bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of the Global Fear Associated with COVID-19 Using Subjectivity Analysis and Deep Learning
Authors: Thakur Nirmalya , Patel Kesha A. , Poon Audrey , Shah Rishika , Azizi Nazif , Han Changhee
Published: June 2024

The work presented in this paper makes multiple scientific contributions related to the investigation of the global fear associated with COVID-19 by performing a comprehensive analysis of a dataset comprising survey responses of participants from 40 countries. First, the results of subjectivity analysis performed using TextBlob, showed that in the responses where participants indicated their biggest concern related to COVID-19, the average subjectivity by the age group of 41–50 decreased from April 2020 to June 2020, the average subjectivity by the age group of 71–80 drastically increased from May 2020, and the age group of 11–20 indicated the least level of subjectivity between June 2020 to August 2020. Second, subjectivity analysis also revealed the percentage of highly opinionated, neutral opinionated, and least opinionated responses per age-group where the analyzed age groups were 11–20, 21–30, 31–40, 41–50, 51–60, 61–70, 71–80, and 81–90. For instance, the percentage of highly opinionated, neutral opinionated, and least opinionated responses by the age group of 11–20 were 17.92%, 16.24%, and 65.84%, respectively. Third, data analysis of responses from different age groups showed that the highest percentage of responses indicating that they were very worried about COVID-19 came from individuals in the age group of 21–30. Fourth, data analysis of the survey responses also revealed that in the context of taking precautions to prevent contracting COVID-19, the percentage of individuals in the age group of 31–40 taking precautions was higher as compared to the percentages of individuals from the age groups of 41–50, 51–60, 61–70, 71–80, and 81–90. Fifth, a deep learning model was developed to detect if the survey respondents were seeing or planning to see a psychologist or psychiatrist for any mental health issues related to COVID-19. The design of the deep learning model comprised 8 neurons for the input layer with the ReLU activation function, the ReLU activation function for all the hidden layers with 12 neurons each, and the sigmoid activation function for the output layer with 1 neuron. The model utilized the responses to multiple questions in the context of fear and preparedness related to COVID-19 from the dataset and achieved an accuracy of 91.62% after 500 epochs. Finally, two comparative studies with prior works in this field are presented to highlight the novelty and scientific contributions of this research work.
A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming
Authors: Sun Shuguang , Wang Haolin , Xu Yanzhi , Wang Tianguang , Liu Ruihua , Chen Wantong
Published: June 2024

The rapid advancement of unmanned aerial vehicle (UAV) technologies has led to an increasing demand for UAV operations in low-altitude, high-density, and complex airspace such as mountains or urban areas. In order to handle complex scenarios and ensure flight safety for UAVs with different flight missions beyond visual line of sight in such environments, a fusion framework of onboard autonomous flight trajectory management and decision-making system using global strategical path planning and local tactical trajectory optimization combination is proposed in this paper. The global strategical path planning is implemented by an enhanced A* algorithm under the multi-constraint of UAV positioning uncertainty and obstacle density to improve the safety and cost-effectiveness. The local tactical trajectory optimization is realized using quadratic programming to ensure smoothness, kinematic feasibility, and obstacle avoidance of the planned trajectory in dynamic environments. Receding-horizon control is used to ensure the flight path and trajectory planning efficiently and seamlessly. To assess the performance of the system, a terrain database and a navigation system are employed for environment and navigation performance simulation. The experimental results confirm that the fusion approach can realize better safety and cost-effectiveness through path planning with kino-dynamic feasible trajectory optimization.
Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making
Authors: Humr Scott , Canan Mustafa
Published: June 2024

Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N = 192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human–AI decision-making and ameliorate the understanding of the interaction dynamics at the confluence of human factors and information features.
Cultural and Religious Equity and Disparity in the Group Climate Experience of Moroccan Girls in Dutch Residential Youth Care Settings
Authors: Sevilir Rabia , Peters-Scheffer Nienke , van der Helm Peer , Graas Dorien , Didden Robert
Published: June 2024

Background: The group climate within residential youth care institutions is considered a transactional process, both within a group of youth from various cultural backgrounds and between them and their group workers. The ongoing interaction between the cultural characteristics of these girls may influence the quality of the group climate. This study aimed to provide an in-depth account of the living group climate experiences and perceptions of Dutch girls with a Moroccan cultural background in Dutch residential groups. Method: Interpretative phenomenological analysis was employed to explore the girls’ group climate experiences. Result: Three major themes emerged, namely (a) level of involvement of Moroccan girls in their living group, (b) perceptions of Moroccan girls’ sense of belonging in a living group, and (c) cultural and religious equality or disparity results in two interaction patterns. The findings revealed that equality or disparity in language, culture, and religion affect Moroccan girls’ experiences and perceptions of the living group climate. A crucial finding was that cultural and religious disparity leads to interaction patterns wherein the girls pre-emptively exclude themselves from receiving support from native group workers. Conclusion: Professionals must be aware of the cultural and religious dynamics, including the interplay and impact of cultural and religious equality and disparity, influencing Moroccan girls’ group climate experiences.
Review on Power Cycling Reliability of SiC Power Device
Authors: Gao Xu , Jia Qiang , Wang Yishu , Zhang Hongqiang , Ma Limin , Zou Guisheng , Guo Fu
Published: June 2024

