bluuebunny/crossref_metadata_2025 huggingface dataset:
Navigating New Norms: Lecturers’ Insights on Digital Learning Integration in Higher Education—A Case of Israel
Authors: Bitar Nizar , Davidovich Nitza
Published: July 2024

This study investigates the implementation of digital learning (DL) methodologies in Israeli higher education, focusing on lecturers’ perceptions and pedagogical transformations. Through in-depth interviews with fifteen lecturers from diverse disciplines, we examine the tension between DL and traditional ‘hevruta’ learning—a collaborative, discussion-based approach rooted in Israeli educational traditions. Thematic analysis reveals three key findings: (1) cultural and institutional factors as primary challenges, especially the tension between ‘hevruta’ learning and digital platforms; (2) a dichotomy in lecturers’ attitudes towards DL; and (3) the evolving role of lecturers from knowledge holders to facilitators. The study proposes the Cultural–Technological Integration Framework (CTIF), extending existing theoretical models by incorporating cultural compatibility as a crucial factor in technology acceptance. Our findings underscore the need for comprehensive institutional support and culturally sensitive approaches to DL implementation, providing valuable perspectives for policymakers and educators in diverse cultural contexts.
Prevalence of Emotional, Intellectual, Imaginational, Psychomotor, and Sensual Overexcitabilities in Highly and Profoundly Gifted Children and Adolescents: A Mixed-Methods Study of Development and Developmental Potential
Authors: Wood Vanessa Reineke , Bouchard Lorraine , De Wit Els , Martinson S. Pickett , Van Petegem Peter
Published: July 2024

This study examined the prevalence of the five forms of overexcitability in children ages 4–13 years who were previously identified as highly or profoundly gifted via a Wechsler Intelligence Scale for Children (WISC) score of 140+. The prevalence of overexcitabilities included an examination of the development and developmental potential according to Dabrowski’s human development theory. The results of this study provided an estimate of the prevalence and distinct types of overexcitability among highly-profoundly gifted young people. These findings will aid the inclusive identification and support of highly-profoundly gifted children and adolescents and help curb historical misunderstanding, misidentification, and misdiagnosis. The mixed-methods study included 88 study participants (parents) from the United States and Belgium who completed the Overexcitabilities Questionnaire II, Adapted (OEQ II, Adapted), the Development and Family History Questionnaire, and a semi-structured interview. The most prevalent OE profile was all five forms of overexcitability—emotional, intellectual, imaginational, psychomotor, and sensual—exhibited a lot of the time or most of the time. Nearly all (99%) of the highly-profoundly gifted children expressed combinations of three or more higher-level overexcitabilities including emotional, intellectual, and imaginational. A holistic assessment of overexcitabilities and giftedness by a professional trained in profound giftedness is warranted for unidentified children and adolescents with behaviors and development similar to the identified highly-profoundly gifted children examined in this study. To aid in the inclusive, early identification of giftedness and highly-profoundly gifted children, universal screening is recommended for all children entering kindergarten.
Tomorrow Never Comes: The Risks of Procrastination for Adolescent Health
Authors: Pérez-Jorge David , Hernández-Henríquez Ana Cristina , Melwani-Sadhwani Roshan , Gallo-Mendoza Anthony Fernando
Published: July 2024

This study explores the relationship between procrastination and declining healthy habits among adolescents, a topic lacking systematic reviews in the existing literature. The primary purpose is to lay the groundwork for promoting mental health and preventing procrastination as risky behavior. This systematic review examined five areas related to procrastination and its influence on healthy lifestyle habits in adolescents: technology and procrastination; sleep and procrastination; academic procrastination; and the COVID-19 pandemic. The findings highlight that technology misuse is linked with procrastination; adolescents tend to procrastinate when going to sleep; academic procrastination negatively impacts long-term educational achievements, and the COVID-19 pandemic has exacerbated this phenomenon. Ultimately, it is concluded that procrastination is related to all these aspects and has detrimental effects on adolescents’ physical and psychological development.
Controller Design for Autonomous Direct Current Microgrid Operation
Authors: Sánchez Hernán A. , Ortega Rubén , Carranza Oscar , Rodríguez Jaime J. , García Víctor H. , Ortega Luis M. , Memije Daniel
Published: July 2024

In the last three decades, the development of new electricity-generation strategies based on renewable energy has been promoted, which has made it possible to offset the increase in electricity consumption, reduce production costs, and positively impact climate change. These new-generation strategies are based on distributed generation (DG) at different points of the electrical power system, which has economic, social, and environmental impacts by reducing greenhouse gas emissions. The new concept of an electric microgrid, which is an integrated energy generation and management system with autonomous capacity, allows efficient production and consumption, with alternating current (AC) and direct current (DC) microgrids being available. This work presents a topology of direct current microgrids for island mode operation, which aims to supply electricity to variable and constant power loads (CPLs), as well as for the autonomous charging and discharging of batteries. To achieve this objective, a microgrid model is developed, current and voltage control loop controllers are designed, and their performance in different types of operation is analyzed. As a contribution to this work, the parasitic elements in the microgrid model are considered, and the design of the controller in the voltage loop is presented for the power supply to the local linear and non-linear loads, in addition to managing the energy coming from CPLs and controls the charge and discharge of the battery. To validate the performance of the controllers, three microgrid operation tests are carried out, which are presented in the Simulations section. In these tests, it is observed that with the PI controller in the voltage loop, the DC_Link signal presents transients with an amplitude of 5 V below and above the 200 V reference, which shows the poor performance of the controller. However, in all three tests with the modified PI-P controller, the DC_Link voltage remains constant at 200 V, with a negligible transient amplitude of almost 0 V, showing better performance.
Real-Time Deepfake Video Detection Using Eye Movement Analysis with a Hybrid Deep Learning Approach
Authors: Javed Muhammad , Zhang Zhaohui , Dahri Fida Hussain , Laghari Asif Ali
Published: July 2024

