Carbohydrate-binding modules (CBMs) are domains of carbohydrate-active enzymes with no catalytic activity but are crucial in recognizing and binding specific polysaccharides. Their structural and functional diversity enables targeted interactions with insoluble substrates, facilitating enzymatic degradation and enhancing process efficiency. CBMs are important tools in biotechnology, particularly in biofuel production, bioremediation, and the food and medical industries, where precise substrate targeting and delivery are critical. Advances in metagenomics and structural bioinformatics have expanded our knowledge of the diversity of CBMs and provided insights into new binding mechanisms and potential industrial applications. Despite their promising properties, challenges remain in engineering CBMs for improved stability and substrate range. We could unlock their full potential by addressing these limitations through protein engineering and computational tools. This review highlights the importance of CBMs in glycobiology and biotechnology. It argues for multidisciplinary approaches to utilize their diversity for innovative solutions in sustainable processes, including bioenergy and biopolymer synthesis, bridging the gap between basic research and industrial application.
A thermostable α-amylase gene (Amyk4) from Bacillus licheniformis k4cm (or similar thermophilic Bacillus), isolated from a Himalayan hot spring in Sikkim, India, was successfully cloned and expressed in Escherichia coli BL21(DE3). The purified recombinant α-amylase (Amyk4) exhibited maximal activity (153.2 ± 1.05 U/mL) at 80 °C and pH 6.0, with a half-life of 30.39 h and enhanced stability in the presence of Ca²⁺, indicating its suitability for high-temperature industrial applications. The cotton fabric desizing was optimized using Response Surface Methodology (Box–Behnken design). Optimal conditions (pH 6.9, 80 °C, enzyme dosage of 190 U/g, and treatment time of 95 min) achieved a TEGEWA rating of 8 and a weight loss of 6.6
The Himalayas comprise a wide diversity of rock types with variations in the rock mass’s inherent textural characteristics and structural features. This terrain has been explored frequently for numerous rock engineering projects and is witnessing progressive infrastructure development owing to the potential of tourism and the prospect of producing hydroelectricity. Moreover, the instability of rock masses continues to be a cause of concern in the region. Therefore, the insight into the physico-mechanical properties of the Himalayan rocks has significant implications for determining appropriate support systems and ensuring cost-effective, sustainable and efficient construction of various structures such as dams, bridges, underground openings, or rock excavations and for optimising the drilling and blasting parameters. Over the period, several researchers have evaluated the engineering properties of the Himalayan rocks from different perspectives. However, a brief review of the variation of rock properties for different tectonostratigraphic zones through the available literature is somewhat elusive to date. In line with this, the present study focused on an in-depth analysis of this issue by scrutinising and assessing accessible research articles on the engineering characteristics of Himalayan rocks. Secondly, possible avenues for further investigations have been presented to provide insight to researchers by identifying the scope and potential of future research in the Himalayan region. This review article can serve as a reference for geotechnical practitioners seeking insights into the properties of rock materials across various rock types in the region.
Sikkim, located in the northeastern Himalaya, is highly vulnerable to natural hazards and increasing depletion of surface and subsurface water resources, particularly springs and lakes. In South Sikkim, several lakes exhibit rapid drainage behavior, among which Nagi Lake shows near-complete water loss shortly after rainfall, indicating the presence of subsurface leakage pathways. This study investigates shallow subsurface moisture dynamics and identifies potential seepage-prone zones beneath the Nagi Lake basin using geoelectrical methods. Electrical resistivity profiling was conducted along seven survey lines during the non-rainy season (October-November 2025) to minimize the influence of transient rainfall-induced moisture variations. Profiling was carried out using the Wenner method, achieving investigation depths of approximately 6.5-9 m. Additionally, Vertical Electrical Sounding (VES) using the Schlumberger configuration was performed at selected locations to examine deeper subsurface conditions. Resistivity results indicate that profiles L1, L2, L3, L4, and L7 contain relatively higher moisture restricted to the upper similar to 5 m, whereas profiles L5 and L6 exhibit persistently low resistivity values from the surface to depths of similar to 9 m, suggesting sustained subsurface moisture accumulation. The downward extension of low-resistivity zones along L5 and L6 indicates possible preferential seepage pathways or localized subsurface water storage. VES results further reveal a higher density of subsurface anomalies below similar to 14 m in these areas. These low-resistivity anomalies are interpreted as potential subsurface flow pathways. Although confirmation of active seepage requires additional hydrological or time-lapse investigations, the findings provide important baseline geophysical insights for lake rejuvenation.
The emergence of Large Language Models (LLMs) has profoundly reshaped computational linguistics, enabling unprecedented reasoning, context awareness, and semantic understanding capabilities. Integrating these sophisticated models into Internet-of-Things (IoT) ecosystems holds transformative potential for enabling intelligent, autonomous, and contextually-aware applications. This article begins with an extensive state-of-the-art survey of existing literature on the integration of LLMs within IoT environments, establishing foundational insights into current capabilities, limitations, and deployment frameworks. Subsequently, the manuscript contributes a comprehensive analysis of lightweight LLMs and embedding models suitable for resource-constrained IoT platforms while introducing a taxonomy of sub-billion–parameter ( < 1B), mid-range (1B–2B), and exact 2B–parameter LLMs—spanning families such as Qwen, Llama, SmolLM, and IBM’s Granite—as well as embedding models under 1B parameters optimized for low-latency retrieval. Comparative assessments elucidate trade-offs in model size, inference latency, context windows, energy consumption, and performance across models categorized by parameter count. Next, a diverse spectrum of prospective use cases—including home healthcare, smart agriculture, industrial optimization, and environmental monitoring—demonstrates the practical efficacy of deploying tailored LLM-IoT frameworks for real-world problem-solving. Later, the article systematically explores key challenges that must be addressed to fully realize the integration of LLMs within IoT contexts, encompassing resource constraints, heterogeneous data processing, privacy and security risks, latency requirements, model interpretability, and ethical considerations. Finally, we outline critical directions for future research, advocating advancements in IoT-specific model architectures, multimodal sensor fusion strategies, real-time adaptive inference methods, energy-aware inference scheduling, and privacy-preserving federated learning paradigms.
