Indus University, formerly Indus Institute of Technology and Engineering[citation needed] is a private university established in 2006. In 2012, Indus received university status and is recognized by the University Grants Commission (UGC). Indus university is an All India Council for Technical Education (AICTE) approved university. Indus University is located in Rancharda, Ahmedabad, Gujarat, India.Indus University had a sign-up memorandum of understanding (MoU) with Dassault Systemes for a Collaborative Learning and Innovation Centre (CLIC) at the Indus campus. CLIC will allow students of Indus University to use Dassault Systemes V6 Technologies, along with LEGO Mindstorms and an Arduino micro-controller, to build cyber-physical projects that will answer problems through inter-disciplinary engineering and management approach.
Due to technological advancements, the energy demand on electronic devices has been reduced from milliwatts (mW) to microwatts (µW), and this microwatt power will be manageable with the usage of thermoelectric generators (TEGs). The TEGs work on the Seebeck effect and generate electricity due to temperature differences. To ascertain the temperature difference required for the power generation, phase change materials (PCMs) are widely recommended to integrate with TEGs. It is desired to prefer the PCMs of low temperature and high temperature types for cold and hot sides of TEGs, and as a result, it could be indeed beneficial to achieve a larger temperature difference for the power generation. The power output from TEGs could be sufficient to feed low power devices such as sensors, IoTs, ships, locomotive industries, wearable devices and signal indicators. The latest research progresses on the materials used for fabricating TEGs, placement of TEGs in the waste heat areas so as to achieve a maximum conversion efficiency, and heat transfer enhancement of PCMs used in TEGs. Due to their environmental sustainability, reliability, minimal maintenance costs, and direct power generation, TEGs are widely employed in various industries. The Internet of Things periphery devices are classified into three categories: Smart Home, Smart Factory, and Energy Efficiency. The high thermal capacity of PCM protects TEG and prevents device failure. The expansion of PCM-TEG's cooling capacity enhances its efficacy. The results indicate that the operating duration is extended by higher thermal power levels and that PCM reduces output voltage fluctuations. Inadequate heat source power may lead to partial PCM melting, which could result in a reduction in electricity output during non-heating periods. This study illustrates the great potential of thermoelectric power generators to herald in a new era of Internet of Things sensing devices by extracting energy from the ambient temperatures. This work could portray the wide area from the development of the novel generators and materials for better performance (Figure of merit), less space, and economically feasible, and the mechanism of heat transfer, critical analysis, importance of IoT, applications, advantages to drawbacks of TEG-PCM module.
Retinal vessel segmentation underpins computer-assisted screening and monitoring of ocular and systemic disease. While encoder–decoder networks such as U-Net are widely used, their behavior is strongly shaped by the training objective. This work presents a controlled empirical study of loss functions for vessel segmentation using a U-Net architecture that employs strided convolutions in the encoder, together with a consistent pre-processing pipeline based on morphological enhancement and principal component analysis. We compare cross-entropy, weighted cross-entropy, and Dice losses on the DRIVE and STARE datasets under identical settings, reporting pixel-wise and overlap-based measures to reflect both detection and spatial agreement. The configuration with weighted cross-entropy provides a balanced outcome, achieving sensitivity and accuracy of 0.873 and 0.969 on DRIVE, and 0.821 and 0.961 on STARE. Rather than proposing architectural novelty, the contribution of this study is a reproducible data-driven comparison that clarifies the tradeoffs each loss imposes on recall, specificity, and boundary fidelity, offering practical guidance for selecting objectives in retinal vessel segmentation.
Higher education institutions (HEIs) today represent a blend of traditional classroom learning and online education, with institutions increasingly incorporating e-learning into their existing teaching methods. However, many HEIs face challenges in enhancing the student learning experience during this transition. Simultaneously, students often struggle to demonstrate effective learning behaviors, which in turn affects their online learning outcomes. This study investigates the impact of e-learning quality on students’ learning behavior and examines its subsequent effects on their continued intention to use e-learning platforms and their perceived net benefits. Drawing on service-dominant (S-D) logic, the study introduces a novel perspective by incorporating value co-creation behavior as a key component in understanding students’ engagement in the e-learning environment. A three-wave, time-lagged research design was employed, involving a sample of 452 university students with prior experience in e-learning. Hypotheses were tested using Hayes’ PROCESS macro. The findings reveal that students’ perceptions of e-learning quality, specifically information quality, service quality, system quality, and instructor quality significantly influence their participative behavior. Additionally, student citizenship behavior mediates the relationship between participative behavior and both continued intention and perceived net benefits. The results affirm that students’ value co-creation behavior plays a crucial role in fostering sustained engagement and positive learning outcomes. These insights contribute to a deeper understanding of how e-learning quality and student behavior interact and offer practical implications for educators and researchers. The proposed framework serves as a strategic tool for HEIs to enhance value co-creation behavior among students, ultimately leading to improved e-learning effectiveness and student success.
Double perovskite materials incorporating rare earth elements, such as La₂BB′O₆ (here B represents Ni and B′ denotes Mn), have been extensively investigated. In this study, La₂NiMnO₆ (LNMO) was synthesized by employing solid-state reaction approach. X-ray diffraction (XRD) analysis confirmed development of a monoclinic crystal structure with P2₁/n space group. Diffuse reflectance spectroscopy demonstrated strong reflectance and minimal absorbance in visible and near-infrared regions, indicating optical transparency. Direct optical band gap, estimated to be 2.96 eV using the Kubelka–Munk-transformed Tauc plot, highlights its potential for UV-filtering and optoelectronic applications. FTIR spectra exhibited pronounced antisymmetric stretching at 556.78 cm−1 and bending modes around 913.24 cm−1, affirming the presence of Ni–O and Mn–O bonds within (Ni/Mn) O₆ octahedral network. Ferromagnetic transitions observed near 150 K and 280 K were attributed to B-site cation ordering. Dielectric studies over the 20 kHz–2 MHz range, modelled by the Maxwell–Wagner and Koops theories, revealed Debye relaxation behaviour and characteristic impedance trends.
