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The current research has proposed an innovative methodology to enhance PVA/CSP/AgNPs hybrid biocomposite films by integrating the CRITIC and CoCoSo multi-criteria decision-making (MCDM) frameworks with machine learning techniques. Bio composite films can be used in medicine, packaging, and environmental applications. The biocomposite films were developed by mixing polyvinyl alcohol (PVA), coconut shell powder (CSP), and silver nanoparticles (AgNPs) to attain better mechanical, thermal, and antibacterial properties. The experimental design comprises 25 distinct trials to assess the tensile strength (TS), Young’s Modulus (YM), percentage elongation (%E), and maximum degradation temperature (T) of the fabricated composite films. CRITIC method was adopted to evaluate the weights of the criteria in an unbiased way, and CoCoSo technique ranked the alternatives. The results were analyzed using supervised machine learning algorithms, including Random Forest, Neural Network, Linear Regression, and AdaBoost. AdaBoost algorithm demonstrated superior performance with an R 2 value greater than 0.98 for all output responses. Decision tree analysis revealed that the composition of silver nitrate significantly influences tensile strength, while coconut shell powder affects Young’s modulus. The optimized film, composed of PVA/20%CSP/4 mM AgNPs, exhibited improved UV-shielding, reduced water absorption (26.2%), and decreased soil weight loss (41.4%). SEM images confirmed the uniform dispersion of AgNPs within the PVA matrix. The integration of CRITIC-CoCoSo with machine learning provides a scalable approach for designing multifunctional bio-composites with enhanced properties for sustainable applications.

Abstract Each year, millions of tons of fly ash (FA), a hazardous byproduct of coal-fired power plants, are disposed of in landfills, causing serious environmental pollution and substantial health risks. Consequently, effective FA management has become critically important. Despite its popularity in the construction sector, FA has rarely been used to create multifunctional porous materials capable of addressing heat insulation, CO 2 capture, and wastewater purification. This paper reports the successful development of highly durable, lightweight, flexible and multifunctional xerogels derived from FA. The FA was chemically modified and blended with cellulose nanofibers (CNFs), a binder, and a crosslinker to enhance structural stability and multifunctionalities. The resulting xerogel exhibited a compressive strength of 293.5 ± 9.6 kPa and a Young’s modulus of 264.1 ± 6.8 kPa at 70% strain, representing an increase of 86.6% in strength and 94% in modulus compared to pure CNF xerogel. Moreover, it exhibited excellent thermal conductivity of 32.1 mW/m.K, and a CO 2 adsorption capacity of 1.67 mmol/g, superior to many contemporary materials. Interestingly, the xerogel also performed extremely well in selectively removing both cationic and anionic dyes from water. These results highlight the effectiveness of our formulation and design approach in producing a robust multifunctional xerogel. With its simple fabrication process, lightweight structure, mechanical robustness and multifunctionality, the developed xerogel emerges as an attractive solution for wastewater management, CO 2 mitigation and building insulation.

In present work, tribology tests were enacted to analyze the wear behavior of composite reinforced with Aloe vera, Corn, Eucalyptus and Soybean Fiber and Epoxy resin. Fatigue and creep tests were accomplished to evaluate the strength of prepared composite specimen under fluctuating and static load conditions. Tribological analysis revealed that hybrid composites achieved higher frictional force as compared to single fiber-based composites. Hybrid ACESE composite exhibits maximum frictional force of 14.01 N, 35.78 N and 37.46 N at 10, 30 and 50 N load for 5 m/s sliding speed as compared to other prepared composite specimens. COF of prepared composite samples were calculated at different sliding speeds (3, 5 and 7 m/s) and applied load of 10, 30 and 50 N load. Hybrid ACESE composite shows maximum value of COF at 10, 30 and 50 N load for 3, 5 and 7 m/s sliding speed. Sliding speed (3, 5 and 7 m/s) with respect to applied load (10, 30 and 50 N) shows the variations in specific wear rate (SWR) for all prepared specimens. During tribological analysis, higher frictional force achieved by hybrid ACESE composite exhibits the highest value of interfacial temperature at 5 m/s sliding speed for 10, 30 and 50 N applied load. While the lower value of frictional force achieved by CE composite displays the lowest value of interfacial temperature at 5 m/s sliding speed for 10, 30 and 50 N applied load. The fatigue test illustrated that hybrid ACESE composite attained the highest fatigue number of cycles at 3822, 3217 and 2218 for 25, 50 and 75% UTS. While CE composites resulted in the lowest fatigue number of cycles at 2234, 1826, and 1219 for 25, 50 and 75% UTS among all prepared specimens.

