bluuebunny/crossref_metadata_2025 huggingface dataset:
Prioritizing Public Policy Implementation for Rural Development in a Developing Country via Multicriteria Classification
Authors: Peña-Orozco Diego León , Londoño-Escobar María Eugenia , Paredes Rodríguez Andrés Mauricio , Gonzalez-Feliu Jesús , Navarrete Meneses Gonzalo
Published: December 2023

Despite the growing global interest in promoting rural development as a strategy to guarantee food security, in developing countries there are large gaps to achieve a sustainable countryside, mainly in rural areas. This research work delves into a methodological approach definition to establish the baseline for the public policy implementation and prioritize the intervention needs in the different items considered in an integral rural development public policy. The proposed methodology combines a qualitative characterization of needs and goals, a social cartography, a quantitative characterization of indicators and the use of multicriteria classification for prioritizing development policies. Eight localities with sixteen small rural settlements are taken as a research unit, to apply the proposed methodology and determine the implementation level of a public policy. The results show that a set of priority policies that both meet the authorities’ objectives and the population’s needs can be defined. Moreover, a vector of priority is proposed to define the weakest items, as a guide to local government administrations to focus efforts on interventions to achieve greater impacts on the rural community development under study. Finally, via a double field validity assessment, those strategic lines are hierarchized and analyzed regarding their potential relationships, as a social system.
The Effects of Oil Price Volatility on South African Stock Market Returns
Authors: Musampa Kongolo , Eita Joel Hinaunye , Meniago Christelle
Published: December 2023

The aim of this study is to assess the response of the South African stock market returns to oil price volatility, based on the daily South African stock market index, using the GARCH-Copula modelling technique. The results of the analysis show evidence of an asymmetric impact of fluctuations in oil prices on South African stock market returns, using a copula model specification, particularly the bivariate symmetrized Joe-Clayton (SJC) copula. The results also revealed that the EGARCH process is the best univariate model to capture oil price volatility. Interestingly, this study also revealed that the tourism industry is most dependent on oil price fluctuations, due to its heavy reliance on transportation costs. The economic implications of this study also suggest that sectors affected by oil price fluctuations need specific long-term and short-term monetary policy strategies. It is recommended that in the short term, expansionary monetary policy could assist in mitigating the impact of higher oil prices, while in the long-term, policies aimed at reducing the volatility in oil prices would be of great help in alleviating its harmful effect on stock market returns.
Social and Individual Factors Predicting Students’ Resilience: A Multigroup Structural Equation Model
Authors: Kassis Wassilis , Vasiou Aikaterini , Govaris Christos , Favre Céline , Aksoy Dilan , Graf Ulrike
Published: December 2023

We investigated students’ resilience predictors during the COVID-19 pandemic. With a two-wave longitudinal sample (n = 713) of students from Greece, Germany, and Switzerland (eighth grade in autumn 2020 and ninth grade in autumn 2021), we determined which social and individual predictors longitudinally predicted resilience before and during the COVID-19 pandemic. We identified the high likelihood of individual factors (self-esteem, self-efficacy) fostering resilience by social factors (teacher, parents, and social resources). Multigroup structural equation modeling analyses demonstrated that the adolescent population was best typified by two growth trajectory classes: a low-anxiety class characterized by a low initial level of depression/anxiety and a high-anxiety class characterized by a higher initial level of depression/anxiety. The model was gender-, migration-, and country-invariant. Overall, the model showed that teachers overlook adolescents with high anxiety or depression levels, but parents support them more. These findings highlight the necessity to pay attention to students’ depression or anxiety symptoms and to satisfy their basic psychological needs, as vital prerequisites for their meaningful, coherent engagement in modern societies despite the odds.
Perspectives on Using Storytelling as a Means of Teaching and Learning: Reflections from Diverse Groups of Participants on the Theme “What’s in Your River?”
Authors: Medupin Cecilia
Published: December 2023

This study explores learners’ views and perspectives on the use of the storytelling strategy to study the basics of ecology through the theme “What’s in your river?” at a field and laboratory summer school for thirty-two college students aged sixteen to eighteen years; and in the lecture theatre to teach ecological concepts to nineteen first-year university undergraduate students. The mixed method approach was applied in the study, with the collection of qualitative and quantitative responses. Questionnaires were administered to the undergraduate students with selected questions that relate to the use of storytelling and its application in learning. The undergraduate students were asked the following key questions: did you enjoy the use of storytelling as a learning resource and strategy? how has storytelling helped you in your learning of the basics of ecology? The main findings of the study are that after using storytelling as a learning strategy, 89% of the respondents said it helped them to think more clearly about the story structure; 84% of the students said it helped them to understand the course contents better; 63% stated that it motivated them to learn; and 58% said it was more useful and helpful than the direct dissemination of lectures. Knowledge of river organisms acquired by the students correlated highly (R-square of 0.7112) with the use of storytelling as a tool for learning. The reason why the students enjoyed the use of storytelling is that it is both affective and cognitive. This article will benefit educators as it suggests different ways of thinking in the transformation of information for positive environmental change.
Navigating the New Normal: Adapting Online and Distance Learning in the Post-Pandemic Era
Authors: Sato Simone Nomie , Condes Moreno Emilia , Rubio-Zarapuz Alejandro , Dalamitros Athanasios A. , Yañez-Sepulveda Rodrigo , Tornero-Aguilera Jose Francisco , Clemente-Suárez Vicente Javier
Published: December 2023

