Current and further trajectories in designing functional materials for solid oxide electrochemical cells:A review of other reviews

Complex oxides are an important class of materials with enormous potential for electrochemical appli-cations.Depending on their composition and structure,such complex oxides can exhibit either a single conductivity(oxygen-ionic or protonic,or n-type,or p-type electronic)or a combination thereof gener-ating distinct dual-conducting or even triple-conducting materials.These properties enable their use as diverse functional materials for solid oxide fuel cells,solid oxide electrolysis cells,permeable membranes,and gas sensors.The literature review shows that the field of solid oxide materials and related electro-chemical cells has a significant level of research engagement,with over 8,000 publications published since 2020.The manual analysis of such a large volume of material is challenging.However,by examining the review articles,it is possible to identify key patterns,recent achievements,prospects,and remaining obstacles.To perform such an analysis,the present article provides,for the first time,a comprehensive summary of previous review publications that have been published since 2020,with a special focus on solid oxide materials and electrochemical systems.Thus,this study provides an important reference for researchers specializing in the fields of solid state ionics,high-temperature electrochemistry,and energyconversiontechnologies.

Contribution to Development of Reliability and Optimization Methods Applied to Mechanical Structures

In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is called the Probabilistic Transformation Method (PTM). This method is readily applicable when the function between the input and the output of the system is explicit. However, the situation is much more involved when it is necessary to perform the evaluation of implicit function between the input and the output of the system through numerical models. In this work, we propose a technique that combines Finite Element Analysis (FEA) and Probabilistic Transformation Method (PTM) to evaluate the Probability Density Function (PDF) of response where the function between the input and the output of the system is implicit. This technique is based on the numerical simulations of the Finite Element Analysis (FEA) and the Probabilistic Transformation Method (PTM) using an interface between Finite Element software and Matlab. Some problems of structures are treated in order to prove the applicability of the proposed technique. Moreover, the obtained results are compared to those obtained by the reference method of Monte Carlo. A second aim of this work is to develop an algorithm of global optimization using the local method SQP, because of its effectiveness and its rapidity of convergence. For this reason, we have combined the method SQP with the Multi start method. This developed algorithm is tested on test functions comparing with other methods such as the method of Particle Swarm Optimization (PSO). In order to test the applicability of the proposed approach, a structure is optimized under reliability constraints.

Spatial patterns of the Brusselator model with asymmetric Lévy diffusion

The formation of spatial patterns is an important issue in reaction–diffusion systems.Previous studies have mainly focused on the spatial patterns in reaction–diffusion models equipped with symmetric diffusion(such as normal or fractional Laplace diffusion),namely,assuming that spatial environments of the systems are homogeneous.However,the complexity and heterogeneity of spatial environments of biochemical reactions in vivo can lead to asymmetric diffusion of reactants.Naturally,there arises an open question of how the asymmetric diffusion affects dynamical behaviors of biochemical reaction systems.To answer this,we build a general asymmetric L´evy diffusion model based on the theory of a continuous time random walk.In addition,we investigate the two-species Brusselator model with asymmetric L´evy diffusion,and obtain a general condition for the formation of Turing and wave patterns.More interestingly,we find that even though the Brusselator model with symmetric diffusion cannot produce steady spatial patterns for some parameters,the asymmetry of L´evy diffusion for this model can produce wave patterns.This is different from the previous result that wave instability requires at least a three-species model.In addition,the asymmetry of L´evy diffusion can significantly affect the amplitude and frequency of the spatial patterns.Our results enrich our knowledge of the mechanisms of pattern formation.

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.

