bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Color Cues on the Learning Performance in Video Lectures
Authors: She Linwei , Wang Zhiguo , Tao Xiaohui , Lai Liqi
Published: July 2024

This study explores the learning effects of color cues in video lectures and their underlying mechanisms. With the rapid growth of online education, lifelong learning, and blended learning, video lectures have become integral to teaching and learning. Color, a crucial element in visual design, directs attention, organizes content, and integrates information. Evaluating 78 college students, we assessed learning performance by comparing video content with no-color, single-color, and multi-color cues using eye-tracking technology and cognitive load scales. Results indicate that students viewing videos with color cues demonstrated better retention and transfer test performance, while absence or excess of color cues increased cognitive load. These findings have practical implications for video producers and provide a theoretical foundation for enhancing learners’ viewing experience and overall effectiveness. This study not only offers an in-depth analysis of color cue utilization in video lectures, highlighting their positive impact on learning outcomes but also introduces fresh perspectives for educational technology and cognitive psychology research. Future investigations should consider color cue effects in diverse cultural contexts and subject areas, exploring varied strategies to optimize the learning experience.
Autonomous Self-Healing Agents in Cementitious Materials: Parameters and Impacts on Mortar Properties
Authors: Lima Geannina Terezinha dos Santos , Silvestro Laura , Tambara Júnior Luís Urbano Durlo , Cheriaf Malik , Rocha Janaíde Cavalcante
Published: July 2024

The concept of self-healing materials and the development of encapsulated curing agents represent a cutting-edge approach to enhancing the longevity and reducing the maintenance costs of cementitious structures. This systematic literature review aims to shed light on the parameters involved in the autonomous self-healing of cementitious materials, utilizing various encapsulated healing agents such as pellets, granules, and capsules. This review also identifies and selects studies that offer additional insights into the efficacy of the self-healing process in cementitious materials and the influence of these specific encapsulated healing agents on the physical mechanical properties of mortars. This comprehensive approach provides a deep understanding of the interplay between self-healing and the physical–mechanical properties of mortars containing these encapsulated healing agents. The main findings indicate that the cement-to-sand ratio, characteristics of fine aggregates, and encapsulation methods significantly impact crack control, self-healing efficiency, and properties of mortar in both fresh and hardened states. The content of encapsulated healing agents within the cementitious matrix affects both the initial workability or flow and subsequent mechanical properties. While pellets coated with PVA film typically reduce workability in the fresh state and compressive strength, capsules coated with Portland cement and sodium silicate mitigate these effects and improve crack sealing in fresh and hardened states without compromising the self-healing capacity of cracks. The three-point flexural test has emerged as the preferred method for a pre-crack assessment over 28 days, with variations depending on the type of healing agent used. As noted in the literature, water has been identified as the optimal environment for autonomous healing. These findings underscore the potential of encapsulation techniques to enhance self-healing capabilities through the controlled release of agents within the cementitious matrix, thereby advancing the research on and development of intelligent construction materials and increasing the durability of cement-based structures.
Point Estimation-Based Dynamic Reliability Analysis of Beam Bridges under Seismic Excitation Considering Uncertain Parameters
Authors: Wu Luo-Cheng , Zeng Meng-Lan , Yan Ke-Zhen
Published: July 2024

Beam bridges, as the primary structural form of medium and small-sized bridges, are extensively utilized for road and railway crossings over rivers and valleys. Ensuring their reliability during earthquakes is crucial not only for maintaining traffic flow but also for mitigating the seismic impact on the economy and society. Considering earthquake intensity and uncertain parameters, this paper proposes an innovative method for assessing the seismic reliability of simply-supported beam bridges under three different levels of seismic design: minor, moderate, and major earthquakes. The proposed method first estimates the probability of encountering three typical earthquake intensities during the design life of simply-supported beam bridges based on crowd intensity, benchmark intensity, and major earthquake intensity. It then introduces uncertain parameters and employs the point estimation method to calculate the probability of bridge passage under specific earthquake intensities. Finally, it combines these earthquake intensities to calculate the overall seismic reliability of simply-supported beam bridges. The effectiveness and efficiency of this method are demonstrated through calculations for a three-span, double-degree-of-freedom simply-supported beam bridge, and validated using Monte Carlo simulations. This research provides solid theoretical support for seismic assessment, design, and intensity-based reliability analysis of simply-supported beam bridges.
A Study of Factors Influencing the Compliance of Design Estimates at the Construction Stage of Residential Buildings
Authors: Vitasek Stanislav , Macek Daniel
Published: July 2024