This review examines the transformation of educational practices to online and distance learning during the COVID-19 pandemic. It specifically focuses on the challenges, innovative approaches, and successes of this transition, emphasizing the integration of educational technology, student well-being, and teacher development. The COVID-19 pandemic has significantly transformed the educational landscape, necessitating a rapid shift to online and distance learning. This review has highlighted key strategies and innovations in online teaching, emphasizing the importance of engaging, adaptable, and inclusive educational practices. It underscores the need for continuous professional development for educators, the integration of advanced technological tools, and the importance of prioritizing student well-being and academic success in digital environments. As we move forward, the insights gathered from this experience will be invaluable in shaping a resilient and dynamic educational framework that is well suited for the post-pandemic era, focusing on inclusivity, engagement, and the effective use of technology to enhance learning experiences.
Integration of Physics and Mathematics in STEM Education: Use of Modeling
Authors: Dominguez Angeles , De la Garza Jorge , Quezada-Espinoza Monica , Zavala Genaro
Published: December 2023

Within STEM (Science, Technology, Engineering, and Mathematics) education, integrating real-world problem scenarios is paramount. Within interdisciplinary education, modeling is an approach to fostering student learning and skill development in a student-centered learning environment. This study focuses on an integrated physics and mathematics course in STEM education based on modeling for first-year engineering students. The main objectives of this study are to analyze students’ models, assess the effectiveness of the pedagogical approach, and evaluate the benefits of integrative education. This study uses a Model-Application Activity as a closure for the quadratic model and extends the models built to a two-dimensional motion situation. The core activity involves a real-world experiment where students attempt to roll a ball down a ramp into a cup. While most groups successfully constructed theoretical models, only a few hit the target, highlighting the complexities of applying theoretical knowledge to real-world scenarios. The study also emphasizes the seamless integration of physics and mathematics, enriching the learning experience and making the models more robust and versatile. Despite the promising results, the study identifies a gap between theoretical understanding and practical application, suggesting the need for more hands-on activities in the curriculum. In conclusion, this study underlines the value of integrating physics and mathematics through modeling and a student-centered approach, setting the stage for future research to enhance the effectiveness of STEM education.
SMART: Selection Model for Assessment Resources and Techniques
Authors: Gil-García Isabel C. , Fernández-Guillamón Ana
Published: December 2023

The European Higher Education Area has ushered in a significant shift in university teaching, aiming to engage students more actively in classes. Professors have leveraged virtual platforms and external tools to introduce interactive tasks. With the proliferation of technology, educators face a challenge in choosing the most suitable approach. This paper presents SMART (Selection Model for Assessment Resources and Techniques), a methodology that determines the optimal assessment activities for university-level education. The methodology employs multicriteria decision-making techniques, specifically AHP and TOPSIS methods, to optimize activities based on various subject-, lecturer-, activity-, and student-related criteria. According to SMART, the top five assessment tasks are group and individual report submissions, workshops, complex H5P activities, and questionnaires. Therefore, it is advisable to prioritize these activities based on the methodology’s results, emphasizing their importance over other assessment methods.
Prompt: ChatGPT, Create My Course, Please!
Authors: Davis Robert O. , Lee Yong Jik
Published: December 2023

The introduction of ChatGPT for public use has generated increasing interest among educational researchers in evaluating the utility of artificial intelligence (AI) in pedagogical settings. This study aims to contribute to this growing body of research by developing an entire course curriculum and lesson plans exclusively using ChatGPT. Case study findings support many of the affordances and limitations observed in previous studies, such as identifying appropriate topics and subtopics for lessons, as well as identifying the occurrence of hallucinations that fabricate data. This study also revealed new limitations in the design capabilities of ChatGPT. Specifically, lessons are designed as standalone units of information. Unlike human educators, AI lacks the ability to integrate prior lessons with current learning experiences or strategically prepare students for future learning outcomes. Understanding these affordances and limitations allows ChatGPT to be a useful tool for educators engaging in instructional design.
A Novel Coupler of Capacitive Power Transfer for Enhancing Underwater Power Transfer Characteristics
Authors: Zhang Xueqiang , Lian Jing
Published: December 2023

