Research Work Research Articles

Modeling, Optimization, and Simulation of Nanomaterials-Based Organic Thin Film Transistor for Future Use in pH Sensing.

Applications of Organic Thin Film Transistor (OTFT) range from flexible screens to disposable sensors, making them a prominent research issue in recent decades. A very accurate and exact pH sensing determination, including biosensors, is essential for these sensors. In this present research work, authors have proposed a nanomaterial-based OTFT for future pH monitoring and other biosensing applications. This work presents a numerical model of a pH sensor based on Carbon Nano Tubes (CNTs). Sensing in harsh conditions may be possible with the CNTs due to their strong chemical and thermal resilience. This research work describes the numerical modeling of Bottom-Gate Bottom-Contact (BGBC) OTFTs with a Semiconducting Single-Walled Carbon Nanotube (s-SWCNT) and C60 fullerene blended active layer. The design methodology of organic nanomaterial-based OTFTs has been presented with various parameter extraction precisely its electrical characteristics, modeled by adjusting the parameters of the basic semiconductor technology. For an active layer thickness of 200 nm, the drain current of the highest-performing s-SWCNT:C60 -based OTFT structure was around 4.25 A. This allows for an accurate representation of the device's electrical characteristics. Using Gold (Ag) Source/Drain (S/D) and back-gate electrodes as the medium for sensing, it has been realized how the thickness of the active layer impacts the performance of an OTFT for pH sensor applications.

Development of physical qualities of high school students in the football section of the school sports club

Introduction. According to the World Study of School Physical Education, sports games, gymnastics and athletics make up almost 80% of physical education curricula in primary and secondary schools. Possibilities of using certain types of sports games, for example, football, in the physical education of high school students are not sufficiently considered. The study aims to determine the effective methodology for developing the physical qualities of high school students in the football section of the school sports club. Materials and methods of research. 20 students aged 16 took part in the experiment. The research work was carried out at the Secondary School of the Betty-Mokhk village of Nozhai-Yurtovsky district, the Chechen Republic (Russian Federation). A sectional group was formed from the students of the experimental group (10 people), which, in addition to the school physical education program, additionally played football at the school sports club. The total volume of the educational and training program was 144 hours. Methods of mathematical statistic were the arithmetic mean and Student's t-test. Research results. The experimental methodology was developed in the form of an educational and training program for football based on a standard sports training program for football players aged 15-17 by the Russian Football Union and recommended for football sports training organizations in the Russian Federation. During the experiment, testing was carried out according to the standards included in the All-Russian Physical Culture and Sports Complex ‘Ready for Labor and Defense’ to identify the level of physical fitness of students. Comparative analysis and mathematical processing of the experiment results showed statistically significant changes in indicators of agility (t = 3.9; p < 0.01), speed (t = 2.3; p < 0.05), endurance (t = 2.9; p < 0.05), strength (t = 3.8; p < 0.01), and flexibility (t = 3.3; p < 0.01). Conclusion. The data obtained demonstrate the positive dynamics of the methodology for a sportsoriented approach through football in sectional classes within the school’s sports club. This proves the effectiveness of the chosen method, namely increasing the volume of physical activity through sectional work in the school sports club, taking into account the interests of students in physical education and sports activities.

Effect of Laterally Substituted Methoxy Group on the Liquid Crystalline Behavior of Novel Ester Molecules