The rising demand for increased integration and higher power outputs poses a hidden risk to the long-term reliable operation of third-generation semiconductors. Thus, the power cycling test (PCT) is widely regarded as the utmost critical test for assessing the packaging reliability of power devices. In this work, low-thermal-resistance packaging design structures of SiC devices are introduced, encompassing planar packaging with dual heat dissipation, press-pack packaging, three-dimensional (3D) packaging, and hybrid packaging. PCT methods and their control strategies are summarized and discussed. Direct-current PCT is the focus of this review. The failure mechanisms of SiC devices under PCT are pointed out. The electrical and temperature-sensitive parameters adopted to monitor the aging of SiC devices are organized. The existing international standards for PCT are evaluated. Due to the lack of authoritative statements for SiC devices, it is difficult to achieve comparison research results without consistent preconditions. Furthermore, the lifetimes of the various packaging designs of the tested SiC devices under PCTs are statistically analyzed. Additionally, problems related to parameter monitoring and test equipment are also summarized. This review explores the broader landscape by delving into the current challenges and main trends in PCTs for SiC devices.
Cost-Optimality Assessment of a Solar Trigeneration System for Tertiary Sector Buildings in Greece
Authors: Tziritas Dimitrios , Braimakis Konstantinos , Bakirtzis Dimitris , Stavrakakis George M. , Yfanti Sofia , Terzis Konstantinos , Langouranis Panagiotis , Zervas Panagiotis L. , Karellas Sotirios
Published: June 2024

To pave the way towards buildings’ decarbonization in the context of the European Union’s (EU) policy, the methodology of cost-optimality assessment based on regulation 244/2012/EU is a useful tool to explore and foster the application of energy technologies in buildings. Meanwhile, the fostering of concentrated solar power is included in the EU solar energy strategy. In this study, the cost-optimal methodology is employed for the techno-economic assessment of the integration of a novel solar, multi-purpose energy technology, namely a parabolic trough collector-based trigeneration system, in two building types with different characteristics, namely an office and a hospital, in Greece, thus allowing the evaluation of the cost-optimal system design and the impact of the building type on the system’s techno-economic performance. Reference buildings are defined and their energy demand is calculated through dynamic energy simulations. The trigeneration system’s performance for different design scenarios is then parametrically investigated using a simulation model. For each scenario, energy, environmental and economic indicators are calculated and the cost-optimal designs are extracted. In the cost-optimal implementation, the system covered 18.19–36.39% and 3.58–15.71% of the heating and cooling demand, respectively, while the reduction of the primary energy consumption and emissions was estimated at 10–14% and 10–16%, respectively. However, differences between the buildings related to the operation schedule and the loads led to the implementation of the system being economically more attractive in the hospital, while for the office, financial support is necessary for a viable investment.
Robust Energy Management Policies for Solar Microgrids via Reinforcement Learning
Authors: Jones Gerald , Li Xueping , Sun Yulin
Published: June 2024

As the integration of renewable energy expands, effective energy system management becomes increasingly crucial. Distributed renewable generation microgrids offer green energy and resilience. Combining them with energy storage and a suitable energy management system (EMS) is essential due to the variability in renewable energy generation. Reinforcement learning (RL)-based EMSs have shown promising results in handling these complexities. However, concerns about policy robustness arise with the growing number of grid intermittent disruptions or disconnections from the main utility. This study investigates the resilience of RL-based EMSs to unforeseen grid disconnections when trained in grid-connected scenarios. Specifically, we evaluate the resilience of policies derived from advantage actor–critic (A2C) and proximal policy optimization (PPO) networks trained in both grid-connected and uncertain grid-connectivity scenarios. Stochastic models, incorporating solar energy and load uncertainties and utilizing real-world data, are employed in the simulation. Our findings indicate that grid-trained PPO and A2C excel in cost coverage, with PPO performing better. However, in isolated or uncertain connectivity scenarios, the demand coverage performance hierarchy shifts. The disruption-trained A2C model achieves the best demand coverage when islanded, whereas the grid-connected A2C network performs best in an uncertain grid connectivity scenario. This study enhances the understanding of the resilience of RL-based solutions using varied training methods and provides an analysis of the EMS policies generated.
A Novel Method to Calculate Water Influx Parameters and Geologic Reserves for Fractured-Vuggy Reservoirs with Bottom/Edge Water
Authors: Yao Chao , Yan Ruofan , Zhou Fei , Zhang Qi , Niu Ge , Chen Fangfang , Cao Wen , Wang Jing
Published: June 2024

