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Wireless Sensor Networks (WSN) play a pivotal role in various domains, including monitoring, security, and data transmission. However, their susceptibility to intrusions poses a significant challenge. This paper proposes a novel Intrusion Detection System (IDS) leveraging Particle Swarm Optimization (PSO) and an ensemble machine learning approach combining Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN) models to enhance the accuracy and reliability of intrusion detection in WSNs. The system addresses key challenges such as the imbalanced nature of datasets and the evolving complexity of network attacks. By incorporating Synthetic Minority Oversampling Technique Tomek (SMOTE-Tomek) techniques to balance the dataset and employing explainable AI methods such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), the proposed model achieves significant improvements in detection accuracy, precision, recall, and F1 score while providing clear, interpretable results. Extensive experimentation on WSN-DS dataset demonstrates the system’s efficacy, achieving an accuracy of 99.73%, with precision, recall, and F1 score values of 99.72% each, outperforming existing approaches. This work offers a robust, scalable solution for securing WSNs, contributing to both academic research and practical applications.

The programmability of radio access networks was already introduced in the fifth generation of mobile networks by leading the pathway towards the Open RAN. To cater the stringent requirements emerging with the novel services and use cases, Open RAN is becoming integral to 6G as well. The most highlighted characteristics of Open RAN such as disaggregated architecture, cloudification of network functions, open interfaces, and intelligent network management are also associated with many challenges related to the security of the end-to-end telco networks. Most existing research works and specifications focus on security in Open RAN in 5G. In this paper, we discuss the role of Open RAN in 6G focusing on three aspects such as security, privacy, and trust, while highlighting their emerging trends and relevance to 6G. Our contributions include presenting a holistic view of Open RAN security within the 6G ecosystem, with insights into use case and stakeholder perspectives and standardization efforts. This includes a concise update on Open RAN security in the 5G era and their applicability to 6G exploration. Moreover, we identify the key research endeavors focused on privacy and trust in Open RAN, with an emphasis on their development toward 6G. Finally, we discuss the possibilities of incorporating energy awareness, quantum-safe solutions, and other emerging technological enablers with Open RAN security.

In regenerative medicine, extracellular vesicles (EVs) possess the potential to repair injured cells by delivering modulatory factors. However, the therapeutic effect of EVs in large-scale tissue defects, which are subject to prolonged timelines for tissue architecture and functional restoration, remains poorly understood. In this study, we introduce EVs and cell-tethering hybrid hydrogels composed of tyramine-conjugated gelatin (GelTA) that can be in-situ crosslinked with EVs derived from human induced pluripotent stem cell-derived myofibers (hiPSC-myofibers) and hiPSC-muscle precursor cells. This hybrid hydrogel sustains the release of EVs and provides a beneficial nano-topography and mechanical properties for creating a favorable extracellular matrix. Secreted EVs from the hiPSC-myofibers contain specific microRNAs, potentially improving myogenesis and angiogenesis. Herein, we demonstrate increased myogenic markers and fusion/differentiation indexes through the combinatory effects of EVs and integrin-mediated adhesions in the 3D matrix. Furthermore, we observe a unique impact of EVs, which aid in maintaining the viability and phenotype of myofibers under harsh environments. The hybrid hydrogel in-situ crosslinked with hiPSCs and EVs is facilely used to fabricate large-scale muscle constructs by the stacking of micro-patterned hydrogel domains. Later, we confirmed a combinational effect, whereby muscle tissue regeneration and functional restoration were improved, via an in vivo murine volumetric muscle loss model.

This study investigates the factors influencing the adoption of fintech AI among 397 government college students from middle-class families in Haryana, India. Structural equation modeling was employed to study the relationships between the adoption of fintech AI and its predictors, namely Perceived Usefulness, Perceived Privacy and Security, Perceived Ease of Use, and Satisfaction of Fintech AI. This study utilized a cross-sectional research design. In this study, a non-probability convenience sampling method was utilized. The researchers opted for a sample of 397 government college students, consisting of 200 males and 197 females, hailing from middle-class families in Haryana, India. This sampling approach was selected because of its simplicity and ease of access, as it enabled the researchers to effortlessly connect with participants without resorting to random selection. The participants were drawn from various subject streams to guarantee a diverse representation of the student body. Data collection occurred between November 2022 and January 2023. The findings suggest that as perceived usefulness, ease of use, and satisfaction with fintech AI increase, the likelihood of adoption also increases. Conversely, as perceived Privacy and Security increase, the likelihood of adoption decreases. It is important to note that the strength of these relationships varies, with the strongest positive relationship observed between Adoption and Satisfaction of Fintech AI, while the relationships with Perceived Usefulness, Perceived Privacy and Security, and Perceived Ease of Use are weaker. These insights provide valuable information for organizations and policymakers seeking to promote the adoption of fintech AI among college students. Strategies aimed at addressing Privacy and Security concerns, enhancing user satisfaction, and improving perceived usefulness and ease of use may prove effective in increasing the adoption of fintech AI within this demographic.

This study investigates the determinants of FinTech adoption in the agricultural economy of developing countries, with a specific focus on Indian farmers. This paper examines the impact of price value (PV) and hedonic motivation (HM) on behavioral intention and actual adoption of FinTech products and services. This empirical study of 362 farmers from different districts in Uttar Pradesh, India, uses structural equation modeling (SEM) to address the gap in understanding what factors affect farmers' adoption of FinTech in agriculture. Using stratified sampling to ensure the representativeness of small and medium sized farmers, the study also includes input from local governance bodies, Gram Pradhans and Lekhpals, to help with accuracy of the data. The findings indicate a positive and significant influence of PV and HM on the behavioral intention. The analysis also demonstrates that PV and HM directly determine actual adoption of FinTech. This article enriches the literature, as it applies UTAUT2 to depict PV, HM, and actual adoption mediation by behavioral intention. The findings enable policymakers, FinTech service providers and stakeholders to build targeted strategies to promote financial inclusion and the adaptation of FinTech by farmers. This work extends the research field by bridging the gap in research and contributes to understanding the interplay between economic and psychological factors in the process of technology adoption as well as with moral implications of the sustainable development of the agricultural economy.