Background: The aim of this research is to study the effect of length-to-width ratio on mesomorphism to enhance the understanding of its potential applications. This will be achieved by synthesizing a unique and innovative series of identical ester molecules, including lateral methoxy groups and terminal ethyl benzoate groups. In this research, we aim to find new insights into the relationship between molecular structures and mesomorphic behaviors, which could have significant implications for the development of advanced materials with adapted properties. : The objective of the study is to investigate the mesophase behavior of new ester mesogens and determine how they are influenced by lateral methoxy groups. By identifying the underlying perspectives and relationships between these variables, we hope to better understand the unique properties and potential applications of these materials. Method: In this research work, the focus is on the synthesis of ethyl(E)-4-((3-(4-alkoxy-3- methoxyphenyl)acryloyl)oxy)benzoate, which is prepared from Steglich esterification method by using (E)-3-(4-alkoxy-3-methoxyphenyl)acrylic acid and ethyl 4- hydroxybenzoate. Synthesis processes involve precise reaction sequences designed to ensure maximum yield and purity of the final product. By providing a detailed report on the experimental process, this study contributes to the ongoing research efforts aimed at the development of innovative compounds with various applications in liquid crystals Result: A new set of liquid crystal derivatives has been synthesized and studied to investigate the effect of molecular structure on the behavior of liquid crystals, with particular attention to the group -OCH3 located laterally. This series was composed of 12 derivatives (C1-C16). Among them, the first six derivatives (C1-C6) did not have the characteristics of a liquid crystal, while the remaining derivatives (C7 and C8) had a monotropic behavior, and C10-C16 had an enantiotropic smectogenic liquid crystal behavior without exhibition of the nematic phase. The average thermal stability of the smectic property was 87.33 °C, and the mesophase range was 2 °C to 14 °C. The molecular structure was confirmed by analytical and spectral analysis. The properties of liquid crystals of this new series were compared with those of other known structurally similar homologous series. The transition temperatures were determined with an optical polarizing microscope equipped with a heating phase. Conclusion: The mesomorphic thermal and optical properties of the compounds have been validated by DSC and POM techniques. The result shows that the length of the alkyl chain has a significant influence on the mesomorphic characteristics and thermal stability of the different mesophases. Evaluation of the compounds studied indicates that the molecules are sensitive to their lateral substituent, which influences the thermal characteristics and stability of the mesophase.

Medicinal Importance and Therapeutic Benefit of Bioactive Flavonoid Eriocitrin: An Update on Pharmacological Activity and Analytical Aspects

Background: Polyphenols are micronutrients and an important class of secondary metabolites that are naturally present in various types of plants and their derived byproducts. Humans obtain polyphenols and phytochemicals from different vegetables, fruits, spices, and teas. More than 8,000 different types of polyphenols have been isolated from different natural sources. Polyphenols play an important role in the plants’ resistance against pests and diseases. Plants from citrus class contain flavonoids, carotenoids, and ascorbic acid. Eriocitrin is a flavanone glycoside present in lemon juice and other citrus plants. Methods: Scientific research data on eriocitrin have been collected from Google, Google Scholar, Pub- Med, and Scopus databases, and analyzed in the present paper to determine the biological importance of eriocitrin in medicine. The medicinal importance and therapeutic benefit of eriocitrin in medicine have been investigated through literature data analysis of various research works. Pharmacological activity data have been thoroughly collected in the present work and analyzed in order to assess the health-beneficial aspects of eriocitrin against human disorders. Results: Scientific database analysis has revealed the medicinal importance and therapeutic benefit of eriocitrin in medicine. Scientific data analysis has revealed the anti-inflammatory, anti-nociceptive, and anti-obesity activities of eriocitrin. Further scientific data analysis has revealed the biological effect of eriocitrin on osteoarthritis, hepatocellular carcinoma, oral carcinogenesis, hepatic steatosis, oxidative damage, and different types of enzymes. In addition, eriocitrin has also been reported to exert potent antioxidant and suppressive effects on oxidative stress. Scientific data analysis has also revealed the significance of analytical techniques for the separation, isolation, and identification of eriocitrin. Conclusion: The present work indicates the biological importance of eriocitrin in medicine and other allied health sectors.

Framework for the Classification of Facial Emotions Using Soft Computing Techniques

