bluuebunny/crossref_metadata_2025 huggingface dataset:
Wide-Load-Range Double-T Resonant Converter for CC/CV Battery Charging
Authors: Wei Xile , Shi Yicheng , Li Gang , Zhang Zhen , Chang Siyuan
Published: January 2024

This article proposes a load-independent constant current (CC) or constant voltage (CV) output Double-T circuit (DT) for electrical vehicles (EVs) or electrical bikes (EBs) charging systems to improve the conversion efficiency over a wide-load range during battery charging processes. Among available studies, the LLC converter is a widely adopted resonant topology for EV or EB charging. However, in CC-CV charging, the wide output voltage caused by the wide-load range requires a wide switching frequency range to achieve, which decreases the efficiency in the wide-load range. To address such issues, in this article, two T-circuits are cascaded to form an output load-independent DT with fixed duty cycle and frequency, which can implement CC-CV modes and zero phase angle at the resonant frequency simultaneously, which not only significantly reduces reactive power in energy storage elements but also eliminates the adverse effect of efficiency reduction owing to switching frequency variation. Finally, based on experimental results, the variation of current in CC mode is within 4.18%, and that of voltage is within 4.44% in CV mode, which demonstrates the inherent load-independent capability of the DT converter. During the battery pack charging experiment, the peak dc-dc conversion efficiency reached 96.70% and the average conversion efficiency was higher than 94.01%.
Employing Deep Reinforcement Learning to Cyber-Attack Simulation for Enhancing Cybersecurity
Authors: Oh Sang Ho , Kim Jeongyoon , Nah Jae Hoon , Park Jongyoul
Published: January 2024

In the current landscape where cybersecurity threats are escalating in complexity and frequency, traditional defense mechanisms like rule-based firewalls and signature-based detection are proving inadequate. The dynamism and sophistication of modern cyber-attacks necessitate advanced solutions that can evolve and adapt in real-time. Enter the field of deep reinforcement learning (DRL), a branch of artificial intelligence that has been effectively tackling complex decision-making problems across various domains, including cybersecurity. In this study, we advance the field by implementing a DRL framework to simulate cyber-attacks, drawing on authentic scenarios to enhance the realism and applicability of the simulations. By meticulously adapting DRL algorithms to the nuanced requirements of cybersecurity contexts—such as custom reward structures and actions, adversarial training, and dynamic environments—we provide a tailored approach that significantly improves upon traditional methods. Our research undertakes a thorough comparative analysis of three sophisticated DRL algorithms—deep Q-network (DQN), actor–critic, and proximal policy optimization (PPO)—against the traditional RL algorithm Q-learning, within a controlled simulation environment reflective of real-world cyber threats. The findings are striking: the actor–critic algorithm not only outperformed its counterparts with a success rate of 0.78 but also demonstrated superior efficiency, requiring the fewest iterations (171) to complete an episode and achieving the highest average reward of 4.8. In comparison, DQN, PPO, and Q-learning lagged slightly behind. These results underscore the critical impact of selecting the most fitting algorithm for cybersecurity simulations, as the right choice leads to more effective learning and defense strategies. The impressive performance of the actor–critic algorithm in this study marks a significant stride towards the development of adaptive, intelligent cybersecurity systems capable of countering the increasingly sophisticated landscape of cyber threats. Our study not only contributes a robust model for simulating cyber threats but also provides a scalable framework that can be adapted to various cybersecurity challenges.
Entity Matching by Pool-Based Active Learning
Authors: Han Youfang , Li Chunping
Published: January 2024

The goal of entity matching is to find the corresponding records representing the same entity from different data sources. At present, in the mainstream methods, rule-based entity matching methods need tremendous domain knowledge. Machine-learning-based or deep-learning-based entity matching methods need a large number of labeled samples to build the model, which is difficult to achieve in some applications. In addition, learning-based methods are more likely to overfit, so the quality requirements of training samples are very high. In this paper, we present an active learning method for entity matching tasks. This method needs to manually label only a small number of valuable samples, and use these labeled samples to build a model with high quality. This paper proposes hybrid uncertainty as a query strategy to find those valuable samples for labeling, which can minimize the number of labeled training samples and at the same time meet the requirements of entity matching tasks. The proposed method is validated on seven data sets in different fields. The experiments show that the proposed method uses only a small number of labeled samples and achieves better effects compared to current existing approaches.
An Applied Framework for Smarter Buildings Exploiting a Self-Adapted Advantage Weighted Actor-Critic
Authors: Papaioannou Ioannis , Dimara Asimina , Korkas Christos , Michailidis Iakovos , Papaioannou Alexios , Anagnostopoulos Christos-Nikolaos , Kosmatopoulos Elias , Krinidis Stelios , Tzovaras Dimitrios
Published: January 2024

