Sustainable control of optimized coolant quantity using an effervescent atomizer for enhanced machining performance

Experimental and machine learning-based analysis of encapsulated lauric acid in geopolymer composites for thermal energy storage

Small Scale Metal Industries (SMI) require huge amounts of energy to function. Although small-scale industries play a vital role in contributing to the economic development of the nation by way of exporting manufactured goods, the use of outdated systems and equipment results in a loss of energy efficiency and a lack of visibility and transparency of the process flow. As a solution to the problems that arise from poor digital integration in such processes, this paper presents a framework referred to as DLCMF (Digital Life Cycle Management Framework) for energy-intensive small and medium enterprises. In order to monitor the consumption and energy flow in the processes, process mining, real-time analytics, and discrete event simulations have been incorporated into the framework. In the context of this research, a simulation of a Machining industry in the state of Maharashtra was undertaken using the software Flexsim, which has been designed with Industry 4.0 and IoT capabilities. It has proven to be effective in reducing the consumption of energy (by 22%) and the amount of materials used (17%). In addition to this, the model facilitates a seamless integration process with respect to smart sensors, PLCs, and ERP systems, resulting in digital transformation within traditional manufacturing settings. The results resonate well with the Sustainable Development Goals, especially SDG 9, SDG 7, and SDG 13, due to improved energy efficiency, cleaner energy usage, and lower emissions of greenhouse gases. The paper provides good implications for policymakers, SME owners, and researchers who would want to align small-scale industrial practices with global sustainability objectives.

In multirotor-based photogrammetry, high-quality multi-view stereo modelling of a site requires a flight path that captures images from a wide range of viewing angles and altitudes. However, aerodynamic models show that vertical manoeuvring consumes notably more energy than horizontal cruising, and flight paths with many vertical transitions therefore lead to rapid battery depletion and lower operational time. Existing planning strategies typically address trajectory generation within continuous spaces, volumetric grids, or fixed uniform layers, but these approaches often fail to limit the vertical search space effectively and struggle to reduce energy usage without sacrificing the specific camera placements needed to reconstruct complex structural details. In this work, we propose a multi-stage planning framework that addresses the trade-off between energy efficiency and reconstruction quality by restricting the sampling space of feasible cameras to a sparse subset of vertical layers. To satisfy photogrammetric requirements within this reduced space, we introduce a view selection scheme based on global co-visibility analysis. This scheme prioritises surface regions that lack viewpoint observation redundancy and allocates camera viewpoints to capture intricate structural details that might otherwise be inadequately observed. Finally, a path planning stage generates collision-free trajectories within this sparse configuration, and this design implicitly minimises energy-intensive vertical transitions. Validation on real-world scanned scenes demonstrates that this approach reduces total flight energy by 6% to 25% compared to the next most energy-efficient baseline and maintains reconstruction completeness comparable to coverage-driven methods.

Abstract - This paper presents the design and development of a smart power cord capable of monitoring usage duration and providing real-time visual feedback through color-changing LEDs. Conventional power cords do not provide any indication of how long a device has been plugged in, which can lead to energy wastage and potential safety hazards such as overheating. The proposed system integrates a microcontroller with a current sensing module to detect active power flow and initiate time tracking. Based on predefined time intervals, an RGB LED changes color to indicate the duration of usage, while an optional display module shows the exact time elapsed. The system enhances user awareness, promotes efficient energy usage, and improves electrical safety. The design is cost-effective and suitable for both household and industrial applications. Key Words: Smart Power Cord, Energy Monitoring, RGB LED, Usage Tracking, Electrical Safety, IoT

Vertical farming is a controlled environment agriculture system with the potential to support resilient and scalable urban food production. However, its economic viability is challenged by high capital and operational costs, a substantial part of which is due to high energy expenditure. Meanwhile, global energy systems are undergoing rapid transformation due to increasing integration of renewable energy sources and the democratization of market-based mechanisms for maintaining grid stability. This evolving landscape creates an opportunity for vertical farms to participate in energy markets such as day-ahead, balancing, and flexibility markets by adjusting their energy usage for lighting and climate control in response to market signals. Doing so could reduce operational costs by reducing energy costs and generating additional revenue for providing services to the energy grid. This work examines the potential and challenges of integrating vertical farms as flexibility providers in energy markets. We discuss the relevant energy market structure and the potential economic benefits for vertical farms, and we formulate the underlying decision-making problem that governs this integration. We then identify gaps in understanding plant responses to variable lighting as critical barriers to implementation and outline research directions to address them. Finally, we support our discussion with small-scale laboratory experiments that illustrate the challenges of this integration.

