The energy production and consumption ecosystem is undergoing a profound transformation with the emergence of smart energy technologies. This survey examines the trends, opportunities, and challenges of promoting smart energy, with a comprehensive analysis of its evolution, current state, and prospects. This research employs a mixed-methods approach, combining quantitative data analysis with qualitative insights from industry experts, policymakers, and academics. Through surveys and interviews, key themes and trends are identified, including the drivers of smart energy solution adoption, the opportunities they offer for sustainability, efficiency, and resilience, and the challenges hindering their widespread implementation. Findings reveal a notable shift toward decentralized energy systems, renewable energy integration, and the Digitalisation of energy infrastructure, driven by the need to mitigate climate change, ensure energy security, and capitalize on technological advancements. It also emphasizes the significance of ethical considerations and legal frameworks in shaping the future of technology-driven, sustainable, and efficient energy practices. These encompass cybersecurity vulnerabilities in interconnected systems, operational issues arising from the use of diverse technologies, regulatory complexities that hinder rapid adoption, and the need to address equity concerns in the energy transition. Hence, this survey study contributes towards understanding Smart Energy’s trajectory.

This study optimised methane yield from pig manure (PM) co-digested with banana peel, cabbage and pumpkin residues (BCP). A two-factor response surface methodology was used, with PM fraction and batch digestion time as factors. Blank-corrected biochemical methane potential (BMP) was the main response, with methane volume and energy yield as supporting responses. Design-Expert was used for quadratic modelling, while Python supported model checking and sensitivity analysis. BMP ranged from 191.48 to 468.23 N mL CH₄ g⁻¹ VS. The PM fraction contributed 98.975% of the model sum of squares, compared with 0.855% for digestion time. The absolute maximum occurred at PM0:BCP100 after 60 days, but this was mono-digestion. The highest tested co-digestion BMP was 332.85 N mL CH₄ g⁻¹ VS at PM50:BCP50 after 60 days. At 45 days, the same blend achieved 324.95 N mL CH₄ g⁻¹ VS, retaining 97.6% of the 60-day yield. Feedstock composition affected methane yield more strongly than digestion time. PM50:BCP50 at 45 days is a time-efficient laboratory-scale condition requiring validation in continuous digesters.

The advancement of pest detection in smart agriculture has outpaced the development of technologies for detecting avian crop threats. Recently, the rise in quelea populations due to ecological improvements has resulted in significant agricultural damage as these birds feed on grains and seedlings of crops such as millet, maize, and rice. This study presents an artificial intelligence-based quelea recognition method suitable for embedded systems. This research enhances the Tiny-YOLOV3 network, developing the Enhanced Tiny-YOLO (ET-YOLO) for real-time detection of queleas in complex outdoor environments. A dataset comprising 3,500 high-resolution quelea images, taken under various conditions and distances, was used for training and testing. Experimental results demonstrate that ET-YOLO achieves an average detection accuracy of 88.5% and a speed of 62 frames per second in video feeds, improving accuracy by 15 percentage points and speed by 2 frames per second over Tiny-YOLOV3. Additionally, ET-YOLO outperforms SSD_MobileNetV2, YOLOV3, and Faster-RCNN, with accuracy improvements of 17, 1.6, and 1.4 percentage points, and speed increases of 1, 35, and 44 frames per second, respectively. With a model size of 56 MB, ET-YOLO is well-suited for deployment in embedded systems within agricultural robots and smart machinery. The comparison shows that the ET-YOLO network proposed in this paper achieves higher detection accuracy and speed than the original Tiny-YOLOV3 lightweight target detection network.

Integrating Agriculture, a vital sector for global food security, is increasingly challenged by resource scarcity, climate variability, and rising operational costs. Efficient management of resources, especially water and energy, has become crucial for sustainable farming. This paper investigates integrating microelectronic systems, including microcontrollers and sensors, into greenhouse environments to enhance agricultural practices. Additionally, it proposes a predictive model for greenhouse air temperature using a Long Short-Term Memory (LSTM) neural network combined with an attention mechanism (LSTM-AT). This hybrid model addresses the limitations of traditional LSTM models in handling long-term data improving prediction accuracy. The LSTM-AT model was validated against multiple models, such as GRU and RNN, under varying prediction horizons and weather conditions. Results show that the LSTM-AT model outperforms the alternatives, achieving a minimum R2 of 0.95, a maximum RMSE of 1.35°C, and a maximum MAPE of 12.01%. These findings highlight the potential of microelectronic systems and advanced prediction models to optimize greenhouse environments, reducing energy consumption and increasing agricultural productivity.

