Satish Kumar

The rapid expansion of social media platforms has transformed communication, social interaction, and identity formation among young adults. While these platforms facilitate social connectedness and self-expression, growing evidence suggests that maladaptive patterns of social media use are associated with anxiety, depression, sleep disturbance, emotional dysregulation, and reduced psychological well-being. This narrative review critically synthesizes current evidence from empirical studies, longitudinal investigations, systematic reviews, and meta-analyses published between 2010 and 2025 concerning the relationship between social media use and mental health among young adults. Evidence indicates that passive browsing, upward social comparison, cyberbullying, problematic social media use, fear of missing out (FOMO), and disrupted sleep architecture are major mechanisms contributing to psychological distress. Importantly, recent findings support a bidirectional relationship in which vulnerable individuals may engage in maladaptive online behaviors that further exacerbate anxiety and depressive symptoms. Emerging neurobehavioral evidence additionally suggests that social media engagement activates reward-processing pathways analogous to those implicated in behavioral addictions. Moderating factors including gender, personality traits, emotional vulnerability, and algorithm-driven exposure patterns further influence psychological outcomes. Despite methodological limitations such as reliance on cross-sectional data and self-reported measures, the cumulative evidence highlights an urgent need for digital literacy programs, early clinical screening, and platform-level interventions aimed at promoting healthier online environments. Future research should prioritize longitudinal, cross-cultural, and mechanistic approaches to clarify causality and identify protective factors capable of mitigating digital-age psychological distress.

High Resolution Image Download MS PowerPoint Slide Metal–semiconductor interfaces play a central role in micro- and nanoelectronic devices, as heat dissipation or temperature drop across these interfaces can significantly affect device performance. Prediction of accurate thermal boundary resistance (TBR) across these interfaces, considering realistic structures and their correlation with underlying thermal transport, remains challenging. In this work, we develop a unified Neuroevolution Potential (NEP) for the Si–Ti system that accurately reproduces energies, forces, and phonon properties of bulk Si, Ti, and TiSi 2 and extends naturally to interfacial environments to analyze interfacial transport. An important development over current machine-learned interatomic potentials is the capability to model complex structures at metal–semiconductor interfaces, as the NEP enables large-scale nonequilibrium molecular dynamics simulations of epitaxial Si/Ti interfaces to elucidate the effect of amorphous or crystalline silicide interfacial layers. Simulated TBRs show excellent agreement with our time-domain thermoreflectance (TDTR) measurements, validating the robustness of our predictions. Spectral analyses reveal that the amorphous TiSi 2 interfacial layer helps in efficient interfacial transport when the thickness is less than 1.5 nm compared to the crystalline TiSi 2 layer, due to the high spectral conductance in the 3–6 THz frequency range and also due to the opening of channels for anharmonic transport, but this trend reverses when the interfacial layer thickness increases beyond 1.5 nm. Comparison of TBRs at the Si/TiSi 2 interface for different crystalline phases of TiSi 2 establishes that the C54 phase has reduced TBR compared to the C49 phase, which is correlated with the difference in their phonon density of states (PDOS) overlap with Si. These results provide atomistic insight into the role of crystalline versus amorphous silicides in interfacial heat transport and demonstrate a transferable machine-learned potential for studying heat dissipation in advanced semiconductor devices.

A method to process boron nitride nanotube (BNNT) fibers with a high degree of alignment, high modulus, and good tensile strength is presented. This has been achieved by dispersing BNNTs in a polymer solution, spinning the resulting polymer/BNNT dispersion into fibers, and removing the polymer. Significant alignment is imparted to the BNNTs within the fiber during drawing and heat treatment under tension. These BNNT fibers are characterized structurally and elementally to confirm the BNNT structure. This work has resulted in a highly oriented BNNT fiber with a modulus as high as 396 GPa and a tensile strength as high as 500 MPa. These tensile values represent the current state of the art for BNNT fibers, and the alignment of BNNTs in the fiber is the highest ever achieved for nanotubes-based fibers. Significant porosity is observed from the TEM images of the BNNT fibers' cross section, indicating that further processing optimization can be expected to further increase these properties. A knot can be made in some of the resulting BNNT fibers, suggesting that some of these BNNT fibers are suitable for typical textile processing techniques. BNNT fibers, their textile preforms, and BNNT fiber containing composites will be suitable for applications requiring high thermal conductivity without electrical conductivity, high temperature oxidative resistance, and low dielectric constant, particularly in aerospace and electronics areas.