Smart buildings are rapidly becoming more prevalent, aiming to create energy-efficient and comfortable living spaces. Nevertheless, the design of a smart building is a multifaceted approach that faces numerous challenges, with the primary one being the algorithm needed for energy management. In this paper, the design of a smart building, with a particular emphasis on the algorithm for controlling the indoor environment, is addressed. The implementation and evaluation of the Advantage-Weighted Actor-Critic algorithm is examined in a four-unit residential simulated building. Moreover, a novel self-adapted Advantage-Weighted Actor-Critic algorithm is proposed, tested, and evaluated in both the simulated and real building. The results underscore the effectiveness of the proposed control strategy compared to Rule-Based Controllers, Deep Deterministic Policy Gradient, and Advantage-Weighted Actor-Critic. Experimental results demonstrate a 34.91% improvement compared to the Deep Deterministic Policy Gradient and a 2.50% increase compared to the best Advantage-Weighted Actor-Critic method in the first epoch during a real-life scenario. These findings solidify the Self-Adapted Advantage-Weighted Actor-Critic algorithm’s efficacy, positioning it as a promising and advanced solution in the realm of smart building optimization.
Mitigating Cyber Anomalies in Virtual Power Plants Using Artificial-Neural-Network-Based Secondary Control with a Federated Learning-Trust Adaptation
Authors: Taheri Seyed , Davoodi Mohammadreza , Ali Mohd
Published: January 2024

Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive communication layer. FL-trust, an improved federated learning (FL) approach, has been recently introduced as a mitigation system for cyber-attacks. However, current FL-trust enhancements, relying solely on proportional-integral (PI), exhibit drawbacks like sensitivity to controller gain fluctuations and a slow response to sudden disturbances, and conventional FL-trust is not directly applicable to the non-independent and identically distributed (non-IID) datasets common in VPPs. To address these limitations, we introduce an artificial neural network (ANN)-based technique to adapt FL-trust to non-IID datasets. The ANN is designed as an intelligent anomaly mitigation control method, employing a dynamic recurrent neural network with exogenous inputs. We consider the effects of the most common VPP attacks, poisoning attacks, on the distributed cooperative controller at the secondary control level. The ANN is trained offline and tested online in the simulated VPP. Using MATLAB simulations on a HOMER-modeled VPP, the proposed technique demonstrates its superior ability to sustain normal VPP operation amidst cyber anomalies, outperforming a PI-based mitigation system in accuracy and detection speed.
Recent Trends and Issues of Energy Management Systems Using Machine Learning
Authors: Lee Seongwoo , Seon Joonho , Hwang Byungsun , Kim Soohyun , Sun Youngghyu , Kim Jinyoung
Published: January 2024

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS.
Powering Electricity Forecasting with Transfer Learning
Authors: Kamalov Firuz , Sulieman Hana , Moussa Sherif , Avante Reyes Jorge , Safaraliev Murodbek
Published: January 2024

Accurate forecasting is one of the keys to the efficient use of the limited existing energy resources and plays an important role in sustainable development. While most of the current research has focused on energy price forecasting, very few studies have considered medium-term (monthly) electricity generation. This research aims to fill this gap by proposing a novel forecasting approach based on zero-shot transfer learning. Specifically, we train a Neural Basis Expansion Analysis for Time Series (NBEATS) model on a vast dataset comprising diverse time series data. Then, the trained model is applied to forecast electric power generation using zero-shot learning. The results show that the proposed method achieves a lower error than the benchmark deep learning and statistical methods, especially in backtesting. Furthermore, the proposed method provides vastly superior execution time as it does not require problem-specific training.
Simulations of CO2 Dissolution in Porous Media Using the Volume-of-Fluid Method
Authors: Golestan Mohammad Hossein , Berg Carl Fredrik
Published: January 2024

