bluuebunny/crossref_metadata_2025 huggingface dataset:
A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease
Authors: Chatzimichail Theodora , Hatjimihail Aristides T.
Published: February 2024

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study’s aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.
Predictors of Occult Metastasis and Prognostic Factors in Patients with cN0 Oral Cancer Who Underwent Elective Neck Dissection
Authors: Yamagata Kenji , Fukuzawa Satoshi , Noguchi Atsuro , Takaoka Shohei , Uchida Fumihiko , Ishibashi-Kanno Naomi , Bukawa Hiroki
Published: February 2024

Elective neck dissection (END) is recommended for the management of patients with oral squamous cell carcinoma (OSCC) because of the risk of occult metastasis (OM). We hypothesized that some factors predict poor prognosis regardless of a cN0 END. This study aimed to investigate the predictors of OM and prognostic factors in patients with cN0 OSCC who underwent supraomohyoid neck dissection (SOHND). A retrospective cohort study design was created and implemented. The primary predictive variables in this study were OM and risk factors for poor prognosis after SOHND. A Cox proportional hazard model was used to adjust for the effects of potential confounders on the risk factors for poor prognoses. Among 86 patients with OSCC, OMs were observed in 9 (10.5%). The neutrophil-to-lymphocyte ratio (NLR) and vascular invasion are good markers for detecting OM. A Cox multivariable analysis identified two independent predictors of overall survival: pathologic node (pN) and laterality of END. An independent predictive factor for disease-free survival, the surgical margin, was also identified in this study. According to the pN classification, pN1 patients had a worse survival rate than pN2 patients. Therefore, in the case of pN1, regardless of being cN0, additional adjuvant therapy may be necessary.
Quantum Switch as a Thermodynamic Resource in the Context of Passive States
Authors: Molitor Otavio A. D. , Rudnicki Łukasz
Published: February 2024

In recent years, many works have explored possible advantages of indefinite causal order, with the main focus on its controlled implementation known as quantum switch. In this paper, we tackle advantages in quantum thermodynamics, studying whether quantum switch is capable of activating a passive state, either alone or with extra resources (active control state) and/or operations (measurement of the control system). By disproving the first possibility and confirming the second one, we show that quantum switch is not a thermodynamic resource in the discussed context, though it can facilitate work extraction given external resources. We discuss our findings by considering specific examples: a qubit system subject to rotations around the x and y axes in the Bloch sphere, as well as general unitaries from the U(2) group; and the system as a quantum harmonic oscillator with displacement operators, as well as with a combination of displacement and squeeze operators.
Estimation of Weighted Extropy with Focus on Its Use in Reliability Modeling
Authors: Irshad Muhammed Rasheed , Archana Krishnakumar , Maya Radhakumari , Longobardi Maria
Published: February 2024

In the literature, estimation of weighted extropy is infrequently considered. In this paper, some non-parametric estimators of weighted extropy are given. The validation and comparison of the estimators are implemented with the help of simulation study and data illustration. The usefulness of the estimators is demonstrated using real data sets.
Collaboration and Competition: A Social Network Analysis of Thailand’s Music Industry
Authors: Peechapat Wichaya , Puttanapong Nattapong
Published: February 2024

This study quantitatively investigates the collaborative framework and competitive landscape of Thailand’s evolving music industry, driven by technological progress and changing consumer preferences. By examining data obtained from Thailand’s Department of Intellectual Property, specifically 138,868 songs, it explores the complex network of relationships among music creators, artists, and various rights-holders, including those associated with recording, music, melodies, and lyrics. Utilizing social network analysis, this research uncovers a power law distribution in these networks, reflecting a scale-free market configuration. This characteristic is marked by a few dominant players exercising considerable market influence, contrasted with numerous less-interconnected participants. This investigation notes regular patterns of collaboration between artists and different rights-holders. Furthermore, the network of music creators displays small-world properties, with short collaborative distances fostering efficient information exchange and creative synergy. Crucially, this study identifies key influential players instrumental in directing the industry’s major trends, highlighting their role in market concentration. These significant findings will provide critical evidence for informing future policy development aimed at improving efficiency and equity in the digital content industries.
Chinese Students Learning English as a Second Language
Authors: McQuade Maria , Pan Dora Jue , Ho Jana Chi-San , Ong JingTong , Ng Melody Chi Ying , Meng Xiangzhi , McBride Catherine
Published: February 2024