Boron nitride nanotubes are promising materials for polymeric composites due to their electrical insulation and thermal conductivity properties. In this work, boron nitride nanotubes (BNNTs) are dispersed in a polymer solution that is then spun and drawn to make polymeric fibers with aligned bundles of long BNNTs. These polymer/BNNT fibers are then studied to determine the relationship among processing, structure, and properties. A sonication-centrifuge procedure was conducted to preserve longer BNNTs within the polymer structure and improve the alignment of BNNTs within the fiber. Herein, changes in the internal structure of PAN/BNNT fiber are mapped, highlighting the importance of the dry-jet wet spinning air gap and cold and hot drawing stages to achieve high orientation of BNNTs and a higher draw ratio of 25x. These fibers can be used to produce thermally conductive, electrically insulating composites, which have significant applications in electronics. This approach is scalable and can also be used to produce high-performance neat BNNT fibers.

Fisheries and aquaculture play a critical role in global food security, nutritional health, economic development, and livelihood generation, supporting more than three billion people as a primary source of animal protein. With increasing global population, urbanization, climate change, and pressure on natural fish stocks, sustainable fisheries and environmentally responsible aquaculture have become essential components of the blue economy. Overexploitation of marine and freshwater resources, habitat degradation, pollution, invasive species, emerging diseases, and climate variability threaten the long-term sustainability of aquatic ecosystems. Recent advances in biotechnology, genomics, precision aquaculture, artificial intelligence (AI), Internet of Things (IoT), biofloc technology, recirculating aquaculture systems (RAS), environmental DNA (eDNA), and digital monitoring have transformed fish production and conservation strategies. These innovations improve production efficiency, reduce environmental impacts, enhance fish health, and strengthen biodiversity conservation. This review summarizes recent developments in sustainable fisheries and aquaculture, emphasizing technological innovations, ecosystem-based management, biodiversity conservation, climate resilience, disease management, and future research priorities for achieving sustainable aquatic resource utilization.

Environmental DNA (eDNA) has emerged as a transformative molecular tool for biodiversity assessment, wildlife monitoring, and ecosystem conservation. Unlike conventional survey methods that rely on direct observation, trapping, or specimen collection, eDNA enables the detection of organisms through genetic material naturally released into environmental matrices such as water, soil, sediments, snow, and air. This non-invasive approach has significantly improved the efficiency, sensitivity, and cost-effectiveness of monitoring rare, elusive, endangered, and invasive species while minimizing disturbance to natural habitats. Recent advances in high-throughput sequencing, metabarcoding, quantitative polymerase chain reaction (qPCR), digital PCR (dPCR), and bioinformatics have substantially expanded the applications of eDNA in ecological research and conservation biology. Environmental DNA has become an indispensable tool for species inventory, habitat assessment, population monitoring, community structure analysis, invasive species surveillance, pathogen detection, and evaluation of ecosystem health across aquatic, terrestrial, and aerial ecosystems. Moreover, integration of eDNA with geographic information systems (GIS), remote sensing, artificial intelligence (AI), machine learning (ML), and ecological modelling has enhanced the accuracy and predictive capability of biodiversity assessments. Despite its considerable advantages, several technical challenges remain, including DNA degradation, environmental inhibition, contamination risks, incomplete reference databases, and limitations in quantitative interpretation. Standardization of sampling protocols, laboratory procedures, and bioinformatics pipelines is therefore essential for improving reproducibility and reliability. This review provides a comprehensive overview of the principles, methodologies, analytical approaches, and diverse applications of environmental DNA in wildlife monitoring and biodiversity conservation. Furthermore, recent technological developments, current limitations, future research priorities, and policy implications are critically discussed to highlight the expanding role of eDNA in supporting evidence-based conservation strategies and sustainable biodiversity management under changing global environmental conditions.

IoT and wireless sensor networks integration in agriculture allows the development of smart monitoring systems which track resource sustainability while providing real-time assessments. The research demonstrates an energy-efficient IoT-based framework for precision agriculture that assesses soil quality parameters while it uses adaptive irrigation controllers. The system organizes itself into three operational layers that include sensors for measuring soil parameters in addition to LoRa and ZigBee for extended-range power-efficient communication and an application component for cloud analysis and decision making. An edge preprocessing method using Kalman filters and adaptive sampling strategy enhances data reliability and diminishes energy usage during operation. Multiple field tests spanning various agricultural ecological regions reached accuracy measurements at 94.7% for moisture sensors and 96.5% for temperature sensors and 92.8% for pH sensors. This module showed water conservation at 30% higher than standard practices by achieving an 18.7% boost in crop productivity. High system uptime reached 97.6% while the data integrity level stood at 98.1% because of real-time dashboards and secure transmission methods. The research shows that sensor-based automation systems will convert traditional farming into effective data-centered operations to boost sustainability along with yield in agricultural production.

