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RESEARCH OBJECTIVE: The research investigates how young boys progress in life after the loss of their fathers. It explores the adjustments they make, the coping mechanisms they develop to navigate life, and the male roles they assume in the family. Additionally, it examines how family can aid and support their coping and adjustment process. THE RESEARCH PROBLEM AND METHODS: This study utilises a case study methodology focused on gaining an understanding of the lives of two adolescent boys who experienced the death of their fathers in the early formative years of their lives. THE PROCESS OF ARGUMENTATION: The paper focuses on five themes- family dynamics, life transitions with and without their fathers, post-loss reassignment of family roles, present circumstances, and future goals. RESEARCH RESULTS: A qualitative analysis of the data revealed that participants’ belief systems, family support, and striving toward future goals helped them to be resilient in this event. CONCLUSIONS RECOMMENDATIONS AND APPLICABLE VALUE OF RESEARCH: While most research typically emphasises the negative impacts of death on people, this study demonstrates how individuals’ can be resilient, dependent on both person-related factors and environmental factors. To help adolescents who are grieving become resilient, the paper offers intervention and coping strategies through counselling, the availability of support networks, and combined efforts by the state and educational institutions.

The textile industry is a very resource-intensive sector, considerably contributing to environmental deterioration via substantial use of water, electricity, and chemicals. With the global transition towards sustainable practices, the notion of a circular economy has become more significant, focusing on prolonging product lifespans, reducing waste, and facilitating material recovery. An effective method for achieving circularity in the textile sector is Design for Disassembly (DfD), which facilitates the straightforward deconstruction of items for recycling or reuse. This study examines the use of Design for Disassembly (DfD) in textile production, emphasizing methods that enable the straightforward separation of fibers, trimmings, and fasteners, hence improving garment recyclability. Data was gathered via a comprehensive examination of textile product designs, production methodologies, and recycling methods throughout the sector. Findings demonstrate that the use of DfD may substantially decrease waste and enhance material recovery rates. Nonetheless, obstacles like redesign expenses, customer awareness, and compatibility with existing production techniques persist. This study presents a detailed technique for incorporating Design for Disassembly (DfD) into textile manufacturing and examines its possible effects on the circular economy. The results substantiate the assertion that the implementation of DfD may be a crucial advancement toward attaining sustainability in the textile sector.

This study examines the potential of polymer matrix nanocomposites for creating lightweight and sustainable automotive parts. It conducts a thorough analysis of mechanical properties, thermal properties, environmental impact, and cost considerations. A variety of nanofillers and polymer matrices were used to create nanocomposites, which were then analyzed to assess their suitability for use in automotive applications. The results indicate variations in the mechanical properties of the nanocomposites. Composite D demonstrates the highest tensile strength (95 MPa), Young's modulus (13 GPa), flexural strength (135 MPa), and impact strength (20 kJ/m^2), suggesting superior mechanical performance compared to the other nanocomposites. The analysis of thermal properties shows variations in glass transition temperature (Tg), melting temperature, thermal conductivity, and heat deflection temperature across different nanocomposites. Among them, Composite D exhibits the highest Tg (88°C) and melting temperature (160°C), along with superior thermal conductivity (0.7 W/mK) and heat deflection temperature (130°C). The assessment of environmental impact metrics reveals that Composite D has the lowest carbon footprint (8 kg CO2/kg), embodied energy (45 MJ/kg), and water usage (90 L/kg), as well as the highest recyclability (90%). These findings emphasize the potential of Composite D to minimize environmental impact and foster sustainability in the automotive manufacturing sector. There are variations in material and processing costs among nanocomposites. Composite D has the highest material cost ($25/kg) and processing cost ($18/kg). This study offers important insights into the performance, environmental impact, and cost considerations of polymer matrix nanocomposites for lightweight sustainable automotive parts. It contributes to the development of more environmentally friendly and efficient vehicles with improved performance characteristics.

This review article delves into the advancements in the realm of nanogrids and their potential in addressing energy challenges, particularly in regions with limited access to centralized power grids. The paper reviews the work in the areas of nanogrids as solutions for regions like sub-Saharan Africa, where a significant population lacks access to main grid electricity. The integration of multiple nanogrids within a community, coupled with an investor energy bank, is explored as a means to alleviate the economic burden of energy storage and to harness the full potential of solar energy. The article also examines the challenges faced by centralized power grids and the shift towards distributed generation (DG) as a remedy. The concept of nanogrids is further elaborated, discussing their control topologies, techniques, and the potential of interconnecting multiple nanogrids to form a microgrid. Lastly, the co-design of solar generationbased nano-grids and water treatment in remote areas is studied, emphasizing the importance of providing essential drinking water and electricity to underdeveloped regions. The integration of solar-powered electricity with water treatment processes offers a unique solution to address both electricity and water needs in such areas.