Traditional investigations of fluid flow in porous media often rely on a continuum approach, but this method has limitations as it does not account for microscale details. However, recent progress in imaging technology allows us to visualize structures within the porous medium directly. This capability provides a means to confirm and validate continuum relationships. In this study, we present a detailed analysis of the dissolution trapping dynamics that take place when supercritical CO2 (scCO2) is injected into a heterogeneous porous medium saturated with brine. We present simulations based on the volume-of-fluid (VOF) method to model the combined behavior of two-phase fluid flow and mass transfer at the pore scale. These simulations are designed to capture the dynamic dissolution of scCO2 in a brine solution. Based on our simulation results, we have revised the Sherwood correlations: We expanded the correlation between Sherwood and Peclet numbers, revealing how the mobility ratio affects the equation. The expanded correlation gave improved correlations built on the underlying displacement patterns at different mobility ratios. Further, we analyzed the relationship between the Sherwood number, which is based on the Reynolds number, and the Schmidt number. Our regression on free parameters yielded constants similar to those previously reported. Our mass transfer model was compared to experimental models in the literature, showing good agreement for interfacial mass transfer of CO2 into water. The results of this study provide new perspectives on the application of non-dimensional numbers in large-scale (field-scale) applications, with implications for continuum scale modeling, e.g., in the field of geological storage of CO2 in saline aquifers.
Issues in Implementation of EU Regulations in Terms of Evaluation of Water Losses: Towards Energy Efficiency Optimization in Water Supply Systems
Authors: Ociepa-Kubicka Agnieszka , Deska Iwona , Ociepa Ewa
Published: January 2024

The water and sewage sector is responsible for approximately 3.5% of energy consumption in the European Union (EU). Leaks causing water losses in water distribution systems (WDSs) are responsible for approximately 24% of water consumption in the EU, which contributes to additional energy losses and emissions of greenhouse gases (GHGs). The implementation of the Directive of the European Parliament and the EU Council on the quality of drinking water (Directive (EU) 2020/2184) introduces the obligation to report water losses by large water utilities in EU Member States. The reported indicator will be the infrastructure leakage index (ILI) which is the ratio between current annual real loss (CARL) and unavoidable annual real loss (UARL). The paper presents a comparative analysis of selected water loss performance indicators calculated for 12 Polish WDSs. Results show that values of calculated indicators were diverse. The overestimation of both the reported value of operating pressure and total length of service connections may lead to the overestimation of UARL and thus to the underestimation of ILI. Obtaining a satisfactory, but incorrect, value of ILI may result in the abandonment of activities aimed at water loss reduction. Water losses in water distribution systems (WDSs) contribute to a significant increase in both energy consumption and GHG emissions. Total approximated electrical energy related to CARL consumed in 2021 by eleven utilities (except for one company) amounted to 3.276 GWh and total approximated carbon emissions amounted to 2807.84 MgCO2eq. In the case of four WDSs, reduction of ILI to the value of 1.5 may reduce GHG emissions by 31–54%. It can be concluded that the implementation of Directive (EU) 2020/2184 will require unification of methodology for calculation of parameters used in ILI evaluation in all EU Member States.
Chemostratigraphic Approach to the Study of Resources’ Deposit in the Upper Silesian Coal Basin (Poland)
Authors: Krzeszowska Ewa
Published: January 2024

The Upper Silesian Coal Basin (USCB), located in southern Poland, is the major coal basin in Poland, and all technological types of hard coal, including coking coal, are exploited. It is also an area of high potential for coal-bed methane (CBM). Despite the increasing availability of alternative energy sources globally, it is a fact that the use of fossil fuels will remain necessary for the next few decades. Therefore, research on coal-bearing formations using modern research methods is still very important. The application of geochemistry and chemostratigraphy in reservoir characterization has become increasingly common in recent years. This paper presents the possibility of applying chemostratigraphic techniques to the study of the Carboniferous coal-bearing succession of the Upper Silesian Coal Basin. The material studied comes from 121 core samples (depth 481–1298 m), representing the Mudstone Series (Westphalian A, B). Major oxide concentrations of Al2O3, SiO2, Fe2O3, P2O5, K2O, MgO, CaO, Na2O, K2O, MnO, TiO2, and Cr2O3 were obtained using X-ray fluorescence (XRF) spectrometry. Trace elements were analyzed using inductively coupled plasma mass spectrometry (ICP/MS). The geochemical record from the Mudstone Series shows changes in the concentration of major elements and selected trace elements, leading to the identification of four chemostratigraphic units. These units differ primarily in the content of Fe, Ca, Mg, Mn, and P as well as the concentration of Zr, Hf, Nb, Ta, and Ti. The study also discusses quartz origin (based on SiO2 and TiO2), sediment provenance and source-area rock compositions (based on Al2O3/ TiO2, TiO2/Zr, and La/Th), and paleoredox conditions (based on V/Cr, Ni/Co, U/Th, (Cu+Mo)/Zn, and Sr/Ba) for the chemostratigraphic units. Chemostratigraphy was used for the first time in the study of the Carboniferous coal-bearing series of the USCB, concluding that it can be used as an effective stratigraphic tool and provide new information on the possibility of correlating barren sequences of the coal-bearing succession.
Internet of Things-Based Control of Induction Machines: Specifics of Electric Drives and Wind Energy Conversion Systems
Authors: Ioannides Maria G. , Stamelos Anastasios P. , Papazis Stylianos A. , Stamataki Erofili E. , Stamatakis Michael E.
Published: January 2024