Terahertz (THz) radiation by beating of two laser beams in a groove density and magnetized hot plasma is discussed theoretically. In this mechanism, the ponderomotive force leads to a nonlinear oscillatory current that resonantly excites THz radiation at the frequency upper hybrid. And the study in which discussed about Terahertz radiation Generation, Magnetized plasma, Detection of terahertz radiations

Abstract In the age of digitalization and artificial intelligence (AI), documentary filmmaking has experienced a significant metamorphosis. Documentaries have grown thanks to digital tools, online platforms, and artificial intelligence (AI) technologies that change storytelling, aesthetics, and audience engagement. Historically, they were constrained by analog film processes and distribution barriers. The evolution of documentary filmmaking from film-based methods to the digital revolution and the emergence of AI-driven techniques is examined in this paper. It raises ethical and authenticity concerns while critically examining the ways AI supports visual effects, editing, scripting, and personalization. The article emphasizes how AI can be both an enabler and a disruptor, citing case studies like Welcome to Chechnya (2020), which used AI-based face replacement, Netflix's algorithmic documentary recommendations, and YouTube's role in activist storytelling. The study links theoretical viewpoints on digital media with practical applications through a qualitative, case-study-based methodology, demonstrating how artificial intelligence is changing the way documentaries are distributed, viewed, and told. According to the study's findings, artificial intelligence (AI) both challenges traditional ideas of truth and authorship that serve as the cornerstone of documentary practice and creates new opportunities for immersive and democratized storytelling. Keywords: Documentary filmmaking, Artificial intelligence, Digital narratives, Storytelling, Online platforms, Ethics, Audience engagement.

Digital signatures are fundamental cryptographic primitives that ensure the authenticity and integrity of digital documents. In the post-quantum era, classical public key-based signature schemes become vulnerable to brute-force and key-recovery attacks due to the computational power of quantum algorithms. Multivariate polynomial based signature schemes are among the one of the cryptographic constructions that offers strong security guarantees against such quantum threats. With the growing capabilities of neural networks, it is natural to explore their potential application in the design of cryptographic primitives. Neural networks inherently captures the non-linear relationships within the data, which are encoded in their synaptic weight matrices and bias vectors. In this paper, we propose a novel construction of a multivariate polynomial based digital signature scheme that leverages neural network architectures. A neural network with binary weights is employed to define the central structure of the signature scheme. The design introduces a recurrent random vector, functionally analogous to an attention mechanism, which contributes dynamic randomness based on the previous state, thereby enhancing the scheme's security. It is demonstrated that the proposed signature scheme provide security against Existential Unforgeability under adaptive Chosen-Message Attacks (EUF-CMA). Furthermore, it is proven that direct attacks aimed to recover the private keys are computationally infeasible within polynomial time, even in the presence of quantum computing abilities. The operational characteristics of the proposed scheme are also evaluated, with results indicating notable efficiency and practical viability in post-quantum cryptographic applications.

space during epidural anesthesia. In addition, miscalculation of dose in smaller patients, pregnant patients or the elderly can occur. Moreover, physiological factors like increased intra-abdominal pressure or decreased cerebrospinal fluid (CSF) volume can lead to significant effects on sympathetic and motor systems to cause serious manifestations

The need for real-time, user-friendly communication interfaces has driven gesture recognition system development. This need spurred system growth. This need has pushed Human-Computer Interaction (HCI) development forward. A hybrid deep learning architecture called GestureNet-HCI extracts spatial data using CNNs and LSTM networks to mimic temporal sequences. GestureNet-HCI integrates these neural networks. This study would benefit from our suggested structure. The technology is designed for real-time applications and accurately detects static and dynamic hand motions. The system had this feature. This was meant to streamline the procedure. It can collect and examine gesture data from depth video sources and RGB. Using video frames, the CNN module encodes visual-spatial characteristics. The LSTM, the other hand, learns motion continuity and temporal links. It is its responsibility. Resilience is attained by the model using an efficient sliding window frame sampling method and a data augmentation pipeline. You do this to become more resilient. Both elements offer the appropriate strength. With little Latency, GestureNet-HCI reaches state-of-the-art accuracy. Experiments with publicly available gesture datasets indicate that it is suitable for real-time uses. Among the pertinent data in these sets are Chalearn LAP IsoGD, NVGesture, and others. The proposed design allows for natural and effective interaction in smart surroundings, assistive technology, and virtual reality. This is a significant advancement. This allows a new standard for smart, responsive human-computer interaction (HCI) systems.