The way in which different cities teach children to read in Chinese may have an impact on the skills they later utilize to acquire English word reading skills. This study examined the relative contributions of several cognitive–linguistic measures to English word reading for Chinese students learning English as a second language in two Chinese cities, one whose school system teaches Pinyin (Beijing) and one whose school system does not teach Pinyin (Hong Kong). Students in grades 2–3 completed measures on Chinese morphological awareness (MA), Chinese phonological awareness (PA), Pinyin writing, and English word reading. In the Beijing group, it was found that PA (β = 0.334, p < 0.01) and Pinyin (β = 0.257, p < 0.05) were significant predictors of English word reading. In contrast, in the Hong Kong group, only MA (β = 0.263, p < 0.05) was found to be a significant predictor of English word reading. The difference in predictors could be due to the availability of a phonological tool (Pinyin) for the Beijing students when learning Chinese, while the Hong Kong group may have relied more heavily on learning using MA and rote memory techniques. Overall, the results from this study provide data supporting the benefits of having a phonological tool like Pinyin for Chinese children when learning to read in English.
Investigating the Factors Contributing to the Formation of Secondary School Students’ Interest towards Higher Education Studies
Authors: Mitsopoulou Athina G. , Pavlatou Evangelia A.
Published: February 2024

The present study’s objective constitutes the examination of the prognostic factors that influence the inclination of students in secondary school towards pursuing higher education. To achieve this goal, an existing questionnaire was utilized and appropriately altered to align with the Greek educational system. The survey involved the participation of 301 secondary school students from Piraeus, which comprises one of Greece’s major cities. The outcomes of the research yield substantial endorsement for the principles outlined in the social cognitive career theory. Specifically, the study highlights the significant role of family background, encompassing the educational levels of the parents, the students’ perceptions of the family’s financial situation, and the financial support provided by the family during the students’ academic journey, in shaping the students’ intent towards pursuing higher education. Moreover, the presence of a secure attachment bond between students and their parents suggests a favorable inclination towards higher education. Conversely, students deriving from low-income families are prone to exhibit hesitancy in pursuing higher education. The acquired data reveal a constructive relationship among outcome expectations, social support, as well as the process of students’ interest in developing a desire for higher education. Conversely, factors such as gender and age, as well as the presence of siblings studying in higher education, appear to have little influence in this regard.
Learning the Practice from the Practice: Theory–Practice Courses in Teacher Education
Authors: Oved Orit , Raichel Nirit
Published: February 2024

In teacher education programs, it is important to deepen knowledge alongside developing practices through practical experience. One practice of the Professional Development School’s (PDS) model in clinical experience is designing courses linking theory to practice. The present study examines the perception of the administrative officers in colleges of education in Israel regarding the Ministry of Education’s Theory–Practice (TP) courses and the challenges in implementing them in the curriculum. This study was conducted in 16 state academic colleges for education and 37 administrative personnel participated: presidents, vice presidents, rectors, deans, and heads of courses and practical training. A semi-structured interview was used, and the data was analyzed thematically. The research participants believe that TP courses as a tool implemented as part of the PDS model may be effective in training teachers to integrate theory with practical experience. The participants raised three major challenges to implementation: systemic, pedagogical, and organizational. The participants emphasize that designing and implementing TP courses is a complex, slow process requiring organizational change and the mindset of administration and teaching staff at both the colleges of education and the schools. Long-term assessment is required to examine the effect of reducing hours dedicated to education theory and subject knowledge.
Motion Artifact Detection Based on Regional–Temporal Graph Attention Network from Head Computed Tomography Images
Authors: Liu Yiwen , Wen Tao , Wu Zhenning
Published: February 2024

Artifacts are the main cause of degradation in CT image quality and diagnostic accuracy. Because of the complex texture of CT images, it is a challenging task to automatically detect artifacts from limited image samples. Recently, graph convolutional networks (GCNs) have achieved great success and shown promising results in medical imaging due to their powerful learning ability. However, GCNs do not take the attention mechanism into consideration. To overcome their limitations, we propose a novel Regional–Temporal Graph Attention Network for motion artifact detection from computed tomography images (RT-GAT). In this paper, head CT images are viewed as a heterogeneous graph by taking regional and temporal information into consideration, and the graph attention network is utilized to extract the features of the constructed graph. Then, the feature vector is input into the classifier to detect the motion artifacts. The experimental results demonstrate that our proposed RT-GAT method outperforms the state-of-the-art methods on a real-world CT dataset.
Remanufacturing a Synchronous Reluctance Machine with Aluminum Winding: An Open Benchmark Problem for FEM Analysis
Authors: Katona Mihály , Bányai Dávid Gábor , Németh Zoltán , Kuczmann Miklós , Orosz Tamás
Published: February 2024