Background:: Facial emotion recognition (FER) technology is enumerated as a productive interface in several operations, which has been specifically focused on as a substitute communication path among a user and an appliance for human computer interface in the previous decade. The efficiency of the facial identification model straightaway relies on the capability of classification methods. In addition, an appropriate swap between recognition efficiency and computational cost is reckoned as the most important factor for planning such models. Methods:: The objective of this paper was to classify the facial emotion electromyogram (EMG) signals by means of a neural network algorithm (NN), support vector machine (SVM) algorithm, and Naive-Bayes algorithm. This research work was directed towards the correlation among the classification accuracies by applying distinct feature extraction procedures on fEMGs. At first, eight participants (six male and two female) were recruited for data recording. Four electrodes were placed on each participant's face for capturing facial gestures (happy, angry, sad, and fear) and two electrodes were placed on the wrist for grounding purposes. Data were recorded by using BIOPAC MP150. After this, the signals were filtered using a band-pass filter and segmentation techniques for enhanced processing. After that, the time-domain and frequency-domain feature extraction procedures were carried out. Time domain and frequency domain features were applied to recorded signals. In this research, we used LabVIEW and MATLAB to produce a set of characteristics from fEMG signals for four emotional conditions, such as anger, sad, fear, and happy. After the feature extraction process, the extracted features were aligned into respective emotions by applying classifiers. The extracted features were further trained and classified by applying the SVM classifier, neural network classifier, and Naive Bayes classifier in MATLAB 2020. Results:: The SVM classifier and neural network classifier generated an accuracy of 93.80% and 96.90%, respectively, whereas the Naive Bayes classifier generated an accuracy of 90.60%. Conclusion:: Facial emotion recognition (FER) is foresighted as a progressive or futuristic model, which has attracted the attention of researchers in several areas of learning due to its higher prospects in distinct applications. Acknowledgment of the emotions through biomedical signals produced from movements of facial muscles is lately presented using an explicit and authentic route.

A Complete Sojorum on Thermosensitive Hydrogels for Wound Healing: Recent Developments and Ongoing Research

Abstract: The nature of the wound typically dictates the available wound-healing therapies. On the other hand, inadequate care results in persistent wounds, fibrosis, tissue loss of function, and ultimately dismemberment. Suturing, unloading, irrigation, debridement, negative pressure therapies, growth factor supplementation, and grafting are examples of current medicines that all have limitations in terms of meeting the needs of full treatment. Natural or synthetic materials/polymers have been utilized to prepare smart thermosensitive hydrogels to facilitate quick and targeted action on wounds. These are intelligent hydrogel system that shows sol-gel transitions at physiological body temperature. Hydrogel provides a moist environment, scaffold-like structure, and localized delivery of drug/growth factor that speed up wound healing even if it eliminates side effects associated with systemic administration. In this article, we summarized the detailed mechanisms of wound healing, conventional strategies, and ongoing thorough research work in the field of thermosensitive hydrogels utilized for wound healing. Moreover, the clinical needs of this formulation, as evident from the commercially available systems are also described in the prior art.

Design of a Power Efficient Model of PWM Generator for Green Communication using High Performance FPGAs

Aim: This paper will focus on promoting the ideas of green communication Background: Pulse Width Modulation (PWM) generator is used to control the power transfer in any communication model. It is one of the most crucial parts of an electrical circuit. Our main focus in this research work is to develop an energy and power-efficient PWM generator by using Field Programmable Gate Array (FPGA) logic to elevate green communication Objective: Our main focus in this research work is to develop an energy and power-efficient PWM generator by using Field Programmable Gate Array (FPGA) logic to elevate green communication. Methods: In order to make the PWM suitable for GC, we have implemented the design on VIVADO ISE from Xilinx. The power analysis as well as resource utilization are targeted on three different FPGAs. Results: Different IO standards of Stub Series Terminated Logic (SSTL) family are explored at different FPGA’s. Power analysis is deployed on the 7 series FPGAs of 3 different categories i.e., Spartan 7 (channel length = 28 nm), Kintex 7 (channel length = 20 nm) and ultra scale Zynq 7 (channel length = 16 nm). Conclusion: It can be concluded from power analysis that SPARTAN-7 device is the most powerefficient and ZYNQ Ultra scale+utilizes the highest amount of power. However, KINTEX-7 Ultrascale device lies in the middle of both these devices as far as power consumption is concerned. There is a reduction of 43.07% TP consumption for SPARTAN-7 device with SSTL135 IO when equated with ZYNQ Ultra scale+ with SSTL18_I IO. Also, it can be observed from sections 5.1, 5.2 and 5.3 that there is more contribution of DP in TP consumption than SP. Hence the device utilizes additional power when it is in active state than static state. Since PWM generator is an integral part of data and wireless communication, it should require less power for proficient transmission and wellorganized green computing and communication.