Next-generation high-performance computing (HPC) systems require power delivery solutions capable of sustaining beyond 1 kW per monolithic chip, with current densities expected to reach or exceed $2 \mathrm{~A} / \mathrm{mm}^{2}$. Distributed vertical power delivery (DVPD) architectures with integrated voltage regulators (IVRs) address this challenge by placing conversion stages closer to the processor, thereby reducing conduction losses. However, the interplay between temperature-dependent power dissipation and substrateembedded microfluidic cooling-particularly in 3D-stacked configurations where inner tiers have limited heat dissipation pathways-has been underexplored. This paper presents a PyAEDT-driven, automated electro-thermal modeling framework for 48 -to- 1 V DVPD architectures to accurately capture realistic power losses and pumping demands. Results demonstrate that ignoring electro-thermal feedback leads to significant underestimation of both power loss and IVR area. An electrical-only analysis predicts a total converter loss of 251.86 W, whereas integrating thermal effects raises this value to 285.91 W -an increase of about 13.5%. Likewise, IVR sizing grows by approximately 11% to mitigate elevated on-resistance and switching losses at higher temperatures. To maintain hot-spot temperatures below $85^{\circ} \mathrm{C}$, the required microfluidic flow rate must rise from $1 \mathrm{~g} / \mathrm{s}$, which is needed without considering electro-thermal interactions, to $2.47 \mathrm{~g} / \mathrm{s}$, generating a pressure drop of 47.9 kPa. Despite this higher flow rate, the pumping overhead remains only 122.32 mW, which is negligible compared to the extensive conduction and switching losses that would occur in inadequately cooled systems.

Effective cooling strategy is critical to achieve improved performance and efficiency in electric-drive vehicle motors. Among approaches, direct winding heat exchangers (DWHXs), positioned inside the motor component slots, have demonstrated superior potential for cooling compared to conventional methods such as forced convection air cooling and liquid jacket cooling. In this work, an in-slot heat exchanger (HEx) based on the DWHX concept is developed for an outer-rotor motor with a 100 kW peak and 55 kW continuous power output, and 50 $\mathrm{kW} / \mathrm{L}$ power density. Initial work developed a baseline additively manufactured aluminum oxide heat exchanger to cool concentrated stator windings in an 18 -slot, 16 -pole outer-rotor motor; however, it lacked performance in cooling stator endwindings. A new design was envisioned to address this issue. The present study introduces a novel in-slot HEx design, which also incorporates a cooling solution for end-windings at both sides of the motor. The thermal performance of this new design is assessed and compared with the baseline concept. The results from the new design indicate an over 50% reduction in thermal resistance and more than 30% reduction in hot-spot temperature. The new design reduces end-winding temperature while maintaining improved thermal uniformity across the winding. The increase in pressure drop in the new design adds only 0.013 W to the pumping power. Furthermore, the results indicate that a potting material used as an interface material - with a thermal conductivity of 3$4 \mathrm{~W} / \mathrm{m} \cdot \mathrm{K}$ - to attach these heat exchangers to motor components is found optimal to ensure effective thermal performance. The achieved performance is realized without altering critical electromagnetic parameters, facilitating seamless integration with the current motor design. These results underscore the potential of ceramic-based in-slot HExs in improving the thermal performance and efficiency of modern electric-drive vehicle motors, representing a substantial advancement in the development of high-power-density electric motors.

The Jammu region, situated in the northwestern Himalayan foothills, represents an ecologically diverse landscape characterized by subtropical forests,riverine ecosystems,wetlands, grasslands, and the Shivalik mountain ranges. The region supports rich floral and faunal diversity, including several threatened, endemic, and economically important species. Its varied ecosystems provide essential services such as climate regulation, soil conservation, carbon sequestration, freshwater supply, and livelihood support for local communities. However, rapid urbanization, habitat fragmentation, deforestation, climate change, invasive species, and increasing anthropogenic pressures pose significant threats to biodiversity conservation. This short communication highlights the ecological importance of the Jammu region, examines major conservation challenges, and discusses sustainable management strategies that integrate scientific research, community participation, habitat restoration, and modern conservation technologies. Strengthening biodiversity monitoring and promoting ecosystem-based conservation are essential for maintaining ecological integrity and supporting sustainable regional development.

