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Abstract The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

Abstract Cybersecurity threats have become more worldly, demanding advanced detection mechanisms with the exponential growth in digital data and network services. Intrusion Detection Systems (IDSs) are crucial in identifying illegitimate access or anomalous behaviour within computer network systems, consequently opposing sensitive information. Traditional IDS approaches often struggle with high false positive rates and the ability to adapt embryonic attack patterns. This work asserts a novel Hybrid Adaptive Ensemble for Intrusion Detection (HAEnID), an innovative and powerful method to enhance intrusion detection, different from the conventional techniques. HAEnID is composed of a string of multi-layered ensemble, which consists of a Stacking Ensemble (SEM), a Bayesian Model Averaging (BMA), and a Conditional Ensemble method (CEM). HAEnID combines the best of these three ensemble techniques for ultimate success in detection with a considerable cut in false alarms. A key feature of HAEnID is an adaptive mechanism that allows ensemble components to change over time as network traffic patterns vary and new threats appear. This way, HAEnID would provide adequate protection as attack vectors change. Furthermore, the model would become more interpretable and explainable using Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The proposed Ensemble model for intrusion detection on CIC-IDS 2017 achieves excellent accuracy (97-98%), demonstrating effectiveness and consistency across various configurations. Feature selection further enhances performance, with BMA-M (20) reaching 98.79% accuracy. These results highlight the potential of the ensemble model for accurate and reliable intrusion detection and, hence, is a state-of-the-art choice for accuracy and explainability.

Abstract Background In today’s digital age, various diseases drastically reduce people’s quality of life. Arthritis is one amongst the most common and debilitating maladies. Osteoarthritis affects several joints, including the hands, knees, spine, and hips. This study focuses on the medical disorder underlying Knee Osteoarthritis (KOA) which severely impairs people’s quality of life. KOA is characterised by restricted mobility, stiffness, and terrible pain and can be caused by a range of factors such as ageing, obesity, and traumas. This degenerative disorder leads to progressive wear and tear of the knee joint. Methods To combat arthritis in the kneecap, this study employs a 12-layer Convolutional Neural Network (CNN) to reach deep learning capabilities. A collection of data from the Osteoarthritis Initiative (OAI) is used to classify KOA. Through the use of medical image processing; the study ascertains whether an individual has this ailment. A sophisticated CNN architecture created especially for binary classification and KOA severity utilising deep learning algorithms is the main component of this work. Results The cross-entropy loss function is an important component of the model’s laborious design that classifies data into two groups. The remaining section uses the Kellgren-Lawrence (KL) grade to classify the disease’s severity. In the binary classification, the proposed algorithm outperforms previous methods with an accuracy rate of 92.3%, and in the multiclassification, its accuracy rate is 78.4% which is superior to the previous findings. Conclusion Looking ahead, the research broadens the scope of this work by gathering information from various sources and using these methods on a wider range of datasets and situations. The potential for major advancements in the field of osteoarthritis detection and classification is highlighted by this forward-looking approach. Furthermore, this method reduces the intervention of medical practitioners and ultimately results in accurate diagnosis. Clinical trial number Not applicable.

Abstract Several parameters affect our brain's neuronal system and can be identified by analyzing electroencephalogram (EEG) signals. One of the parameters is alcoholism, which affects the pattern of our EEG signals. By analyzing these EEG signals, one can derive information regarding the alcoholic or normal stage of an individual. Many road accident cases around the world, including drinking and driving scenarios, which result in loss of life, have been reported. Another reason for such incidents is that riders avoid wearing helmets while driving two-wheelers. Many road accident cases involving two-wheelers, including drinking, driving, overspeeding, and nonwearing helmets, have been reported. Therefore, to solve such issues, the present work highlights the features of an intelligent model that can predict the alcoholism level of the subject, wearing of a helmet, vehicle speed, location, etc. The system is designed with the latest technologies and is smart enough to make decisions. The system is based on multilayer perceptron, histogram of oriented gradients (HoG) feature extraction, and random forest to make decisions in real time. The accuracy of the proposed method is approximately 95%, which will reduce the fatality rate due to road accidents. The system is tested under different working environments, i.e., indoor and outdoor, and satisfactory outcomes are observed.

Hydroponics is a soilless farming technique that has emerged as a sustainable alternative. However, new technologies such as Industry 4.0, the internet of things (IoT), and artificial intelligence are needed to keep up with issues related to economics, automation, and social challenges in hydroponics farming. One significant issue is optimizing growth parameters to identify the best conditions for growing fruits and vegetables. These parameters include pH, total dissolved solids (TDS), electrical conductivity (EC), light intensity, daily light integral (DLI), and nutrient solution/ambient temperature and humidity. To address these challenges, a systematic literature review was conducted aiming to answer research questions regarding the optimal growth parameters for leafy green vegetables and herbs and spices grown in hydroponic systems. The review selected a total of 131 papers related to indoor farming, hydroponics, and aquaponics. The review selected a total of 123 papers related to indoor farming, hydroponics, and aquaponics. The majority of the articles focused on technology description (38.5%), artificial illumination (26.2%), and nutrient solution composition/parameters (13.8%). Additionally, remaining 10.7% articles focused on the application of sensors, slope, environment and economy. This comprehensive review provides valuable information on optimized growth parameters for smart hydroponic systems and explores future prospects and the application of digital technologies in this field.