Deepfake technology uses artificial intelligence to create realistic but false audio, images, and videos. Deepfake technology poses a significant threat to the authenticity of visual content, particularly in live-stream scenarios where the immediacy of detection is crucial. Existing Deepfake detection approaches have limitations and challenges, prompting the need for more robust and accurate solutions. This research proposes an innovative approach: combining eye movement analysis with a hybrid deep learning model to address the need for real-time Deepfake detection. The proposed hybrid deep learning model integrates two deep neural network architectures, MesoNet4 and ResNet101, to leverage their respective architectures’ strengths for effective Deepfake classification. MesoNet4 is a lightweight CNN model designed explicitly to detect subtle manipulations in facial images. At the same time, ResNet101 handles complex visual data and robust feature extraction. Combining the localized feature learning of MesoNet4 with the deeper, more comprehensive feature representations of ResNet101, our robust hybrid model achieves enhanced performance in distinguishing between manipulated and authentic videos, which cannot be conducted with the naked eye or traditional methods. The model is evaluated on diverse datasets, including FaceForensics++, CelebV1, and CelebV2, demonstrating compelling accuracy results, with the hybrid model attaining an accuracy of 0.9873 on FaceForensics++, 0.9689 on CelebV1, and 0.9790 on CelebV2, showcasing its robustness and potential for real-world deployment in content integrity verification and video forensics applications.
Wearable Sensing Devices and Technology
Authors: Jing Lei , Zhou Jiehan , Zhang Zhan
Published: July 2024

The field of wearable sensing devices and technology has witnessed remarkable advancements, driven by the need for more integrated, efficient, and user-friendly systems [...]
Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method
Authors: Vladov Serhii , Kovtun Viacheslav , Sokurenko Valerii , Muzychuk Oleksandr , Vysotska Victoria
Published: July 2024

A neural network method has been developed for helicopter turboshaft engine residual life determination, the basis of which is a hierarchical system, which is represented in neural network model form, consisting of four layers, which determines the numerical value of the residual life. To implement a hierarchical system, a justified multilayer perceptron is used. A multilayer perceptron training algorithm has been developed, which, by introducing an initial parameter to the output layer, yields a prediction accuracy of up to 99.3%, and the adaptive Adam training rate ensures an accuracy of up to 99.4% in helicopter turboshaft engine residual life determination. A method for constructing a degradation curve has been developed that takes into account both the parameter predictions and similarities with past patterns, allowing you to determine the range of possible values of the residual life estimate, with a probability of up to 95%. The article considers an example of solving the task of determining the thermally stressed state of helicopter turboshaft engine compressor turbine blades and assessing their residual life. A computational experiment was carried out to determine the residual life of helicopter turboshaft engine compressor turbine blades, and the results, with 160 training epochs, recorded an accuracy of 99.3%, with a reduction in losses from 2.5% to 0.5% thanks to training process optimization by applying an adaptive training rate. The comparative analysis results showed that use of the multilayer perceptron as a hierarchical system gives better results than the classical RBF network and the least squares method. The first and second types of error were reduced by 2.23 times compared to the RBF network and by 4.74 times compared to the least squares method.
SEDAT: A Stacked Ensemble Learning-Based Detection Model for Multiscale Network Attacks
Authors: Feng Yan , Yang Zhihai , Sun Qindong , Liu Yanxiao
Published: July 2024

Anomaly detection for network traffic aims to analyze the characteristics of network traffic in order to discover unknown attacks. Currently, existing detection methods have achieved promising results against high-intensity attacks that aim to interrupt the operation of the target system. In reality, attack behaviors that are commonly exhibited are highly concealed and disruptive. In addition, the attack scales are flexible and variable. In this paper, we construct a multiscale network intrusion behavior dataset, which includes three attack scales and two multiscale attack patterns based on probability distribution. Specifically, we propose a stacked ensemble learning-based detection model for anomalous traffic (or SEDAT for short) to defend against highly concealed multiscale attacks. The model employs a random forest (RF)-based method to select features and introduces multiple base learning autoencoders (AEs) to enhance the representation of multiscale attack behaviors. In addressing the challenge of a single model’s inability to capture the regularities of multiscale attack behaviors, SEDAT is capable of adapting to the complex multiscale characteristics in network traffic, enabling the prediction of network access behavior. Comparative experiments demonstrate that SEDAT exhibits superior detection capabilities in multiscale network attacks. In particular, SEDAT achieves an improvement of at least 5% accuracy over baseline methods for detecting multiscale attacks.
DANet: A Domain Alignment Network for Low-Light Image Enhancement
Authors: Li Qiao , Jiang Bin , Bo Xiaochen , Yang Chao , Wu Xu
Published: July 2024

