bluuebunny/crossref_metadata_2025 huggingface dataset:
Gender-Driven English Speech Emotion Recognition with Genetic Algorithm
Authors: Yue Liya , Hu Pei , Zhu Jiulong
Published: June 2024

Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC.
Feasibility Study of Applying Enzyme-Induced Carbonate Precipitation (EICP) without Calcium Source for Remediation of Lead-Contaminated Loess
Authors: Zhang Kun , Zhang Shixu
Published: June 2024

To assess the long-term stability of lead-contaminated loess treated with calcium-free Enzyme-Induced Carbonate Precipitation (EICP) technology while avoiding significant soil strength increases, various parameters such as the pH value, heavy metal ion leaching rate, and soil heavy metal speciation were evaluated. This study investigated the remediated soil’s stability under complex environmental conditions, including dry–wet cycles and acid rain leaching. The intrinsic mechanisms were elucidated through the Zeta potential, scanning electron microscopy (SEM), and X-ray diffraction (XRD) analyses. The results showed that compared to the untreated lead-contaminated loess, the surface strength of the loess treated with EICP technology increased by 3.86 times, with a 1.47-fold increase observed with the calcium-free EICP treatment. Carbonate precipitation improved the erosion resistance by adsorbing or coating fine particles and forming bridging connections with coarse particles. As the number of dry–wet cycles increased, the soil pH gradually decreased but remained above 8.25. The heavy metal leaching rate increased with the leaching cycles until reaching a plateau. The acid rain influence showed a decrease in the Pb2+ content in the leachate as the acid rain solution pH increased, meeting hazardous waste disposal regulations. These findings offer new insights for improving heavy metal-contaminated loess site remediation and understanding the underlying geochemical mechanisms.
Effect of Oak Powdery Mildew on Ascorbate–Glutathione Cycle and Other Antioxidants in Plant—Erysiphe alphitoides Interaction
Authors: Skwarek-Fadecka Monika , Nawrocka Justyna , Sieczyńska Katarzyna , Patykowski Jacek , Posmyk Małgorzata Maria
Published: June 2024

Erysiphe alphitoides is a species of powdery mildew responsible for the major foliar disease of oak trees, including Quercus robur. Infection with E. alphitoides leads to a reduction in the growth of the trees and in their ability to survive. This paper reports on the biochemical changes characteristic of defence responses in oak leaves with different infection area sizes, collected in July, August, and September during three growing seasons. The study highlights the effect of E. alphitoides infection on changes in the ascorbate-glutathione cycle, phenolic compound profile, and metal content (mineral distribution). Visible symptoms of pathogen infection appeared gradually in July, but the most intense biochemical plant responses in oak leaves were detected mainly in August and September. These responses included increased ascorbate-glutathione enzyme activities, phenolic compounds, and metal contents. In addition, microscopic analyses revealed a strong fluorescence signal of lignin in the epidermis of pathogen-infected leaves. The involvement of the studied compounds in the basic defence mechanisms of oak against E. alphitoides infection is discussed in the paper.
Proprioception, Emotion and Social Responsiveness in Children with Developmental Disorders: An Exploratory Study in Autism Spectrum Disorder, Cerebral Palsy and Different Neurodevelopmental Situations
Authors: Riquelme Inmaculada , Hatem Samar M. , Sabater-Gárriz Álvaro , Martín-Jiménez Elisabeth , Montoya Pedro
Published: June 2024

Proprioception has long been linked with emotional dysregulation in neurotypical adults. Neuropediatric disorders such as autism spectrum disorder (ASD) and cerebral palsy (CP) are distinct entities and yet both present with deficits and challenges in sensory processing and the regulation of emotions. This study aimed to explore the relationship between proprioception and emotional–social performance in children and to compare proprioception and emotional–social performance in different underlying neurodevelopmental conditions. For this purpose, this cross-sectional study included 42 children with ASD, 34 children with CP and 50 typically developing peers. Proprioceptive acuity, proprioceptive reactive behavior as well as emotion regulation and social responsiveness were assessed. The results show a significant correlation between proprioceptive deficits and emotional difficulties in this pediatric sample, with distinct proprioceptive impairment patterns according to the underlying neurological disorder. Children with CP showed significant emotional knowledge deficits, while children with ASD predominantly showed challenges in social responsiveness. These data thus suggest a differentiated impact of proprioception on emotional–social performance in neurodevelopmental disorders and highlight proprioception as a potential therapeutic target for balancing emotion regulation in children with neurodevelopmental conditions.
Understanding the Multiple Influences on Black Parents’ School Involvement: A Longitudinal Perspective
Authors: Gale Adrian , Williams Ed-Dee , Boyd Donte , Lateef Husain
Published: June 2024

