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The electronic and electrical industrial sector is exponentially growing throughout the globe, and sometimes, these wastes are being disposed of and discarded with a faster rate in comparison to the past era due to technology advancements. As the application of electronic devices is increasing due to the digitalization of the world (IT sector, medical, domestic, etc.), a heap of discarded e-waste is also being generated. Per-capita e-waste generation is very high in developed countries as compared to developing countries. Expansion of the global population and advancement of technologies are mainly responsible to increase the e-waste volume in our surroundings. E-waste is responsible for environmental threats as it may contain dangerous and toxic substances like metals which may have harmful effects on the biodiversity and environment. Furthermore, the life span and types of e-waste determine their harmful effects on nature, and unscientific practices of their disposal may elevate the level of threats as observed in most developing countries like India, Nigeria, Pakistan, and China. In the present review paper, many possible approaches have been discussed for effective e-waste management, such as recycling, recovery of precious metals, adopting the concepts of circular economy, formulating relevant policies, and use of advance computational techniques. On the other hand, it may also provide potential secondary resources valuable/critical materials whose primary sources are at significant supply risk. Furthermore, the use of machine learning approaches can also be useful in the monitoring and treatment/processing of e-wastes. Highlights In 2019, ~ 53.6 million tons of e-wastes generated worldwide. Discarded e-wastes may be hazardous in nature due to presence of heavy metal compositions. Precious metals like gold, silver, and copper can also be procured from e-wastes. Advance tools like artificial intelligence/machine learning can be useful in the management of e-wastes. Graphical abstract

Abiotic stress is one of the severe stresses of environment that lowers the growth and yield of any crop even on irrigated land throughout the world. A major phytohormone abscisic acid (ABA) plays an essential part in acting toward varied range of stresses like heavy metal stress, drought, thermal or heat stress, high level of salinity, low temperature, and radiation stress. Its role is also elaborated in various developmental processes including seed germination, seed dormancy, and closure of stomata. ABA acts by modifying the expression level of gene and subsequent analysis of - and -acting regulatory elements of responsive promoters. It also interacts with the signaling molecules of processes involved in stress response and development of seeds. On the whole, the stress to a plant can be susceptible or tolerant by taking into account the coordinated activities of various stress-responsive genes. Numbers of transcription factor are involved in regulating the expression of ABA responsive genes by acting together with their respective -acting elements. Hence, for improvement in stress-tolerance capacity of plants, it is necessary to understand the mechanism behind it. On this ground, this article enlightens the importance and role of ABA signaling with regard to various stresses as well as regulation of ABA biosynthetic pathway along with the transcription factors for stress tolerance.

Peer-to-Peer (P2P) networks are less expensive, simple to use, and do not require the traditional client–server model. It has particular advantages in data sharing and resource utilization, so it is recommended to use it for various applications. P2P networks have been used in many applications, especially in data sharing and resource utilization. Load balancing and security is an essential task to improve the performance of P2P networks. Hence, in this paper, probability-based load balancing control and security enhancement is developed in P2P networks. The probability of peer can be computed with chicken swarm optimization (CSO), which selects the best peer in P2P networks to achieve load balancing and resource utilization. The proposed method is developed to attain two main objective functions: load balancing control and security enhancement. A probability-based CSO algorithm is used to control load balancing. The security is achieved with Enhanced Rumour Riding protocol (ERR) and SXOR (Split XOR) operation. The proposed method is implemented in the NS2 platform, and the performance of the proposed method is analysed with performance metrics such as delay, delivery ratio, packet loss, encryption time, decryption time, and throughput. The proposed method is compared with existing methods such as Biased Contribution Index based Rumour Riding protocol (BCIRR), Ant Colony Optimization (ACO), and Catching Algorithms (CA). The proposed technique achieves a 98.75% packet delivery ratio, with a minimum 3.8 s delay. Ultimately the performance suggests that the proposed system can perform better for load balancing and security in the P2P network.

Addressing water scarcity and pollution is imperative in tackling global environmental challenges, prompting the exploration of innovative techniques for effective water and wastewater treatment. Nanotechnology presents promising solutions through the customization of nanoparticles and nanocomposites specifically designed for water purification applications. This review delves into recent advancements in nanoparticle-based technologies for water treatment, with a particular focus on their synthesis methodologies, intrinsic properties, and versatile applications. A wide range of nanoparticles, ranging from metal nanoparticles to carbon-based nanomaterials, along with hybrid nanocomposites such as metal/metal oxide-based nanocomposites, polymer-based nanocomposites, and others, were emphasized for eliminating contaminants from water and wastewater matrices. Furthermore, this review elucidates the underlying mechanisms governing pollutant removal processes, encompassing adsorption, catalysis, and membrane filtration, facilitated by nanoparticles. Additionally, it explores the environmental implications and challenges associated with the widespread deployment of nanoparticle-based water-treatment technologies. By amalgamating existing research findings, this review provides valuable insights into the potential of nanoparticles and nanocomposites in mitigating water-related challenges and presents recommendations for future research trajectories and technological advancements in this domain.

This study examines the relationship between life skills, parent-child relationships, and school environment among adolescents in the Nainital district. Life skills, critical for adolescent development, are influenced by supportive parenting practices and conducive school environments. Using a descriptive survey method, the research sampled 800 adolescents through simple random sampling. Data were collected using standardized tools, including the parent-child relationship scale, school environment inventory, and a life skills assessment questionnaire developed by the investigator; the reliability of LSAQ is 0.80. Findings revealed significant correlations between adolescents' life skills and specific dimensions of father-child and mother-child relationships. Positive parenting traits such as protecting, loving, symbolic, and object reward were positively associated with life skills, while negative traits like neglect and punishment showed adverse impacts. The study underscores the pivotal roles of family and school in fostering critical life skills, contributing to adolescent well-being and resilience.