In practical oilfield production, the phenomenon of water influx typically shortens the water-free recovery period of wells, leading to water flooding and causing a sharp decline in the production well yields, bringing great harm to production. Water invasion usually occurs as a result of the elastic expansion of the water as well as the compaction of the aquifer pore space. However, it can be due to the special characteristics of fractured-vuggy reservoirs such as non-homogeneity and the discrete distribution of the pore spaces. It is challenging to use traditional seepage flow theories to analyze the characteristics of water influx. Also, reservoir numerical simulation methods require numerous parameters which are difficult to obtain, which significantly reduces the accuracy of the results. In this study, considering the driving energy for water influx, a water influx characteristic model was obtained by fitting a graph plate. Subsequently, an iterative calculation method was used to simultaneously obtain water influx volume and OOIP. The aquifer to hydrocarbon ratio was determined by fitting the water influx curve with the graphic plate. Results show that the calculation method is sensitive to the values of reservoir pressure and the crude oil formation volume factor. After applying the method to one field case, it was discovered that water influx performance can be characterized into two types, i.e., linear water influx and logarithmic water influx. In the early stages, the water influx rate of logarithmic water influx is greater compared to linear water influx. However, the volume and energy of waterbody are limited, and the water invasion phenomenon occurs almost exclusively within a short period after the invasion. On the other hand, the volume of waterbody invaded by linear water influx is larger, and it can maintain a stable rate of water influx. The results of the study can provide theoretical support for the waterbody energy evaluation and dynamic analysis of water influx, as well as the control and management of water in these types of reservoirs.
The Inter-Regional Embodied Carbon Flow Pattern in China Based on Carbon Peaking Stress
Authors: Xiao Qianqian , Chu Zi’ang , Shi Changfeng
Published: June 2024

Embodied carbon flows among regions have led to unfair carbon emission responsibility accounting based on production. However, the heterogeneity of carbon peaking stress between regions is significantly neglected for those embodied carbon flows. Incorporating the carbon peaking stress into the embodied carbon flows can more clearly show what causes the carbon peaking stress and which carbon flow paths are more critical. In this study, the decoupling index of carbon emissions and economy development was applied to characterize the carbon peaking stress in each region, and the environmental extended multi-regional input–output model was applied to re-evaluate the criticality of regional embodied carbon flows. The results showed that the carbon peaking stress in China improved from 2007 to 2012, but the rebound of carbon peaking stress in 2017 made most regions reverse the previous downward trend. The stress to reach carbon peaks varies considerably from region to region, and the stress in the northwest is much higher than that in developed eastern China. Considering the heterogeneity of carbon peaking stress, additional concerns should be given to the net embodied carbon output in the northwestern, northern, and central regions, which can help avoid the dilemma between outsourcing embodied carbon and reducing carbon emissions from production. The policy to reduce emissions should be implemented in all regions that benefit from the net embodied carbon output of the northern and northwestern regions, where the carbon peaking stress is higher. The focus should be on the actual improvement of the carbon peaking stress, not just on the transfer of stress. The increasing urgency of achieving carbon peaking targets and unequal stress for regional peaking emissions calls for differentiated regional mitigation measures to help the Chinese government scientifically and in an orderly manner promote the overall and local carbon peaking work.
Suitability Analysis of Selected Methods for Modelling Infrasound and Low-Frequency Noise from Wind Turbines
Authors: Stępień Bartłomiej , Wszołek Tadeusz , Mleczko Dominik , Małecki Paweł , Pawlik Paweł , Kłaczyński Maciej , Czapla Marcjanna
Published: June 2024

Wind turbines emit infrasound and low-frequency noise (ILFN), which can be annoying for people living near wind farms. To assess the acoustic impact of wind turbines on the environment, it is essential to model ILFN propagation during the forecasting stage. This study assesses the effectiveness of three commonly used sound propagation models (ISO 9613-2, CNOSSOS-EU for favourable propagation conditions, Nord2000) in predicting ILFN generated by wind turbines. The performance of these models in modelling ILFN is generally not validated or guaranteed. The analysis covers octave frequency bands ranging from 4 Hz to 250 Hz, and comparisons are made against measurements conducted at a wind farm in Poland. Non-parametric statistical tests were used with a significance level of α=0.05 to determine significant differences between measured and predicted results. The results show that the Nord2000 method provides accurate calculations, while the ISO 9613-2 method can be used for simplified assessments of ILFN generated by wind turbines during the investment preparation phase.
Thermodynamic Analysis of the Combustion Process in Hydrogen-Fueled Engines with EGR
Authors: Szwaja Stanislaw , Piotrowski Andrzej , Szwaja Magdalena , Musial Dorota
Published: June 2024