This study explores longitudinal influences of various factors on Black parents’ involvement in their children’s education. Guided by Hoover-Dempsey & Sandler’s Model of Parent Involvement, this research examines whether parents’ school climate perceptions, attitudes about involvement, self-efficacy, and children’s academic performance predict parent involvement over time. Utilizing data from the Maryland Adolescence in Context Study with a sample of 560 Black parents, we found that positive school climate perceptions and favorable attitudes towards involvement significantly predict increased parent involvement in later years. The results underscore the importance of supportive school environments and parent attitudes in fostering their involvement.
Survey of Security Issues in Memristor-Based Machine Learning Accelerators for RF Analysis
Authors: Lillis Will , Hoffing Max Cohen , Burleson Wayne
Published: June 2024

We explore security aspects of a new computing paradigm that combines novel memristors and traditional Complimentary Metal Oxide Semiconductor (CMOS) to construct a highly efficient analog and/or digital fabric that is especially well-suited to Machine Learning (ML) inference processors for Radio Frequency (RF) signals. Analog and/or hybrid hardware designed for such application areas follows different constraints from that of traditional CMOS. This paradigm shift allows for enhanced capabilities but also introduces novel attack surfaces. Memristors have different properties than traditional CMOS which can potentially be exploited by attackers. In addition, the mixed signal approximate computing model has different vulnerabilities than traditional digital implementations. However both the memristor and the ML computation can be leveraged to create security mechanisms and countermeasures ranging from lightweight cryptography, identifiers (e.g., Physically Unclonable Functions (PUFs), fingerprints, and watermarks), entropy sources, hardware obfuscation and leakage/attack detection methods. Three different threat models are proposed: (1) Supply Chain, (2) Physical Attacks, and (3) Remote Attacks. For each threat model, potential vulnerabilities and defenses are identified. This survey reviews a variety of recent work from the hardware and ML security literature and proposes open problems for both attack and defense. The survey emphasizes the growing area of RF signal analysis and identification in terms of commercial space, as well as military applications and threat models. We differ from other recent surveys that target ML, in general, neglecting RF applications.
Study on High Temperature Properties of Yttrium-Modified Aluminide Coating on K444 Alloy by Chemical Vapor Deposition
Authors: Yang Hanzhe , Wu Yong , Sun Qingyun , Yang Fu , Xia Chunhuai , Xia Siyao , Du Jianrong
Published: June 2024

This work aims to explore a method of improving the high-temperature oxidation resistance and thermal corrosion resistance of a hollow blade of gas turbine. The yttrium-modified aluminide coating was prepared on the surface of nickel-based superalloy K444 by chemical vapor deposition (CVD). The microstructure, high temperature oxidation resistance, and thermal corrosion resistance of the modified aluminide coating deposited at 950 °C, 1000 °C, and 1050 °C were compared. The microstructure and morphology of the coatings were observed and analyzed by XRD, SEM, and EDS. The results showed that adding yttrium and changing the deposition temperature had no effect on the double-layer structure (outer layer and diffusion layer) of the coating. Compared with adding yttrium, the deposition temperature had a greater effect on the coating thickness. When the deposition temperature was 1050 °C and the deposition time was 2 h, the thickness of the yttrium-modified aluminide coating increased by 33% compared to that of a single aluminide coating. The high temperature oxidation resistance and thermal corrosion resistance of the three groups of yttrium-modified aluminide coatings are better than that of the single aluminide coating. The resistance to high temperature oxidation and hot corrosion of the yttrium-modified aluminide coating deposited at 1050 °C was better than that of yttrium-modified aluminide coating deposited at 1000 °C, and both were better than that of the modified coating deposited at 950 °C. The higher the deposition temperature, the higher the yttrium content of the coating, the faster the film-forming speed of α-Al2O3, and the better the high temperature oxidation resistance and thermal corrosion resistance of the coating.
Exploring the Role and Variability of 3d Transition Metal Complexes in Artistic Coloration through a Bottom-Up Scientific Approach
Authors: Coia Alexandra , Ruddick Jackson , Kuang Olivia , Wang Li-Qiong
Published: June 2024