We propose restoring low-light images suffering from severe degradation using a deep-learning approach. A significant domain gap exists between low-light and real images, which previous methods have failed to address with domain alignment. To tackle this, we introduce a domain alignment network leveraging dual encoders and a domain alignment loss. Specifically, we train two dual encoders to transform low-light and real images into two latent spaces and align these spaces using a domain alignment loss. Additionally, we design a Convolution-Transformer module (CTM) during the encoding process to comprehensively extract both local and global features. Experimental results on four benchmark datasets demonstrate that our proposed A Domain Alignment Network(DANet) method outperforms state-of-the-art methods.
Adaptive Event-Triggered Voltage Control of Distribution Network Subject to Actuator Attacks Using Neural Network-Based Sliding Mode Control Approach
Authors: Zhang Fang
Published: July 2024

This paper studies an adaptive event-triggered sliding mode control (AET-SMC) strategy for a distribution network voltage control (DN-VC) system subject to actuator attacks. In the network environment, a distribution network voltage deviation model under actuator attack is established. In order to save network resources in the DN-VC system, an adaptive event-triggered scheme (AETS) is designed. Considering the network-induced delay, the closed-loop system is transformed into an event-based delay system. Considering the network attack, the sliding mode control (SMC) method is used to offset the influence of the actuator attack. In order to eliminate the buffeting phenomenon, neural network (NN) technology is used to estimate the attack signal and obtain the sliding mode controller with better performance. The stability and stabilization criteria of the DN-VC system are obtained by using the Lyapunov–Krasovskii method and a linear inequality operation. Finally, numerical examples are used to verify the effectiveness of the method.
Research on the Optimization of the Heating Effect of Lithium-Ion Batteries at a Low Temperature Based on an Electromagnetic Induction Heating System
Authors: Wang Borui , Yan Mingyin
Published: July 2024

Based on an electromagnetic induction heating system that was recently developed in a previous work, an orthogonal test with three elements and nine levels was carried out to improve the heating effect of the system. This was intended to achieve a balance between the heating rate and temperature uniformity, where the electrochemical and thermal behaviors of the heated lithium-ion battery could be characterized by a high-accuracy electrochemical–thermal coupling model. This was validated against constant-current discharge and HPPC test data at room temperature and different low temperatures. Under the optimal parameter combination that was found in the orthogonal test, the battery temperature could rise to 293.15 K from 243.15 K in 494 s, with a maximum temperature rise rate of 0.133 K·s−1. The temperature difference after heating reached 4.21 K, which resulted from the heat conductivity of the battery material due to the skin depth of the battery shell and the material properties inside the battery. Due to the internal resistance, which decreased to no more than a quarter of the low-temperature level, both the usable energy and pulse power were increased more than 2.5 and 3 times, respectively. The enhancement of the energy output ability could provide a greater cruise range and improved dynamics for electric vehicles. The capacity calibration results obtained during the heating cycles indicated that there was only a 3.61% reduction in capacity retention after 120 repetitive heating cycles, which was 0.008 Ah below the normal cycle at 293.15 K, even compared with room-temperature capacity calibration, thus reducing the effect on the battery’s lifetime. Therefore, the electromagnetic induction heating system with a heating strategy could achieve a beneficial compromise between the temperature rise behavior, cycle lifetime, and working ability, indicating considerable potential for the optimization of the heating effect.
P2P Energy Exchange Architecture for Swarm Electrification-Driven PV Communities
Authors: Taouil Khaled , Aloulou Rahma , Bradai Salma , Gassara Amal , Kharrat Mohamed Wajdi , Louati Badii , Giordani Michel
Published: July 2024

Swarm electrification-driven communities face significant challenges, including implementing advanced distributed control in areas with limited ICT access and establishing trust among villagers hesitant to grant access to their assets. This paper proposes a distributed DC microgrid architecture for P2P energy exchange in these communities, ensuring stability and an effective exchange operation. By implementing a Blockchain marketplace specifically designed to suit the rural context, the proposed architecture ensures tracing of exchange transactions to fairly settle participants. Validation experiments demonstrate its efficacy in achieving peak shaving. It provides 11% of the requester’s total demand from the community even while maintaining the constraint of reducing discharge–charge cycles to one per day, thereby preserving battery life. Additionally, the solution reduces prosumer production losses by 16% of the total PV production.
The Importance of Public Sources of Financing the Development of Renewable Energy in Agriculture, Using the Example of Eastern Poland
Authors: Przygodzka Renata , Badora Aleksandra , Kud Krzysztof , Mioduszewski Jarosław , Woźniak Marian , Stec Artur
Published: July 2024