Compared to inductive power transfer (IPT) technology, capacitive power transfer (CPT) technology offers unique advantages such as being cost-effective, lightweight, and free from eddy-current losses, making it more suitable for underwater power transfer. Unlike air, water can conduct electricity and the electric conductivity of different kinds of waters varies with different ion concentrations, which would greatly affect the equivalent model of the underwater couplers. To address this issue, multiple types of underwater coupler working in different kinds of water are compared and analyzed. The influence of the electrical conductivity of water on the capacitive coupler is comprehensively analyzed, and the novel capacitive coupler and its equivalent model are proposed to improve power transfer efficiency. To verify the theoretical analysis, the double-sided LC-compensated CPT circuit is built and tap water is used in the experiment. The experimental results are consistent with the theoretical analysis. In addition, the experimental results also validate the superiority of the proposed capacitive coupler compared to existing research.
EDI-C: Reputation-Model-Based Collaborative Audit Scheme for Edge Data Integrity
Authors: Yang Fan , Sun Yi , Gao Qi , Chen Xingyuan
Published: December 2023

The emergence of mobile edge computing (MEC) has facilitated the development of data caching technology, which enables application vendors to cache frequently used data on the edge servers close to the user, thereby providing low-latency data access services. However, in an unstable MEC environment, the multi-replica data cached by different edge servers is prone to corruption, making it crucial to verify the consistency of multi-replica data on different edge servers. Although the existing research realizes data integrity verification based on the cooperation of multiple edge servers, the integrity proof generated by multiple copies of data is the same, which has low verification efficiency and is vulnerable to attacks such as replay and replace. To address the above issues, based on homomorphic hash and sampling algorithms, this paper proposes an efficient and lightweight multi-replica integrity verification algorithm, which has significantly less storage cost and computational cost and can resist forgery and replay and replace attacks. Based on the verification algorithm, this paper further proposes a multi-replica edge data integrity collaborative audit scheme EDI-C based on the reputation model. EDI-C realizes the efficient collaborative audit of multiple edge servers in a distributed discrete environment through an incentive mechanism to avoid the trust problem of both sides caused by centralized audit. Also, it supports batch auditing of multiple copies of original data files at the same time through parallel processing and data block auditing technology, which not only significantly improves the verification efficiency but also realizes the accurate location and repair of corrupted data at the data block level. Finally, the security analyses and performance evaluation show the security and practicability of EDI-C. Compared with the representative schemes, the results show that EDI-C can ensure the integrity verification of cached data more efficiently in an MEC environment.
RFTNet: Region–Attention Fusion Network Combined with Dual-Branch Vision Transformer for Multimodal Brain Tumor Image Segmentation
Authors: Jiao Chunxia , Yang Tiejun , Yan Yanghui , Yang Aolin
Published: December 2023

Brain tumor image segmentation plays a significant auxiliary role in clinical diagnosis. Recently, deep learning has been introduced into multimodal segmentation tasks, which construct various Convolutional Neural Network (CNN) structures to achieve excellent performance. However, most CNN-based segmentation methods have poor capability for global feature extraction. Transformer is good at modeling long-distance dependencies, but it can cause local information loss and usually has a high computational complexity. In addition, it is difficult to fully exploit the brain tumor features of different modalities. To address these issues, in this paper, we propose a region–attention fusion (RAF) network that combines a dual-branch vision Transformer (DVT), called RFTNet. In RFTNet, the DVT is exploited to capture the delicate local information and global semantics separately by two branches. Meanwhile, a novel RAF is employed to effectively fuse the images of the different modalities. Finally, we design a new hybrid loss function, called region-mixed loss function (RML) to calculate the importance of each pixel and solve the problem of class imbalance. The experiments on BrasTS2018 and BraTS2020 datasets show that our method obtains a higher segmentation accuracy than other models. Furthermore, ablation experiments prove the effectiveness of each key component in RFTNet.
Background Material Identification Using a Soft Robot
Authors: Jeong Nathan , Lee Wooseop , Jeong Seongcheol , Mahendran Arun Niddish , Vikas Vishesh
Published: December 2023

Soft robotics is an emerging technology that provides robots with the ability to adapt to the environment and safely interact with it. Here, the ability of these robots to identify the surface of interaction is critical for grasping and locomotion tasks. This paper describes the capability of a four-limb soft robot that can identify background materials through the collection of reflection coefficients using an embedded antenna and machine learning techniques. The material of a soft-limb robot was characterized in terms of the relative permittivity and the loss tangent for the design of an antenna to collect reflection coefficients. A slot antenna was designed and embedded into a soft limb in order to extract five features in reflection coefficients including the resonant frequency, −3 dB bandwidth taken from the lowest S11, the value of the lowest S11, −3 dB bandwidth taken from the highest S11, and the number of resonant frequencies. A soft robot with the embedded antenna was tested on nine different background materials in an attempt to identify surrounding terrain information and a better robotic operation. The tested background materials included concrete, fabric, grass, gravel, metal, mulch, soil, water, and wood. The results showed that the robot was capable of distinguishing among the nine different materials with an average accuracy of 93.3% for the nine background materials using a bagged decision-tree-based ensemble method algorithm.
Scalable Data Concentrator with Baseline Interconnection Network for Triggerless Data Acquisition Systems
Authors: Zabołotny Wojciech Marek
Published: December 2023