Transition metal complexes have historically played a pivotal role in creating vibrant pigments utilized across artistic mediums such as ceramics, paintings, and glass mosaics. Despite their extensive historical use, our understanding of the mechanisms governing transition metal complex behavior has predominantly emerged in recent times, leaving numerous aspects of this process ripe for exploration. These complexes exhibit striking color variations under diverse conditions when employed in pigment formulations. This review utilizes a bottom-up scientific approach, spanning from microscopic to macroscopic scales, to unravel the molecular origins of the colors generated by transition metal complexes in pigments and ceramic glazes. Advanced spectroscopy techniques and computational chemistry play pivotal roles in this endeavor, highlighting the significance of understanding and utilizing analytical data effectively, with careful consideration of each technique’s specific application. Furthermore, this review investigates the influence of processing conditions on color variations, providing valuable insights for artists and manufacturers aiming to enhance the precision and quality of their creations while mitigating environmental impact.
Study of Leakage Current Transport Mechanisms in Pseudo-Vertical GaN-on-Silicon Schottky Diode Grown by Localized Epitaxy
Authors: El Amrani Mohammed , Buckley Julien , Kaltsounis Thomas , Arguello David Plaza , El Rammouz Hala , Alquier Daniel , Charles Matthew
Published: June 2024

In this work, a GaN-on-Si quasi-vertical Schottky diode was demonstrated on a locally grown n-GaN drift layer using Selective Area Growth (SAG). The diode achieved a current density of 2.5 kA/cm2, a specific on-resistance RON,sp of 1.9 mΩ cm2 despite the current crowding effect in quasi-vertical structures, and an on/off current ratio (Ion/Ioff) of 1010. Temperature-dependent current–voltage characteristics were measured in the range of 313–433 K to investigate the mechanisms of leakage conduction in the device. At near-zero bias, thermionic emission (TE) was found to dominate. By increasing up to 10 V, electrons gained enough energy to excite into trap states, leading to the dominance of Frenkel–Poole emission (FPE). For a higher voltage range (−10 V to −40 V), the increased electric field facilitated the hopping of electrons along the continuum threading dislocations in the “bulk” GaN layers, and thus, variable range hopping became the main mechanism for the whole temperature range. This work provides an in-depth insight into the leakage conduction transport on pseudo-vertical GaN-on-Si Schottky barrier diodes (SBDs) grown by localized epitaxy.
Spatio-Temporal Structure of Two Seaweeds Communities in Campeche, Mexico
Authors: Hernández-Casas Cynthia Mariana , Mendoza-González Ángela Catalina , García-López Deisy Yazmín , Mateo-Cid Luz Elena
Published: June 2024

Macroalgae populations are influenced by various factors that define their spatial and temporal distribution in different habitats and regions. In Mexico, studies addressing the abundance and diversity of macroalgae communities related to environmental factors are scarce. The objective is to determine the spatio-temporal variation of the structure of the community of seaweeds in Xpicob and Villamar, Campeche, during three climatic seasons. Sampling took place during each season using transects and quadrants; additionally, the type of substrate, water temperature, transparency, depth, salinity, and dissolved oxygen, were recorded. The total richness was 74 taxa, corresponding to three classes: Phaeophyceae (3), Florideophyceae (36), and Ulvophyceae (35). Filamentous algae dominate in species richness in the intertidal zone at low depths, while fleshy and calcareous algae predominate in number and biomass in the subtidal zone at higher depths (60–200 cm). Twenty-eight species were common to both sites; meanwhile, 46 taxa were exclusive of specific sites, including 13 found exclusively in Xpicob and 33 in Villamar. The most favorable climatic season for the macroalgae located in Xpicob was the winter rain. For the macroalgae community in Villamar, the most favorable climatic season was the dry. These differences are likely attributed to the predominant environmental and physicochemical characteristics of each site.
Requirements and Characteristics for the Development and Selection of Design Methods
Authors: Matschewsky Johannes , Brambila-Macias Sergio A. , Neramballi Abhijna , Sakao Tomohiko
Published: June 2024

While many design methods are developed, tested and reported in the literature, their utilization in industry practice remains low. Design methods are receiving substantial scholarly focus and are considered central to efficiently achieving reliable outcomes in the engineering design process. They are particularly vital as industrial companies increasingly transition to integrated offerings of products and services with a lifecycle perspective, leading to additional uncertainty and complexity. Thus, the presented research aims to support method selection and development, focusing on resource-efficient offerings. This is achieved through an in-depth, practice-centric, empirical study of users’ requirements of design methods and the corresponding characteristics of design methods aimed at meeting these requirements in resource-efficient offerings. Highly relevant insight supporting a broad set of stakeholders is reported. Firstly, the user requirements and method characteristics reported support practitioners seeking to identify a design method fitting their needs. Secondly, academics and practitioners aiming to enhance the usefulness and impact of a design method may benefit from considering these requirements and characteristics during method development. Lastly, the systematic approach taken in this research can be applied by both method developers and potential users to identify additional requirements and corresponding characteristics specific to their conditions. Two use cases for the results attained are reported, focusing on applying the research results for method selection and deriving overall guidelines for developing design methods directed toward resource-efficient offerings.
Effects of Aging on Secreted Adipocytokines in Visceral Fat of Female C3H/HeJ Mice Consuming a Long-Term High-Fat Diet
Authors: Mubtasim Noshin , Barr Benjamin , Boren Caleb , Gollahon Lauren
Published: June 2024