The European Union’s increasing focus on sustainable and eco-friendly product design has resulted in significant pressure on original equipment manufacturers to adopt more environmentally conscious practices. As a result, the remanufacturing of end-of-life electric machines is expected to become a promising industrial segment. Identifying the missing parameters of these types of machines will play an essential role in creating feasible and reliable redesigns and remanufacturing processes. A few case studies related to this problem have been published in the literature; however, some novel, openly accessible benchmark problems can facilitate the research and function as a basis for comparing and validating novel numerical methods. This paper presents the identification process of an experimental synchronous machine. It outlines methodologies for identifying material properties, winding schemes, and other critical parameters for the finite element analysis and modelling of electric machines with incomplete information. The machine in question is intended for remanufacturing, with the plan to replace its faulty winding with an aluminium-based alternative. It also serves as an open benchmark problem for researchers, designers, and practitioners.
Fast Coordinated Predictive Control for Renewable Energy Integrated Cascade Hydropower System Based on Quantum Neural Network
Authors: Ye Xi , Chen Zhen , Zhu Tong , Wei Wei , Peng Haojin
Published: February 2024

The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a quantum neural network (QNN)-based coordinated predictive control approach is proposed. It manipulates coordinated dispatch of multiple clean energy sources, including hydro, wind, and solar power, leverages QNN to conquer intricate multi-uncertainty and learn intraday predictive control patterns, by taking renewable power, load, demand response (DR), and optimal unit commitment as observations. This enables us to exploit the stability and exponential memory capacity of QNN to extrapolate diversified dispatch policies in a reliable manner, which can be hard to reach for traditional learning algorithms. A closed-loop warm start framework is finally presented to enhance the dispatch quality, where the decisions by QNN are fed to initialize the optimizer, and the optimizer returns optimal solutions to quickly evolve the QNN. A real-world case in the ZD sub-grid of the Sichuan power grid in China demonstrates that the proposed method hits a favorable balance between operational cost, accuracy, and efficiency. It realizes second-level elapsed time for intraday predictive control.
Real-Time Performance Benchmarking of RISC-V Architecture: Implementation and Verification on an EtherCAT-Based Robotic Control System
Authors: Yoo Taeho , Choi Byoung Wook
Published: February 2024

RISC-V offers a modular technical approach combined with an open, royalty-free instruction set architecture (ISA). However, despite its advantages as a fundamental building block for many embedded systems, the escalating complexity and functional demands of real-time applications have made adhering to response time deadlines challenging. For real-time applications of RISC-V, real-time performance analysis is required for various ISAs. In this paper, we analyze the real-time performance of RISC-V through two real-time approaches based on processor architectures. For real-time operating system (RTOS) applications, we adopted FreeRTOS and evaluated its performance on HiFive1 Rev B (RISC-V) and STM3240G-EVAL (ARM M). For real-time Linux, we utilized Linux with the Preempt-RT patch and tested its performance on VisionFive 2 (RISC-V), MIO5272 (x86-64), and Raspberry Pi 4 B (ARM A). Through these experiments, we examined the response times on the real-time mechanisms of each operating system. Additionally, in the Preempt-RT experiments, scheduling latencies were evaluated by means of the cyclictest. These are very important parameters for implementing real-time applications comprised of multi-tasking. Finally, in order to show the real-time capabilities of RISC-V practically, we implemented motion control of a six-axis collaborative robot, which was performed on the VisionFive 2. This implementation provided a comparative result of RISC-V’s performance against the x86-64 architecture. Ultimately, the results indicated that the real-time performance of RISC-V for real-time applications was feasible. A noticeable achievement of this research is its first implementation of an EtherCAT master on RISC-V designed for real-time applications. The successful implementation of the EtherCAT master on RISC-V shows real-time capabilities for a wide range of real-time applications.
Sum Rate Optimization for Multi-IRS-Aided Multi-BS Communication System Based on Multi-Agent
Authors: Fathy Maha , Fei Zesong , Guo Jing , Abood Mohamed Salah
Published: February 2024

Intelligent reflecting surface (IRS) is a revolutionizing technology for improving the spectral and energy efficiency of future wireless networks. In this paper, we consider a downlink large-scale system empowered by multi-IRS to aid communication between the multiple base stations (BSs) and multiple user equipment (UEs). We target maximizing the sum rate by jointly optimizing the UE association, the transmit powers of BSs, and the configurations of the IRS beamforming. Due to the applicability restrictions of conventional optimization methods and their high complexity with large-scale networks in dynamic environments, deep reinforcement (DRL) learning is adopted as an alternative approach to finding optimal solutions. First, we model the optimization problem as a multi-agent Markov decision problem (MAMDP). Then, because large-scale wireless networks are naturally complex and changeable, and because many entities interact and affect how the whole system works, it is important to use a multi-agent approach to understand the complex dependencies and relationships between the different parts. In order to solve the problem, we propose a cooperative multi-agent deep reinforcement learning (MADRL)-based algorithm that works for both continuous and discrete IRS phase shifts. Simulation results validate that the proposed algorithm surpasses iterative optimization benchmarks regarding both sum rate performance and convergence.
Enhancement of the Synaptic Performance of Phosphorus-Enriched, Electric Double-Layer, Thin-Film Transistors
Authors: Mah Dong-Gyun , Park Hamin , Cho Won-Ju
Published: February 2024

