bluuebunny/crossref_metadata_2025 huggingface dataset:
A van der Waals Model of Solvation Thermodynamics
Authors: Tortorella Attila , Graziano Giuseppe
Published: August 2024

Exploiting the van der Waals model of liquids, it is possible to derive analytical formulas for the thermodynamic functions governing solvation, the transfer of a solute molecule from a fixed position in the ideal gas phase to a fixed position in the liquid phase. The solvation Gibbs free energy change consists of two contributions: (a) the high number density of all liquids and the repulsive interactions due to the basic fact that each molecule has its own body leading to the need to spend free energy to produce an appropriate cavity to contain the solute molecule; (b) the ubiquitous intermolecular attractive interactions lead to a gain in free energy for switching-on attractions between the solute molecule and neighboring liquid molecules. Also the solvation entropy change consists of two contributions: (a) there is an entropy loss in all liquids because the cavity presence limits the space accessible to liquid molecules during their continuous translations; (b) there is an entropy gain in all liquids, at room temperature, due to the liquid structural reorganization as a response to the perturbation represented by solute addition. The latter entropy contribution is balanced by a corresponding enthalpy term. The scenario that emerged from the van der Waals model is in qualitative agreement with experimental results.
A Federated Adversarial Fault Diagnosis Method Driven by Fault Information Discrepancy
Authors: Sun Jiechen , Zhou Funa , Chen Jie , Wang Chaoge , Hu Xiong , Wang Tianzhen
Published: August 2024

Federated learning (FL) facilitates the collaborative optimization of fault diagnosis models across multiple clients. However, the performance of the global model in the federated center is contingent upon the effectiveness of the local models. Low-quality local models participating in the federation can result in negative transfer within the FL framework. Traditional regularization-based FL methods can partially mitigate the performance disparity between local models. Nevertheless, they do not adequately address the inconsistency in model optimization directions caused by variations in fault information distribution under different working conditions, thereby diminishing the applicability of the global model. This paper proposes a federated adversarial fault diagnosis method driven by fault information discrepancy (FedAdv_ID) to address the challenge of constructing an optimal global model under multiple working conditions. A consistency evaluation metric is introduced to quantify the discrepancy between local and global average fault information, guiding the federated adversarial training mechanism between clients and the federated center to minimize feature discrepancy across clients. In addition, an optimal aggregation strategy is developed based on the information discrepancies among different clients, which adaptively learns the aggregation weights and model parameters needed to reduce global feature discrepancy, ultimately yielding an optimal global model. Experiments conducted on benchmark and real-world motor-bearing datasets demonstrate that FedAdv_ID achieves a fault diagnosis accuracy of 93.09% under various motor operating conditions, outperforming model regularization-based FL methods by 17.89%.
Quantum Dynamical Interpretation of the Mean Strategy
Authors: Wang Fang , Wang Peng , Jiao Yuwei
Published: August 2024

The method of quantum dynamics is employed to investigate the mean strategy in the swarm intelligence algorithm. The physical significance of the population mean point is explained as the location where the optimal solution with the highest likelihood can be found once a quantum system has reached a ground state. Through the use of the double well function and the CEC2013 test suite, controlled experiments are conducted to perform a comprehensive performance analysis of the mean strategy. The empirical results indicate that implementing the mean strategy not only enhances solution diversity but also yields accurate, efficient, stable, and effective outcomes for finding the optimal solution.
Competence Profile of the Intra-Hospital Emergency Team Nurse: A Scoping Review
Authors: Rabiais Isabel , Pereira Lisete , Moniz Nuno , Almeida António , Monteiro Paulo , Severino Sandy , Sousa Luís , José Helena
Published: August 2024

Background: Nurses in intra-hospital emergency teams are required to be competent in responding to situations of clinical deterioration and emergency. This study aimed to map the extent, variety, and nature of the literature on the competences of the intra-hospital emergency teams nurse. Methods: A scoping review based on the principles of the Joanna Briggs Institute. The search of the databases was conducted in July and August 2023, and the search strategy was adjusted. The analysis of the relevance of the articles, data extraction, and synthesis were conducted by two reviewers. Discrepancies were resolved by a third reviewer. The data were extracted using the competency outcomes and performance assessment (COPA) model. Results: Fourteen results were analyzed, and the competences organized by the domains of the COPA model which are considered by some authors to be advanced or specialized are as follows: assessment and intervention, communication, critical thinking, teaching, human caring relationships, management, leadership, and knowledge integration. Other competences, such as self-confidence, ability to remain calm, concentration, quick reaction, and a sense of humor, were mentioned. Conclusions: The results highlighted the competences of the intra-hospital emergency team nurse. This knowledge could contribute to educational programs and shape the competency profile of intra-hospital emergency team nurses.
Enhancing Women’s Entrepreneurship in Rural Ecuador: A Study Protocol Aligned with the Sustainable Development Goals
Authors: Sánchez-Riofrío Angélica-María , Faytong-Haro Marco , Roa Olga , Valderrama-Álvarez Jonathan
Published: August 2024