Precision agriculture is transforming traditional farming practices through data-driven approaches that enable more efficient management of resources such as water, nutrients, and pesticides. This study investigates the implementation of a Narrow-Band Internet of Things (NB-IoT) enabled precision agriculture system integrated with a Long Short-Term Memory (LSTM) deep learning model to optimize crop management through real-time environmental monitoring and data-driven decision-making. The system deployed sensors to monitor essential parameters, including soil moisture, temperature, humidity, light intensity, pH, and nutrient levels, enabling continuous data collection. The LSTM model was trained on historical data to predict crop health, irrigation needs, and pest risk, achieving a prediction accuracy of 92% for soil moisture and 89% for pest risk. Results showed that datadriven irrigation adjustments reduced water consumption by 10-13% while increasing crop yield by 10-13%. Real-time nutrient monitoring facilitated targeted fertilization, reducing over-application by 15% and improving resource efficiency. The study demonstrates that an NB-IoT and deep learning approach can significantly enhance crop productivity and sustainability in agriculture. This model offers a scalable and adaptive solution for precision farming, providing actionable insights for optimized resource use and environmental conservation.

Purpose The paper aims to investigate the effects of slurry erosion on hydro turbine components, focusing on the experimental analysis of SS-304 using sand as the erodent material. The study was conducted on a tester under varying parameters to assess the material’s erosion behavior. In this work, the experimental investigation of SS-304 with sand was done with sand as the erodent material on a tester with various parameters. Further, the materials were made more resistant to wear by a WC-10Co-4Cr coating, done by the high-velocity oxygen fuel method. The mass loss of the specimens with and without coating was calculated. SEM was carried out on the specimens. The specimens with coating showed greater erosion resistance than the base material; however, wear mechanisms such as craters, lip formation, pores, etc. were discovered on the specimens. Design/methodology/approach The wear tests were carried out on the specimens with parameters of rotating speeds of 1,000, 1,150, 1,300 and 1,450 rpm; time duration 80, 130 and 180 min with sand concentrations of 30% and 50% in water. The base material was coated with WC-10Co-4Cr by the HVOF method of thermal spray. Findings In the results, it was observed that the wear resistance of the coated specimen increased significantly as compared to the uncoated material. Concentration proved to be the major factor influencing the wear erosion followed by rotational speed and time period. Various surface defects such as ploughing, crater formation, lip formation and micro-cutting were also found. Originality/value Slurry concentration was found to be the more dominant factor in increasing the wear of the specimens. The tests proved that the coating proved to be highly wear-resistant as compared to the uncoated base material and increased the wear resistance up to 3 times.

The pipeline method of coal transportation has numerous benefits, such as a cost-effective solution to transport high volumes of material, continuous operation, environmental benefits, reduced labour costs compared to traditional modes of transport like rail or truck. Moreover, coal slurry transport is crucial in many industrial applications, such as coal-fired power plants, steel manufacturing, coal liquefaction plants, mining operations, etc. Understanding the pressure drop characteristics of coal slurry behaviour inside the pipeline is vital for efficient system design and operation. In this experimental study, pilot-level pipe loop experiments were conducted to analyse the coal slurry flow behaviour inside a pilot plant test loop having sudden contractions, and bends. Experimental setup was fabricated to transport coal slurry, mixture of coal and water, through pipeline transitions. The coal particles characteristics were examined using various characterization techniques such as scanning electron microscope, thermal analyzer etc. The pressure drops were examined for a flow rate ranging from 10 m3/hr. to 40 m3/hr. and coal concentration (Cw) of 3%, 7%, and 10% over the bend and sudden contraction pipeline transitions. The results revealed complex relationships between pressure drop, slurry properties, flow conditions, and pipeline geometry. The understanding of key flow regimes, assessment of the relationship between particle size distribution and pressure loss, and the development of correlations developed empirically for pressure drop prediction over pipeline transitions were amongst the key findings. The present study provides valuable insights for optimizing coal slurry transport systems and contributed to the broader understanding of multiphase flow dynamics in complex geometries.

