Enhance Energy Efficiency Research Articles

DAPNet: A Dual-Attention Parallel Network for the Prediction of Ship Fuel Consumption Based on Multi-Source Data

The precise prediction of ship fuel consumption (SFC) not only serves to enhance energy efficiency to benefit shipping enterprises but also to provide quantitative foundations to aid in carbon emission reduction and ecological environment protection. On the other hand, SFC-related data represent typical multi-source characteristics and heterogeneous features, which lead to several methodological issues (e.g., feature alignment and feature fusion) in SFC prediction. Therefore, this paper proposes a dual-attention parallel network named DAPNet to solve the above issues. Firstly, we design a parallel network structure containing two kinds of long short-term memory (LSTM) and improved temporal convolutional networks (TCNs) for time-series analysis tasks so that different source data can be applied to suitable networks. Secondly, a local attention mechanism is included in each single parallel network so as to improve the ability of feature alignment from different-scale training data. Finally, global attention is employed for the fusion of all parallel networks, which can enrich representation features and simultaneously enhance the performance of SFC prediction. In experiments, DAPNet is compared with 10 methods, including baseline and attention models. The comparison results show that DAPNet and several of its variants obtain the highest accuracy in SFC prediction.

Aspects of Developing an Innovative, Energy-Efficient, Low-Emission Co-Generator

Purpose. To develop a model and construct an experimental, innovative co-generator with high energy efficiency and low emissions. This involves developing a system that can efficiently generate both electricity and heat simultaneously while minimizing emissions, contributing to sustainability efforts, and addressing energy demands in a more environmentally friendly manner. Methodology. To achieve the goal, a system approach is utilized, enabling the selection of modelling types for the development of an experimental, cutting-edge co-generation system capable of efficiently producing both electricity and heat with superior energy efficiency and minimal emissions. For this purpose, the following steps were completed: processing and summarizing available literature and patent sources, analysing scientific and technical papers on the selection and application of modelling types in co-generation systems, and considering principles and individual approaches to input data formation for mathematical modelling. This process enables the selection of a mechanism and the creation of simulation models for effective energy production at various enterprises. Findings. Assessment is performed of the energy efficiency of the co-generator system under various operating conditions, comparing it with existing conventional methods of electricity and heat generation. Results are presented of performance testing to determine the system’s capability to simultaneously generate electricity and heat efficiently, considering factors such as output stability, load responsiveness, and overall reliability. Identification and evaluation are performed of innovative technologies and methodologies integrated into the co-generator design, highlighting their effectiveness in enhancing energy efficiency and reducing emissions. Insights into any operational challenges encountered during the construction, testing, and optimization phases, along with proposed solutions or improvements to solve these problems. The analysis of the overall environmental impact of deploying the co-generator showed potential benefits in terms of reduced greenhouse gas emissions and local air quality improvement. Originality. Using a combination of scientific approaches encompassing physics and heat transfer engineering, in accordance with the first law of thermodynamics, such as the conservation laws of energy and entropy, and principles of heat exchange employed to transfer heat between different mediums, gas kinetics have yielded values for the energy transformation coefficient, indicating qualitative characteristics of fuel thermolysis and power generation as the final product. Practical value. The results provide for developing a comprehensive model of an advanced co-generation system capable of efficiently producing both electricity and heat with superior energy efficiency and minimal emissions. It also entails determining the types of models for mathematical modelling at all management levels and establishing a new method for input data formation for both technologies and their subsystems, incorporating additional technological implementations.