This article presents a novel approach to the analysis of heat release in a hydrogen-fueled internal combustion spark-ignition engine with exhaust gas recirculation (EGR). It also discusses aspects of thermodynamic analysis common to modeling and empirical analysis. This new approach concerns a novel method of calculating the specific heat ratio (cp/cv) and takes into account the reduction in the number of moles during combustion, which is characteristic of hydrogen combustion. This reduction in the number of moles was designated as a molar contraction. This is particularly crucial when calculating the average temperature during combustion. Subsequently, the outcomes of experimental tests, including the heat-release rate, the initial combustion phase (denoted CA0-10) and the main combustion phase (CA10-90), are presented. Furthermore, the impact of exhaust gas recirculation on the combustion process in the engine is also discussed. The efficacy of the proposed measures was validated by analyzing the heat-release rate and calculating the mean combustion temperature in the engine. The application of EGR in the range 0-40% resulted in a notable prolongation of both the initial and main combustion phases, which consequently influenced the mean combustion temperature.
Framework to Develop Electric School Bus Vehicle-to-Grid (ESB V2G) Systems Supplied with Solar Energy in the United States
Authors: Haces-Fernandez Francisco
Published: June 2024

Federal and state governments in the United States (US) are promoting the transition from traditional Diesel School Buses to Electric School Buses (ESBs). This would prevent the emission of deleterious air pollutants that affect students and communities while simultaneously contributing to a reduction in greenhouse gases, aiding in the fight against climate change. However, due to their significant size and long routes, ESBs require large batteries with significant electricity demand. If this additional electricity demand is supplied to hundreds of thousands of EBSs at peak consumption times, the strain on the grid may be detrimental, while transportation costs for schools could dramatically increase. Furthermore, if EBSs are charged using traditional hydrocarbon generation, the environmental benefits of these projects may be significantly reduced. Therefore, applying renewable energy presents a host of synergistic opportunities to reduce emissions while providing inexpensive electricity to schools. Solar energy is abundant in large portions of the US, potentially providing many schools with ample inexpensive and sustainable electricity to power their transportation equipment and meet other requirements at their facilities. This research developed a novel framework to integrate publicly available big data provided by federal and state agencies in the US, as well as National Laboratories, to provide stakeholders with actionable information to develop EBS grid-to-vehicle (V2G) systems across the US. Geographic Information Systems, data analytics and Business Intelligence were applied to assess and characterize solar energy generation and consumption patterns. The novel integration of the systems in the proposed framework provided encouraging results that have practical implications for stakeholders to develop successful and sustainable ESB V2G facilities. These results identified many schools across the US that would significantly benefit from the use of solar energy and be able to supply their local communities during idle times with renewable energy through V2G. The renewable energy resource would be capable of charging ESBs at a low cost for operational availability as required. The results indicate that the proposed ESB V2G system will provide significant benefits to both schools and their local communities. The feasibility of the proposed endeavor was validated by the results of the study, providing both school and solar energy stakeholders with insights into how to better manage such a complex system.
Comprehensive Overview of Recent Research and Industrial Advancements in Nuclear Hydrogen Production
Authors: Venizelou Venizelos , Poullikkas Andreas
Published: June 2024

As new sources of energy and advanced technologies are used, there is a continuous evolution in energy supply, demand, and distribution. Advanced nuclear reactors and clean hydrogen have the opportunity to scale together and diversify the hydrogen production market away from fossil fuel-based production. Nevertheless, the technical uncertainties surrounding nuclear hydrogen processes necessitate thorough research and a solid development effort. This paper aims to position pink hydrogen for nuclear hydrogen production at the forefront of sustainable energy-related solutions by offering a comprehensive review of recent advancements in nuclear hydrogen production, covering both research endeavors and industrial applications. It delves into various pink hydrogen generation methodologies, elucidating their respective merits and challenges. Furthermore, this paper analyzes the evolving landscape of pink hydrogen in terms of its levelized cost by comparatively assessing different production pathways. By synthesizing insights from academic research and industrial practices, this paper provides valuable perspectives for stakeholders involved in shaping the future of nuclear hydrogen production.
Energy Poverty and Democratic Values: A European Perspective
Authors: Kwilinski Aleksy , Lyulyov Oleksii , Pimonenko Tetyana
Published: June 2024