The operational temperature range and the specific requirement of the application direct the choice of a working fluid in a heat pipe.Methanol and ammonia are two fluids with distinct characteristics that make them suitable for various thermal environments.However, establishing a consistent testing environment for all fluids, especially nitrogen or ammonia, presents significant cost challenges.Consequently, experiments were conducted exclusively on methanol, considering the heat pipe's vertical, horizontal, and bent configurations.Methanol and ammonia heat pipes were modeled using the simulation package COMSOL Multiphysics.The methanol heat pipe experiment was then considered to evaluate the accuracy of the model and look for performance differences between the two working fluids.Such a method carries some interesting implications for the thermal behavior and feasibility of using methanol and ammonia as working fluids in heat pipes.

The study examines the efficacy of using a 20% blend of juliflora methyl ester (JFME20) as a diesel fuel alternative, focusing on injection timings of 19°, 23°, and 27° bTDC. At 27° bTDC, JFME20 exhibited a 4.53% increase in brake thermal efficiency (BTE) compared to standard conditions. Emission reductions at full load were notable: smoke decreased by 8%, hydrocarbons (HC) by 17.3%, and carbon monoxide (CO) by 25.45%. However, nitrogen oxide (NOx) emissions increased, necessitating additional mitigation strategies. To address this, exhaust gas recirculation (EGR) was implemented at 10% and 20% with JFME20 at 27° bTDC, resulting in substantial NOx reductions by 16.74% and 33%, respectively, compared to JFME20 without EGR. These findings underscore that JFME20 enhances thermal efficiency, reduces CO, HC, and smoke emissions, and highlights the necessity of managing NOx emissions. EGR emerges as a vital technique for optimizing the environmental performance of biofuels in diesel engines, balancing efficiency and emission control. This study demonstrates the potential of JFME20 as a viable diesel substitute, with EGR playing a critical role in mitigating its environmental impact.

In this paper, we propose a digital signature scheme, the MQQ-Sigv scheme, that relies on the difficulty of solving the multivariate quadratic (MQ) problem. The central map of the proposed scheme will be designed using the multivariate quadratic quasigroup (MQQ). We will prove that the MQQ-Sigv scheme is secure against various attacks including existential unforgeability under chosen message attack (EUF-CMA), Min-Rank attack, High-Rank attack, Direct attack, and Differential attack. Furthermore, we will prove that finding an equivalent good key for the MQQ-Sigv scheme is infeasible in polynomial time, and analyze the operating characteristics of the scheme.

Bamboo powder-reinforced polyethylene (PE) composite was developed as an ecologically friendly engineering material, and its mechanical properties were systematically investigated. To enhance adhesion between bamboo powder and the polyethylene matrix, maleic anhydride-grafted polyethylene (MAPE) was used as a compatibilizer. Given the growing demand for high-density polyethylene (HDPE) products, integrating natural and synthetic reinforcements, such as bamboo powder and glass fiber, presents a sustainable alternative in materials engineering. In this study, a composite matrix composed of HDPE, bamboo powder (BP), and glass fiber (GF) was fabricated using compression molding. The mechanical performance of the samples was assessed through flexural and compressive tests, considering different mass ratios of the components. Results indicate that the inclusion of BP and GF enhances compressive and flexural strengths by 20%, making the composites suitable for load-bearing applications, such as stress-bearing sheets, films, and pipes. However, a decrease in tensile and impact strengths was observed due to insufficient interfacial adhesion between the reinforcements and the HDPE matrix, highlighting a trade-off in mechanical performance. The findings suggest that optimizing fiber-matrix compatibility, potentially through improved surface treatments or alternative compatibilizers, could further enhance the overall mechanical performance of these composites. This research contributes to the advancement of sustainable composite materials by demonstrating the potential of BP/GF/HDPE composites in structural applications while addressing environmental concerns associated with plastic waste and non-renewable materials.

Ghoongroo pig is the first recognized pig breed in India from the North Bengal and adjoining districts of Assam. This breed has been evolved through continuous breeding and selection within its native breeding tract. The present investigation aimed to evaluate the genetic performance of Ghoongroo pigs across generations, utilizing data collected over a 13-year period (2008-2021) from the Nucleus Pig Breeding Farm at the ICAR-National Research Centre on Pig in Guwahati, Assam. The analysis of productive, reproductive and carcass traits revealed continuous improvements in litter size, litter weight, weaning weight and other traits over successive generations due to selective breeding. The study highlighted the consistent genetic progress achieved through selective breeding within the Ghoongroo pig breed. The scientifically managed nucleus herd of Ghoongroo pigs at the research institute serves as a valuable repository for preserving the breed’s genetic resources and acts as a conservation unit for this unique indigenous breed.