Women entrepreneurs in rural Ecuador face significant obstacles, including limited access to education, financial services, and business networks. Despite their vital role in the economy, gender inequalities hinder their success. This study protocol aims to evaluate the impact of a tailored business training program designed to empower rural women entrepreneurs and promote sustainable economic development. Aligned with the Sustainable Development Goals (SDGs) of Quality Education (Goal 4), Gender Equality (Goal 5), and Decent Work and Economic Growth (Goal 8), the program will assess improvements in women’s agency, confidence, and business performance. Key results include a 54% increase in perceived self-efficacy and a 200% increase in locus of control observed in the pre-pilot phase, indicating enhanced decision-making capacities and program effectiveness. Expected improvements in business performance will be measured by sales figures and financial growth, with anticipated positive impacts on SDG 8. The program will also track participation rates, with high enrollment and completion rates contributing to SDG 4. Additionally, financial stability and the number of engaged suppliers will be monitored, supporting SDG 8. By incorporating additional structural interventions, the study will offer insights into enhancing the empowerment of rural women entrepreneurs, creating a holistic impact that fosters both individual and community development.
Memory-Based Dynamic Bayesian Networks for Learner Modeling: Towards Early Prediction of Learners’ Performance in Computational Thinking
Authors: Hooshyar Danial , Druzdzel Marek J.
Published: August 2024

Artificial intelligence (AI) has demonstrated significant potential in addressing educational challenges in digital learning. Despite this potential, there are still concerns about the interpretability and trustworthiness of AI methods. Dynamic Bayesian networks (DBNs) not only provide interpretability and the ability to integrate data-driven insights with expert judgment for enhanced trustworthiness but also effectively process temporal dynamics and relationships in data, crucial for early predictive modeling tasks. This research introduces an approach for the temporal modeling of learners’ computational thinking abilities that incorporates higher-order influences of latent variables (hereafter referred to as memory of the model) and accordingly predicts learners’ performance early. Our findings on educational data from the AutoThinking game indicate that when using only first-order influences, our proposed model can predict learners’ performance early, with an 86% overall accuracy (i.e., time stamps 0, 5, and 9) and a 94% AUC (at the last time stamp) during cross-validation and 91% accuracy and 98% AUC (at the last time stamp) in a holdout test. The introduction of higher-order influences improves model accuracy in both cross-validation and holdout tests by roughly 4% and improves the AUC at timestamp 0 by roughly 2%. This suggests that integrating higher-order influences into a DBN not only potentially improves the model’s predictive accuracy during the cross-validation phase but also enhances its overall and time stamp-specific generalizability. DBNs with higher-order influences offer a trustworthy and interpretable tool for educators to foresee and support learning progression.
Gamification for Teaching Integrated Circuit Processing in an Introductory VLSI Design Course
Authors: Diéguez Ángel , Canals Joan , Moreno Sergio , Vilà Anna
Published: August 2024

Gamification is being incorporated into university classrooms due to its educational benefits for students learning, including encouraging student behavior and engagement, and consequently improving learning outcomes. Despite gamification being increasingly used in education, little has been developed related to Very-Large-Scale Integration (VLSI). In this article, we describe two different gamification experiences applied to integrated circuit processing and design in an introductory VLSI design course for Electronic Engineers. While gamification in universities is still not very mature and our experience spans only two academic years, we observed that, with the practice of gamifying part of our course, the topics treated in games were profoundly learned and the experience was very positive in every aspect of the teaching–learning process.
Perceptions and Use of AI Chatbots among Students in Higher Education: A Scoping Review of Empirical Studies
Authors: Schei Odin Monrad , Møgelvang Anja , Ludvigsen Kristine
Published: August 2024

With the recent arrival of publicly available AI chatbots like ChatGPT, Copilot, Gemini, and Claude follows a need for knowledge about how students in higher education perceive and use these tools, and what this might mean for their learning processes. This scoping review analyzes 24 empirical articles published between 1 January 2022 and 5 September 2023 on students’ perceptions and use of AI chatbots in higher education. The articles were reviewed using a five-stage scoping review methodology. The findings underscore a global research interest in how students engage with AI chatbots, which is especially pronounced in Asia. The studies span diverse disciplines, with a predominance in science, technology, engineering, and mathematics disciplines. The empirical findings reveal that students perceive AI chatbots as highly useful and motivating as personal task assistants and for getting immediate feedback and help with writing, coding, and academic tasks. However, students are concerned about the accuracy and reliability of the responses from the chatbots, as well as potential negative impacts on their learning processes, critical thinking, discipline, and creativity. The purpose-driven use of AI chatbots among students and their potentially positive influence on motivation and learning processes offer insights for educators and policymakers. Our research concludes that while positive attitudes, perceptions, and critical use prevail, addressing students’ concerns is crucial for responsible AI integration in higher education.
Forgiveness Education in Fourth- through Eighth-Grade Classrooms: Development and Evaluation of a New Forgiveness Curriculum
Authors: Toussaint Loren , Kueny Angela , Stevens Allison
Published: August 2024