This paper reviews the advancements in machine learning techniques for enhanced solar power generation forecasting. Solar energy, a potent alternative to traditional energy sources, is inherently intermittent due to its weather-dependent nature. Accurate forecasting of photovoltaic power generation (PVPG) is paramount for the stability and reliability of power systems. The review delves into a deep learning framework that leverages the long short-term memory (LSTM) network for precise PVPG forecasting. A novel approach, the physics-constrained LSTM (PCLSTM), is introduced, addressing the limitations of conventional machine learning algorithms that rely heavily on vast data. The PC-LSTM model showcases superior forecasting capabilities, especially with sparse data, outperforming standard LSTM and other traditional methods. Furthermore, the paper examines a comprehensive study from Morocco, comparing six machine learning algorithms for solar energy production forecasting. The study underscores the Artificial Neural Network (ANN) as the most effective predictive model, offering optimal parameters for real-world applications. Such advancements not only bolster the accuracy of solar energy forecasting but also pave the way for sustainable energy solutions, emphasizing the integration of these findings in practical applications like predictive maintenance of PV power plants.

This study enhances the domain of optimum energy storage system selection by offering a complete decision support framework that incorporates technical, economic, and environmental factors. The technical investigation examines energy and power density measurements, which demonstrate the exceptional volumetric energy storage capacities of lithium-ion batteries at 250 Wh/L. This highlights their appropriateness for applications that prioritize compactness. Lead-acid batteries are a cost-effective option that have a lower energy density of 80 Wh/L. This makes them particularly advantageous in situations where volume limitations are not as important. Flow batteries and supercapacitors are positioned in the middle, with flow batteries offering a well-balanced profile of energy and power density, while supercapacitors excel in applications that need fast charging and discharging. An in-depth evaluation of the startup costs and ongoing expenditures is conducted to thoroughly assess the economic feasibility of adopting an energy storage system. Although lithium-ion batteries have a higher upfront cost of $100,000, they demonstrate a well-balanced economic profile by offering a significant 66% decrease in operating expenditures compared to lead-acid batteries. Lead-acid batteries, which have an initial price tag of $60,000, demonstrate cost-effectiveness but result in higher ongoing costs. Flow batteries and supercapacitors are positioned as premium alternatives, with respective beginning prices of $150,000 and $200,000. Environmental sustainability is a fundamental factor that is assessed via life cycle analysis. Lithiumion batteries, while very efficient, give rise to problems over resource depletion and environmental damage linked to mining activities. Lead-acid batteries, due to their recyclability, provide a more ecologically sound alternative. Flow batteries, which use readily available and environmentally safe components, are very compatible with objectives aimed at promoting sustainability. Supercapacitors, which often use ecologically friendly materials, have potential in reducing negative environmental impacts. MultiCriteria Decision Analysis (MCDA) is used to integrate technical, economic, and environmental assessments. Lithium-ion batteries are considered the most favorable choice due to their exceptional technical performance and economic viability. Lead-acid batteries have a strong market position due to their cost-efficiency and ecofriendliness. Flow batteries and supercapacitors, while they have distinct benefits, are only suitable for certain applications. The stability of the framework is shown by sensitivity analysis, which identifies energy density and beginning costs as crucial factors. Engaging stakeholders helps confirm the framework’s usefulness, ensuring that it can be practically used and remains relevant in guiding well-informed choices in the ever-changing field of energy storage technologies. This study provides vital insights into the discussion on sustainable and efficient energy solutions, presenting a clear plan for decision-makers to navigate the intricacies of selecting energy storage systems.

Magnesium (Mg) has become a focus in the transportation industry due to its potential in reducing fuel consumption and gas emissions while improving recyclability. Mg alloys are also known for their low neutron absorption, good resistant of carbon dioxide as well as thermal conductivity which makes them suitable for use in industrial equipment for nuclear energy. there has been an increasing interest in the research and development of Mg alloys. These are the lightest of all metallic structural materials and are approximately 33% lighter than aluminium (Al) and 75% lighter than ferrous (Fe) alloys and have excellent specific mechanical properties. In this work, FSW of AZ31B Alloy was examined at the various rotational speeds of 900 -1440 rpm, with fixed welding speed of 40mm/min and 2° tool tilt angle using an HSS tool. The mechanical properties were compared for the different rotational speeds. The quality of FSW joints is dependent on input value of heat and material flow rate, which are prejudiced by process parameters., higher rotation speeds may cause abnormal stirring, resulting in a tunnel defect at the weld nugget due to increased strain rate and turbulence.

This study report performed a thorough data-driven analysis to evaluate the economic sustainability of smart home investments in the quickly changing residential living scene. The research provided a comprehensive view of the financial ramifications of implementing smart home technology by taking into account initial investment costs, energy savings, maintenance and operating expenditures, and user satisfaction. The results show a considerable potential for improved user happiness and energy savings, which supports the financial viability of smart home investments. The findings highlight the multifarious significance of these technologies in establishing more practical and efficient living environments and provide insightful information for policymakers, industry stakeholders, and homeowners.