The Internet of Things (IoT) is introduced in systems with electrical machines, such as in electric drive systems, wind energy generating systems, and small and special machines, to remote monitor and control the operation for data acquisition and analysis. These systems can integrate with the equipment and retrofit the existing installations. At the end of the control loops there are always motors, or actuators, of big or small ratings, of rotating or linear movements, electrical or nonelectrical, which must produce the motion. This article analyses selected aspects of research and applications of IoT-based control in electric drive systems and of wind energy conversion systems with induction machines. Various applications and study cases of control systems of electrical machines with IoT technology are described. With the IoT-based control of induction machine systems operators can remotely monitor parameters and obtain accurate real-time feedback during fast changing duty cycle operation. Thus, IoT creates multipurpose instruments in the remote control of induction machines. The paper offers a comprehensive analysis of IoT-based control applications in the field of induction machines, with technical details of design, construction, experimental testing, and prototyping, that are useful to energy engineering specialists in the sector of electric drives and wind energy conversion systems.
Diagnosis of the Development of Energy Cooperatives in Poland—A Case Study of a Renewable Energy Cooperative in the Upper Silesian Region
Authors: Gajdzik Bożena , Jaciow Magdalena , Wolniak Radosław , Wolny Robert , Grebski Wieslaw Wes
Published: January 2024

Renewable energy sources (RESs) offer key transformative potential from a societal point of view due to their modularity and ability to generate energy at the local level, allowing for the development of grassroots democratic and participatory initiatives. The paper aims to share insights into the processes of creating RES cooperatives in Poland. One of the first cooperatives to be established in the Upper Silesian region in Poland was the energy cooperative (EC) “Our Energy”. This study presents an in-depth empirical analysis of a community-based renewable energy cooperative. The study employed a case study methodology, including a SWOT analysis framework, to describe the research subject and identify its strengths, weaknesses, opportunities, and threats. Key findings indicate that members benefit from stable energy prices and full recovery of the energy produced, and the cooperative is at the forefront of energy-sharing practices that minimize costs through direct transactions with the local municipality. The strategic goals of the EC focus on expanding membership, increasing the number of photovoltaic installations, implementing energy balancing, combating energy poverty, and reducing emissions. Challenges such as financial constraints and a lack of real-time monitoring of energy distribution are acknowledged, and carbon footprint reduction innovations and stakeholder engagement are highlighted as forward-looking approaches. The study highlights the role of cooperatives as a model for community-led sustainable energy initiatives. However, the study acknowledges the limitations of its small sample size, suggesting the need for broader research to understand the impact of collaborative energy on decarbonization. Future research directions are proposed, focusing on the long-term sustainability and socioeconomic impacts of energy cooperatives. This study contributes to the scholarly discourse on renewable energy cooperatives by offering insight into their potential to bridge the gap between energy producers and consumers and support sustainable community development. The main novelty of this paper lies in its detailed examination of a specific renewable energy cooperative, incorporating SWOT analysis, stakeholder perspectives, quantitative assessments, and a forward-thinking approach. This multifaceted analysis contributes to the existing literature on renewable energy initiatives, providing a valuable reference for researchers, policymakers, and practitioners in the field.
Assessing the Reliability and Optimizing Input Parameters of the NWP-CFD Downscaling Method for Generating Onshore Wind Energy Resource Maps of South Korea
Authors: Kim Jeonghyeon , Moon Hyungoo , Kim Jin-Yong , Kim Keon Hoon , Kim Hyun-Goo , Park Sung Goon
Published: January 2024

The numerical weather prediction (NWP) method is one of the popular wind resource forecasting methods, but it has the limitation that it does not consider the influence of local topography. The NWP-CFD downscaling considers topographic features and surface roughness by performing computational fluid dynamics (CFD) with the meteorological data obtained by the NWP method as a boundary condition. The NWP-CFD downscaling is expected to be suitable for wind resource forecasting in Korea, but it lacks a quantitative evaluation of its reliability. In this study, we compare the actual measured data, the NWP-based data, and the NWP-CFD-based data quantitatively and analyze the three main input parameters used for the calculation of NWP-CFD (minimum vertical grid size Δzmin, the difference angle Δdir, and the forest model activation reference length l0). Compared to the actual measurement data, the NWP-based data overestimate wind resources by more than 35%, while the NWP-CFD-based data show an error of about 8.5%. The Δzmin and Δdir have little effect on the results, but the l0 has a large effect on the simulation results, and it is necessary to adjust the values appropriately corresponding to the characteristics of an area.
The Role of AI in Improving Environmental Sustainability: A Focus on Energy Management
Authors: Uriarte-Gallastegi Naiara , Arana-Landín Germán , Landeta-Manzano Beñat , Laskurain-Iturbe Iker
Published: January 2024