Abstract The paper addresses the issue of ensuring the authenticity and copyright of medical images in telemedicine applications, with a specific emphasis on watermarking methods. While several systems only concentrate on identifying tampering in medical images, others also provide the capacity to restore the tampered regions upon detection. While several authentication techniques in medical imaging have successfully achieved their goals, previous research underscores a notable deficiency: the resilience of these schemes against unintentional attacks has not been sufficiently examined or emphasized in previous research. This indicates the need for further development and investigation in improving the robustness of medical image authentication techniques against unintentional attacks. This research proposes a Reversible-Zero Watermarking approach as a solution to address these problems. The new approach merges the advantages of both the reversible and zero watermarking techniques. This system is comprised of two parts. The first part is a zero-watermarking technique that uses VGG19-based feature extraction and watermark information to establish an ownership share. The second part incorporates this ownership share into the image in a reversible manner using a combination of a discrete wavelet transform, an integer wavelet transform, and a difference expansion. Research findings confirm that the suggested watermarking approach for medical images demonstrates substantial enhancements compared to current methodologies. Research findings indicate that NC values are often around 0.9 for different attacks, whereas BER values are close to 0. It demonstrates exceptional qualities in being imperceptible, distinguishable, and robust. Additionally, the system provides a persistent verification feature that functions independently of disputes or third-party storage, making it the preferred choice in the domain of medical image watermarking.

Abstract Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.

Abstract The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset’s feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model.

Abstract Direct AC–AC converters are strong candidates in the power converting system to regulate grid voltage against the perturbation in the line voltage and to acquire frequency regulation at discrete step levels in variable speed drivers for industrial systems. All such applications require the inverted and non-inverted form of the input voltage across the output with voltage-regulating capabilities. The required value of the output frequency is gained with the proper arrangement of the number of positive and negative pulses of the input voltage across the output terminals. The period of each such pulse for low-frequency operation is almost the same as the half period of the input grid or utility voltage. These output pulses are generated by converting the positive and negative input half cycles in noninverting and inverting forms as per requirement. There is no control complication to generate control signals used to adjust the load frequency as the operating period of the switching devices is normally greater than the period of the source voltage. However, high-frequency pulse width modulated (PWM) control signals are used to regulate the output voltage. The size of the inductor and capacitor is inversely related to the value of the switching frequency. Similarly, the ripple contents of voltage and currents in these filtering components are also inversely linked with PWM frequency. These constraints motivate the circuit designer to select high PWM frequency. However, the alignment of the high-frequency control input with the variation in the input source voltage is a big challenge for a design engineer as the switching period of a high-frequency signal normally lies in the microsecond. It is also required to operate some high-frequency devices for various half cycles of the source voltage, creating control complications as the polarities of the half cycles are continuously changing. This requires at least the generation of two high-frequency signals for different intervals. The interruption of the filtering inductor current is a big source of high voltage surges in circuits where the high-frequency transistors operate in a complementary way. This may be due to internal defects in the switching transistors or some unnecessary inherent delay in their control signals. In this research work, a simplified AC–AC converter is developed that does not need alignment of high-frequency control with the polarity of the source voltage. With this approach, high-frequency signals can be generated with the help of any analog or digital control system. By applying this technique, only one high-frequency control signal is generated and applied in AC circuits, as in a DC converter, without applying a highly sensitive polarity sensing circuit. So, controlling complications is drastically simplified. The circuit and configuration always avoid the current interruption problem of filtering the inductor. The proposed control and circuit topology are tested both in computer-based simulation and practically developed circuits. The results obtained from these platforms endorse the effectiveness and validation of the proposed work.

Abstract A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.

Detection of aerial activities, including drones and birds, has practical implications for automating bird surveys and developing radar systems for aerial object collision detection. Convolutional neural networks (CNNs) have been extensively utilized for image recognition and classification tasks, albeit prior research predominantly focuses on single-class 'drone' classification. However, a gap persists in achieving high accuracy for multi-class classification. To address the limitations of traditional CNNs, such as vanishing gradients and the necessity for numerous layers, this study introduces a novel model termed ''MobVGG.” This model combines the architectures of MobileNetV2 and VGG16 to accurately classify images as either 'bird' or 'drone'. The dataset comprises 4212 images for each category of 'bird' and 'drone'. The stringent methodology was applied for dataset preparation and model training to ensure the reliability of the findings. Comparative analysis with previous research demonstrates that the proposed MobVGG model, trained on both 'bird' and 'drone' images, achieves superior accuracy (96 %) compared to benchmark studies. Our paper targets researchers and graduate students as its primary audience.

A serious eye condition called cataracts can cause blindness. Early and accurate cataract detection is the most effective method for reducing risk and averting blindness. The optic nerve head is harmed by the neurodegenerative condition known as glaucoma. Machine learning and deep learning systems for glaucoma and cataract detection have recently received much attention in research. The automatic detection of these diseases also depends on deep learning transfer learning platforms like VeggNet, ResNet, and MobilNet. The authors proposed MobileNetV1 and MobileNetV2 based on an optimized architecture building lightweight deep neural networks using depth-wise separable convolutions. The experiments used publicly available data sets with both cataract & normal and glaucoma & normal images, and the results showed that the proposed model had the highest accuracy compared to the other models.

The concept of the Internet of Things (IoT) threat surface refers to the overall susceptibility of smart devices to potential security risks. This vulnerability includes the combined impact of security weaknesses, gaps in protective measures, and potential vulnerabilities within the device OS, installed libraries, and applications, as well as the infrastructure involved. This comprises both identified and unforeseen risks that could potentially compromise the device’s integrity, data, logs, and hosted applications. By minimizing the extent to which the device’s components are exposed, it becomes possible to reduce the vulnerabilities inherent in the device, thereby decreasing its overall threat surface area. This research introduces an innovative framework for assessing Smart IoT cameras within the ecosystem. This framework involves the identification and categorization of webcam devices, followed by an analysis of potential threats based on various exposure indicators present within each layer. Subsequently, this information is used to determine the possible paths through which a device might be compromised, allowing for the evaluation of severity and both maturity levels. The authors present metrics that aid in reevaluating and recalibrating the security levels, considering the discovered threat surface elements. These refined metrics offer a fresh perspective on security, offering valuable insights for stakeholders who are engaged in the development, deployment, and evaluation of the security aspects of such devices.

