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Abstract Researchers are now avoiding perovskite materials containing lead due to their toxicity and instability in open air and heat. In this study, our research group worked on potassium-based K2MgSiCl6 & K2MgSiBr6 perovskite compounds and thoroughly investigated these materials’ basic behaviours. Goldschmidt and formation energy results certify the structure and thermodynamic stabilities of the studied compounds. The band gaps noted for the K2MgSiCl6 & K2MgSiBr6 perovskites were 2.42 eV & 1.88 eV, respectively. After the mechanical observations of the investigated compounds, it became apparent that both the studied compounds own brittle and anisotropic nature. Based on the tendency to absorb electromagnetic radiations both in the ultra-violet and visible range, it enables the K2MgSiCl6 & K2MgSiBr6 perovskites suitable for detectors, solar cells, and many other optoelectronic devices. The K2MgSiBr6 material showed the first response to the electromagnetic radiations in the visible range and acquired the optical conductivity value of 1219 (Ω.cm)-1; however, K2MgSiCl6 material showed its response to a bit higher energetic photons in the visible range than K2MgSiBr6 material, but its optical conductivity was higher (i.e., 1270 (Ω.cm)-1) than the K2MgSiBr6 material in the visible range.

Abstract Solar cells are increasingly being utilised for both energy harvesting and reception in free‐space optical (FSO) communication networks. The authors focus on the implementation of a mid‐band p‐In0.01Ga0.99 N/p‐In0.5Ga0.5 N/n‐In0.5Ga0.5 N (PPN) solar cell, boasting an impressive 26.36% conversion efficiency (under 1.5AM conditions) as a receiver within an indoor FSO communication network. Employing a solar cell with dimensions of 1 mm in length and width, the FSO system underwent simulation using Optisystm software, while the solar cell's behaviour was simulated using SCAPS‐1D. The received power from the solar cell was then compared to that of four commercially available avalanche photodiode (APD) receivers. Exploring incident wavelengths spanning 400–700 nm within the visible spectrum, across transmission distances of 5, 10, 15, and 20 m, the study presented current‐voltage (IV) and power‐voltage curves. Notably, the InGaN solar cell exhibited superior electrical power output compared to all commercial APDs. In conclusion, the findings underscore that augmenting received power has the potential to enhance FSO network quality and support extended transmission distances.

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study’s results.

Abstract The line of sight (LoS) probability is a key factor for the channel modeling of air‐to‐ground (A2G) communication. However, the existing LoS probability models do not account for the effects of airframe shadowing (AS) and building density, which can cause serious link obstruction and performance loss due to the six‐dimensional (6D) mobility and self‐body of unmanned aerial vehicle (UAV). In this article, a new LoS probability model is proposed that considers the AS and building density for different UAV heights. Adding to this, the AS is derived in terms of UAV framework and 6D mobility. Next, the machine learning (ML) based graph neural network (GNN) method is developed to learn the features and structure of the urban environment and predict the LoS probability. Then, the GNN model is trained and evaluated based on the ray tracing (RT) data to establish the relationship between model parameters and UAV heights under the building density and AS factors. The interpretation and explanation of the proposed GNN model and prediction are also discussed in this article. It is shown from the simulation analysis that the GNN model accurately captures the effects of AS, building height distributions, and UAV heights, with high accuracy compared to the baseline 3GPP, GCM and NYU models.

This paper explores the challenges and potential solutions related to data collection, integration, processing, and utilization in defense manufacturing within the context of Industry 4.0. While Industry 4.0 envisions the integration of various technologies to achieve seamless operations in industries, the unique characteristics of defense manufacturing, such as stringent data limitations and security requirements, make direct translation challenging. Through a comprehensive review of academic literature, key themes were identified, including quality control, digitalization, cyber–physical aspects, sustainability, risk management, ownership of information, and security. Drawing from the reviewed publications, potential solutions were distilled into related approaches, such as data governance frameworks, data exchange standards, blockchain, additive manufacturing, transparent digital supply chains, and smart factories. These solutions present opportunities for the Australian defense manufacturing industry to overcome the identified challenges and leverage the benefits of Industry 4.0, including improved quality control, increased efficiency, enhanced security, and optimized supply chains. By embracing these opportunities, the defense manufacturing sector can successfully navigate the complexities of Industry 4.0 and realize its vision of seamless integration for continued growth and success.

Abstract The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts’ groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems

ABSTRACTMoney laundering (ML) is a critical source of extracting the money illegally from the financial system. It is linked to various types of crimes, including corruption, exploitation of a specific community, drug use, and many others. Detection of ML operations is a difficult task on a global scale due to the large volume of financial transactions. However, it also allows criminals to use financial systems to carry out fraudulent transactions. It mainly concern minimizing the potentially risks associated with money laundering. Anti-money laundering-(AML) tools based on AI-driven applications are now tracking transactions to overcome this challenge. A total of 112 research papers are assessed to identify the literature’s gaps and suggest new directions for the research area accordingly. The findings of this systemic literature review work will not only open new paths for the research community, but will also assist the state agencies in developing an optimal AML system to counter these major issues and provide a healthy environment for their residents. This article seeks to assess the existing situation from various angles and open up new pathways for future research directions to investigate and build high levels of authenticity and security in the financial industry using artificial intelligence (AI).

Background: Anopheles stephensi is a significant malaria vector in Pakistan, and understanding its feeding behavior is necessary to control the spread of malaria. However, limited information is available on the host preferences of A. stephensi in Pakistan. Therefore, we aimed to explore the feeding behavior of A. stephensi, a malaria vector, in the District Khyber, Khyber Pakhtunkhwa, Pakistan. Methods: A total of 7462 mosquitoes were collected between March and September 2021, with 1674 (22.4%) identified as A. stephensi (952 female and 722 male). Among the female A. stephensi, 495 (52%) were blood-fed. DNA was extracted from the blood-fed female A. stephensi mosquitoes using the Ammonium Acetate Precipitation Method followed by PCR analysis, blood meal sources were identified. Nested PCR on 191 pooled samples was used to detect Plasmodium falciparum and Plasmodium vivax. Results: Cattle blood meals were predominant (73%), followed by human (20%) and chicken (7%), with no dog blood meals detected. All individual mosquito samples were negative for Plasmodium falciparum, while two pooled samples (out of 191) tested positive for P. vivax. Conclusion: A. stephensi in Khyber District primarily displayed anthropophagic feeding behavior, with a small portion of the population infected with P. vivax. The results underscore the importance of targeted vector control strategies, environmental management, community engagement and continuous monitoring to suppress malaria transmission.

