contribute to the increase in soil strength.Vetiver grass is widely planted in tropical and subtropical areas of the world for soil and water conservation.This study investigates the characteristics of unsaturated soil slope covered with capillary barrier system and Vetiver grass in comparison with the original slope through numerical analyses and field measurements.The analyses were carried out under the advanced,normal,and delayed rainfall patterns.The results of the analyses indicated that the capillary barrier system played a more significant role than the Vetiver grass in maintaining slope stability,although both the capillary barrier system and Vetiver grass contributed to the slope stability.In addition,both numerical analyses and field measurements showed that under the delayed and normal rainfall patterns,when antecedent rainwater infiltration could increase the soil moisture,the capillary barrier system performed much better compared to Vetiver grass in maintaining soil matric suctions and slope stability.

Role of plastic zone porosity and permeability in sand production in weak sandstone reservoirs

Sand production is often characterized as a two-stage process,in which material failure occurs near the cavity,leading to the formation of a plastic zone,from which particles are detached and transported out because of continuous hydrodynamic erosion under the effects of the produced fluid flow.The plastic zone porosity is affected by coupled processes,while the plastic zone permeability has a significant impact on the performance of sand production prediction,especially in weak sandstone reservoirs.Large-scale sand production experiments were conducted using a customized high-pressure consolidation apparatus.The results show that specific stress-fluid pressure conditions may create a plastic zone around a hole,which has lower permeability than the intact zone.The plastic zone comprises two subzones:a high-permeability shear band zone and a low-permeability compaction zone.During sand production,sand migrates from the compaction zone through the shear band zone to the perforation hole.Thus,sand production is associated with the increased permeability and porosity in the compaction zone.Existing sand prediction models were modified according to the new findings,resulting in a modified model with improved performance.The modified model was validated using the sanding data from a weak sandstone reservoir in Kazakhstan.

Nickel embedded porous macrocellular carbon derived from popcorn as sulfur host for high-performance lithium-sulfur batteries

Due to the demands for high performance and ecological and economical alternatives to conventional lithium-ion batteries(Li Bs),the development of lithium-sulfur(Li-S)batteries with remarkably higher theoretical capacity(1675 m A h g-1)has become one of the extensive research focus directions worldwide.However,poor conductivity of sulfur,critical cyclability problems due to shuttle of polysulfides as intermediate products of the cathodic reaction,and large volume variation of the sulfur composite cathode upon operation are the major bottlenecks impeding the implementation of the next-generation Li-S batteries.In this work,a unique three-dimensional(3D)interconnected macrocellular porous carbon(PC)architecture decorated with metal Ni nanoparticles was synthesized by a simple and facile strategy.The as-fabricated Ni/PC composite combines the merits of conducting carbon skeleton and highly adsorptive abilities of Ni,which resulted in efficient trapping of lithium polysulfides(Li PSs)and their fast conversion in the electrochemical process.Owing to these synergistic advantageous features,the composite exhibited good cycling stability(512.3 mA h g^(-1)after 1000 cycles at 1 C with an extremely low capacity fading rate 0.03%per cycle),and superior rate capability(747.5 mA h g^(-1)at 2 C).Accordingly,such Ni nanoparticles embedded in a renewable puffed corn-derived carbon prepared via a simple and effective route represent a promising active type of sulfur host matrix to fabricate high-performance Li-S batteries.

Defect-rich porous tubular graphitic carbon nitride with strong adsorption towards lithium polysulfides for high-performance lithium-sulfur batteries

The commercialization of the lithium-sulfur(Li-S)batteries is severely hampered by the shuttle effect and sluggish kinetics of lithium polysulfides(Li PSs).In this study,porous tubular graphitic carbon nitride(PTCN)was synthesized as the sulfur host by hydrothermal treatment,thermal shock and etching methods.By etching technology,the hollow nanotube tentacles grow on the tube wall of PTCN,the mesoporous appears on the inner wall,and a large number of nitrogen defects are introduced.The verticallyrooted hollow nanotube tentacles on the PTCN surface facilitate electron conduction for sulfur redox reactions.The hollow and porous architecture exposes plentiful active interfaces for accelerated redox conversion of polysulfide.Furthermore,the nitrogen defects in PTCN enable more excellent intrinsic conductivity,higher adsorbability and conversion catalytic activity to Li PSs.Based on the above synergetic effect,the batteries with PTCN/S cathodes realize a high discharge capacity of 504 m Ah g^(-1) at 4 C and a stable cycling behavior over 500 cycles with a low capacity decay of 0.063%per cycle.The results indicate a promising approach todesigning a high performance electrode material for Li-S batteries.

High voltage outdoor insulator surface condition evaluation using aerial insulator images

High voltage insulator detection and monitoring via drone-based aerial images is a cost-effective alternative in extreme winter conditions and complex terrains.The authors examine different surface conditions of the outdoor electrical insulator that generally occur under winter condition using image processing techniques and state-of-the-art classification methods.Two different types of classification approaches are compared:one method is based on neural networks(e.g.CNN,InceptionV3,MobileNet,VGG16,and ResNet50)and the other method is based on traditional machine learning classifiers(e.g.Bayes Net,Decision Tree,Lazy,Rules,and Meta classifiers).They are evaluated to discriminate the images of insulator surface exposed to freezing,wet,and snowing conditions.The results indicate that traditional machine learning methods with proper selection of features can show high classification accuracy.The classification of the insulator surfaces will assist in determining the insulator conditions,and take preventive measures for its protection.