One of the most common types of cybercrime is phishing, which uses social engineering and technological deceit to obtain sensitive personal and financial data through email spoofing, malicious websites, and destructive software installations. Phishing attack detection is an important field of study in cybercrime forensics because attackers use a variety of communication methods, such as emails, URLs, messaging apps, and phone calls. In order to overcome this obstacle, this work builds a new model that combines group convolution with a symmetric structure rather than using a conventional CNN. It then uses the SMOTE to control the data's class imbalance. Incorporating snapshot ensemble improves the model's generalisability without drastically raising computational costs, while cyclic cosine annealing learning rates further boost the training process. With a classification accuracy of 99.12%, the suggested method for detecting phishing attacks outperforms four existing ensemble methods, according to the results. Using ensemble learning techniques in conjunction with group convolution greatly improves accuracy and adaptability, as seen below. Finally, the suggested approach provides a solid defence against the increasing danger of phishing attempts by providing a dependable, effective, and extensible cybercrime forensics solution. https://doi.org/10.65470/james.11

Abstract A conversational orchestration framework named A-LLM is presented in which large language models (LLMs) are employed to jointly optimize energy efficiency and security in wireless sensor networks (WSNs). Sink–sensor interactions are modeled as structured dialogues, through which compressed network state summaries are translated into actionable policies for cluster-head selection, duty-cycle scheduling, and anomaly mitigation. A risk-aware energy optimization objective that incorporates the cost of LLM queries is formalized, and a secure conversational reconfiguration protocol (SCR-LLM) is proposed to isolate compromised nodes and refresh group keys under adversarial conditions. To improve practicality, a prompt-distillation approach is introduced so that LLM behavior can be transferred to a lightweight edge policy, thereby reducing both latency and query overhead. The framework has been designed for evaluation through extensive simulations and an emulated testbed under heterogeneous traffic patterns and attack scenarios. Improvements in network lifetime, energy consumption, and detection accuracy have been observed when compared to classical baselines. All datasets used were either publicly available or derived from public sources, ensuring reproducibility and methodological transparency.

Interpersonal affectionate touch plays a crucial role in social bonding, stress regulation, and psychological well-being. However, individual differences in past touch experiences and attitudes toward touch remain understudied. This study aimed to validate the German version of the touch experiences and attitudes questionnaire (TEAQ-G) and investigate how the subscales childhood touch experiences, current intimate touch, and attitudes toward touch relate to social relationships, mental health, emotional states, cortisol and oxytocin levels in everyday life. Data from 1,319 study participants suggest good psychometric properties of the TEAQ-G, confirming its reliability and validity as a tool for assessing touch experiences and attitudes. Regression analyses revealed that retrospectively reported more positive childhood touch and current intimate touch were associated with lower levels of attachment avoidance, stress, anxiety, depression, and loneliness, as well as higher resilience, family functioning, and relationship satisfaction. These findings were further supported by ecological momentary assessment data from 253 subjects (6 measures à two days, resulting in 3036 data points), associating positive childhood touch experiences with more favorable daily emotional states in adulthood. Specifically, individuals reported lower levels of stress, anxiety, loneliness, and burden related to the COVID-19 pandemic, along with increased happiness. Similarly, current intimate touch was linked to lower stress, pandemic-related burden, reduced loneliness, higher happiness, and moderately higher salivary oxytocin levels. Moreover, both childhood touch experiences and attitudes toward intimate touch significantly moderated the relationship between daily affectionate touch reports and individuals’ happiness, stress levels, pandemic-related burden, and cortisol concentrations. Our results emphasize the developmental importance of early touch experiences and highlight the lasting impact of both early and ongoing touch on attachment, social relationships, psychological well-being, and hormonal responses in everyday life.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-20885-y.

Background It is only since 2014 that Hijra have been legally able to access gender affirming surgeries through public hospitals in India. Many Hijras have and continue to access informal surgeries known as Nirvanam, practiced by unqualified local healers with the help of community members. Aims To compare the experiences of Hijra who undertook Nirvanam, and those who had accessed formal public hospital care for gender affirming surgery. Methods Interviews were conducted with five Hijra who undertook Nirvanam, and five who had accessed formal public hospital care. Interview questions pertaining to surgeries were analyzed thematically, focusing on each cohort separately. Results For participants who undertook Nirvanam, two themes were developed: (1) Exclusion from hospital care leads to culturally-mandated informal care, and (2) Suffering as a pathway into womanhood. For participants who had accessed formal hospital care, two themes were also developed: (1) Formal medical care requires time, and (2) The importance of therapy without judgment or pressure. Conclusions The paper concludes by considering the importance of community education about Nirvanam, and the potential for the translation of the experience of nonjudgmental hospital care into the space of Hijra houses.

Atherosclerosis is characterized by the accumulation of lipids and immune cells in the arterial wall, leading to the narrowing and stiffening of blood vessels. Innate and adaptive immunity are involved in the pathogenesis of human atherosclerosis. However, spatial organization and roles of immune cells during disease progression remain poorly understood. A better understanding of the immune response’s contribution to atherosclerosis progression could unveil novel therapeutic targets to mitigate plaque development and rupture, ultimately reducing cardiovascular events burden. Here, we utilised GeoMx® and CosMx™ technologies to analyse serial sections of human coronary arteries from patients with varying degrees of atherosclerotic lesion severity. Our work comprises a series of investigations and integrates findings from both datasets, including pathway analyses, cell typing, and neighbourhood analysis. This workflow highlights the power of combining these spatial transcriptomics platforms to elucidate biological processes at the single-cell level. Our approach unbiasedly identifies molecules and pathways of relevance to support the understanding of atherosclerosis pathogenesis and assess the potential for novel therapies.