Abstract Solid-phase synthesis underpins many advances in synthetic and combinatorial chemistry, biology, and material science. The immobilization of a reacting species on the solid support makes interfacing of reagents an important challenge in this approach. In traditional synthesis columns, this leads to reaction errors that limit the product yield and necessitates excess consumption of the mobile reagent phase. Although droplet microfluidics can mitigate these problems, its adoption is fundamentally limited by the inability to controllably interface microbeads and reagent droplets. Here, we introduce Dielectrophoretic Bead-Droplet Reactor as a physical method to implement solid-phase synthesis on individual functionalized microbeads by encapsulating and ejecting them from microdroplets by tuning the supply voltage. Proof-of-concept demonstration of the enzymatic coupling of fluorescently labeled nucleotides onto the bead using this reactor yielded a 3.2-fold higher fidelity over columns through precise interfacing of individual microreactors and beads. Our work combines microparticle manipulation and droplet microfluidics to address a long-standing problem in solid-phase synthesis with potentially wide-ranging implications.

Objective: To estimate the Knowledge, Attitude and Practice (KAP) with regarding to bio-medical waste management among the healthcare workers in hospitals of Bikaner district. Place and duration of study: The study was carried out at the affiliated hospitals of S. P. Medical College, Bikaner and at the hospitals under C.M.H.O., Bikaner from October 2022 to December 2022. Method: The study employed a quantitative research methodology, specifically utilizing a descriptive research design and simple random sampling technique to evaluate the levels of knowledge, attitude, and practice (KAP). A self-designed questionnaire and checklist were created to gather data before and after the trial. A preliminary study was carried out to assess the knowledge, attitude and practice level of 120 healthcare professionals working in hospitals in the Bikaner district regarding the management of biomedical waste. Subsequently, they were educated on this topic using a self-instructional module. A follow-up study was then conducted to measure the improvement in their knowledge, attitude and practice level regarding biomedical waste management. Result: After providing healthcare personnel at Bikaner district hospitals with a Self-Instructional Module, it was noted that their knowledge and level of practice regarding biomedical waste management improved. Conclusion: The study's findings indicate that strict compliance with regulations and the implementation of an enhanced environmental management system are necessary for the proper handling, sorting, transportation, storage, and disposal of biomedical waste. Additionally, training is essential for garbage handlers, scavengers, sweepers, as well as higher authorities and nursing staff.

This theoretical review explores the intricate relationship between Denison’s organizational culture and worker productivity within the context of municipalities, with a specific focus on workplace incivility as a potential mediator. Denison’s model, encompassing four key cultural traits - involvement, consistency, adaptability, and mission - provides a comprehensive framework for understanding how organizational culture impacts various organizational outcomes. The main goal of this review is to introduce the conceptual model framework that suggests the connections between the dimensions of Denison’s organizational culture and employee productivity as well as workplace incivility. The effect of Denison’s organizational culture on the productivity of municipal workers will be explored. By combining existing theoretical perspectives and empirical evidence, this study aims to offer a comprehensive understanding of how each dimension of Denison’s model interacts with and influences productivity through incivility in the distinct settings of municipal organizations. Furthermore, this review contributes to both theoretical and practical domains by enhancing our understanding of how Denison organizational culture manifests in municipal settings and its subsequent influence on worker productivity. Moreover, it furnishes a comprehensive insight into workplace incivility, presenting theoretical arguments and substantiating evidence. The discourse advocates for the vigilant monitoring, effective management, and elimination of adaptable organizational cultural practices within municipalities. These efforts are proposed to not only mitigate workplace incivility but also foster an environment conducive to heightened employee performance and productivity.

Entrepreneurship helps to mainstream the disadvantaged. In order to design policy for the promotion of entrepreneurship among disadvantaged people a proper understanding of entrepreneurship is imperative and cognition has been considered as the best interpreter of entrepreneurship. This study aimed to explore the driving forces and hindrances that influence disadvantaged individuals in their decision to initiate entrepreneurial endeavors. We are using a sample of 1173 people taken from the low-income population of India. The data is analyzed through the use of the Logistic regression technique. The result reflects that attitude, subjective norms, self-efficacy, perceived opportunities and role models encourage Indian disadvantaged to start business. This research contributes to two existing theories: challenge-based entrepreneurship and the theory of planned behavior. It also sheds light on a previously understudied group - disadvantaged entrepreneurs in developing economies. This knowledge can be used by policymakers to create programs that encourage entrepreneurship at the grassroots level.

In engineering and applied sciences, several physical phenomena can be more precisely characterized by employing nonlinear fractional partial differential equations. The primary goal of this research is to examine the traveling wave solution in closed form for the nonlinear acoustic wave propagation model known as the time fractional simplified modified Camassa–Holm equation, which is used to explain the unidirectional propagation of shallow-water waves with non-hydrostatic pressure and explains the dispersion properties of numerous phenomena like fluid flow, control theory, liquid drop patterning in plasma, acoustics, fusion, and fission processes, etc. The utmost potential approach, namely the new auxiliary equation technique, is applied for analyzing the time nonlinear fractional simplified modified Camassa-Holm equation in the logic of the newest established truncated M-fractional derivative. The fractional partial differential equations have been transformed to the ordinary differential equation using the complex wave transformation in the sense of truncated M-fractional derivative. A variety of soliton solutions, including anti-kink, single soliton, anti-bell, bell, kink, multiple soliton, double soliton, singular-kink, compacton shape, periodic shape, and so many, are displayed in the diagram of 3D and contour plots by taking into account a number of various parameters. It is essential to point out that all derived outcomes are directly compared to the original solutions to certify their exactness. Results show that the used scheme is capable, simple, and straightforward and can be useful to a variety of complex phenomena. The acquired results are unique for the model equation and could be applied to the analysis of several nonlinear study fields.

The current study, guided by resource-based and signaling theories, investigated the mediator's function of employer branding in the connection between human resources practices and employee engagement. Data analysis for this research employed PLS-SEM, with a sample size comprising 220 individuals employed in various IT companies within the Delhi-NCR region. The restraint to this probe is that it is transverse analysis and employs quantitative research methodologies. The outcomes of the present work unveiled a direct and statistically substantial relationship among human resource practices and employee engagement. Furthermore, employer branding was identified as a mediator in the relationship between human resource practices and engagement among employees. This research provides targeted guidance for HR professionals and leaders in the IT sector, presenting in-depth insights into human resources dynamics, specifically focusing on employee engagement. Emphasizing the intermediary role of employer branding, our study empowers managers with actionable insights to enhance HR strategies, fostering heightened workplace satisfaction and increased employee engagement for a resilient and thriving workforce.

This study investigated the moderating effect of collectivism as a national culture on the interaction between organizational culture (measured in terms of clan, adhocracy, market, and hierarchy cultures) and commitment in the context of SMEs. A total of 1200 questionnaire surveys were delivered to 155 SMEs, of which 356 were deemed valid. The hypotheses were tested using a hierarchical multiple regression analysis. According to the findings of the study, a significantly positive relationship between organizational culture and commitment was discovered, and collectivism, as a national culture, moderates this relationship significantly. This study offers several recommendations for future research in this field. SMEs should prioritize the development of a better culture to generate a higher level of organizational commitment. Future scholars could use additional organizational contextual components as mediating or moderating variables to explore the association between organizational culture and commitment.