This paper explores the complex relationship between energy poverty and the maintenance of democratic values within the European Union (EU), suggesting that energy poverty not only impacts economic stability and health outcomes but also poses significant challenges to democratic engagement and equity. To measure energy poverty, a composite index is developed using the entropy method, which surpasses traditional measures focused solely on access to energy or its developmental implications. To assess the level of democratic governance in EU countries, the voice and accountability index (VEA), which is part of the World Governance Indicators compiled by the World Bank, is utilized. By analyzing EU data from 2006 to 2022, the findings suggest that a 1% improvement in VEA quality, represented by a coefficient of 0.122, is correlated with a notable improvement in the energy poverty index. This suggests that the EU should focus on enhancing transparency and public participation in energy decision-making, along with ensuring accountability in policy implementation. The research also differentiates between full and flawed democracies, noting that tailored approaches are needed. In full democracies, leveraging economic prosperity and trade is crucial due to their significant positive impacts on the energy poverty index. In contrast, in flawed democracies, enhancing governance and accountability is more impactful, as evidenced by a higher coefficient of 0.193. Strengthening legal and regulatory frameworks, improving regulatory quality, and ensuring public engagement in governance could substantially mitigate energy poverty in these contexts. In addition, this paper demonstrates that this relationship is influenced by factors such as income inequality, energy intensity, and trade openness.
Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland
Authors: Vakhnin Aleksei , Ryzhikov Ivan , Brester Christina , Niska Harri , Kolehmainen Mikko
Published: June 2024

Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network’s measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests.
Influence of γ-Fe2O3 Nanoparticles Added to Gasoline–Bio-Oil Blends Derived from Plastic Waste on Combustion and Emissions Generated in a Gasoline Engine
Authors: Palmay Paul , Barzallo Diego , Puente Cesar , Robalino Ricardo , Quinaluisa Dayana , Bruno Joan Carles
Published: June 2024

The environmental pressure to reduce the use of fossil fuels such as gasoline generates the need to search for new fuels that have similar characteristics to conventional fuels. In this sense, the objective of the present study is the use of commercial gasoline in mixtures with pyrolytic oil from plastic waste and the addition of γ-Fe2O3 nanoparticles (NPs) in a spark ignition engine to analyze both the power generated in a real engine and the emissions resulting from the combustion process. The pyrolytic oil used was obtained from thermal pyrolysis at low temperatures (450 °C) of a mixture composed of 75% polystyrene (PS) and 25% polypropylene (PP), which was mixed with 87 octane commercial gasoline in 2% and 5% by volume and 40 mg of γ-Fe2O3 NPs. A standard sample was proposed, which was only gasoline, one mixture of gasoline with bio-oil, and a gasoline, bio-oil, and NPs mixture. The bio-oil produced from the pyrolysis of PS and PP enhances the octane number of the fuel and improves the engine’s power performance at low revolutions. In contrast, the addition of iron NPs significantly improves gaseous emissions with a reduction in emissions of CO (carbon monoxide), NOx (nitrogen oxide), and HCs (hydrocarbons) due to its advantages, which include its catalytic effect, presence of active oxygen, and its large surface area.
Combinatorial Component Day-Ahead Load Forecasting through Unanchored Time Series Chain Evaluation
Authors: Kontogiannis Dimitrios , Bargiotas Dimitrios , Fevgas Athanasios , Daskalopulu Aspassia , Tsoukalas Lefteri H.
Published: June 2024

Accurate and interpretable short-term load forecasting tasks are essential to the optimal operation of liberalized electricity markets since they contribute to the efficient development of energy trading and demand response strategies as well as the successful integration of renewable energy sources. Consequently, performant day-ahead consumption forecasting models need to capture feature nonlinearities, analyze system dynamics and conserve evolving temporal patterns in order to minimize the impact of noise and adapt to concept drift. Prominent estimators and standalone decomposition-based approaches may not fully address those challenges as they often yield small error rate improvements and omit optimal time series evolution. Therefore, in this work we propose a combinatorial component decomposition method focused on the selection of important renewable generation component sequences extracted from the combined output of seasonal-trend decomposition using locally estimated scatterplot smoothing, singular spectrum analysis and empirical mode decomposition methods. The proposed method was applied on five well-known kernel models in order to evaluate day-ahead consumption forecasts on linear, tree-based and neural network structures. Moreover, for the assessment of pattern conservation, an intuitive metric function, labeled as Weighted Average Unanchored Chain Divergence (WAUCD), based on distance scores and unanchored time series chains is introduced. The results indicated that the application of the combinatorial component method improved the accuracy and the pattern conservation capabilities of most models substantially. In this examination, the long short-term memory (LSTM) and deep neural network (DNN) kernels reduced their mean absolute percentage error by 46.87% and 42.76% respectively and predicted sequences that consistently evolved over 30% closer to the original target in terms of daily and weekly patterns.
Non-Linear Phenomena in Voltage and Frequency Converters Supplying Non-Thermal Plasma Reactors
Authors: Komarzyniec Grzegorz Karol , Stryczewska Henryka Danuta , Boiko Oleksandr
Published: June 2024