Persistence behavior is a tactic advanced adversaries use to maintain unauthorized access and control of compromised assets over extended periods. Organizations can efficiently detect persistent adversaries and reduce the growing risks posed by highly skilled cyber threats by embracing creative techniques and utilizing sophisticated tools. By taking a proactive stance, businesses may increase their entire cybersecurity posture by anticipating and mitigating possible risks before they escalate. Security analysts perform thorough investigations and extract meaningful insights from large datasets with greater technical advantage by using Elasticsearch in conjunction with a variety of linguistic tools. This research presents a novel methodology for proactive threat intelligence to identify and mitigate advanced adversaries that use persistent behaviors. The authors designed and set up an Elasticsearch-based advanced Security Information and Event Management platform to offer a proactive threat-hunting strategy. This enables comprehensive analysis and detection by integrating Lucene, Kibana, and domain-specific languages. The goal of this research is to locate hidden advanced enemies who exhibit persistent behavior during cyberattacks. The framework can help improve the organization’s resilience to identify and respond to threats by closely examining activities like boot or logon auto-start execution in registry keys, tampering with system processes and services, and unauthorized creation of local accounts on compromised assets. This study emphasizes proactive actions over reactive reactions, which advances danger detection techniques. This technical study provides security practitioners seeking to improve defenses against new advanced attacks to stay ahead in a dynamic threat landscape.

Abstract The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Abstract The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.

Key Clinical Message This case underscores the critical importance of timely recognition and management of NMDAR encephalitis in adolescents to mitigate potential long‐term sequelae. If a pediatric patient presents with suspected viral encephalitis, autoimmune etiology must be excluded via cerebrospinal fluid antibody assay to guide appropriate immunosuppressive therapy, and improve patient outcomes. Abstract Autoimmune encephalitis particularly involving the n‐methyl‐d‐aspartate receptor (NMDAR) is recognized as a rare cause of acute encephalopathy in pediatric patients. The following case is of a 14‐year‐old female diagnosed with anti‐NMDAR encephalitis who initially presented with fever, episodic convulsions, and loss of consciousness. She subsequently developed right‐sided body weakness, expressive aphasia, and visual hallucinations. Clinical examination revealed prominent neuropsychiatric manifestations such as altered sensorium, motor deficits, hallucinations, and visual disturbances. Cerebello‐bulbar signs were not appreciable in this particular case. She was treated for viral encephalitis but showed no improvement. Laboratory investigations revealed the presence of NMDAR antibodies in the cerebrospinal fluid confirming the diagnosis of autoimmune etiology. The patient demonstrated notable improvement following immunosuppressive treatment.

In the development of image forensics, detection of Copy-Move Forgery (CMF) has become a major challenge due to the proliferation of image forgery techniques. The CMF is widely utilized to alter the content of the original image to spread false information or to use such forged digital images for illegal purposes e.g. false evidence in the court of law, or to blackmailing any individual. This paper presents a new method for CMF Detection (CMFD) that uses the power of Graph Convolution Networks (GCNs) and its multiple layers with ReLU activation, for CMFD and analysis. The aim to use GCN is due to its ability to improve the feature extraction process by utilizing the spatial and structural affiliation between elements in the digital images. Also, the GCN aims to store information about images and use it to graphically describe images with pixels or image areas as features, spatial and correlation relationships as edges. By pulling data from this image, GCN is able to obtain content rich features that are very powerful at detecting CMF regions. In proposed methodology, we utilized Support Vector machine (SVM) for classification and the binary cross-entropy loss, and the Adam optimizer for improving accuracy. Our scheme successfully achieves high accuracy and is effective in CMFD. We use the MICC F220, and CoMoFoD datasets to test the GCN in our proposed CMFD method. Through much testing and evaluation, we have found that GCN has the tremendous ability for CMFD in digital images in term of accuracy.

Brain tumors, a significant health concern, are a leading cause of mortality globally, with an annual projected increase of 5% by the World Health Organization. This work aims to comprehensively analyze the performance of transfer learning methods in identifying the types of brain tumors, with a particular emphasis on the necessity of prompt identification. The study demonstrates how useful it is to use pre-trained models, including models VGG-16, VGG-19, Inception-v3, ResNet-50, DenseNet, and MobileNet—on MRI datasets and used to obtain a precise classification. Using these methods model accuracy and efficiency have been enhanced. The research aims to contribute to improved treatment planning and patient outcomes by implementing optimal methodologies for precise and automated brain tumor analysis, evaluation framework encompasses vital metrics such as confusion matrices, ROC curves, and the achieved Area Under the Curve (AUC) for each approach. The comprehensive methodology outlined in this paper serves as a systematic guide for the implementation and evaluation of brain tumor classification models utilizing deep learning techniques. The integration of visual representations, code snippets, and performance metrics significantly enhances the clarity and understanding of the proposed approach. Among our proposed algorithms, VGG-16 attains the highest accuracy at 97% and consumes only 22% of time as compared to our previous proposed methodology.