This article primarily addresses the factors affecting the possibility of achieving the costs estimated in the design stage of a building after its completion. The authors rely on an information base of twenty-three apartment buildings erected by twelve construction companies between 2017 and 2023, divided into two phases (2017–2020 and 2021–2023). The outputs of the article present the conclusions of several years of research into the identification of factors and risks affecting construction costs, capturing the development of price indicators over time, creating a realistic picture of working with costs from the building’s design stage during its execution and the application of sustainable and digitalization technologies within a selected segment of the building industry. The presented conclusions are based on statistical dependencies compiled using regression analysis to explore the relationships between the cost, time and technological parameters of selected buildings. These outputs provide an interesting and well-founded perspective on the obtained data, thus overcoming the lack of relevant methods, techniques and fitting algorithms for a sophisticated and long-term approach to pricing in the construction sector.
Evaluation Model for Bearing Capacity of Grouting Sleeves Based on Conventional Material Parameters
Authors: Zhu Hongguang , Zhao Zhijian , Shen Zhengyan , Pang Seng
Published: July 2024

The existing bearing capacity model for grouting sleeves is synthesized in this study. Utilizing the principles of thick-walled cylinders in elastic mechanics, a comprehensive ultimate bearing capacity model for grouting sleeves is developed using conventional material parameters. To validate the efficacy of the model, 18 steel bar grouting sleeves of varying anchor lengths, steel bar diameters, and designs were tested. The outcomes indicate minimal errors in the computational results and demonstrate superior applicability when juxtaposed with established grouting sleeve bearing capacity formulas.
How Does the One Belt One Road Initiative Affect the Chinese International Architecture, Engineering, and Construction Firms? Empirical Analysis Based on Propensity Score Matching and Difference-in-Differences Method
Authors: Ye Jinglei , Zhang Na , Deng Xiaopeng , Niu Yanliang
Published: July 2024

With the proposal of the “One Belt One Road (OBOR) initiative”, the Chinese architecture, engineering, and construction (AEC) industry has increasingly been exploring the overseas markets. This paper adopted the propensity score matching and difference-in-differences (PSM-DID) method to evaluate the impact of the OBOR Initiative on Chinese international contractors and consulting firms, respectively. The results shows that the OBOR Initiative significantly stimulated the overseas market development of contractors, whereas it had no positive impact on consulting firms. The results may provide comprehensive guidance for industry practitioners, policymakers, and scholars to correctly understand the different characteristics of international contractors and consulting firms, thereby formulating a targeted development strategy.
The Microalgae Chlamydomonas for Bioremediation and Bioproduct Production
Authors: Bellido-Pedraza Carmen M. , Torres Maria J. , Llamas Angel
Published: July 2024

The extensive metabolic diversity of microalgae, coupled with their rapid growth rates and cost-effective production, position these organisms as highly promising resources for a wide range of biotechnological applications. These characteristics allow microalgae to address crucial needs in the agricultural, medical, and industrial sectors. Microalgae are proving to be valuable in various fields, including the remediation of diverse wastewater types, the production of biofuels and biofertilizers, and the extraction of various products from their biomass. For decades, the microalga Chlamydomonas has been widely used as a fundamental research model organism in various areas such as photosynthesis, respiration, sulfur and phosphorus metabolism, nitrogen metabolism, and flagella synthesis, among others. However, in recent years, the potential of Chlamydomonas as a biotechnological tool for bioremediation, biofertilization, biomass, and bioproducts production has been increasingly recognized. Bioremediation of wastewater using Chlamydomonas presents significant potential for sustainable reduction in contaminants and facilitates resource recovery and valorization of microalgal biomass, offering important economic benefits. Chlamydomonas has also established itself as a platform for the production of a wide variety of biotechnologically interesting products, such as different types of biofuels, and high-value-added products. The aim of this review is to achieve a comprehensive understanding of the potential of Chlamydomonas in these aspects, and to explore their interrelationship, which would offer significant environmental and biotechnological advantages.
Oxalic Acid-Assisted Photo-Fenton Catalysis Using Magnetic Fe3O4 Nanoparticles for Complete Removal of Textile Dye
Authors: Bhavsar Sunil , Dudhagara Pravin , Ghelani Anjana , Wirajana I Nengah , Phi Quyet-Tien , Chen Yih-Yuan , Shyu Douglas J. H.
Published: July 2024

Textile industry effluents contain several hazardous substances, such as dye-containing effluents, which pose environmental and aesthetic challenges. Presently, the microbial-based remediation process is in use. This study investigated the application of ferrous–ferric oxide (Fe3O4) nanoparticles, a readily formulated nanoadsorbent, to remove scattered dye molecules from industrial effluents. The ferrous–ferric oxide nanoparticles were prepared using a chemical co-precipitation method. The nanoparticles had 26.93 emu g−1 magnetization, with sizes smaller than 20 nm, and possessed a highly purified cubic spinel crystallite structure. The catalytic activity of the iron oxide depended on the dose, photocatalytic enhancer, i.e., H2O2 level, pH of the reaction medium, and dye concentration. We optimized the Fenton-like reaction to work best using 1.0 g/L of ferrous–ferric oxide nanoparticles, 60 mM oxalic acid at pH 7.0, and 60 ppm of dye. Iron oxides act as photocatalysts, and oxalic acid generates electron–hole pairs. Consequently, higher amounts of super-radicals cause the rapid degradation of dye and pseudo-first-order reactions. Liquid chromatography–mass spectrometry (LC-MS) analysis revealed the ferrous–ferric oxide nanoparticles decolorized and destroyed Disperse Red 277 in 180 min under visible light. Hence, complete demineralization is observed using a photo-Fenton-like reaction within 3 h under visible light. These high-capacity, easy-to-separate next-generation adsorption systems are suggested to be suitable for industrial-scale use. Ferrous–ferric oxide nanoparticles with increased adsorption and magnetic properties could be utilized to clean environmental pollution.
Co-Encapsulation of Paclitaxel and Doxorubicin in Liposomes Layer by Layer
Authors: Mota Díaz Isaac Izcoatl , Douda Janna , García López Patricia , Cabrera Becerra Sandra Edith , Gómez Álvarez Miguel Ángel , Jiménez Rodríguez Rebeca , Jurado León Rafael , López Sánchez Pedro
Published: July 2024