Atmospheric pressure cold plasmas have recently been the subject of intense research and applications for solving problems in the fields of energy, environmental engineering, and biomedicine. Non-thermal atmospheric pressure plasma sources, with dielectric barrier discharges, plasma jets, and arc discharges, are non-linear power loads. They require special power systems, which are usually designed separately for each type of plasma reactor, depending on the requirements of the plasma-chemical process, the power of the receiver, the type of process gas, the current, voltage and frequency requirements, and the efficiency of the power source. This paper presents non-linear phenomena accompanying plasma generation in the power supply plasma reactor system, such as harmonic generation, resonance, and ferroresonance of currents and voltages, and the switching of overvoltages and pulse generation. When properly applied, this can support the operation of the above-mentioned reactors by providing improved discharge ignition depending on the working gas, thus increasing the efficiency of the plasma process and improving the cooperation of the plasma-generation system with the power supply.
Biomethane Production from the Two-Stage Anaerobic Co-Digestion of Cow Manure: Residual Edible Oil with Two Qualities of Waste-Activated Sludge
Authors: de la Cruz-Azuara Jesus Eduardo , Ruiz-Marin Alejandro , Canedo-Lopez Yunuen , Aguilar-Ucan Claudia Alejandra , Ceron-Breton Rosa Maria , Ceron-Breton Julia Griselda , Anguebes-Franseschi Francisco
Published: June 2024

Wastewater treatment systems produce large volumes of sludge which is not used; its final disposal is in soil or landfill. This sludge represents a biomethane-energy alternative through anaerobic co-digestion, contributing to reducing the environmental impacts caused by their inadequate disposal. Biomethane production by the two-stage production method in batch digesters with pH and temperature control was evaluated by two qualities of waste-activated sludge (SLB50 and SLB90) and with a mixture of two co-substrates: cow manure (CEV50 and CEV90) and residual edible oil (CAV50 and CAV90). Bacteria in good-quality sludge (SLB90) showed a faster adaptation of 2 days than those in low-quality sludge (SLB50), with a 25-day lag phase. The highest CH4 production was for SLB90 (303.99 cm3 d−1) compared to SLB50 (4.33 cm3 d−1). However, the cow manure–sludge mixture (CEV90) contributed to the increased production of CH4 (42,422.8 cm3 d−1) compared to CEV50 (12,881.45 cm3 CH4 d−1); for CAV90 and CAV50, these were 767.32 cm3 d−1 and 211.42 cm3 d−1, respectively. The addition of sludge co-substrates improves the nutrient balance and C/N ratio; consequently, methane production improves. This methodology could be integrated into concepts of the circular economy.
Phylogenetic Diversity, Host Specificity, and Distribution of the Wood-Decaying Fungus Phellinotus teixeirae in Western Colombia’s Seasonally Dry Tropical Forest
Authors: Bolaños-Rojas Ana C. , Londoño-Caicedo Jorge M. , Cortés Andrés J. , Motato-Vásquez Viviana
Published: June 2024

Phellinotus (Polyporales) is a common genus of wood-decay fungi in tropical and subtropical areas, endemic to the Seasonally Dry Tropical Forest (SDTF) biome. However, Phellinotus diversity remains unexplored, despite being a major threat to living trees. Therefore, this study is aimed at confirming and characterizing through morphological and molecular data the first isolates of Phellinotus teixeirae in Pithecellobium dulce (Fabaceae) trees (locally referred to as ‘Chiminango’) from the endangered Colombian SDTF biome. Fifteen fungal specimens were recovered from living P. dulce trees, in the urban area and at the Universidad del Valle campus, and classified as P. teixeirae based on taxonomical descriptors. Phylogenetic relationships were inferred from a four-loci dataset (ribosomal and gene-coding regions), including 82 taxa covering 3991 nucleotide positions. The analysis recovered seven highly supported (>90% bootstrapping) monophyletic taxa of the ‘Phellinotus Clade’, and confirmed the new distribution range of P. teixeirae (100% bootstrap support), which extends approx. 1000 km north in the Neotropics. Hierarchical stratified Analysis of MOlecular VAriance (AMOVA) provided a clear genetic distinction between species (70% of variation, p-value = 0.001) and low differentiation among country of origin within species (11%, p-value = 0.044). Discriminant Analysis for Principal Components (DAPC) indicated complex clustering including closely related species, probably a signal of recent radiation and weak species boundaries. Median-joining haplotype network analysis identified unique haplotypes, which may correlate with new host colonization and population expansion (Tajima’s D ≤ −0.5). In conclusion, this study provides the first assessment of the genetic diversity of P. teixeirae in a novel geography (SDTP) and host tree (P. dulce). However, increasing the number of isolates remains critical to understand further the genus’ distribution patterns and drivers of genetic diversity.
Abundance and Species Richness of Lianas in a Karst Seasonal Rainforest: The Influence of Abiotic and Biotic Factors
Authors: Li Jianxing , Lu Fang , Li Dongxing , Wang Bin , Guo Yili , Wen Shujun , Huang Fuzhao , Tao Wanglan , Tang Nianwu , Li Xiankun , Xiang Wusheng
Published: June 2024

