bluuebunny/crossref_metadata_2025 huggingface dataset:
Drone Based RGBT Tracking with Dual-Feature Aggregation Network
Authors: Gao Zhinan , Li Dongdong , Wen Gongjian , Kuai Yangliu , Chen Rui
Published: September 2023

In the field of drone-based object tracking, utilization of the infrared modality can improve the robustness of the tracker in scenes with severe illumination change and occlusions and expand the applicable scene of the drone object tracking task. Inspired by the great achievements of Transformer structure in the field of RGB object tracking, we design a dual-modality object tracking network based on Transformer. To better address the problem of visible-infrared information fusion, we propose a Dual-Feature Aggregation Network that utilizes attention mechanisms in both spatial and channel dimensions to aggregate heterogeneous modality feature information. The proposed algorithm has achieved better performance by comparing with the mainstream algorithms in the drone-based dual-modality object tracking dataset VTUAV. Additionally, the algorithm is lightweight and can be easily deployed and executed on a drone edge computing platform. In summary, the proposed algorithm is mainly applicable to the field of drone dual-modality object tracking and the algorithm is optimized so that it can be deployed on the drone edge computing platform. The effectiveness of the algorithm is proved by experiments and the scope of drone object tracking is extended effectively.
What Changes Have Occurred in the Pattern of Paediatric Burns in the Last Years with Special Attention to the COVID-19 Pandemic?
Authors: Mok Sophie Y. , Adams Susan E. , Holland Andrew J. A.
Published: September 2023

Burns in children remain a prominent mode of injury, resulting in considerable morbidity and mortality globally and are a key cause of disability-adjusted life-years. Paediatric burns present a unique challenge, in part due to the developmental, physical and emotional differences between adults and children. Those living in low- and middle-income settings are particularly vulnerable, facing problems such as overcrowding and floor-level cooking. During the COVID-19 pandemic, stay at home orders and the closure of schools and childcare changed the pattern of paediatric injury across the world, resulting in a general increase in trauma-related presentations. This review will examine recent global trends in paediatric burns, including the impact of COVID-19, specifically focusing on the pattern of burn aetiology over the past decade. It will also look at any changes with regard to epidemiological characteristics; burn site, severity and extent; first aid and location; and management and outcomes.
Exploring the Growth and Predictors of Fine Motor Skills in Young Children Aged 4–8 Years
Authors: Józsa Krisztián , Oo Tun Zaw , Borbélyová Diana , Zentai Gabriella
Published: September 2023

Fine motor skills (FMS) are fundamental for children’s growth, influencing their writing progress and everyday activities. However, the development of these skills is a multifaceted process that is affected by various factors. Consequently, it becomes essential to comprehend the disparities in children’s fine motor skills (FMS) development and the predictive factors. To address this aspect, we aimed to investigate the development of children’s FMS, and the factors that can predict their development. Our analysis encompassed comparing FMS development between the two countries, examining gender and age discrepancies, and discerning the variables that predict FMS development. Employing a reliable instrument (the FMS development battery), we conducted a survey study involving 3050 Hungarian children living in both Slovakia and Hungary. The analysis, powered by R packages, yielded significant findings. Notably, students from Slovakia showcased significantly higher levels of FMS compared to their counterparts from Hungary. Furthermore, we observed a noteworthy gender disparity, with female students outperforming male students in both countries. Additionally, differences emerged across various age groups, indicating that older age groups exhibited more advanced FMS compared to their younger counterparts. With the help of regression analyses, our research also revealed the direct impact of gender and age on students’ FMS development, while parental education entered an indirect influence on this aspect. These discoveries offer valuable insights for educational leaders and policymakers seeking to enhance children’s FMS.
Developing a Scientific Literacy Assessment Instrument for Portuguese 3rd Cycle Students
Authors: Coppi Marcelo , Fialho Isabel , Cid Marília
Published: September 2023

Teachers and researchers, in accordance with the main Portuguese curriculum documents in the area of Physical and Natural Sciences, agree that the development of scientific literacy is an integral part of basic education and report that the teaching of these skills is taking place in schools. However, few scientific literacy assessment instruments are available to assess students’ proficiency in using these skills. In this article, we describe the design and processes for gathering validity evidence for the development of the Avaliação da Literacia Científica Essencial (ALCE) instrument. The ALCE assesses scientific literacy skills of students at the end of the 3rd cycle of Basic Education, in the cognitive domains of understanding; analysing and evaluating phenomena; problems and everyday situations involving content knowledge and skills developed in the subjects of Natural Sciences and Physical Chemistry. Our validity argument, which includes the gathering of evidence based on the content and internal structure of the instrument and is grounded in the current literature on the validation of assessment instruments, supports the use of the instrument for the assessment of students’ scientific literacy level at the end of the 3rd cycle of Basic Education. The ALCE may be a useful tool to identify possible gaps between the teaching objectives and the students’ scientific literacy proficiency and reflect on the methodologies, lesson plans and strategies used in the classroom in order to change them to better develop the students’ scientific literacy.
How Has the Pandemic Affected Access and the Feeling of Belonging in Portuguese Higher Education?
Authors: Martins Susana da Cruz , Mauritti Rosário , Machado Bernardo
Published: September 2023