Nonlinear fractional partial differential equations can explain a vast scope of engineering and science, like atomic physics, wireless transmission, nonlinear optics, acoustics, economics, materials science, control theory, plasma physics, quantum plasma in mechanics, biological systems of nonlinear case and so on. In this study, the new generalized (G′/G)-expansion technique have been studied for examine the nonlinear fractional equations and built exact analytical traveling as well as solitary wave solutions namely space–time one-dimensional fractional wave equation used to simulate wave transmission in a nonlinear medium with blending and space–time fractional regularized long wave equation enables bore expansion and wave generating in nonlinear model of solitary waves, ion acoustic plasma waves, shallow waves in water, and nonlinear dispersive waves with the help of conformable derivatives. The creatable equations are transformed into ordinary differential equations by fractional complex transformation. Some dynamical wave shapes of multiple solitons, single soliton, periodic anti-kink, anti-kink, flat-kink type solitary wave models have been created, and 3D, and contour have been built to represent these solutions. The results are expressed using hyperbolic, rational functions, and trigonometric and Maple or Mathematica utilized to graphically represent the obtained solutions. It is essential to point out that all resultant solutions are directly compared to the original solutions to ensure their exactness. The new generalized (G′/G)-expansion method is an operative, compatible, and fruitful approach for analyzing nonlinear fractional traveling waves.

The space–time fractional Landau-Ginzburg-Higgs equation and coupled Boussinesq-Burger equation describe the behavior of nonlinear waves in the tropical and mid-latitude troposphere, exhibiting weak scattering, extended connections, arising from the interactions between equatorial and mid-latitude Rossby waves, fluid flow in dynamic systems, and depicting wave propagation in shallow water. The improved Bernoulli sub-equation function method has been used to achieve new and wide-ranging closed-form solitary wave solutions to the mentioned nonlinear fractional partial differential equations through beta-derivative. A wave transformation is applied to renovate the fractional-order equation into an ordinary differential equation. Some standard wave shapes of multiple soliton type, single soliton, kink shape, double soliton shape type, triple soliton shape, anti-kink shape, and other types of solitons have been established. The more updated software Python is used to display the solutions by using 3D and contour plotlines to describe the physical significances of attained solutions more clearly. The findings of this study are straightforward, adaptable, and quicker to simulate. It has been notable that the improved Bernoulli sub-equation function method is practical, effective, and offers more sophisticated solutions that can help to generate a large number of wave solutions for various models.

This study aimed to predict the bankruptcy risk of companies listed on the National Stock Exchange (NSE) in India using the Altman Z-score model. By examining the impact of variable factors such as market value equity/book value of total liabilities, retained earnings/total assets, working capital/total assets, earnings before interest and taxes/total assets, and sales/total assets, the research sought to assess their influence on the financial condition of listed companies. The Altman Z-score model is a widely accepted tool for predicting bankruptcy risk and has been utilized in various industries across different countries. The model comprises five financial ratios that capture a company's liquidity, profitability, leverage, solvency, and activity. By analyzing these ratios, the model calculates a composite Z-score, which can be used to classify companies into different risk categories. Using a sample of NSE-listed companies, this study employed the Altman Z-score model to predict bankruptcy risk and examined the impact of the five variable factors on the firms' financial health. The findings provided insights into the relationship between these financial ratios and the companies' bankruptcy risk, offering valuable information for investors, policymakers, and other stakeholders in the Indian financial market. The study demonstrated the applicability of the Altman Z-score model in predicting bankruptcy risk for NSE-listed companies in India and highlighted the importance of each variable factor in assessing a firm's financial condition. These findings can help stakeholders make informed decisions regarding investment, risk management, and policy formulation in the Indian financial market.

Raw and citric acid chemically treated date palm stone agro-waste biomass (RDSB and CA-MDSB) powders were used to remove an important class of emerging industrial pollutants, i.e., 2,4,6-trichlorophenol (2,4,6-TCP) from aqueous solutions towards sustainable waste utilization to develop cost-effective technology for treating wastewater. The biomass characterization was performed by using different analytical techniques such as CHN elemental analysis, particle size, BET, FTIR, and SEM-EDX, TGA analysis. The FTIR spectral analysis revealed that the main chemical groups (N–C, O=C, H-O, H-C, and O–C) were involved in trapping 2,4,6-TCP. The highest adsorption was achieved with a contact time of 150 and 120 min, an initial concentration of 50-200 mg/L, and a biosorbent dosage ranging from 0.1 to 1.0 g/L RDSB and CA-MDSB, respectively. The experimental kinetic data of the adsorption process for both adsorbents (RDSB and CA-MDSB) fitted very well with the pseudo-second-order kinetic model and Langmuir equilibrium data. The 2,4,6-TCP maximum monolayer adsorption capacities were 53.7, and 123.8 mg/g for RDSB and CA-MDSB, respectively. The present research confirms that the date palm stone biomass could be used as an effective and low-cost biosorbent for the remediation of 2,4,6-TCP from an aqueous environment.

Employing carbon-neutral (NH3) and low carbon footprint (biodiesel) fuels in Reactivity Controlled Compression Ignition (RCCI) engine mode is one possible method for minimizing carbon emissions in diesel engines. In this study, a Compression Ignition (CI) engine was customized to run in RCCI mode, employing High Reactive Fuel (HRF) as biodiesel and Low Reactive Fuel (LRF) as ammonia (NH3). Based on our earlier findings, the proportion of ammonia in premixing is fixed at 40 %. In the first phase, the primary injection timing of the algal biodiesel varied between 12 and 21°CA bTDC at a preset pre-injection timing of 46°CA bTDC. In the next phase, the pre-injection time varied between 46 and 54°CA bTDC, with the optimal main injection time being 18°CA. The result suggested that the optimal main and pre-injection at 18 and 58°CA bTDC increased the Cylinder Pressure (CP) by 30.1 %, and peak HRR and combustion phase angle were advanced. Brake Thermal Efficiency exhibited an 11 % enhancement, with Brake Specific Energy Consumption decreasing by 24.1 %. Nitrogen Oxide levels saw an increase of around 39.5 %. In contrast, reductions of 19.2 %, 23.5 %, 39.7 %, and 21.7 % were observed in Hydrocarbons, Carbon Monoxide, smoke emissions, and Exhaust Gas Temperature, respectively, compared to the single injection under full load conditions.