Abstract Western European economies are among the top innovative countries in the globe and show a remarkable commitment to environmental sustainability through innovations and renewable energy targets. Particularly, renewable energy transition (RET) may shift Western European economies from unclean to clean energy sources and could reduce pollution. Therefore, we analyze the impact of RET and patents on CO2 emissions in 25 Western Europe countries using a period 1995–2020 and cross-sectional dependence techniques. Moreover, the impact of economic growth is also analyzed to examine the Environmental Kuznets Curve (EKC). The results suggest the presence of EKC with a turning point at GDP per capita of 50,287 dollars and Denmark, Iceland, Sweden, Ireland, Norway, Luxembourg, and Switzerland are found at the second stage of the EKC in the long run. Moreover, RET reduces CO2 emissions in the long and short run, Thus, RET helps to achieve environmental sustainability by reducing CO2 emissions in Western European economies. However, patents have negative effects in the long run but do not affect emissions in the short run. Thus, innovation helps to sustain an environment in the long run.

In this manuscript, gain improvement is observed for an ultra-wideband (UWB) antenna. The proposed arrangement is optimally designed for high gain wideband communications. This arrangement comprises a low profile and easily circuit integrated antenna covering spectrum ranges from 3.1 GHz to 10.6 GHz for low power UWB communications. Extended impedance improvement is also achieved by employing a co-planar waveguide (CPW) feeding mechanism incorporating a castle shaped profile and wider radiation element. Moreover, a periodic structure based single layered frequency selective surface (FSS) for gain improvement is also suggested. This split center resonator (SCR) FSS is designed and employed owing to a smaller footprint and stable performance. The performance of this antenna-FSS arrangement is analyzed by varying the inter-element spacing of SCR, distance between SCR layer and the proposed CPW fed antenna. The simulations and measurements validate at least 5 dBi of gain improvement while offering wideband impedance matching and stable radiation performance. This improved performance and low-profile design makes the proposed arrangement suitable for modern high gain UWB communication services.

This paper discusses the alternating-current (AC) and direct-current (DC) characteristics of Trigate FinFETs. A modified non linear DC model is proposed to predict $I-V$ characteristics with effect of an efficient technique for the extraction of AC small signal parameters. The extraction of small signal parameters using S-measurements can be developed on advance design system (ADS) software which is based on the electrical behavior of the device. It examines the electrical response as it depends on bias voltages and the extrinsic and intrinsic parameters of Si FinFETs. The approaches discussed here are valid over a range of frequency starting from few Hz up to several tens of GHz. In this paper equivalent circuit procedure has been adopted to compute device AC parameters based on its measured AC response. The characteristics were then simulated by developing a Matlab code based on particle swam optimization (PSO) technique and compared with technology computer aided design (TCAD) data. The results shows that the good agreement are achieved between simulated and TCAD data.

More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has promise for forecasting such illnesses, but its opaque nature, difficulty in explaining predictions, and difficulty in recognizing predicted mistakes limit its use in healthcare. Addressing these challenges, our research introduces an explainable ML model designed for the early detection of CKD. Utilizing a multilayer perceptron (MLP) framework, we enhance the model’s transparency by integrating Local Interpretable Model-agnostic Explanations (LIME), providing clear insights into the predictive processes. This not only demystifies the model’s decision-making but also empowers healthcare professionals to identify and rectify errors, understand the model’s limitations, and ascertain its reliability. By improving the model’s interpretability, we aim to foster trust and expand the utilization of ML in predicting CKD, ultimately contributing to better healthcare outcomes.

While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the importance of spatial spillover effects and nonlinear effects of covariates on childhood stunting are inevitable in dealing with an enduring issue of regional health inequalities. The present study aims to investigate the spatial inequalities in childhood stunting at the district level in Pakistan and validate the importance of spatial lag in predicting childhood stunting. Furthermore, it examines the presence of any nonlinear relationships among the selected independent features with childhood stunting. The study utilized data related to socioeconomic features from MICS 2017–2018 and climatic data from Integrated Contextual Analysis. A multi-model approach was employed to address the research questions, which included Ordinary Least Squares Regression (OLS), various Spatial Models, Machine Learning Algorithms and Explainable Artificial Intelligence methods. Firstly, OLS was used to analyse and test the linear relationships among selected variables. Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. Finally, EXAI methods such as SHapley were utilized to identify potential nonlinear relationships. The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. In contrast, low climate vulnerability, high exposure to mass media and high women’s literacy were found to reduce childhood stunting. The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. Furthermore, EXAI methods such as partial dependency plot reveal the existence of a nonlinear relationship between multidimensional poverty and childhood stunting. The study’s findings provide valuable insights into the spatial distribution of childhood stunting in Pakistan, emphasizing the importance of considering spatial effects in predicting childhood stunting. Individual and household-level factors such as exposure to mass media and women’s literacy have shown positive implications for childhood stunting. It further provides a justification for the usage of EXAI methods to draw better insights and propose customised intervention policies accordingly.

The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge computing, and cloudlets. Cloud computing offers services in a centralized way through a cloud server. On the contrary, the fog computing paradigm offers services in a dispersed manner providing services and computational facilities near the end devices. Due to the distributed provision of resources by the fog paradigm, this architecture is suitable for large-scale implementation of applications. Furthermore, fog computing offers a reduction in delay and network load as compared to cloud architecture. Resource distribution and load balancing are always important tasks in deploying efficient systems. In this research, we have proposed heuristic-based approach that achieves a reduction in network consumption and delays by efficiently utilizing fog resources according to the load generated by the clusters of edge nodes. The proposed algorithm considers the magnitude of data produced at the edge clusters while allocating the fog resources. The results of the evaluations performed on different scales confirm the efficacy of the proposed approach in achieving optimal performance.