Triggerless data acquisition systems (DAQs) require that the data stream is transmitted from multiple links and into the processing node. The short input data words must be concentrated and packed into the longer bit vectors the output interface (e.g., PCI Express) uses. In that process, the unneeded data must be eliminated, and a dense stream of useful DAQ data must be created. Additionally, the time order of the data should be preserved. This paper presents a new solution using the Baseline Network with Reversed Outputs (BNRO) for high-speed data routing. A thorough analysis of the network’s operation enabled increased scalability compared to the previously published concentrator based on an 8 × 8 network. The solution may be scaled by adding additional layers to the BNRO network while minimizing resource consumption. Simulations were performed for four and five layers (16 and 32 inputs). The FPGA implementation and tests in the actual hardware were successfully performed for 16 inputs. The pipeline registers may be added in each layer independently, shortening the critical path and increasing the maximum acceptable clock frequency.
A High-Accuracy, Scalable and Affordable Indoor Positioning System Using Visible Light Positioning for Automated Guided Vehicles
Authors: Boixader Aleix , Labella Carlos , Catalan Marisa , Paradells Josep
Published: December 2023

Indoor Positioning Systems (IPSs) have multiple applications. For example, they can be used to guide people, to locate items in a warehouse and to support the navigation of Automated Guided Vehicles (AGV). Currently most AGVs use local pre-defined navigation systems, but they lack a global localisation system. Integrating both systems is uncommon due to the inherent challenge in balancing accuracy with coverage. Visible Light Position (VLP) offers accurate and fast localisation, but it encounters scalability limitations. To overcome this, this paper presents a novel Image Sensor-based VLP (IS-VLP) identification method that harnesses existing Light Emitting Diode (LED) lighting infrastructure to substitute both navigation and localisation systems effectively in the whole area. We developed an IPS that achieves six-axis positioning at 90 Hz refresh rate using OpenCV’s solvePnP algorithm and embedded computing. This IPS has been validated in a laboratory environment and successfully deployed in a real factory to position an operative AGV. The system has resulted in accuracies better than 12 cm for 95% of the measurements. This work advances towards positioning VLP as an appealing choice for IPS in industrial environments, offering an inexpensive, scalable, accurate and robust solution.
Composite Switched Lyapunov Function-Based Control of DC–DC Converters for Renewable Energy Applications
Authors: Hashemi Tohid , Mahboobi Esfanjani Reza , Jafari Kaleybar Hamed
Published: December 2023

Renewable energy sources play a pivotal role in the pursuit of sustainable and eco-friendly power solutions. While offering environmental benefits, they present inherent challenges. Photovoltaic systems rely on surrounding conditions, wind systems contend with variable wind speeds, and fuel cells are both costly and inefficient. Furthermore, the energy injected by renewable energy sources (RES) exhibits unpredictable behavior. To tackle these problems, researchers employ diverse power electronic devices and converters like inverters, power quality filters, and DC–DC choppers. Among these, DC–DC converters stand out for effectively regulating DC voltage and enhancing the efficiency of RESs. The meticulous selection of a suitable DC–DC converter, coupled with the integration of an efficient control technique, significantly influences overall power system performance. This paper introduces a novel approach to the design of switching controllers for DC–DC converters, specifically tailored for application in renewable energy systems. The proposed controller leverages the power of composite switched Lyapunov functions (CSLF) to enhance the efficiency and performance of DC–DC converters, addressing the unique challenges posed by renewable energy sources. Through comprehensive analysis and simulation, this study demonstrates the efficacy of the controller in optimizing power transfer, improving stability, and ensuring reliable operation in diverse renewable energy environments. Moreover, the small-scale DC–DC converter experiment’s findings are presented to confirm and validate the proposed scheme’s practical applicability.
TrustDFL: A Blockchain-Based Verifiable and Trusty Decentralized Federated Learning Framework
Authors: Yang Jinsheng , Zhang Wenfeng , Guo Zhaohui , Gao Zhen
Published: December 2023