This article addresses the issue of public support for the development of renewable energy and the assessment of this support from the perspective of farmers in Eastern Poland. Since it is a region characterized by a relatively high share of agriculture in the economy, a diversified structure of this sector, and at the same time, a region with a relatively high level of energy poverty, it can be considered a good example for illustrating the research problem. To achieve the goal, surveys were carried out covering 519 farms. Upon the consideration of a literature review and the research results of other authors, the collected opinions of the owners of these farms regarding the motivators encouraging them to invest in renewable energy technologies and the assessment of the role of the state in supporting energy transformation allowed for the formulation of conclusions. The research results indicate that renewable energy technologies are present in 47% of the surveyed farms, while in 36% of them, investments were financed from farmers’ own funds. This means that the possibility of obtaining support from public funds is not a necessary condition for implementing renewable energy investments.
Structural and Compositional Analysis of CZTSSe Thin Films by Varying S/(S+Se) Ratio
Authors: Zaki Mohamed Yassine , Sava Florinel , Simandan Iosif Daniel , Mihai Claudia , Velea Alin
Published: July 2024

The development of kesterite (Cu2ZnSn(S,Se)4, CZTSSe) thin films for photovoltaic applications is highly necessary, given their composition of Earth-abundant, environmentally friendly elements and their compatibility with established photovoltaic technologies. This study presents a novel synthesis approach for CZTSSe films with varied S/(S+Se) ratios, ranging from 0.83 to 0.44, by a two-step magnetron sputtering deposition/annealing process. The first step consists in an initial deposition of stacked Mo/SnS2/Cu layers, which, upon thermal treatment in a sulfur atmosphere, were transformed into Cu2SnS3 (CTS) films. In the second step, further deposition of ZnSe and subsequent annealing in a tin and selenium atmosphere resulted in the formation of a CZTSSe phase. These processes were optimized to fabricate high-quality and single-phase CZTSSe films, thereby mitigating the formation of secondary phases. Characterization techniques, including scanning electron microscopy, demonstrated a clear correlation between decreased S/(S+Se) ratios and enhanced film densification and grain size. Moreover, grazing incidence X-ray diffraction and Raman spectroscopy confirmed a compositional and structural transition from close to CZTS to nearly a CZTSe phase as the S/(S+Se) ratios decreased. This study advances kesterite-based solar cell technology by enhancing the structural properties and crystallinity of the absorber layer, necessary for improving photovoltaic performance.
Evaluation of Prediction Model for Compressor Performance Using Artificial Neural Network Models and Reduced-Order Models
Authors: Jeong Hosik , Ko Kanghyuk , Kim Junsung , Kim Jongsoo , Eom Seongyong , Na Sangkyung , Choi Gyungmin
Published: July 2024

In order to save the time and material costs associated with refrigeration system performance evaluations, a reduced-order model (ROM) using highly accurate numerical analysis results and some experimental values was developed. To solve the shortcomings of these traditional methods in monitoring complex systems, a simplified reduced-order system model was developed. To evaluate the performance of the refrigeration system compressor, the temperature of several points in the system where the compressor actually operates was measured, and the measured values were used as input values for ROM development. A lot of raw data to develop a highly accurate ROM were acquired from a VRF system installed in a building for one year, and in this study, specific operating conditions were selected and used as input values. In this study, the ROM development process can predict the performance of compressors used in air conditioning systems, and the research results on optimizing input data required for ROM generation were observed. The input data are arranged according to the design of experiments (DOE), and the accuracy of ROM according to data arrangement is compared through the experiment results.
Load Frequency Control of Multiarea Power Systems with Virtual Power Plants
Authors: Wang Zeyi , Wang Yao , Xie Li , Pang Dan , Shi Hao , Zheng Hua
Published: July 2024

Virtual power plants (VPPs) integrate diverse energy resources using advanced communication technologies and intelligent control strategies. This integration enhances the utilization and efficiency of distributed generation. This paper explores the incorporation of VPPs into load frequency control (LFC) systems. It includes an analysis of VPP-aggregated resources’ frequency regulation characteristics and a VPP-inclusive LFC model. Additionally, a decentralized automatic generation control strategy is proposed to distribute power outputs effectively, enabling swift grid frequency adjustments. This study uses MATLAB simulations to demonstrate the benefits and efficacy of VPPs in LFC, underscoring their role in advancing grid management and stability.
Multivariable Control-Based dq Decoupling in Voltage and Current Control Loops for Enhanced Transient Response and Power Delivery in Microgrids
Authors: Srikanth Mandarapu , Venkata Pavan Kumar Yellapragada , Pradeep Reddy Challa , Mallipeddi Rammohan
Published: July 2024