The primary objective of neuromorphic electronic devices is the implementation of neural networks that replicate the memory and learning functions of biological synapses. To exploit the advantages of electrolyte gate synaptic transistors operating like biological synapses, we engineered electric double-layer transistors (EDLTs) using phosphorus-doped silicate glass (PSG). To investigate the effects of phosphorus on the EDL and synaptic behavior, undoped silicate spin-on-glass-based transistors were fabricated as a control group. Initially, we measured the frequency-dependent capacitance and double-sweep transfer curves for the metal-oxide-semiconductor (MOS) capacitors and MOS field-effect transistors. Subsequently, we analyzed the excitatory post-synaptic currents (EPSCs), including pre-synaptic single spikes, double spikes, and frequency variations. The capacitance and hysteresis window characteristics of the PSG for synaptic operations were verified. To assess the specific synaptic operational characteristics of PSG-EDLTs, we examined EPSCs based on the spike number and established synaptic weights in potentiation and depression (P/D) in relation to pre-synaptic variables. Normalizing the P/D results, we extracted the parameter values for the nonlinearity factor, asymmetric ratio, and dynamic range based on the pre-synaptic variables, revealing the trade-off relationships among them. Finally, based on artificial neural network simulations, we verified the high-recognition rate of PSG-EDLTs for handwritten digits. These results suggest that phosphorus-based EDLTs are beneficial for implementing high-performance artificial synaptic hardware.
High-Payload Data-Hiding Scheme Based on Interpolation and Histogram Shifting
Authors: Weng Chi-Yao , Weng Hao-Yu , Shongwe Njabulo Sinethemba , Huang Cheng-Ta
Published: February 2024

The whole world is quickly transforming into an information world with the setup of smart cities, among other things. Information and other forms of privacy are therefore at risk due to the growing intelligence of hackers. Therefore, to enhance the security of information, a capacity-raising interpolation method based on histogram shifting is proposed. According to the experimental results, the capacity-raising interpolation method based on histogram shifting produces high-quality images with a significantly high embedding capacity, outperforming other methods. Apart from the already-mentioned advantages, the proposed method is reversible and can recover images in a lossless manner.
Overhead Power Line Damage Detection: An Innovative Approach Using Enhanced YOLOv8
Authors: Wu Yuting , Liao Tianjian , Chen Fan , Zeng Huiquan , Ouyang Sujian , Guan Jiansheng
Published: February 2024

This paper presents an enhanced version of YOLOv8 specifically designed for detecting damage in overhead power lines. Firstly, to improve the model’s robustness, an adaptive threshold mechanism is introduced that can dynamically adjust the detection threshold based on the brightness, contrast, and other characteristics of the input image. Secondly, a novel convolution method, GSConv, is adopted in the YOLOv8 framework, which balances the model’s running speed and accuracy. Finally, a lightweight network structure, Slim Neck, is introduced, effectively reducing the model’s complexity and computational load while maintaining good performance. These improvements enable our YOLOv8 model to achieve excellent performance in detecting ‘thunderbolt’ and ‘break’ types of cable damage. Experimental results show that the improved YOLOv8 network model has an average detection accuracy (mAP) of 90.2%, a recall rate of 91.6%, and a precision of 89.8% on the ‘Cable Damage Detection’ dataset from RoboFlow for ‘thunderbolt’. For ‘break’, the mAP is 86.5%, the recall rate is 84.1%, and the precision is 86.1%. Compared with the original YOLOv8 model, these indicators have been significantly improved, highlighting the high practical value and strong generalization ability of the proposed algorithm in detecting damage to overhead power lines. This also demonstrates the high practical value of the method in future research directions.
A P-Q Coordination Control Strategy of VSC-HVDC and BESS for LVRT Recovery Performance Enhancement
Authors: Wang Zhen , Wu Jialiang , Liu Ruixu , Shan Yu
Published: February 2024

