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With the inception of high voltage (HV), requisites on the insulating permanence of HV equipment is becoming increasingly crucial. Mineral/synthetic oil liquid insulation—together with solid insulation materials (paper, pressboard)—is the fundamental insulation constituent in HV apparatuses; their insulation attributes perform a substantial part in a reliable and steady performance. Meanwhile, implications on the environment, scarcity of petroleum oil supplies and discarding complications with waste oil have stimulated investigators to steer their attention towards sustainable, renewable, biodegradable and environmentally friendly insulating substances. The contemporary insulating constituent’s evolution is driven by numerous dynamics—in particular, environmental obligations and other security and economic issues. Consequently, HV equipment manufacturers must address novel specifications concerning to these new standards. Renewable, sustainable and environmentally friendly insulating materials are continuously substituting conventional insulating items in the market place. These are favorable to traditional insulating materials, due to their superior functionality. The also offer explicit security and eco-friendly advantages. This article discusses cutting-edge technology of environmentally friendly insulating materials, including their fabrication, processing and characterization. The new renewable, insulating systems used in HV equipment are submitted and their fundamental gains stated in comparison with conventional insulating materials. Several experimental efforts carried out in various parts of the world are presented, offering an outline of the existing research conducted on renewable insulating systems. The significance of this article lies in summarizing prior investigations, classifying research essence, inducements and predicting forthcoming research trends. Furthermore, opportunities and constraints being experienced in the field of exploration are evidently reported. Last but not least, imminent research proposals and applications are recommended.

Nietzsche’s idea of ‘will to power’ is one of the most influential concepts of history of philosophy that emerges out as a result of his criticism of certain knowledge and tradition morality. The rejection of certain knowledge and traditional morality lead Nietzsche to regard human interest and ‘supremacy’ as most prior. Nietzsche conceives ‘will to power’ as act of ‘free spirit’. He believes that ‘will to power’ being act of ‘free spirit’ is an inner potential by virtue of which men overcome their false beliefs which are barriers in human projection and authenticity of self. Santayana argues that ‘will to power’ is a mistaken concept. He believes that ‘life’ is not necessarily assertion of power to get supremacy over others. He raises an objection that the idea of ‘will to power’ ultimately leads to admiration of ‘egotism’ that takes superiority of human for granted and creates disharmony between human and reality. Santayana links Nietzsche’s thought to German philosophical tradition that pursues ‘free spirit’ and authenticity of self but embraces egotism. For Santayana, one can pursue authenticity of self through his wisdom and creativeness like Greek who had been supporter of ‘free spirit’ but always had harmony with reality. This paper aims to explore Santayana’s question that inquires how authenticity of self can be achieved without egotistic implication. I will revisit the notion of authenticity of self by giving an analysis of Nietzsche’s theory of ‘will to power’ and Santayana’s critique of this theory.

Small-to-medium scale smart buildings are an important part of the Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the major enabler for smart control in such environments. Reliability is among the key performance requirements for many loss-sensitive IoT and WSN applications, while Energy Consumption (EC) remains a primary concern in WSN design. Error-prone links, traffic intense applications, and limited physical resources make it challenging to meet these service goals—not only that these performance metrics often conflict with one another, but also require solving optimization problems, which are intrinsically NP-hard. Correctly forecasting Packet Delivery Ratio (PDR) and EC can play a significant role in different loss-sensitive application environments. With the ever-increasing availability of performance data, data-driven techniques are becoming popular in such settings. It is observed that a number of communication parameters like transmission power, packet size, etc., influence metrics like PDR and EC in diverse ways. In this work, different regression models including linear, gradient boosting, random forest, and deep learning are used for the purpose of predicting both PDR and EC based on such communication parameters. To evaluate the performance, a public dataset of the IEEE 802.15.4 network, containing measurements against more than 48,000 combinations of parameter configurations, is used. Results are evaluated using root mean square error and it turns out that deep learning achieves up to 98% accuracy for both PDR and EC predictions. These prediction results can help configure communication parameters taking into account the performance goals.