Energy consumption in buildings continues to increase due to inefficient use of electrical systems, particularly lighting. Conventional lighting systems rely heavily on manual control, often leading to unnecessary energy wastage when lights remain on in unoccupied spaces. This study presents the design and implementation of an intelligent lighting control system aimed at optimizing energy usage. The system integrates motion detection and ambient light sensing technologies with a microcontroller to automate lighting operation. It ensures that lights are only activated when required and adjusted according to environmental conditions. The proposed solution demonstrates improved energy efficiency, reduced operational costs, and enhanced sustainability.

Plant foods generally supply lower quantities of digestible indispensable amino acids (IAAs) relative to the metabolic requirements. Protein quality can therefore be compromised in vegan diets. Targeted complementation of diverse plant foods in optimal proportions can overcome different limiting IAAs in vegan meals. Four-day food diaries from 193 New Zealand vegans were assessed for protein quality. Meals with a Digestible Indispensable Amino Acid Score (DIAAS) < 100% (n = 3623) were targeted for improvements in protein quality using a systematic pairwise matching procedure. Meal pairs attaining DIAAS of ≥ 100% were incorporated into the Vegan Protein Quality (VPQ) tool, currently accessible at: vegan-diet-tracking.shinyapps.io/Vegans/. The VPQ tool optimises food quantities to match each user's energy, total protein and IAA requirements, tailored to user preferences. The pairwise matching approach improved the amino acid profile of > 99% of meals to a DIAAS of at least 75%, with 41% of meals achieving a DIAAS of ≥ 100%. Test scenarios in the VPQ tool demonstrated the ability of the application to optimise meals to fulfil individual requirements, within serving size constraints. The collective findings provide novel empirical evidence that vegan meals can achieve high protein quality when appropriately planned.

Adaptive beam shaping transforms laser welding and additive manufacturing Adaptive laser beam shaping with a deformable mirror enables efficient energy and material usage in laser welding and additive manufacturing. European Union (EU) manufacturing companies increasingly build their business models on guaranteeing the performance of complex, high value products made from advanced alloys and produced under stringent quality requirements. Ensuring that these products meet demanding performance standards – while avoiding costly or dangerous failures – requires robust quality control and continuous development of more reliable, sustainable and cost effective manufacturing methods.

Purpose To address climate challenges, sustainable adaptive reuse of industrial heritage sites and their impact on reducing the carbon footprint can be considered an influential alternative. Therefore, this article aims to develop a futuristic vision sustainability assessment tool for industrial heritage sites, considering their historical and unique values. Design/methodology/approach In total, 47 indicators in 7 sustainability criteria were identified with a comprehensive perspective. Then, experts' opinions were sought via a questionnaire through the Future Design method. Next, to eliminate the uncertainties, the indicators were weighted through three methods of the multi-criteria decision-making by fuzzy logic with Python language programming to handle extensive data and customize some fuzzy steps. Findings The results show that safety and security measures, including fire safety and security, hold the greatest weight. The environmental indicators, including water management, local environmental impact, life cycle assessment and sustainable resources, are the next priority issues. Moreover, socio-cultural aspects with energy efficiency indicators and user comfort are primary concerns. Then, an adaptively reused industrial heritage was assessed by the proposed tool, comparing it with the DGNB certification. The comparison demonstrated a notable difference in weight and characteristics of indicators, suggesting a potential need for a distinct certificate for industrial heritage sites. Originality/value This study provides a dependable reference for policymakers and cultural heritage experts for the sustainable assessment of industrial heritage sites. Moreover, the presented tool can be applied for the assessment of industrial heritage sites in other countries by utilizing the localization method proposed in this article.

This paper presents the design and development of an intelligent energy meter integrated with Internet of Things (IoT) technology for real-time monitoring and power theft detection. Traditional metering systems are unable to identify unauthorized consumption effectively, resulting in major revenue losses. The proposed system utilizes voltage and current sensors interfaced with a microcontroller to continuously monitor energy usage. The collected data is transmitted to a cloud platform for analysis. Any mismatch or abnormal pattern in consumption is treated as potential theft and triggers an alert. This system ensures transparency, improves efficiency, and reduces manual intervention. The solution is cost-effective, scalable, and suitable for modern smart grid applications.