This research addresses the increasing importance of understanding how Artificial Intelligence can facilitate the transition of companies to a Circular Economy model. This study focuses on energy management, examining its impact on efficiency and emissions across a multi-case analysis of 18 projects in diverse sectors. The findings indicate that Artificial Intelligence positively influences both variables, with variations across applications and sectors. Notably, Artificial Intelligence significantly enhances energy efficiency in four out of six sectors, achieving over 5% improvement in half of the projects. Regarding emissions, positive effects are observed in 15 out of 18 projects, resulting in over 5% reductions in seven cases. Artificial Intelligence plays a pivotal role in emissions reduction in the Design and Energy sectors, with some projects achieving over 20% reductions. Additionally, this study explores how improved energy efficiency positively affects strategic business variables, such as cost, quality, and delivery time. The impact on emissions contributes to reducing occupational risks, particularly those associated with chemical and biological agents. Although managers are satisfied, measures need to be taken to overcome the lack of employee acceptance. These findings are of great interest to the stakeholders involved in the integration of Artificial Intelligence into companies.
Electromagnetic Characterization of Silicon–Iron Additively Manufactured Cores for Electric Machines
Authors: Michieletto Daniele , Alberti Luigi , Zanini Filippo , Carmignato Simone
Published: January 2024

This paper deals with the electromagnetic characterization of a laminated toroidal ferromagnetic core made through additive manufacturing, specifically using the laser powder bed fusion process. The continuing demand for increasingly efficient, lightweight, and higher performance electric machines is creating huge challenges in the design and realization of new electric motor solutions. The constant improvements in additive manufacturing technologies have prompted researchers to investigate the possibility of adopting these production techniques for the manufacture of high-value electric motors. For these reasons, this paper investigates the ferromagnetic characteristics of an additively manufactured core made with FeSi6.5 powder. The BH curve and the specific iron losses of the processed material have been measured so that they can be compared with a commercial lamination, and have the possibility of carrying out more precise finite element simulations.
Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile
Authors: Ghazvini Mina Bagherzade , Sànchez-Marrè Miquel , Naderi Davood , Angulo Cecilio
Published: January 2024

Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures.
A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues
Authors: Andrade Pedro , Laadjal Khaled , Alcaso Adérito Neto , Cardoso Antonio J. Marques
Published: January 2024

The complexity of Fuel Cell (FC) systems demands a profound and sustained understanding of the various phenomena occurring inside of it. Thus far, FCs, especially Proton Exchange Membrane Fuel Cells (PEMFCs), have been recognized as being among the most promising technologies for reducing Green House Gas (GHG) emissions because they can convert the chemical energy bonded to hydrogen and oxygen into electricity and heat. However, their efficiency remains limited. To enhance their efficiency, two distinct factors are suggested. First, the quality of materials plays a significant role in the development of more robust and efficient FCs. Second, the ability to identify, mitigate, and reduce the occurrence of faults through the use of robust control algorithms is crucial. Therefore, more focused on the second point, this paper compiles, distinguishes, and analyzes several publications from the past 25 years related to faults and their diagnostic techniques in FCs. Furthermore, the paper presents various schemes outlining different symptoms, their causes, and corresponding fault algorithms.
Regression-Based Self-Triggered Control of Distributed Economic Dispatch over Lossy Communication Channels
Authors: Ijaz Zohaib , John Jobish , Noor-A-Rahim Md. , Pesch Dirk
Published: January 2024

An essential building block of today’s smart grids is the integration of various renewable power generators into the system. Renewable distributed generators (DGs) provide environment friendly energy at a low cost. In this work, a distributed economic dispatch (ED) system is considered for renewable DGs to address the scalability challenge and reduce single points of failure compared to a centralised ED system. A consensus control mechanism is devised to achieve the incremental cost (IC) consensus among the DGs and utilise a self-triggered control (STC) mechanism to reduce the communication of the distributed DG system. A lossy communication channel between local DGs is considered in this work, which can arise from power lines or congestion on noisy wireless communication channels. A regression-based estimation approach is proposed to account for lost or corrupted data. The proposed method is compared with a centralised ED system and periodic communication. The proposed STC approach achieves the same level of performance as periodic communication while using half the communication resources.
Exploring the Viability of Local Electricity Markets for Managing Congestion in Spanish Distribution Networks
Authors: García-Muñoz Fernando , Jiménez-Martínez Mariana , Eichman Josh , Corchero Cristina , Benveniste Gabriela
Published: January 2024