Lianas are a crucial component of karst seasonal rainforests, yet research on them has predominantly focused on non-karst regions. Consequently, their abundance and species richness remain relatively understudied within karst ecosystems. We aimed to document the abundance and species richness of lianas and investigate their relationships with abiotic and biotic factors, based on data from a fully mapped 15 ha plot in a karst seasonal rainforest of Nonggang (SW China). Structural equation models (SEMs) were employed to estimate the path coefficients and variation of dependent variables, enabling a comprehensive analysis of the factors affecting the abundance and species richness of liana. Within the 15 ha plot, a total of 23,819 lianas were identified, encompassing 113 species from 34 families. These lianas constituted 24.16% of the total woody plant density and 33.44% of the species present, but only 4.32% of the total woody plant basal area. Lianas are primarily influenced by abiotic factors, especially elevation and phosphorus (P), with less impact from biotic factors. Our findings reveal that lianas, despite constituting a relatively small percentage of the total woody plant basal area, significantly contribute to the density and diversity of the forest. Notably, abiotic factors such as elevation and phosphorus availability predominantly shape the distribution and richness of lianas, highlighting the importance of these environmental variables. The findings offer valuable insights for future liana studies and the preservation of karst forests’ biodiversity.
Advancing Thermophilic Anaerobic Digestion of Corn Whole Stillage: Lignocellulose Decomposition and Microbial Community Characterization
Authors: Bokhary Alnour , Ale Enriquez Fuad , Garrison Richard , Ahring Birgitte Kiaer
Published: June 2024

Converting corn grains into bioethanol is an expanding practice for sustainable fuel production, but this is accompanied by the production of large quantities of by-products such as whole stillage. In the present study, the influence of advanced wet oxidation and steam explosion (AWOEx) pretreatment on biogas production and lignocellulose decomposition of corn whole stillage (CWS) was evaluated using semi-continuous thermophilic reactors. The digestion of the CWS was shown to be feasible with an organic loading rate (OLR) of 1.12 ± 0.03 kg VS/m3 day and a hydraulic retention time (HRT) of 30 days, achieving a methane yield of 0.75 ± 0.05 L CH4/g VSfed for untreated stillage and 0.86 ± 0.04 L CH4/g VSfed for pretreated stillage, corresponding with an increase in methane yield of about 15%. However, the reactors showed unstable performance with the highest investigated OLRs and shortest HRTs. Under optimal conditions, the conversion efficiencies of COD, cellulose, hemicellulose, and lignin were 88, 95, 97, and 59% for pretreated CWS, and 86, 94, 95, and 51% for untreated CWS, respectively. Microbial community analysis showed that Proteiniphilum, MBA03, and Acetomicrobium were the dominant genera in the digestate and were likely responsible for the conversion of proteins and volatile fatty acids in CWS.
Yeast Culture Is Beneficial for Improving the Rumen Fermentation and Promoting the Growth Performance of Goats in Summer
Authors: Zhang Shuang , Geng Yuancong , Ling Yan , Wang Dan , Hu Guixue
Published: June 2024

This study aimed to investigate the effects of yeast cultures on the antioxidant capacity, rumen fermentation, and growth performance of goats in the summer. An in vitro experiment was conducted using yeast culture supplemented at 0% (control), 0.6% (test 1), 0.9% (test 2), and 1.2% (test 3) of the dry matter (DM) weight of the basal diet. With a 24 h fermentation, the pH value; the total short-chain fatty acid, acetic acid, propionic acid, and butyric acid concentrations; and the degradability of the DM, the neutral detergent fiber, and the acid detergent fiber were significantly increased (p < 0.05) in tests 2 and 3 compared with the control group. In the feeding experiment, thirty-six crossbreed goats aged 3.0 ± 0.5 months with a body weight of 11.08 ± 1.41 kg were divided, and the yeast culture was supplemented at 0% (control), 0.90% (test 1), and 1.20% (test 2) of the basal diet. Similar effects on rumen fermentation parameters were obtained in test 1 and 2 groups compared to the in vitro experiment. Moreover, the dry matter intake, average daily gain, serum total antioxidant capacity, and the activities of total superoxide dismutase and glutathione peroxidase were significantly higher and the malondialdehyde concentration was significantly lower (p < 0.05) in tests 1 and 2 compared with the control. The results indicated that yeast culture (0.90%) could improve the antioxidant capacity, rumen fermentation, and growth performance of goats in summer. The optimal supplementation concentration is 0.90% DM.
Investigation of Freezing and Freeze-Drying for Preserving and Re-Using a Whole Microbial Cheese Community
Authors: Cao Wenfan , Passot Stéphanie , Irlinger Françoise , Fonseca Fernanda
Published: June 2024