Global DC fast-charging infrastructure is being rapidly developed to accommodate the ever-increasing demand from the electric vehicle market. A major complication in the electric power system's process is the proliferation of ultra-fast charging (UFC) stations, which are known for their enormous power consumption and unpredictable, intermittent performance. By implementing grid-supportive features and ensuring an improved power consumption profile for the grid, installing regional energy storage can solve these challenges. This study presents a control approach for managing the grid-side AC/DC converters of rapid charging stations focused on future-oriented regulation. The paper primarily concentrates on various Vienna rectifier topologies. The technology, characteristics, benefits, and operational aspects of Vienna rectifier topologies are vital to improving the performance, efficiency, and grid integration of electric vehicle charging systems. Fast charging, grid stability, energy economy, and the smooth integration of electric vehicles into the electrical grid are all made possible by Vienna rectifiers. When used in battery energy storage systems (BESS) for electric vehicle charging infrastructure, Vienna rectifiers allow for effective discharge and charging of the batteries. The configurations and assessments of these converters are examined, assessed, and compared based on power output parameters, element count, power factor, THD, and efficiency. The Vienna rectifier has been identified as the best suitable DC fast-charging converter architecture for power levels exceeding 15 kW due to its exceptional efficiency, limited output voltage ripples, high power density, reduced current ripples, and reliable performance. Research on ways to improve electric vehicle charging infrastructure can be spurred by a better grasp of the Vienna rectifier's technological complexities and performance benefits, leading to greener and more efficient transportation options. As a result, the system's power density will eventually rise and elemental stress will decrease.

IntroductionGreen banking (GB) strategies are a set of financial practices and activities implemented by banks to encourage sustainability, environmental responsibility, and low-carbon impact. This bibliometric study examines the present state of research in this field by utilizing the Scopus database for data retrieval and VosViewer for network analysis.MethodA total of 149 researches were retrieved from the Scopus and analyzed using the VosViewer software following the PRISMA guidelines. The study examines year-by-year publication patterns, top-cited articles, contributing countries, journals, co-citation analysis of authors, and co-occurrence analysis of keywords.ResultStrategies such as online banking, solar ATMs, construction of green buildings, and making provisions for green loans are identified as the key GB strategies that can be adopted by the banks. Furthermore, the fundamental challenges banking organizations encounter while implementing GB strategies are underlined.DiscussionThe study suggests that “green brand image,” “green audit,” “environmental economics,” and “carbon footprint” can be considered as an area of interest in future. The study provides researchers, policymakers, regulators, and financial institutions with valuable insights by presenting the current state of green banking research and identifying emerging areas for further exploration. These findings will help others to discover the areas of interest in GB and advancing sustainable finance practices that can foster environmental responsibility in the financial sector.

This study investigated the performance, combustion, and emissions of a modified low heat rejection (LHR) diesel engine fueled with a blend of 90 % coconut waste cooking oil (CWCO) biodiesel and 10 % diethyl ether (DEE). The engine combustion chamber components were coated with 300 μm lanthanum-doped partially stabilized zirconia for thermal insulation. Engine testing was performed at varied loads from 0 to 100 % using an eddy current dynamometer. Exhaust emissions, including hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NOx), and smoke were measured. Compared to conventional diesel, the CWCO-DEE blend showed a 3 % higher brake thermal efficiency of 33.4 % and 2.42 % lower brake-specific fuel consumption at full load. HC, CO, and smoke emissions decreased by 18 % (39 ppm), 11 %, and 19 % at higher loads with the blend. However, NOx emissions increased slightly by 21.2 %. The DEE compensated for CWCO's lower cetane number and viscosity, while the LHR coating enhanced combustion by providing thermal insulation, raising exhaust gas temperatures by 13 %. The improved efficiency and reduced emissions demonstrate the potential of optimized biodiesel-additive blends in conjunction with LHR engine modifications to sustainably utilize inexpensive waste cooking oil feedstocks as renewable diesel replacements. However, further optimization of blend compositions, additives, and coatings is needed to balance performance benefits against possible NOx increases. This study highlights a promising combined approach leveraging engine design and fuel advancements.

This study investigates the feasibility of using grape biodiesel from the wine industry as a sustainable and cost-effective alternative to traditional fuels. The research focuses on optimizing key parameters like blend ratio, injection timing, injection pressure, engine load, and exhaust gas recirculation in a Common Rail Direct Injection engine to achieve low emissions without compromising performance. Due to grape biodiesel's higher viscosity, different energy content, and varying ignition delays compared to diesel, precise adjustments are essential for complete combustion and reduced emissions. This study uses response surface methodology and a Central Composite Design matrix to find the best values for a number of parameters in modern CRDI engines that use advanced electronic control units. Through fifty input combinations, the study aims to minimize specific fuel consumption, hydrocarbons, nitrogen oxides, and carbon monoxide while maximising brake thermal efficiency, brake mean effective pressure, and mechanical efficiency. The optimal configuration includes a fuel injection timing of 6° bTDC, an engine load of 82 %, 6.7 % EGR, 1000 bar injection pressure, and a 33 % grape biodiesel blend. These optimum input conditions yielded outputs of 3.55 bar BMEP, 31.85 % BTE, 64 % mechanical efficiency, 0.278 kg/kWh SFC, 0.127 % CO, 357 ppm NOx, and 8 ppm of HC. These adjustments ensure low emissions and efficient engine operation, highlighting grape biodiesel's potential as a viable alternative fuel.

A high-temperature polymer electrolyte membrane fuel cell (PEMFC) has enormous potential to produce clean energy with minimal environmental pollution along with high energy density and conversion efficiency. In the present work, anhydrous proton-conducting polymer electrolyte membranes for prospective high-temperature fuel cell application were successfully prepared using different protic ionic liquids (PILs) and sulfonated poly(ether ether ketone) (SPEEK). The protic ionic liquids (PILs), 2-hydroxyethylammonium formate, diethylmethyl ammonium triflate, 1-ethylimidazolium bis(trifluoromethylsulfonyl)imide, 1,2-dimethyl imidazolium bis(trifluoro methylsulfonyl)imide, and diethylmethylammonium methanesulfonate were selected based on their favorable physicochemical properties. The effect of the incorporation of the PIL on the structural, thermal, and electrical transport properties of the composite SPEEK polymer electrolyte membranes was studied. The prepared polymer electrolyte membranes (PEMs) were characterized using Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffractometry (XRD), and electrochemical impedance spectroscopy (EIS) techniques. FTIR results indicated an interaction between the PIL and the SPEEK, which resulted in homogeneous and flexible membranes. The DSC results confirmed good miscibility and elucidated plasticizing effect of the incorporated PIL on the SPEEK polymer matrix. All the PEMs showed good thermal stability and high-temperature anhydrous proton conductivity, achieving best proton conductivity, ≈8 × 10–3 S cm−1 at 120 °C and low activation energy ≈0.26 eV, making it suitable for prospective high-temperature PEMFC applications.

Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machine regression (SVMR), and Gaussian regression (GR) techniques for precise solar PV panel power prediction. The investigation into the impact of environmental factors—solar radiation, ambient temperature, and relative humidity—on PV panel output reveals the superior predictive capabilities of SVMR models. With a mean squared error (MSE) of 0.038, a mean absolute error (MAE) of 0.17, and an R2 value of 0.99, SVMR outperforms GR and MR models. Conversely, Gaussian regression demonstrates comparatively weaker performance, yielding an R2 of 0.88, an MSE of 0.49, and an MAE of 0.63.This research underscores the reliability and enhanced accuracy of the proposed SVMR model in forecasting solar PV panel output. The outcomes presented herein carry significant implications for promoting the widespread adoption of PV panels in electricity generation, particularly in challenging environmental conditions. The findings offer valuable insights into optimizing solar PV deployment, ultimately contributing to the expansion of solar power generation in the national energy landscape. Moreover, the comparative analysis provides insights into how anticipated PV power generation can adapt to varying weather conditions, encompassing factors such as temperature, humidity, and solar radiation.

Environmental concerns need to be addressed to make economic growth sustainable. Theories in the literature attempt to establish the relationship between economic growth and environmental degradation. These theories are not deterministic, as concepts like the inverted U-shaped or N-shaped Environmental Kuznets Curve (EKC) have yielded different results for different economies. This study attempts to validate the shape of the EKC and simultaneously test for an asymmetric relationship between economic growth and carbon dioxide emissions. To achieve this, the study uses the simple and non-linear Autoregressive Distributed Lag (ARDL) method on data from 1965 to 2021. The study finds evidence of an inverted U-shaped EKC but finds no evidence of an N-shaped EKC for India. Additionally, the study reports an asymmetric relationship between economic growth, energy consumption, and carbon dioxide emissions. Based on these findings, the study provides suitable policy recommendations.

Abstract The study aims to identify the determinants of digital financial inclusion (DFI) and its role in micro enterprises’ ease of doing business. The study is based on the World Bank’s Enterprises Survey of Micro Firms (ESM) 2022 data of 998 micro-enterprises. The variables that measure access and use of digital finance are extracted from the data. Two sets of independent variables, namely digital resource capability and firms and owners’ characteristics, are taken as explanatory variables of digital financial inclusion. Analysis of variance (ANOVA) has been applied to analyze the difference in perceived business obstacles across the micro-enterprises with and without access and use of digital finance. Further, a logistic regression model is developed to analyze the determinants of DFI. The findings of the study reveal that digital financial inclusion helps to face obstacles in business regulation and handle market externalities. Further, estimates of both the logistic regression model and marginal effects suggest that access to the internet, education, and owner experience are instrumental in digital financial inclusion among micro-enterprises. This study may be helpful for various stakeholders, such as the government, promoters of entrepreneurship, banks, and international organizations working in digital financial inclusion. The originality of the study lies in exploring the determinants of DFI among micro-enterprises, which are still unexplored in the case of India using extensive and specific micro enterprise’s data collected by the World Bank.

Diffusion models have recently exhibited significant potential in generative modeling, surpassing generative adversarial networks concerning perceptual quality and autoregressive models in density estimation. However, a notable drawback of these models is their slow sampling time, requiring numerous model evaluations to generate high-quality samples. This research proposes a technique termed cyclical diffusion sampling that incorporates cyclical stochastic gradient Langevin dynamics (SGLD) with elucidated diffusion models (EDM) sampler to enhance stability when utilizing a limited number of sampling steps. Cyclical step-size scheduling has been demonstrated to enhance the effectiveness of SGLD in learning complex multimodal distributions. We have shown, via empirical assessments, that cyclical diffusion sampling significantly enhances image quality and markedly decreases inference time. Notably, the method preserves simplicity and requires no alterations to the network architecture, which promotes straightforward reproducibility and seamless integration with current methodologies.

The service management and orchestration (SMO) framework plays a major role in the management and orchestration (M&O) of applications, services, and components within the open radio access network (O-RAN) architecture, as defined by the O-RAN Alliance. It comprises the non-real-time RAN intelligence controller (Non-RT RIC) and possesses the capability to incorporate management systems and components from multiple standards development organizations (SDOs), notably the Third Generation Partnership Project (3GPP) and the European Telecommunications Standards Institute (ETSI). This multi-SDO-based SMO framework aims to provide a rich set of M&O services in a coherent and unified manner. Leveraging management data analytics (MDA) to deliver M&O services can enhance SMO capabilities. This article proposes an intelligence-driven approach by integrating MDA into the 3GPP and ETSI management systems within the context of SMO. Furthermore, it introduces the artificial intelligence (AI)/machine learning (ML) Function into the Non-RT RIC, consolidating existing intelligent components and introducing novel ones to enhance the intelligence capabilities of the Non-RT RIC. The article further proposes an architectural solution that unifies and facilitates interoperability among the intelligent systems of the 3GPP, ETSI, and Non-RT RIC – collectively referred to as “three modules” – within SMO. It also presents the end-to-end lifecycle workflow of the AI/ML model across the three modules. Finally, the article outlines key research challenges related to integrating MDA within the SMO framework.

Abstract The space–time fractional nonlinear Klein-Gordon and modified regularized long-wave equations explain the dynamics of spinless ions and relativistic electrons in atom theory, long-wave dynamics in the ocean, like tsunamis and tidal waves, shallow water waves in coastal sea areas, and also modeling several nonlinear optical phenomena. In this study, the improved Bernoulli sub-equation function method has been used to generate some new and more universal closed-form traveling wave solutions of those equations in the sense of beta-derivative. Using the fractional complex wave transformation, the equations are converted into nonlinear differential equations. The achieved outcomes are further inclusive of successfully dealing with the aforementioned models. Some projecting solitons waveforms, including, kink, singular soliton, bell shape, anti-bell shape, and other types of solutions are displayed through a three-dimensional plotline, a plot of contour, and a 2D plot for definite parametric values. It is significant to note that all obtained solutions are verified as accurate by substituting the original equation in each case using the computational software, Maple. Additionally, the results have been compared with other existing results in the literature to show their uniqueness. The proposed technique is effective, computationally attractive, and trustworthy to establish more generalized wave solutions.