This study is based on an in-depth analysis of the Portuguese reality. The main question addressed concerns the effects of the pandemic on the social profile and conditions of participation, and higher education students’ sense of belonging in Portugal. The changes resulting from the pandemic will also be considered in light of the policies that have been pursued and implemented at the national level with the aim of increasing and enhancing students’ integration within the higher education community. The application of a longitudinal approach was made possible through institutional data and data produced by the EUROSTUDENT project. The results obtained clarify some of the immediate effects that the pandemic has had on higher education and on the social and academic conditions and contexts of students in Portugal. On a more structural level, these results also emphasise the importance of political choices in the process of democratisation and extending the system to new segments of the population.
Interprofessional Climate Change Curriculum in Health Professional Programs: A Scoping Review
Authors: Dalapati Trisha , Nick Sophie E. , Chari Tristan A. , George Ian A. , Hunter Aitchison Alexandra , MacEachern Mark P. , O’Sullivan Aine N. , Taber Kreager A. , Muzyk Andrew
Published: September 2023

Background: We conducted this scoping review to (1) comprehensively compile the published literature on interprofessional education focused on climate change and health, (2) assess the quality and outcomes of the existing curriculum interventions, and (3) highlight potential areas for further growth. By evaluating published curricular interventions, our goal was to highlight effective and validated developments that could be utilized by health professional educators seeking to include topics on climate change in the curriculum. Methods: We searched Ovid MEDLINE databases (MEDLINE, In-Process, In-Data-Review and Other Non-Indexed Citations, and Epub Ahead of Print), Embase, Clarivate Web of Science, and EBSCOhost Education Abstracts from inception through March 2023. Resulting articles were screened for relevance, and data were extracted from the included studies. The Medical Education Research Study Quality Instrument (MERSQI) was used to assess the quality of each included study. Findings: We screened 688 unique articles, and 6 met inclusion criteria. Publications dated from 2013 to 2023. Three studies included students or healthcare professionals from at least three health profession education programs. The mean MERSQI score was 8·17 (SD = 1·34; range 6–10). Curricular interventions varied by study, and topics included effects of climate change on human health, effect of climate change on extreme weather events and infectious disease exposures, and role of health professionals in climate change communication. Studies did not utilize a two-group comparison design. Most studies used an assessment tool with content validity evidence, but no study provided evidence of validity for internal structure or relationships to other variables. Studies reported that curricular interventions improved outcomes related to both climate change and interprofessional education. Interpretation: Despite the growing call for health professional education focused on climate change and health, there remains a gap in the interprofessional curricular interventions that have been assessed.
The Validation and Psychometric Properties of the Gaming Instinctual Motivation Scale
Authors: Teoh Ai Ni , Dillon Roberto , Kaur Divjyot
Published: September 2023

Being able to quantify gaming motivation in a valid, systematic way has important implications for game designers and gaming user experience researchers. In the present study, we aimed to develop and validate a 30-item Gaming Instinctual Motivation Scale (GIMS) based on Dillon’s 6–11 Framework on instinctual gaming motivation and Lazzaro’s gaming experience model. To validate the scale, we recruited 194 regular gamers (Mage = 22.70 years old, SD = 4.38) to complete the GIMS based on their general gaming experience and their experience playing role-laying games (RPGs), first-person shooters (FPSs), real-time strategy, puzzle, and action games. We used a cross-validation approach and performed exploratory factor analysis and confirmatory factor analysis to test the structure of the scale and the reliability and validity of the scale, respectively. The final version of the GIMS had a one-dimensional structure with 15 items. It also had good construct validity, χ2 (N = 117, df = 86) = 126.28, p = 0.003, CFI = 0.93, TLI = 0.92, and RMSEA = 0.064 (90% CI [0.04, 0.09]), and reliability (CR = 0.89), and an acceptable convergent validity (AVE = 0.35). The one-dimensional structure was generalizable to RPG and FPS games, demonstrating the applicability of the scale to these two gaming genres. Higher scores on the GIMS were also associated with a greater intention to play games.
An Improved Spatio-Temporally Smoothed Coherence Factor Combined with Delay Multiply and Sum Beamformer
Authors: Guo Ziyang , Geng Xingguang , Yao Fei , Liu Liyuan , Zhang Chaohong , Zhang Yitao , Wang Yunfeng
Published: September 2023