During the current COVID-19 pandemic, many digital solutions around the world have been proposed to cope with the deadly virus but the role of mobile-based applications is dominant one. In Pakistan, during the current COVID-19 pandemic, an array of mobile health applications (apps) and platforms have been launched to grapple with the impacts of the COVID-19 situation. In this survey, our major focus is to explore and analyze the starring role of mobile apps based on the features and functionalities to tackle the COVID-19 disease, particularly in Pakistan. In this study, over fifty (50) mobile apps have been scrapped from the well-known three different sources i.e. Google Play Store, iOS Play Store, and web source. We developed our own data set after searching through the different play stores. We have designed two criteria such that the first criteria are known as eligibility criteria, while the second one is known as assessment criteria. The features and functions of each mobile app are pinpointed and discussed against the parameters of the assessment criteria. The major parameters of assessment criteria are: (i) Home monitoring; (ii) COVID-19 awareness; (iii) contact tracing; (iv) telemedicine; (v) health education; (vi) COVID-19 surveillance; (vii) self-assessment; (viii) security; and (ix) accessibility. This study conducted exploratory analysis and quantitative meta-data analysis by adopting PRISMA guidelines. This survey article is not only discussing the function and features of each COVID-19-centered app in Pakistan, but it also sheds light on the limitations of every mobile app as well. The results of this survey might be helpful for the mobile developers to review the current app products and enhance the existing mobile platforms targeted towards the COVID-19 pandemic. This is the first attempt of its kind to present a state-of-the-art survey of the COVID-19-centered mobile health apps in Pakistan.

The current COVID-19 pandemic has affected almost every aspect of life but its impact on the healthcare landscape is conspicuously adverse. However, digital technologies played a significant contribution in coping with the challenges spawned by this pandemic. In this list of applied digital technologies, the role of immersive technologies in battling COVID-19 is notice-worthy. Immersive technologies consisting of virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), metaverse, gamification, etc. have shown enormous market growth within the healthcare system, particularly with the emergence of pandemics. These technologies supplemented interactivity, immersive experience, 3D modeling, touching sensory elements, simulation, and feedback mechanisms to tackle the COVID-19 disease in healthcare systems. Keeping in view the applicability and significance of immersive technological advancement, the major aim of this study is to identify and highlight the role of immersive technologies concerning handling COVID-19 in the healthcare setup. The contribution of immersive technologies in the healthcare domain for the different purposes such as medical education, medical training, proctoring, online surgeries, stress management, social distancing, physical fitness, drug manufacturing and designing, and cognitive rehabilitation is highlighted. A comprehensive and in-depth analysis of the collected studies has been performed to understand the current research work and future research directions. A state-of-the-artwork is presented to identify and discuss the various issues involving the adoption of immersive technologies in the healthcare area. Furthermore, the solutions to these emerging challenges and issues have been provided based on an extensive literature study. The results of this study show that immersive technologies have the considerable potential to provide massive support to stakeholders in the healthcare system during current COVID-19 situation and future pandemics.

Abstract A continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts.

Objective: To study the adjuvant per-operative epidural administration of Triamcinolone in lumbar disc surgery to alleviate post-operative pain. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Neurosurgery, Combined Military Hospital, Rawalpindi Pakistan, from Oct 2021 to Sep 2022 Methodology: Hundred patients aged 20 to 80 years, of either gender, with a single-level prolapsed intervertebral disc and nerve root compression were included and underwent unilateral single-level decompression. A pre-operative MRI scan of the lumbo-sacral spine was done to confirm the diagnosis. All patients initially received conservative management (analgesics and physiotherapy) but failed to respond to it. They were divided into two groups by the lottery method. In Group A, an epidural steroid was administered, while Group B acted as the control. Pain was recorded pre-operatively at 24 hours and then at 1 and 2 weeks post-operatively using the Visual Analogue Scale. Results: The pre-operative median VAS of Groups A and B was not significant at pre-operation. After 24 hours of operation, the median VAS of Group A patients was VAS 3 (1), while in Group-B it was 2 (1), (p-value of 0.001). After 1 week, the median VAS of Group-A patients was 2 (2) and Group- B was 3 (1), with a significant p value of 0.024. After 2 weeks, the median VAS of Group-A patients was 1 (1) and Group B was 2 (1), with a significant p value of 0.015. Conclusion: Intra-operative epidural and foraminal administration of steroids in single-level lumbar disc surgery significantly reduce...

Automated early detection and classification of paddy diseases help in applying treatment efficiently according to the detected diseases. Early detection also minimises the usage of chemical substances and pesticides and hinders the spread of the disease to healthy crops. On a broader scale, it aids in halting the global spread of diseases. Thus, it ultimately promotes healthier rice crops and increased yield. In this survey paper, we present a thorough exploration of deep learning (DL) models for the classification of paddy diseases. Our paper delves into the motivation behind this research study, reveals different paddy diseases and their associated symptoms, and unravels various deep-learning models employed for disease detection. We have also discussed strategies used by researchers for improving the performance of DL models, along with adaptations tailored for application-specific contexts. Additionally, we illustrate relevant research findings, explore datasets utilised in this domain, and analyse approaches for data augmentation. Through an exhaustive investigation, we emphasise existing research gaps, challenges, and open issues, concluding in a discussion on avenues for future exploration.

Physical Layer-based Secret Key Generation (PLSKG) between the legitimate nodes from the reciprocal wireless channel is a vastly studied area of Physical Layer Security (PLS). PLSKG aims to secure the wireless link between the legitimate nodes by symmetrically encrypting the wirelessly transmitted information via a secret key that is extracted from the common randomness of the stochastic wireless channel. PLSKG encompasses the intermediate steps of channel sampling, quantization, information reconciliation, and privacy amplification. The PLSKG algorithms are evaluated in terms of quantifiers such as Key Generation Rate (KGR), Key Agreement Probability (KAP), and randomness. The practical PLSKG algorithms (level-crossing algorithms) extract a secret key by analyzing the channel samples and assigning bit sequences to the channel samples lying in different quantization regions. Level-crossing algorithms are lossy and extract a secret key from the central samples of matched excursions between the legitimate nodes. This results in a reduced KGR as there is a scarcity of such matched excursions considering the fast variations of the wireless link between the legitimate nodes. This paper proposes a Two-Round Channel Parsing (TRCP) algorithm that exploits the correlation between the excursion lengths of the channel samples in addition to the sample correlation. TRCP effectively utilize the channel samples by reducing the sample losses incurred by lossy quantizers exploring a new dimension of correlated excursions of the channel samples between legitimate nodes. Simulation results demonstrate that the proposed TRCP scheme enhances the KGR and KAP performance of the secret key and also passes the National Institute of Standards and Technology (NIST) test suite of randomness.

Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling decentralized model training across multiple devices while preserving data privacy. However, the decentralized nature of FL introduces significant security challenges, making it vulnerable to various attacks targeting models, data, and privacy. This survey provides a comprehensive overview of the defense strategies against these attacks, categorizing them into data and model defenses and privacy attacks. We explore pre-aggregation, in-aggregation, and post-aggregation defenses, highlighting their methodologies and effectiveness. Additionally, the survey delves into advanced techniques such as homomorphic encryption and differential privacy to safeguard sensitive information. The integration of blockchain technology for enhancing security in FL environments is also discussed, along with incentive mechanisms to promote active participation among clients. Through this detailed examination, the survey aims to inform and guide future research in developing robust defense frameworks for FL systems.

Hexavalent chromium (Cr (VI)) and para-chlorophenol (4-CP) are prevalent industrial wastewater contaminants that are recalcitrant to natural degradation and prone to migration in aquatic systems, thereby harming biological health and destabilizing ecosystems. Consequently, their removal is imperative. Compared to conventional chemical treatment methods, CW-MFC technology offers broader application potential. Leersia hexandra Swartz can enhance Cr (VI) and 4-CP absorption, thereby improving wastewater purification and electricity generation in CW-MFC systems. In this study, three CW-MFC reactors were designed with L. hexandra Swartz in distinct configurations, namely, stacked, multistage, and modular, to optimize the removal of Cr (VI) and 4-CP. By evaluating wastewater purification, electrochemical performance, and plant growth, the optimal influent hydraulic retention time (HRT) was determined. The results indicated that the modular configuration at an HRT of 5 days achieved superior removal rates and power generation. The modular configuration also supported the best growth of L. hexandra, with optimal photosynthetic parameters, and physiological and biochemical responses. These results underscore the potential of modular CW-MFC technology for effective detoxification of complex wastewater mixtures while concurrently generating electricity. Further research could significantly advance wastewater treatment and sustainable energy production, addressing water pollution, restoring aquatic ecosystems, and mitigating the hazards posed by Cr (VI) and 4-CP to water and human health.

Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance compared to those utilizing cadmium sulfide (CdS). Likewise, CIGS-based devices are more efficient according to their device performance, environmentally benign nature, and thus, reduced cost. Therefore, the paper introduces an optimization process of three-layered n-CdS/p-CIGS/p-GaAs (NPP)) solar cell architecture based on thickness and carrier charge density. An in-depth investigation of the numerical analysis for homojunction PPN-junction with the ’GaAs’ layer structure along with n-ZnO front contact was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Subsequently, various computational optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture were examined. The electronic characteristics by adding the GaAs layer on the top of the conventional (PN) junction further led to optimized values of the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF), and short-circuit current density (JSC) of the solar cell. Lastly, the paper concludes by highlighting the most promising results of our study, showcasing the impact of adding the GaAs layer. Hence, using the optimized values from the analysis, thickness of 5 (μm) and carrier density of 1×1020 (1/cm) resulted in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16 V, 89.52%, and 43.88 (mA/m2), respectively, for the proposed solar cell architecture. The outcomes of the study aim to pave the path for highly efficient, optimized, and robust multi-junction solar cells.

Gallic acid (GA), an important phenolic component, is gaining popularity due to its biological and industrial applications. However, its rapid expansion can be hazardous, causing cancer and gene damage, making the design of a low-cost and fast GA sensor difficult. We used a single-step hydrothermal approach to synthesize MoS2 nanoparticles for colorimetric detection of GA. The nanoparticles were analyzed using techniques like; UV–Vis spectroscopy, FT-IR spectroscopy, SEM, EDX and XRD. The optimization of key parameters such as MoS2 concentration (2.0 mg), temperature (30 °C), and pH (7) resulted in a limit of detection (LOD) of 0.125 × 10−6 M with a dynamic range of 0.5 to 36 × 10−6 M. MoS2 nanoflowers performed as nanozymes in the filter paper-based sensor, catalyzing 3, 3′, 5, 5′-tetramethylbenzidine (TMB) oxidation, while GA acted as an inhibitor to prevent further reaction progression. The detection was made feasible through capturing an image support by an ordinary smartphone and the steady-state kinetic study validated MoS2 nanoflowers' affinity for sensing H2O2. The sensor performed well in real-world samples such as diet tea, green tea, water, blood serum, and urine, with recovery rates ranging from 93.2 % to 102.1 %. Density functional theory calculations were applied to provide an insight into GA-MoS2 binding interactions and changes in electronic properties. With all of these merits, we believe MoS2 nanoparticles can provide low-cost and portable filter paper-based strips as a sensing platform for visual assessment of GA.

Abstract The supply chain management (SCM) of COVID-19 vaccine is the most daunting task for logistics and supply managers due to temperature sensitivity and complex logistics process. Therefore, several technologies have been applied but the complexity of COVID-19 vaccine makes the Internet of Things (IoT) a strong use case due to its multiple features support like excursion notification, data sharing, connectivity management, secure shipping, real-time tracking and monitoring etc. All these features can only feasible through choosing and deploying the right IoT platform. However, selection of right IoT platform is also a major concern due to lack of experience and technical knowledge of supply chain managers and diversified landscape of IoT platforms. Therefore, we introduce a decision making model for evaluation and decision making of IoT platforms that fits for logistics and transportation (L&T) process of COVID-19 vaccine. This study initially identifies the major challenges addressed during the SCM of COVID-19 vaccine and then provides reasonable solution by presenting the assessment model for selection of rational IoT platform. The proposed model applies hybrid Multi Criteria Decision Making (MCDM) approach for evaluation. It also adopts Estimation-Talk-Estimation (ETE) approach for response collection during the survey. As, this is first kind of model so the proposed model is validated and tested by conducting a survey with experts. The results of the proposed decision making model are also verified by Simple Additive Weighting (SAW) technique which indicates higher results accuracy and reliability of the proposed model. Similarly, the proposed model yields the best possible results and it can be judged by the precision, accuracy and recall values i.e. 93%, 93% and 94% respectively. The survey-based testing also suggests that this model can be adopted in practical scenarios to deal with complexities which may arise during the decision making of IoT platform for COVID-19 SCM process.