The purpose of this study was to examine the efficacy of a new middle-school-classroom forgiveness curriculum in promoting forgiveness and reducing anger. The students in this study were 153 fourth through eighth graders in three regional parochial schools. The students were assigned to forgiveness education or control groups in the fall semester and reassigned to the opposite group in the spring semester. Before-and-after assessments of forgiveness and anger were collected in both the fall and spring semesters. The quantitative results showed that levels of forgiveness increased as expected in both semesters, but anger did not change in expected ways. The qualitative results showed that the children’s conceptualizations of the forgiveness process aligned closely with the core forgiveness curriculum content. The results suggest that meaningful work can be accomplished to promote forgiveness in the middle school classroom using this newly developed, efficient, and effective curriculum that can be implemented by teachers and staff.
AI Eye-Tracking Technology: A New Era in Managing Cognitive Loads for Online Learners
Authors: Šola Hedda Martina , Qureshi Fayyaz Hussain , Khawaja Sarwar
Published: August 2024

Eye-tracking technology has emerged as a valuable tool for evaluating cognitive load in online learning environments. This study investigates the potential of AI-driven consumer behaviour prediction eye-tracking technology to improve the learning experience by monitoring students’ attention and delivering real-time feedback. In our study, we analysed two online lecture videos used in higher education from two institutions: Oxford Business College and Utrecht University. We conducted this analysis to assess cognitive demands in PowerPoint presentations, as this directly affects the effectiveness of knowledge dissemination and the learning process. We utilised a neuromarketing-research consumer behaviour eye-tracking AI prediction software called ‘Predict’, which employs an algorithm constructed on the largest neuroscience database (comprising previous studies conducted on live participants n = 180,000 with EEG and eye-tracking data). The analysis for this study was carried out using the programming language R, followed by a series of t-tests for each video and Pearson’s correlation tests to examine the relationship between ocus and cognitive demand. The findings suggest that AI-powered eye-tracking systems have the potential to transform online learning by providing educators with valuable insights into students’ cognitive processes and enabling them to optimise instructional materials for improved learning outcomes.
Performance Analysis of a 50 MW Solar PV Installation at BUI Power Authority: A Comparative Study between Sunny and Overcast Days
Authors: Yakubu Rahimat Oyiza , Ijeoma Muzan Williams , Yusuf Hammed , Abdulazeez Abdulazeez Alhaji , Acheampong Peter , Carbajales-Dale Michael
Published: August 2024

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation’s dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system’s performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year’s worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month’s sunniest and cloudiest days. The goal is to aid in predicting the system’s output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant’s performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant’s performance from deteriorating. Optimizing a solar PV system’s design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability.
Inkjet Printing of a Gate Insulator: Towards Fully Printable Organic Field Effect Transistor
Authors: Bai Huiwen , Voyles Richard M. , Nawrocki Robert A.
Published: August 2024

In this work, a gate insulator poly (4-vinylphenol) (PVP) of an organic field effect transistor (OFET) was deposited using an inkjet printing technique, realized via a high printing resolution. Various parameters, including the molecular weight of PVP, printing direction, printing voltage, and drop frequency, were investigated to optimize OFET performance. Consequently, PVP with a smaller molecular weight of 11 k and a printing direction parallel to the channel, a printing voltage of 18 V, and a drop frequency of 10 kHz showed the best OFET performance. With a direct ink writing-printed organic semiconductor, this work paves the way for fully inkjet-printed OFETs.
Robust PI-PD Controller Design: Industrial Simulation Case Studies and a Real-Time Application
Authors: Alyoussef Fadi , Kaya Ibrahim , Akrad Ahmad
Published: August 2024

PI-PD controllers have superior performance compared to traditional PID controllers, especially for controlling unstable and integrating industrial processes with time delays. However, computing the four tuning parameters of this type of controller is not an easy task. Recently, there has been significant interest in determining the tuning rules for PI-PD controllers that utilize the stability region. Currently, most tuning rules for the PI-PD controller are presented graphically, which can be time-consuming and act as a barrier to their industrial application. There is a lack of analytical tuning guidelines in the literature to address this shortfall. However, the existing analytical tuning guidelines do not consider a rigorous design approach. This work proposes new robust analytical tuning criteria based on predefined gain and phase margin bounds, as well as the centroid of the stability region. The proposed method has been tested using various simulation studies related to a DC–DC buck converter, a DC motor, and a heat exchanger. The results indicate that the proposed tuning rules exhibit strong performance against parameter uncertainty with minimal overshoots. Furthermore, the suggested technique for simultaneous control of yaw and pitch angles has been tested in a real-time application using the twin rotor multi-input multi-output system (TRMS). Real-time results indicate that, compared to other methods under investigation, the suggested approach provides nearly minimal overshoots.
Horizontally Scalable Implementation of a Distributed DBMS Delivering Causal Consistency via the Actor Model
Authors: Camilleri Carl , Vella Joseph G. , Nezval Vitezslav
Published: August 2024