Bone fragility represents a significant global health burden, characterized by the deterioration of bone strength, increased brittleness, and heightened fracture susceptibility. Osteoporosis substantially elevates the risk of fragility fractures, the principal clinical manifestation of the disease. Current diagnostic approaches, including biomedical imaging, bone strength assessment, and bone mineral density measurement, are closely linked to identifying bone fragility through various predictive models and tools. Although numerous studies have employed predictors to characterize fragility fractures, few have comprehensively examined the morpho-structural features of bone across multiple hierarchical scales, limiting the ability to fully elucidate the underlying mechanisms of bone fragility. This review summarizes recent advancements in predictive modeling and novel diagnostic tools, focusing on multiscale approaches for assessing bone fragility. We critically evaluate the translational potential of these tools for the early detection of fragility fractures and their clinical application in mitigating fracture risk. Moreover, this study discusses the integration of multiscale predictive methodologies, which promise to enhance early-stage bone fragility detection and potentially prevent severe fractures through timely intervention. Finally, the study reflects on current research limitations, addressing the challenges associated with multiscale predictive modeling of bone fragility, and proposes future directions to refine these tools to improve the accuracy and utility of fragility fracture prediction and prevention strategies.

The long-term anticipation of electricity supply (ELS) and demand has supposed substantial prominence in the elementary investigation to offer sustainable resolutions to electricity matters. In this editorial, an outline of the organization of the electricity segment of Pakistan and analysis of historical supply and demand statistics, an up-to-date position of the contrary set of energy plans is presented. The intention of this analysis is to explore the Granger causality relationship between electricity supply and economic growth (EG) by using a multivariate context with time series statistics covering 1990 to 2015 in Pakistan. Augmented dickey-fuller (ADF) and Philips-Perron (PP) unit root tests indicate that variables are non-stationary and integrated in a similar order (1). Our findings also reveal that variables economic growths (GDP), electricity supply (ELS), investment (INV), and export (EX) are co-integrated. The study also finds the Granger causality runs from EG to ELS deprived of any feedback effect. Therefore, the policy implications from our findings indicate that electricity preservation strategies may be implemented without any economic adverse impacts.

With the growing demand for energy and the limitations of fossil fuel resources, the utilization of renewable energy sources has become a vital and sustainable solution. However, identifying optimal locations for the development of these resources remains a major challenge in energy planning. Accurate spatial potential assessment can play a critical role in enhancing efficiency and reducing production costs. This study aims to present a scenario-based framework for assessing solar and wind energy potential in Pakistan. A total of 19 spatial criteria were used, categorized into evaluation and constraint factors. The full consistency method (FUCOM) was applied to weight the criteria, while the ordered weighted averaging (OWA) method was employed to model various potential scenarios. The results revealed that global horizontal irradiation (GHI) and proximity to transmission lines are the most significant factors for solar energy, whereas wind speed and wind power density are crucial for wind energy potential. Scenario analysis indicated that, under the AND scenario, the area with very high potential for solar and wind energy is 8005.72 km2 and 968.98 km2, respectively. These values increase to 63,607.52 km2 and 16,288.32 km2 under the OR scenario. The spatial agreement map for the simultaneous development of solar and wind energy showed an overlap of 461.42 km2 in the AND scenario and 11,836 km2 in the OR scenario. These findings highlight the importance of scenario-based decision-making approaches and accurate spatial evaluations in the development of multiple renewable energy plant sites under various investment and policy conditions. Moreover, the proposed framework can serve as a practical model for simulating and assessing renewable energy development potential in other regions of the world.

Drought-induced stress presents a substantial threat as it disrupts the normal growth of cereal crops and leads to decreased yields. The persistent occurrence of drought conditions significantly impacts the growth and development of pearl millet. This study aimed to explore how calcium chloride (CaCl 2 ) regulates the growth of pearl millet when it faces a lack of water. Over two years, field experiments were conducted at the College of Agriculture, Bahauddin Zakariya University, Bahadur Sub-Campus Layyah. During the study, we exposed pearl millet to various foliar applications of CaCl 2 (0 mg/L, 25 mg/L, 50 mg/L, and 75 mg/L) while subjecting it to two different irrigation conditions: full irrigation and drought stress during the booting stage. Results revealed that a significant reduction in the growth (plant height; PH, stem diameter; SD, fresh leaf weight; FLW, stem fresh weight; SFW, stem dry weight; SDW, root fresh weight; RFW, root dry weight; RDW, and plant dry weight; PDW), yield (panicle length; PL, grain per panicle; GPP, grain weight; GW, thousand grain weight; TGW, grain yield; GY, biological yield; BY, and harvest index; HI), and physiological attributes (membrane stability index; MSI, and soil plant analysis development; SPAD) were found under water drought stress condition, while increment in antioxidant level was observed due to low moisture contents in soil. In both years, foliar applied CaCl 2 enhanced all the physiological, growth and yield traits as well as some of the antioxidants like superoxide dismutase (SOD), peroxidase (POD) and ascorbate peroxidase (APX). Study concluded that a concentration of 50 mg/L of CaCl 2 is optimal for enhancing all examined attributes of pearl millet under both drought and full irrigation conditions. The results strongly advocate for the use of CaCl 2 as the most effective treatment for the cultivation of pearl millet in arid and semi-arid regions.