Classification and Comprehension of Software Requirements Using Ensemble Learning

The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

Advancing Al-doped ZnO thin films structural,optical and electrical properties of low temperature PET substrates via flash lamp annealing

In this study, aluminum-doped zinc oxide(AZO) thin films were deposited onto a low-temperature polyethylene terephthalate(PET) substrate using DC magnetron sputtering. Deposition parameters included power range of 100-300 W, a working pressure of 15 mTorr, and a substrate temperature of 50 ℃. Post-deposition, flash lamp annealing(FLA) was employed as a rapid thermal processing method with a pulse duration of 1.7 ms and energy density of 7 J·cm-2, aimed at enhancing the film's quality while preserving the temperature-sensitive PET substrate. FLA offers advantages over conventional annealing,including shorter processing times and improved material properties. The structural, optical, and electrical characteristics of the AZO films were assessed using X-ray diffraction, field emission scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy, ultraviolet-visible spectroscopy, and Hall effect measurements. The results demonstrated that properties of AZO films varied with deposition and annealing conditions. Films deposited at 200 W and subjected to FLA exhibited superior crystallinity, with average visible light transmittance exceeding 80% and resistivity as low as 0.38 Ω·cm representing 95%improvement in transmittance. Electrical analysis revealed that carrier concentration, mobility, and resistivity were influenced by both sputtering and annealing parameters. These findings underscore the effectiveness of FLA in optimizing AZO thin film properties, highlighting potential in optoelectronics applications.

Factors Contributing to Contamination of Street Foods in Bamako, Mali

The World Health Organization states that foodborne diseases are a worldwide public health issue. Although street foods can provide nutritious and affordable ready-to-eat meals for city dwellers, their health risks can outweigh the benefits. A cross-sectional study was conducted in the Bamako district, focusing on street food vendors near schools, universities, extensive markets, administrative centers, and major roads. We aimed to sample fifty (50) sellers per municipality, making 300 sellers for the Bamako district. We developed a survey sheet to collect data, and six teams rotated between the municipalities each month. Before starting the collection, the teams were provided administrative papers approved by the municipal authority. The survey revealed three types of sales sites: fixed (65%), semi-fixed (30%), and mobile (4.40%). The proportion of sellers was 26.8%, 23.2%, 19.7%, and 4.2% in municipalities III, IV, and I. In municipalities I, II, III, IV, and VI, respectively, 92%, 95.70%, 93%, 87.2%, and 100% of the sellers were female. The age distribution of sellers was 65.63%, 46.81%, 40.82%, 38.30%, 36.17%, 36%, and 32% in the 25-34 and 35 - 44 age groups. Illiteracy rates were 59.20%, 61.70%, 55.30%, 75%, and 56% in municipalities I, II, III, IV, and VI, respectively. The study identified two categories of sellers: 48.3% in type 1 and 51.7% in type 2. The first category comprised 154 sellers, and the second 165 sellers. The survey found that 66.00%, 56.00%, 48.90%, 44.90%, 38.30%, and 34.40% of municipal V, VI, III, I, II, and IV sales sites were open-air. In municipality I, 63.30% of the sites were under hangars, while in municipalities II and IV, the corresponding percentages were 51.10% and 59.40%, respectively. Moreover, 46.00%, 31.90%, 31.30%, 30.60%, and 27.70% of the sites in municipalities VI, II, IV, I, and III were located next to gutters. In conclusion, this study identified several factors that could compromise the quality of street foods sold in the six municipalities of Bamako.

Phytochemical analysis of Tinospora cordifolia and Withania somnifera and their therapeutic activities with special reference to COVID-19

Various important medicines make use of secondary metabolites that are produced by plants.Medicinal plants,such as Withania somnifera and Tinospora cordifolia,are rich sources of chemically active compounds and are reported to have numerous therapeutic applications.The therapeutic use of medicinal plants is widely mentioned in Ayurveda and has folkloric importance in different parts of the world.The aim of this review is to summarize the phytochemical profiles,folkloric importance,and primary pharmacological activity of W.somnifera and T.cordifolia with emphasis on their action against the novel coronavirus.