Abstract Fin-Tech is the merging of finance and technology, to be considered a key term for technology-based financial operations and money transactions as far as Fin-Tech is concerned. In the massive field of business, mobile money transaction security is a great challenge for researchers. The user authentication schemes restrict the ability to enforce the authentication before the account can access and operate. Although authentication factors provide greater security than a simple static password, financial transactions have potential drawbacks because cybercrime expands the opportunities for fraudsters. The most common enterprise challenge is mobile-based user authentication during transactions, which addresses the security issues against fraudsters. The confirmation of a user legitimation before the money transaction is highlighted by mechanisms and technologies from previous studies that may be helpful in user authentication. This study aims to identify the technologies for user authentication and the opportunity for their transformation to mobile money transaction security despite having all the legally required data for a transaction. This proposed review has identified the role of multifactor authentication techniques for authentication to mitigate the risk of fraudulent transactions—the analysis through 92 articles taken from famous publishers. The most relevant articles address authentication problems, of which 54 percent describe money transaction security, and the rest highlight the supporting technology for user authentication. The study platform described the technology-based approaches with the appreciation of new ideas for secure money transactions. The QR code and multifactor authentication are key terms that increased security by 46%. In addition, this also ensures the user is legitimate using advanced technologies and algorithms to predict and discover transaction risks and discourage fraudsters from trying.

In this paper, we explore the integration of building information modeling (BIM) technology to assess carbon emissions, emphasizing the unique contributions to smart and sustainable approaches in prefabricated buildings and focusing on the application of digital construction strategies facilitated by BIM to evaluate carbon emissions in green prefabricated buildings, with a detailed case study on C-House at Southeast University, Nanjing, China. The research methodology involved creating a BIM model of C-House in Rhino and collecting data from the operationalization phase. This research work delves into analyzing the structural components, on-site assembling process, and evaluation of carbon emissions by using a BIM-based assessment, as well as the energy load and consumption of prefabricated components, including sustainable PV panels, to enhance building efficiency and sustainability. The findings uncover the life cycle of C-House, which spans seven stages, compared with the five stages of conventional builds. Currently in its third cycle, C-House exhibits significant reductions of 70.57% in carbon emissions during the second cycle and 43.53% in the first one. This highlights the pattern showing that the prolonged reuse of prefabricated buildings leads to decreasing emissions over time. Such results underscore the potential carbon emission reductions and environmental advantages of reusing green prefabricated buildings. Furthermore, this study provides insights into the entire life cycle of the building, from inception to occupation and post-phase performance evaluation. By employing BIM for modeling, simulation, and analysis, we offer practical insights into the application of smart technologies for sustainable construction practices, significantly contributing to the advancement of green and digital construction technologies.

Abstract The accurate prediction of environmental Se (selenium) adsorption levels is critical for sustainable development and management perception. The concept of sorting massive quantities of data to find important information using machine learning (ML) has recently been applied to environmental remediation, particularly the science-based design of a 'green' carbonaceous and an effective functional material (e.g., biochar and Fe modified biochar) with high Selenium (Se) removal capacity. The present study focuses on presenting ML models that utilize the random-forest (RF) support vector regression (SVR) and SHAP (SHapley Additive exPlanations) models to forecast the adsorption of Se by modified biochar. The RF, SVR and SHAP models, which were constructed using basic surface properties of Fe-modified biochar and environmental conditions showed accuracy and predictive performance for Se (removal capacity in the test group with R 2 of 0.98, 0.98 and 0.95 and RMSE of 0.35, 0.14 and 0.23 mg-kg−1, respectively). The SVR model was highly effective for predicting Se adsorption, indicating potentially higher accuracy than the RF and SHAP models. This may be due to the small size of our data. According to the feature analysis and partial dependence plot analysis of all three models, the most significant component regulating Se adsorption was oxygen (%) followed by carbon (%), temperature, pH and Fe in all three models. The relative importance of variables may offer guidance for researchers to develop improved Se treatment of actual water and wastewater. Moreover, the ML models developed in this study took into account the surface functionalities of biochar and Fe-modified biochar to provide a more accurate prediction of Se removal, and offer a comprehensive guideline for the long-term development of biochar adsorbents for Se removal. Graphical Abstract

Abstract Internet of Things (IoT) driven systems have been sharply growing in the recent times but this evolution is hampered by cybersecurity threats like spoofing, denial of service (DoS), distributed denial of service (DDoS) attacks, intrusions, malwares, authentication problems or other fatal attacks. The impacts of these security threats can be diminished by providing protection towards the different IoT security features. Different technological solutions have been presented to cope with the vulnerabilities and providing overall security towards IoT systems operating in numerous environments. In order to attain the full-pledged security of any IoT-driven system the significant contribution presented by artificial neural networks (ANNs) is worthy to be highlighted. Therefore, a systematic approach is presented to unfold the efforts and approaches of ANNs towards the security challenges of IoT. This systematic literature review (SLR) is composed of three (3) research questions (RQs) such that in RQ1, the major focus is to identify security requirements or criteria that defines a full-pledge IoT system. This question also focusses on pinpointing the different types of ANNs approaches that are contributing towards IoT security. In RQ2, we highlighted and discussed the contributions of ANNs approaches for individual security requirement/feature in comprehensive and detailed fashion. In this question, we also determined the various models, frameworks, techniques and algorithms suggested by ANNs for the security advancements of IoT. In RQ3, different security mechanisms presented by ANNs especially towards intrusion detection system (IDS) in IoT along with their performances are comparatively discussed. In this research, 143 research papers have been used for analysis which are providing security solutions towards IoT security issues. A comprehensive and in-depth analysis of selected studies have been made to understand the current research gaps and future research works in this domain.

The presented study estimated the aboveground biomass (AGB) of Pinus roxburghii (chir pine) natural forests and plantations, and created biomass maps using a relationship (regression model) between AGB and Sentinel-2 spectral indices. The mean AGB and BGB (belowground biomass) of natural forests were 79.54 Mg.ha-1 and 20.68 Mg.ha-1, respectively, whereas the mean AGB and BGB of plantations were 94.48 Mg.ha-1 and 24.56 Mg.ha-1, respectively. Correlation showed that mean diameter at breast height (DBH) and mean height have weak relationships with AGB, and BGB has shown correlation coefficients (R2 = 0.46) and (R2 = 0.56) for polynomial models. Regression models between AGB (Mg.ha-1) of Pinus roxburghii natural forest and Sentinel-2 spectral indices showed a strong relationship with Ratio Vegetation Index (RVI) with R2 = 0.72 followed by Normalized Difference Vegetation Index (NDVI) and Atmospherically Resistant Vegetation Index (ARVI) with R2 = 0.70. In contrast, the lower performance of spectral indices has been shown in regression with plantation AGB. Correlation coefficients (R2) were 0.41, 0.41, and 0.40 for RVI, NDVI, and ARVI, respectively. All indices showed that the distribution of AGB data was not the best fit with the linear regression model. Therefore, non-linear exponential and power models were considered the best fit for NDVI, RVI, and ARVI. A biomass map was developed from RVI for both natural forests and plantation because RVI has the highest R2 and lowest P-value.