Experimental and numerical study on ventilated cavitation of high-speed projectile

In this study, ventilated cavitating flow characteristics around an axisymmetric projectile are investigated by combining experiments and numerical simulations. Experiments were carried out with a Split–Hopkinson pressure bar launch system and the pressure-equaling exhaust technology. Modular projectiles are designed to experimentally investigate the influence of head shape and ventilatory volume on flow characteristics. Large eddy simulation model is applied to obtain more flow field information. Compared with the conical head projectile, the hemispherical head projectile has a thinner attached cavity and more local detachment of the cavity. The statistical structure of the velocity and pressure fluctuations are analyzed by combining histograms and Q–Q diagrams. The results show that the pressure drag is dominant in the total drag and the periodic pulsation of the tail cavity and the stable vortex structure at the tail cause the variation of drag. The larger cavity volume changes the actual shape of the projectile, making the drag of the conical head projectile higher. The evolution characteristics of the cavitating flow field around the projectile with different ventilatory volumes are obtained, and the relationship between pressure fluctuation and chamber volume is derived. It is found that the reentrant jet causes a reverse flow at the nozzle, which leads to local pressure rise at the same interval. The above research work could contribute to the design and flow control of the ventilated cavity body.

Biological Importance, Pharmacological Activities, and Nutraceutical Potential of Capsanthin: A Review of Capsicum Plant Capsaicinoids.

Carotenoids are natural hydrocarbons that play an important role in photomorphogenesis, photosynthesis, photoprotection, development, and defense mechanism of plants. Carotenoids have good anti-oxidants and provitamin A contents with their additional colorant nature, which are indispensable to plants and human diets. Capsicum species are well known for their culinary uses worldwide; they are not only cultivated as vegetables but used in numerous medicinal preparations as well due to their medicinal aspects. This article aims to collect data on the beneficial aspects of capsaicinoids with a major emphasis on capsanthin. In order to instigate the biological potential and therapeutic benefit of capsanthin in medicine, in the present work, scientific research data on capsanthin were collected from different literature sources and analyzed. The biological potential of Capsicum annuum in medicine was also investigated through literature data analysis of different scientific research work. Scientific data on capsanthin were collected from Google, Google Scholar, PubMed, Science Direct, and Scopus using the term capsanthin and capsicum in the present work. Detailed pharmacological activities of capsanthin were presented and discussed in the present work through scientific data analysis of research work. Analytical techniques for the separation, isolation, and identification of capsanthin were taken into consideration in this work. Scientific data analysis revealed the biological importance and therapeutic benefit of capsanthin and capsicum in medicine. Capsicum annuum is a member of the Solanaceae family, which is one of the most cultivated spices worldwide. Capsaicinoids are one of the main classes of phytochemicals found in chili peppers, i.e., Capsicum annuum, and are mainly responsible for the pungent and spicy flavor of chili peppers. Capsanthin is a crystalline red color pigment found as the main component of Capsicum annuum fruits during ripening. Capsanthin is also found in Lilium, Aesculus, Berberis, and Asparagus officinalis. Chemically, capsanthin contains a cyclopentane ring, 11 conjugated double bonds, and a conjugated keto group. Capsanthin is a powerful antioxidant, exhibits anti-tumor activities, attenuates obesity-induced inflammation, and raises plasma HDL cholesterol levels. Scientific studies have proven the pharmacological benefits of capsanthin in medicine as it is helpful in pain relief, cardioprotection, weight loss, and body temperature regulation. Moreover, it also has anti-inflammatory, anticancer, antioxidant, and antimicrobial activities. In the literature database, numerous extraction and isolation techniques have been documented for capsanthin. In addition, the analytical techniques and other bioanalytical tools for the isolation and identification of capsanthin were also discussed in the present article. Medicinal importance and pharmacological activities of capsanthin were reviewed and discussed in this paper. This review aimed to highlight the literature on capsanthin in drug discoveries with their analytical development.

The WellComm Toolkit: Impact on Practitioner Skills and Knowledge and Implications for Evaluation Research