Abstract Globally, green spaces are considered as a fundamental part of biodiversity to balance the environment in an ecosystem particularly in urban areas. Contrary to this, urban green is declining in the least developing nations particularly due to rapid urbanization. This impact alter the condition of environmental services in term of non-provision and insufficient urban green spaces for the local population. This study takes Shahdara Town as an area of interest that is a congested urban area of Lahore Pakistan, and currently facing social injustice in terms of a dense urban area with few green spaces. The study uses Geographical Information Science for the identification of green spaces, calculation of their total area, and quantification of green cover extent by using NDVI, to comparing them with population to analyzed the environmental justice. To check the social perspective of the population regarding green areas and their importance among urban people, a comparison has been made with the presence of parks in the area, their association with living conditions and quality of life of locals. This social prospective was investigated by applying the Multiple Linear Regression Model (MLRM) by taking the availability of park/urban green in area as dependent variable and accessibility to parks, its total distance from a house, and frequency of visit as independent variables. The resultant analysis shows that according to international standards, per capita green spaces are less in this town, of Lahore. This imbalance provision of environmental services is a challenging task, for policymakers and a hurdle for urban sustainability. Finally, the outcomes of the research provide a documented base evidence and social analysis for environmental sustainability in terms of provisions of environmental services for the urban dwellers in the region.

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Wireless Sensor Networks (WSNs) are revolutionizing the globe with their sensing technologies. This initiative aims to design and deploy a cattle health monitoring system (CHMS) for the agriculture sector for the well-being of livestock and the eradication of hunger. The production and consumption of all dairy and meat products can only be conducted responsibly if the health of the source animal is protected. In this regard, WSNs, a component of the Internet-of-things (IoT) system, can be utilized for monitoring cow health due to their adaptability and portability, which enables them to be applied to expansive domains such as cattle healthcare. The integration of IoT and artificial intelligence enables the prediction of livestock illnesses. The primary purpose of this proposed system is to forecast cattle diseases utilizing real-time data from non-invasive body-area sensors and Artificial Neural Networks (ANN) and to display the expected results to authorized personnel via a web application. Popular authentication schemes are used in this system as it is susceptible to hacking and requires robust network security to protect the confidentiality, integrity, and availability of its resources. The presented results validates the accuracy of the proposed novel system for cow diseases prediction that is approximately 98 percent. The proposed CHMS can assist concerned farmers in remotely monitoring the health of their livestock from a variety of locations and in taking appropriate and timely measures to protect animal health. Technological automation will lower prices and labor inputs while enhancing farm output.

Abstract Background The co‐occurrence of depression among tuberculosis (TB) patients is a critical issue, contributing to poor treatment outcomes, prolonged hospitalisations and increased healthcare expenses. Objective The objective of this study was to assess the feasibility of delivering a co‐designed depression care pathway within TB services in Pakistan. Design Mixed‐method study. Setting and Participants Routine depression screening for TB patients was conducted at three TB facilities in Peshawar, Pakistan, encompassing primary, secondary and tertiary care settings. All patients aged 18 or above (male and female) attending the three TB facilities between November 2021 and February 2022 were included in the study using the consecutive sampling technique. Results A total of 301 people with confirmed TB, within the past 4 weeks, visited the three TB care facilities; 191/301 patients were screened for depression. Approximately 35% of the 191 TB patients screened positive for depression, with varying severity levels. Qualitative findings highlighted the acceptability of integrated depression care, emphasising the importance of open communication and empathetic attitudes. Barriers to integration include stigma, logistical challenges, patient noncompliance and cost burdens. Facilitators included the empathetic attitude of healthcare providers and the availability of mental health services within the same facility. Conclusion There is a high burden of depression in patients with TB, highlighting the pressing need for mental health support in this population. Acceptability of integrated care was evident, with factors such as co‐located mental health services, training healthcare providers and provider empathetic attitudes playing a crucial role. Further research is required to evaluate the effectiveness of the integrated TB‐depression screening systems towards improved health outcomes, implementation, scalability and impact on the broader healthcare system. Patient and Public Contribution To create a more inclusive and comprehensive TB and depression care pathway, we gathered input from both service providers and service users (TB patients, their carers). Reflective meetings with community leaders, social activists and health professionals from various sectors were also conducted during pathway delivery to get their insights. Power, gender and age imbalances were addressed by encouraging participation of patients and carers across gender and age groups. This approach ensured that the perspectives of all stakeholders were considered in the development of the care pathway.

Abstract The population growth and urbanization has caused an exponential increase in waste material. The proper disposal of waste is a challenging problem nowadays. The proper disposal site selection with typical sets and operators may not yield fruitful results. To handle such problems, the exponential aggregation operators based on neutrosophic cubic hesitant fuzzy sets are proposed. For appropriate decisions in a decision-making problem, it is important to have a handy environment and aggregation operators. Many multi attribute decision making methods often ignore the uncertainty and hence yields the results which are not reliable. The neutrosophic cubic hesitant fuzzy set can efficiently handle the complex information in a decision-making problem, as it combines the advantages of neutrosophic cubic set and hesitant fuzzy set. In this paper first we establish exponential operational laws in neutrosophic cubic hesitant fuzzy sets, in which the exponents are neutrosophic cubic hesitant fuzzy numbers and bases are positive real numbers. In order to use neutrosophic cubic hesitant fuzzy sets in decision making, we are developing exponential aggregation operators and investigate their properties in the current study. In many multi expert decision-making methods there are different decision matrices but same weighting vector for attributes. The results of a multi expert decision-making problem becomes more reliable if every decision expert has its own decision matrix along with his own weighting vector for attributes. In this study, we are developing multi expert decision-making method that uses different weights for an attribute corresponding to different experts. At the end we present two applications of exponential aggregation operators in environmental protection multi attribute decision making problems.

In the present research, we consider a biological model of serum hepatitis disease. We carry out a detailed analysis of the mentioned model along with a class with asymptomatic carriers to explore its theoretical and numerical aspects. We initiate the study by using the piecewise fractal–fractional derivative (FFD) by which the crossover effects within the model are examined. We split the time interval into subintervals. In one subinterval, FFD with a power law kernel is taken, while in the second one, FFD with an exponential decay kernel of the proposed model is considered. This model is then studied for its disease-free equilibrium point, existence, and Hyers–Ulam (H-U) stability results. For numerical results of the model and a visual presentation, we apply the Lagrange interpolation method and an extended Adams–Bashforth–Moulton (ABM) method, respectively.