Causal Consistency has been proven to be the strongest type of consistency that can be achieved in a fault-tolerant, distributed system. This paper describes an implementation of D-Thespis, which is an approach that employs the actor mathematical model of concurrent computation to establish a distributed middleware that enforces causal consistency on a widely used relational database management system (RDBMS). D-Thespis prioritises developer experience by encapsulating the intricacies of causal consistency behind an interface that is accessible over standard REST protocol. Here, we discuss several novel results. Firstly, we define a method that builds a causally consistent DBMS supporting elastic horizontal scalability. Secondly, we deliver a cloud-native implementation of the middleware and provide results and insights on 6804 benchmark configurations executed on our implementation while running on a public cloud infrastructure across several data centres. The evaluation concerns transaction processing performance, an evaluation of our implementation’s update visibility latency, and a memory profiling exercise. The results of our evaluation show that under a transactional workload, a single-node installation of our implementation of D-Thespis is 1.5 times faster than a relational DBMS running serialisable transaction processing, while the performance of the middleware can improve by more than three times when scaled horizontally within the same data centre. Our study of the memory profile of the D-Thespis implementation shows that the system distributes its memory requirements evenly across all the available machines, as it is scaled horizontally. Finally, we also illustrate how our middleware propagates data changes across geographically-distributed infrastructures in a timely manner: our tests show that most of the effects of data change operations in one data centre are available in a remote data centre within less than 300 ms over and above the network round trip latency between the two data centres.
DNN Adaptive Partitioning Strategy for Heterogeneous Online Inspection Systems of Substations
Authors: Fu Qincui , Deng Fangming , Xue Xianfa , Zeng Jianjun , Wei Baoquan
Published: August 2024

With the explosive development of power edge equipment and the continuous improvement in power inspection performance, the requirements of substations and terminal equipment, such as drones with limited resources, cannot meet the strict delay and energy consumption requirements. This paper proposes an adaptive partitioning strategy for heterogeneous substation inspection systems. First, a layer delay prediction model and layer energy consumption prediction model are established on each heterogeneous node, and nonlinear characteristics related to delay and energy consumption are trained. On this basis, a deep neural network (DNN) hybrid partitioning strategy is proposed. The DNN task is divided into synchronous cooperative reasoning between terminal devices and multi-heterogeneous edge nodes. The experimental results show that the average absolute percentage error (MAPE) of the delay model was reduced by 31.49% on average. On drones and mobile edge nodes, the energy consumption model MAPE reduced the average by 21.92%, and the DNN end-to-end latency was reduced by 31.48%. The total cost of the system was reduced and the efficiency of UAV inspection was improved.
A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms
Authors: Shen Minxian , Lv Tianyi , Liu Yi , Zhang Jialiang , Ju Mingye
Published: August 2024

Images captured under adverse weather conditions often suffer from blurred textures and muted colors, which can impair the extraction of reliable information. Image defogging has emerged as a critical solution in computer vision to enhance the visual quality of such foggy images. However, there remains a lack of comprehensive studies that consolidate both traditional algorithm-based and deep learning-based defogging techniques. This paper presents a comprehensive survey of the currently proposed defogging techniques. Specifically, we first provide a fundamental classification of defogging methods: traditional techniques (including image enhancement approaches and physical-model-based defogging) and deep learning algorithms (such as network-based models and training strategy-based models). We then delve into a detailed discussion of each classification, introducing several representative image fog removal methods. Finally, we summarize their underlying principles, advantages, disadvantages, and give the prospects for future development.
Investigation of Wall Boiling Closure, Momentum Closure and Population Balance Models for Refrigerant Gas–Liquid Subcooled Boiling Flow in a Vertical Pipe Using a Two-Fluid Eulerian CFD Model
Authors: Shaparia Nishit , Pelay Ugo , Bougeard Daniel , Levasseur Aurélien , François Nicolas , Russeil Serge
Published: August 2024