The excess consumption of a high-fat diet has been identified as one of the factors contributing to obesity. Women are at higher risk of adult obesity than men, predisposing them to a different set of detrimental disease conditions. Furthermore, aging studies show that physiological decline also has a serious impact on changes in the endocrine properties of white adipose tissue. However, there is still relatively little known about the factors associated with obesity and aging and their compounding impacts on women’s health. To investigate changes in adipocytokine secretion profiles, obesity was induced in female C3H/HeJ mice through the long-term consumption of a high-fat diet. Weight gain measurements and the Echo MRI analysis of fat composition showed that increases were due solely to the high fat content in the diet. Adipocytokine secretions were analyzed in media conditioned from harvested visceral fat tissue that was organ-cultured ex vivo. Adipocytokine analysis performed across diets and ages showed that there were significant increases in Adiponectin and Leptin secretion in high-fat diets, accelerating increases in Resistin secretion in high-fat diets. Aging induced the increased secretion of Lipcalin-2, Pentraxin-3, Serpin E1, MCP-1, and ICAM-1, regardless of diet. Furthermore, the comparisons of organoid-cultured protein secretions and flash-frozen tissue samples differed greatly, suggesting the WAT organoid cultures may yield information that is more reflective of in situ conditions. Taken together, our results show that high-fat diets and aging in C3H/HeJ female mice significantly impact secretions from adipose tissue, which may contribute to women’s health issues.
Dual-Tower Counterfactual Session-Aware Recommender System
Authors: Song Wenzhuo , Xing Xiaoyu
Published: June 2024

In the complex dynamics of modern information systems such as e-commerce and streaming services, managing uncertainty and leveraging information theory are crucial in enhancing session-aware recommender systems (SARSs). This paper presents an innovative approach to SARSs that combines static long-term and dynamic short-term preferences within a counterfactual causal framework. Our method addresses the shortcomings of current prediction models that tend to capture spurious correlations, leading to biased recommendations. By incorporating a counterfactual viewpoint, we aim to elucidate the causal influences of static long-term preferences on next-item selections and enhance the overall robustness of predictive models. We introduce a dual-tower architecture with a novel data augmentation process and a self-supervised training strategy, tailored to tackle inherent biases and unreliable correlations. Extensive experiments demonstrate the effectiveness of our approach, outperforming existing benchmarks and paving the way for more accurate and reliable session-based recommendations.
Unveiling Potential: Fostering Students’ Self-Concepts in Science Education by Designing Inclusive Educational Settings
Authors: Graichen Martina , Mikelskis-Seifert Silke , Hinderer Linda , Scharenberg Katja , Rollett Wolfram
Published: June 2024

The relevance of experimentation in natural sciences and the importance of inclusion of all students are widely acknowledged. Successful scientific experimentation in the classroom is based on higher levels of science self-concept and appropriate instructions for completing the experiment. To facilitate the experimentation process, we developed a learning environment for magnetism with minimal barriers, aimed at fostering experimentation and self-concept development in dimensions such as language, visibility, and action. In a study involving 348 students from Grades 5 and 6 from German secondary schools, we investigated how students perceived the learning environment in terms of accessibility, how their self-concept in engaging with experimental instructions developed, and how these two concepts, accessibility and self-concept, are related. The results indicated that the students found the instructions of the digital learning environment to be accessible and showed a significant increase in self-concept when utilizing experimental instructions. It was also shown that the more accessible the students perceived the experimental instructions, the greater the increase in their self-concept with experimental instructions. However, only a small amount of variance was explained. This shows that such a digital learning environment can have positive effects on students, although the remaining open aspects (e.g., the specific support of low-achieving students) are being addressed and should be investigated in the future.
A Scoping Review of Research on the Use of Digital Technologies for Teaching Reading Fluency
Authors: Oakley Grace
Published: June 2024