The synergistic effect of antineoplastic drug co-encapsulation systems has made them highly regarded due to their improved pharmacological efficacy. Biopolymer-coated liposomes were evaluated for paclitaxel and doxorubicin co-encapsulation in MCF-7 and MDA-MB-231 breast cancer cell lines. These nanosystems are characterized by dynamic light scattering, transmission electron microscopy, and UV–VIS spectroscopy. The conventional and hybrid liposomal systems presented sizes of 150 to 230 nm and %EE greater than 80% for the encapsulated active ingredients. These drug-laden liposomal systems significantly decreased cell viability in both breast cancer cell lines compared with liposome-free drugs. The delivery of antineoplastic drugs in breast cancer therapy could potentially benefit from new hybrids for drug co-encapsulation.
Statistical Interdependence between Daily Precipitation and Extreme Daily Temperature in Regions of Mexico and Colombia
Authors: Zabaleta-Ortega Álvaro , Mercado-Fernández Teobaldis , Reyes-Ramírez Israel , Angulo-Brown Fernando , Guzmán-Vargas Lev
Published: July 2024

We study the statistical interdependence between daily precipitation and daily extreme temperature for regions of Mexico (14 climatic stations, period 1960–2020) and Colombia (7 climatic stations, period 1973–2020) using linear (cross-correlation and coherence) and nonlinear (global phase synchronization index, mutual information, and cross-sample entropy) synchronization metrics. The information shared between these variables is relevant and exhibits changes when comparing regions with different climatic conditions. We show that precipitation and temperature records from La Mojana are characterized by high persistence, while data from Mexico City exhibit lower persistence (less memory). We find that the information exchange and the level of coupling between the precipitation and temperature are higher for the case of the La Mojana region (Colombia) compared to Mexico City (Mexico), revealing that regions where seasonal changes are almost null and with low temperature gradients (less local variability) tend to display higher synchrony compared to regions where seasonal changes are very pronounced. The interdependence characterization between precipitation and temperature represents a robust option to characterize and analyze the collective dynamics of the system, applicable in climate change studies, as well as in changes not easily identifiable in future scenarios.
Fast Finite-Time Observer-Based Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems with Full-State Constraints
Authors: Zhou Kewei , Wang Xin
Published: July 2024

This article studies a class of uncertain nonlinear multiagent systems (MASs) with state restrictions. RBFNNs, or radial basis function neural networks, are utilized to estimate the uncertainty of the system. To approximate the unknown states and disturbances, the state observer and disturbance observer are proposed to resolve those issues. Moreover, a fast finite-time consensus control technique is suggested in order to accomplish fast finite-time stability without going against the full-state requirements. It is demonstrated that every signal could be stable and boundless, and an event-triggered controller is considered for the saving of resources. Ultimately, the simulated example demonstrates the validity of the developed approach.
Bounding Quantum Correlations: The Role of the Shannon Information in the Information Causality Principle
Authors: Oughton Natasha , Timpson Christopher G.
Published: July 2024

The Information Causality principle was proposed to re-derive the Tsirelson bound, an upper limit on the strength of quantum correlations, and has been suggested as a candidate law of nature. The principle states that the Shannon information about Alice’s distant database gained by Bob after receiving an m bit message cannot exceed m bits, even when Alice and Bob share non-local resources. As originally formulated, it can be shown that the principle is violated exactly when the strength of the shared correlations exceeds the Tsirelson bound. However, we demonstrate here that when an alternative measure of information, one of the Renyi measures, is chosen, the Information Causality principle no longer arrives at the correct value for the Tsirelson bound. We argue that neither the assumption of particular ‘intuitive’ properties of uncertainties measures, nor pragmatic choices about how to optimise costs associated with communication, are sufficient to motivate uniquely the choice of the Shannon measure from amongst the more general Renyi measures. We conclude that the dependence of the success of Information Causality on mere convention undermines its claimed significance as a foundational principle.
Efficient and Flexible Method for Reducing Moderate-Size Deep Neural Networks with Condensation
Authors: Chen Tianyi , Xu Zhi-Qin John
Published: July 2024