Delay multiply and sum beamforming (DMAS) is a non-linear method used in ultrasound imaging which offers superior performance to conventional delay and sum beamforming (DAS). While the combination of DMAS and coherence factor (CF) can further improve single plane-wave imaging lateral resolution, by using CF to weight the DMAS output, the main lobe width and aberration effects can be suppressed, which will improve the disadvantage of low lateral resolution when imaging with a single plane-wave. However, in low signal-to-noise ratio (SNR) environments, the speckle variance of the image increases, and there are black area artifacts around high echo objects. To improve the quality of the scatter without significantly reducing the lateral resolution of the DMAS-CF, this paper proposes an adaptive spatio-temporally smoothed coherence factor (GSTS-CF) combined with delay multiply and sum beamformer (DMAS + GSTS-CF), which uses the generalized coherence factor (GCF) as a local coherence detection tool to adaptively determine the subarray length to obtain an improved adaptive spatio-temporally smoothed factor, and uses this factor to weight the output of DMAS. The simulation and experimental data show that the proposed method improves lateral resolution (20 mm depth) by 86.87% compared to DAS, 52.13% compared to DMAS, 15.84% compared to DMAS + STS-CF, and has a full width at half maxima (FWHM) similar to DMAS-CF. The proposed method improves the speckle signal-to-noise ratio (sSNR) by 87.85% (simulation) and 77.84% (in carotid) compared to DMAS-CF, 20.37% (simulation) and 40.74% (in carotid) compared to DMAS, 15.03% (simulation) and 13.46% (in carotid) compared to DMAS + STS-CF, and has sSNR and scatter variance similar to DAS. This indicates that the method improves scatter quality (lower scatter variance and higher sSNR) without significantly reducing lateral resolution.
A CMA-Based Electronically Reconfigurable Dual-Mode and Dual-Band Antenna
Authors: Russo Nicholas E. , Zekios Constantinos L. , Georgakopoulos Stavros V.
Published: September 2023

In this work, an electronically reconfigurable dual-band dual-mode microstrip ring antenna with high isolation is proposed. Using characteristic mode analysis (CMA), the physical characteristics of the ring antenna are revealed, and two modes are appropriately chosen for operation in two sub-6 GHz “legacy” bands. Due to the inherent orthogonality of the characteristic modes, measured isolation larger than 37 dB was achieved in both bands without requiring complicated decoupling approaches. An integrated electronically reconfigurable matching network (comprising PIN diodes and varactors) was designed to switch between the two modes of operation. The simulated and measured results were in excellent agreement, showing a peak gain of 4.7 dB for both modes and radiation efficiency values of 44.3% and 64%, respectively. Using CMA to gain physical insights into the radiative orthogonal modes of under-researched and non-conventional antennas (e.g., antennas of arbitrary shapes) opens the door to developing highly compact radiators, which enable next-generation communication systems.
Attention-Mechanism-Based Models for Unconstrained Face Recognition with Mask Occlusion
Authors: Zhang Mengya , Zhang Yuan , Zhang Qinghui
Published: September 2023

Masks cover most areas of the face, resulting in a serious loss of facial identity information; thus, how to alleviate or eliminate the negative impact of occlusion is a significant problem in the field of unconstrained face recognition. Inspired by the successful application of attention mechanisms and capsule networks in computer vision, we propose ECA-Inception-Resnet-Caps, which is a novel framework based on Inception-Resnet-v1 for learning discriminative face features in unconstrained mask-wearing conditions. Firstly, Squeeze-and-Excitation (SE) modules and Efficient Channel Attention (ECA) modules are applied to Inception-Resnet-v1 to increase the attention on unoccluded face areas, which is used to eliminate the negative impact of occlusion during feature extraction. Secondly, the effects of the two attention mechanisms on the different modules in Inception-Resnet-v1 are compared and analyzed, which is the foundation for further constructing the ECA-Inception-Resnet-Caps framework. Finally, ECA-Inception-Resnet-Caps is obtained by improving Inception-Resnet-v1 with capsule modules, which is explored to increase the interpretability and generalization of the model after reducing the negative impact of occlusion. The experimental results demonstrate that both attention mechanisms and the capsule network can effectively enhance the performance of Inception-Resnet-v1 for face recognition in occlusion tasks, with the ECA-Inception-Resnet-Caps model being the most effective, achieving an accuracy of 94.32%, which is 1.42% better than the baseline model.
Exploring the Physical-World Adversarial Robustness of Vehicle Detection
Authors: Jiang Wei , Zhang  Tianyuan , Liu  Shuangcheng , Ji  Weiyu , Zhang  Zichao , Xiao  Gang
Published: September 2023