The General Health Questionnaire (GHQ-12) is widely accepted as an instrument used to examine diagnosable psychiatric disorders. It is also routinely used as a measure of psychological morbidity. This study aimed to assess and establish the psychometric properties of the GHQ-12 among Indian teenagers. The tool was administered to 212 adolescents aged from 12 to 18 years. Following two-stage cluster sampling, the data were collected from Aligarh district, India. Fleiss Kappa analysis was used to determine test reliability and showed an overall value of 0.94, based on the rater agreement for the instrument. Furthermore, the Rasch measurement model was used, with values of 0.79 and 0.83 for person and item reliability, respectively. Moreover, the value of unidimensionality was found to be 37.9%. Additionally, item fit statistics and item analysis were conducted for the instrument. Based on the preliminary data and findings, the study provides primary evidence for the reliability and validity of GHQ-12. Hence, the questionnaire can lead to more multi-site studies in India.

Various infrastructure financing methods are being used worldwide, each with its own set of advantages and obstacles. Basically, the advantages and disadvantages of a financing method manifest differently depending on each country's economic system, geographical location, economic dependency, political conditions, cultures, and beliefs. Using a funding strategy in one country may be beneficial under certain situations but harmful in another. As a result, the usefulness and efficacy of financing methods should be studied in light of the countries' circumstances. The Asian Landlocked Developing Countries (LLDC), which share the same fate in terms of geographical constraints and economic reliance, are grappling with severe infrastructural gaps in all sectors. These countries have an emerging economy, are largely dependent on agriculture, have a poor management structure, are unorganised and have weak financial markets, and rely heavily on their neighbours’ transit routes to interact with the global economy. Some of these countries depend upon foreign assistance, and the lack of transparency and binding nature of this funding has had little impact on developing such countries' infrastructure. As a result, in order to finance their infrastructure projects, these countries must select the most acceptable financing option for their circumstances. The national budget constraint, inadequate local financial markets, the risk of foreign credits, and cultural and religious values in these countries are among the obstacles that contemporary financing methods present. Consequently, looking for alternative financing methods for these countries' infrastructure projects is critical. This paper intends to look at infrastructure financing methods and their challenges in Asian landlocked developing countries and evaluate Islamic finance as an alternative to conventional finance. The qualitative research method is used for this study. Furthermore, document analysis has been utilised as a data collection tool. First, the concepts of Islamic finance will be presented before moving on to the definitions and types of infrastructure. Afterwards, the conventional infrastructure financing methods, along with the shortcomings of these methods in Asian landlocked developing countries, will be discussed. Then the development of Islamic finance in Asian LLDCs will be studied. Finally, how Islamic finance addresses the challenges of conventional finance methods will be perused. The study found that the Islamic finance models, which are founded on the principle of shared income or risk sharing, can be implemented as an alternative to conventional finance in infrastructure financing. Public-private partnership (PPP) initiatives can be implemented effectively within the framework of Islamic financing concepts because the elements of the public-private partnership initiatives are congruent with Islamic finance principles. First of all, public-private partnership initiatives allow for risk sharing among project participants, including financiers. In addition, public-private partnership projects allow Islamic financiers to become project participants rather than just lenders. Following that, PPP infrastructure projects are by definition, free of speculation and gambling. Finally, project contracts are frequently well-specified and free of uncertainty. Besides this, creating financial inclusion and risk sharing in the framework of Islamic finance reduces the currency risk and the cost of financing and causes equitable distribution of wealth in society.

Digitalized agriculture methods and other technical improvements are required to be integrated with finance technology (FinTech) as a critical path and through this integration, trade-offs between resource use and profitability may be lessened, supporting the development of sustainable agricultural models. This research explores the complex interrelationships of factors influencing adoption of fintech by farmers. This study understands the relationship between Social Influence (SI), Performance Expectancy (PE), Convenience (C), Behavioural Intention to adopt agricultural fintech products and services (BIAFPS) and Actual Adoption of agricultural products and services (AAFPS). By utilizing a multifaceted methodology drawn as an extension of Unified Theory of Acceptance and Use of Technology (UTAUT) model by intercepting BIAFPS as mediator, the study explores the intricate relationships and interdependencies among these variables, illuminating the critical function of BIAFPS in this intricate structure. This study intends to improve the understanding of the mechanisms impacting farmer’s perspective by analysing 362 farmers as respondent of 6 different districts of the third largest agriculture cultivation state of India i.e. Uttar Pradesh during April 2023- July 2023. Authors applied stratified sampling technique to collect the data. Smart PLS4 was utilized and SEM and mediation analysis was conducted to study the data. The study concluded that SI, PE, and C influence BIAFPS and AAFPS respectively in a very positive manner and also full mediation of BIAFPS was established among SI, PE, C and AAFPS. The importance of FinTech integration in promoting sustainable farming practices eventually leading to sustainable development is emphasized in this study. Stakeholders may unleash the transformative potential of FinTech and promote a paradigm shift towards environmentally responsible and commercially successful agriculture systems in developing countries by addressing the factors driving adoption.

In applied physics and engineering, non-linear fractional types of partial differential equations become increasingly prominent as an estimation technique to explain a wide variety of non-linear phenomena. Throughout this study, we choose to use an adaptable extended tanh-function scheme over a conformable derivative in order to construct a comprehensive closed-form traveling wave solution of the time fractional Cahn–Hilliard and modified Kawahara equations. The mentioned equations, have been used as frameworks for various phenomena such as quantum theory, geosciences, water wave mechanics, and solitary waves in shallow water in the porous medium. The different values of the fractional order of the model can be employed successfully to organize the wave velocity. We recognized numerous forms of solutions by using the computational software, namely solitons type, bell types, kink types, periodic types, multiple periodic, single soliton, singular kink, and other types of solutions that are illustrated using 3D, contour, and spherical. The attained results have effectively handled the previously stated phenomena in various fields of engineering and mathematical physics. The results originating in this study were confirmed by the computational software, which was used to rewrite them as non-linear fractional partial differential equations. We ensure that the technique is revised to make it more effective, pragmatic, and credible, and we pursue more comprehensive exact results for traveling waves, and then for solitary waves.