Physicochemical Analysis of Locally Made Yoghurt (Kossam) Commercialised in the City of Douala-Cameroon

Yogurt is a traditional dairy product well known in all the regions of the world. In Cameroon, the most popularly known type is “kossam” also called curdled milk. Kossam is a set of milk based beverage from northern Cameroon presenting great symbolic, economic and social values for local population [1]. 150 Kossam samples were collected from neighborhoods of PK8, Bonamoussadi, Nyalla, cite des palmier, Deido and Bedi community and later on reconstituted into 50 different samples of 350 mL, each containing 1/3 of 3 individual samples. They were analyzed for their physiochemical properties such as: PH, titratable acidity, density, brix and dry matter using most at times the standard Association of Official Analytical Chemists (AOAC) methods with slight modifications and results compared to a licensed brand sold in the Cameroonian market. The results of the study showed that, the physico-chemical properties of the locally made yogurts were different within the different samples. Analysis of variance revealed a significant difference in the levels of the parameters analyzed in the different yogurt samples (p −1 Kg/L), Brix (8˚ - 24˚B), Dornic (23˚ - 160˚D). others contents per 100 g fresh matter are as follows: dry matter (average mean of 16.54%). Hence, the significant variations in the physico-chemical properties of kossam are a call for concern since as it impacts on the health of the population consuming this product.

Assessment of Awareness and Understanding of Hemolytic Disease of the Fetus and Newborn in the Beninese Population

Background: Hemolytic Disease of the Fetus and Newborn (HDFN) arises from blood group incompatibility, especially the RhD antigen. In Benin, systematic ABO RhD blood grouping is poorly understood by many midwives and nurses. Nearly one in ten women risk having children with HDFN. This study aimed to determine the level of knowledge of the Beninese population on HDFN. Methods: Data were collected from June 2023 to March 2024. Participants completed a Kobotoolbox questionnaire on WhatsApp, with in-person assistance for illiterate participants. The study involved 521 participants from across Benin. Data were analyzed using SigmaPlot version 14.0. Results: Among the 521 participants, 298 were women (57.20%) aged 18 to 77 years. The majority (40.69%) were aged 26 - 35. Over a third (35.51%) did not know their RhD blood group. Most (59.12%) were unaware of the risks for RhD discordant couples. Among those with a partner, 25.16% were in at-risk couples for HDFN, and over half (59.12%) were unaware of this risk. There was no significant association between being in a high-risk union and knowledge of the risk or education level. Conclusion: Only 40.88% of the Beninese population are aware of HDFN, indicating a low level of knowledge.

A Study on the Challenges of Human-Centric Cyber-Security and the Guarantee of Information Quality

Information security and quality management are often considered two different fields. However, organizations must be mindful of how software security may affect quality control. This paper examines and promotes methods through which secure software development processes can be integrated into the Systems Software Development Life-cycle (SDLC) to improve system quality. Cyber-security and quality assurance are both involved in reducing risk. Software security teams work to reduce security risks, whereas quality assurance teams work to decrease risks to quality. There is a need for clear standards, frameworks, processes, and procedures to be followed by organizations to ensure high-level quality while reducing security risks. This research uses a survey of industry professionals to help identify best practices for developing software with fewer defects from the early stages of the SDLC to improve both the quality and security of software. Results show that there is a need for better security awareness among all members of software development teams.

Cytotoxic and antibacterial substances against multi-drug resistant pathogens from marine sponge symbiont:Citrinin,a secondary metabolite of Penicillium sp.

Objective:To Isolate,purify,characterize,and evaluate the bioaclive compounds from the sponge-derived fungus Penicillium sp.FF001 and to elucidate its structure.Methods:The fungal strain FF001 with an interesting bioactivity profile was isolated from a marine Fijian sponge Melophlus sp.Based on conidiophores aggregation,conidia development and mycelia morphological characteristics,the isolate FF001 was classically identified as a Penicillium sp.The bioactive compound was identified using various spectral analysis of UV,high resolution electrospray ionization mass spectra,1H and 13C NMR spectral data.Further minimum inhibitory concentrations(MICs)assay and brine shrimp cytotoxicity assay were also carried out to evaluate the biological properties of the purified compound.Results:Bioassay guided fractionation of the EtOAc extract of a static culture of this Penicillium sp.by different chromatographic methods led the isolation of an antibacterial,anticryptococcal and cytotoxic active compound,which was identified as citrinin(1).Further,citrinin(1)is reported for its potent antibacterial activity against methicillin-resistant Staphylococcus aureus(S.aureus),rifampicin-resistant 5.aureus,wild type S.aureus and vancomycin-resistant Enterococcus faecium showed MICs of 3.90,0.97,1.95 and7.81μg/mL,respectively.Further citrinin(1)displayed significant activity against the pathogenic yeast Cryptococcus neoformans(MIC 3.90μg/mL),and exhibited cytotoxicity against brine shrimp larvae LD_(50)of 96μg/mL.Conclusions:Citrinin(1)is reported from sponge associated Penicillium sp.from this study and for its strong antibacterial activity against multi-drug resistant human pathogens including cytotoxicity against brine shrimp larvae,which indicated that sponge associated Penicillium spp.are promising sources of natural bioactive metabolites.