Federated learning is a privacy-preserving machine learning framework where multiple data owners collaborate to train a global model under the orchestra of a central server. The local training results from trainers should be submitted to the central server for model aggregation and update. Busy central server and malicious trainers can introduce the issues of a single point of failure and model poisoning attacks. To address the above issues, the trusty decentralized federated learning (called TrustDFL) framework has been proposed in this paper based on the zero-knowledge proof scheme, blockchain, and smart contracts, which provides enhanced security and higher efficiency for model aggregation. Specifically, Groth 16 is applied to generate the proof for the local model training, including the forward and backward propagation processes. The proofs are attached as the payloads to the transactions, which are broadcast into the blockchain network and executed by the miners. With the support of smart contracts, the contributions of the trainers could be verified automatically under the economic incentive, where the blockchain records all exchanged data as the trust anchor in multi-party scenarios. In addition, IPFS (InterPlanetary File System) is introduced to alleviate the storage and communication overhead brought by local and global models. The theoretical analysis and estimation results show that the TrustDFL efficiently avoids model poisoning attacks without leaking the local secrets, ensuring the global model’s accuracy to be trained.
Comparison of Energy Storage Management Techniques for a Grid-Connected PV- and Battery-Supplied Residential System
Authors: Martínez-Caballero Luis , Kot Radek , Milczarek Adam , Malinowski Mariusz
Published: December 2023

The use of renewable energy sources (RES) such as wind and solar power is increasing rapidly to meet growing electricity demand. However, the intermittent nature of RES poses a challenge to grid stability. Energy storage (ES) technologies offer a solution by adding flexibility to the system. With the emergence of distributed energy resources (DERs) and the transition to prosumer-based electricity systems, energy management systems (EMSs) have become crucial to coordinate the operation of different devices and optimize system efficiency and functionality. This paper presents an EMS for a residential photovoltaic (PV) and battery system that addresses two different functionalities: energy cost minimization, and self-consumption maximization. The proposed EMS takes into account the operational requirements of the devices and their lower-level controllers. A genetic algorithm (GA) is used to solve the optimization problems, ensuring a desired State of Charge (SOC) at the end of the day based on the next day forecast, without discretizing the SOC transitions allowing a continuous search space. The importance of adhering to the manufacturer’s operating specification to avoid premature battery degradation is highlighted, and a comparative analysis is performed with a simple tariff-driven solution, evaluating total cost, energy exchange, and peak power. Tests are carried out in a detailed model, where Power Electronics Converters (PECs) and their local controllers are considered together with the EMS.
Safe-Learning-Based Location-Privacy-Preserved Task Offloading in Mobile Edge Computing
Authors: Min Minghui , Liu Zeqian , Duan Jincheng , Zhang Peng , Li Shiyin
Published: December 2023

Mobile edge computing (MEC) integration with 5G/6G technologies is an essential direction in mobile communications and computing. However, it is crucial to be aware of the potential privacy implications of task offloading in MEC scenarios, specifically the leakage of user location information. To address this issue, this paper proposes a location-privacy-preserved task offloading (LPTO) scheme based on safe reinforcement learning to balance computational cost and privacy protection. This scheme uses the differential privacy technique to perturb the user’s actual location to achieve location privacy protection. We model the privacy-preserving location perturbation problem as a Markov decision process (MDP), and we develop a safe deep Q-network (DQN)-based LPTO (SDLPTO) scheme to select the offloading policy and location perturbation policy dynamically. This approach effectively mitigates the selection of high-risk state–action pairs by conducting a risk assessment for each state–action pair. Simulation results show that the proposed SDLPTO scheme has a lower computational cost and location privacy leakage than the benchmarks. These results highlight the significance of our approach in protecting user location privacy while achieving improved performance in MEC environments.
DetTrack: An Algorithm for Multiple Object Tracking by Improving Occlusion Object Detection
Authors: Gao Xinyue , Wang Zhengyou , Wang Xiaofan , Zhang Shuo , Zhuang Shanna , Wang Hui
Published: December 2023

Multi-object tracking (MOT) is an important problem in computer vision that has a wide range of applications. Currently, object occlusion detecting is still a serious challenge in multi-object tracking tasks. In this paper, we propose a method to simultaneously improve occluded object detection and occluded object tracking, as well as propose a tracking method for when the object is completely occluded. First, motion track prediction is utilized to improve the upper limit of occluded object detection. Then, the spatio-temporal feature information between the object and the surrounding environment is used for multi-object tracking. Finally, we use the hypothesis frame to continuously track the completely occluded object. Our study shows that we achieve competitive performances compared to the current state-of-the-art methods on popular multi-object tracking benchmarks such as MOT16, MOT17, and MOT20.
Depth-Quality Purification Feature Processing for Red Green Blue-Depth Salient Object Detection
Authors: Feng Shijie , Zhao Li , Hu Jie , Zhou Xiaolong , Chan Sixian
Published: December 2023