Global renewable energy transition in fossil fuel dependent regions

The worldwide shift towards renewable energy, as defined by the United Nations' Sustainable Development Goals (SDGs) in 2015, is a crucial milestone in achieving a sustainable future. The core focus of SDG 7 is to advance the accessibility, dependability, and environmentally-friendly nature of energy, aiming to substantially enhance the proportion of renewable energy sources and enhance energy efficiency. Although there have been significant developments in renewable energy, notably in solar and wind technologies, the rate of transition is still inadequate to achieve climate objectives. This study examines the imperative of implementing policy changes and integrated strategies to align global and local goals, while tackling the issues of resource allocation and the pressing need to transition away from fossil fuels in order to mitigate global temperature increase. Case studies showcase effective national endeavors, such as Brazil's Proalcool, Germany's Energiewende, and India's ambitious renewable energy objectives, illustrating diverse strategies for bolstering renewable energy capability. The report also analyzes the economic consequences for emerging nations that heavily rely on fossil fuels and the expected effects of reduced fossil fuel consumption. This article offers a detailed analysis of the current advancements, difficulties, and variations across different regions in the field of renewable energy. It gives valuable information on the future of global renewable energy plans, highlighting the importance of unified worldwide endeavors to attain a sustainable energy future.

Artificial Intelligence in Climate Change Mitigation: A Review of Predictive Modeling and Data-Driven Solutions for Reducing Greenhouse Gas Emissions

Artificial Intelligence (AI) is increasingly recognized as a powerful tool for addressing the challenges of climate change. Its ability to process vast amounts of data and generate advanced predictive models positions AI as a key player in efforts to reduce greenhouse gas (GHG) emissions and develop sustainable solutions. This review delves into the multifaceted role of AI in climate change mitigation, highlighting its potential in several critical areas. Firstly, AI is revolutionizing predictive climate modeling by providing more accurate forecasts and simulations, enabling better-informed policy and decision-making. Secondly, it is optimizing energy systems through smart grid management, demand forecasting, and the integration of renewable energy sources, thereby enhancing energy efficiency and reducing reliance on fossil fuels. Furthermore, AI is advancing carbon capture and storage technologies by improving the identification of optimal sites and enhancing process efficiency. In environmental monitoring, AI-driven solutions are enabling real-time detection and analysis of environmental data, contributing to more effective conservation efforts. This review also presents case studies and data that demonstrate the tangible impact of AI applications in driving progress towards global emission reduction targets. However, the adoption of AI in this domain is not without challenges. Issues such as data privacy, algorithmic transparency, and the ethical implications of AI deployment need to be carefully addressed. The paper concludes by outlining future research directions and emphasizing the need for interdisciplinary collaboration to fully harness the potential of AI in combating climate change.

Integration of renewable energy-powered cold storage solutions for reducing post-harvest food waste in rural agricultural areas

Post-harvest food loss remains a critical challenge in rural agricultural areas, exacerbated by inadequate storage facilities and unreliable energy access. This study develops and optimizes an advanced renewable energy-powered cold storage system tailored for rural settings, integrating solar and wind energy with phase change materials (PCMs) for efficient energy storage. The system incorporates Internet of Things (IoT)-based sensors and artificial intelligence (AI)-driven energy management to maintain optimal storage conditions and enhance energy efficiency. Field trials conducted in Lincolnshire, UK, and Appalachian regions of the US demonstrated significant reductions in post-harvest food loss by an average of 43.5%, extensions in produce shelf-life by up to 300%, and increased income for smallholder farmers by approximately 43%. The system also achieved an 80% reduction in greenhouse gas emissions compared to conventional diesel-powered systems. Economic analyses revealed a shorter payback period and higher return on investment, confirming the system's viability. High user satisfaction and adoption rates indicate the system's practicality and potential for widespread implementation. The findings suggest that integrating renewable energy with smart technologies in cold storage solutions offers a scalable and sustainable approach to enhancing food security, promoting economic growth in rural areas, and supporting environmental objectives globally.