Adversarial attacks can compromise the robustness of real-world detection models. However, evaluating these models under real-world conditions poses challenges due to resource-intensive experiments. Virtual simulations offer an alternative, but the absence of standardized benchmarks hampers progress. Addressing this, we propose an innovative instant-level data generation pipeline using the CARLA simulator. Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks. Our findings highlight diverse model performances under adversarial conditions. YOLO v6 demonstrates remarkable resilience, exhibiting just a marginal 6.59% average drop in average precision (AP). In contrast, the ASA attack yields a substantial 14.51% average AP reduction, twice the effect of other algorithms. We also note that static scenes yield higher recognition AP values, and outcomes remain relatively consistent across varying weather conditions. Intriguingly, our study suggests that advancements in adversarial attack algorithms may be approaching its “limitation”. In summary, our work underscores the significance of adversarial attacks in real-world contexts and introduces the DCI dataset as a versatile benchmark. Our findings provide valuable insights for enhancing the robustness of detection models and offer guidance for future research endeavors in the realm of adversarial attacks.
Research on the Architecture of Transactional Smart Contracts Based on Blockchains
Authors: Liu Zhixiang , Feng Wenlong , Zhang Yu , Zhu Chengcheng
Published: September 2023

At present, smart contracts are designed based on the application field, and their structure and functions are closely related to specific businesses. Even smart contracts within the same field and the same business have different structures and functions due to different developers, resulting in structural confusion, repeated development, and low levels of sharing. In response to this problem, this study conducts a full investigation of smart contracts in various fields, using big data technology to compare and analyze the structures of each contract and extracting the common content of their main bodies to study each feature, as well as to conduct induction and fusion. This study also generally designs a hierarchical structure and formulates structural modules such as transaction rules and the analysis of rights and responsibilities, as well as a reward and punishment mechanism. Data traceability is established, and the overall architectural specification of smart contracts is constructed. Additions, deletions, and improvements are made based on specific application environments to realize the dynamic updates of the architecture of contracts. Experiments show that the architecture of contracts can realize the various functions required in a transaction, solve the problem of the repeated development of current transactional smart contracts, and improve the sharing level.
Multi-Phase Focused PID Adaptive Tuning with Reinforcement Learning
Authors: Ding Ye , Ren Xiaoguang , Zhang Xiaochuan , Liu Xin , Wang Xu
Published: September 2023

The Proportional-Integral-Derivative (PID) controller, a fundamental element in industrial control systems, plays a pivotal role in regulating an extensive array of controlled objects. Accurate and rapid adaptive tuning of PID controllers holds significant practical value in fields such as mechatronics, robotics, and automatic control. The three parameters of the PID controller exert a substantial influence on control performance, rendering the tuning of these parameters an area of significant interest within related research fields. Numerous tuning techniques are widely employed to optimize its functionality. Nonetheless, their adaptability and control stability may be constrained in situations where prior knowledge is inadequate. In this paper, a multi-phase focused PID adaptive tuning method is introduced, leveraging the deep deterministic policy gradient (DDPG) algorithm to automatically establish reference values for PID tuning. This method constrains agent actions in multiple phases based on the reward thresholds, allowing the output PID parameters to focus within the stable region, which provides enhanced adaptability and maintains the stability of the PID controller even with limited prior knowledge. To counteract the potential issue of a vanishing gradient following action constraints, a residual structure is incorporated into the actor network. The results of experiments conducted on both first-order and second-order systems demonstrate that the proposed method can reduce the tracking error of a PID controller by 16–30% compared with the baseline methods without a loss in stability.
Enhanced Social Recommendation Method Integrating Rating Bias Offsets
Authors: Han Lu , Qin Jiwei , Xia Boshen
Published: September 2023

Current social recommendations based on Graph Neural Networks (GNNs) often neglect to extract rating bias from user and item statistics, leading to misinterpreting real user preferences. For example, a high rating from a user with lenient rating standards and a high average rating does not always indicate a real preference for the item. This situation highlights inherent flaws in existing recommendation algorithms that do not adequately account for bias in user and item ratings and rating trends. To address this problem, this paper proposes an enhanced social recommendation method based on GNNs with integrated rating bias offsets (SR-BS). Firstly, we obtain rating bias from users and items by subtracting their average rating value from the historical rating value for each user/item. To enhance the model’s learning capability, we transform the rating biases into vector representations. Secondly, in the model learning, diverse meta-paths are predefined for modeling interaction relations between graph nodes (e.g., user–item–user, user–user). The aggregation of semantic information from these relational paths is achieved by stacking multiple GNN layers, enabling the fusion of higher-order information. Finally, the experimental results on four datasets—Ciao, Epinions, Douban, and FilmTrust—show that our method outperforms other state-of-the-art methods in social recommendation tasks, exhibiting high stability and personalization.
High-Resolution Indoor Sensing Using Channel State Information of WiFi Networks
Authors: Zhou Hongjian , Zhang Yongwei , Temiz Murat
Published: September 2023