Effects of oil contamination and bioremediation on geotechnical properties of highly plastic clayey soil

Leakage of oil and its derivatives into the soil can change the engineering behavior of soil as well as cause environmental disasters.Also,recovering the contaminated sites into their natural condition and making contaminated materials as both environmentally and geotechnically suitable construction materials need the employment of remediation techniques.Bioremediation,as an efficient,low cost and environmentalfriendly approach,was used in the case of highly plastic clayey soils.To better understand the change in geotechnical properties of highly plastic fine-grained soil due to crude oil contamination and bioremediation,Atterberg limits,compaction,unconfined compression,direct shear,and consolidation tests were conducted on natural,contaminated,and bioremediated soil samples to investigate the effects of contamination and remediation on fine-grained soil properties.Oil contamination reduced maximum dry density(MDD),optimum moisture content(OMC),unconfined compressive strength(UCS),shear strength,swelling pressure,and coefficient of consolidation of soil.In addition,contamination increased the compression and swelling indices and compressibility of soil.Bioremediation reduced soil contamination by about 50%.Moreover,in comparison with contaminated soil,bioremediation reduced the MDD,UCS,swelling index,free swelling and swelling pressure of soil,and also increased OMC,shear strength,cohesion,internal friction angle,failure strain,porosity,compression index,and settlement.Microstructural analyses showed that oil contamination does not alter the soil structure in terms of chemical compounds,elements,and constituent minerals.While it decreased the specific surface area of the soil,and the bioremediation significantly increased the mentioned parameters.Bioremediation resulted in the formation of quasi-fibrous textures and porous and agglomerated structures.As a result,oil contamination affected the mechanical properties of soil negatively,but bioremediation improved these properties.

Effectiveness of sloping agricultural land technology on soil fertility status of mid-hills in Nepal

Hedgerows with intercropping systems were established at the ICIMOD test and demonstration site at Godawari to assess the effective- ness of Sloping Agricultural Land Technology （SALT） in reducing run- off water volume, controlling soil loss, increasing crop production, and improving soil fertility in the mid-hills of Nepal. Runoff water volume （1996-2002）, soil loss （1996-2002） and maize yield （1995-2001）, and soil fertility-related parameters were assessed on SALT models with three factors： the type of nitrogen-fixing plant, the farmers＇ practice, and fertilizer use. Results showed a significant effect of Alnus nepalensis and/or Indigofera dosua on runoff water volume, soil loss, crop produc- tion, soil water retention, and soil nutrients （NPK）. Farmers＇ practice and fertilization did not play a significant role in reducing runoff water and soil loss. However, farmers＇ practice significantly increased crop produc- tion. Therefore, integrating soil conservation approaches on SALT sys- tems enhances stable economic output to hills and mountain farmers.

<i>In-Vitro</i>Fermentation by Human Fecal Bacteria and Bile Salts Binding Capacity of Physical Modified Defatted Rice Bran Dietary Fiber

Defatted rice bran dietary fiber (DRBDF) was modified by micronization, ultrasound, microwave and extrusion cooking. We investigated the impacts of these physical treatments on the fermentation ability and bile salts binding capacity of DRBDF. In-vitro fermentation by human fecal bacteria of modified fibers showed that the major fermentation products were propionic, acetate and butyrate acid. Fermentation of extruded fiber gave the highest amounts of propionic and acetic acid 135.76 and 25.45 mmol/L respectively, while, the fermented product with microwaved fiber had the highest butyric acid content (10.75 mmol/L). The amount of short-chain fatty acid increased from 12 h to 24 h and propionic acid was the predominant. On the other hand,in-vitrobile salts binding showed that extruded fiber had higher affinity with sodium deoxycholate and sodium chenodeoxycholate (66.14% and 30.25% respectively) while microwaved fiber exhibited the highest affinity with sodium taurocholate (14.38%). In the light of obtained results we can affirmed that these physical treatments significantly improved the fermentation products and bile salts binding capacity of DRBDF. Extrusion compared to the other physical treatment methods used in this study has greatly and positively influenced the fermentation and bile binding capacity of DRBDF.