Voltage source converter (VSC)-based multi-terminal direct current (MTDC) transmission technology has been a research focus, and the low-voltage ride-through (LVRT) and recovery in receiving-end systems is one of the major problems to consider. A coordinated control strategy for a VSC-MTDC system is proposed to improve the frequency and voltage dynamics in the receiving-end system during the LVRT and recovery processes. A battery energy storage system (BESS) plays a significant role in providing frequency and voltage support with its flexible power control capability. During the LVRT process, the BESS can provide reactive current injection and active current absorption to improve system stability in the AC side, and during the recovery process, an adaptive current limitation method is proposed for the BESS converter to dynamically adjust the active and reactive power outputs according to the frequency and voltage deviation severity. Meanwhile, the coordination of the sending-end systems and DC chopper can reduce the power output to avoid DC overvoltage during LVRT, and it can also provide frequency support to the receiving-end system with the DC voltage transmitting frequency information during the recovery process. A simulation was carried out on the MATLAB/Simulink platform, and a three-terminal VSC-MTDC system was used to validate the effectiveness of the proposed strategy.
Analysis of Variability in Electric Power Consumption: A Methodology for Setting Time-Differentiated Tariffs
Authors: Duarte Javier E. , Rosero-Garcia Javier , Duarte Oscar
Published: February 2024

The increasing concern for environmental conservation has spurred government initiatives towards energy efficiency. One of the key research areas in this regard is demand response, particularly focusing on differential pricing initiatives such as Time-of-Use (ToU). Differential tariffs are typically designed based on mathematical or statistical models analyzing historical electricity price and consumption data. This study proposes a methodology for identifying time intervals suitable for implementing ToU energy tariffs, achieved by analyzing electric power demand variability to estimate demand flexibility potential. The methodology transforms consumption data into variation via the coefficient of variation and, then, employs k-means data analysis techniques and the a priori algorithm. Tested with real data from smart meters in the Colombian electrical system, the methodology successfully identified time intervals with potential for establishing ToU tariffs. Additionally, no direct relationship was found between external variables such as socioeconomic level, user type, climate, and consumption variability. Finally, it was observed that user behavior concerning consumption variability could be categorized into two types of days: weekdays and non-working days.
Changes in the Characteristics of Pine Logging Residue during Storage in Forest Stands
Authors: Wieruszewski Marek , Kawalerczyk Jakub , Stuper-Szablewska Kinga , Walkiewicz Joanna , Lieskovský Martin , Jarzębski Maciej , Mirski Radosław
Published: February 2024

A significant amount of logging residue is produced during roundwood harvesting. Logs are often left in forest sites due to, for example, ecological or logistical aspects. Taking into account the fact that the number of studies focused on changes in the properties of the residue is very low, it was decided to conduct research on the effect of a three-year storage period in forest stands on the chemical properties and energy potential of the wood. The research design allowed for the determination of changes during up to three years of storage. The performed analysis showed a highly negative impact on the characteristics of the material. These adverse effects were probably caused by the activity of microorganisms such as fungi and bacteria, as evidenced by the increased concentrations of ergosterol and low-molecular-weight organic acids. Moreover, it was found that wood stored for three years was characterized by a lower cellulose content; an increased percentage of lignin; a reduced content of sterols (desmosterol, stigmasterol, lanosterol and β-sitosterol) and phenolic acids; and reduced antioxidant activity, as determined with the ABTS+ method. Storing logs also led to a reduction in the energy potential of the residues, as shown by a reduction in net and gross calorific value and an increased ash content.
Prediction of Electricity Generation Using Onshore Wind and Solar Energy in Germany
Authors: Walczewski Maciej Jakub , Wöhrle Hendrik
Published: February 2024

Renewable energy production is one of the most important strategies to reduce the emission of greenhouse gases. However, wind and solar energy especially depend on time-varying properties of the environment, such as weather. Hence, for the control and stabilization of electricity grids, the accurate forecasting of energy production from renewable energy sources is essential. This study provides an empirical comparison of the forecasting accuracy of electricity generation from renewable energy sources by different deep learning methods, including five different Transformer-based forecasting models based on weather data. The models are compared with the long short-term memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as a baseline. The accuracy of these models is evaluated across diverse forecast periods, and the impact of utilizing selected weather data versus all available data on predictive performance is investigated. Distinct performance patterns emerge among the Transformer-based models, with Autoformer and FEDformer exhibiting suboptimal results for this task, especially when utilizing a comprehensive set of weather parameters. In contrast, the Informer model demonstrates superior predictive capabilities for onshore wind power and photovoltaic (PV) power production. The Informer model consistently performs well in predicting both onshore wind and PV energy. Notably, the LSTM model outperforms all other models across various categories. This research emphasizes the significance of selectively using weather parameters for improved performance compared to employing all parameters and a time reference. We show that the suitability and performance of a prediction model can vary significantly, depending on the specific forecasting task and the data that are provided to the model.
Factors Influencing the Decision-Making Process at the End-of-Life Cycle of Onshore Wind Farms: A Systematic Review
Authors: Agra Neto João , González Mario Orestes Aguirre , Castro Rajiv Lucas Pereira de , Melo David Cassimiro de , Aiquoc Kezauyn Miranda , Santiso Andressa Medeiros , Vasconcelos Rafael Monteiro de , Souza Lucas Honorato de , Cabral Eric Lucas dos Santos
Published: February 2024