Innovative Electrostatic Precipitator Solutions for Efficient Removal of Fine Particulate Matter: Enhancing Performance and Energy Efficiency

The removal of particulate matter (PM) from air streams is essential for advancing environmental technologies and safeguarding public health. This study explores the performance of an electrostatic precipitator (ESP) in eliminating fine and ultra-fine PM under varied experimental conditions. It uniquely examines the influence of PM size and feed rate on ESP removal efficiency. The system’s use of low voltages enhances energy sustainability, while its innovative design improves corona discharge, leading to significant reductions in fine and ultra-fine PM emissions. Plants using electrical devices are increasingly being incorporated into material processing lines to reduce pollution in the surrounding work area, as well as to collect particle emissions in the atmosphere. It is also possible to recycle some raw materials in this way with low energy consumption. This cleaning technology increases the added value of industrial equipment, which affects its competitiveness and its impact on sustainable manufacturing. The experimental results indicate a steady electrostatic field voltage of 15.1 kilovolts, with an airflow maintained at 0.8 m/s through a doser at 2.5 bar, eliminating the need for a fan. The PM feed rate varied between 2 and 20 mm/h, with six trials conducted to ensure the data were consistent. Preliminary studies devoid of ESP intervention demonstrated little PM removal, since buildup on the chamber walls distorted the results. The installation of the ESF markedly enhanced the removal efficiency, achieving up to 95.5%. Further analysis revealed that ESP performance depended on PM concentration in the agglomeration chamber, achieving a clearance rate exceeding 98% under optimal conditions. Fine PM (0.35 to 8.7 µm) was more efficiently removed than ultra-fine PM (0.2 to 0.35 µm). The highest removal efficiency was observed at a feed rate of 0.962 mg/s, while the lowest occurred at 0.385 mg/s. A strong positive correlation between particle concentration and removal efficiency (Pearson value up to 0.829) was observed, particularly at feed rates of 0.128, 0.641, and 1.283 mg/s. The study’s findings confirm that the ESP is highly effective in removing particulate matter, particularly fine and ultra-fine particles, with an optimal feed rate, significantly enhancing the system’s performance.

Hybrid membranes incorporating microencapsulated PCM for enhanced hygrothermal performance in enthalpy recovery ventilation systems

Advancements in building technologies are increasingly centered on improving energy efficiency, particularly within ventilation systems. Integrating Microencapsulated Phase Change Materials (MPCMs) into paper-based membranes enhances hygrothermal performance in enthalpy recovery ventilation systems. MPCMs, known for their latent heat storage capabilities, were incorporated at concentrations of 0 %, 5 %, 10 %, and 20 % by weight. The hybrid membranes exhibited a significant latent heat storage capacity, achieving up to 34.38 J/g at MPCM 20 wt% content. Although the latent heat decreased by approximately 10 % after 1000 cycles of extreme temperature fluctuations between 15 °C and 60 °C, the membranes still exhibited durability sufficient to endure a replacement cycle of over three years, maintaining their effectiveness in long-term applications. The addition of MPCMs also led to improvements in moisture resistance, with the moisture resistance coefficient increasing from 8.88 to 19.19 at MPCM 20 wt%, while still preserving sufficient moisture permeability for efficient enthalpy exchange. These improvements in thermal and hygrothermal properties underscore the potential of MPCM-integrated membranes to enhance energy efficiency and thermal comfort in HVAC systems, providing a sustainable solution for modern building designs. The findings suggest further exploration into optimizing MPCM content for varying climate conditions and assessing the long-term performance of these membranes in diverse practical applications.

Multipath Routing with Dynamic Load Balancing for Enhanced Energy Efficiency in Zone-Based Wireless Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of independent, self-organized mobile nodes capable of wirelessly communicating with each other in a reliable and secure manner. One of the major challenges in MANETs is energy consumption, as the lifetime of a node's battery is limited. This limitation can affect both network connectivity and the lifetime of individual nodes. Various protocols, proposed by the IETF and other solution providers, have aimed to optimize bandwidth, transmission quality, and power control, but none have significantly improved energy consumption. In response to these challenges, the proposed protocol is the Dynamic Load Balancing in Multipath Energy-Consuming Routing Protocol (DLB-MERP) for Wireless Ad hoc Networks. DLB-MERP can be viewed as an extension of the AOMDV protocol and is classified as a zone-based routing protocol, though it incorporates superior load balancing and energy management techniques. It introduces the concept of virtual zones to manage and distribute energy consumption in an organized way. The protocol selects communication paths based on the availability of node energy and power levels. A leader node is defined within each zone, chosen based on its energy level, load, and channel strength, to optimize energy usage. This method increases network resiliency through multipath routing, thereby enhancing overall network performance and extending network lifetime. DLB-MERP effectively addresses the limitations of existing energy-aware routing methodologies by balancing energy consumption, leading to prolonged network operation.