Neural networks have been extensively applied to a variety of tasks, achieving astounding results. Applying neural networks in the scientific field is an important research direction that is gaining increasing attention. In scientific applications, the scale of neural networks is generally moderate size, mainly to ensure the speed of inference during application. Additionally, comparing neural networks to traditional algorithms in scientific applications is inevitable. These applications often require rapid computations, making the reduction in neural network sizes increasingly important. Existing work has found that the powerful capabilities of neural networks are primarily due to their nonlinearity. Theoretical work has discovered that under strong nonlinearity, neurons in the same layer tend to behave similarly, a phenomenon known as condensation. Condensation offers an opportunity to reduce the scale of neural networks to a smaller subnetwork with a similar performance. In this article, we propose a condensation reduction method to verify the feasibility of this idea in practical problems, thereby validating existing theories. Our reduction method can currently be applied to both fully connected networks and convolutional networks, achieving positive results. In complex combustion acceleration tasks, we reduced the size of the neural network to 41.7% of its original scale while maintaining prediction accuracy. In the CIFAR10 image classification task, we reduced the network size to 11.5% of the original scale, still maintaining a satisfactory validation accuracy. Our method can be applied to most trained neural networks, reducing computational pressure and improving inference speed.
Behavior of Correlation Functions in the Dynamics of the Multiparticle Quantum Arnol’d Cat
Authors: Mantica Giorgio
Published: July 2024

The multi-particle Arnol’d cat is a generalization of the Hamiltonian system, both classical and quantum, whose period evolution operator is the renowned map that bears its name. It is obtained following the Joos–Zeh prescription for decoherence by adding a number of scattering particles in the configuration space of the cat. Quantization follows swiftly if the Hamiltonian approach, rather than the semiclassical approach, is adopted. The author has studied this system in a series of previous works, focusing on the problem of quantum–classical correspondence. In this paper, the dynamics of this system are tested by two related yet different indicators: the time autocorrelation function of the canonical position and the out-of-time correlator of position and momentum.
Inquiry-Based Activities with Woodlice in Early Childhood Education
Authors: García-Rodeja Isabel , Barros Sara , Sesto Vanessa
Published: July 2024

This study aims to describe the implementation of a teaching sequence where preschool-age children participate in activities related with woodlice. Although there is extensive literature on inquiry-based activities, most studies have been conducted in higher educational stages. Data were collected though audio and video recordings of the sessions, which have then been transcribed. The children participated in research activities in which they had no difficulty in posing hypotheses and making predictions. In relation to the experimental design, we observed difficulties in understanding the purpose of the experiment and how to reach conclusions from the experimental results. From the results of this study, we can conclude that children from an early age can engage in inquiry activities where they are given opportunities to make predictions, formulate hypotheses, and, with the help of the teacher, plan simple experiments to test their ideas.
Distance Learning through the Grand Egyptian Museum: Leveraging Modern Technology to Teach about Ancient Egypt among Schools and National Museums
Authors: Zakaria Nevine Nizar
Published: July 2024

By considering the increasing emphasis on lifelong learning and the ongoing significant debates over the value and utility of digital technologies in accelerating long-established learning tasks, the paper suggests new approaches for the national museums of Egypt to incorporate distance learning and museum-based online programs as a means to improve their traditional educational programs. This would significantly help school students better understand the ancient history of Egypt, “Pharaonic Egypt”, which is an essential part of their formal school curriculum. Moreover, it provides much-needed support to museums with limited or insufficient educational programs to promote more meaningful learning experiences. The proposed approach can be effectively implemented through the Grand Egyptian Museum (GEM), the world’s largest archaeological museum complex dedicated to the culture of ancient Egyptian Pharaonic Culture. The study discusses the GEM’s educational and digital infrastructure capabilities in creating a robust online learning platform that provides access to rich educational content and digital resources across Egypt’s various geographic regions, enhancing museum education concerning pharaonic Egypt in line with the state’s cultural policy to strengthen national identity among future generations. By analyzing both qualitative and quantitative data, the study investigates the potential benefits of integrating online digital technologies into the pedagogical and educational practices of Egypt’s national museums. It concludes by providing practical recommendations for future research and actions encouraging online learning among Egyptian museums, aligned with recent national efforts toward Egypt’s digital transformation.
State of the Art and Future Trends in Monitoring for Industrial Induction Heating Applications
Authors: Esteve Vicente , Bellido Juan L. , Jordán José
Published: July 2024

Induction surface hardening (ISH) processes are widely used in the heat treatment of numerous industrial components, especially in the automotive industry. Since this industry operates under very demanding quality standards, it is crucial for these heat treatment processes to meet rigorous specifications to ensure the safety and reliability of the produced components. This implies the precise and repeatable control of certain parameters throughout the manufacturing process of each of the parts treated, through precise and reliable instrumentation in electromagnetically harsh environments. The main objective of this work is to define the monitoring process for an industrial IHS application, determining the control needs and the methods of measurement, recording, and verification of the parameters that ensure the quality of the process. This paper describes the monitoring process of induction surface hardening, emphasizing the use of sensors and modern measurement and control systems. A comprehensive monitoring system supported by a programmable logic controller (PLC) and mixed acquisition and instrumentation systems (analog and digital) implemented with a high-performance Field Programmable Gate Array (FPGA) will be presented.
Optimal Allocation of Hybrid Energy Storage Capacity Based on ISSA-Optimized VMD Parameters
Authors: Luo Xin , He Yu , Zhang Jing , Li Jia
Published: July 2024