It is observed that the number of onshore wind farms that reach the end of their service life is continually increasing. The decision-making process that defines the future of the farm is a challenge for the owners. This systematic review aimed to identify which factors influence the decision-making process at the end-of-life cycle of onshore wind farms. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a research strategy was developed and used the Scopus, Web of Science and EMBASE databases. Initially, 2767 articles were identified, but, after double-blind screening, 26 articles were analyzed in full. The scarcity of studies on this topic and little elucidation are limitations of this review. The results include (i) a systematization of six options for decision making, (ii) thirteen factors influencing the decision-making process associated with categories of external factors (logistics and infrastructure aspects, regulatory aspects and public policies, national energy guidelines, the technological development of the sector); and internal factors (economic/financial, operational and environmental aspects). It is concluded that most of the publications consist of simulations and theoretical studies highlighting a bottleneck in experiences and feasible data to support decisions at the end of service life. It is highlighted that most of the studies showed that partial decommissioning with partial repowering, as well as total decommissioning, were the most feasible options for the end-of-life cycle, with aspects related to public policies and regulatory aspects, as well as environmental, operational and economic/financial aspects, being the most influential, especially due to the wake effect, operation and maintenance costs (OPEX) and the protection of guarantees and incentives for operation in a new operating cycle.
Integration of Floating Photovoltaic Panels with an Italian Hydroelectric Power Plant
Authors: Venturini Paolo , Gagliardi Gabriele Guglielmo , Agati Giuliano , Cedola Luca , Migliarese Caputi Michele Vincenzo , Borello Domenico
Published: February 2024

The potential of applying a floating PV (FPV) system in an Italian context (namely, Cecita dam and Mucone hydroelectric power plants) is studied. The additional PV energy production, as well as the effect of non-evaporated water on the productivity of the hydropower plant, is analyzed by varying the basin surface coverage. The simulations highlight that the amount of additional hydroelectricity is quite small if compared to the non-FPV system, reaching about 3.56% for 25% basin surface coverage. However, the annual PV energy production is noticeable even at low coverage values. The expected gain in electricity production in the case of 25% basin surface coverage with the FPV plant rises to 391% of that of the actual hydropower plant. This gain becomes even larger if a vertical axis tracking system is installed and the increase is about 436%. The economic analysis confirms that the production costs (USD/kWh) of FPV systems are comparable to those of land-based PV (LBPV) plants, becoming smaller in the case that a tracking system is installed. In particular, the best solution is the one with 15% coverage of the lake. In this case, the levelized cost of electricity for the LBPVs is 0.030 USD/kWh and for the FVPs, with and without tracking, it is equal to 0.032 and 0.029 USD/kWh, respectively.
Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN
Authors: Perez-Anaya Eduardo , Jaen-Cuellar Arturo Yosimar , Elvira-Ortiz David Alejandro , Romero-Troncoso Rene de Jesus , Saucedo-Dorantes Juan Jose
Published: February 2024

Energy generation through renewable processes has represented a suitable option for power supply; nevertheless, wind generators and photovoltaic systems can suddenly operate under undesired conditions, leading to power quality problems. In this regard, the development of condition-monitoring strategies applied to the detection of power quality disturbances becomes mandatory to ensure proper working conditions of electrical machinery. Therefore, in this work we propose a diagnosis methodology for detecting power quality disturbances by means of the continuous wavelet transform (CWT) and convolutional neural network (CNN). The novelty of this work lies in the image processing that allows us to precisely highlight the discriminant patterns through spectrograms into 2D images; the images are cropped and reduced to a standard size of 128x128 pixels to retain the most relevant information. Subsequently, the identification of six power quality disturbances is automatically performed by a convolutional neural network. The effectiveness of the proposed method is validated under a set of synthetic data as well as a real data set; the obtained results make the proposal suitable for being incorporated into the monitoring of power quality disturbances in renewable energy systems.
Geothermal Resource Exploration in Reshi Town by Integrated Geophysical Methods
Authors: Ahmed Ijaz , Liu Haifei , Chen Rujun , Ahmad Jawad , Shah Shahid Ali , Fahad Shah , Rahim Osama Abdul , Ullah Farid , Rui Li
Published: February 2024