Indoor sensing is becoming increasingly important over time as it can be effectively utilized in many applications from digital health care systems to indoor safety and security systems. In particular, implementing sensing operations using existing infrastructures improves our experience and well-being, and exhibits unique advantages. The physical layer channel state information for wireless fidelity (WiFi) communications carries rich information about scatters in the propagation environment; hence, we exploited this information to enable detailed recognition of human behaviours in this study. Comprehensive calibration and filtering techniques were developed to alleviate the redundant responses embedded in the channel state information (CSI) data due to static objects and accidental events. Accurate information on breathing rate, heartbeat and angle of arrival of the incoming signal at the receiver side was inferred from the available CSI data. The method and procedure developed can be extended for sensing or imaging the environment utilizing wireless communication networks.
Differences in the Structure of Household Electricity Prices in EU Countries
Authors: Matuszewska-Janica Aleksandra , Żebrowska-Suchodolska Dorota , Zalewska Mariola E. , Ala-Karvia Urszula , Hozer-Koćmiel Marta
Published: September 2023

Private households are an important group of energy consumers. Based on Eurostat data, their energy consumption constituted 30% of the final consumption of energy use across the European Union in 2021. The cost of energy is one of the main components of household budgets; thus, the prices provided by energy carriers have a significant impact on energy consumption. The price offered to the final consumer consists of three components: the price of energy and its supply, network costs, and taxes and levies. The values of the three components, however, depend on several factors, among which the structure of the energy markets and energy policies in individual EU countries play a key role. This work aimed to analyze and assess the structure of electricity prices offered to households across EU countries in the years 2019–2021. The differences and similarities between the pricing policies of selected products in the EU and their impacts on households’ purchasing capacity were captured and a non-pattern classification method (k-means) was applied as a research tool. The results indicated that the heterogeneity of the electricity price structure increased significantly over the period analyzed. This may be a consequence of the use of strongly differentiated tools to mitigate electricity price increases and the steps being taken towards low-carbon economies.
Impact of Alkyl-Based Side Chains in Conjugated Materials for Bulk Heterojunction Organic Photovoltaic Cells—A Review
Authors: Moineau-Chane Ching Kathleen Isabelle
Published: September 2023

The research for efficient organic materials organized in bulk heterojunction (BHJ) thin films for organic photovoltaics (OPVs) has shown a significant breakthrough in the past decade. Desired structural organization can be attained through various strategies. In this regard, the current review highlights tuning of alkyl chains introduced on molecular structures of active materials. The recent wide literature is classified based on the introduction of alkyl chains on polymers and small molecules used as donor and acceptor materials. The design of these materials, the morphological aspects of the active layers, and the performances of the related photovoltaic cells are detailed. A comprehensive discussion on chemical structures of the different material families considered, their modification by alkyl chains of various natures, and the morphological aspects are reported and tabulated.
Monitoring and Preventing Failures of Transmission Pipelines at Oil and Natural Gas Plants
Authors: Bęben Dariusz , Steliga Teresa
Published: September 2023

In recent years, the increase in energy prices and demand has been driven by the post-pandemic economic recovery. Of the various energy sources, oil and natural gas remain the most important source of energy production and consumption after coal. Oil and gas pipelines are a key component of the overall energy infrastructure, transporting oil and gas from mines to end users, so the reliability and safety of these pipelines is critical. The oil and gas industry incurs large expenses for the removal of failures related to, among others, corrosion of pipelines caused by the presence of Hg, CO2 H2S, carbonates and chlorides in reservoir waters. Therefore, pipeline operators must constantly monitor and prevent corrosion. Corrosion failure losses are a major motivation for the oil and gas industry to develop accurate monitoring models using non-destructive NDT methods based on test results and failure frequency observations. Observing the locations of frequent pipeline failures and monitoring and applying corrosion protection to pipelines play an important role in reducing failure rates and ultimately increasing the economic and safety performance of pipelines. Monitoring and prevention efforts support the decision-making process in the oil and gas industry by predicting failures and determining the timing of maintenance or replacement of corroded pipelines. We have presented methods of prevention through the use of corrosion inhibitors in crude oil and natural gas transmission pipelines, as well as various factors that influence their application. In this article, a review of corrosion rate monitoring systems is conducted, and a range of control and monitoring scenarios is proposed. This knowledge will aid scientists and practitioners in prioritizing their policies, not only to choose the appropriate monitoring technique but also to enhance corrosion protection effectiveness.
The Impact of Degradation of PV/Battery-Independent System Components on Technical and Economic Indicators and Sizing Process
Authors: Mielcarek Agata , Ceran Bartosz , Jurasz Jakub
Published: September 2023