Background: Antimicrobial resistance is one of the research priorities of health organizations due to increased risk of morbidity and mortality. Outbreaks of nosocomial infections caused by carbapenem-resistant Acinetobacter Baumannii (CRAB) strains are at rise worldwide. Antimicrobial resistance to carbapenems reduces clinical therapeutic choices and frequently led to treatment failure. The aim of our study was to determine the prevalence of carbapenem resistance in A. baumannii isolated from patients in intensive care units (ICUs). Methods: This cross-sectional study was carried out in the Department of Microbiology, Basic Medical Sciences Institute (BMSI), Jinnah Postgraduate Medical Centre (JPMC), Karachi, from December 2016 to November 2017. Total 63 non-repetitive A. baumannii were collected from the patients’ specimens, admitted to medical and surgical ICUs and wards of JPMC, Karachi. The bacterial isolates were processed according to standard microbiological procedures to observe for carbapenem resistance. SPSS 21 was used for data analysis. Results: Out of the 63 patients, 40 (63.5%) were male. The age of the patient ranged from 15-85 year, with average of 43 year. 34.9% patients had been hospitalized for 3 days. Chronic obstructive pulmonary disease was present in highest number with average of 58.7% for morbidity. Number of patients on mechanical ventilation was highest (65.1%). All isolates were susceptible to colistin. The resistance to ampicillin-sulbactam, ceftazidime, ciprofloxacin, amikacin, piperacillin- tazobactam and meropenem was 82.5%, 81%, 100%, 87.3%, 82.5% and 82% respectively. Out of 82% CRAB, 77% were obtained from ICUs. Conclusion: This study has revealed the high rate of carbapenem resistance in A. baumannii isolates in ICUs thus leaving behind limited therapeutic options.

Background: There were a number of restorative materials, which were fulfilling the requirements of a dental restorative material partially or somewhat completely with some limitations. 3M ESPE recently introduced a new dental restorative material, Filtek™ Silorane, which was claimed to fulfil the main objective completely, which was even confirmed by the recent studies. In order to make the best available dental restorative material an ideal restorative material it has to be bioactive. This study has been planned for the invention of a Silorane based bioactive material. It was proposed to incorporate the known bioactive materials (Hydroxyapatite and Fluoroapatite) in Silorane and analyze its chemical characterization. The main objective of a restorative dental material is to restore the function and aesthetics of the dental patient. Methods: Hydroxyapatite and Fluoroapatite were synthesized and incorporated in Silorane by 5, 10, 40, 50 and 60% weight ratio. The resultant samples were chemically analyzed by FTIR and Raman Spectroscopy. Results: The incorporation of synthesized Hydroxyapatite and Fluoroapatite into Silorane resulted in the innovation of a new bioactive restorative material. The resultant FTIR and Raman spectra shows that the Silorane incorporated by 40, 50 and 60% hydroxyapatite and fluoroapatite were altering the chemical structure of Silorane while spectrum of the samples with 5 and 10% of hydroxyapatite and fluoroapatite shows the apatites presence without changing the chemical structure of Silorane. Conclusion: A novel bioactive restorative material was invented by the incorporation of synthesized nano-Hydroxyapatite and Fluoroapatite into Filtek™ Silorane. The chemical characterization showed that bond was formed between the apatites and siloxane functional groups which results in the best available dental restorative material.

An innovative approach to this research is to evaluate the effects of different concentrations of biodiesel with premixed ammonia energy in a dual-fuel combustion engine and determine the best operating conditions for optimal engine responses. Experimental results of a 40% ammonia energy premixing fraction with direct injection of a biodiesel blend of 30% (40%AEPF + B30) at 80% load improved brake thermal efficiency (BTE) by 8.42% while reducing brake specific energy consumption (BSEC) by 13.28%. It also reduces the hydrocarbon (HC) emission by 16.8%, the carbon monoxide (CO) emission by 15.06%, and the smoke opacity by 26.8%. As opposed to this, carbon dioxide (CO2) and nitrogen oxide (NOx) emissions are elevated by 21.5% and 16.54%, respectively, relative to the 40% ammonia energy premixing fraction with direct injection of diesel (40%AEPF + Diesel). Thereafter, the ANOVA technique is also utilized to analyze the impact of operational parameters, and the findings show that the responses are statistically significant. Additionally, the results are utilized in a grey relational analysis (GRA) based optimization process to determine the appropriate operational variables. According to optimization results, 40%AEPF + B30 at 80% load provides the most efficiency and fuel economy. It is capable of being used as an alternate fuel instead of conventional diesel.

This paper presents a robust hybrid super-twisting reaching law-based sliding mode controller in conjunction with a complementing recursive backstepping controller for DC-DC boost converters with constant power loads (CPLs). The key control goal is to keep the DC-bus voltage steady and constant while continuously powering the CPL. The nonlinear dynamical model of the DC-DC boost converter is translated into Brunovsky’s canonical forms, utilizing the precise feedback linearization technique to attain this control goal. It is worth mentioning that the dynamical model has been modified with bounded external uncertainties to demonstrate the restraint performance for huge loads and input voltage variations. The proposed hybrid controller is then constructed using this model. Furthermore, to ensure the large signal stability of the whole system under the designed control law, the Lyapunov stability theory is used. Numerical simulation analysis are carried out in MATLAB/Simulink to validate the performance of the designed robust controller for a DC–DC boost converter feeding a CPL when the operating point change. The results show that the designed controller outperforms both existing nonlinear controllers and a traditional PI controller in terms of ensuring the system’s stability and fast response in a variety of operating modes. Finally, in order to confirm the theoretical design and simulation results of the designed robust hybrid controller, experimental results are also provided in this paper.

The development of high wear/erosion resistance materials for defense application is found to be a challenging task among the researchers. The current study investigated the reinforcement effect of TiC and Rice husk ash (RHA) on Al6061 hybrid composites made using a two-step ultrasonic-assisted stir casting process. Four distinct Al6061 hybrid composites were created with varying TiC weight percentages (3, 6, 9, and 12 wt.%). RHA weight percentage was kept constant at 3%. The solid particle erosion test was carried out on each composite using a Taguchi L27 (33) orthogonal array. Experiments were conducted by varying the erodent particle impact angle, erodent velocity, and erodent discharge rate. The effect of each parameter on the erosion rate of the TiC/RHA Al6061 composite was discussed. The composite containing 12% TiC and 3% RHA showed improved erosion resistance. It was found that on increasing the TiC addition in the composite resulted in an increase in erosion resistance. SEM images were used to examine the surface failure on the composites caused by erodent impact.