Being multivariable in nature, voltage and current control loops have controllers in the forward and cross-coupling paths. Most methods discussed in the literature focus on tuning the controllers in the forward paths to reduce the dq coupling. A modified pole-zero cancellation (MPZC) technique has recently been discussed, which uses the concepts of pole-zero cancellation and particle swarm optimization to effectively tune the forward path controllers. However, given the fixed gains in the cross-coupling paths, it is not possible to realize a superior transient response from this technique. Therefore, to achieve enhanced vector control of VSIs under transient conditions, this paper proposes a hybrid MPZC (HMPZC) method, which incorporates multivariable control along with the MPZC technique for both voltage/current control loops. In the proposed HMPZC method, the MPZC method is used to tune the forward path controllers, and multivariable control-based PI controllers are assigned in the cross-coupling paths of dq-axes loops rather than fixed gains. In this paper, these multivariable control-based PI controllers are designed using direct synthesis method-based internal model control (IMC). From the simulation results, it is verified that the proposed HMPZC method has reduced the coupling between the d- and q-axes loops of the current/voltage, leading to the improved transient response and power delivery capability of VSIs.
Meteorological Data Mining and Synthesis for Supplementing On-Site Data for Regulatory Compliance
Authors: Sonpon Ben , Usman Shoaib , Smith Joseph , Kovaleski Sarah , Wibbenmeyer Jason
Published: July 2024

Many regulatory requirements add significant delay in the licensing of new nuclear power stations. One area of particular interest is the environmental impact of potential atmospheric release. The purpose of this research is to demonstrate effectiveness of meteorological data mining and synthesis from offsite locations to reduce need for onsite data, hence allowing rapid licensing. The automated procedures tested for data mining and extraction of meteorological data from multiple offsite sources and the data analytic tool developed for data fusion are presented here. Three important meteorological parameters from regulatory compliance are considered for this analysis: wind velocity, wind direction, and atmospheric stability. Callaway Nuclear Power Plant (CNPP) is used as our reference site. CNPP uses the ΔTΔz approach while we use the Vogt method to estimated stability for the offsite locations. Stability classification correlation coefficients between the reference plant and Columbia Regional Airport range from −0.087 to 0.826 for raw with an average of 0.317 ± 0.313. With travel time, correction changed slightly, i.e., a 10 m observation 0.064 ± 0.249 and 0.028 ± 0.123 and a 60 m observation 0.103 ± 0.265 and 0.063 ± 0.155 for the wind from the reference plant to the airport and vice versa, respectively. For Jefferson City Memorial Airport, raw data correlation was from −0.083 to 0.771, with an average of 0.358 ± 0.321. With travel time, correction changed slightly, i.e., 10 m observation 0.075 ± 0.208 and −0.073 ± 0.255 and 60 m observation 0.018 ± 0.223 and −0.032 ± 0.248 for wind from the reference plant to the airport and vice versa, respectively. Stability classification correlation coefficients between the reference plant and St. Louis Lambert International Airport ranged from −0.083 to 0.763 for raw with an average of 0.314 ± 0.295. With travel time, correction changed slightly, i.e., 10 m observation −0.003 ± 0.307 and −0.030 ± 0.277 and 60 m observation −0.030 ± 0.193 and −0.005 ± 0.215 for wind from the reference plant to the airport and vice versa, respectively. It is important to observe that mathematically. stability class correlation coefficients were not great, but in most cases the predicted and observed values were only off by one stability class. Similar correlations were calculated for wind direction and velocities. Our result, when applied to a proposed nuclear power station, can significantly reduce time and effort to prepare a robust environmental protection plan required for license application.
Co-Pyrolysis of Sewage Sludge, Two-Component Special Municipal Waste and Plastic Waste
Authors: Tomasek Szabina , Miskolczi Norbert
Published: July 2024

In this study, the co-pyrolysis of sewage sludge (SS), two-component special municipal waste (SMW) and plastic waste (Plastic) was studied using thermogravimetric equipment and a scaled-up tubular reactor. During the experiments, the effects of the raw material composition and pyrolysis temperature on the decomposition processes, the product yields and compositions were investigated. It was found that co-pyrolysis was a series of complex reactions and resulted in more volatile and lower residue yield than the pyrolysis of the individual raw materials. In some cases (e.g., 25%SMW + 75%Plastic, 25%SS + 75%Plastic, 50%SS + 50%Plastic blends), it also caused a higher synthesis gas yield and H2/CO ratio. During the thermogravimetric analysis, the beneficial effects occurred at lower temperatures, but in the scaled-up experiments, these effects only prevailed at 900 °C as a result of the larger amount of raw materials and the worse heat transfer. The produced gases can be characterized by a lower heating value of 17.3–35 MJ/Nm3. Therefore, they can be used for energetic purposes; however, before chemical use, further quality improvement is needed due to the lower H2/CO ratios.
An Analysis of Greenhouse Gas Emissions in Electrolysis for Certifying Clean Hydrogen
Authors: Kim Yunji , Min Inhong , Lee Jieun , Yang Heena
Published: July 2024