This article presents research on modelling the operation of an independent electricity generation system consisting of a photovoltaic installation and energy storage in the form of electrochemical batteries (PV/BAT). The generation system was considered primarily in the context of its sizing process, i.e., the selection of the installed power of the photovoltaic installation (PV) and the rated capacity of the battery (BAT). Traditionally, the model includes a one-year analysis of the generation system based on initial (nominal) parameters without considering component performance degradation. The novelty of this research lies in the long-term simulation of the system operation, considering the degradation of its components. The sizing process was based on the numerical method. The best solution is selected on the basis of the economic criterion, while satisfying the reliability condition. The simulations were conducted using Matlab software. Using a comparative analysis, the scale of technical and economic oversizing of the system was determined by considering long-term reliability. For the assumed customer load profile, insolation profile, and battery operation in the range of 25–100% of the available capacity, providing the assumed level of reliability after accounting for degradation in the sizing process resulted in a 33.33% increase in the rated battery capacity, an 18.75% increase in the installed photovoltaic plant capacity, and a 19.5% increase in the system cost of electricity (LCOE) relative to the results of the sizing process without accounting for component performance degradation over the years of operation.
Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks
Authors: Böhm Laura , Kolb Sebastian , Plankenbühler Thomas , Miederer Jonas , Markthaler Simon , Karl Jürgen
Published: September 2023

Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models’ resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023.
Photovoltaic Spectral Responsivity and Efficiency under Different Aerosol Conditions
Authors: Kouklaki Dimitra , Kazadzis Stelios , Raptis Ioannis-Panagiotis , Papachristopoulou Kyriakoula , Fountoulakis Ilias , Eleftheratos Kostas
Published: September 2023

While solar power applications are growing rapidly worldwide, information about solar energy availability, its characteristics and the factors that affect it are essential. Among other parameters, a reference spectrum (ASTMG-173-03) is adopted, relying on Standard Test Conditions (STC), under which Photovoltaic (PV) devices are evaluated. However, these rigorously defined conditions can vary considerably from realistic environmental conditions. The objective of the present work is to assess the impact of the variability of atmospheric composition on the spectral distribution of the incident solar spectral irradiance (SSI) and, therefore, its implication on various PV materials performance. Ground-based measurements of global horizontal SSI have been conducted using a Precision Spectroradiometer (PSR) in the framework of the ASPIRE (Atmospheric parameters affecting SPectral solar IRradiance and solar Energy) project in Athens, Greece. The gathered data in combination with spectrally resolved radiative transfer under clear-sky conditions contributed to the investigation of the atmospheric variables that attenuate irradiance (e.g., aerosols). In addition, since PV modules’ spectral absorptivity differs according to the semiconductor material used, the impact of the above-mentioned spectral features on PV performance has been investigated in order to estimate the spectral impact between the theoretical and outdoor conditions on the yield of different PV technologies. Overall, the results denote that smoke has a more significant effect than dust, while the effect on various technologies varies. The highest deviation compared to the STC was observed in the case of a-Si, reaching an absolute difference of 45% in the case of smoke particles in the atmosphere, while the maximum deviation between the different technologies reached approximately 7%.
Accelerating the Energy Transition through Power Purchase Agreement Design: A Philippines Off-Grid Case Study
Authors: Barroco Jose , Vithayasrichareon Peerapat
Published: September 2023

As renewable energy (RE) costs decrease, private non-subsidized revenue sources, such as power purchase agreements (PPA), will increase in off-grid areas. This paper’s objective is to improve policymakers’ and utilities’ understanding of PPA’s role in mitigating private investors’ risks in off-grid areas to accelerate the energy transition. The paper built a PPA dataset for the largest off-grid area in the Philippines and developed novel and efficient techniques to evaluate the risk mitigation ability of a PPA. While oil-based technologies are expensive, they are low-capital-intensive, and the fuel, the bulk of the cost, is passed through to consumers and primarily funded by subsidies. In contrast, the most affordable energy source, RE, requires higher upfront capital investments, financed primarily with equity. Investors chose low-capital-intensive technologies (oil), rehabilitated power plants, and utilized old equipment, all investment decisions to mitigate residual PPA risks, i.e., distribution utilities’ low creditworthiness and capital recovery uncertainty. Presenting the investment as a PPA residual mitigation tool is one of the paper’s contributions to the literature. The distribution utility needs to reduce investors’ uncertainty by covering reasonable investors’ costs. Policymakers need to level the playing field between fossil fuels and RE by reducing subsidies and strengthening distribution utility creditworthiness.
Fuzzy-Ball Fluids Enhance the Production of Oil and Gas Wells: A Historical Review
Authors: Li Xiaoyong , Zheng Lihui , Chen Yuanbo , Huang Xiaowei , Zhai Xiaopeng , Wei Panfeng , Tao Xiujuan , Nie Shuaishuai
Published: September 2023