Background: Reading fluency is a crucial component of reading. Research indicates that the use of digital technologies can help students with reading difficulties and disabilities improve their reading fluency. Objectives: The objective of this scoping review was to identify and describe research focusing on the use of digital technologies for teaching reading fluency to primary or elementary students in English-speaking settings. Design: Online databases were used to identify papers published between 2013 and 2023. Eighty-six papers that met the inclusion criteria were selected for analysis. Results: The review indicates that research has primarily focused on the use of digital technologies as interventions to support students at risk of reading difficulties and students with disabilities, with relatively little research emphasis on general classroom teaching of reading fluency. Moreover, uses of digital technologies for the teaching of reading fluency could mostly be categorised as “enhancements” of common non-digital strategies for teaching reading fluency, such as explicit teaching, drill and practice, and repeated readings. Much of the research has focused on the use of programs as opposed to the innovative use of open-ended digital tools. Conclusions: This paper raises questions about the relatively narrow uses of digital technologies in the teaching and research of reading fluency and calls for an expanded research agenda to include a broader range of pedagogical goals and approaches.
A Systematic Review of Generative AI for Teaching and Learning Practice
Authors: Ogunleye Bayode , Zakariyyah Kudirat Ibilola , Ajao Oluwaseun , Olayinka Olakunle , Sharma Hemlata
Published: June 2024

The use of generative artificial intelligence (GenAI) in academia is a subjective and hotly debated topic. Currently, there are no agreed guidelines towards the usage of GenAI systems in higher education (HE) and, thus, it is still unclear how to make effective use of the technology for teaching and learning practice. This paper provides an overview of the current state of research on GenAI for teaching and learning in HE. To this end, this study conducted a systematic review of relevant studies indexed by Scopus, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search criteria revealed a total of 625 research papers, of which 355 met the final inclusion criteria. The findings from the review showed the current state and the future trends in documents, citations, document sources/authors, keywords, and co-authorship. The research gaps identified suggest that while some authors have looked at understanding the detection of AI-generated text, it may be beneficial to understand how GenAI can be incorporated into supporting the educational curriculum for assessments, teaching, and learning delivery. Furthermore, there is a need for additional interdisciplinary, multidimensional studies in HE through collaboration. This will strengthen the awareness and understanding of students, tutors, and other stakeholders, which will be instrumental in formulating guidelines, frameworks, and policies for GenAI usage.
DPShield: Optimizing Differential Privacy for High-Utility Data Analysis in Sensitive Domains
Authors: Thantharate Pratik , Bhojwani Shyam , Thantharate Anurag
Published: June 2024

The proliferation of cloud computing has amplified the need for robust privacy-preserving technologies, particularly when dealing with sensitive financial and human resources (HR) data. However, traditional differential privacy methods often struggle to balance rigorous privacy protections with maintaining data utility. This study introduces DPShield, an optimized adaptive framework that enhances the trade-off between privacy guarantees and data utility in cloud environments. DPShield leverages advanced differential privacy techniques, including dynamic noise-injection mechanisms tailored to data sensitivity, cumulative privacy loss tracking, and domain-specific optimizations. Through comprehensive evaluations on synthetic financial and real-world HR datasets, DPShield demonstrated a remarkable 21.7% improvement in aggregate query accuracy over existing differential privacy approaches. Moreover, it maintained machine learning model accuracy within 5% of non-private benchmarks, ensuring high utility for predictive analytics. These achievements signify a major advancement in differential privacy, offering a scalable solution that harmonizes robust privacy assurances with practical data analysis needs. DPShield’s domain adaptability and seamless integration with cloud architectures underscore its potential as a versatile privacy-enhancing tool. This work bridges the gap between theoretical privacy guarantees and practical implementation demands, paving the way for more secure, ethical, and insightful data usage in cloud computing environments.
A Low-Latency CORDIC Algorithm Based on Pre-Rotation and Its Application on Computation of Arctangent Function
Authors: Li Kun , Fang Hongji , Ma Zhenguo , Yu Feng , Zhang Bo , Xing Qianjian
Published: June 2024

This paper presents a low-latency coordinate rotation digital computer (CORDIC) algorithm to accelerate the computation of arctangent functions, and it describes the corresponding iterative and pipelined architecture of this novel algorithm. As compared to the existing methods based on CORDIC, the proposed method can effectively reduce the number of iterations by dedicated pre-rotation and comparison processes. Moreover, the proposed CORDIC algorithm supports all vectors with arbitrary angles while maintaining convergence. By error analysis, the proposed algorithm can achieve the same accuracy as the conventional CORDIC algorithm during floating-point arctangent function computation and reduce the number of iterations by approximately 50%. This paper presents two new architectures—the iterative architecture, which can be more resource efficient, and the pipelined architecture, which can achieve a throughput rate of one datum per clock. Finally, the experimental comparison results indicate that the proposed method outperforms extant methods as it exhibits low latency, requires fewer resources to compute the arctangent function for floating-point inputs, and necessitates no digital signal processing (DSP) and memory for fixed-point inputs.
Correction: Hussain et al. The Causal Connection of Natural Resources and Globalization with Energy Consumption in Top Asian Countries: Evidence from a Nonparametric Causality-in-Quantile Approach. Energies 2020, 13, 2273
Authors: Hussain Hafezali Iqbal , Haseeb Muhammad , Tvaronavičienė Manuela , Mihardjo Leonardus W. W. , Jermsittiparsert Kittisak
Published: June 2024