The WellComm toolkit is used across many areas of the UK to identify and support early years children with their speech, language and communication. There is some evidence for positive outcomes for children who are assessed and supported using the toolkit. However, wider implications of implementing the toolkit have not been fully investigated. This study aimed to explore the impact of implementing the WellComm toolkit on practitioners, practically and for their knowledge and skills. An additional aim was to reflect on the impact this may have on evaluation research. Early years practitioners (EYPs) in the UK completed an online survey asking about their experiences of using the WellComm toolkit, and a sub-sample participated in interviews. Survey and interview respondents spanned different early years settings. Practitioners described positive ways in which using the WellComm toolkit had impacted their knowledge of speech, language and communication, supported improvements in practice, and the quality and specificity of speech and language referrals. Negative implications, such as time costs and staffing burdens, were also discussed but were found not to outweigh the benefits of use. The positive implications of using the WellComm toolkit on practitioners’ own development and knowledge are likely to support the outcomes of children, though the usefulness of the WellComm toolkit for children who speak English as an additional language (EAL) and children with special educational needs (SEND) is questioned. Such findings have implications for the work of evaluation researchers, who need to be sensitive to the use of such toolkits in settings where interventions are being evaluated.

Meta-Transfer Metric Learning for Time Series Classification in 6G-Supported Intelligent Transportation Systems

Deep learning-based time series classification in 6G-supported Intelligent Transportation Systems (ITS) helps transport decision-making. Deep learning classifier training necessitates a large amount of labeled data for feature extraction. Labeling time series data in 6G-supported ITS is tough. Meta-learning can be used to train deep classifiers with limited data. However, in meta-learning, the tasks are frequently modeled by a low-complexity base learner. It is unable to use more complicate and powerful structures. The meta-learning-pretrained classifier can only perform new classification problems with the same number of classes. Most pre-training strategies do not prioritize enhancing the pre-training phase’s convergence rate and lowering the computational cost. Most research work aims to improve classification performance by increasing the complexity of the classification model. However, this raises computing costs. In this paper, we propose a one-dimensional Multi-Scale Dilated Convolution Neural Network time series classifier (MSDCNN). MSDCNN combines multi-scale CNN and dilated CNN. It can extract multi-scale characteristics from time series and reduce the complexity of the classifier. Furthermore, we propose a pre-training strategy, called Meta-transfer metric Learning using Scale function (MLS). MLS allows the classifier to gain experience from different tasks with various numbers of classes. Experiments show that MLS reduces pre-training computation costs during the pre-training phase. The pre-trained classifier, without using any fine tuning techniques, achieves the highest accuracy by comparing with the state-of-the-art methods. Finally, we present a case study of applying MSDCNN and MLS to detect road accidents in 6G-supported transportation systems.

Facial Physiological Characteristics and Skin Microbiomes Changes are Associated with Body Mass Index (BMI).

Overweight and obesity have become public health problems worldwide. An increasing number of research works are focusing on skin physiology and the manifestations of obesity-associated skin diseases, but little is known about the correlations between body mass index (BMI), facial skin physiological parameters, and the facial skin microbiome in healthy women. To investigate the correlations between BMI, facial skin physiological parameters and facial bacteria and fungi in 198 women aged 18 to 35 years in Shanghai. According to the international BMI standard and Chinese reference standard, subjects were divided into three groups, "lean" B1, "normal" B2 and "overweight" B3, and the physiological parameters of facial skin were measured by non-invasive instrumental methods, and the skin microbiota was analyzed by 16S rRNA and ITS high-throughput sequencing. Compared with the skin physiological parameters of the normal group, those of the overweight group exhibited a significant increase in trans-epidermal water loss (TEWL), which indicated that the skin barrier was impaired. The skin haemoglobin content was significantly increased, and skin surface pH was significant decreased in those with a high BMI. Furthermore, α-diversity, analysed using the Shannon, Chao, Sobs, and Ace indexes, was increased in the overweight group, suggesting that the diversity and species abundance of facial bacterial and fungal microbiota were also increased. Moreover, the overweight group had higher abundances of Streptococcus, Corynebacterium, Malassezia, and Candida. Notably, skin surface pH was significantly and negatively correlated with the relative abundances of Malassezia, Candida, and Cladosporium. Besides, the abundance of Malassezia was positively associated with the abundances of Staphylococcus and Corynebacterium. These results indicate that BMI is associated with differences in the biophysical properties and microbiome of the facial skin. A high BMI affects the integrity of skin barrier and changes the skin flora diversity and species composition.