The recognition of different activities in sports has gained attention in recent years for its applications in various athletic events, including soccer and cricket. Cricket, in particular, presents a challenging task for automatic activity recognition methods due to its closely overlapped activities such as cover drive, and pull short, to name a few. Existing methods often rely on hand-crafted features as the limited availability of public data has restricted the scope of research to only the significant categories of cricket activities. To this end, we proposed a cricket activities dataset and an intuitive end-to-end deep learning model for cricket activity recognition. The data is collected from online sources and pre-processed through cleaning, resizing, and organizing. Similarly, an intuitive deep model is designed with a combination of time-distributed 2D CNN layers and LSTM cells for extracting and learning the spatiotemporal information from the input sequences. For benchmarking, we evaluated the model on our cricket datasets and four standard datasets namely UCF101, HMDB51, YouTube action, and Kinetics. The quantitative results show that the proposed model outperforms different variants of recurrent neural networks and achieved an accuracy of 92%, recall of 91%, and F1 score of 91%. Our code and dataset is publicly available for further research on https://drive.google.com/file/d/1c9qcAz4q00qvx4yFA3pSudWFczm1cWUL/view?usp=sharing.

Verbs in Arabic Language are preceded with specific prepositions: each preposition is called sila for the verb it precedes to. A difference of opinion exists among exegetists and Quran scholars about these prepositions followed by a non-dedicated verb which are used at variance in rhetoric and syntax. Rhetorically, it refers to metaphorical as of majāz, allegory, symbolism and simile whereas syntactically it clears the position of verb and assists in giving the required meaning accordingly. This research aims at a stylistic, rhetoric and syntactic analysis of these preceding prepositions through an empirical study.

Entrepreneurial ventures are established in large numbers in China. The success rate of these entrepreneurial ventures is lower than that of new startups. Mismanagement and a lack of creative skills among entrepreneurs are cited as reasons for entrepreneurial failure in China. The current study investigates the impact of entrepreneurial networking and new venture intention on entrepreneurial success in China, with psychological capital and entrepreneurial optimism serving as moderators. 483 responses were collected from business students in China for data analysis. The findings of the study reveal that the impact of entrepreneurial networking and new venture intention on entrepreneurial success in China, with the moderating role of psychological capital and entrepreneurial optimism, is significant. The theoretical framework of this research has novelty as it introduces new moderating relationships of psychological capital and entrepreneurial optimism in the model of entrepreneurial success. Practically, this study has revealed that entrepreneurial success can be achieved with entrepreneurial networking, entrepreneurial optimism, psychological capital, and new venture intention. The directions of this research point out additional gaps in the literature that scholars should discuss in subsequent studies.

Bacillus species are well reported plant growth promoting rhizobacteria which play an important role in improving soil fertility, nutrient recycling, and controlling phytopathogens. Their use as biocontrol agents is an ecofriendly strategy but being a rhizospheric community, they could harbor antimicrobial resistance genes. In current study, rhizobacteria were isolated from the mono cropped wheat. Ten out of 50 isolates showed antifungal activity against Fusarium spp., economically important pathogens of wheat. The strain HU-91 with highest antifungal activity showed the biochemical traits of lipopeptide production. It also resisted multiple antibiotics of different classes (index ≥ 0.2) and was categorized as multidrug resistant bacteria. The strain HU-91 was identified as Bacillus velezensis based on its 16S rRNA homology (99 %). Whole genome analysis of B. velezensis HU-91 revealed 76 contigs with a total length of 3,952,697 bp, GC content 46.3 %, and a total of 3912 genes. Functional classification of genome predicted genes involved in drug resistance, and antagonism. A detailed genome analysis identified CAZymes and 10 biosynthetic gene clusters involved in antagonism. Various antimicrobial resistance genes associated with the resistance to different antimicrobial classes such as β lactams, tetracyclines, aminoglycosides, fluoroquinolones, macrolides, sulfonamides, and lincosamides were also found in the genome of HU-91. Furthermore, HU-91 lacked plasmid but harbored a well-developed CRISPR Cas system. The underlying mechanisms involved in the antimicrobial resistance were depicted as intrinsic based on the genes encoding production of antibiotics degrading enzymes and efflux pumps. These findings depict that HU-91 adapts the intrinsic mechanisms of drug resistivity to tolerate environmental stress. The lack of plasmid and presence of CRISPR Cas system advocate its safety to be used as bioinoculant.

Rhipicephalus sanguineus can complete up to four generations per year, depending on factors such as climate and host availability. Biomorphic studies of R. sanguineus were conducted under laboratory conditions. The larval, nymphal and adult ticks were fed on sheep at 250C, 60 % relative humidity (RH) and exposed to daylight. The incubation period was lasted 35 days. The average mean of premoulting and moulting period of larvae was 70 ± 0.91 and 7.4 ± 0.6 days, respectively. The time durations of premoulting and moulting of nymphs were 62 ± 1.66 and 13 ± 6.4 days, respectively. The average developmental periods were 6.09, and 5.15 days for larval pre-feeding, and feeding, respectively. The average developmental periods were 3.54, and 6.11 days for nymphal pre-feeding, and feeding, respectively. The complete life cycle of R. sanguineus was 180 days with range of 120-286 days. The average weights of the unfed larvae, and nymphs were 0.04 ± 0.03, and 0.40 ± 0.09 mg, respectively while 0.60 ± 0.05, and 5.02 ± 0.20 mg for engorged larvae and nymphs, respectively. The index of reproduction efficiency (REI), egg production efficiency (EPE), and the index of reproductive fitness (RFI) averaged 14.99 ± 3.65, 0.31 ± 0.08, and 11.74 ± 3.01, respectively. Scutum length and width of male was 2.2 and 1.2mm, respectively while length and width of female was 1.04 and 0.99 mm, respectively. The current study findings could help the workers in adopting different management studies for ticks in the world.