AI-driven decarbonization of buildings: Leveraging predictive analytics and automation for sustainable energy management

The decarbonization of buildings is a pivotal strategy in addressing global climate change, and artificial intelligence (AI) is increasingly recognized as a powerful tool in this process. This paper explores the application of AI in optimizing building energy consumption, focusing on predictive analytics, real-time energy monitoring, and smart energy systems. By leveraging machine learning algorithms, AI enhances energy efficiency through precise automation, particularly in heating, ventilation, and air conditioning (HVAC) systems. AI-powered demand response solutions dynamically adjust energy use based on factors like occupancy, weather patterns, and energy prices, further minimizing carbon footprints. Additionally, the integration of renewable energy sources such as solar and wind with AI-driven energy management systems enables smarter utilization of clean energy. The paper examines both new construction projects and the retrofitting of existing infrastructures, offering insights into AI’s role in reducing carbon emissions and improving energy sustainability. Case studies highlight successful implementations, underscoring AI’s ability to drive significant energy savings and carbon reduction. The study also discusses challenges such as data privacy, the high cost of AI technology, and the regulatory framework required for widespread adoption. The findings present AI as an essential component of the future of sustainable, carbon-neutral buildings.

Leveraging Contemporary Project Management Techniques and Technology for Sustainable Telecommunications Advancement in Yemen

The digital techniques and technology have transformed formal project management field dramatically by enhancing communication, resource management and efficiency. This study aims to evaluate the efficacy of contemporary project management methodologies in promoting and facilitating sustainable development within the telecommunications sector in Yemen. Situated within the broader discourse on achieving the United Nations Sustainable Development Goals, the research employs a mixed methods approach to gather both quantitative and qualitative data. Quantitative data was collected through a survey of 150 professionals across major telecommunications firms in Yemen. The survey examined familiarity and application of techniques and technology such as waterfall, agile, six sigma, and lean. Qualitative insights were derived from 22 in-depth interviews with project managers and stakeholders. The findings of the study indicate that there is a narrow scope for integrating sustainability in Yemen’s telecommunications sector projects. Despite the research indicating a considerable level of awareness and utilization of contemporary methodologies, the integration of sustainability objectives varies considerably between projects. While numerous projects adopt environmental sustainability strategies, such as enhancing energy efficiency to reduce environmental impact and minimize carbon emissions, they lack comprehensive sustainability strategies that encompass economic, social, and environmental aspects in an integrated manner. The qualitative interviews yielded insights into the significant challenges to sustainable implementation, including insufficient training, organizational obstacles, and the effects of the ongoing conflict. In light of these findings, the paper addresses these challenges and provides recommendations and effective strategies to overcome them.

BIM-based energy consumption analysis for residential buildings: effects of orientation, size, and type of windows