To allow the original publication to be better understood [...]
Development of Concrete Façade Sandwich Panels Incorporating Phase Change Materials
Authors: Niall Dervilla , West Roger
Published: June 2024

Using the mass of a building to store or dissipate heat can reduce the demand on auxiliary heating and/or cooling systems. There is a scarcity of full-scale and full-year studies in the literature, which this study seeks to address, as it is critical to assess the performance of real phase change material (PCM) composites throughout all seasons. This study involved the design and manufacture of precast cladding sandwich panels with a PCM–concrete inner leaf used in three full-scale huts which were instrumented to record thermal data over 18 months. Analysis of these data showed that when the internal air temperature fluctuated through the phase change temperature, the PCM–concrete composite was effective at reducing the internal air temperatures by up to 16% if overnight ventilation was provided and 12% without overnight ventilation in a temperate climate. Furthermore, the PCM located deeper than 60 mm from the internal surface of the wall is ineffective at reducing internal air temperatures. The data also highlighted that the thermal conditions required to activate the PCM only occurred during 30% of the year. The thermal data were used to validate a simulation model which can be used to derive bespoke solutions for this form of technology in real scenarios in any geographical location.
Climate Characterization and Energy Efficiency in Container Housing: Analysis and Implications for Container House Design in European Locations
Authors: Figaj Rafal Damian , Laudiero Davide Maria , Mauro Alessandro
Published: June 2024

The present study investigates the energy efficiency of different container house configurations across thirty European locations. By employing Heating Degree Days (HDDs) and Cooling Degree Days (CDDs), the research delves into climatic zone exploration, providing a simplified climatic classification for residential purposes and comparing it with the Köppen–Geiger model. The authors use specific hourly climatic data for each location, obtained through dynamic simulations with TRNSYS v.18 software. Initially, the CDDs are calculated by using different base temperatures (comfort temperatures that minimize energy demand) tailored to the specific conditions of each case. Then, the thermal loads of container houses are evaluated in different climatic scenarios, establishing a direct correlation between climatic conditions and the energy needs of these innovative and modular housing solutions. By comparing stacked and adjacent modular configurations in container housing, particularly in post-disaster scenarios, the study underscores the importance of adaptive design to optimize energy efficiency. The analysis conducted by the authors has allowed them to propose a climate characterization model based on HDDs, CDDs, and solar irradiance, obtaining an effective novel correlation with the Köppen–Geiger classification, especially in extreme climates. The present model emerges as a powerful tool for climate characterization in residential applications, offering a new perspective for urban planning and housing design. Furthermore, the results reveal a significant correlation between climate classification and the specific energy needs of container houses, emphasizing the direct influence of regional climatic characteristics on energy efficiency, particularly in small-sized dwellings such as container houses.
Multi-Port Energy Router in Mobile Energy Storage for Emergency Power Outage in Urban Cities
Authors: Nie Yu , Nasr Esfahani Mohammad , Hu Yihua , Li Xinian , Alkahtani Mohammed
Published: June 2024

A multi-port energy router (MER) is an important infrastructure for power management and energy storage after an unexpected power outage. In addition, MERs can relate to various emergency electric power sources (EEPSs) and power grids at the same time. Moreover, by putting an MER in mobile energy storage, an MER allows for more flexible energy management and regulation in urban cities. This paper proposes a new topology of MERs composed of a bidirectional AC/DC converter and a partial power processing-based triple active bridge (PPP-based TAB) converter to supply emergency power for an unexpected power outage. This design of an MER improves the power efficiency significantly to realize peer-to-peer (P2P) trading which realizes the bidirectional power transmission among the main power grid, EEPSs such as the battery of electric vehicles (EV), and clients’ loads. The control methods of the power transmission of two power electronic converters in MER are also illustrated in this paper to realize the power transmission among the main power grid, clients’ loads, and EEPSs. This allows any power source connected to the MER to be used as an input or output to receive or send out power, which greatly improves the utilization of power. In conclusion, the MATLAB/Simulink and experiment test are executed and meticulously presented to validate the practicality of the proposed configuration.
Optimizing Thermal Energy Sharing in Smart District Heating Networks
Authors: Dattilo Anna , Melino Francesco , Ricci Mattia , Sdringola Paolo
Published: June 2024