Worldwide populations have historically experienced serious issues from infectious diseases, requiring coordinated and inclusive prevention measures. HIV is one of the most hazardous of these as it attacks CD4 + cells, or T-cell lymphocytes, which are crucial to human immunity. To explore the variability of HIV/AIDS transmission, this study introduces a nonlinear stochastic mathematical model that incorporates a recovery compartment to account for hospitalized patients’ progression to complete recovery and to better capture the intricate dynamics of disease transmission. Fractional derivatives are used with a generalized Caputo operator to enhance the accuracy of the model, effectively mixing the memory and genes that exist in biological systems. The model’s validity is affirmed through considerations of positivity, boundedness, reproduction number, stability, and sensitivity analysis. Stability theory is employed to explore both local and global stabilities. Sensitivity analysis identifies parameters with a significant impact on the reproduction number. To establish the existence and uniqueness of solutions, the model is qualitatively examined via fixed-point theory. Apart from that, a new numerical technique for simulations focused on the predictor-corrector strategy is implemented and MATLAB is used to verify the results. By comparing the fractional-order and integer-order derivatives, it is noted that the fractional-order method is the more accurate and realistic depiction of the dynamics of the disease. The suggested technique unlocks the door for more effective interventions giving researchers a competitive edge in learning about and managing the complex mechanisms of HIV/AIDS transmission.

Vicilin has nutraceutical potential and different noteworthy medicative health-promoting biotic diversions, and it is remarkable against pathogenic microorganisms and insects. In this study, Vigna aconitifolia vicilin (VacV) has been identified and characterized from the seed of Vigna aconitifolia (Jacq.) Marechal (Moth beans). LC-MS/MS analysis of VacV provided seven random fragmented sequences comprising 238 residues, showing significant homology with already reported Vigna radiata vicilin (VraV). VacV was purified using ammonium sulfate precipitation (60%) followed by size exclusion chromatography on Hi-Load 16/60 Superdex 200 pg column and anion-exchange chromatography (Hi trap Q FF column). Purified VacV showed a major ~50 kDa band and multiple lower bands on 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) under both reduced and non-reduced conditions. After all, a three-dimensional molecular structure of VacV was predicted, which showed β-sheeted molecular conformation similar to crystallographic structure of VraV. All Vicilins from V. aconitifolia and other plants were divided into six sub-groups by phylogenetic analysis, and VacV shared a high degree of similarity with vicilins of Vigna radiata, Pisum sativum, Lupinus albus, Cicer arietinum and Glycine max. Additionally, VacV (20 μg) has significant growth inhibition against different pathogenic bacteria along strong antifungal activity (50 μg). Likewise, VacV (3.0 mg) produced significant growth reduction in Rice Weevil Sitophilus oryzae larvae after 9 days compared with control. Furthermore, by using MMT assay, the cytotoxicity effect of VacV on the growth of HepG2 liver cancerous cells was tested. VacV showed cytotoxicity against the HepG-2 line and the acquired value was 180 µg after 48 h. Finally, we performed molecular docking against caspase-3 protein (PDB ID: 3DEI) for VacV bioactive receptor interface residues. Hence, our results reveal that VacV, has nutraceutical potential and moth beans can be used as a rich resource of functional foods.

The transportation of construction waste involves various complexities, including logistics, monitoring, and resource management. Nevertheless, conventional transportation methods struggle to meet the combined requirements of environmental sustainability and efficiency in modern urban development due to problems such as high idle rates and insufficient management. The swift advancement of Internet of Things (IoT) technology offers an innovative solution for the intelligent and effective management of construction waste transportation in response to these issues. This study explores how IoT technology can enhance construction waste transportation management by developing an evaluation framework using the Delphi method, analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). This research focuses on the application of IoT to optimize the transportation and logistics process through real-time monitoring and data analysis. The capability of IoT technology to analyze real-time data facilitates the modification of routes to minimize empty mileage and transportation time, thus improving transport efficiency. Ultimately, the potential and challenges of IoT in construction waste transportation management have been discussed.