The drive for carbon neutrality has led to legislative measures targeting reduced greenhouse gas emissions across the transportation, construction, and industry sectors. Renewable energy sources, especially solar and wind power, play a pivotal role in this transition. However, their intermittent nature necessitates effective storage solutions. Green hydrogen and ammonia have gained attention for their potential to store renewable energy while producing minimal emissions. Despite their theoretical promise of zero greenhouse gas emissions during production, real-world emissions vary based on system configurations and lifecycle assessments, highlighting the need for detailed evaluations of their environmental impact. Therefore, in this study, calculations were performed for the actual amount of produced greenhouse gas emissions that are associated with the production of green hydrogen using electrolysis, from raw material extraction and processing to hydrogen production, with these assessed from well-to-gate emission estimates. Emissions were also evaluated based on various types of renewable energy sources in South Korea, as well as hydrogen production volumes, capacities, and types. Using these data, the following factors were examined in this study: carbon dioxide emissions from the manufacturing stage of electrolysis equipment production, the correlation between materials and carbon dioxide emissions, and process emissions. Current grades of clean hydrogen were verified, and the greenhouse gas reduction effects of green hydrogen were confirmed. These findings are significant against the backdrop of a country such as South Korea, where the proportion of renewable energy in total electricity production is very low at 5.51%. Based on the domestic greenhouse gas emission efficiency standard of 55 kWh/kgH2, it was found that producing 1 kg of hydrogen emits 0.076 kg of carbon dioxide for hydropower, 0.283 kg for wind power, and 0.924 kg for solar power. The carbon dioxide emissions for AWE and PEM stacks were 8434 kg CO2 and 3695 kg CO2, respectively, demonstrating that an alkaline water electrolysis (AWE) system emits about 2.3 times more greenhouse gasses than a proton exchange membrane (PEM) system. This indicates that the total carbon dioxide emissions of green hydrogen are significantly influenced by the type of renewable energy and the type of electrolysis used.
Maximum Power Transfer of a Photovoltaic Microgeneration System Using PSO-Based Dynamic Modeling
Authors: Chavez Giovanny , Tipán Luis
Published: July 2024

This research aims to implement an already developed algorithm to obtain the maximum power transfer of a solar generation field based on a dynamic approach. The study addresses the sizing of the load to be supplied, which is a residential building. On the other hand, it also considers the field sizing as a function of the load and the operating characteristics of the selected inverter. The irradiance data correspond to the hourly record of a station that is part of the network of meteorological stations in Quito. Quito was chosen as the location for this research due to the optimization algorithm’s practical application and the availability of experimental equipment. The demand sizing is based on the regulations of the distribution company with jurisdiction in the area, which makes it a suitable test bed for the algorithm. The optimization algorithm is developed using Python (version 3.9), and the analysis of the behavior of the solar panels is performed by dynamic modeling using the Vensim software (version 10.1.2). Finally, comparative results are presented between using and not using the investigated circuit and algorithm in the photovoltaic system, obtaining an improvement in the generation over a system without the use of these improvements, validating these results by implementing them in a test system, obtaining ranges higher than 10% of the initially generated power.
Electrochemical Performance of a Hybrid NiCo2O4@NiFelt Electrode at Different Operating Temperatures and Electrolyte pH
Authors: Niyati Ataollah , Moranda Arianna , Beigzadeh Arough Pouya , Navarra Federico Maria , Paladino Ombretta
Published: July 2024

Transition metals such as nickel and cobalt as an alternative to Pt and Pd can be used for oxygen evolution reactions (OERs) and hydrogen production reactions (HERs) in alkaline environments, facilitating green hydrogen production as a sustainable alternative to fossil fuels. In this study, an NiCo2O4 catalyst was produced by a sono-hydrothermal method using urea as a hydrolysis agent. The electrochemical performance of the catalyst-coated NiFelt electrode was evaluated at different KOH concentrations (0.25, 0.5, and 1 M) and four operating temperatures in the interval of 20–80 °C. The electrode characteristics were investigated via electrochemical spectroscopy (cyclic voltammetry, EIS, multistep chronopotentiometry, multistep chronoamperometry) using two different reference electrodes (Ag/AgCl and Hg/HgO), to obtain insight into the anodic and cathodic peaks. XRD, SEM, EDS, and TEM analyses confirmed the purity, structure, and nanoscale particle size (20–45 nm) of the NiCo2O4 catalyst. The electrode showed symmetric CV with Ag/AgCl, making this reference electrode more appropriate for capacitance measurements, while Hg/HgO proved advantageous for EIS in alkaline solutions due to reduced noise. The overpotential of the catalyst-coated NiFelt decreased by 108 mV at 10 mA/cm2 compared to bare NiFelt, showing a good potential for its application in anion exchange membranes and alkaline electrolyzers at an industrial scale.
Periodontitis in Psoriatic Patients: Epidemiological Insights and Putative Etiopathogenic Links
Authors: Di Spirito Federica , Di Palo Maria Pia , Rupe Antonio , Piedepalumbo Federica , Sessa Alessandra , De Benedetto Giuseppina , Russo Barone Serena , Contaldo Maria
Published: July 2024

