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ABSTRACT. This article introduces NauSim, an open-source simulation tool designed for developing control algorithms for Autonomous Underwater Vehicles (AUVs). NauSim is targeted at researchers and developers in underwater robotics, with a focus on Machine Learning (ML) based applications. Key design principles include a clean, flexible, and modular architecture that can easily integrate with various existing control paradigms. The simulation tool is made in Python, acknowledging its prominence in ML research. NauSim emphasizes simplicity in deploying control algorithms from the simulator to target hardware. To ensure applicability in real-world scenarios, NauSim strives to provide an experience that closely mimics real-life conditions, encompassing both sensory and physical interaction models.

ABSTRACT. The fast growth of digital communication via smartphones and social media has made textual data as one of the most essential forms of people's expressions. Accurate emotion discovery from text is thus important for applications in customer sentiment analysis and interactive technologies, be it a web, desktop, or mobile application, an intelligent robot, or minor connected devices. This paper is intended to evaluate the performance of five state-of-the-art transformer models, including BERT, ALBERT, DistilBERT, Transformer XL, and RoBERTa, for multiclass emotion detection using the IEMOCAP dataset. From the results, it is observed that DistilBERT has the highest accuracy of 79.2%, implying an effective approach in balancing model size and performance. These research show that smaller models like DistilBERT often outperform larger models by reducing their overfitting and computational demands. The findings also highlights the need for task-specific fine-tuning and optimization in emotion detection.

ABSTRACT. Peripheral nerve injuries (PNIs) present significant clinical challenges due to their potential to cause chronic disability and substantial healthcare costs. Traditional treatments often fail due to inconsistent outcomes and long recovery times. In this paper, we present a comprehensive review that summarizes current advancements in PNI treatment and outlines a framework for overcoming existing barriers and improving patient outcomes through innovative therapies. Unlike reviews that focus solely on the limitations of existing treatments, our approach provides a detailed analysis of future challenges and integration strategies. Innovations in neurostimulation, materials science, and miniaturization are driving the development of next-generation neural interfaces aimed at restoring sensory and motor functions. Understanding the detailed electrical model of the device-tissue interface is crucial for optimizing electrode design for improved neural recording and stimulation. Continuous research and development are essential for overcoming existing challenges and providing widespread access to these treatments. We highlight the importance of a multidisciplinary approach to achieve more effective and personalized therapies for PNI, contributing valuable insights to the field and setting the stage for future advancements.

ABSTRACT. This article analyses the effects of seasonal variations and weather effects on the electricity consumption of residential consumers. To optimize energy usage, precise load profile forecasts are critical, and Demand Side Management (DSM) is a key strategy. DSM reduces the cost of energy acquisition and the associated penalties by continuously monitoring energy use and managing appliance schedules. The proposed approach utilizes DSM-assisted agent-based modeling to anticipate electricity usage patterns for 300 households. It also models inductive and non-inductive loads separately and selects specific loads to operate at specific times. This research work investigates the impact of climate on residential electricity usage, including air conditioning and heating demands and overall power consumption. Results are compared with a similar study to validate our approach.

ABSTRACT. In this article domestic base and shift based load profile are estimated for effective Demand Side Management (DSM). Domestic electricity consumption in Pakistan accounts for approximately 40% of total electricity sales. Effective DSM can reduce electricity consumption. Power generation companies are increasingly recognising the importance of analysing their customers' consumption patterns. This article proposes an agent-based modelling approach for forecasting household electricity consumption profile. AnyLogic software is used for establishing the model that uses variables and functions to estimate base and shiftable load of three hundred households. This article also estimates the weekly and 24-hour load profile for effective DSM operations by smart grid infrastructure. The results show that the agent-based simulation closely approximates real-time data, with a difference of only 2.4%.