This article presents the methodology and results developed as part of the Integration of Energy Resources through Local Electricity Markets (IREMEL) project, whose aim is to assess the capability of flexibility markets to manage eventual distribution network (DN) congestion produced by a high penetration of distributed energy resources (DERs), including photovoltaic (PV) panels, battery energy storage systems (BESSs), and electric vehicles (EVs). The distribution system simulator OpenDSS has been used to simulate three Spanish DNs under multiple DER penetration scenarios considering an urban and rural low-voltage network and an industrial medium-voltage DN. Likewise, the congestion events detected in the annual simulations have been used to measure the potential of flexibility markets under different DER penetrations and energy pricing. The results suggest that oversized distribution networks could prevent a profitable flexibility market implementation since the simulations developed in this article shows that networks with high congestion levels are prime candidates to solve this issue through a market mechanism. Likewise, the results suggest that a proper price for the energy managed through a local flexibility market (LFM) could have a bigger effect on market viability than DER penetration.
Investigation of Energy Consumption of Office Spaces with Active Workstations
Authors: Park Jinhyung , Choi Kwangwon , Talib Abu , Joe Jaewan
Published: January 2024

This study investigates the impact of an active workstation on heating and cooling energy consumption in commercial buildings. The improved performance and health of office workers are well-known and supported by several surveys and studies. However, the preferred and revised indoor air temperature for different activities of the employees, such as standing and walking, directly impacts the energy consumption of the building. Therefore, this study systematically quantifies the impact of indoor environments on building energy consumption using a simulation tool. Based on literature reviews with multidisciplinary fields, revised setpoint temperature, metabolic rate, and required outdoor air rate due to the level of active workstation (standing and walking) are applied to EnergyPlus reference building models. The impact on heating and cooling energy consumption is analyzed by considering several representative cities in the United States. Their average sensitivity was found to be 7.28% lower and 14.28% higher for heating and cooling energy, respectively. The total increment of $13,887 represents approximately 0.16% of the total profit of the corporation. This increment can be justified given the benefits to workers’ health, which include lower insurance costs and improved performance. This suggests that it is economically desirable to deploy active workstations widely.
Magnesium Hydride: Investigating Its Capability to Maintain Stable Vapor Film
Authors: Skvorčinskienė Raminta , Eimontas Justas , Bašinskas Matas , Vorotinskienė Lina , Urbonavičius Marius , Kiminaitė Ieva , Maziukienė Monika , Striūgas Nerijus , Zakarauskas Kęstutis , Makarevičius Vidas
Published: January 2024

In order to implement timely sustainability solutions, road transportation is gradually transitioning to electric power. However, the maritime sector faces challenges in finding ways to shift towards more sustainable fuel. From the perspective of long-distance shipping, electric transport is economically impractical. Therefore, alternative solutions or proposals contributing to the global reduction of pollutant gas emissions in maritime transport are vitally important. This investigation aims to find solutions that enhance the ecological efficiency of intercontinental cargo ships. In this study, an assessment of a magnesium hydride coating was conducted as it is a prospective coating capable of reducing hydrodynamic resistance to save fuel. Due to MgH2’s ability to release hydrogen at higher temperatures or during a reaction with water, it is expected that this could contribute to an enhancement of the Leidenfrost effect, maintaining a vapor layer on the surface. Samples prepared in situ via reactive magnetron sputtering were submitted to thermal analysis for dehydrogenation range evaluation and the experimental rig for critical (Leidenfrost) temperature identification. In conclusion, thermogravimetric (TG) analysis indicated that the volatile content, primarily hydrogen, in the sample reached approximately 13% by mass. The TG curve exhibited variations in MgH2 mass, with the most significant mass loss occurring at 300 °C. After conducting critical temperature experiments, the potential of MgO coating was observed to be greater than anticipated when compared to the main material, MgH2.
Hundred-Watt Implantable TEG Module for Large-Scale Exhaust Gas Waste Heat Recovery
Authors: Gu Zhien , He Shi , Li Xiang , Sun Peng , Wu Jiehua , Hu Haoyang , Zhang Qiang , Jiang Jun
Published: January 2024