Deep Reinforcement Learning (DRL) is transforming autonomous racing by enabling agents to make real-time, high-stakes decisions with the least supervision. Yet, strong generalization over multiple varied tracks is a key bottleneck. Within this paper, a rigorous examination of the relationship between reward system design and hyperparameter tuning for autonomous racing agents using the AWS DeepRacer platform as a unified benchmark is conducted. A comprehensive comparison of Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) algorithms on two vastly different reward structures with the aid of an extensive tuning of batch size, learning rate, discount factor, and entropy is performed. The results identify that a well-engineered reward mechanism, under the optimized hyperparameter (batch size 128, learning rate 0.0003, discount factor 0.99, entropy 0.01), allows PPO to outperform standard benchmarks with an average lap time of 12.464 s on 21 unseen tracks. These results demonstrate not only enhanced performance but also improved generalization, enabling the models to perform effectively on previously unseen tracks. Additionally, significant emphasis was placed on reward shaping and analyzing hyperparameter sensitivity in large-scale DRL systems to ensure their practical applicability in autonomous scenarios.

Dense crowd counting and modeling at different gatherings has ignited a new flame in the visual surveillance research community. There is a high possibility of mishappenings in the form of stampede, mob fighting at these gatherings and the administration is helpless in these scenarios. There is a requirement of analyzing the crowd to prevent these dangerous situations. The proposed work is a case study of Kumbh Mela which models the crowd counting in densely populated images. In the proposed work, the orthographic projection of the crowd is captured using a camera attached to a drone, to reduce the effect of occlusion and scaling which, otherwise, may get introduce during image acquisition process. The captured data is fed to a Convolutional Neural Network for training the model to count head of persons present in the frame. The results obtained from the trained model are validated using geometry and imaging techniques. The proposed model has achieved a mean-absolute-error of 94.3 and a mean-squared-error of 104.6 which seems to outperform the existing state-of-the-art models with respect to the reported performance parameters. The proposed model can be used as a viable solution in applications related to modeling the crowd behavior.

Segment losses due to intermittent connectivity and mobility lead to sub optimal performance of the Transmission Control Protocol (TCP). This is due to the fact that segment loss is considered as a binary signal for triggering congestion control and retransmission mechanisms at the TCP sender. In wired networks, segments are dropped due to congestion at the routers and the strategy of taking missed acknowledgment as an implicit signal for congestion control performs well. However, in wireless networks, segment losses are primarily due to mobility and transmission errors. Unlike many previous efforts, this paper proposes the design and implementation of Software Defined Network (SDN) assisted TCP which does not require the wireless Access Points (APs) to be TCP-aware and preserves end to end semantics. Further, no changes are required to be done in TCP protocol implementation at the end-hosts. The proposed approach utilizes the programmability provided by the SDN paradigm to intelligently trigger the spurious timeout detection and response algorithms, already implemented in standard TCP. The proposed approach is compared with the standard TCP and SDN assisted Zero Window based approach on Linux kernels using virtual data-plane switches and APs provided by the Mininet-WiFi platform. The implementation results establish the applicability of the proposed approach.

By addressing the drawbacks of static optimization techniques, this research seeks to improve the dynamic energy management of thermoelectric generators (TEGs). Finding the best deep reinforcement learning (DRL) algorithm to maximize energy distribution, prolong battery life, and boost system efficiency in the face of the variable conditions present in waste heat recovery systems is the goal. The TEG system was modeled using the Markov decision process and implemented in a computer-simulated environment. Three advanced DRL algorithms were used: soft actor-critic (SAC), proximal policy optimization (PPO), and deep Q-networks (DQN); which were trained to act as intelligent controllers. The performance of each algorithm was systematically evaluated and compared using key metrics, including average cumulative reward, battery health, system efficiency, and a novel metric termed the energy fulfillment rate, which measures the ability to meet demand while storing surplus energy. The comparative analysis revealed a critical trade-off between maximizing performance and ensuring hardware longevity. The SAC algorithm demonstrated the best overall performance, achieving the highest average reward (− 7.03) and energy fulfillment rate (22.84%). However, the A2C, DDPG, and PPO algorithms all achieved a perfect average battery health of 100.00%, highlighting their superior capability for preserving system longevity, albeit with slightly lower rewards. The DQN algorithm consistently showed the least effective performance across all metrics, particularly in maintaining battery health (60.73%). The SAC algorithm is the most suitable of the methods tested for dynamically managing TEG systems. Its underlying principle of maximization of entropy enables a better exploration of control strategies, leading to a better balance between immediate energy dispatch and long-term storage goals. The findings confirm the significant potential of DRL to create efficient and adaptive controllers for renewable energy applications, although further validation of physical hardware is required to confirm real-world viability.

This paper outlines linear and nonlinear systems, presenting some key features of each while pointing out the major problems brought by the nonlinearities into control applications. Linear systems exhibit proportional and additive relationships and are comparatively easier to model and control. On the contrary, the nonlinear systems possess complex and unpredictable patterns, thus often forcing their simplification. Linearization, performed via Jacobian-based approximations and feedback linearization, is very important in allowing the application of linear control strategies to be employed for nonlinear systems. The paper presents proportional-integral-derivative (PID) control and Computed torque control (CTC) as the major control methods for manipulation, showing the corresponding design principles and also discusses other control techniques in brief.