The precise design of heat exchangers in automobile air conditioning systems for more sustainable electric vehicles requires an enhanced assessment of CFD mechanistic models for the subcooled boiling flow of pure eco-friendly refrigerant. Computational Multiphase Flow Dynamics (CMFDs) relies on two-phase closure models to accurately depict the complex physical phenomena involved in flow boiling. This paper thoroughly examines two-phase CMFD flow boiling, incorporating sensitivity analyses of critical parameters such as boiling closures, momentum closures, and population balance models. Three datasets from the DEBORA experiment, involving vertical pipes with subcooled boiling flow of refrigerant at three different pressures and varying levels of inlet liquid subcooling, are used for comparison with CFD simulations. This study integrates nucleate site density and bubble departure diameter models to enhance wall boiling model accuracy. It aims to investigate various interfacial forces and examines the S-Gamma and Adaptive Multiple Size-Group (A-MuSiG) size distribution methods for their roles in bubble break up and coalescence. These proposed approaches demonstrate their efficacy, contributing to a deeper understanding of flow boiling phenomena and the development of more accurate models. This investigation offers valuable insights into selecting the most appropriate sub-closure models for both boiling closure and momentum closure in simulating boiling flows.
Precision Control for Room Temperature of Variable Air Volume Air-Conditioning Systems with Large Input Delay
Authors: Shi Jinfeng , Liu Haoyang , Yang Xiaowei
Published: August 2024

A large input delay, parametric uncertainties, matched disturbances and mismatched disturbances exist extensively in variable air volume air-conditioning systems, which can deteriorate the control performance of the room temperature and even destabilize the system. To address this problem, an adaptive-gain command filter control framework for the room temperature of variable air volume air-conditioning systems is exploited. Through skillfully designing an auxiliary system, both the filtered error and the input delay can be compensated concurrently, which can attenuate the effect of the filtered error and the input delay on the control performance of the room temperature. Then, a smooth nonlinear term with an adjusted gain is introduced into the control framework to compensate for parametric uncertainties, matched disturbances and mismatched disturbances, which relieves the conservatism of the controller gain selection. With the help of the Lyapunov theory, both the boundedness of all the system signals and the asymptotic tracking performance for the room temperature can be assured with the presented controller. Finally, the contrastive simulation results demonstrate the validity of the developed method.
Design of a High-Precision Self-Balancing Potential Transformer Calibrator
Authors: Li Mengjia , Zhou Feng , Jiang Jiandong , Liu Hao , Xiong Bo , Wang Xue , Yao Teng
Published: August 2024

Potential transformers are vital for measuring and protecting the power grid. Their accuracy and reliability directly impact the stability and security of the power system. To address the issues with traditional high-precision potential transformer calibrators, such as cumbersome operation and low efficiency, a high-precision potential transformer calibrator has been developed. The calibrator is based on an embedded system architecture of FPGA and ARM. It uses a high-precision current comparator along with feedback control technology. By monitoring and adjusting the error feedback voltage, it can perform the automated calibration of potential transformers with an accuracy class of 0.0001. The measurement ranges from 0.00001% to 200.0%. This study can be adapted to meet the development needs of modern digital measurement systems.
Study of the Dynamics of a Single Bubble
Authors: Pavlenko Anatoliy , Koshlak Hanna
Published: August 2024

The behaviour of bubbles in cavitation and boiling processes is determined by the thermodynamic parameters of the two-phase medium and the intensity of heat and mass transfer, which affect the final dynamic effects. In this review, we analyse the influences of these factors on bubble behaviour, as described in existing mathematical models. In particular, we analyse the physical processes that govern bubble behaviour, the influence of mass transfer, vapor and liquid temperature, vapour, and liquid pressure on the inertial and dynamic stages of development. In conclusion, we summarize the problems associated with modelling, the accuracy of numerical predictions, and propose directions for further research.
Estimation of the Availability of Electrical Energy from the Thermal Energy Extracted by Thermoelectric Modules: Case Study in Monterrey, México
Authors: Montaño-Moreno Julio C. , Quiroga-Ocaña Guillermo , Noriega José R. , Félix-Herrán Luis C. , Granados-Monge Rodolfo , Benitez Victor H. , Lozoya-Santos Jorge de-J.
Published: August 2024

This research proposes the utilization of Peltier modules to convert electrical energy from thermal energy to show its potential as a renewable energy source for residential and commercial application. The study, whose results are presented in this manuscript, was conducted in the city of Monterrey, located in the northeast of Mexico. The energy source was tested and analyzed utilizing a set of statistical metrics and further comparison against experimental test results. The Distrito Tec area in Monterrey city is an academic complex and it was chosen for this study, which consisted of the indirect measurement of the average annual heat energy stored within the buildings’ cement structure. The aim was to obtain the annual accumulated electrical power in Wh per year that Peltier modules could provide in Distrito Tec, which is located in a city with solar irradiation levels above the world average. The proposal in this paper could encourage further investigation regarding this energy that is currently waste heat. More specifically, the results of this research highlight the importance of thermoelectric modules and seek to motivate research to improve their properties and make them more efficient and more viable as well. Thermoelectric modules have the potential to be part of the solution to sustainable development as presented in the United Nations SDG-7—ensure access to affordable, reliable, sustainable and modern energy for all.
Optimizing Energy Efficiency in Deep-Sea Mining: A Study on Swirling Flow Transportation of Double-Size Mineral Particles
Authors: Chen Xiaodong , Chen Yaoyao , Wu Xu , Zhu Peilin , Yang Lele
Published: August 2024