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours.The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work.The non-active cells in brain region are known to be benign and they will never cause the death of the patient.These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels.The Magnetic Resonance(MR)image contrast is improved by the cost map construction technique.The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method.This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features.Using k-mean clustering algorithm the tumor regions in glioma are classified.The proposed algorithm provides high sensitivity,specificity and tumor segmentation accuracy.

Severity Recognition of Aloe vera Diseases Using AI in Tensor Flow Domain

Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agriculturalists spend a huge amount of money.However,the manual detection method of plant diseases is complicated and time-consuming.Consequently,automated systems for plant disease detection using machine learning(ML)approaches are proposed.However,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data.To address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural networks.In the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained architectures.It is capable to obtain the feature extraction of the given data with fine-tuning details.After convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural Network.The validation of the proposed method is carried out on a self-collected dataset generated through the augmentation step.The achieved average accuracy of our method is more than 96%and outperforms the recent techniques.

Absolute Stability of Nonlinear Systems with Two Additive Time-varying Delay Components

In this paper, we present a new sufficient condition for absolute stability of Lure system with two additive time-varying delay components. This criterion is expressed as a set of linear matrix inequalities （LMIs）, which can be readily tested by using standard numerical software. We use this new criterion to stabilize a class of nonlinear time-delay systems. Some numerical examples are given to illustrate the applicability of the results using standard numerical software.

Tensile Properties of Mechanically-Defibrated Cellulose Nanofiber-Reinforced Polylactic Acid Matrix Composites Fabricated by Fused Deposition Modeling

Biodegradable polymers are highly attractive as potential alternatives to petroleum-based polymers in an attempt to achieve carbon neutrality whilst maintaining the mechanical properties of the structures.Among these polymers,polylactic acid(PLA)is particularly promising due to its good mechanical properties,biocompatibility and thermoplasticity.In this work,we aim to enhance the mechanical properties of PLA using mechanically-defibrated cellulose nanofibers(CNFs)that exhibit remarkable mechanical properties and biodegradability.We also employ fused deposition modeling(FDM),one of the three-dimensional printing methods for thermoplastic polymers,for the low-cost fabrication of the products.Mechanically-defibrated CNF-reinforced PLA matrix composites are fabricated by FDM.Their tensile properties are investigated in two printing directions(0°/90°and+45°/-45°).The discussion about the relationship between printing direction and tensile behavoir of mechanically-defibrated CNF-reinforced PLA matrix composite is the unique point of this study.We further discuss the microstructure and fracture surface of mechanically-defibrated CNF-reinforced PLA matrix composite by scanning electron microscope.

Field survey of a sustainable sanitation system in a residential house

Sustainable sanitation is an approach for more ecological and sustainable water resources management. In this paper, we proposed one of the new integrated waste treatment systems： an ＂sustainable sanitation system＂ that includes separation of the black water from water system by a non-flushing toilet （bio-toilet）, and a gray water treatment based on a biological and ecological concept. Sustainable sanitation system also converts the domestic waste to soil conditioners and fertilizers, for farmland use. As one of the case studies, Environmentally Symbiotic Housing in which people actually live using the bio-toilet for the black water treatment and the household wastewater treatment facility for the gray water was introduced. The availability of this system was investigated by analyzing the sawdust used in the bio-toilet and the quality of the effluent in the household wastewater treatment facility. As the result, the water content of the sawdust did not exceed 60% in any of the sampling points and the BOD and COD of the effluent of the household wastewater treatment facility were below 10 and 20 mg/L respectively, due to the low loading. Compared to the pollution load on the water environment created by the conventional system, it was found that the effluent of the house has a lower load than the tertiary treatment and the volume of the water consumption is 75% of the conventional system.