Geothermal resources are a sustainable and valuable source of energy that offers considerable economic and social advantages. The present investigation centers on the accessibility of geothermal reservoirs in Reshi Town, Taoyuan County, Changde City, located in the Hunan Province of China. Geophysical exploration techniques are of paramount importance in the identification and exploration of geothermal resources. The present investigation utilized an integrated geophysical approach that incorporates induced polarization (IP), magnetotelluric (MT), and joint profile techniques. The primary objective of this study was to examine the distribution of formation lithology, subsurface electrical structures, karst fracture development zones, and the location and occurrence of deep and large thermal reservoirs in Reshi Town, Changde City. The research encompassed a comprehensive process that included the collection of data, its subsequent interpretation, inversion, and validation through drilling. The joint profile approach provided comprehensive data on fault structures within the study region. Using magnetotelluric sounding, areas with lower electrical resistance were found along lines L2, L3, and L4. This showed that thermal water reservoirs were underground. The induced polarization sounding method exhibited a distinct response to geothermal water, including minerals, suggesting the presence of a high-temperature geothermal reservoir, along line 1. Drilling operations carried out at two different locations, ZK01 on line L3 and ZK02 on line L4, confirmed the existence of underground hot water. The drilling findings have verified the existence of faults F3 and F4, which act as important channels for geothermal fluids. The present research offers a dependable geophysical foundation for the forthcoming development of geothermal resources in Reshi Town, and areas with similar geological conditions.
Study of the Thermal and Hydraulic Performance of Porous Block versus Gyroid Structure: Experimental and Numerical Approaches
Authors: Saghir Mohamad Ziad , Kerme Esa D. , Hajialibabei Mahsa , Rasheed Heba , Welsford Christopher , Al-Ketan Oraib
Published: February 2024

Various researchers in the field of engineering have used porous media for many years. The present paper studies heat enhancement using two different types of porous media. In the first type, porous metal foam media was used experimentally and numerically for heat extraction. The porous medium was replaced with a porous structure using the Gyroid model and the triply periodic minimum surfaces technique in the second type. The Darcy–Brinkman model combined with the energy equation was used for the first type, whereas Navier–Stokes equations with the energy equation were implemented for the second type. The uniqueness of this approach was that it treated the Gyroid as a solid structure in the model. The two types were tested for different heat fluxes and different flow rates. A comparison between the experimental measurements and the numerical solution provided a good agreement. By comparing the performance of the two types of structure, the Gyroid structure outperformed the metal foam for heat extraction and uniformity of the temperature distribution. Despite an 18% increase in the pressure drop in the presence of the Gyroid structure, the performance evaluation criteria for the Gyroid are more significant when compared to metal foam.
Morphological Trait Correlations, Gonadal Development Characteristics and Pleopod Nutrient Compositions of the Whelk Volutharpa perryi perryi
Authors: Yu Linghui , Yin Weijun , Han Senrong , Zhao Tanjun , Hao Zhenlin , Yin Donghong , Zhan Yaoyao , Chang Yaqing
Published: February 2024

The aim of this study was to clarify the “morphological-trait–body weight” correlation, gonadal development characteristics, and pleopod (main edible part) nutrient composition of the whelk (Volutharpa perryi perryi). Live body mass (BM), soft tissue mass (STM), and eight other morphological traits of the whelk were measured, and path coefficients, correlation indices (R2), and coefficients of determination were then calculated. Gonadal development characteristics were investigated by histological observation. Pleopod nutrient composition was analyzed by standard biochemical assays. The results indicated that (1) shell aperture width (SAW) and body whorl height (BWH) were positively correlated with both live BM and STM (p < 0.01), and shell height (SH) was positively correlated with both live BM and STM (p < 0.01) in male whelks; (2) similar gonadal development characteristics were observed in both female and male whelks; and (3) pleopod nutrient composition was consistent in both female and male whelks, whereas sex-specific variation in pleopod nutrient content was observed in the whelks. The observations in this study will provide theoretical support for the development of the whelk aquaculture industry.
The Statistical Mechanics of Ideal Magnetohydrodynamic Turbulence and a Solution of the Dynamo Problem
Authors: Shebalin John V.
Published: February 2024