Integration of Different Storage Technologies towards Sustainable Development—A Case Study in a Greek Island

The necessity for transitioning to renewable energy sources and the intermittent nature of the natural variables lead to the integration of storage units into these projects. In this research paper, wind turbines and solar modules are combined with pumped hydro storage, batteries, and green hydrogen. Energy management strategies are described for five different scenarios of hybrid renewable energy systems, based on single or hybrid storage technologies. The motivation is driven by grid stability issues and the limited access to fresh water in the Greek islands. A RES-based desalination unit is introduced into the hybrid system for access to low-cost fresh water. The comparison of single and hybrid storage methods, the exploitation of seawater for the simultaneous fulfillment of water for domestic and agricultural purposes, and the evaluation of different energy, economic, and environmental indices are the innovative aspects of this research work. The results show that pumped hydro storage systems can cover the energy and water demand at the minimum possible price, 0.215 EUR/kWh and 1.257 EUR/m3, while hybrid storage technologies provide better results in the loss of load probability, payback period and CO2 emissions. For the pumped hydro–hydrogen hybrid storage system, these values are 21.40%, 10.87 years, and 2297 tn/year, respectively.

Research progress of direct contact heat storage based on phase change heat storage

AbstractPhase change heat storage presents a promising solution to address challenges related to new energy intermittency and waste heat recovery. However, for it to function as an effective energy hub, heat storage becomes crucial. Among the available options, the direct contact heat accumulator stands out due to its simple structure, substantial heat transfer area, and minimal heat transfer resistance, making it a favorable choice for enhancing energy storage rates. Although a great deal of research has been done on phase change materials and heat accumulators over the years, a systematic review of direct‐contact heat accumulators is lacking. This article reviews the related research on direct contact heat storage, aiming to summarize the research work on direct contact phase change heat storage systems. Various phase change materials (PCMs) for direct contact systems are analyzed, and the study of PCM melting crystallization behavior, heat transfer coefficients and system performance in direct contact systems is summarized, as well as the improvement methods for direct contact vessels and PCMs. It provides a useful reference for future research on direct‐contact heat storage systems.

Impact and Integration of Electric vehicles on renewable energy based Microgrid: Frequency profile improvement by a-SCA optimized FO-Fuzzy PSS approach

The modelling of an electric vehicle along with its integration and impact over a renewable energy based microgrid topology is well addressed in this manuscript. The frequent charging and discharging of the electric vehicle makes an oscillation over grid frequency. The performance especially frequency of an islanded AC microgrid is also affected seriously under the actions of different uncertainties like load dynamics, wind fluctuation in wind plant, solar intensity variation of PV plant etc. In order to maintain standard frequency, this research work aims to regulate the net power generation of the system in response to total demand. To monitor net generation, this work has intended a fractional order fuzzy power system stabilizer (FO-Fuzzy PSS) control scheme in several dynamic situations. The proposed FO-Fuzzy PSS control scheme acts as most potential candidate to pertain stability in system frequency in above discussed disturbances. The controller gains are tuned optimally with suggesting an advanced- Sine Cosine Algorithm (a-SCA) under different conditions. The performance of the optimal FO- Fuzzy PSS controller is compared over standard fuzzy controller and PID controller in regard to frequency regulation of microgrid system. It is observed that proposed FO-Fuzzy PSS control scheme has the credential to reduce settling time of ΔF1 (area1 microgrid frequency) by 98.60% and 250.82% over fuzzy controller & PID controller correspondingly. Further, the dynamic optimal performance of the proposed a-SCA is compared over original SCA and PSO techniques to justify its superiority.

Cost and performance aware scheduling technique for cloud computing environment

Recently, lot of interest have been put forth by researchers to improve workload scheduling in cloud platform. However, execution of scientific workflow on cloud platform is time consuming and expensive. As users are charged based on hour of usage, lot of research work have been emphasized in minimizing processing time for reduction of cost. However, the processing cost can be reduced by minimizing energy consumption especially when resources are heterogeneous in nature; very limited work have been done considering optimizing cost with energy and processing time parameters together in meeting task quality of service (QoS) requirement. This paper presents cost and performance aware workload scheduling (CPA-WS) technique under heterogeneous cloud platform. This paper presents a cost optimization model through minimization of processing time and energy dissipation for execution of task. Experiments are conducted using two widely used workflow such as Inspiral and CyberShake. The outcome shows the CPA-WS significantly reduces energy, time, and cost in comparison with standard workload scheduling model.