Energy efficiency in computing has emerged as a critical concern due to escalating environmental and financial costs, particularly in the context of cluster computing, where there is an ever-increasing software workload. Achieving meaningful improvements in energy efficiency requires a comprehensive understanding of the interplay between hardware and software. This research investigates how algorithmic optimisations, language choice, and parallelisation strategies influence energy efficiency and how hardware-level strategies such as underclocking, overclocking, cooling, and on-demand computing can further impact energy usage. A set of measures that can be used generally to show the impact trade-off of power and performance are defined, including the Energy Factor (EF) and a new Efficiency–Performance Score (EPS). Validation experiments on a custom-built Raspberry Pi Bramble cluster used workloads like Monte Carlo Pi simulations in Python and C. Energy and performance trade-offs were evaluated using the Energy Factor and Efficiency–Performance Score on a small example cluster to validate the approach. Results show parallelisation greatly improves energy efficiency over serial execution. Cooling slightly boosts speed under heavy loads but increases total energy use. Perhaps counter-intuitively, underclocking actually raises total energy consumption, while overclocking reduces it. Language choice also impacts efficiency, with C offering notable energy savings over Python. The findings support the hypothesis that software optimisation alone can improve energy efficiency, but the most impactful results are achieved when both software and hardware strategies are jointly considered. These insights contribute to the design of future energy-aware computing systems and provide a foundation for sustainable, high-performance computing architectures.

The rapid growth of urban populations and smart city initiatives has increased the demand for efficient urban utility management systems, with over 55% of the global population living in cities and municipal inefficiencies causing nearly 20–30% resource wastage annually, while IoT-based smart city solutions are projected to grow at over 19% annually. Additionally, issues such as water overflow, inefficient streetlight operation, and manual pump control contribute to energy loss, safety hazards, and increased operational costs. Traditional urban utility management systems rely heavily on manual monitoring and fixed schedules, which often lead to delayed responses, water wastage, overflow incidents, and inefficient energy consumption. Furthermore, conventional systems lack realtime data visibility, remote accessibility, and automated control mechanisms, reducing their effectiveness in dynamic urban environments. To address these challenges, the proposed IoT Urban Utilities Management System utilizes the ESP32 microcontroller to develop an intelligent and automated municipal management solution. The system integrates ultrasonic sensors to monitor manhole water levels and prevent overflow, while a GSM module sends real-time alerts to authorities. An RTC module enables scheduled control of water pumps and streetlights, ensuring efficient energy usage. The ESP32 processes sensor data and supports IoT connectivity for remote monitoring and data visualization, while an LCD and buzzer provide local alerts and system status updates. This smart system enhances operational efficiency, reduces resource wastage, improves public safety, and supports the development of sustainable and intelligent urban infrastructure management.

This article presents the development and validation of a digital twin of a galvanic production line and a multi-agent system using the Unity environment as a simulation platform. The primary objective was to create a tool that supports the analysis, testing and optimization of process flows and intralogistics involving autonomous mobile robots. The model replicates the actual structure of the technological line, taking into account the sequence of operations, energy consumption, processing times and transport logic. By applying a modular architecture, input data—such as layout configuration, energy usage and operation sequences—are imported from external configuration files, enabling rapid generation and comparison of various scenarios. A series of simulation experiments was conducted and the results were compared with an idealized mathematical model. The analysis revealed the presence of bottlenecks, shifting throughput limitations, as well as queuing and routing conflicts that cannot be captured by simplified analytical models. Furthermore, it was shown that expanding the process structure increases total energy consumption but improves energy efficiency per produced unit. The developed digital twin enables safe testing of structural and operational changes and provides a foundation for future integration with MES or SCADA systems, as well as the advancement of predictive production process management.

ABSTRACT Energy is an essential element of economic development. In maintaining a stable energy supply, countries worldwide are facing a transition towards cleaner energy sources and technologies. While gains in the usage and promotion of cleaner energy have been made in this pursuit, the transition toward cleaner energy faces obstacles concerning energy-related uncertainties and economic policy uncertainties, hindering investor confidence. This study explores the connectedness between a key element in the energy transition—clean energy stock returns, the energy-related uncertainty index, and the economic policy uncertainty index, using monthly data from 2005 to 2022. In using a quantile spillover approach to assess the dynamic relationship between the variables, the results indicate that in times of volatility within the market, the connectedness between clean energy returns, energy-related uncertainty, and economic policy uncertainty increases. In a less volatile market environment, the results, however, yield that clean energy returns, energy-related uncertainty, and economic policy uncertainty interdependency increase. These results underscore the need to create clean energy investment strategies and informed policy responses amid an increasingly volatile market and geopolitical tensions.