To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation optimization method based on variational mode decomposition (VMD) and a multi-strategy improved salp swarm algorithm (ISSA). From typical wind load power and contact line agreement power, the HESS power is obtained. VMD decomposes this power into high- and low-frequency power, respectively, for the super capacitor and the Li-ion battery. Considering charging and discharging power and state of charge (SOC) constraints, an optimization model minimizing the system equivalent annual value cost is established. ISSA optimizes the best decomposition layer K and penalty coefficients α in VMD. The optimal cut-off point and corresponding energy storage allocation scheme are analyzed. A simulation and analysis on MATLAB show that the proposed ISSA-VMD HESS capacity allocation scheme saves 7.53% in costs compared to an empirical mode decomposition (EMD) scheme, proving the method’s effectiveness and superiority.
A Study on GAN-Based Car Body Part Defect Detection Process and Comparative Analysis of YOLO v7 and YOLO v8 Object Detection Performance
Authors: Jung Do-Yoon , Oh Yeon-Jae , Kim Nam-Ho
Published: July 2024

The main purpose of this study is to generate defect images of body parts using a GAN (generative adversarial network) and compare and analyze the performance of the YOLO (You Only Look Once) v7 and v8 object detection models. The goal is to accurately judge good and defective products. Quality control is very important in the automobile industry, and defects in body parts directly affect vehicle safety, so the development of highly accurate defect detection technology is essential. This study ensures data diversity by generating defect images of car body parts using a GAN and through this, compares and analyzes the object detection performance of the YOLO v7 and v8 models to present an optimal solution for detecting defects in car parts. Through experiments, the dataset was expanded by adding fake defect images generated by the GAN. The performance experiments of the YOLO v7 and v8 models based on the data obtained through this approach demonstrated that YOLO v8 effectively identifies objects even with a smaller amount of data. It was confirmed that defects could be detected. The readout of the detection system can be improved through software calibration.
Techno-Economic Analysis of Clean Hydrogen Production Plants in Sicily: Comparison of Distributed and Centralized Production
Authors: Massaro Fabio , Ferraro Marco , Montana Francesco , Riva Sanseverino Eleonora , Ruffino Salvatore
Published: July 2024

This paper presents an assessment of the levelized cost of clean hydrogen produced in Sicily, a region in Southern Italy particularly rich in renewable energy and where nearly 50% of Italy’s refineries are located, making a comparison between on-site production, that is, near the end users who will use the hydrogen, and centralized production, comparing the costs obtained by employing the two types of electrolyzers already commercially available. In the study for centralized production, the scale factor method was applied on the costs of electrolyzers, and the optimal transport modes were considered based on the distance and amount of hydrogen to be transported. The results obtained indicate higher prices for hydrogen produced locally (from about 7 €/kg to 10 €/kg) and lower prices (from 2.66 €/kg to 5.80 €/kg) for hydrogen produced in centralized plants due to economies of scale and higher conversion efficiencies. How-ever, meeting the demand for clean hydrogen at minimal cost requires hydrogen distribution pipelines to transport it from centralized production sites to users, which currently do not exist in Sicily, as well as a significant amount of renewable energy ranging from 1.4 to 1.7 TWh per year to cover only 16% of refineries’ hydrogen needs.
Energy Consumption Prediction in Residential Buildings—An Accurate and Interpretable Machine Learning Approach Combining Fuzzy Systems with Evolutionary Optimization
Authors: Gorzałczany Marian B. , Rudziński Filip
Published: July 2024

This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in residential buildings. The proposed FRBPSs are characterized by a genetically optimized accuracy–interpretability trade-off. The trade-off optimization is carried out by means of multi-objective evolutionary optimization algorithms—in particular, by our generalization of the well-known strength Pareto evolutionary algorithm 2 (SPEA2). The proposed FRBPSs’ designing procedure is our original extension and generalization (for regression problems operating on continuous outputs) of an approach to designing fuzzy rule-based classifiers (FRBCs) we developed earlier and published in 2020 in this journal. FRBCs operate on discrete outputs, i.e., class labels. The experimental contribution of this work includes designing the collection of FRBPSs for residential building energy consumption prediction using the data set published in 2024 and available from Kaggle Database Repository. Moreover, the comparison with 20 available alternative approaches is carried out, demonstrating that our approach significantly outperforms alternative methods in terms of interpretability and transparency of the energy consumption predictions made while remaining comparable or slightly superior in terms of the accuracy of those predictions.
Energy-Consuming Right Trading Policy and Corporate ESG Performance: Quasi-Natural Experimental Evidence from China
Authors: Yan Qiuyan , Wan Kai
Published: July 2024