We review and extend the theory of ideal, homogeneous, incompressible, magnetohydrodynamic (MHD) turbulence. The theory contains a solution to the ‘dynamo problem’, i.e., the problem of determining how a planetary or stellar body produces a global dipole magnetic field. We extend the theory to the case of ideal MHD turbulence with a mean magnetic field that is aligned with a rotation axis. The existing theory is also extended by developing the thermodynamics of ideal MHD turbulence based on entropy. A mathematical model is created by Fourier transforming the MHD equations and dynamical variables, resulting in a dynamical system consisting of the independent Fourier coefficients of the velocity and magnetic fields. This dynamical system has a large but finite-dimensional phase space in which the phase flow is divergenceless in the ideal case. There may be several constants of the motion, in addition to energy, which depend on the presence, or lack thereof, of a mean magnetic field or system rotation or both imposed on the magnetofluid; this leads to five different cases of MHD turbulence that must be considered. The constants of the motion (ideal invariants)—the most important being energy and magnetic helicity—are used to construct canonical probability densities and partition functions that enable ensemble predictions to be made. These predictions are compared with time averages from numerical simulations to test whether or not the system is ergodic. In the cases most pertinent to planets and stars, nonergodicity is observed at the largest length-scales and occurs when the components of the dipole field become quasi-stationary and dipole energy is directly proportional to magnetic helicity. This nonergodicity is evident in the thermodynamics, while dipole alignment with a rotation axis may be seen as the result of dynamical symmetry breaking, i.e., ‘broken ergodicity’. The relevance of ideal theoretical results to real (forced, dissipative) MHD turbulence is shown through numerical simulation. Again, an important result is a statistical solution of the ‘dynamo problem’.
Iron Content of Wheat and Rice in Australia: A Scoping Review
Authors: Cheung Yee Lui , Zheng Belinda , Rehman Yumna , Zheng Zi Yin Joanne , Rangan Anna
Published: February 2024

With a shift towards plant-based diets for human and planetary health, monitoring the mineral content of staple crops is important to ensure population nutrient requirements can be met. This review aimed to explore changes in the iron content of unprocessed wheat and rice in Australia over time. A comprehensive systematic search of four electronic databases and the gray literature was conducted. A total of 25 papers published between 1930 and 2023 that measured the iron content of unprocessed wheat and rice were included. Triticum aestivum was the most common wheat type studied, including 26 cultivars; iron content ranged from 40 to 50 µg/g in the 1930s and 1970s and was more variable after this time due to the introduction of modern cultivars, with most values between 25 and 45 µg/g. The iron content of rice (Oryza sativa) was more consistent at 10–15 µg/g between the 1980s and 2020s. Variations over the years may be attributed to environmental, biological, and methodological factors but these were not well documented across all studies, limiting the interpretation of findings. As the number of individuals following plant-based diets continues to rise, the ongoing monitoring of the mineral content in commonly consumed plant-based foods is warranted.
Microbiological and Toxicological Investigations on Bivalve Molluscs Farmed in Sicily
Authors: Castello Annamaria , Alio Vincenzina , Cammilleri Gaetano , Sciortino Sonia , Macaluso Andrea , Ferrantelli Vincenzo , Dall’Ara Sonia , Pino Fiorella , Servadei Irene , Oliveri Giuseppa , Costa Antonella
Published: February 2024

Bivalves can concentrate biological and chemical pollutants, causing foodborne outbreaks whose occurrence is increasing, due to climatic and anthropic factors that are difficult to reverse, hence the need for improved surveillance. This study aimed to evaluate the hygienic qualities of bivalves sampled along the production and distribution chain in Sicily and collect useful data for consumer safety. Bacteriological and molecular analyses were performed on 254 samples of bivalves for the detection of enteropathogenic Vibrio, Arcobacter spp., Aeromonas spp., Salmonella spp., and beta-glucuronidase-positive Escherichia coli. A total of 96 out of 254 samples, collected in the production areas, were processed for algal biotoxins and heavy metals detection. Bacterial and algal contaminations were also assessed for 21 samples of water from aquaculture implants. Vibrio spp., Arcobacter spp., Aeromonas hydrophila, Salmonella spp., and Escherichia coli were detected in 106/254, 79/254, 12/254, 16/254, and 95/254 molluscs, respectively. A total of 10/96 bivalves tested positive for algal biotoxins, and metals were under the legal limit. V. alginolyticus, A. butzleri, and E. coli were detected in 5, 3, and 3 water samples, respectively. Alexandrium minutum, Dinophysis acuminata, Lingulodinium polyedra, and Pseudonitzschia spp. were detected in water samples collected with the biotoxin-containing molluscs. Traces of yessotoxins were detected in molluscs from water samples containing the corresponding producing algae. Despite the strict regulation by the European Commission over shellfish supply chain monitoring, our analyses highlighted the need for efficiency improvement.