Abstract Environmental pollution is a global concern that has economic and health implications. Therefore, proper estimation using precise modeling can help in decision-making to address this externality. In science and engineering, there are a lot of different theories to help deal with the complex frame of the environment. The prime objective of these theories is to impart a plan of action to handle fuzzy data more precisely. Furthermore, humans need a platform that can correctly assign a value to optimize credence in a belief system. The indeterminacy is further classified into contradiction, ignorance, and unknown by a pentapartitioned neutrosophic set. On the other hand, a cubic set characterizes both the combined and the crisp value. The study introduces pentapartitioned neutrosophic cubic set, as it illustrates all of these attributes, allowing credence to be appropriately handled. The study also explained its operational laws and aggregation operators. Finally, this technique is used to develop and evaluate the air pollution models in major Pakistani cities like Karachi, Lahore, Islamabad, and Peshawar. It will help the legislators to reevaluate current policies to mitigate this externality.

In today’s world, services are improved and advanced in every field of life. Especially in the health sector, information technology (IT) plays a vigorous role in electronic health (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment’s multiple benefits, privacy and security loopholes exist. As the number of users grows, the Electronic Healthcare System’s (EHS) response time becomes slower. This study presented a trust mechanism for access control (AC) known as role-based access control (RBAC) to address this issue. This method observes the user’s behavior and assigns roles based on it. The AC module has been implemented using SQL Server, and an administrator develops controls for users with roles and access to multiple EHS modules. To validate the user’s trust value, A.net-based framework has been introduced. The framework of e-health proposed in this research ensures that users can protect their data from intruders and other security threats.

In 5G cellular communication systems, achieving low latency is crucial to support the ‘tactile’ Internet with response times of less than one millisecond. Traditional initial synchronization methods face challenges due to high delays. This manuscript presents a novel approach using an EXtrinsic Information Transfer (EXIT) chart-aided method to investigate the concurrence of $m$ -sequences using Iterative Soft Sequence Estimation (ISSE) in a communication channel. The ISSE technique leverages the concept of an Automatic Gain Controller (AGC), which gradually increases the gain as the number of chips in the $m$ -sequence generator grows, both at the transmitter and the receiver. Our ISSE method stands out by achieving sequence synchronization at the receiver with as few as $F$ successive chips for a sequence of ( $2^{F}-1$ ) chips. We base our work on the EXIT chart, eliminating the need for interleavers, which introduce transmission delays. To address the delay issue, we exploit the inherent interrelationship of the $m$ -sequence generator’s concern chips, which have a duration of ( $2^{F}-1$ ), as influenced by the Linear Feedback Shift Register (LFSR) in our ISSE model. We observe that low-order Primitive Polynomials (PPs) exhibit lower Erroneous Loading Probability ( $P_{e}$ ) than higher-order PPs at a specific Signal-to-Noise Ratio (SNR). PPs with identical order but fewer connection taps outperform those with more connection taps. The EXIT chart analysis reveals that lower-order PPs exhibit a larger opening tunnel between the outer and inner curves, resulting in higher achievable rates in our proposed system. Moreover, PPs with lower order achieve higher Mutual Information (MI) more efficiently with the assistance of our ISSE system compared to higher-order PPs.

Meloidogyne spp. is an important pest of cucurbits in tunnel farming of vegetables in Pakistan. A cucumber germplasm was evaluated for resistance against the root-knot nematode (RKN, Meloidogyne incognita) based on the number of galls and egg masses recorded under glass house conditions. All the cucumber accessions showed varying responses towards the RKN inoculation. Out of the fifteen cucumber lines, two were found to be moderately susceptible to M. incognita, ten were susceptible while the other three were highly susceptible. For the management of the nematodes, resistance was induced in a highly susceptible cucumber accession (28294) by the application (both drench and foliar) of elicitors, i.e., salicylic acid (SA) and benzothiadiazole (BTH). The application of SA and BTH significantly enhanced the defence mechanism of the cucumber plants when compared to the control. Increased enzymatic activities in the cucumber plants as the result of the elicitor applications were determined through spectrophotometer to correlate the induced resistance. There was a significant increase in the enzymatic activities when compared to the control in the cucumber plants, which ultimately enhanced the resistance as there was a decrease in the number of galls and egg masses per plant. The enzymatic antioxidant activity was also found to increase in response to the nematode infection. Both SA and BTH were reported to play pivotal roles in inducing resistance in cucumber plants against M. incognita.

The thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition, several maximum power points (MPPs) appear on the P/V curve. In multiple MPPs, the true global maximum power points (GMPP) are very important for optimum action. The existing conventional technologies have slow tracking speed, low productivity, and unwanted fluctuations in voltage curves. To overcome the TEG system behavior and shortcomings, A novel control technology for the TEG system is proposed, which utilizes the improved generalized regression neural network and fitness dependent optimization (GRNNFDO) to track the GMPP under dynamic operating conditions. Conventional TEG system control techniques are not likely to trace true GMPP. Our novel GRNNFDO can trace the true GMPP for NUTD and under varying temperature conditions In this article, some major contributions in the area of the TEG systems are investigated by solving the issues such as NUTD global maxima tracking, low efficiency of TEG module due to mismatch, and oscillations around optimum point. The results of GRNNFDO are compared with the Cuckoo-search algorithm (CSA), and grasshopper optimization (GHO) algorithm and particle swarm optimization (PSO) algorithm. Results of GRNNFDO are verified with experiments and authenticated with MATLAB/SIMULINK. The proposed GRNNFDO control technique generates up to 7% more energy than PSO and 60% fast-tracking than meta-heuristic algorithms.