With the advances in deep learning technology, Red Green Blue-Depth (RGB-D) Salient Object Detection (SOD) based on convolutional neural networks (CNNs) is gaining more and more attention. However, the accuracy of current models is challenging. It has been found that the quality of the depth features profoundly affects the accuracy. Several current RGB-D SOD techniques do not consider the quality of the depth features and directly fuse the original depth features and Red Green Blue (RGB) features for training, resulting in enhanced precision of the model. To address this issue, we propose a depth-quality purification feature processing network for RGB-D SOD, named DQPFPNet. First, we design a depth-quality purification feature processing (DQPFP) module to filter the depth features in a multi-scale manner and fuse them with RGB features in a multi-scale manner. This module can control and enhance the depth features explicitly in the process of cross-modal fusion, avoiding injecting noise or misleading depth features. Second, to prevent overfitting and avoid neuron inactivation, we utilize the RReLU activation function in the training process. In addition, we introduce the pixel position adaptive importance (PPAI) loss, which integrates local structure information to assign different weights to each pixel, thus better guiding the network’s learning process and producing clearer details. Finally, a dual-stage decoder is designed to utilize contextual information to improve the modeling ability of the model and enhance the efficiency of the network. Extensive experiments on six RGB-D datasets demonstrate that DQPFPNet outperforms recent efficient models and delivers cutting-edge accuracy.
A Review of Smart Energy Management in Residential Buildings for Smart Cities
Authors: Qayyum Faiza , Jamil Harun , Ali Faiyaz
Published: December 2023

This survey critically examines the integration of energy management systems within smart residential buildings, serving as key nodes in the smart city network. It systematically maps out the intricate relationships between smart grid technologies, energy storage capabilities, infrastructure development, and their confluence in residential settings. From the evolution of power generation methods, incorporating both traditional and renewable sources, to the cutting-edge progress in energy-efficient transport systems, we assess their cumulative impact on the smart urban environment. While our approach is rooted in theoretical exploration rather than mathematical modeling, we provide a comprehensive review of the prevailing frameworks and methodologies that drive energy management in smart urban ecosystems. We also discuss the implications of these systems on urban sustainability and the critical importance of integrating various energy domains to facilitate effective energy governance. By bringing together a diverse array of scholarly insights, our paper aspires to enhance the understanding of energy interdependencies in smart cities and to catalyze the development of innovative, sustainable policies and practices that will define the future of urban energy management. Through this expanded perspective, we underscore the necessity of cross-disciplinary research and the adoption of holistic strategies to optimize energy usage, reduce carbon footprints, and promote resilient urban living in the era of smart cities.
A Method for the Characterization of the Energy Demand Aggregate Based on Electricity Data Provided by AMI Systems and Metering in Substations
Authors: Bustos-Brinez Oscar A. , Duarte Javier E. , Zambrano-Pinto Alvaro , González Fabio A. , Rosero-Garcia Javier
Published: December 2023

This paper presents a methodology developed to perform the processing, analysis, and characterization of AMI measurement data from the substations of three network operators of the Colombian electrical grid. This methodology includes the analysis of the data, which presents the sources of information used by the model, along with the preprocessing and exploratory analysis of the substations data. It also includes the formulation of the data reconstruction method, which uses a constrained optimization model to characterize the substations, based on the different behaviors of the end users of the Colombian electrical grid. In addition to the proposed methodology, the results of its application to the data provided by the operators are provided. These results show the capacity of the proposed methodology to adequately identify the most common behaviors of the users in a given area and characterize most of the energy demand profiles of each substation.
Revolutionizing Solar Power Forecasts by Correcting the Outputs of the WRF-SOLAR Model
Authors: Huang Cheng-Liang , Wu Yuan-Kang , Tsai Chin-Cheng , Hong Jing-Shan , Li Yuan-Yao
Published: December 2023

Climate change poses a significant threat to humanity. Achieving net-zero emissions is a key goal in many countries. Among various energy resources, solar power generation is one of the prominent renewable energy sources. Previous studies have demonstrated that post-processing techniques such as bias correction can enhance the accuracy of solar power forecasting based on numerical weather prediction (NWP) models. To improve the post-processing technique, this study proposes a new day-ahead forecasting framework that integrates weather research and forecasting solar (WRF-Solar) irradiances and the total solar power generation measurements for five cities in northern, central, and southern Taiwan. The WRF-Solar irradiances generated by the Taiwan Central Weather Bureau (CWB) were first subjected to bias correction using the decaying average (DA) method. Then, the effectiveness of this correction method was verified, which led to an improvement of 22% in the forecasting capability from the WRF-Solar model. Subsequently, the WRF-Solar irradiances after bias correction using the DA method were utilized as inputs into the transformer model to predict the day-ahead total solar power generation. The experimental results demonstrate that the application of bias-corrected WRF-Solar irradiances enhances the accuracy of day-ahead solar power forecasts by 15% compared with experiments conducted without bias correction. These findings highlight the necessity of correcting numerical weather predictions to improve the accuracy of solar power forecasts.
Quantifying the Operational Flexibility of Distributed Cross-Sectoral Energy Systems for the Integration of Volatile Renewable Electricity Generation
Authors: Berg Sebastian , Blaume Lasse , Nilges Benedikt
Published: December 2023