Abstract As power densities increase in heterogeneously integrated systems, with the introduction of new 3D architectures and the increasing number of transistors on chips, there exists a continued bottleneck for thermal management. High temperatures have a drastic impact on memory performances and refresh cycles. Moreover, thermal coupling between neighboring chiplets on a package is increasing as the types of chips on a heterogeneously integrated package diversify, and this, in turn, creates different heat flux densities within a heterogeneously integrated package. Thus, there arises a need for the implementation of efficient thermal design and solutions that cater to high heat fluxes within a package as well as different heights for different chip stacks within a package. In this paper, we present a parametric thermal design of heterogeneously integrated packages for high-performance computing. We focus on a 2.5D packaging structure, which includes components including artificial intelligence (AI) accelerators and high bandwidth memory (HBM) on a silicon interposer. Analytically and numerically, we investigate the thermal challenges stemming from high power density in stacked dies, variations in die heights, and cooling limitations at the package surface. To mitigate temperature gradients within the package, we propose a thermal-aware package structure, emphasizing the inside architecture. Also, the thermal coupling effect is studied for multiple cooling technologies on the outer surface using a thermal violation region graph. This research has shown that not only the internal structure of the package but also its ability to transfer heat to the outer surface has a significant impact on the thermal coupling effect. Using our approach, we can design package architecture systematically considering the external cooling environment in the early design stage.

Purpose The purpose of this paper is to investigate the silt erosion performance of Bare, 75%Cr 2 O 3 + 25%Al 2 O 3 and 85%Cr 2 O 3 + 15Al 2 O 3 -coated SS304 under various control parameters such as rotation speed, concentration of silt and particle size of silt used for making slurry. This can provide insight for using chromia and alumina-based coatings for hydro-turbines. Design/methodology/approach Taguchi approach was used to identify the effect of three input parameters on the bare and coated alloys. L 16 orthogonal array is used for determining the signal-to-noise (S/N) ratio for each process parameter. For each level of parameters taken into consideration about the erosion wear, the arithmetic mean of the S/N ratio is calculated. On the essence of the results of S/N ratios, it is possible to determine the effect of the most dominating parameters of the erosion wear. Findings Results show that the erosion increases with an increase in silt concentration (Wt.%). It has been analyzed that the rotational speed has the most significant effect followed by the particle size and concentration on erosion wear for all uncoated and coated SS-304 samples. Maximum resistance to erosion is provided by 85%Cr 2 O 3 + 15%Al 2 O 3 . The least erosion wear for process parameters has occurred at the optimal parametric combination of rotational speed (N) = 415 rev/min, concentration (C) = 15 Wt.% and particle size range as <53 µm for uncoated and coated stainless steel. Originality/value The study clearly shows the silt erosion performance of chromia and alumina coatings of different compositions at different input parameters. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0028/

High Power Impulse Magnetron Sputtering (HIPIMS) has emerged as a promising technique for the deposition of thin films with superior properties. In this study, HIPIMS was combined with a positive target bias (Kick voltage) process to enhance AlN film quality further. Aluminum Nitride (AlN) thin films were deposited using this method and characterized using Transmission Electron Microscopy (TEM) and X-Ray Diffraction (XRD). The thermal properties of the films were investigated using Frequency Domain Thermoreflectance (FDTR). The results show that the HIPIMS plus Kick process significantly improves the film quality, leading to enhanced thermal conductivity (112 W/m·K) compared to conventional techniques. This work demonstrates the potential of HIPIMS in producing high-quality thin films with improved thermal properties, making it suitable for various applications in electronics and thermal management.