The energy-consuming right trading policy (ECRTP) represents a significant initiative to promote the sustainable development of Chinese enterprises. This study employs the difference-in-differences methodology to analyze how ECRTP influences ESG performance based on data from A-share listed industrial enterprises in China from 2006 to 2020. The findings indicate that ECRTP effectively enhances corporate ESG performance, and this conclusion holds valid following a battery of robustness checks. Moreover, ECRTP improves corporate ESG performance by promoting green technological innovation and green perceptions among executives. Tests for heterogeneity show that the ECRTP exerts a more pronounced influence on ESG performance for enterprises located in regions with high public environmental awareness, heavily polluting industries, and coastal areas. This study broadens the literature on ECRTP effectiveness evaluation, providing valuable insights for refining the design of these policies, promoting their implementation, and facilitating the achievement of dual control targets for energy consumption.
Does Anyone Care about the Opinion of People on Participating in a “Social” Metaverse? A Review and a Draft Proposal for a Surveying Tool
Authors: Mottura Stefano
Published: July 2024

In recent years, the attention paid to the metaverse in the scientific world has increased; the hottest topics include system architecture and enabling technologies, as well as business, privacy, ethical, and security issues. On the other side, at the mainstream level, it is well known that the company Meta (formerly Facebook) is striving to realize its interpretation of a “social” metaverse. As Meta is a big leader of social media, it is reasonable to guess that, in the future, users will participate in a new social platform, such as that which the company is building by depicting unlimited and engaging opportunities. Regardless of Meta, we ask what the opinion of people is about this possible future scenario. A literature search of previous works about this topic has been done; the few results we found were not properly on topic and showed heterogeneous content. A survey on interpretations of the metaverse of major information and communication technologies (ICT) companies that impact the consumer world was undertaken; the results show that Meta is the most prominent company with the mission of building a ”social” metaverse worldwide. Finally, a draft of a tool for assessing the predilection of people for a “social” metaverse, based on various facets of the future social platform, is proposed.
Effects of High Dietary Starch Levels on the Growth Performance, Liver Function, and Metabolome of Largemouth Bass (Micropterus salmoides)
Authors: Sun Lihui , Guo Jianlin , Li Qian , Jiang Jianhu , Chen Jianming , Gao Lingmei , Yang Bicheng , Peng Jun
Published: July 2024

In this study, we conducted a 16-week feeding trial to investigate the effects of a high-cassava starch diet on growth performance, liver function, and metabolism in largemouth bass (Micropterus salmoides). We formulated five diets containing varying levels of cassava starch: 12%, 9%, 6%, 3%, and 0% (termed M12, M9, M6, M3, and M0, respectively). We distributed these diets among largemouth bass with the initial body weight of 83.33 ± 0.55 g via an in-pond “raceway” aquaculture system. Our findings suggest that high level (12%) of cassava starch dietary inclusion adversely affected growth performance metrics such as weight gain rate and specific growth rate, along with feed utilization efficiency indicators, including protein efficiency, protein deposition rate, and the apparent digestibility of dry matter and protein. This negative impact was accompanied by a decrease in intestinal amylase activity. Through further transcriptomic analysis, we identified several key genes associated with carbohydrate metabolism, which underwent changes influencing liver function. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed the involvement of these differentially expressed genes (DEGs) in the tricarboxylic acid cycle (TCA cycle). Comparative metabolomics analysis further indicated that the M9 group showed significant enrichment in pathways related to amino acid metabolism and alterations in the levels of metabolites involved in carbohydrate metabolism. In conclusion, our study demonstrates that incorporating up to 9% cassava starch in the diet can enhance growth performance in largemouth bass by stimulating digestive enzyme activities and promoting glucose utilization.
Nutrient Composition of Four Dietary Patterns in Italy: Results from an Online Survey (the INVITA Study)
Authors: Baroni Luciana , Bonetto Chiara , Rizzo Gianluca , Galchenko Alexey , Guidi Giada , Visaggi Pierfrancesco , Savarino Edoardo , Zavoli Martina , de Bortoli Nicola
Published: July 2024

Though Italy is a native land of Mediterranean diet, its adherence in the Italian population is low, witnessed by the high rates of overweight in its inhabitants. Vegetarian dietary patterns (i.e., lacto-ovo-vegetarian and vegan) are increasing in western countries, and also in Italy, where 9.5% of the population self-declared as vegetarian in 2023. Though the vegetarian diet has been associated with beneficial health effects, speculation on its alleged nutrient inadequacy exists. For this reason, we assessed the nutrient composition of the diet of 470 participants enrolled in an online survey (the INVITA study), who completed a weighted food questionnaire on three different days. Participants were divided into four dietary groups obtained according to their self-declared dietary intakes: 116 Meat Eaters (MEs), 49 Fish Eaters (FEs), 116 Lacto-Ovo-Vegetarians (LOVs), and 189 VegaNs (VNs). The mean intake of most of the main nutrients was similar among all groups and within the normal range expected for the Italian population, supporting the adequacy of diets within our Italian sample, especially the LOV and VN diet. Since the Mediterranean diet is a plant-based diet, some of its components still persist in the current Italian diet, representing a staple also for people adopting a vegetarian diet.
Fermentation with Lactic Acid Bacteria for Bean Flour Improvement: Experimental Study and Molecular Modeling as Complementary Tools
Authors: Sabater Carlos , Sáez Gabriel D. , Suárez Nadia , Garro Marisa S. , Margolles Abelardo , Zárate Gabriela
Published: July 2024