ABSTRACT. The linearly increasing demand of electricity demand around the globe has witness a heavy burden on the electrical power infrastructure ranging from power generation to utilization. The most of fuel used for power generations are from fossil fuels. The increasing demand and its fossil fuel consumption made the world to increase the global temperature and prices of the electricity. Therefore, power experts and environmentalist has urged the solution to use the viable source of energy which should be free and less pollutant. Among the different options, the solar energy is free and non-exhausted. But its variability and use in low voltage grid is really challenging. Therefore, this paper proposes solar photovoltaic (solar PV) based distributed generation in radial distribution system. The intermittency of solar irradiance and its capacity and location in distribution system greatly affects the power network dynamics. Therefore, this paper utilizes the multi-state time series modeling of solar PV for effective prediction of solar generation into grid. Optimal capacity and location is further evaluated by AI technique such as particle swarm optimization technique. Four case studies have conducted to maximize the network parameters i.e. without distributed generation (DG), solar PV, capacitor, both solar PV and capacitor. The capacitor is for providing reactive power in the network. Moreover, proposed model have been tested on IEEE 33 radial distribution system with stochastic load model considering the power loss, voltage profile and voltage stability index as system objectives.

ABSTRACT. Hearing or speaking deficiency raises a huge issue in communication thus making it difficult for the hearing-impaired population to participate fully within society and educational environments. Moreover, deaf children who use sign language as their first language face even greater challenges as they try to access pedagogical resources predominantly designed for oral learners such as the Noorani Qaida. To address this, we have developed a deep learning-based system that can recognize Arabic Sign Language (ArSL) gestures. This system enables people with hearing impairments to learn the Noorani Qaida using sign language and be part of Quranic education. It is fed video or images and recognizes sign language in real time with the help of CNNLSTM architecture. We are particularly interested in developing a learning system suitable for children, especially when learning Arabic. The system has shown good results regarding precision and student performance tests.

ABSTRACT. The tremendous development in the use of smart applications, modern communication technologies, and the Internet of Things revolution has made it necessary for the city administration to use these technologies to improve and develop cities and exploit them to solve the problems facing residents and make their lives better. The problems of traffic congestion, climate change, energy sources, natural resources, transportation, and health are among the most important dilemmas that can be faced using the concept of smart cities. This research summarizes the current state of Amman City as a case study to analyze its reality and the extent of its progress in the field of using smart city technologies by studying existing or future projects. There are many challenges facing the city of Amman in its use of smart city systems, such as financing problems, lack of technical expertise in this field, and the lack of integrated and comprehensive planning for a smart city. The researchers developed proposals and recommendations related to technologies and services that contribute to overcoming the dilemmas facing Amman in transitioning towards a smart city similar to cities in the developed world.

ABSTRACT. Robotic Container for Protecting Sensitive Hardware.

ABSTRACT. Naegleria fowleri, a free-living, thermophilic ameba, is found worldwide in warm fresh water (lakes, rivers, and hot springs) and soil. The only human infecting Naegleria is fowleri. Most of the time, Naegleria fowleri feeds on bacteria in freshwater. Rarely, the amebae can infect humans through the nose during water activities. The ameba moves from the nose to the brain, causing primary amebic meningoencephalitis (PAM). which is usually fatal. The aim of this work was to examine the resistance of a strain of N. fowleri from Pakistan to antibiotics and determine its phylogenetic links by examining its genome using bioinformatics tools. The mitochondrial genome sequence of N. fowleri Karachi strain NF001 was acquired and subsequently compared to the genomes of other strains using the BLAST program. An investigation was conducted using MEGA to do multiple sequence alignment and phylogenetic analysis on 21 distinct Naegleria nucleotide sequences. The objective of this inquiry was to examine the evolutionary connections existing among various strains. According to the results, it was concluded that the Pakistani strain exhibited significant evolutionary resemblances to other strains. During the molecular typing study, the genome of N. fowleri was compared to the sequences of bacterial plasmids contained in the PubMLST database. The objective of this study was to identify potential loci that may be associated with antibiotic resistance. The comparative analysis, however, revealed discrepancies in alignment with just a minimal level of consistency among the variables. To ascertain the presence of antibiotic resistance in N. fowleri, the ResFinder tool was employed, but no resistance genes were detected. This study showcases the efficacy of advanced technologies like genetic analysis and bioinformatics in the field of disease research. Comparative genomics offers valuable insights into resistance development, while phylogenetic studies demonstrate the evolutionary relationships among different organisms. Considering the global increase in temperatures, it is advisable to do further study to forecast the transmission of diseases using machine learning predictive modelling. The molecular type of study indicated that N. fowleri has the potential to develop antibiotic resistance due to similarities in bacterial plasmids. Ultimately, the genomic characterization and phylogenetic analysis enhance our comprehension of this potentially lethal central nervous system infection, and they hold the potential to aid in the advancement of more effective diagnostics, therapeutic strategies, and preventive measures. This study emphasizes the significance of global collaborative endeavors in the management of N. fowleri and the battle against antibiotic resistance.