In this study, we have designed and developed an implantable thermoelectric generator (TEG) module tailored for large-scale flue gas waste heat recovery. We also have established a test stand to simulate diverse operational conditions, and systematically examined the influence of different operating conditions, including flue gas temperature, flue gas velocity, and cooling water temperature, on the electrical performance of the TEG module. When the flue gas temperature is 139 °C, the flue gas flow rate is 3.4 m/s, and the cooling water temperature is 20 °C, the TEG module operates at its peak performance. It achieves an open-circuit voltage of 856.3 V and an output power of 150.58 W. Furthermore, the TEG module demonstrates a notable power generation capacity of 3.86 kW/m3 and a waste heat recovery capacity of 135.85 kW/m3. The results prove the TEG module as an effective solution for large-scale flue gas waste heat recovery in industrial settings, contributing to sustainable energy practices. This study supports the application of thermoelectric power generation in the industrial sector, offering significant potential for advancements in energy efficiency.
Biohythane, Biogas, and Biohydrogen Production from Food Waste: Recent Advancements, Technical Bottlenecks, and Prospects
Authors: Sahota Shivali , Kumar Subodh , Lombardi Lidia
Published: January 2024

Food waste (FW) is a significant global issue with a carbon footprint of 3.3 billion tonnes (Bt), primarily generated due to improper food supply chain management, storage issues, and transportation problems. Acidogenic processes like dark fermentation, anaerobic digestion, and a combination of DF-AD can produce renewable biofuels (Bio-CH4, Bio-H2) by valorising FW, aligning with the UN SDGs. FW is an ideal substrate for acidogenic processes due to its high moisture content, organic matter, and biodegradability. However, the choice of FW valorisation pathways depends on energy yield, conversion efficiency, and cost effectiveness. Acidogenic processes are not economically viable for industrial scale FW treatment due to reduced energy recovery from stand-alone processes. So, this study reviews comparative studies on biogas, biohydrogen, and biohythane production from FW via acidogenic processes, focusing on energy yield, energy recovery, and environmental and economic impact to provide a clear understanding of energy recovery and yield from all acidogenic processes. Additionally, this review also explores the recent advancements in digestate slurry management and the synergistic effects of AD and HTC processes. Lastly, a futuristic integrated bio-thermo-chemical process is proposed for maximum energy recovery, valuing food waste to energy vectors (Bio-H2, Bio-CH4, and hydro-char) along with digestate management and biofertilizer production.
Forecast Zoning of Forest Fire Occurrence: A Case Study in Southern China
Authors: Jing Xiaodong , Li Xusheng , Zhang Donghui , Liu Wangjia , Zhang Wanchang , Zhang Zhijie
Published: January 2024

Forest fires in the southern region of China pose significant threats to ecological balance, human safety, and socio-economic stability. Forecast zoning the occurrence of these fires is crucial for timely and effective response measures. This study employs the random forest algorithm and geospatial analysis, including kernel density and standard deviation ellipse methods, to predict forest fire occurrences. Historical fire data analysis reveals noteworthy findings: (i) Decreasing Trend in Forest Fires: The annual forest fire count in the southern region exhibits a decreasing trend from 2001 to 2019, indicating a gradual reduction in fire incidence. Spatial autocorrelation in fire point distribution is notably observed. (ii) Excellent Performance of Prediction Model: The constructed forest fire prediction model demonstrates outstanding performance metrics, achieving high accuracy, precision, recall, F1-scores, and AUC on the testing dataset. (iii) Seasonal Variations in High-Risk Areas: The probability of high-risk areas for forest fires in the southern region shows seasonal variations across different months. Notably, March to May sees increased risk in Guangxi, Guangdong, Hunan, and Fujian. June to August concentrates risk in Hunan and Jiangxi. September to November and December to February have distinct risk zones. These findings offer detailed insights into the seasonal variations of fire risk, providing a scientific basis for the prevention and control of forest fires in the southern region of China.
Effects of Audio–Visual Interaction on Physio-Psychological Recovery of Older Adults in Residential Public Space
Authors: Shu Shan , Meng Lingkang , Piao Xun , Geng Xuechuan , Tang Jiaxin
Published: January 2024