Buildings have a significant environmental impact due to their reliance on nonrenewable energy sources. To address this concern, computer software is utilized to estimate building energy requirements. Building Information Modeling (BIM) has emerged as a valuable tool in this process, with BIM 6D offering a particularly beneficial feature. BIM 6D provides a comprehensive energy model of the building, simulating its actual energy performance. By analyzing natural and artificial lighting systems, particularly daylighting, BIM 6D can assist in optimizing energy efficiency. Despite being underutilized, the potential of this feature to enhance building sustainability is noteworthy. BIM 6D facilitates well-informed design and operational decisions for both new constructions and the renovation of existing buildings. The study examines design criteria such as building orientation, window orientation, size, and type to optimize energy consumption in a bungalow in the Sangli region of Maharashtra, underscoring the importance of energy-efficient buildings to local communities. Energy analysis and optimization are conducted using the Autodesk Insight tool. The methodology involves creating 3D models with Autodesk Revit Architecture software and performing various analyses, including Building Energy Modeling (BEM) simulations, Illuminance Analysis, Solar Access, and Daylight Autonomy assessments. The analysis findings indicate that low-e glass windows of small size and oriented between 0° and 90° significantly enhance energy efficiency under certain conditions, while changing the window type does not affect the analysis. The paper concludes with recommendations for window orientation and size to enhance energy performance in residential buildings, contributing to the broader goal of sustainable and energy-efficient construction practices. This study provides valuable insights for designing energy-efficient residential buildings in rural areas, addressing awareness gaps, and promoting sustainable construction practices.

OSC-GOR: Optimal Security-Aware Cluster-Based Hybrid Geographical and Opportunistic Routing for Vehicular ad hoc Networks

Vehicular ad hoc networks (VANETs) are crucial for infrastructure-less vehicular communication. The dynamic and complex nature of VANETs necessitates efficient routing strategies. This paper introduces an optimal security-aware cluster-based hybrid geographical and opportunistic routing (OSC-GOR) for VANETs. By utilizing the improved bald eagle optimization (IBEO) algorithm, we achieve optimal clustering that enhances energy efficiency. Cluster heads (CHs) are elected based on trust, calculated from metrics like energy consumption and signal strength. A Poplar optimization algorithm (POA) further refines these metrics. Subsequently, an optimal min–max recurrent neural network (OMM-RNN) determines the next forwarder nodes, facilitating data transfer in VANETs. Implemented using the NS-2 tool and SUMO traffic generator, OSC-GOR showcases optimized clustering and superior network efficiency. Comparative simulations reveal OSC-GOR’s advantages in energy use, latency, delivery ratio, goodput, and routing overhead, outperforming existing methods. Notably, OSC-GOR effectively counters VANET attacks such as GPS spoofing and Sybil threats.

A comparative study of vehicle powertrain efficiency: Data-driven analyzing energy consumption and environmental impact

This study investigates the energy consumption of electric vehicles (EVs) in real-world driving conditions, using in-vehicle sensor data and location tracking. The energy consumption of EVs is compared to that of an internal combustion engine vehicle (ICEV) under similar driving tests. Furthermore, an energy-centric life cycle assessment was conducted to assess the environmental impact of the transition to electrified transportation by reporting the carbon emissions of the EVs and ICEV. Since a large volume of driving data was acquired in the experiment, machine learning (ML) techniques are employed to interpret the obtained data and identify a key feature. ML algorithms can help develop predictive models that estimate energy consumption and explain the major factors that impact such consumption. The findings also offer valuable insights into energy consumption, emission, and the underlying factors that can inform policy discussion related to transport electrification and enhance energy efficiency. This research is significant in comprehending the potential of current EVs and their superiority over traditional vehicles. It is vital to facilitate the shift toward electric transportation, which can be driven by the results.

Research on Intelligent Energy System and Power Metering Optimization Based on Multi-objective Optimization Decision Algorithm