This paper presents a framework for achieving the intelligent manufacturing of tomorrow through smart factories initiatives, using a real-world case study to bridge the gap between high-level strategy and shop-floor results. End-Pipe-Cloud-Application architecture with machine connectivity to solve the critical challenge of heterogeneous integration, seamlessly merging data from legacy production systems, new IoT sensors, and business software into a single, intelligent platform has been implemented in this study. The results posit a 30% reduction in production-line labor costs, a 2% increase in overall capacity, and delivered a return on investment (ROI) exceeding 130% with a payback period of approximately two years. The solution enabled true closed-loop control over the “Man, Machine, Material, Method, and Environment” factors, transforming the traditional Manufacturing Execution System (MES) into the proactive “brain” of the operation. Cellular IoT is a viable and powerful engine for intelligent manufacturing integration, translating strategic initiatives into measurable, synergistic outcomes.

Metal Additive Manufacturing (MAM) offers unparalleled design freedom but is fundamentally limited by precision-related defects, including poor surface finish, geometric inaccuracies, and thermal residual stresses. This comprehensive review systematically analyzes these inherent limitations through the critical lens of the Equipment-Material-Process triad. It then establishes Additive-Subtractive Hybrid Manufacturing (ASHM) as the principal paradigm for precision enhancement. The paper synthesizes research progress across ASHM implementations based on arc, laser, and plasma energy sources, evaluating each for its capability to rectify specific MAM shortcomings. A core contribution is the critical examination of intelligent process planning strategies—including model decomposition, adaptive slicing, and the optimization of alternating sequences—which are essential for mitigating geometric complexity and tool accessibility issues. Finally, the review codifies the persistent technical challenges impeding industrial maturation, such as the need for integrated real-time control, material-process certification, and standardized machine tool interfaces. This work provides a foundational reference for advancing hybrid manufacturing from a promising solution to a robust, industrially viable technology.

Wearable mechatronic systems rapidly transform healthcare, particularly real-time health monitoring. These systems rely heavily on the performance of the sensors they incorporate, and recent breakthroughs in electronics, biocompatible materials, and nanomaterials have paved the way for more efficient and reliable wearable devices. These advancements, including tiny sensors and biomedical tools, have revolutionized diagnostics and health prediction, significantly improving patient care and quality of life. This paper explores emerging technologies in wearable mechatronic systems, focusing on innovations such as artificial intelligence (AI), flexible electronics, and non-invasive biosensors. The paper examines how these technologies are shaping the future of wearable devices, addressing key challenges in sensor reliability, scalability, and integration with healthcare systems and ultimately revolutionizing real-time health analysis.

This study focuses on investigating the manufacturing, characterization, and assessment of palm kernel nut oil as a cutting fluid (CF) in the machining of aluminium 6061 alloy. Cutting fluids are vital in machining operations as they reduce friction, dissipate heat, and prolong the lifespan of tools. Palm kernel nut oil, derived from the fruit of a palm kernel, has attracted attention due to its environmentally friendly and readily biodegradable characteristics. This study involved the extraction, refinement, and characterization of palm kernel nut oil for its potential application as a cutting fluid. An experimental investigation was conducted to evaluate the performance of palm kernel nut oil (PKNO) as a CF through turning operations on aluminium 6061 alloy. The experimental parameters included the cutting speed, feed rate, and depth of cut, while the effectiveness of the CF was assessed based on key performance indicators such as surface roughness and cutting temperature. The findings demonstrated that the PKNO-CF exhibited favourable physical properties, including optimal viscosity, density, and pH levels. Furthermore, a detailed chemical analysis confirmed the absence of hazardous components, establishing palm kernel nut oil as a safer and more environmentally friendly alternative to conventional cutting fluids. This study aligns with United Nations Sustainable Development Goal (SDG) 12: Responsible Consumption and Production as it promotes the use of an environmentally friendly and biodegradable cutting fluid, reducing reliance on conventional, potentially hazardous cutting fluids and reducing environmental pollution. By utilizing palm kernel nut oil as a sustainable alternative, this research supports eco-friendly manufacturing practices and minimizes environmental impact in machining operations

Increasing awareness of and need for sustainable agriculture methods has highlighted technology and how it can aid resource management and, eventually, enhance productivity. IoTenabled devices use advanced sensors, communication networks, and automated controls to track real-time crop requirements, weather, and soil moisture. These systems promote sustainability and efficiency through maximum water utilization, minimizing waste, and reducing human interference. The methodology in the paper covers an analysis of IoT technologies, the underlying theories, and the emerging trends per challenges and limitations of IoT in smart agriculture. The present study, therefore, points to the revolutionary role IoT could play in agriculture and calls for the broad adoption of IoTs to guarantee food security and sustainable agricultural methods. Despite high costs, technological difficulties, and obstacles to adoption persisting, these would hopefully be resolved with continued advancements and encouraging regulations to establish IoT-based smart irrigation as an essential part of contemporary agriculture.