As a part of the transition in higher-level energy systems, distributed cross-sectoral energy systems (DCESs) play a crucial role in providing flexibility in covering residual load (RL). However, there is currently no method available to quantify the potential flexibility of DCESs in covering RL. This study aimed to address this gap by comparing the RL demand of a higher-level energy system with the electricity flow between a DCES and the electricity grid. This can allow for the quantification of the flexibility of DCES operation. Our approach was to categorize existing methods for flexibility quantification and then propose a new method to assess the flexibility of DCESs in covering RL. For this, we introduced a new quantification indicator called the Flexibility Deployment Index (FDI), which integrates two factors: the RL of the higher-level energy system and the electricity purchase and feed-in of a DCES. By normalizing both factors, we could compare the flexibility to cover RL with respect to different DCES concepts and scenarios. To validate the developed quantification method, we applied it to a case study of a hospital’s DCES in Germany. Using an MILP optimization model, we analyzed the variation in FDI for different technology concepts and scenarios, including fixed electricity tariffs, dynamic electricity tariffs, and CO2-emission-optimized operation. The results of our calculations and the application of the FDI indicate that high-capacity combined heat and power units combined with thermal storage units provide higher flexibility. Additionally, the results highlight higher flexibility provision during the winter period compared to the summer period. However, further application and research are needed to confirm the robustness and validity of the FDI assessment. Nonetheless, the case study demonstrates the potential of the new quantification method.
Line-Start Permanent Magnet Synchronous Motor Supplied with Voltage Containing Negative-Sequence Subharmonics
Authors: Gnaciński Piotr , Pepliński Marcin , Muc Adam , Hallmann Damian
Published: December 2023

In some power systems, the voltage waveform contains frequency components less than fundamental, called subharmonics or subsynchronous interharmonics. Voltage subharmonics can be both positive- and negative-sequence, independent of their frequency (order). Subharmonics exert harmful effects on sundry electrical equipment, especially on rotating machinery; they cause various noxious phenomena, such as a local saturation of the magnetic circuit, increases in power losses and windings temperature, and torque pulsations leading to vibration of unacceptable severity. Notably, previous works reported excessive vibration of rotating machinery only under no-load, while under full load, rather moderate vibration occurred. This study deals with vibration analysis of a line-start permanent magnet synchronous motor (LSPMSM) supplied with the voltage containing negative-sentence subharmonics. Experimental investigations were conducted for a 3 kW, four-pole production LSPMSM for subharmonics of various values and frequencies. Voltage subharmonics of values significantly less than reported in real power systems were found to cause unacceptable vibration, especially under full load.
Evaluation of Technical and Economic Indicators for the Production Process of Microalgae Lipids Considering CO2 Capture of a Thermoelectric Plant and Use of Piggery Wastewater
Authors: Valdovinos-García Esveidi Montserrat , Cabrera-Capetillo Christian Ariel , Bravo-Sánchez Micael Gerardo , Barajas-Fernández Juan , Olán-Acosta María de los Ángeles , Petriz-Prieto Moisés Abraham
Published: December 2023

Microalgae are highly studied microorganisms for the production of high-value products due to their high content of proteins, lipids, carbohydrates, and chlorophyll. These compounds are refined to obtain profitable industrial products. This article analyzes the lipid production of Chlorella sp. biomass, considering 18 scenarios for its production, with 9 of these being partially supplemented with swine wastewater. A 1 ha area was considered for biomass cultivation, primary and secondary biomass harvesting, and lipid extraction. Using simulation in the software SuperPro Designer v10, parameters such as CO2 capture (from a thermoelectric power plant), freshwater consumption, wastewater consumption, energy consumption, and unit production cost were evaluated. The results show that the production cost is high, ranging from 836.9 US $/kg to 1131.5 US $/kg of produced lipids, with a maximum CO2 capture of 454 kg of CO2/kg of lipids. The use of wastewater reduces the production cost by approximately 10%. The evaluation of technical and economic parameters allows us to identify bottlenecks and implement strategies to reduce production costs.
Method of Reducing the Effects of Repeated Ignition during Earth Faults in Compensated Medium Voltage Networks
Authors: Andruszkiewicz Jerzy , Lorenc Józef , Olejnik Bartosz , Weychan Agnieszka , Staszak Bogdan
Published: December 2023