ABSTRACT. The widespread stealing of mobile phones and electronic devices presents a significant challenge, leading to financial loss and contributing to criminal activities. Developing countries in the world have been fighting street crime for a long time. This problem has become so serious that many times people's lives are lost in this robbery besides the loss of property. The biggest tragedy is that there is no restriction on the free sale of such snatched or stolen items within the market and there is no record of such items anywhere to show that these devices are from someone snatched or stolen and no one should buy them in the market. To address this problem, a user-friendly web application has been proposed that enables users to report stolen devices by providing their IMEI and other relevant identifiers along with location and time of events. Additionally, potential buyers can verify the legitimacy of a product before purchase. This platform aims to create a centralized database to curb the resale of stolen items, enhancing the chances of recovery. The proposed system is based on two algorithms to process the date and to produce results in the form of reports. First algorithm has been presented to provide area wise based stolen items with location and time. Second algorithm provides total no. stolen items with their classification. This solution has the potential to significantly reduce the circulation of stolen devices in the market, benefiting both original owners and potential buyers. Further the application is enable to report the events to law enforcement agencies to control these kind of events.

ABSTRACT. This research presents active contours technique for breast tumor segmentation. A mathematical model along with iterative solution for computation of active contours has been shown. Images from different imaging modalities have been used and segmentation curves around the lesion, tumor and microcalcifications have been computationally drawn.

ABSTRACT. During the past two decades, green consumerism has become a trend as consumers become conscious of their consumption's impact on the environment. To study the antecedents of green products, purchase behaviour, and to identify the potential gaps between purchase intentions and actual purchase behaviour. This study adopted the well-known Theory of Planned Behavior (TPB) and extended it with perceived environmental concern (PEC) and perceived consumer effectiveness (PCE). Three hundred and thirty-three Malaysian respondents were asked to assess their beliefs regarding green product consumption. The findings revealed that the key purchasing intention and purchase behaviour predictors were attitudes toward green products and subjective norms. The results further highlight that PCE and PEC positively correlate with purchase intention, while perceived behavioural control failed to influence. Moreover, it is evident that these constructs significantly contribute to consumers' intention-behaviour relation towards green products.

ABSTRACT. This paper analyzes the impact of Russian-Ukrainian conflict events on Asia-Pacific stock markets using an event study approach. February 25, 2022, was chosen as the date when the Russian-Ukrainian conflict occurred. The first result is that the stock market with the largest market capitalization in Asia-Pacific is not negatively affected by the Russia-Ukraine conflict. However, the Hong Kong Exchange and Clearing stock market was negatively affected. Second, based on statistical results regarding abnormal returns, the conflict had the majority of significant adverse impacts in the event period. Third, the Russia-Ukraine conflict had a big before and after effect on the Asia-Pacific stock market because there was no difference in abnormal returns. Lastly, stock markets in Asia-Pacific reacted to the conflict due to the negative impacts that emerged as a result of the conflict. This paper illustrates that investors must have a backup plan when investing in the Asia-Pacific stock market during a conflict.