Objective: To study the efficacy of per-operative angiogram and Doppler ultrasound as an adjunct to assess the cerebral vessels during intracranial aneurysm clipping surgery. Study Design: Cross-sectional study Place and Duration of Study: Department of Neurosurgery, Combined Military Hospital, Rawalpindi Pakistan, from Jan 2020 to Dec 2021. Methodology: Forty-eight diagnosed cases of subarachnoid haemorrhage secondary to ruptured aneurysm were included. All the patients who were not coiled-able (broad-based, multi-lobed, complex) were operated on (craniotomy and clipping of aneurysm). Intraoperative angiogram and Doppler ultrasound of cerebral vasculature were done in all cases to see the patency of vessels and the position of clips. Results: Mean age of the patients was 43.97±8.23 years. 24(50.00%) patients had anterior communicating artery aneurysms, 18(37.50%) had middle cerebral artery aneurysms, whereas 6(12.50%) were of an anterior cerebral artery. Craniotomy and clipping of aneurysm were done in all the cases; among them, 15(31.25%) were operated on early (between 48-96 hrs), whereas 33(68.75%) were operated on after 10-14 days. Per-operatively, cerebral angiogram and Doppler ultrasound were used to assess the cerebral vasculature and position of the clip. About of 5(10.00%) required clip readjustment, 4(8.33%) had parent vessel occlusion, 10(20.83%) required vasodilator therapy due to vasospasm, and 4(8.33%) had residual aneurysm neck. All these were rectified during the same procedure. Conclusion: Per-operative angiography and Doppler ultrasound are useful adjuncts in aneurysmal brain surgery to prevent complications like cerebral ischemia, recurrence, residual aneurysm and re-bleed.

Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low‐cost photovoltaic (PV) cells in miniaturized electronic devices. Indeed, ML is set to significantly contribute to the development of more efficient and cost‐effective solar cells. This systematic review offers an extensive analysis of recent ML techniques in designing novel solar cell materials and structures, highlighting their potential to transform the low‐cost solar cell research and development landscape. The review encompasses a variety of ML approaches, such as Gaussian process regression (GPR), Bayesian optimization (BO), and deep neural networks (DNNs), which have proven effective in boosting the efficiency, stability, and affordability of solar cells. The findings of this review indicate that GPR combined with BO is the most promising method for developing low‐cost solar cells. These techniques can significantly speed up the discovery of new PV materials and structures while enhancing the efficiency and stability of low‐cost solar cells. The review concludes with insights on the challenges, prospects, and future directions of ML in low‐cost solar cell research and development.

Purpose: There is extensive use of the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extensions by researchers and practitioners in the field of businesses especially in banking sectors to measure the adoption of new and improved technologies like mobile banking, e-commerce and its relevance to the consumer behaviors worldwide and particularly in Pakistan where there is lower literacy rate and higher use of technology. This study is focused on sketching the previous studies in the form of a systematic review to provide the new pathways in mobile banking and also suggests a refined and comprehensive model for further study. Design/Methodology/Approach: The twenty-two (22) papers were included regarding the empirical and theory-based research of UTAUT & Internet Banking whereas twenty-six (26) empirical and theory-based evidence from UTAUT2 & mobile banking adoption. Findings: The most important indicators to look at from previous studies in this theory are: social influence, price value, hedonic motivation, behavioral intention and actual use behavior, effort expectancy, facilitating conditions, and performance expectancy. Implications/Originality/Value: It is analyzed from the previous studies that these factors may play a vital role in the adoption of mobile banking and its applications and it is also further suggested that fairer studies are required to find better directions and results

The roots of hyperaccumulators can significantly alter soil pH and thus change the chromium (Cr) availability in the rhizosphere. The pH dynamics in the rhizosphere of Cr hyperaccumulator Leersia hexandra Swartz remains unknown. In this study, the spatial dynamics of pH in the rhizosphere of L. hexandra at different Cr exposure were examined using planar optode (PO). The effects of different Cr concentrations on the biomass, physiological parameters, and soil enzyme activity were investigated. The results showed that pH in the rhizosphere of L. hexandra was highly heterogeneous and followed the root shape. There were obvious soil acidification in all groups and the average pH values in the control, Cr50, and Cr100 groups decreased by 0.26, 0.27, and 0.35 pH unit, respectively. At a certain concentration (50 mg kg–1), Cr significantly increased the plant height and biomass of L. hexandra compared to the control (p

For an exhaustive removal of Cr(VI), three different types of biochar were prepared, modified and evaluated for Cr(VI) removal from wastewater. Magnetic eucalyptus biochar (MBC) was synthesized via a facile pyrolysis process of eucalyptus biochar pretreated with FeCl3 firstly coupled with K2CO3 activation. As a comparison, raw biochar (BC) and FeCl3 modified biochar (FBC) were prepared. The physicochemical property of adsorbent synthesis and removal mechanism was examined using BET, SEM with EDS, zeta potential, XRD, FTIR, XPS and Vibrating sample magnetometer (VSM), and the effect of various reaction conditions and parameters were evaluated for Cr(VI) removal. Results demonstrated that compared to BC and FBC, MBC has a larger specific surface area (870.3264 m2/g), higher content of Fe mainly existed as γ-Fe2O3 and Fe3O4, and higher zeta potential (10.66). This is due to the significant alleviation of pore blockage caused by iron oxides and more efficient conversion of iron oxides with the participation of K2CO3 during modification process. Upon batch tests, the removal efficiency could achieve more than 91% for 200 mg/L Cr(VI) solution with optimum adsorbent dosage (0.01 g) at desired acid condition (pH = 2) within 9 h. The separation behavior of MBC for Cr(VI) was highly identical with elovich and Freundlich model. MBC showed better adsorption performance up to three cycles and under various co-existing ions. Besides, the removal ability of MBC gained an obvious increase of 2–5 folds and 3–10 folds at different initial Cr(VI) concentrations, compared with BC and FBC. Characterization analyses unraveled that physicochemical removal process was responsible for the efficient elimination of Cr(VI) in an aqueous solution, resulting from adsorption, electrostatic attraction, pore filling, complexation, ion exchange, redox reaction and precipitation due to the fabrication of iron oxides into biochar.