The article presents the results of research in the field of limiting the effects of overvoltages and improving the conditions for the self-extinguishing of transient faults in medium voltage networks with earth fault current compensation. The aim of the research was to estimate the level of overvoltages generated by an earth fault during re-ignition of the electric arc and to assess the possibility of reducing them by increasing the attenuation of the earth fault circuit. The results of the conducted tests show that to increase the attenuation, which ensures a significant effect of limiting such overvoltages, it is enough to change the way of operation of the devices forcing an additional active component of the earth fault current (AWSCz/ACFA). In Poland, such devices are commonly used to improve the effectiveness of earth fault protection. It was also found that the proposed solution enables accurate tuning of the Petersen coil in networks with natural asymmetry of earth capacitances. Therefore, changes in the operation of AWSCz/ACFA devices may have a beneficial effect on the limiting the effects of repeated ignitions during earth faults and, at the same time, enable the accurate tuning of the Petersen coil and increase its ability to extinguish arc-fed faults. Research and theoretical analysis of the issue were carried out on the basis of data characterizing the parameters of the earth fault circuits of the real 15 kV network.
Understanding Energy Poverty among the Elderly: Insights from a Household Survey in Greece
Authors: Sardianou Eleni
Published: December 2023

Understanding the determinants of energy poverty among the elderly is crucial for shaping energy policies with significant economic and social implications. This research provides valuable insights into the factors influencing energy poverty among the elderly in Greece. To achieve the study’s objectives, household survey data from 3651 elderly individuals for the year 2022 were utilized. The empirical findings, based on statistical and econometric analysis, affirm the influence of socioeconomic factors on the ability of the elderly to meet their energy needs and address energy poverty challenges. The analysis suggests that both household size and work-related pensions positively impact their ability to address their heating requirements. Elderly individuals with a higher level of education are less likely to experience energy poverty. Additionally, it is found that as perceived health status declines, elderly individuals are more likely to encounter difficulties in covering utility payments. These results underscore the importance of tailoring strategies to promote the adoption of energy poverty measures that cater to diverse demographic and income groups. This information is vital for ensuring that the elderly can afford to maintain a comfortable living environment without sacrificing other essential needs.
A New Hybrid Model Based on SCINet and LSTM for Short-Term Power Load Forecasting
Authors: Liu Mingping , Li Yangze , Hu Jiangong , Wu Xiaolong , Deng Suhui , Li Hongqiao
Published: December 2023

A stable and reliable power system is crucial for human daily lives and economic stability. Power load forecasting is the foundation of dynamically balancing between the power supply and demand sides. However, with the popularity of renewable energy sources and electric vehicles, it still struggles to achieve accurate power load forecasting due to the complex patterns and dynamics of load data. To mitigate these issues, this paper proposes a new hybrid model based on a sample convolution and integration network (SCINet) and a long short-term memory network (LSTM) for short-term power load forecasting. Specifically, a feed-forward network (FFN) is first used to enhance the nonlinear representation of the load data to highlight the complex temporal dynamics. The SCINet is then employed to iteratively extract and exchange information about load data at multiple temporal resolutions, capturing the long-term dependencies hidden in the deeper layers. Finally, the LSTM networks are performed to further strengthen the extraction of temporal dependencies. The principal contributions of the proposed model can be summarized as follows: (1) The SCINet with binary tree structure effectively extracts both local and global features, proving advantageous for capturing complex temporal patterns and dynamics; (2) Integrating LSTM into the SCINet-based framework mitigates information loss resulting from interactive downsampling, thereby enhancing the extraction of temporal dependencies; and (3) FNN layers are strategically designed to enhance the nonlinear representations prior to feeding the load data fed into the SCINet and LSTM. Three real-world datasets are used to validate the effectiveness and generalization of the proposed model. Experimental results show that the proposed model has superior performance in terms of evaluation metrics compared with other baseline models.
Impact of K-H Instability on NOx Emissions in N2O Thermal Decomposition Using Premixed CH4 Co-Flow Flames and Electric Furnace
Authors: Park Juwon , Kim Suhyeon , Yu Siyeong , Park Dae Geun , Kim Dong Hyun , Choi Jae-Hyuk , Yoon Sung Hwan
Published: December 2023

This study systematically investigates the formation of NOx in the thermal decomposition of N2O, focusing on the impact of Kelvin–Helmholtz (K-H) instability in combustion environments. Using premixed CH4 co-flow flames and an electric furnace as distinct heat sources, we explored NOx emission dynamics under varying conditions, including reaction temperature, residence time, and N2O dilution rates (XN2O). Our findings demonstrate that diluting N2O around a premixed flame increases flame length and decreases flame propagation velocity, inducing K-H instability. This instability was quantitatively characterized using Richardson and Strouhal numbers, highlighting N2O’s role in augmenting oxygen supply within the flame and significantly altering flame dynamics. The study reveals that higher XN2O consistently led to increased NO formation independently of nozzle exit velocity (ujet) or co-flow rate, emphasizing the influence of N2O concentration on NO production. In scenarios without K-H instability, particularly at lower ujet, an exponential rise in NO2 formation rates was observed, due to the reduced residence time of N2O near the flame surface, limiting pyrolysis effectiveness. Conversely, at higher ujet where K-H instability occurs, the formation rate of NO2 drastically decreased. This suggests that K-H instability is crucial in optimizing N2O decomposition for minimal NOx production.