Pulses are considered superfoods for the future world due to their properties, but they require processing to reduce antinutritional factors (ANFs) and increase bioactivity. In this study, bean flour (Phaseolus vulgaris L.) was fermented under different conditions (addition of Lactiplantibacillus plantarum CRL 2211 and/or Weissella paramesenteroides CRL 2182, temperature, time and dough yield) to improve its nutri-functional quality. Fermentation for 24 h at 37 °C with the mixed starter increased the lactic acid bacteria (LAB) population, acidity, polyphenol content (TPC) and ANF removal more than spontaneous fermentation. Statistical and rep-PCR analysis showed that fermentation was mainly conducted by Lp. plantarum CRL 2211. Metabolic modeling revealed potential cross-feeding between Lp. plantarum and W. paramesenteroides, while the molecular docking and dynamic simulation of LAB tannases and proteinases involved in ANF removal revealed their chemical affinity to gallocatechin and trypsin inhibitors. Fermentation was better than soaking, germination and cooking for enhancing bean flour properties: it increased the free amino acids content by 50% by releasing glutamine, glutamic acid, arginine, leucine and lysine and modified TPC by increasing gallic acid and decreasing caffeic, ferulic and vanillic acids and quercetin-3-glucoside. The combination of experimental and simulation data may help us to understand fermentation processes and to design products with desirable features.
Effects of Salinity on Physicochemical Properties, Flavor Compounds, and Bacterial Communities in Broad Bean Paste-Meju Fermentation
Authors: Guo Qingyan , Peng Jiabao , Zhao Jingjing , Lei Jie , Huang Yukun , Shao Bing
Published: July 2024

Broad bean paste (BBP) is a traditional fermented soy food, and its high salt content not only prolongs the fermentation time but also threatens human health. In this study, three BBP-meju with different salt concentrations were prepared, and the effects of varying salinity on fermentation were comprehensively compared. The results showed that salt-reduced fermentation contributed to the accumulation of amino acid nitrogen, reducing sugars, free amino acids, and organic acids. Alcohols, esters, aldehydes, and acids were the main volatile flavor compounds in BBP-meju, and the highest total volatile flavor compounds were found in medium-salt meju. Bacillus, Staphylococcus, Aspergillus, and Mortierella were the dominant microbial communities during fermentation, and there were also three opportunistic pathogens, Enterobacter, Pantoea, and Brevundimonas, respectively. According to Spearman correlation analysis, Wickerhamomyces, Bacillus, Staphylococcus, and Mortierella all showed highly significant positive correlations with ≥3 key flavor compounds, which may be the core functional flora. Furthermore, the dominant microbial genera worked synergistically to promote the formation of high-quality flavor compounds and inhibit the production of off-flavors during salt-reduced fermentation. This study provides a theoretical reference for the quality and safety control of low-salt fermented soy foods.
Microbial Population and Physicochemical Properties of Miang Fermented in Bamboo Tubes by the Luar Ethnic Group in Lao PDR
Authors: Phovisay Somsay , Abdullahi Aliyu Dantani , Kham Nang Nwet Noon , Unban Kridsada , Shetty Kalidas , Khanongnuch Chartchai
Published: July 2024

Miang is a traditional fermented food made from Assam tea leaves and consumed as a snack. This study investigated the underground Miang fermentation process practiced by the Luar ethnic group in Laos, specifically examining the nutritional composition and microbial dynamics. Lactic acid bacteria and yeast were dominant in the fermentation process, reaching 8.43 and 8.50 log CFU/g after one week before gradually declining, while the coliform bacterial count was at 5.31 log CFU/g in the initial week but became undetectable in the later stages of fermentation. Next-generation sequencing identified Firmicutes (75.02%) and Proteobacteria (23.51%) as the primary phyla. Bacterial genera included Lactobacillus (73.36%) and Acetobacter (21.06%), with fungi mainly represented by Pichia (85.52%) and Candida (13.45%). Fundamental microbes such as Lactobacillus and Acetobacter were predominantly present, alongside Pichia and Candida, in the fungal communities. Microbial activities played a crucial role in generating essential enzymes for Miang’s transformation. The nutritional transformation appears to be complete at 5 weeks of fermentation. The moisture content in the final products was approximately 74% and correlated with a change in nitrogen-free extract (NFE) and crude fiber. The fat content showed a slight increase from 1.3% to 2.52%, but protein content slightly declined from 17.21% to 16.05%, whereas ash content did not change significantly. Key polysaccharide-degrading enzymes, particularly pectinase and β-mannanase, were revealed and peaked at 48.32 and 25.32 U/g Miang, respectively. The total polyphenols increased from 103.54 mg/g dry Miang to 144.19–155.52 mg/g during fermentation. The lowered IC50 value indicated an increase in antioxidant activity. A fermentation period of at least 3 weeks proved to be optimal for enhancing antioxidant properties and bioactive compounds, and mitigating the risk of coliform bacteria.
Amazon Fruits as Healthy Ingredients in Muscle Food Products: A Review
Authors: Rios-Mera Juan D. , Arteaga Hubert , Ruiz Roger , Saldaña Erick , Tello Fernando
Published: July 2024