Chronic postsurgical pain (CPSP) results from a cascade of events in the peripheral and central nervous system following surgery. Several clinical predictors, including the prior pain state, premorbid psychological state (e.g., anxiety, catastrophizing), intraoperative surgical load (establishment of peripheral and central sensitization), and acute postoperative pain management, may contribute to the patient’s risk of developing CPSP. However, research on the neurobiological and biobehavioral mechanisms contributing to pediatric CPSP and effective preemptive/treatment strategies are still lacking. Here we evaluate the peri-surgical process by identifying key problems and propose potential solutions for the pre-, intra-, and postoperative pain states to both prevent and manage the transition of acute to chronic pain. We propose an 8-step process involving preemptive and preventative analgesia, behavioral interventions, and the use of biomarkers (brain-based, inflammatory, or genetic) to facilitate timely evaluation and treatment of premorbid psychological factors, ongoing surgical pain, and postoperative pain to provide an overall improved outcome. By achieving this, we can begin to establish personalized, precision medicine for children and adolescents presenting to surgery and subsequent treatment selection.

Urban development and clean energy are two crucial components of sustainable development. Cities around the world are facing increasing pressure to reduce their carbon footprint and ensure that they are equipped to meet the energy needs of their growing populations. The use of clean energy for urban development has become an important topic of discussion as it presents a unique opportunity to address both of these challenges simultaneously. The present paper focuses on various aspects such as the benefits for transition to adopt clean energy, developing a framework for adoption of clean energy products for urban areas, possible clean energy products and to understand the satisfaction level of the consumers using the clean energy products in the urban areas of Bhubaneswar, Odisha.

Internet of Things (IoT) innovation is one of the fastest growing fields in various regions or aspects which include irrigation. IoT works on the character of our lives by way of bringing and cultivating modifications in many fields of exercises to motive them to come to be convenient, savvy and enriched with the aid of adequate guy-made recognition. As a result of this innovation, smart cultivating frameworks recognize a social trade towards current agri-business that is more useful, consumes less water, and is extraordinarily less luxurious. The primary goal of this paper is to make use of IoT within the agribusiness subject to accumulate facts right away (soil Moister, temperature), with a purpose to help one with staring at a few climate situations distantly, effectively and improve massively the creation and thus the pay of ranchers. The modern version is created utilizing NodeMCU innovation, which contains express sensors, and a Wifi module that assists with amassing moment records on the internet. It is worth concentrating on the testing of this model created, profoundly precise data in light of the fact that any herbal changes were outstanding in a flash and taking into consideration to decide. This paper speculating about integrating the IoT with different other technologies.

In recent years, ransomware attacks have become a more significant source of computer penetration. Only general-purpose computing systems with sufficient resources have been harmed by ransomware so far. Numerous ransomware prediction strategies have been published, but more practical machine learning ransomware prediction techniques still need to be developed. In order to anticipate ransomware assaults, this study provides a method for obtaining data from artificial intelligence and machine learning systems. A more accurate model for outcome prediction is produced by using the data science methodology. Understanding the data and identifying the variables are essential elements of a successful model. A variety of machine learning algorithms are applied to the pre-processed data, and the accuracy of each technique is compared to determine which approach performed better. Additional performance indicators including recall, accuracy, and f1-score are also taken into account while evaluating the model. It uses machine learning to predict how the ransomware attack would pan out.

Today, there are tens of millions of Facebook groups and over 200 million Facebook users all over the world who are members of Facebook groups that they consider to be a meaningful part of their lives. These groups cover a range of topics from parenting to rare diseases to fitness, but they all enable people to build relationships with the communities that matter to them, no matter where they are. Over 7 million of the Facebook groups that users have identified as meaningful are local groups that are bringing together the people and places that make up local communities, whether that be a neighborhood, a city, or a region. The connections and information these groups foster among local communities has meant that when crisis strikes, local groups have proven themselves to be critical sources of support and assistance for their members. Lack of resources and recognition are the main challenges faced by these groups, which are entirely based on civic engagement and the personal commitment of their leaders. To overcome these limits, Facebook launched in 2018 the Facebook Community Leadership Program to empower community leaders from all over the world.

Deadlock is a highly unfavourable situation, the deadlock problem becomes further complicated if the underlying system is distributed. Deadlocks in distributed systems are similar to deadlocks in single processor systems, only worse. They are harder to avoid, prevent or even detect. They are hard to cure when tracked down because all relevant information is scattered over many machines.In deadlock situations the whole system or a part of it remains indefinitely blocked and cannot terminate its task. Therefore it is highly important to develop efficient control scheme to optimize the system performance while preventing deadlock situations.In this research paper, a new deadlock prevention algorithm have been offered with the emergence of grid computing. The main objective of paper is to prevent deadlock problem in Grid environment in order to preserve the data consistency and increase the throughput by maximizing the availability of resources and to ensure that all the resources available in the grid are effectively utilized.