Vimal Jyothi Engineering College

The current research focuses on hemp biodiesel (HB), procured from industrial-grade crude hemp oil-a novel, non-edible feedstock processed via dual-stage transesterification. HB exhibits physiochemical properties akin to fossil diesel and meets national (I.S 15607) and international (ASTM D6751) biodiesel specifications, showcasing its viability as a sustainable fuel option. Blended with petro-diesel at five ratios (HB10 to HB50), the performance was assessed in a Kirloskar TV1 diesel engine at the default compression ratio (C.R) 17.5:1. Fossil diesel demonstrated the highest brake thermal efficiency (BTE) of 26.78 %, followed closely by HB30 (25.81 %) and HB20 (24.05 %). HB blends significantly lowered emissions across the board, except for NOx, and exhibited longer combustion durations compared to diesel. Further evaluation involved varying the C.R to 19:1 and 16:1 while maintaining other parameters constant. At higher C.R 19:1, HB30 achieved a BTE of 29.47 %, slightly below diesel's 30.51 %. HB30 excelled in emission reduction -CO, UBHC, and smoke was lowest at C.R 19:1 due to superior combustion. It also recorded the highest heat release rate after diesel across all C.R settings, with C.R 19:1 exhibiting the shortest combustion duration. Based on these findings, HB30 shows the most promising performance at the optimal compression ratio of 19:1.

Abstract – The AI-Powered Smart Distribution Board is designed to address critical inefficiencies in conventional electrical systems, such as load imbalance, low power factor, and the absence of real-time monitoring. By integrating artificial intelligence and automation, the system provides advanced energy management features. The primary functions include predicting electrical load demand using AI-based forecasting, which is crucial for optimized energy management. It automatically balances phase loads through intelligent switching mechanisms using relays or contactors. Power quality is improved, and energy costs are reduced by integrating capacitor banks for automated power factor correction. Furthermore, the system incorporates an IoT-enabled, real-time dashboard that facilitates remote monitoring, control, and fault alerts, significantly enhancing operational reliability and efficiency. This innovative solution is scalable, promotes sustainable energy practices, and is ideal for modern residential and commercial applications. Key Words: AI, Smart Distribution Board, Load Forecasting, Power Factor Correction, IoT, ESP32, Load Balancing, Energy Management, Automation

Abstract - Robotic and autonomous systems often operate in environments with varying terrain conditions, ranging from smooth indoor surfaces to rough and uneven outdoor landscapes. Conventional rigid wheels perform efficiently on flat surfaces but suffer from poor traction and stability on irregular terrains. This paper presents the design and development of a terrain-adaptable shape-shifting wheel capable of dynamically altering its geometry to suit different surface conditions. The proposed system transitions between a circular configuration for smooth motion and a triangular configuration for improved traction and obstacle negotiation. A servo-actuated rack and pinion mechanism controlled by an ESP32 microcontroller enables precise shape transformation. The system also supports wireless control through Wi-Fi, allowing flexible operation. A prototype was developed and tested on multiple terrains, including smooth surfaces and uneven ground. The results demonstrate improved stability, reduced slippage, and better obstacle-handling capability compared to conventional wheels. The proposed design offers a simple, cost-effective, and scalable solution for multi-terrain robotic applications such as exploration, agriculture, and rescue operations. Key Words: Shape-shifting wheel, terrain adaptability, rack and pinion, mobile robots, ESP32.

It is a conventional understanding that all businesses desire to grow and expand in terms of revenue, bottom-line profit margins, and employee count. In an apt environment, growth is natural, and all small businesses – including family businesses - desire to scale up into medium and eventually large firms. Nevertheless, despite supportive macroeconomic factors, some family businesses deliberately choose to remain small. This research aims to identify the reasons behind this counterintuitive but conscious decision of some small family businesses to remain small. Specifically, this paper challenges the conventional development framework of small family businesses and questions the assumption that all small family business owners desire their firms to grow. The purpose of this paper is to explore the reasons for maintaining such intentional resistance to expand and also to generate research propositions that support the implausible argument that at least some small family business owners deliberately remain small for varied reasons. A qualitative grounded theory method was used with small family business owners who were willing to participate in the study. Purposive sampling was used to identify small family business owners, and unstructured, in-depth interviews with 12 such participants were conducted, recorded, and transcribed. Open and axial coding identified two major and three minor themes. A major inference was an unwillingness to share tacit knowledge outside their family. The participants revealed that, as they did not want their embodied family knowledge to lose its boundaries and to maintain tacit knowledge sovereignty, they opted to stay small while remaining content with the growth of their business. Another major reason was to ensure the members’ well-being, making succession planning easier. In addition, three minor themes emerged to explain why they are reluctant to relinquish their smallness: core skill focus, relinquishing authority, and compliance overhead. The identified themes were validated through member checking, thereby enhancing the credibility of the findings. Research propositions are developed from these themes and could fuel further research.

Dense wireless sensor networks (WSNs) form an essential part of smart agriculture as they provide real-time, continuous monitoring regarding soil conditions, the state of crops, irrigation requirements and the environment. Nevertheless, when the readings of high-frequency sensors are transmitted continuously through each node, too much energy is wasted, congestion and packet collisions occur, which lower the network lifetime and reliability. The traditional method of aggregating a few sensor values into a single transmission addresses these concerns. Still, they frequently rely on fixed aggregation periods that do not respond in real-time to rapidly changing conditions in the agricultural environment. Due to this, any crisis, including weather-related issues (such as a sharp decrease in soil moisture, an outbreak of disease, or a heavy rise in temperature), can be postponed, and timely action will be prevented, which will threaten crop yields. AgriCAPA (Context-Aware Packet Aggregation Architecture) is a solution to this problem, realised in the form of a dynamic, intelligent approach to forming packet aggregates using real-time, low-power agricultural sensor networks. AgriCAPA has in place a lightweight Context Engine, situated in edge nodes, which is used to interpret information on environmental variability, data urgency, and remaining power levels. It sorts Data at the time of arrival by its level of severity, so that critical events can bypass the aggregation process and be sent electronically. Meanwhile, other stable or low-priority data can be aggregated and compressed to save bandwidth. To support longer network life and to ensure responsiveness, energy-conscious scheduling and event-based wakeup radios are introduced into the system; as a result, the system has (almost) minimal idle listening and therefore network lifetime. The large-scale simulations in an innovative farm environment demonstrate that AgriCAPA achieves 58% energy savings, a 42% reduction in the latency of high-priority events, and a 25% improvement in overall network lifetime compared to conventional aggregation approaches. Context-awareness, priority-based transmission, and rational energy use are three mechanisms integrated into AgriCAPA, enabling its reliable and real-time monitoring of agricultural processes with minimal power consumption. This makes it a scalable, mindful, sustainable, and innovative farming solution, especially in the grand scale advancements that will eventually change manual productivity and yield barriers. wireless sensor networks (WSNs), AgriCAPA, agriculture, crops, yield barriers.