It is now well established that everyday interaction with nature has a restorative potential on the elderly population’s health and well-being. However, empirical evidence on the restorative effects of neighborhood greenspace is still lacking, and scant attention has been given to the cross-effect of the visual–audio experience. The present study examined the restorative effects of audio–visual interactions on older adults in typical residential public spaces in Chinese cities. A pretest–post-test design was used to measure changes in participants’ physiological responses, mood states, and mental restoration. Participants (mean age = 68.88 years) were asked to experience six simulated audio–visual conditions (3 scenes × 2 sounds) of residential public space. The results showed that: (1) A green scene combined with nature sounds showed the most restorative effect on the elderly participants’ psycho-physiological health. (2) Viewing green scenes facilitated the most psycho-physiological recovery for the elderly, followed by viewing the activity scene. (3) Compared to the traffic noise, adding nature sounds could promote many more benefits in HR recovery, positive mood promotion, and perceived restorative effects, and the advantage of nature sounds over traffic noise was mainly demonstrated in the green scene. (4) Visual scenes demonstrated a greater impact on the elderly participants’ psycho-physiological recovery than the sounds. Our findings suggested the necessity of providing residential nature and activity spaces, encompassing both sound and vision, to promote healthy aging in Chinese residential contexts.
Relationships between Regeneration of Qinghai Spruce Seedlings and Soil Stoichiometry across Elevations in a Forest in North-Western China
Authors: Wu Xiurong , Chong Peifang , Xu Erwen , Zhao Weijun , Jing Wenmao , Jin Ming , Zhao Jingzhong , Wang Shunli , Wang Rongxin , Ma Xuee
Published: January 2024

Qinghai spruce (Picea crassifolia Kom.) is an ecologically important species in the forest ecosystem of the Qilian Mountains region in China. Natural regeneration of this species is critical to maintaining forest ecosystem function. Here, we analyzed several biological indicators among naturally regenerating Qinghai spruce across several elevations in the Pailugou watershed. Specifically, seedling density, basal diameter (BD), and plant height were measured, as were soil physicochemical parameters, at 2700 m, 3000 m, and 3300 m above sea level. Differences in the regeneration indicators and correlations between the indicators and soil parameters were then assessed across elevations. The results showed that soil stoichiometry was more sensitive to changes in elevation than seedling indicators were. Furthermore, seedling density was positively correlated with soil pH, whereas BD was positively correlated with the carbon-to-nitrogen ratio (C/N), the carbon-to-phosphorus ratio (C/P), and soil organic carbon (SOC) contents. None of the analyzed soil stoichiometry parameters had a significant impact on elevation-specific differences in seedling density. However, soil pH, SOC, and C/N significantly affected variations in seedling basal diameter at different elevations. Finally, soil pH, SOC, C/N, and the carbon-to-phosphorus ratio significantly affected variations in seedlings’ heights at different elevations. This study provides a strong theoretical basis for further understanding of the mechanisms associated with Qinghai spruce regeneration, ultimately contributing to rational protection and management strategies for this important natural resource.
Effects of Nitrogen Forms on Root Morphology and Nitrogen Accumulation in Pinus tabuliformis carr. Seedlings under Exponential Fertilization
Authors: Liu Ping , Li Xinye , Hu Shiyu , He Wenting , Zhou Yiming , Wang Yutao
Published: January 2024

In this study, the effects of different fertilization methods and nitrogen forms on the root morphology and nitrogen accumulation of Pinus tabuliformis carr. were investigated, and the interaction mechanism between roots and nitrogen fertilizer was analyzed to provide a theoretical basis for the rational fertilization of Pinus tabuliformis. The total amount of nitrogen fertilizer applied to seedlings was 100 mg·plant−1; four nitrogen form treatments of ammonium nitrogen, nitrate nitrogen, ammonium nitrate 1:1, and amide nitrogen were set up; and two fertilization methods of conventional and exponential fertilization were applied, with a total of eight experimental treatments. By measuring root parameters, plant biomass, and nitrogen accumulation, the correlation between fertilization method and nitrogen form on the root index of seedlings was analyzed, and the effects of fertilization methods and nitrogen forms on the root growth of seedlings were discussed. Compared with conventional fertilization, exponential fertilization significantly promoted root growth and development, and amide nitrogen was the best nitrogen form. The total length, total surface area, total volume, average diameter, root tips, root/shoot ratio, root tissue density of seedlings’ roots, biomass, and nitrogen accumulation of seedlings in exponential fertilization with amide nitrogen EF3 treatment all increased substantially compared with the others, The effects of fertilization mode, nitrogen form, and their interaction on the partial growth of Pinus tabuliformis seedlings were significant (p < 0.05). The correlation analysis of each index showed that the correlation between nitrogen accumulation and biomass was strongest. Therefore, considering the morphological and structural characteristics of the root system and the nitrogen accumulation in the root system, amide nitrogen in the experimental fertilization can better promote the growth and development of the root system of seedlings.
Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level
Authors: Gkontzis Andreas F. , Kotsiantis Sotiris , Feretzakis Georgios , Verykios Vassilios S.
Published: January 2024

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.