This research manuscript is devoted to investigate a smoking tobacco cancer model by applying some advanced techniques based on second derivative. The snuffing class is included in the construction of our model. The stability of the spread of the cancer disease due to tobacco is examined using the reproductive number concept. It is also advisable to look into the many scenarios in which the infectious classes might decline or vanish. Researchers calculated the reproductive number to create projections for the future. It hardly guarantees a thorough investigation, though. In light of this, the concept of strength number and the study of second derivatives for mathematical models are recommended using Laypunove functions to get more detailed information for future predictions. As a result, we investigate the proposed model using the second derivative technique to compute strengthen number. By calculating the aforesaid number, future proposals can be predicted. Finally, we make an effort to examine the proposed model under fractional order derivative to present some graphical simulations using different fractional order. In this regard, some numerical tools are utilized to approximate the proposed model for graphical investigations.

In this paper, we develop a new mathematical model for an in-depth understanding of COVID-19 (Omicron variant). The mathematical study of an omicron variant of the corona virus is discussed. In this new Omicron model, we used idea of dividing infected compartment further into more classes i.e asymptomatic, symptomatic and Omicron infected compartment. Model is asymptotically locally stable whenever R0

ZnO is an important II-IV n-type direct bandgap semiconductor material that has exhibited wide consideration due to its applications in optoelectronic devices. In the present investigation, untreated and plasma-treated ZnO NPs were fabricated by a sol-gel route and analyzed with XRD, Raman, UV-Vis spectra, and I–V characteristic techniques. To be used in electronic applications, the ZnO nanoparticles were subjected to a plasma treatment process to tune their optical, vibrational mode, structural and electrical properties. XRD confirmed the pure Wurtzite tetragonal phase crystalline nature of produced samples. The average crystalline size, lattice constant, unit cell volume, and porosity were found in the range of 3.81–4.68 nm, a = 3.217–3.323 Å and c = 2.032–2.178 Å, 5.662–11.274 Å3 and 7.09–5.03%, respectively. The Raman spectra revealed that the intensity of Raman bands was increased due to the increase in the vibrational amplitude and increase in force constants with the enhancement in grain size, these spectra results are in good agreement with XRD and reveal the purity of samples. The bandgap found reduced from 3.56 eV to 3.39 eV by plasma treatment. The electrical studies revealed that plasma treatment improves the electrical properties of ZnO NPs without producing any structural distortions. I–V characteristic curves confirmed that the conductivity was increased from 8.3 X 10−6 to 5.5 X 10−4 ℧ cm−1 on plasma treatment which caused an increase in leakage current. The improved structural, vibrational mode, optical and electrical properties of prepared ZnO NPs make them a suitable candidate for application in electronic devices.

Hesitancy is an imperative part of belief system. In order to counter the hesitancy in neutrosophic cubic set (NCS), the notion of neutrosophic cubic hesitant fuzzy set (NCHFS) is presented. NCHFS couple NCS with hesitant fuzzy set (HFS). Operational laws in NCHFS are developed with examples. To meet the challenges of decision making problems, neutrosophic cubic hesitant fuzzy geometric (NCHFG) aggregation operators, neutrosophic cubic hesitant fuzzy Einstein geometric (NCHFEG) aggregation operators, neutrosophic cubic hesitant fuzzy hybrid geometric (NCHFHEG) aggregation operators are developed in the current study. At the end a multi expert decision making (MEDM) process is proposed and furnished upon numerical data of a company as applications.

Objectives: To assess the prevalence of chronic pain, its physical and psychosocial impact on daily life, and the various therapies adopted to alleviate pain. Method: The cross-sectional population-based telephonic survey was conducted from May to July 2021 at Shaukat Khanum Memorial Cancer Hospital, Lahore, Pakistan, and comprised patients of either gender aged at least 18 years suffering from chronic pain who visited the institutional laboratory collection centres. In the first phase, people who were suffering from chronic pain were screened, while in the second phase, data was collected using a detailed questionnaire exploring pain history, treatment and its effects. The data was compiled and analysed using Antlere’s AI based software. Results: Of the 4,801 patients contacted, 757(15.75%) were suffering from chronic pain (15.75%). (201, 20%) subjects reported that there pain score was 5/10 on the numerical rating scale. Back pain was the major complaint (183, 18%) among the subjects. Of the total, 335(44.25%) were having active treatment, and 226 (67%) of them said the medication was effective. Overall, 706 (93%) patients had never visited a pain management specialist. Furthermore, 252 (33%) participants were diagnosed with depression, and 106 (14%) patients said that they were suicidal at some point in life. Conclusion: There was found to be a serious lack of awareness regarding pain management. Key Words: Chronic, Pain, Pakistan, Survey, Prevalence, Back Pain.

Clinical picture of patients taking methamphetamine for long duration includes rampant caries of the smooth surfaces of the whole dentition. The increasing use of methamphetamine in homosexuals is leading to the spread of HIV (human immunodeficiency virus). Easy availability and rapidly spreading nature of this drug (methamphetamine) results in worldwide increase of patients with medical and dental problems. Its effect on human dentition is highly damaging as patients with a beautiful smile begin to present a horrible picture of black, broken, and painful teeth within one year of methamphetamine use. Restoration of aesthetics and function of these teeth is not an easy task, and usually the first step to deal with this condition is counselling the patient to stop using this drug. Knowledge of methamphetamine-induced undesirable effects on the human body is important for the general dental practitioner as referral to mental health services is necessary in this condition.