Advancements in drilling technology are pivotal to optimizing the production and sustainability of oil and gas wells. One of the emerging innovations is the application of a specialized fluid known as “Fuzzy-ball” fluid. This paper comprehensively reviews the historical evolution and advancements in the utilization of fuzzy-ball fluid in drilling and well-repair processes. Fuzzy-ball fluid has been discovered to bolster a formation’s pressure-bearing capabilities and systematically augment resistance against oil, gas, and water flow. These attributes have been instrumental in the phased integration of fuzzy-ball fluid into procedures aiming to enhance production in oil and gas wells. This paper bridges the knowledge gap in the industry regarding the application of fuzzy-ball fluid, thereby circumventing the challenges of inadequate understanding and suboptimal designs. However, the study acknowledges the potential limitation of information loss despite the extensive data collection from diverse sources, such as articles, patents, and reports. As a future direction, this paper emphasizes the need for a more encompassing and critical evaluation of fuzzy-ball fluid’s performance and applications. This will enable a more informed decision-making process, fostering the expanded use and understanding of this fluid’s mechanism within the industry.
Robust Nonlinear Control of a Wind Turbine with a Permanent Magnet Synchronous Generator
Authors: Acosta Lúa Cuauhtemoc , Bianchi Domenico , Martín Baragaño Salvador , Di Ferdinando Mario , Di Gennaro Stefano
Published: September 2023

This paper addresses the design of a robust nonlinear dynamic controller for a wind turbine. The turbine is equipped with a permanent magnet synchronous generator. The control problem involves tracking a suitable reference value for the turbine’s angular velocity, which corresponds to the wind speed. This issue is tackled by compensating for variations in the electrical and mechanical parameters present in the mathematical model. Additionally, the problem is approached under the assumption that wind speed cannot be directly measured, a fact verified in practical scenarios. This situation is particularly relevant for real-world applications, where only nominal parameter values are accessible and accurate wind speed measurement is challenging due to disturbances caused by the turbine or other factors, despite the use of appropriate sensors. To achieve precise tracking of the angular velocity reference, effective compensation of perturbation terms arising from parameter uncertainties and errors in wind estimation becomes crucial. To address this problem, a wind velocity estimator is employed in conjunction with high-order sliding mode parameter estimators, ensuring the turbine’s operation attains a high level of performance.
Numerical Analysis of Dual Fuel Combustion in a Medium Speed Marine Engine Supplied with Methane/Hydrogen Blends
Authors: Cameretti Maria Cristina , De Robbio Roberta , Palomba Marco
Published: September 2023

Compression ignition engines will still be predominant in the naval sector: their high efficiency, high torque, and heavy weight perfectly suit the demands and architecture of ships. Nevertheless, recent emission legislations impose limitations to the pollutant emissions levels in this sector as well. In addition to post-treatment systems, it is necessary to reduce some pollutant species, and, therefore, the study of combustion strategies and new fuels can represent valid paths for limiting environmental harmful emissions such as CO2. The use of methane in dual fuel mode has already been implemented on existent vessels, but the progressive decarbonization will lead to the utilization of carbon-neutral or carbon-free fuels such as, in the last case, hydrogen. Thanks to its high reactivity nature, it can be helpful in the reduction of exhaust CH4. On the contrary, together with the high temperatures achieved by its oxidation, hydrogen could cause uncontrolled ignition of the premixed charge and high emissions of NOx. As a matter of fact, a source of ignition is still necessary to have better control on the whole combustion development. To this end, an optimal and specific injection strategy can help to overcome all the before-mentioned issues. In this study, three-dimensional numerical simulations have been performed with the ANSYS Forte® software (version 19.2) in an 8.8 L dual fuel engine cylinder supplied with methane, hydrogen, or hydrogen–methane blends with reference to experimental tests from the literature. A new kinetic mechanism has been used for the description of diesel fuel surrogate oxidation with a set of reactions specifically addressed for the low temperatures together with the GRIMECH 3.0 for CH4 and H2. This kinetics scheme allowed for the adequate reproduction of the ignition timing for the various mixtures used. Preliminary calculations with a one-dimensional commercial code were performed to retrieve the initial conditions of CFD calculations in the cylinder. The used approach demonstrated to be quite a reliable tool to predict the performance of a marine engine working under dual fuel mode with hydrogen-based blends at medium load. As a result, the system modelling shows that using hydrogen as fuel in the engine can achieve the same performance as diesel/natural gas, but when hydrogen totally replaces methane, CO2 is decreased up to 54% at the expense of the increase of about 76% of NOx emissions.
Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations
Authors: Erdogan Gülsah , Fekih Hassen Wiem
Published: September 2023