The world at large makes use of forklifts, most especially during production as a means of transporting objects from one place to another. To solve the problem of poor visibility in the design of the forklift, a design of a remote-controlled forklift also equipped with voice control can be used, eliminating the need for a secondary person’s directions while lifting objects. This research was carried out to develop a remote and voicecontrolled forklift with the use of Arduino’s, transceivers to receive and send signals as well as interpret the signal. The forklift was constructed using Acrylonitrile Butadiene Styrene (ABS) reinforced plastic, a hydraulic to control the lifting mechanism and a steering system to drive the forklift. Several tests were carried out on the forklift to test the flexibility of the voice control module as well as the degree of sensitivity, especially in noisy areas. The operable distance of the prototype is within 100 m (about 328.08 ft). Tests on the lifting speed, as well as the speed of the forklift, were also carried out during this research. The sensitivity tests done reveal that the forklift cannot be operated in too noisy areas as the noise creates an interference, hindering the forklifts’ ability to receive and interpret the given instructions. The research suggests further tests be carried out on the sensitivity and flexibility of the voice control module. Also, enhancements can be applied to the device by incorporating more robotic concepts to allow the vehicle to become autonomous.

This study presents a hybrid feedback system integrating personalized and collaborative strategies to enhance learning and teamwork in augmented reality (AR) environments. Traditional feedback mechanisms often fail to accommodate individual differences in skills, preferences, and learning styles, limiting their effectiveness in educational and professional applications. Furthermore, many collaborative AR platforms lack integrated feedback systems, leading to miscommunication and inefficiencies in team-based tasks. It posits using artificial intelligence (AI) to provide adaptive, user-specific guidance while synchronizing visual and auditory cues to improve group coordination. Experimental evaluations demonstrate a 40% reduction in feedback latency, a 25% increase in user satisfaction, and over 90% compatibility across various devices, including smartphones, tablets, and head-mounted displays (HMDs). These results reinforce AR's potential to enhance collaboration, optimize cognitive load management, and improve task performance. The findings highlight the transformative impact of hybrid feedback systems in advancing AR applications across education and industry.

Implementing a 5G network in industrial robotics is on the verge of changing the manufacturing sector by strengthening connectivity, designing systems for high-speed data communication, and assisting in increasing automation. This paper research shows that 5G networks deal with limited industrial systems as insufficient bandwidth, slow response time, and capacity constraints; characteristics of 5G, which are high response time, sufficient bandwidth, and the ability to accommodate multiple devices, help to improve the performance of the robots in the industry by bringing about swift response time, swift data been more reliable and improve communication in rapidly changing environments. The paper also dives into key applications of 5G in industrial robotics, comprising autonomous systems, industrial connectivity, enhanced robotics safety systems, and telemetric monitoring. It posits the challenges encountered in infrastructure associated with using 5G in industrial environments. By examining research done currently and realworld case studies, this paper provides the radical effects of 5G on industrial robotics, providing a deeper understanding of the future of next-generation factories and Industry 4.0. The research results propose that 5G-enabled robotics will notably improve the efficiency of the operation, adaptability, and innovative excellence across industries.

The literature on using ceramic particles from agroindustrial wastes to enhance the engineering performance of metallic materials is limited. This study explores using rice husk (RH) and white clay (WC) particulates to develop zinc‐based composite coatings on A36 steel. Four cathode specimens (80 × 40 × 2 mm) of A36 steel and two zinc anodes (50 × 30 × 2 mm) were prepared. The steel specimens were coated with Zn‐10RHWC(t25), Zn‐10RHWC(t30), Zn‐15RHWC(t25), and Zn‐15RHWC(t30), denoted as S1, S2, S3, and S4, respectively. The concentrations used were 10/15 g/L, with deposition times of 25/30 min at a constant cell voltage of 0.5 V. Corrosion rates (CRs) in 3.5 wt.% NaCl were investigated according to ASTM and NACE standards. The coated samples’ hardness, tensile strength (TS), and wear rate (WR) properties were also examined. Scanning electron microscopy (SEM) and X‐ray diffraction (XRD) were employed to study the morphology and crystallization of the coatings. The coated specimens exhibited significantly lower CR than the uncoated steel (CR = 8.45 ± 0.58 mm/year). CR values for S1 to S4 were 5.74 ± 0.41, 2.18 ± 0.42, 3.09 ± 0.38, and 5.92 ± 0.45 mm/year, respectively. All coated specimens showed substantial improvements in TS over the uncoated specimen (4.81%, 2.83%, and 4.29% for upper, middle, and lower sections, respectively). Regarding deformation modulus, the Zn‐15RHWC(t25) samples exhibited improvements of about 1.31% and 1.38% in the upper and middle sections, respectively, while the lower section experienced a decrease of 2.03%. The study demonstrates significant enhancements in the engineering properties of A36 steel coated with Zn‐13, RH, and WC using the dual‐anode electrolytic codeposition technique.