This paper presents a LoRaWAN-assisted smart energy meter designed for real-time monitoring and efficient management of electrical energy consumption. The proposed system integrates voltage and current sensors with an ESP32 controller to continuously measure energy parameters such as voltage, current, and power. The collected data are transmitted securely over a LoRaWAN network to a cloud-based platform for real-time visualization and analysis, similar to the edge-enabled smart metering approach. The system provides automated alerts for abnormal energy usage, along with daily and monthly consumption reports and departmental energy comparison features. By enabling long-range, low-power communication and remote accessibility, the proposed solution reduces manual intervention, enhances operational safety, and supports scalable energy management for institutional and industrial environments

Understanding the drivers of energy consumption is essential for addressing global energy challenges. This study examines energy usage in Türkiye using Index Decomposition Analysis (IDA) and the Autoregressive Distributed Lag (ARDL) model. The IDA, applied to sectoral energy data for the industrial, service, and agricultural sectors from 1990 to 2023, reveals that production effect increased total energy consumption by nearly 87%, while structural changes reduced it by approximately 14%, and improvements in energy intensity reduced it by about 21%. The ARDL analysis, based on annual data from 1960 to 2023, confirms a long-run relationship among energy consumption per capita (EC pc), Gross Domestic Product per capita (GDP pc), (CO₂ pc), and the share of renewable energy in total energy production. The results indicate an adjustment toward the long-run equilibrium, with approximately 46% of any deviation corrected each year, reflecting both short-run and long-run relationships. We also examine the Environmental Kuznets Curve (EKC) and Energy-Environmental Kuznets Curve (EEKC) hypotheses. Our results support an inverted U-shaped EEKC between EC pc and GDP pc, and an EKC between CO₂ pc and GDP pc. To ensure robustness, Ordinary Least Squares (OLS) estimates are reported as a benchmark specification.

China is faced with both challenges of global climate shifts and population aging, so energy consumption structure transformation towards cleanliness has become the first key issue if our country wants to achieve sustainable development. The paper utilizes a decade of provincial data (2012-2021) to explore how an aging populace reconfigures energy usage under different levels of environmental governance. Based on fixed effect and threshold estimations, the following results can be obtained: 1) The emergence of a large elderly population acts as an inhibitor of coal reliance and can expedite the adjustment to green energy; 2) Environmental regulation has one threshold in the impact on the energy consumption structure of population aging, and with the continuous improvement of the environmental regulation level, the positive effects of population aging are enhanced; 3) There is a clear spatial divide: The promoting effect in the eastern region is the largest. This study offers reference for promoting the development of sustainable development and differentiated environmental regulation policies in the context of aging and promoting the low-carbon transformation of the energy structure.

Monitoring electricity usage is important for electricity consumers. The electrical installation lines installed at the customer's premises are distributed via Miniature Circuit Breakers (MCBs) to each existing area or room and are then used for various electronic equipment. The challenge of presenting a system that can identify and view electricity usage patterns based on time series data on each route has the potential to support wise electricity use. The aim of this research is to build an electricity usage monitoring system architecture which is then called SMARTIK. The advantage proposed by SMARTIK is the detection of electrical energy usage anomalies, which are read from each existing electrical installation line. Anomaly detection is performed using a machine learning method, namely a Isolation Forest

In the current era of rapid urbanization and digital transformation, the efficient use of electrical energy has become an essential factor in promoting sustainable living and industrial development. The Intelligent Energy Meter with Alerting System using Arduino Uno and IoT technology is designed to provide users with real-time monitoring, control, and management of electrical power consumption. The proposed system continuously measures the power usage of household or industrial appliances and transmits the data to an IoT-based cloud platform for analysis, visualization, and reporting. The Arduino Uno serves as the central processing unit that collects data from voltage and current sensors such as the ACS712current sensor and a voltage divider circuit, calculates the power consumption, and communicates with an IoT Wi-Fi module (ESP8266) to upload the data online. When the energy usage exceeds a predefined threshold, the system automaticallygenerates alerts to the user through IoT-based notifications or buzzer indications. This threshold-based cost management mechanism helps prevent unnecessary energy wastage and high electricity bills. The system can also control or automate appliances based on user-defined parameters, thereby improving energy efficiency. Furthermore, users can remotely access detailed energy consumption analytics through the IoT platform, which provides insights into load patterns, daily or monthly usage reports, and anomaly detection. Overall, the proposedsystem enables proactive energy management, encourages conservation, and contributes to the vision of smart homes and smart grids. It represents an economical and scalable solution adaptable to both residential and industrial environments.