When looking for new ingredients to process red meat, poultry, and fish products, it is essential to consider using vegetable resources that can replace traditional ingredients such as animal fat and synthetic antioxidants that may harm health. The Amazon, home to hundreds of edible fruit species, can be a viable alternative for new ingredients in processing muscle food products. These fruits have gained interest for their use as natural antioxidants, fat replacers, colorants, and extenders. Some of the fruits that have been tested include açai, guarana, annatto, cocoa bean shell, sacha inchi oil, and peach palm. Studies have shown that these fruits can be used as dehydrated products or as liquid or powder extracts in doses between 250 and 500 mg/kg as antioxidants. Fat replacers can be added directly as flour or used to prepare emulsion gels, reducing up to 50% of animal fat without any detrimental effects. However, oxidation problems of the gels suggest that further investigation is needed by incorporating adequate antioxidant levels. In low doses, Amazon fruit byproducts such as colorants and extenders have been shown to have positive technological and sensory effects on muscle food products. While evidence suggests that these fruits have beneficial health effects, their in vitro and in vivo nutritional effects should be evaluated in muscle food products containing these fruits. This evaluation needs to be intended to identify safe doses, delay the formation of key oxidation compounds that directly affect health, and investigate other factors related to health.
One Justice for All? Social Dilemmas, Environmental Risks and Different Notions of Distributive Justice
Authors: Liebe Ulf , Bruderer Enzler Heidi , Diekmann Andreas , Preisendörfer Peter
Published: July 2024

A just or fair distribution of environmental bads and goods is important for solving environmental social dilemmas and is a core idea of environmental justice politics and research. Environmental justice is mostly associated with egalitarianism as the sole justice principle for all people. In contrast, we argue that it is important to uncover and consider heterogeneity in justice concerns to achieve socially accepted solutions to environmental social dilemmas. With noise pollution as an example, we explore citizens’ preferences for justice principles regarding the allocation of politically initiated environmental benefits. In our survey in four European cities, respondents were asked to choose between different outcomes of a program to reduce road traffic noise in line with the following four notions of distributive justice: equal shares, equal outcomes, the greatest benefit for the least advantaged (Rawls), and the greatest benefit for the greatest number (Bentham). We found that most respondents chose Rawls’ principle, a preference that was stable over time but weaker when explicitly introducing the veil of ignorance. The preference for Rawls notwithstanding, we observed substantial heterogeneity in justice preferences. Multinomial logit analyses of survey and geo-referenced data on noise exposure showed that respondents with a higher socio-economic status and lower exposure to traffic noise were more likely to choose Rawls’ principle. Taken together, our study confirms the prominence of Rawls’ principle, demonstrates empirically the heterogeneity of justice preferences, and calls for more direct measurements of such preferences in research on environmental social dilemmas, environmental justice, and beyond.
Prevalence of Malocclusion Traits in Primary Dentition, 2010–2024: A Systematic Review
Authors: Chen Hanyi , Lin Lude , Chen Jieyi , Huang Fang
Published: July 2024

The present review was aimed to describe the prevalence and the regional distribution of malocclusion among preschool children worldwide. Two independent reviewers performed a systematic literature search to identify English publications from January 2010 to May 2024 using PubMed, ISI Web of Science and Embase. Search MeSH key words were “malocclusion”, “primary dentition” and “child, preschool”. The reporting quality was assessed by the modified Newcastle–Ottawa Quality Assessment Scale. We identified 2599 publications and recruited 47 articles. Fourteen of the included studies were conducted in Asia, four in Europe, twenty-eight in South America and one in Africa. The prevalence of malocclusion ranged from 28.4% to 83.9%, and half of the reported prevalences were higher than 50%. The highest percentage was in Asia (61.81%), followed by Europe (61.50%), South America (52.69%) and Africa (32.50%). Statistically significant differences existed in deep overbite, anterior open bite, posterior crossbite, edge-to-edge incisor relationship and distal step between continents (p < 0.05). Europe showed the highest prevalence (33.08%) of deep overbite. Africa showed the highest prevalence (18.60%) of anterior open bite. Europe showed the highest prevalence (15.38%) of posterior crossbite. The most common malocclusion traits were increased overjet and deep overbite. To conclude, malocclusion remained prevalent in the primary dentition and varied between countries.