In this paper, the intricate problem of optimizing power metering within an intelligent energy system, utilizing a multi-objective optimization decision-making algorithm, is thoroughly explored. Given the current energy landscape, achieving efficient energy utilization and environmental sustainability has become a focal point of research. As a pivotal aspect of future energy management, the precision and optimization of power metering in intelligent energy systems directly influence the effectiveness and cost of energy consumption. To begin, this paper delves into the fundamental principles and application backdrop of intelligent energy systems, highlighting the significance of power metering in such systems. Subsequently, addressing the multi-objective optimization challenges in power metering, a novel optimization method based on a multi-objective optimization decision algorithm is introduced. This algorithm achieves comprehensive optimization of power metering, encompassing multiple objectives such as power cost reduction, enhanced energy efficiency, and environmental protection. The experimental results underscore the remarkable performance of this algorithm, which not only elevates the precision of power metering but also achieves substantial savings in energy costs and significantly boosts energy efficiency. Furthermore, the algorithm exhibits robust adaptability, making it capable of addressing power metering optimization challenges across diverse scenarios. Finally, this paper discusses the practical application prospects of the optimization algorithm in intelligent energy systems, and points out the direction of future research. The research in this paper provides new ideas and methods for the optimization of power metering in intelligent energy systems, and has important theoretical and practical significance for promoting the intelligence and refinement of energy management.

An Energy-Efficient Dynamic Feedback Image Signal Processor for Three-Dimensional Time-of-Flight Sensors

With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively researched, often in conjunction with augmented reality (AR) and virtual reality (VR). However, for more precise analysis, high-quality images are required, necessitating significantly larger parameters and computations. These requirements can pose challenges, especially in developing AR and VR technologies for low-power portable devices. Therefore, we propose a dynamic feedback configuration image signal processor (ISP) for 3D ToF sensors. The ISP achieves both accuracy and energy efficiency through dynamic feedback. The proposed ISP employs dynamic area extraction to perform computations and post-processing only for pixels within the valid area used by the application in each frame. Additionally, it uses dynamic resolution to determine and apply the appropriate resolution for each frame. This approach enhances energy efficiency by avoiding the processing of all sensor data while maintaining or surpassing accuracy levels. Furthermore, These functionalities are designed for hardware-efficient implementation, improving processing speed and minimizing power consumption. The results show a maximum performance of 178 fps and a high energy efficiency of up to 123.15 fps/W. When connected to the hand pose estimation (HPE) accelerator, it demonstrates an average mean squared error (MSE) of 10.03 mm, surpassing the baseline ISP value of 20.25 mm. Therefore, the proposed ISP can be effectively utilized in low-power, small form-factor devices.

Review of Angular-Selective Windows with Guest–Host Liquid Crystals for Static Window Applications

This review focuses on the development and advancements in angular-selective smart windows, with particular emphasis on static windows utilizing guest–host liquid crystal (GHLC) systems. Angular-selective windows are designed to adjust their transmittance based on the angle of incident light, offering enhanced energy efficiency and visual comfort in both architectural and automotive applications. By leveraging the anisotropic absorption properties of dichroic dyes, GHLC-based windows can selectively block oblique sunlight while preserving clear visibility from normal viewing angles. Various liquid crystal (LC) alignment configurations, including vertically aligned, homogeneously aligned, hybrid aligned, uniformly lying helix, and twisted aligned LC cells, have been investigated to optimize light control for different installation angles, such as for automotive windshields and building windows. These advancements have demonstrated significant improvements in energy conservation and occupant comfort by reducing cooling demands and regulating sunlight penetration. This review summarizes key findings from recent studies, addresses the limitations of current technologies, and outlines potential future directions for further advancements in smart window technology.

Analytical comparison of the boil-off gas recondensation process’s energy consumption and carbon emissions

With 28 operational liquefied natural gas receiving terminals in China by 2023, totaling a receiving capacity of 1.2 × 108 t/a, managing carbon emissions and boil-off gas has attracted increased attention. This study presents the optimization of 8 boil-off gas (BOG) recondensation processes, considering energy consumption and carbon emissions as assessment criteria, along with two types of vaporizers. The method involves analyzing how process parameters affect system performance using Hydraulic System Simulation Software, followed by optimizing system parameters with a genetic algorithm. It explores the impact of compressor output pressure, after-cooler inlet material ratio, and pre-cooler inlet material ratio on system energy consumption and carbon emissions. Results show that increased recondensing, compression, and cooling measures slightly elevate energy consumption but significantly reduce carbon emissions. Moreover, a higher BOG content enhances the carbon reduction effect. This study represents the first optimization of BOG recondensation processes focusing on carbon emissions, offering fresh theoretical insights and significant potential for enhancing energy efficiency and reducing carbon emissions.