The constant attention to sustainability aimed at reconciling economic and social development with environmental protection is the driving force of the continuous growth of renewable energy in the energy sector. Among the numerous actions taken by the European Commission (EC) in this direction, an important initiative towards the complete decarbonization is represented by the Renewable Energy Communities (RECs). According to the EC, “energy communities enable collective and citizen-driven energy actions to support the clean energy transition. They can contribute to increasing public acceptance of renewable energy projects and make it easier to attract private investments in the clean energy transition”. At the European level, numerous energy communities are emerging, although they are all based on photovoltaic production and, consequently, focus only on electricity flows. The aim of this paper is to define a thermal energy community in which thermal energy sharing can be achieved by exploiting the concept of the smart district heating network. Starting from a small existing district heating network, its conversion into a smart one will be analyzed and optimized with the aim of studying the sharing of thermal energy between the various prosumer and non-prosumer users connected to the district heating network.
Exploring Energy Poverty in Urban and Rural Contexts in the Era of Climate Change: A Comparative Analysis of European Countries and Israel
Authors: Dokupilová Dušana , Stojilovska Ana , Palma Pedro , Gouveia João Pedro , Paschalidou Eleftheria G. , Barrella Roberto , Feenstra Marielle , Horta Ana , Sánchez-Guevara Carmen , Kádár József , Tesanovic Majda , Thomaidis Nikolaos S. , Hamed Tareq Abu
Published: June 2024

This article examines the multidimensional problem of energy poverty, focusing on its connections to climate change and its manifestation at rural and urban scales across selected European countries and Israel. The study examined 31 locations in eight countries with diverse geographical and economic backgrounds: Bosnia and Herzegovina, Greece, North Macedonia, The Netherlands, Portugal, Spain, Slovakia, and Israel. The article aims to understand how winter energy vulnerability in rural and urban locations in these countries could be identified using selected energy poverty indicators and how it evolves under the influence of climate change. A set of sociodemographic, infrastructural, and economic variables, combined with climate analysis, were selected and assessed for their impact on energy poverty. We found that energy poverty in most countries depends significantly on location and regional development. Due to a combination of factors influencing energy poverty, rural households tend to be more vulnerable. Furthermore, climate change consequences will likely leave rural areas more likely to experience energy poverty in the future.
A Review of Control Techniques for Inverter-Based Distributed Energy Resources Applications
Authors: Hasheminasab Seyedmohammad , Alzayed Mohamad , Chaoui Hicham
Published: June 2024

The escalating adoption of low-carbon energy technologies underscores the imperative to transition from conventional fossil fuel-dependent sources to sustainable alternatives. The expansion of Distributed Energy Resources (DERs) signifies an essential shift towards a more resilient and environmentally friendly energy landscape. However, integrating inverter-based DERs introduces challenges, particularly in system inertia and grid instability. This review delves into the critical area of inverter-based grid control strategies, focusing on the primary and secondary control mechanisms. Primary controls are investigated, including traditional droop control and low-voltage ride-through (LVRT) capability. The secondary control strategies, involving virtual impedance (VI) and load frequency control (LFC), are vital in maintaining grid stability and reliability are reviewed. The aim is to offer a comprehensive understanding of the principles, advancements, and challenges associated with inverter-based grid controls, contributing valuable insights for the seamless integration of DERs into modern power grids.
Precision Fermentation as an Alternative to Animal Protein, a Review
Authors: Knychala Marilia M. , Boing Larissa A. , Ienczak Jaciane L. , Trichez Débora , Stambuk Boris U.
Published: June 2024

The global food production system faces several challenges, including significant environmental impacts due to traditional agricultural practices. The rising demands of consumers for food products that are safe, healthy, and have animal welfare standards have led to an increased interest in alternative proteins and the development of the cellular agriculture field. Within this innovative field, precision fermentation has emerged as a promising technological solution to produce proteins with reduced ecological footprints. This review provides a summary of the environmental impacts related to the current global food production, and explores how precision fermentation can contribute to address these issues. Additionally, we report on the main animal-derived proteins produced by precision fermentation, with a particular focus on those used in the food and nutraceutical industries. The general principles of precision fermentation will be explained, including strain and bioprocess optimization. Examples of efficient recombinant protein production by bacteria and yeasts, such as milk proteins, egg-white proteins, structural and flavoring proteins, will also be addressed, along with case examples of companies producing these recombinant proteins at a commercial scale. Through these examples, we explore how precision fermentation supports sustainable food production and holds the potential for significant innovations in the sector.
Functional Tea Extract Inhibits Cell Growth, Induces Apoptosis, and Causes G0/G1 Arrest in Human Hepatocellular Carcinoma Cell Line Possibly through Reduction in Telomerase Activity
Authors: Chen Yuan , Chen Changsong , Xiang Jiaxing , Gao Ruizhen , Wang Guojun , Yu Wenquan
Published: June 2024