Zinc-bas ed composite coatings developed from synthetic ceramics (Si3N4, SiC, and Al2O3) have recently been employed as reinforcement to enhance their resistance to deterioration. However, there is limited literature on the utilization of ceramic particles sourced from agro-industrial wastes in the formulation of these coatings. This study investigated the effect of the surface improvement process (SIP) using rice husk (RH) nanoparticles on the hardness and wear rate of A36 steel. The A36 steel, zinc bar, and RH nanoparticles were procured and characterized using Energy Dispersive Spectroscopy (EDS). Four cathode specimens were produced, including an as-received specimen of A36 steel and two anodes of zinc. Four steel specimens coated with Zn-10RH(t25), Zn-10RH(t30), Zn-15RH(t25), and Zn-15RH(t30), denoted as S1, S2, S3, and S4, respectively, were developed with concentrations of 10 or 15 g/L and deposition times of 25 or 30 minutes at a constant cell voltage of 0.5 V. The as-received substrate steel was used as the control specimen (CS). The hardness and wear rate (WR) properties of the deposited samples were examined using Vickers hardness (HV) and a Pin-on-disc tribometer, respectively. All coated specimens exhibited substantial improvements in hardness and wear rate properties compared to CS (Hardness = 85.82±0.45 HV and WR = 2.45±0.34 g/min). For the coated specimens, the hardness and WR values ranged from 188.50 to 288.37 HV, 260.34 to 284.38 MPa, and 0.01 to 0.02 g/min, respectively. The inclusion of the coatings significantly enhanced the mechanical properties of the deposited specimens.

First-order growth models are fundamental in economics, finance, and various scientific disciplines for modeling dynamic processes. Solving these models numerically is often challenging due to their complex nature. Hybrid block methods have emerged as promising tools for efficiently and accurately solving such models. This article provides a comprehensive development, analysis and implementation of hybrid block method applied to the numerical solution of first-order growth models. We discuss the theoretical foundations, algorithmic implementations, and practical applications of these methods, highlighting their advantages and limitations. Additionally, we present numerical examples and comparisons with other numerical techniques to assess the performance of hybrid block method. Through these insights, we aim to provide insights into the state-of-the-art techniques for solving first-order growth models numerically.

The increasing demand for renewable energy sources has led to a renewed interest in using biomass as an energy source. Machine learning (ML) can potentially improve the efficiency and effectiveness of energy production from biomass. However, ML's impact on biomass energy production has not yet been fully explored. This study aims to systematically review the current literature on ML use in biomass energy production and investigate ML's impact through multiple case studies. A systematic literature review is conducted to identify relevant studies on the use of ML in energy production from biomass. The multiple case designs involve analyzing diverse real-world cases of machine learning applications in biomass energy production to gain a deeper understanding of the technology's practical implications and potential benefits. The findings of this study provide insights into the possible benefits and challenges of using ML in energy production from biomass. They will inform the development of future research and policy in this area.

The way man interacts with his place of abode has now been revolutionized by smart homes, which have become a more dominant trend in our ever-growing modern way of living. The trend of smart homes is marked by the rapid evolution of technology, the use of Internet of Things (IoT) devices, intelligent sensors, actuators, and control systems to improve convenience, security, increase autonomy and energy consumption. Features in the smart home systems range from lighting systems to voice automated appliances and even predictive analytics; providing a new shift for maintenance and usage of the home environment. However, the adoption of this technological change has drawbacks. As more devices are networked, the issue of privacy and security issues continues to pose worries in the minds of homeowners. Prospective advancements in smart home technology have huge tendencies to overcome smart home constraints and expand the spread of smart home technology implementation. AI developments have led to the production of more systems that are more human-conscious which drive a more autonomous world. The standardization of cybersecurity policies will help to reduce the worries about security problems, which will yield a more secure and interoperable smart home system.

The high cost of manufacturing intricate machine components, as per high-profile engineering applications, has hindered the widespread adoption of titanium alloys. These alloys pose challenges in terms of geometrical qualities, energy consumption, and the risk of material failure. In contrast, additive manufacturing (AM) technology offers a more adaptable and superior alternative to traditional production methods, simplifying the fabrication of titanium. This article explores the potential of AM for long-term industrial manufacturing. It delineates the benefits it can bring to small and medium enterprises (SMEs) in developing economies, enabling them to compete in high-end titanium/product manufacturing. It emphasizes the potential of additive manufacturing for metal production and the various processes that can replace existing manufacturing methods. The paper discusses the microstructure, characteristics, and economic advantages of titanium alloys produced from AM processes. The study examines the current knowledge regarding the challenges and prospects of AM-fabricated titanium-based alloys. As a result, it provides valuable insights and recommendations for academics and other individuals interested in researching and utilizing the metal AM method for producing/manufacturing components.