Examining The Impact of Macroeconomics Factors on Carbon Emissions in Selected 7-ASEAN Members: A Panel Data Analysis

This study investigates the relationships between urbanization, economic growth, energy consumption, transportation, and carbon emissions in seven ASEAN countries from 2011 to 2022. As released by the World Health Organization (WHO), more than 80 percent of the world’s population living in urban areas were exposed to air quality levels that were above the WHO limits. The region that is most affected comes from those nations categorized as low to middle-income countries. Hence, it is best to examine why these 7 ASEAN nations categorized from the lower to upper-middle income countries are not able to control the increase in the level of their carbon emissions. The analysis would also identify the appropriate empirical model suitable for the data. Using panel data analysis applying econometric modeling which are the Pooled Ordinary Least Square, Fixed Effects Model, and Random Effects Model, the findings support the Environmental Kuznets Curve hypothesis, revealing that urbanization and economic growth significantly increase carbon emissions. The study emphasizes the need for renewable energy adoption and sustainable urban planning to reduce carbon footprints. Key recommendations include enhancing energy efficiency and promoting cleaner transportation technologies to achieve sustainable economic growth in the ASEAN region.

Two-Dimensional Covalent Organic Frameworks with Carbazole-Embedded Frameworks Facilitate Photocatalytic and Electrocatalytic Processes

Catalytic technologies are pivotal in enhancing energy efficiency, promoting clean energy production, and reducing energy consumption in the chemical industry. The pursuit of novel catalysts for renewable energy is a long-term goal for researchers. In this work, we synthesized three two-dimensional covalent organic frameworks (COFs) featuring electron-rich carbazole-based architectures and evaluated their catalytic performance in photocatalytic organic reactions and electrocatalytic oxygen reduction reactions (ORRs). Pyrene-functionalized COF, termed as FCTD-TAPy, demonstrated excellent photocatalytic performance for amino oxidation coupling and showed a remarkable preference for substrates with electron-withdrawing groups (up to >99% Conv. and >99% Sel). Furthermore, FCTD-TAPy favored a four-electron transfer pathway during the ORR and exhibited favorable reaction kinetics (51.07 mV/dec) and a high turnover frequency (0.011 s−1). In contrast, the ORR of benzothiadiazole-based FCTD-TABT favored a two-electron transfer pathway, which exhibited a maximum double-layer capacitance of 14.26 mF cm−2, a Tafel slope of 53.01 mV/dec, and a hydrogen peroxide generation rate of 70.3 mmol g−1 h−1. This work underscores the potential of carbazole-based COFs as advanced catalytic materials and offers new insights into the design of metal-free COFs for enhanced catalytic performance.

Web Server Based Electrical Control System Analysis for Smart Buildings

Energy management in smart buildings still faces challenges in optimizing energy use, particularly for high-load devices, such as HVAC systems and lighting. Conventional control systems are often inadequate for optimizing energy usage based on the operational needs of the building. This study aims to develop and analyze a web server-based electrical energy control system that can be accessed in real time to improve the energy efficiency of smart buildings. This study employed a quasi-experimental method by implementing a web server-based control system in a smart building and comparing the energy consumption before and after the application of the system. The results show that the system reduces the energy consumption by up to 25%, particularly for HVAC systems and lighting. The most significant energy savings occurred during off-peak hours, when the system automatically reduced power for unnecessary devices. The implications of this research suggest that a web server-based control system not only enhances energy efficiency and reduces operational costs, but also provides greater flexibility in energy management through more adaptive and responsive remote control. This research contributes to the development of more sustainable energy management technologies for smart buildings, with wide potential applications in commercial and institutional building scenarios