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semantic autoencoder (SAE) models where the encoder embeds a visual feature vector space into the semantic space and the decoder reconstructs the original visual feature space. The objective is to learn the embedding by leveraging a source data distribution, which can be applied effectively to a different but related target data distribution. Such embedding-based methods are prone to domain shift problems and are vulnerable to biases. We propose an integral projection-based semantic autoencoder (IP-SAE) where an encoder projects a visual feature space concatenated with the semantic space into a latent representation space. We force the decoder to reconstruct the visual-semantic data space. Due to this constraint, the visual-semantic projection function preserves the discriminatory data included inside the original visual feature space. The enriched projection forces a more precise reconstitution of the visual feature space invariant to the domain manifold. Consequently, the learned projection function is less domain-specific and alleviates the domain shift problem. Our proposed IP-SAE model consolidates a symmetric transformation function for embedding and projection, and thus, it provides transparency for interpreting generative applications in ZSL. Therefore, in addition to outperforming state-of-the-art methods considering four benchmark datasets, our analytical approach allows us to investigate distinct characteristics of generative-based methods in the unique context of zero-shot inference.

Biometric authentication is gaining the interest of private, public, consumer electronics and corporate security systems. For the protection of cyberspace from hackers and other harmful people, biometric security is growing more and more popular among organizations, individuals and enterprises. The word “cyber security” refers to the procedures, techniques, and tools used to safeguard data, network system, computer networks and software from potential attacks online. Online financial service delivery is referred to as “cyber banking.” As the trend of exchanging things has changed, internet banking has grown. Despite the benefits, there have been instances of security threat-related issues with Internet banking. To identify persons, biometric security verifies their physical attributes and behavioral traits. For identification verification, it is the most reliable and effective physical security method. According to biometric authentication, people can be recognized precisely based on their innate behavioral or physical traits. Numerous security measures have been implemented throughout the entire Internet banking service to address these issues. Globally, cybercrime has deep roots and poses a significant threat to the occurrence of criminal or terrorist behavior. Without being addressed by a single authority, these risks can compromise security on the inside as well as the outside. If the cybercrime goes unnoticed, both money and personal data are lost. Internet services and information infrastructure have previously been targeted in assaults. Online fraud and hacker attacks are only two examples of the daily computer-related crimes that take place. The Internet of Things (IoT) is the most reliable foundation for facilitating high-quality, comfortable human living. IoT has had a substantial impact across a range of application domains. Smart gadgets are more vulnerable to hackers because of their rapid development and trust in wireless mechanics for data transport. As a result, the rate of cybercrime is rising daily. Artificial Intelligence (AI)-based cybersecurity emerges because of technological advancement and poses a risk to public safety, personal property rights, and privacy protection for people. The study elaborates on the key features of biometrics system in conventional and Islamic banking to counter the risk of cybersecurity and provide high safety and security to the banking industry. For this systematic literature review, the most suitable and most relevant 101 articles from the reputed online libraries are selected. This analysis absorbed four research questions and pertinent keywords from the period of 2009 to 2022 (a part of 2023 was included).

The global livestock sector is expanding at a faster rate than any other agricultural sub-sector. It employs approximately 1.3 billion people and accounts for approximately 40 % of global agricultural output. This industry is one of the most damaging sectors to the world's increasingly scarce water resources, contributing to water pollution from animal wastes, hormones, antibiotics, chemicals from tanneries, pesticides, and fertilizers used to spray feed crop. Climate change harms livestock productivity by changing ecosystem services i.e. water availability, forage quality and quantity, diseases outbreak, and animals stress due to heat shock and reduction of livestock diversity and breeds. Ecosystem and animal health are under the direct influence of climate change. The prevalence and dispersal of animal illnesses and pathogens are presently on the rise due to climate change. Climate change may have a significant impact on farm animal production performance around the world. Heat stress appears to be one of the intriguing environmental variables affecting animals, making animal production challenging in many geographical locations around the world. Intake of feed can decrease at high temperatures while energy demands increase due to the activation of thermoregulation mechanisms, which harms productivity, growth, and development. This is because thermoregulation mechanisms are activated when temperatures are high. The rate at which animals digest their food is impacted, subjected to heat stress, in addition to their rate of growth and development. The animal's heart rate, rectal temperature, and respiratory rate can all be increased during expose to environments with high temperatures.

Abstract Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

Abstract Water constitutes ≈70–90% of the organism's body by mass and is highly important for its survival. Water contains a variety of chemical contaminants introduced by various sectors, resulting in contamination that has a direct impact on the ecosystem. Various approaches are in practice to tackle these issues. Among these, semiconductor photocatalysis appears to be the cutting‐edge technology for the degradation of wastewater contaminants. Herein, the fabrication of Fe3O4−SiO2 nanocomposite via facile co‐precipitation and Stober methods are reported. Various characterization techniques are employed for the structural elucidation, morphology, crystallinity, and stability of the as‐prepared composite. The nanocomposite is employed in catalytic and photocatalytic applications toward the removal of methylene blue (MB) and methyl orange (MO) dyes from a comparative perspective. It is observed that the composite can remove about 93% of MB and 51% of MO within 7 and 6 h, respectively. These findings indicate that the nanocomposite has a higher MB removal effectiveness than the MO. This trend can be accredited to the difference in the chemical structure of both dyes. The nanocomposite is also evaluated for antioxidant and antileishmanial activity, and it is shown to be quite effective even at very low concentrations.

Objective: To determine the efficacy of pre-operative meningioma embolization in terms of operating time and intraoperative bleeding. Study Design: Prospective longitudinal study. Place and Duration of Study: Department of Neurosurgery, Combined Military Hospital, Rawalpindi Pakistan, from Jan to Dec 2020. Methodology: Twelve diagnosed cases of intracerebral meningioma were included in the study. The patients were diagnosed cases of meningioma as per pre-operative signs and symptoms, radiological confirmation was done with magnetic resonance imaging, and all were subjected to pre-operative embolization of the tumour with catheter advancement in the arterial system and embolizing the target vessels with polyvinyl alcohol particles, followed by craniotomy and excision of the tumour. Results: The mean age of the patients was 47±672. 24 years. The mean operating time (from the incision to closure) was 96.17±10.23 minutes. In contrast, the intraoperative blood loss was 325.33±15.43 ml. Six (50.00%) individuals had parasagittal meningioma, 4(33.33%) were of cerebral convexity, while 2(16.66%) had lesions at sphenoid wing. Conclusion: Meningioma is a quite vascular tumour, and the operative outcome can be improved by pre-operative angioembolization. The reduced vascularity makes the resection easier by decreasing the per-op bleed and operating time. Keywords: Intraoperative bleed, Meningioma, Operating time, Pre-operative embolization.

