Institute of Management Technology, Hyderabad

The present research investigates the impact of social media sentiment related to Israel-Palestine and Israel-Iran tensions, alongside deliberations concerning prominent political figures, Benjamin Netanyahu, Ali Khamenei, and Donald Trump, on the equities within the global defense sector. We deploy Google’s TabNet and wavelet quantile correlation frameworks to unearth the predictive nexus and dynamic interlinkage structures between Reddit-derived sentiment indices and daily returns of six major defense stocks worldwide. Empirical results reveal that social media sentiment linked to geopolitical events, specifically attached to the epicentres of conflicts, significantly influences defense equity dynamics in long-run horizons.

Purpose Drawing upon resource dependence theory (RDT), this study benchmarks the operational consequences of working capital risk (WCR) considering organizational structure (business group (BG) vs independent firm). Design/methodology/approach Using a large dataset of 228 manufacturing firms listed on the Bombay Stock Exchange (BSE 500) from 2015 to 2023, the study employs panel data regression to test the proposed hypotheses. All models control for year and industry effect. More importantly, we have addressed the potential endogeneity concern with the help of 2SLS IV regression tests. Findings The findings show that higher WCR lowers production efficiency by 0.291 units and inventory efficiency by 0.443 units. However, BG affiliation positively moderates WCR–efficiency relationship. Consistent with the inherent advantage of BG network in terms of buffering financial constraint, it is observed that BG affiliation not only neutralizes the negative impact of WCR on production efficiency but yields a net positive impact (i.e. net effect size is +3.04). It highlights the dual role of organizational structure in providing financial stability and resource sharing. Originality/value The present study is one of the first studies that attempts to operationalize WCR with the help of receivable cycle and understand its effects on firm performance, considering the role of BG setup. The study contributes to the literature by bridging gaps in WCM research and highlighting practical strategies for firms to optimize working capital. It emphasizes actionable implications for managers to enhance liquidity, strengthen supplier relationships and leverage organizational structure to buffer against WCR.

Edge devices require more and more efficient models of deep learning algorithms that are able to deliver high levels of accuracy while following strict constraints in terms of computation and memory. Existing convolutional neural networks tend to compromise between performance and efficiency and there is a huge gap for real-time vision applications on resource-limited platforms. This study proposes LightClassNet which is a lightweight convolutional architecture to optimise the accuracy, latency and energy cost of edge-based image classification. The model is both strategy-wise by combining depthwise separable convolutions and bottleneck modules as well as pruning & quantization. Slightly improved results were observed in the experiments performed on the CIFAR-10 dataset indicating a 93.4% accuracy using only 2.1MB model size and 18ms inference time on a Raspberry Pi 4, outperforming several state-of-the-art lightweight models. The results confirm the proposed design as a good solution to fill the efficiency-accuracy gap, and an excellent solution to real-time, on-device AI applications.

Lead oxide (PbO) is a crucial component of borate glasses despite its toxicity because its presence significantly enhances their characteristics for specific uses, like radiation shielding and optical devices. The present work deals with the radiation shielding features of the borate glasses containing high density chemicals (PbO & PbCl2). The glasses with composition xPbCl2-(30 − x)PbO−69.5B2O3−0.5MnO2 with 5 ≤ x ≤ 25 mol% were prepared using melt quenching method. To obtain the shielding characteristic of samples like MAC, LAC, Zeff, etc., the well-known and reputable Phy-X/PSD online tool is utilized. The data flattened on the higher energy side (CE and PP) and showed increased MAC and LAC in the lower energy (PE) region. Lower concentrations of PbCl2 demonstrated better LAC and MAC since sample density diminished as PbCl2 increased.

Protective materials are becoming growing in significance in both industrial and daily applications, which has led to an increase in the significance of them in research. MoO3, CdO are such high density chemicals which were used to prepare the present glasses with composition 60B2O3-20CdO-10ZnO-(10-x)K2O-xMoO3 where x = 0, 0.5, 1, 1.5 & 2. To determine the sample's amorphous phase, the produced samples were first exposed to the XRD. The spectra's missing distinct peaks demonstrated the materials' amorphous phase. Since greater density offers more protection against radiation penetration, density is essential in radiation-resistant glasses. The concept of Archimedes is used to analyse the glasses' density, and the variance is attributed to the transition of BO3 and BO4 units. Shielding properties like MAC and LAC are assessed using Phys-x/PSD software. The PE, CE, and PP in various energy areas are used to describe these factors.