Psoriasis, a systemic autoimmune disorder primarily affecting the skin, manifests through erythematous plaques and scales, impacting approximately 2–3% of the global population. Chronic periodontitis, a prevalent oral disease characterized by the destruction of tooth-supporting tissues, affects roughly 10–15% of adults worldwide. Emerging evidence suggests a bidirectional relationship between psoriasis and chronic periodontitis, supported by epidemiological studies indicating a higher prevalence of periodontitis among individuals with psoriasis and vice versa. Both conditions are chronic inflammatory diseases marked by dysregulated immune responses and altered cytokine profiles, notably involving proinflammatory cytokines such as TNF-α and IL-17. Clinical studies highlight a reciprocal impact of treating one condition on the other, underscoring the necessity of interdisciplinary collaboration between dermatologists and periodontists in managing patients with both conditions. This narrative review provides a comprehensive overview of the relationship between psoriasis and chronic periodontitis, examining epidemiological associations, shared inflammatory pathways, genetic insights, microbial dysbiosis, environmental factors, and clinical implications. The review emphasizes the importance of integrated care approaches and the potential for targeted therapeutic interventions to improve both psoriatic and periodontal patient outcomes, advocating for further research into the molecular and cellular mechanisms underpinning the comorbidity of these diseases.
Detection Model and Spectral Disease Indices for Poplar (Populus L.) Anthracnose Based on Hyperspectral Reflectance
Authors: Jia Zhicheng , Duan Qifeng , Wang Yue , Wu Ke , Jiang Hongzhe
Published: July 2024

Poplar (Populus L.) anthracnose is an infectious disease that seriously affects the growth and yields of poplar trees, and large-scale poplar infections have led to huge economic losses in the Chinese poplar industry. To efficiently and accurately detect poplar anthracnose for improved prevention and control, this study collected hyperspectral data from the leaves of four types of poplar trees, namely healthy trees and those with black spot disease, early-stage anthracnose, and late-stage anthracnose, and constructed a poplar anthracnose detection model based on machine learning and deep learning. We then comprehensively analyzed poplar anthracnose using advanced hyperspectral-based plant disease detection methodologies. Our research focused on establishing a detection model for poplar anthracnose based on small samples, employing the Design of Experiments (DoE)-based entropy weight method to obtain the best preprocessing combination to improve the detection model’s overall performance. We also analyzed the spectral characteristics of poplar anthracnose by comparing typical feature extraction methods (principal component analysis (PCA), variable combination population analysis (VCPA), and the successive projection algorithm (SPA)) with the vegetation index (VI) method (spectral disease indices (SDIs)) for data dimensionality reduction. The results showed notable improvements in the SDI-based model, which achieved 89.86% accuracy. However, this was inferior to the model based on typical feature extraction methods. Nevertheless, it achieved 100% accuracy for early-stage anthracnose and black spot disease in a controlled environment respectively. We conclude that the SDI-based model is suitable for low-cost detection tasks and is the best poplar anthracnose detection model. These findings contribute to the timely detection of poplar growth and will greatly facilitate the forestry sector’s development.
Catalytic Mode and Product Specificity of an α-Agarase Reveal Its Direct Catalysis for the Production of Agarooligosaccharides
Authors: Zeng Xiaofeng , Tian Yixiong , Kong Haocun , Li Zhaofeng , Gu Zhengbiao , Li Caiming , Hong Yan , Cheng Li , Ban Xiaofeng
Published: July 2024

Many α-agarases have been characterized and are utilized for producing agarooligosaccharides through the degradation of agar and agarose, which are considered valuable for applications in the food and medicine industries. However, the catalytic mechanism and product transformation process of α-agarase remain unclear, limiting further enzyme engineering for industrial applications. In this study, an α-agarase from Catenovulum maritimus STB14 (Cm-AGA) was employed to degrade agarose oligosaccharides (AGOs) with varying degrees of polymerization (DPs) to investigate the catalytic mechanism of α-agarases. The results demonstrated that Cm-AGA could degrade agarose into agarotetraose and agarohexaose. The reducing ends of agarotetraose and agarohexaose spontaneously release unstable 3,6-anhydro-α-l-galactose molecules, which were further degraded into agarotriose and agaropentose. Cm-AGA cannot act on α-1,3-glucoside bonds in agarotriose, agarotetraose, neoagarobiose, and neoagarotetraose but can act on AGOs with a DP greater than four. The product analysis was further verified by β-galactosidase hydrolysis, which specifically cleaves the non-reducing glycosidic bond of agarooligosaccharides. Multiple sequence alignment results showed that two conserved residues, Asp994 and Glu1129, were proposed as catalytic residues and were further identified by site-directed mutagenesis. Molecular docking of Cm-AGA with agaroheptose revealed the potential substrate binding mode of the α-agarase. These findings enhance the understanding of Cm-AGA’s catalytic mode and could guide enzyme engineering for modulating the production of agarooligosaccharides.
Multi-Elemental Analysis of Edible Insects, Scorpions, and Tarantulas from French (Online) Market and Human Health Risk Assessment Due to Their Consumption: A Pilot Study
Authors: Holowaty Yulianna , Leufroy Axelle , Mazurais Clément , Beauchemin Diane , Jitaru Petru
Published: July 2024