The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.
Dynamic Distributed Collaborative Control for Equitable Current Distribution and Voltage Recovery in DC Microgrids
Authors: Lasabi Olanrewaju , Swanson Andrew , Jarvis Leigh , Aluko Anuoluwapo
Published: September 2023

In a stand-alone DC microgrid featuring several distributed energy resources (DERs), droop control is adopted to achieve a proportional distribution of current among the DERs within the microgrid. The operation of the droop control mechanism leads to a variation in bus voltage, which is further amplified by the line impedance between the DC bus and DERs. This paper proposes an enhanced distributed secondary control technique aimed at achieving equitable current sharing and voltage regulation simultaneously within a DC microgrid. The proposed distributed secondary control is incorporated into the cyber layer of the microgrid, facilitating the exchange of information among the controllers. In the event of a communication link breakdown, this technique upholds the reliability of the entire system. The control loop utilizes a type-II fuzzy logic control framework for the adaptive selection of the control parameters to improve the control response. Furthermore, the proposed technique can handle both resistive and constant power loads without any particular prerequisites. Utilizing the Lyapunov method, appropriate stability criteria for the proposed controller have been formulated. Various tests were performed across a range of operational scenarios to assess the robustness of the proposed control technique through MATLAB/Simulink® models, which have been validated with real-time experiments. The outcomes revealed that the proposed control effectively achieves its control objectives within a DC microgrid, showcasing rapid responsiveness and minimal oscillation.
Study on SI Engine Operation Stability at Lean Condition—The Effect of a Small Amount of Hydrogen Addition
Authors: Leyko Jacek , Słobiński Kamil , Jaworski Jarosław , Mitukiewicz Grzegorz , Bou Nader Wissam , Batory Damian
Published: September 2023

The lean-burn mode is a solution that reduces the fuel consumption of spark-ignition internal combustion engines and keeps the low exhaust emission, but the stability of the lean-burn combustion process, especially at low loads, needs to be addressed. Enhancing gasoline with hybrid hydrogen oxygen (HHO) gas—a mixture of hydrogen and oxygen gases—is proposed to improve combustion of the lean-gasoline mixture. A three-cylinder, spark-ignition, naturally aspirated, MPI engine with HHO gas produced with an alkaline water electrolyzer and introduced as a gasoline enhancement was tested. The amount of hydrogen added to the lean-gasoline mixture (λ = 1.4) was in the range from 0.15 to 1.5%, and the results were compared to the stoichiometric (λ = 1) and pure lean mode (λ = 1.4) gasoline operation. The other authors’ results show that a minimum 3% of the mass fraction of hydrogen is necessary to affect the gasoline combustion process. This paper proved that even a small hydrogen enhancement of gasoline in the amount of 0.3% of the mass fraction improves the combustion stability.
Risk Assessment of Power Supply Security Considering Optimal Load Shedding in Extreme Precipitation Scenarios
Authors: Zhou Gang , Shi Jianxun , Chen Bingjing , Qi Zhongyi , Wang Licheng
Published: September 2023

Extreme rainfall may induce flooding failures of electricity facilities, which poses power systems in a risk of power supply interruption. To quantitatively estimate the risk of power system operation under extreme rainfall, a multi-scenario stochastic risk assessment method was proposed. First, a scenario generation scheme considering waterlogged faults of power facilities was constructed based on the storm water management model (SWMM) and the extreme learning machine method. These scenarios were merged with several typical scenario sets for further processing. The outage of power facilities will induce power flow transfer which may consequently lead to transmission lines’ thermal limit violation. Semi-invariant and Gram–Charlier level expansion methods were adopted to analytically depict the probability density function and cumulative probability function of each line’s power flow. The optimal solution was performed by a particle swarm algorithm to obtain proper load curtailment at each bus, and consequently, the violation probability of line thermal violations can be controlled within an allowable range. The volume of load curtailment as well as their importance were considered to quantitatively assess the risk of power supply security under extreme precipitation scenarios. The effectiveness of the proposed method was verified in case studies based on the Southeast Australia Power System. Simulation results indicated that the risk of load shedding in extreme precipitation scenarios can be quantitatively estimated, and the overload probability of lines can be controlled within the allowable range through the proposed optimal load shedding scheme.