Deep-sea minerals are regarded as the most economically viable and promising mineral resource. Vertical hydraulic lifting represents one of the most promising methods for deep-sea mining lifting systems. To mitigate the potential for clogging due to the aggregation of particles in vertical pipe transport during deep-sea mining operations, this paper employs numerical simulations utilizing the computational fluid dynamics and discrete element method (CFD-DEM) model to investigate the swirling flow transportation of mineral particles. The characteristics of the swirling flow field and the motion law of double-size particles at different swirling ratios are investigated. The findings demonstrate that, in comparison to axial transport within the pipeline, the particle movement observed in swirling flow transport exhibits an upward spiral trajectory. This phenomenon facilitates the orderly movement of particles, thereby enhancing the fluidization of particles within the pipeline. An increase in the swirling ratio (SR) has a considerable impact on the velocity within the pipe. The tangential velocity distribution undergoes a gradual transition from centrosymmetric to non-centrosymmetric as the distance from the inlet increases. An increase in the SR results in an enhanced aggregation of particles at the wall, accompanied by a notable rise in the local particle concentration. The value of SR = 0.3 represents a critical threshold. When SR exceeds this value, the distribution of particles in the cross-section reaches a relatively stable state, rendering it challenging to further alter the distribution and concentration of particles, even if the SR is augmented. Furthermore, the maximum local particle concentration in the vicinity of the wall tends to be stable. These results provide valuable insights into vertical pipe swirling flow transport for deep-sea mining.
A Blockchain and PKI-Based Secure Vehicle-to-Vehicle Energy-Trading Protocol
Authors: Hossain Md Sahabul , Rodine Craig , Tsiropoulou Eirini Eleni
Published: August 2024

With the increasing awareness for sustainable future and green energy, the demand for electric vehicles (EVs) is growing rapidly, thus placing immense pressure on the energy grid. To alleviate this, local trading between EVs should be encouraged. In this paper, we propose a blockchain and public key infrastructure (PKI)-based secure vehicle-to-vehicle (V2V) energy-trading protocol. A permissioned blockchain utilizing the proof of authority (PoA) consensus and smart contracts is used to securely store data. Encrypted communication is ensured through transport layer security (TLS), with PKI managing the necessary digital certificates and keys. A multi-leader, multi-follower Stackelberg game-based trade algorithm is formulated to determine the optimal energy demands, supplies, and prices. Finally, we propose a detailed communication protocol that ties all the components together, enabling smooth interaction between them. Key findings, such as system behavior and performance, scalability of the trade algorithm and the blockchain, smart contract execution costs, etc., are presented through numerical results by implementing and simulating the protocol in various scenarios. This work not only enhances local energy trading among EVs, encouraging efficient energy usage and reducing burden on the power grid, but also paves a way for future research in sustainable energy management.
Experimental Study of Kinetic to Thermal Energy Conversion with Fluid Agitation for a Wind-Powered Heat Generator
Authors: Javed Muhammad Haseeb , Duan Xili
Published: August 2024

In this paper, a heat generator with fluid agitation is developed and experimentally studied. This heat generator can convert kinetic energy from a wind turbine directly to thermal energy through the process of viscous dissipation—a process achieved through the agitation of the working fluid inside a container. In the experimental study, an electric motor (instead of a wind turbine) was used to provide the kinetic energy input to the heat generator. The torque, rotational speed, and temperature rise in the fluid were measured. Using the measured quantities, the efficiency of kinetic energy to sensible heat conversion was calculated. Experiments were conducted to investigate the effects of different impellers, rotational speeds, and working fluids, including distilled water, ethylene glycol (EG), and their respective nanofluids, with Al2O3 nanoparticles at different concentrations. The study also found that the temperature rise in fluids due to viscous dissipation was influenced by the specific heat of the fluid, suggesting that the heat generator can be optimized for energy storage with high-specific-heat fluids, such as water, or for achieving a higher temperature rise with low-specific-heat fluids, such as ethylene glycol. The experimental results indicated that the heat generator was up to 90% efficient in converting kinetic energy to thermal energy. The study revealed that, for constant power input, the heat dissipation rate depends solely on the vessel’s geometry, not the fluid properties. Optimizing the impeller design and baffles within the vessel is crucial for maximizing power input. For applications, a wind turbine can power this heat generator to provide heat to a house or a commercial building.
A Photovoltaic Fault Diagnosis Method Integrating Photovoltaic Power Prediction and EWMA Control Chart
Authors: Su Jun , Zeng Zhiyuan , Tang Chaolong , Liu Zhiquan , Li Tianyou
Published: August 2024