Preserving microbial ecosystems obtained from traditional cheese-making processes is crucial to safeguarding the biodiversity of microbial cheese communities and thus ensuring that the high flavor quality of traditional cheeses is maintained. Few protocols have been proposed for the long-term storage of microbial consortia. This work aimed to develop preservation methods to stabilize the entire microbial community in smear-ripened cheese without multiplication or isolation. A simplified microbial community, capable of reproducing the metabolic pattern of cheese maturation, was used in three independent cheese productions. Cheese samples were taken before and after the ripening step, mixed with maltodextrin or saline solution, and subjected to different stabilization conditions including freezing and freeze-drying, followed by 1 month of storage. Microbial survival was quantified using the colony-forming unit assay. Differential scanning calorimetry was used to relate the physical events occurring within the samples to the microbial storage stability. Freezing at −80 °C resulted in the lowest loss of culturability (<0.8 log unit), followed by freezing at −20 °C and freeze-drying. The ripening bacteria appeared as the most sensitive microorganisms within the community. Moreover, a successful cheese production using the best-stabilized community showed the possibility of preserving and re-using an entire microbial community of interest.
Common Attractors for Generalized F-Iterated Function Systems in G-Metric Spaces
Authors: Nazir Talat , Silvestrov Sergei
Published: June 2024

In this paper, we study the generalized F-iterated function system in G-metric space. Several results of common attractors of generalized iterated function systems obtained by using generalized F-Hutchinson operators are also established. We prove that the triplet of F-Hutchinson operators defined for a finite number of general contractive mappings on a complete G-metric space is itself a generalized F-contraction mapping on a space of compact sets. We also present several examples in 2-D and 3-D for our results.
Self-Esteem and Resilience in Adolescence: Differences between Bystander Roles and Their Implications in School Violence in Spain
Authors: González Moreno Alba , Molero Jurado María del Mar
Published: June 2024

School violence is a social problem that has an impact on the psychological well-being of adolescents. One of the least identified roles within school violence is that of bystander, which refers to students who witness acts of violence perpetrated by their peers in the school environment. Current scientific evidence determines that young people with high self-esteem and resilience tend to have better mental health. The aim of this research is to identify the role of being a bystander of school violence on self-esteem and resilience in this crucial developmental stage of adolescence. The sample is composed of a total of 730 adolescents aged between 14 and 19 years. The results obtained indicate that young people who perceive themselves as non-bystanders of school violence show higher levels of self-esteem. As for the differences according to sex, it was found that non-bystander boys have greater resilience and self-esteem compared to girls. There are negative correlations between a healthy lifestyle and stress, but positive correlations between healthy lifestyle and self-esteem. In addition, we wanted to investigate the likelihood that observant adolescents intervene to help their peers. The results show that resilience acts as a protective factor that encourages such intervention, while self-esteem would be a risk factor. These findings highlight the importance of promoting resilience and self-esteem in school settings to improve peer relationships and foster healthy youth development.
Donkey Gelatin and Keratin Nanofibers Loaded with Antioxidant Agents for Wound Healing Dressings
Authors: Râpă Maria , Gaidau Carmen , Stefan Laura Mihaela , Lazea-Stoyanova Andrada , Berechet Mariana Daniela , Iosageanu Andreea , Matei Ecaterina , Jankauskaitė Virginija , Predescu Cristian , Valeika Virgilijus , Balčiūnaitienė Aistė , Cupara Snezana
Published: June 2024

Acute and chronic wounds present a significant healthcare challenge, requiring innovative solutions for effective treatment. The exploitation of natural by-products with advanced cell regeneration potential and plant-based materials, which possess bioactive properties, is an innovative topic in wound management. This study investigates the potential of donkey gelatin and keratin for blending with natural bioactive extracts such as sumac, curcumin, and oak acorn to fabricate antioxidant and antimicrobial nanofibers with accelerated wound healing processes. The fabricated nanofibers possess good in vitro biocompatibility, except for the sumac-based donkey nanofibers, where cell viability significantly dropped to 56.25% (p < 0.05 compared to non-treated cells). The nanofiber dimensions showed structural similarities to human extracellular matrix components, providing an ideal microenvironment for tissue regeneration. The donkey nanofiber-based sumac and curcumin extracts presented a higher dissolution in the first 10 min (74% and 72%). Curcumin extract showed similar antimicrobial and antifungal performances to rivanol, while acorn and sumac extracts demonstrated similar values to each other. In vitro tests performed on murine fibroblast cells demonstrated high migration rates of 89% and 85% after 24 h in the case of acorn and curcumin nanofibers, respectively, underscoring the potential of these nanofibers as versatile platforms for advanced wound care applications.