Objective: To see if transporting paediatric patents to operation theatres using a battery-operated vehicle decreases preoperative anxiety and reduce incidents of postoperative adverse events. Method: The study was conducted from May to August 2019 at the Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan, and comprised paediatric patients aged 3-5 years assessed as American Society of Anaesthesiologists grade I and II who were scheduled to undergo elective intrathecal chemotherapy under general anaesthesia. The subjects were randomised into intervention group A and control group B. Patients in group A were part of an incentive-based game in the holding bay area and were transported to the operation theatre using a battery-operated toy vehicle. Those in group B watched a pre-decided cartoon on television screen in the holding bay and were transported further using wheel chair. Induction of anaesthesia was done in the presence of guardians in both the groups. Patient anxiety was assessed using the modified Yale Preoperative Anxiety Scale in holding bay area T0, prior to induction of anaesthesia T1, and postoperative anaesthesia-related recovery T2. Data was analysed using SPSS 22. Results: Of the 36 patients, 18(50%) were in group A; 12(66.6%) boys and 6(33.3%) girls with mean age 4.01±0.48 years. The remaining 18(50%) subjects were in group B; 13(72.2%) boys and 5(27.8%) girls with mean age 3.96 years. The difference in anxiety between the groups at T0 was non-significant (p=0.73). At T1 and T2, the scores were significantly lower group A compared to group B (p

The global economy has recorded an unprecedented increase in growth rates in the last few decades occasioned by the expanding interconnectedness among countries through exchanging goods and services. The expanding production base has increased the demand for fossil fuels, specifically crude oil, both in local and foreign markets, leading to inevitable imports. Despite this, factors driving the increasing demand for oil imports have not been extensively researched thus calling for an empirical verification on the matter. Hence, this study contributes to the literature by investigating the tripartite effects of transportation infrastructure, economic growth, and renewable energy on crude oil imports. The empirical evidence relies on the autoregressive distributed lag (ARDL) model for bound test cointegration employed to obtain long- and short-run estimates. The empirical results from the study's analysis show that renewable energy hinders crude oil importation while transportation stimulates it in both economies. The inducing role of economic growth is substantial only in China, and the significant effect of foreign direct investment and industrialization prove to be fundamental in driving crude oil imports. Consequently, the study concludes that other explanatory variables contribute to crude oil dependence in China and India, asides from renewable energy. The post estimation tests conducted further accentuate the validity and reliability of the empirical outcomes. Drawing from the empirical outcome, the study suggests the criticality of renewable energy promotion as a viable policy decision to resolve the growth of oil dependence in the countries under study.

Abstract Automatic modulation classification is a task that is essentially required in many intelligent communication systems such as fibre‐optic, next‐generation 5G or 6G systems, cognitive radio as well as multimedia internet‐of‐things networks etc. Deep learning (DL) is a representation learning method that takes raw data and finds representations for different tasks such as classification and detection. DL techniques like Convolutional Neural Networks (CNNs) have a strong potential to process and analyse large chunks of data. In this work, we considered the problem of multiclass (eight classes) classification of modulated signals, which are, Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16 and 64 Quadrature Amplitude Modulation corrupted by Additive White Gaussian Noise, Rician and Rayleigh fading channels using 3D‐CNN architectures in both frequency and spatial domains while deploying three approaches for data augmentation, which are, random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross‐validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10‐fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10‐fold CV without augmentation in the frequency domain and we found learning in the spatial domain to be better than learning in the frequency domain.

Investor sentiment is believed to play an increasingly significant role in business and economic activities. By analyzing data collected from a sample of listed nonfinancial firms in Pakistan for the period 2009–2018, we quantify investor behavior and how it affects market returns, cash flows, discount rates, and firm performance. We find that investor sentiment has a significant impact on market activities, and our findings are in line with existing behavioral finance theories. Not only does our study offer theoretical confirmation of the significance of investor sentiment in aggregate market- and firm-level indicators, but it also offers new insights concerning market-based, news-based, and social media–based sentiment in the context of the Pakistani market.

A smart energy management controller can improve energy efficiency, save energy costs, and reduce carbon emissions and energy consumption while accurately catering to consumer consumption habits. Having integrated various renewable energy systems (RESs) and a battery storage system (BSS), we proposed an optimization-based demand-side management (DSM) scheduler and energy management controller (SEMC) for a smart home. The suggested SEMC creates a DSM-based operational plan regarding user-centered and comfort-aware preferences. Using the generated appliances operation plan, consumers can reduce energy costs, carbon emissions, peak-to-average ratio (PAR), improve their comfort in terms of thermal, illumination, and appliances usage preferences. A schedule for residential consumers is suggested using ant colony optimization (ACO), teaching learning-based optimization (TLBO), Jaya algorithm, rainfall algorithm, firefly algorithm, and our hybrid ACO and TLBO optimization (ACTLBO) algorithm. Five existing algorithms-based frameworks validate the DSM framework that relies on ACTLBO. The results validate that the integration of RESs and BSS, and adapting our proposed algorithm and SEMC under demand response program real-time price reduced the energy bill costs, PAR and CO2 in Case I: only external grid (EG) usage by 42.14%, 22.05%, and 28.33%, in Case II: EG with RESs by 21.79%, 11.27%, 17.02%, and in Case III: EG with RESs and BSS by 28.76%, 41.53%, 21.86%, respectively as compared to without employing SEMC. Moreover, the user-comfort (UC) improvement index-ratio with scheduling using ACTLBO is 7.77%, 24.73%, 5.00%, and 3.43% in aspects of indoor air quality, average delay, thermal, and visual, respectively. Simulation results show that the proposed DSM-based framework outperforms existing frameworks to reduce energy cost, reduce carbon emission, mitigate peak loads, and improve UC.