The inevitability of faults arises due to prolonged exposure of photovoltaic (PV) power plants to intricate environmental conditions. Therefore, fault diagnosis of PV power plants is crucial to ensure the continuity and reliability of power generation. This paper proposes a fault diagnosis method that integrates PV power prediction and an exponentially weighted moving average (EWMA) control chart. This method predicts the PV power based on meteorological factors using the adaptive particle swarm algorithm-back propagation neural network (APSO-BPNN) model and takes its error from the actual value as a control quantity for the EWMA control chart. The EWMA control chart then monitors the error values to identify fault types. Finally, it is verified by comparison with the discrete rate (DR) analysis method. The results showed that the coefficient of determination of the prediction model of the proposed method reached 0.98. Although the DR analysis can evaluate the overall performance of the inverter and identify the faults, it often fails to point out the specific location of the faults accurately. In contrast, the EWMA control chart can monitor abnormal states such as open and short circuits and accurately locate the string where the fault occurs.
An Analysis of Suspected Microplastics in the Muscle and Gastrointestinal Tissues of Fish from Sarasota Bay, FL: Exposure and Implications for Apex Predators and Seafood Consumers
Authors: Conger Eric , Dziobak Miranda , Berens McCabe Elizabeth J. , Curtin Tita , Gaur Ayushi , Wells Randall S. , Weinstein John E. , Hart Leslie B.
Published: August 2024

Microplastics have been found in the gastrointestinal (GI) fluid of bottlenose dolphins (Tursiops truncatus), inhabiting Sarasota Bay, FL, suggesting exposure by ingestion, possibly via contaminated fish. To better understand the potential for trophic transfer, muscle and GI tissues from 11 species of dolphin prey fish collected from Sarasota Bay were screened for microplastics (particles < 5 mm diameter). Suspected microplastics were found in 82% of muscle samples (n = 89), and 97% of GI samples (n = 86). Particle abundance and shapes varied by species (p < 0.05) and foraging habit (omnivore vs. carnivore, p < 0.05). Pinfish (Lagodon rhomboides) had the highest particle abundance for both tissue types (muscle: 0.38 particles/g; GI: 15.20 particles/g), which has implications for dolphins as they are a common prey item. Findings from this study support research demonstrating the ubiquity of estuarine plastic contamination and underscore the risks of ingestion exposure for wildlife and potentially seafood consumers.
Potential Valorization of Banana Production Waste in Developing Countries: Bio-Engineering Aspects
Authors: Waraczewski Robert , Sołowiej Bartosz G.
Published: August 2024

Plant food production generates a lot of by-products (BPs). These BPs are majorly discarded into the environment, polluting it, or into landfills where they just decompose, providing no benefit and taking up storage space, causing financial costs. These plant BPs are biodegradable, but reusing them may provide a better outcome and profit. The vast majority of plant-based food BPs are polysaccharide polymers like gums, lignin, cellulose, and their derivatives. It is possible to utilize plant food production waste, like banana peels, leaves, pseudostems, and inflorescences, to produce bioethanol, single-cell protein, cellulase, citric acid, lactic acid, amylase, cosmetics, fodder additives, fertilizers, biodegradable fibers, sanitary pads, bio-films, pulp and paper, natural fiber-based composites, bio-sorbents, bio-plastic, and bio-electricity in the agro-industry, pharmaceutical, bio-medical, and bio-engineering fields. Moreover, the use of banana BPs seems to be a way of dealing with many issues in underdeveloped countries, providing a clean and ecological solution. The suggested idea might not only reduce the use of plastic but also mitigate waste pollution.
A Review of Ganoderma lucidum Polysaccharide: Preparations, Structures, Physicochemical Properties and Application
Authors: Zhong Yuanbo , Tan Pingping , Lin Huanglong , Zhang Di , Chen Xianrui , Pang Jie , Mu Ruojun
Published: August 2024

Ganoderma lucidum (GL) is a kind of edible fungus with various functions and a precious medicinal material with a long history. Ganoderma lucidum polysaccharide (GLP) is one of the main bioactive substances in GL, with anti-tumor, anti-oxidation, anti-cancer, and other biological activities. GLP is closely related to human health, and the research on GLP is getting deeper. This paper reviewed the extraction and purification methods of GLP, the relationship between structure and activity, and the qualitative and quantitative methods. This review provides solutions for the analysis and application of GLP. At the same time, some new methods for extraction, purification and analysis of GLP, the relationship between advanced structures and activity, and future applications of and research into GLP were emphasized. As a kind of bioactive macromolecule, GLP has unique functional properties. Through the comprehensive summary of the extraction, purification, and analysis of GLP and its future prospects, we hope that this review can provide valuable reference for the further study of GLP.
Comparison of Antioxidant Properties and Color of Selected Polish Honeys and Manuka Honey
Authors: Majewska Ewa , Drużyńska Beata , Derewiaka Dorota , Ciecierska Marta , Pakosz Paulina
Published: August 2024