ABSTRACT. A novel fractal curve-based metamaterial absorber is introduced in this paper for X, Ku, and K bands. Fractal curve with carbon properties assigned over the Polyethylene Terephthalate (PET) substrate and TPU dielectric spacer backed by copper ground plane is numerically analyzed in ANSYS HFSS 15.0. Grey Relational Analysis (GRA) is used for the optimum substrate selection in which PET is found to be the most suitable one. Perfect absorption is achieved with a total of 14GHz bandwidth and minimum reflection coefficient of -15GHz. Structure is analyzed in TEM, TE, and TM modes exhibiting the same values of minimum reflection coefficient and bandwidth which suggests the polarization insensitivity of the absorber. Incident angle insensitivity is observed by simulating the absorber under incident angles (θ=0°, θ=15°, θ=30°, θ=45°), in which there is no significant effect of incident angle observed on the reflection coefficient. At θ=45°, no absorption is found in X and Ku band whereas 100% absorption is found in K band. The proposed structure showing the perfect absorption, polarization insensitivity, and incident angle insensitivity proves it a better candidate for the potential use as a perfect absorber.

ABSTRACT. In the present work, a microstrip resonator metamaterial absorber was designed and simulated using Computer Simulation Technology Microwave Studio (CST MWS). Annealed copper is chosen for the microstrip structure and FR-4 as a substrate. The 0.8 mm FR-4 is chosen for light weight, flexibility and for the absorption of Electromagnetic Waves. S-parameters at the input port indicated a reduction in the received signal level. The values of S-parameters obtained are less than -24 db at resonance frequency of 10.648 GHz having bandwidth of 152 MHz at -10 db. The S-parameters at various incidence angles showed that increasing the incidence angle resulted in decreased electromagnetic wave absorption. The results demonstrate that the metamaterial absorber provides effective electromagnetic shielding capabilities in the X band.

ABSTRACT. Every second, huge amounts of data are created online through emails, website visits, and uploads of videos or photos. Currently, there are about 44 zettabytes of data globally. To manage this massive amount of data, data centers are essential. They handle all kinds of computing, data storage, and network needs for businesses. While beneficial from a technological, business, and political standpoint, data centers have a negative impact on the environment. They consume vast amounts of electricity for cooling and operating IT equipment, leading to carbon emissions and contributing to climate change. Addressing these environmental issues, such as reducing carbon emissions, generating renewable energy, and managing electronic waste, are significant challenges for the data center industry. Most data centers worldwide still rely on fossil fuels, with only a few adopting renewable energy sources. This paper focuses on making data centers more efficient and sustainable by reducing their carbon footprint and emissions.

ABSTRACT. Intelligent neuromorphic computing has been increasingly used in recent years to improve machine learning approaches for automatically detecting anomalies. This review paper provides a thorough analysis of the progress achieved in this interdisciplinary field in recent years, namely in the areas of machine learning, computer vision, and pattern recognition. The analysis is based on the findings published in high-ranking publications. The combination of neuromorphic computing concepts and machine learning algorithms has enabled the development of innovative methods for detecting anomalies. These methods are known for their effectiveness, flexibility, and capacity to handle data in real-time or almost real-time situations. The main topics examined include the application of spiking neural networks, event-based processing, and bio-inspired computing models to address the difficulties presented by intricate data patterns and changing abnormalities. In addition, this review consolidates the advancements made in computational neuroscience, adaptive systems, and edge computing paradigms to enhance the efficiency and practicality of these intelligent systems. This paper aims to offer a thorough overview of the current advancements in intelligent neuromorphic computing-assisted machine learning for autonomous anomaly detection. It achieves this by analysing recent literature, identifying emerging trends, and outlining potential areas for future research.

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