Abstract The main purpose of this work is to address the problem of robust grid voltage sensor fault‐tolerant control for a single‐phase two‐level rectifier. First, considering a rectifier with disturbances and grid voltage sensor fault, the standard switched system model is constructed. Based on identical transformation, the original system is converted into an augmented system. In the augmented system, the information of the grid voltage sensor fault is included in the state vector. Second, an unknown input observer is designed for the augmented system for sensor fault estimation. The observer error is totally robust against independent deterministic disturbances. In addition, the effect of the disturbance caused by unknown disturbances or a modelling error is minimised with respect to a prescribed H∞ performance index. Finally, the sensor fault‐tolerant control is realised by fault compensation technique, which protects the rectifier from serious performance degradation in the presence of the grid voltage sensor fault. The proposed fault‐tolerant control strategy requires no supplementary hardware and all the inputs involved in the algorithm are directly available. Simulation and experimental results are exhibited to illustrate the capability of the proposed method to tolerate the grid voltage sensor fault.

Teaching science through computer games, simulations, and artificial intelligence (AI) is an increasingly active research field. To this end, we conducted a systematic literature review on serious games for science education to reveal research trends and patterns. We discussed the role of Virtual Reality (VR), AI, and Augmented Reality (AR) games in teaching science subjects like physics. Specifically, we covered the research spanning between 2011 and 2021, investigated country-wise concentration and most common evaluation methods, and discussed the positive and negative aspects of serious games in science education in particular and attitudes towards the use of serious games in education in general.

Globally, with the maturity of information technology, society is now in the information age, magnifying the significance of integrating innovative applications in different financial and other regulatory arenas. The emergence of financial technology (FinTech) based applications has transformed the traditional banking and regulatory systems and enhanced customer satisfaction by providing a balanced environment for protecting its customers from risky behavior or other potential disruptions. Besides these critical applications of FinTech in the financial industry, it is not developed in the Gulf Cooperation Council (GCC) region as in China, United States, and other developed countries. In order to bridge the gaps in the extant by identifying the critical factors involved, this research work presents a systematic analysis of the available literature reported during the period ranging from 2016 to 2021. This systematic mapping of the extant was performed by selecting five different research questions. The key objectives of this systematic research work are, (1) To identify the barriers that restrict the rise of FinTech in the GCC region, (2) Analyze the behavior of different communities regarding the adaptation of FinTech by evaluating the case studies reported, (3) the impacts of FinTech on different communities in the GCC region. (4) The findings of this research work will not only help the state development bodies by encouraging its stakeholders to use FinTech-driven applications in banking, markets, etc. but it will also help the people in maintaining long-term connection with the Ministries of Foreign Affairs, the Dutch regulators, Economic Affairs, the Dutch Central Bank (DNB), as well as with the Authority on Financial Markets (AFM), and (5) this study work will present new research directions for the research community to explore in the near future.

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.

There is an increased demand for high-quality drinking water, which requires the removal of hazardous contaminants emerging from municipal, agricultural, and industrial effluents. An approach towards safe drinking water requires the implementation of various treatment processes for water emerging from natural resources as well as coming in the form of industrial wastewater. Pollutants in water bodies may be present in multiple states, including large suspended particles, dissolved chemical substances, in the form of microorganisms or suspended solids. Over the last few years, researchers have concentrated on developing a practical methodology for extracting pollutants from wastewater. During the course of time, although many advanced and complex wastewater treatment processes have evolved,yet adsorption has its own importance. Owing to its simple operation process, low cost, and less sludge formation, adsorption is being considered the most promising technique for wastewater treatment. In this regard, nanomaterial adsorbents have become a topic of great interest owing to their exceptional properties such as high adsorption strength, greater surface area, and chemical stability. Keeping in view, key features of nanoparticles,researchers have explored the applications of various adsorbents at the nanoscale in addressing wastewater treatment issues. The present review focuses on the use of nano-adsorbents in treating various industrial effluents and also provides a comparative assay in terms of the advantages and drawbacks of these nano-adsorbents,employed in removing hazardous contaminants from industrial effluents.

Human activity recognition (HAR) using wearable sensors enables continuous monitoring for healthcare applications. However, the conventional centralised training of deep learning models on sensor data poses challenges related to privacy, communication costs, and on-device efficiency. This paper proposes a federated learning framework integrating spiking neural networks (SNNs) with long short-term memory (LSTM) networks for energy-efficient and privacy-preserving HAR. The hybrid spiking-LSTM (S-LSTM) model synergistically combines the event-driven efficiency of SNNs and the sequence modelling capability of LSTMs. The model is trained using surrogate gradient learning and backpropagation through time, enabling fully supervised end-to-end learning. Extensive evaluations of two public datasets demonstrate that the proposed approach outperforms LSTM, CNN, and S-CNN models in accuracy and energy efficiency. For instance, the proposed S-LSTM achieved an accuracy of 97.36% and 89.69% for indoor and outdoor scenarios, respectively. Furthermore, the results also showed a significant improvement in energy efficiency of 32.30%, compared to simple LSTM. Additionally, we highlight the significance of personalisation in HAR, where fine-tuning with local data enhances model accuracy by up to 9% for individual users.

Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in the areas of ICT, farming, business, and trade, for example. This study explores the integration of machine learning and MCDM methods, which has been used effectively in diverse application areas. Objective: The objective of the research is to critically analyze state-of-the-art research methods used in intelligent decision support systems and to further identify their application areas, the significance of decision support systems, and the methods, approaches, frameworks, or algorithms exploited to solve complex problems. The study provides insights for early-stage researchers to design more intelligent and cost-effective solutions for solving problems in various application domains. Method: To achieve the objective, literature from the years 2015 to early 2020 was searched and considered in the study based on quality assessment criteria. The selected relevant literature was studied to respond to the research questions proposed in this study. To find answers to the research questions, pertinent literature was analyzed to identify the application domains where decision support systems are exploited, the impact and significance of the contributions, and the algorithms, methods, and techniques which are exploited in various domains to solve decision-making problems. Results: Results of the study show that decision support systems are widely used as useful decision-making tools in various application domains. The research has collectively studied machine learning, artificial intelligence, and multi-criteria decision-making models used to provide efficient solutions to complex decision-making problems. In addition, the study delivers detailed insights into the use of AI, ML and MCDM methods to the early-stage researchers to start their research in the right direction and provide them with a clear roadmap of research. Hence, the development of Intelligent Decision Support Systems (IDSS) using machine learning (ML) and multicriteria decision-making (MCDM) can assist researchers to design and develop better decision support systems. These findings can help researchers in designing more robust, efficient, and effective multicriteria-based decision models, frameworks, techniques, and integrated solutions.