Abstract Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne zoonotic disease with high morbidity and case fatality rates (CFR) in humans, especially among the high-risk groups, including butchers. Therefore, a cross-sectional study was conducted in Kabul City to determine the knowledge gaps and risky behaviors of Kabul City’s butchers about CCHF. Data were collected from 123 butchers by structured interview using a pre-designed questionnaire. The finding of the present study revealed that of the total CCHF cases in the butchers, their family members, and co-workers, 37.9% died. Nearly half of the butchers (45.0%) experienced splashing of animal blood on their faces, while 70.1% of them had cut their hands with a knife during their occupation life. In addition, 27.0% of the respondents held the knife in their mouth while dressing the animal carcasses, and 27.3% of the butchers consumed raw liver. Hand washing and PPE application were practiced by 94.3% and 22.8% of the respondents, respectively. About 92% of the respondents had heard about CCHF, and 67.4% of the participants had good knowledge about CCHF. The knowledge level of the respondents (poor or good) was statistically related to their age, history of blood splash in the face and eyes of the butchers, holding work knives in the mouth, blowing the skin of animals, hand washing with soap and water, and slaughter places (p < 0.05). A medium correlation between education and total knowledge of the butchers was observed (r = 0.3; p < 0.05). It is necessary to change such high-risk behaviors by continually coordinating awareness campaigns through mass media, workshops, and informative leaflet distribution to improve knowledge. One Health impact statement CCHF is a multifaceted zoonotic disease where tick bites, direct contact with the tissue, secretions, and body fluids of the infected animals and humans, and consumption of raw milk, meat, and liver of infected animals are the main transmission methods of the disease to humans. Although livestock including cattle, sheep, goats, camels, and ostrich are the main reservoirs of the virus, they are infected in subclinical form, and humans are the only hosts where fatal clinical disease develops. Since the CCHF virus circulates in nature between tick-vertebrates-ticks, the only method that can effectively control and prevent the disease in humans is by using a targeted One Health approach between the human, veterinary, and environmental health sectors. Meanwhile, it has been proven that public awareness campaigns through the use of mass media, community leaders, and integrated workshops are the most effective tools to prevent the transmission of the CCHF virus from infected animals and ticks to humans.

The time-fractional unstable nonlinear Schrödinger (NLS) equations capture the time evolution of disturbances within media, tailored for describing phenomena in unstable media to help model and understand the intricate dynamics of systems prone to instability, the behavior of disturbances in complex and unstable environments and many more. In this study, we investigate the exact and analytical solutions of time-fractional unstable nonlinear Schrödinger (NLS) equations with the time-fractional β-time derivative, a powerful technique known as the sine-Gordon expansion (SGE) method is used. Non-locality behaviors that cannot be modeled using classical calculus are included in this equation. It discusses the relationships between waves of different frequencies or the same frequency but with distinct polarizations having a diverse set of key applications in nonlinear optics. We begin by using a fixed-point argument to prove the existence and uniqueness of solutions to the time-fractional problem under consideration. The partial differential equation of fractional order is transformed into an ordinary differential equation using the complex transformation. Numerous exact solutions are achieved with all the arbitrary parameters. The specific values of the obtained solutions' associated parameters are analyzed graphically with 3D plots as multiple soliton types of different wave solutions with the help of symbolic software. Understanding that the features of the solutions are far from intuitive because it is based on the parameter selection from the figures is difficult. The main objective of this paper is to investigate some fresh and further general solitary wave solutions to the suggested equation that properly explain the above-stated phenomena.

The most popular design for solar energy receivers that achieves great thermal performance is a cavity receiver. This study experimentally compared two novel shapes of cavity receivers: conical-cylindrical-conical (CCC) and double cylindrical, with conventional conical and cylindrical designs, to examine the effectiveness of solar energy receivers. The findings indicate that the CCC shape demonstrated a higher level of efficiency, as it performed better than all other shapes that were examined. Results emphasized the influence of radiation intensity on receiver efficiency, with all shapes exhibiting improved performance at higher intensities, maximizing at 1000 W/m2. Lower friction factors were desirable for minimizing heat losses and maximizing efficiency, resulting in values of 0.078, 0.09, 0.1, and 0.12 for CCC, conical, double cylindrical, and cylindrical shapes at a flow rate of 2 L/min, respectively. The temperature differences for CCC, conical, double cylindrical, and cylindrical shapes are computed as 6.4 °C, 5.7 °C, 5 °C, and 4.3 °C, respectively. The thermal efficiencies of the CCC, conical, double cylindrical, and cylindrical shapes are computed as 86.5 %, 81.5 %, 79.9 %, and 68.9 %, respectively, at a flow rate of 2 L/min.

Emotion recognition using EEG signals is an emerging area of research due to its broad applicability in Brain-Computer Interfaces. Emotional feelings are hard to stimulate in the lab. Emotions don’t last long, yet they need enough context to be perceived and felt. However, most EEG-related emotion databases either suffer from emotionally irrelevant details (due to prolonged duration stimulus) or have minimal context, which may not elicit enough emotion. We tried to overcome this problem by designing an experiment in which participants were free to report their emotional feelings while watching the emotional stimulus. We called these reported emotional feelings “Emotional Events” in our Dataset on Emotion with Naturalistic Stimuli (DENS), which has the recorded EEG signals during the emotional events. To compare our dataset, we classify emotional events on different combinations of Valence(V) and Arousal(A) dimensions and compared the results with benchmark datasets of DEAP and SEED. Short-Time Fourier Transform (STFT) is used for feature extraction and in the classification model consisting of CNN-LSTM hybrid layers. We achieved significantly higher accuracy with our data compared to DEAP and SEED data. We conclude that having precise information about emotional feelings improves the classification accuracy compared to long-duration recorded EEG signals which might be contaminated by mind-wandering. This dataset can be used for detailed analysis of specific experienced emotions and related brain dynamics.

The definition of the fundamental concepts and the design of the architectural framework for network slicing in fifth-generation communication systems have been successfully concluded; the standardization activities are almost over; and the commercial deployment has already commenced worldwide. To compete for digital supremacy and to be seen as front-runners in the international technological race, researchers from various regions and countries have begun exploring the technical requirements, envisioning potential applications, identifying innovative enablers, developing testbeds for the preliminary validation of several terrestrial and non-terrestrial technologies, and conceptualizing the architectural design for the next generation of mobile communication systems – the sixth-generation (6G) – aiming to connect the human, physical, and digital worlds with a high level of intelligence and openness for the 2030s. In support of such an ambitious vision, this article extends the end-to-end network slicing concepts, methods, solutions, and functioning architectures towards 6G. To this intent, the study first presents several decisive motivating trends behind such an extension of network slicing in order to make forthcoming mobile networks fully slicing-aware. Following that, the paper attempts to highlight the intelligentization of a number of key enabling technologies that will bring a renaissance to network slicing in the next decade. It then proposes a unified architectural framework and its principal building blocks in several layers, paving the way for the implementation of an open and intelligent network and network slicing in 6G. The proposed architectural solution harmonizes the most recent specifications of the relevant de jure and de facto standards development organizations in their applicable layers with the aim of architecting a pre-standard-compliant and preliminary framework for slicing the 6G network. Finally, the article is intended to spur interest and lay the groundwork for further investigations and subsequent research and development by highlighting a number of open research challenges and directions in this flourishing field.