Edible insects are becoming increasingly popular as protein alternatives to traditional animal-based products. As such, information on their elemental composition is important to ensure they are safe for human consumption. This article describes the development and validation of a rapid, reliable method for the simultaneous determination of 19 elements (Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Pb, Se, Sr, and Zn) in edible insects by inductively coupled plasma mass spectrometry (ICP-MS) following closed vessel microwave digestion. The method was validated using three insect certified reference materials, namely black soldier fly larvae meal (BFLY-1), cricket flour (KRIK-1), and mealworm powder (VORM-1). The method was applied to analyze twelve different (whole) insect species. The maximum amount of each sample was calculated for As, Cd, and Pb with respect to their provisional tolerable daily intake values established by the Food and Agricultural Organization/World Health Organization. Most of the samples, except for scorpions and tarantulas, were safe to consume at large doses (1000–10,000 insects per day). Furthermore, most of the samples contained high levels of Fe, K, Na, and Zn, providing a preliminary overview of the nutritional profile of these novel protein alternatives.
Fostering Circular Economy: Brewing By-Products as Innovative Ingredients for Cereal Bar Formulation
Authors: Paciulli Maria , Sogari Giovanni , Rodolfi Margherita , Parenti Ottavia , Andreani Giulia , Chiavaro Emma
Published: July 2024

Brewer’s spent grain (BSG) was used as a sustainable and healthy ingredient in two cereal bar formulations, with honey (H) and chocolate (C) used as the binding systems’ characterizing ingredients. The two bars, formulated using three levels of BSG (H1: 8.5%; H2: 12.7%; H3: 21.2%; C1: 3.9%; C2: 7.7%; C3: 15.5%) and stored for 20 days, were studied from a physicochemical perspective and compared to non-enriched control bars. The analysis showed that BSG enriched the bars with minerals, B vitamins, proteins, and fibers, meeting the required contents for the “high fiber” nutritional claim. Moisture content and water activity decreased with increasing BSG quantity and storage time. Higher BSG content increased flexibility in H bars after 7 days, while decreasing water content and increasing hardness in C bars at 1 storage day. Higher BSG levels darkened the samples’ color with little change during storage. In addition, a consumer sensory test was conducted. The results showed that providing information on BSG had little impact on liking, purchase intent, and sensory perception. In addition, under blind conditions, H bars were considered more natural and healthier than the C bars; however, these differences were not significant in the informed conditions. This study shows the potential use of upcycled ingredients in cereal bars and highlights the central role of the sensory experience on consumer appreciation, considering also information provision.
Effect of Chickpea (Cicer arietinum L.) Flour Incorporation on Quality, Antioxidant Properties, and Bioactive Compounds of Shortbread Cookies
Authors: Felisiak Katarzyna , Przybylska Sylwia , Tokarczyk Grzegorz , Tabaszewska Małgorzata , Słupski Jacek , Wydurska Joanna
Published: July 2024

High nutritional value and antioxidant properties make chickpea flour a valuable substitute for wheat flour, although its texture-forming abilities are different. The aim of this study was to investigate the possibility of increasing the content of bioactive compounds and antioxidant properties of shortbread cookies by simple partial or complete replacement of wheat flour with chickpea flour without considerable changes in texture, color, sensory properties, or acceptability. Shortbread cookies were made from wheat flour (0% of chickpea flour), wheat flour and chickpea flour (replacement of 25%, 50%, and 75%), and chickpea flour (100%). Generally, the increase in chickpea flour share resulted in an increase in protein, fat, and ash content, as well as antioxidant properties. Polyphenol content, flavonoid content, and antioxidant activities increased three- to sixfold in shortbread cookies containing chickpea flour in comparison to wheat cookies. The level of proteins increased about 50% and the antioxidant properties were three to six times higher than in wheat cookies. Cookies containing up to 75% chickpea flour were assessed as very good or good quality, while only cookies without wheat flour were assessed as sufficient quality. It could be concluded that part of the wheat flour content in shortbread cookies can be replaced by chickpea flour. Application of a 25% proportion of chickpea flour increases physicochemical properties without changes in sensory properties. Sensory quality was up to 75% lower, but antioxidant properties were increased. However, complete replacement of wheat flour in shortbread cookies without changing the recipe resulted in a product of slightly lower sensory quality.
The Cashew Pseudofruit (Anacardium occidentale): Composition, Processing Effects on Bioactive Compounds and Potential Benefits for Human Health
Authors: Gutiérrez-Paz Carina , Rodríguez-Moreno María-Constanza , Hernández-Gómez María-Soledad , Fernández-Trujillo Juan Pablo
Published: July 2024

The fruit of the cashew, a tree belonging to the family Anacardiaceae, is composed of approximately 10% nut (cashew) and 90% stalk or pseudofruit, usually discarded in situ and fermented in the soil. This review identifies cashew pseudofruit’s physicochemical characteristics and bioactive compounds and their possible relationship to health benefits. Different processing techniques have been used to preserve the pseudofruit, and the effect of these techniques on its nutrients is also reviewed in this work. Cashew is a highly perishable product with moisture content above 80% w/w and 10% w/w sugars. It also has a high content of polyphenols, flavonoids, and tannins and high antioxidant properties that are best preserved by nonthermal processing techniques. The pseudofruit presents the high inhibitory activity of α-amylase and lipase enzymes, has anti-inflammatory and body weight reduction properties and healing activity, and controls glucose levels, insulinemia, and insulin resistance. For all these reasons, cashews have been promoted as a propitious food/ingredient for preventive and therapeutic management of different pathologies such as diabetes, dyslipidemia, obesity, hypertension, fatty liver, and acne. Moreover, it has positive effects on the intestinal microflora, among others. This pseudofruit has a high potential for the development of functional foods.