With traditional power delivery architectures in state-of-the-art high-power (>1 kW) high-current density systems (>1 A/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), over 30% of the system-wide power is dissipated within the power delivery system, i.e., during the delivery of power from a printed circuit board (PCB) to functional die(s). Historically, in high-power systems, efficient low-power density voltage regulators have been placed on PCB, to minimize the conversion loss and advanced low-resistance interconnect technologies have been utilized to reduce the lateral routing loss in packaging power distribution network (PPDN). While power loss is reduced linearly with lower PPDN resistance, current reduction is desired due to the quadratic dependence of power on current. To efficiently deliver current from PCB to functional die, distributed vertical power delivery (DVPD) is preferred in this work. With this approach, power is delivered horizontally at high-voltage low-current and converted to low-voltage high-current close to functional die, near points-of-load (POLs), with optimal number of compact, power-efficient distributed on/in-interposer voltage regulators (VRs). Thus, lateral distribution of VRs is promising for mitigating conduction loss in both the VRs and horizontal packaging interconnect components. To increase the conversion efficiency and current density, an advanced network of parallel-connected vertically-stacked inductors and Gallium Nitride (GaN) power devices are considered. Analytical loss models and model-guided design methodology for optimizing the PCB-to-POL loss in a DVPD system are proposed in this work. A preferred power architecture for delivering 1-kW power to a functional 500-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> die is determined based on the proposed models and methodology, exhibiting system-wide efficiency of 85.6% and power density of 2 W/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Advancements in high-performance computing (HPC) demand densified interconnect and assembly methods in power delivery networks (PDN). A challenge accordingly emerges with system-on-package (SoP) solutions using vertical power delivery networks (VPDNs), where heat dissipation from the die and the integrated voltage regulator (IVR) is a primary concern. This paper presents a numerical thermal analysis of two VPDN designs: distributed on- and in-interposer dual-phase multi-inductor hybrid (DPMIH) converters. Positioned vertically beneath the die for a 12 V-to-1 V single-stage, high-current power conversion using gallium nitride (GaN), these configurations are evaluated in scenarios with and without embedded microchannel cooling. With microchannel cooling, the analysis incorporates 103 microchannels, each 200 µm wide and 100 µm high, employing water as the working fluid. The aim is to maintain device temperatures below 90 °C during 1 kW power delivery to a monolithic 3D (M3D) die while minimizing pumping losses for microchannel cooling and ensuring total efficiency exceeds 80 % at a high current density. Numerical results show that without embedded microchannel cooling, the maximum system temperatures reach approximately 126.1 °C and 155.5 °C for on- and in-interposer configurations, respectively. Conversely, embedded microchannel cooling with a flow rate of 0.05 kg/s reduces maximum system temperatures to 54.6 °C and 74.6 °C for on- and in-interposer configurations, respectively. For maintaining temperatures just below the threshold of 90 °C, on-interposer conversion requires a flow rate over 0.015 kg/s, with pumping power consumption of at least 9.2 W, whereas in-interposer conversion demands a minimum flow rate of 0.025 kg/s, consuming 39.4 W of pumping power.

Modern high performance integrated systems demand high-power (>1 kW) to be delivered at high current density (>2 A/mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) from PCB to points-of-load (POLs) on-chip. Efficient delivery of high-quality power from PCB to POLs is a primary concern in modern high-power high-density integrated systems. With traditional power delivery approaches, high voltage is converted to high current on PCB, yielding prohibitively high power loss in horizontal packaging interconnect components. One approach to reduce this loss is with vertical power delivery (VPD), i.e., to deliver low current at high voltage horizontally and convert it to high current low voltage close to POLs. Voltage regulators (VRs) integrated within small footprint near POLs, however, exhibit high switching and inductor losses. As a result, state-of-the-art VPD systems still exhibit high IR voltage drops, power efficiency of less than 70%, and high thermal dissipation. Thus, the design of compact power efficient VRs is a primary concern with VPD approach. To enhance the overall performance of the PCB-to-POL power delivery system, distributed VPD is considered and architecture-specific design of VRs is investigated in this paper. The design methodology for determining optimal number and placement of VRs for a given power delivery architecture is also proposed. The approach has been demonstrated with on-interposer 12V/1V power converters, comprising Gallium Nitride (GaN) power devices and state-ofthe-art inductors and capacitors, yielding 85% power efficiency with 1-kA load at 2 A/mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>.

High-power impulse magnetron sputtering (HiPIMS) plus kick is a physical vapor deposition method that employs bipolar microsecond-scale voltage pulsing to precisely control the ion energy during sputter deposition. HiPIMS plus kick for AlN deposition is difficult since nitride deposition is challenged by low surface diffusion and high susceptibility to ion damage. In this current study, a systematic examination of the process parameters of HiPIMS plus kick was conducted. Under optimized main negative pulsing conditions, this study documented that a 25 V positive kick biasing for AlN deposition is ideal for optimizing a high quality film, as shown by X-ray diffraction and transmission electron microscopy as well as optimal thermal conductivity while increasing high speed deposition (25 nm/min) and obtaining ultrasmooth surfaces (rms roughness = 0.5 nm). HiPIMS plus kick was employed to deposit a single-texture 1 μm AlN film with a 7.4° rocking curve, indicating well oriented grains, which correlated with high thermal conductivity (121 W/m·K). The data are consistent with the optimal kick voltage enabling enhanced surface diffusion due to ion-substrate collisions without damaging the AlN grains.