Evaluating text classification with explainable artificial intelligence

<span lang="EN-US">Nowadays, artificial intelligence (AI) in general and machine learning techniques in particular has been widely employed in automated systems. Increasing complexity of these machine learning based systems have consequently given rise to blackbox models that are typically not understandable or explainable by humans. There is a need to understand the logic and reason behind these automated decision-making black box models as they are involved in our day-to-day activities such as driving, facial recognition identity systems, online recruitment. Explainable artificial intelligence (XAI) is an evolving field that makes it possible for humans to evaluate machine learning models for their correctness, fairness, and reliability. We extend our previous research work and perform a detailed analysis of the model created for text classification and sentiment analysis using a popular Explainable AI tool named local interpretable model agnostic explanations (LIME). The results verify that it is essential to evaluate machine learning models using explainable AI tools as accuracy and other related metrics does not ensure the correctness, fairness, and reliability of the model. We also present the comparison of explainability and interpretability of various machine learning algorithms using LIME.</span>

An Experimental Study on the Effectiveness of the Backward-Facing Step Technique on Small-Scale Horizontal-Axis Wind Turbine Rotor Blades

The aim of this research work was to explore how modifying the design of small-scale HAWT rotor blades through the backward-facing step technique affects their efficiency under varying wind speeds. The study involved altering step parameters such as location, length, and depth to create four distinct stepped blade shapes and enhance the aerodynamic performance of a rotor with a diameter of 280 mm. A specific blade profile, NREL S822, was selected to meet both aerodynamic and structural criteria. The rotor models were examined at a Reynolds number of 4.7 × 104 for wind speeds between 8.5 and 15.5 m/s and tip-speed ratios between 2 and 5. The experimental results indicated that for certain geometric step parameter values, the efficiency of the rotor model (B3) increased by approximately 47% compared to the base model (B1), particularly for tip-speed ratios lower than around 3.2. However, beyond this point, the rotor efficiency dropped significantly, reaching approximately 60% in one case. Additionally, a hybrid rotor model (B6) was generated by combining the shape of the rotor model (B4) with the most efficient rotor model from the literature, generated using the leading-edge wavy shape technique. This hybrid rotor model enhanced rotor efficiency for specific values of tip-speed ratio and also ensured its smoother operation. Overall, the rotor model (B2), distinguished by smaller step parameter values and a shift as well as broadening of the power coefficient curve towards lower tip-speed ratio values, exhibited a higher peak power coefficient, approximately 1.4% greater than the base rotor (B1). This increase occurred at a lower tip-speed ratio, allowing the rotor to operate with higher efficiency across a broader range of tip-speed ratios.

A recommender system-using novel deep network collaborative filtering

<p>The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.</p>

Thermodynamic Properties of Pyridine Based 1,3,4-Oxadiazole Scaffolds

Background: The present work describes the synthesis, characterization, and thermo-acoustical parameters of binary solutions of 1-(2-(4-fluorophenyl)-5-(pyridin-4-yl)- 1,3,4-oxadiazol-3(2H)-yl)-3-(pyridin-2-yl)prop-2-en-1-one, (AS1-08) and 1-(2-(4- methoxyphenyl)-5-(pyridin-4-yl)-1,3,4-oxadiazol-3(2H)-yl)-3-(pyridin-2-yl)prop-2-en-1- one, (AS1-12) in dimethyl sulfoxide (DMSO) and N, N-dimethylformamide (DMF) were estimated. Methods: Viscosity, ultrasonic velocity, and density methods at different solvents (DMSO and DMF) and different temperatures (298.15, 308.15, and 318.15 K) were used during the experimental research work. Results: Some acoustic parameters such as adiabatic compressibility (κs), intermolecular free path length (Lf), Rao’s molar sound function (Rm), Van der Waals constant (b), internal pressure (π), free volume (Vf) and solvation number (Sn) were calculated. The results obtained were interpreted in terms of solute-solvent and solute-solute interactions. Conclusion: The thermodynamic and various acoustical properties were determined at three different temperatures viz. (298.15, 308.15, and 318.15 K) at atmospheric pressure. Likewise, the similarity between experimental results of the binary solutions was studied to analyze how the change in the structural modification and solvent changes the values of Gibbs energy of activation (ΔG*), enthalpy of activation (ΔH*), and entropy of activation (ΔS*).