The antioxidant capacity and nutritional value of honey are significantly dependent on the content of phenolic compounds. The aim of this study was to compare the antioxidant properties and color of selected honeys and manuka honeys available in the Polish market. The results showed quantitative differences in phenolic acids, phenolic content and antioxidant activity between the honeys, indicating the influence of floral sources. Dark honeys, including buckwheat honey, showed increased phenolic content and superior antioxidant properties. The study revealed remarkable correlations between phenolic content, antioxidant capacity and color. Buckwheat honey showed higher antioxidant properties compared to manuka honey, which is highly valued in the current market. These results highlight the importance of further research into Polish buckwheat honey and advocate its wider consumption due to its high nutritional value and remarkable bioactive properties. In addition, the study contributes to a deeper understanding of honey diversity and highlights the potential importance of regional honey varieties in promoting health.
The Effects of Nitrogen Application and Varietal Variation on the Product Quality and In Vitro Bioaccessibility of Bioactive Compounds of Baby Spinach Varieties Grown in a Soilless Growth Medium
Authors: Bhengu Nhlanzeko Mbalenhle , Mianda Sephora Mutombo , Maboko Martin Makgose , Sivakumar Dharini
Published: August 2024

Baby spinach is becoming increasingly popular as a salad ingredient and needs high fertiliser rates to grow well and attain higher-quality leaves (dark green leaves). Chemical fertilisers, especially nitrogen (N), boost yields. There are many risks associated with nitrogen fertilisation. Additionally, spinach contains phenolic compounds and carotenoids. Nitrogen fertilisation affects growth, development, yield and metabolites. This study examined the impact of lower concentrations of N (0, 30, 60, 90, 120, 150 mg/L) on yield and colour properties [light intensity (L*) colour coordinates, unique for green colour (a*) and yellow colour (b*)], as well as the impact of varying N concentrations on the total phenolic content and p-coumaric acid, quercetin, ferulic acid, kaempferol, lutein, zeaxanthin, β-carotene and antioxidant activities in the baby spinach varieties ‘Acadia’, ‘Crosstrek’ and ‘Traverse’, and it was established that N fertilisation improves phytochemical bioaccessibility and antioxidant activity. In a split strip plot design, three baby spinach varieties were treated with different N concentrations, including 0, 30, 60, 90, 120 and 150 mg/L. For 40 days, three baby spinach varieties were grown on soilless Mikskaar Professional substrate 300. During both seasons, ’Crosstrek’ had the highest fresh mass (921.4 g/m2, 856.3 g/m2) at 120 mg/L N, while ‘Traverse’ had the highest fresh mass at 554.8 g/m2 and at 564.3 g/m2 at 90 mg/L N and did not differ significantly from 90 to 150 mg/L N during either season. During both seasons, ‘Acadia’ at 90 mg/L N increased fresh mass to 599 g/m2 and 557.9 g/m2. The variety × N supply interaction significantly affected the leaf colour; chlorophyll content across seasons; the levels of bioactive compounds, p-coumaric acid, quercetin, ferulic acid, kaempferol, lutein, zeaxanthin and β-carotene in spinach varieties; the in vitro bioaccessibility; and the antioxidant activity. Varietal differences influenced the bioaccessibility of phenolic compounds and carotenoid components. The appropriate N levels can be used during plant cultivation to optimise the bioaccessibility of this spinach variety. Thus, fertilising ‘Traverse’ with 90 mg/N mL increased the in vitro bioaccessibility of β-carotene (35.2%), p-coumaric acid (7.13%), quercetin (8.29%) and ferulic acid (1.92%) without compromising the yield.
Preventing Fungal Spoilage from Raw Materials to Final Product: Innovative Preservation Techniques for Fruit Fillings
Authors: Bento de Carvalho Teresa , Silva Beatriz Nunes , Tomé Elisabetta , Teixeira Paula
Published: August 2024

Spoilage fungi are a significant cause of financial loss in the food and beverage industry each year. These fungi thrive in challenging environments characterized by low acidity, low water activity and high sugar content, all of which are common in fruit fillings used in pastry products. Fruit fillings are therefore highly susceptible to fungal spoilage. Fungal growth can cause sensory defects in foods, such as changes in appearance, odor, flavor or texture, and can pose health risks due to the production of mycotoxins by certain mold species. To reduce food loss and waste and extend product shelf-life, it is critical that we prevent fungal spoilage. Synthetic chemicals such as sorbic acid and potassium sorbate are commonly used as preservatives to prevent fungal spoilage. However, with consumer demand for ‘natural’ and ‘chemical-free’ foods, research into clean-label preservative alternatives to replace chemical preservatives has increased. The objectives of this review are (i) to provide an overview of the sources of fungal contamination in fruit filling production systems, from pre-harvest of raw materials to storage of the final product, and to identify key control factors; and (ii) to discuss preservation techniques (both conventional and novel) that can prevent fungal growth and extend the shelf-life of fruit fillings.