The functional tea CFT-1 has been introduced into China as a nutraceutical beverage according to the “Healthy China” national project. The effects on human hepatocellular carcinoma (HCC) cells remain unclear and were investigated with the functional tea extract (purity > 98%). The morphological changes in the cells were observed with microscopes. Cell proliferation, migration, cycle distribution, and apoptotic effects were assessed by MTT, Transwell assays, and flow cytometry, respectively, while telomerase inhibition was evaluated with telomerase PCR ELISA assay kits. The CFT-1 treatment resulted in cell shrinkage, nuclear pyknosis, and chromatin condensation. CFT-1 suppressed the growth of Hep3B cells with IC50 of 143 μg/mL by inducing apoptosis and G0/G1 arrest in Hep3B cells. As for the molecular mechanism, CFT-1 treatment can effectively reduce the telomerase activity. The functional tea extract inhibits cell growth in human HCC by inducing apoptosis and G0/G1 arrest, possibly through a reduction in telomerase activity. These results indicate that CFT-1 extract exhibited in vitro anticancer activities and provided insights into the future development and utilization of CFT-1 as functional foods to inhibit the proliferation of HCC cells.
Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks
Authors: Aghamohammadesmaeilketabforoosh Kimia , Nikan Soodeh , Antonini Giorgio , Pearce Joshua M.
Published: June 2024

Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study, two separate sets of strawberry images, along with their associated diseases, were collected and subjected to resizing and augmentation. Subsequently, a combined dataset consisting of nine classes was utilized to fine-tune three distinct pretrained models: vision transformer (ViT), MobileNetV2, and ResNet18. To address the imbalanced class distribution in the dataset, each class was assigned weights to ensure nearly equal impact during the training process. To enhance the outcomes, new images were generated by removing backgrounds, reducing noise, and flipping them. The performances of ViT, MobileNetV2, and ResNet18 were compared after being selected. Customization specific to the task was applied to all three algorithms, and their performances were assessed. Throughout this experiment, none of the layers were frozen, ensuring all layers remained active during training. Attention heads were incorporated into the first five and last five layers of MobileNetV2 and ResNet18, while the architecture of ViT was modified. The results indicated accuracy factors of 98.4%, 98.1%, and 97.9% for ViT, MobileNetV2, and ResNet18, respectively. Despite the data being imbalanced, the precision, which indicates the proportion of correctly identified positive instances among all predicted positive instances, approached nearly 99% with the ViT. MobileNetV2 and ResNet18 demonstrated similar results. Overall, the analysis revealed that the vision transformer model exhibited superior performance in strawberry ripeness and disease classification. The inclusion of attention heads in the early layers of ResNet18 and MobileNet18, along with the inherent attention mechanism in ViT, improved the accuracy of image identification. These findings offer the potential for farmers to enhance strawberry cultivation through passive camera monitoring alone, promoting the health and well-being of the population.
Estimation of Fractal Dimension and Segmentation of Brain Tumor with Parallel Features Aggregation Network
Authors: Sultan Haseeb , Ullah Nadeem , Hong Jin Seong , Kim Seung Gu , Lee Dong Chan , Jung Seung Yong , Park Kang Ryoung
Published: June 2024

The accurate recognition of a brain tumor (BT) is crucial for accurate diagnosis, intervention planning, and the evaluation of post-intervention outcomes. Conventional methods of manually identifying and delineating BTs are inefficient, prone to error, and time-consuming. Subjective methods for BT recognition are biased because of the diffuse and irregular nature of BTs, along with varying enhancement patterns and the coexistence of different tumor components. Hence, the development of an automated diagnostic system for BTs is vital for mitigating subjective bias and achieving speedy and effective BT segmentation. Recently developed deep learning (DL)-based methods have replaced subjective methods; however, these DL-based methods still have a low performance, showing room for improvement, and are limited to heterogeneous dataset analysis. Herein, we propose a DL-based parallel features aggregation network (PFA-Net) for the robust segmentation of three different regions in a BT scan, and we perform a heterogeneous dataset analysis to validate its generality. The parallel features aggregation (PFA) module exploits the local radiomic contextual spatial features of BTs at low, intermediate, and high levels for different types of tumors and aggregates them in a parallel fashion. To enhance the diagnostic capabilities of the proposed segmentation framework, we introduced the fractal dimension estimation into our system, seamlessly combined as an end-to-end task to gain insights into the complexity and irregularity of structures, thereby characterizing the intricate morphology of BTs. The proposed PFA-Net achieves the Dice scores (DSs) of 87.54%, 93.42%, and 91.02%, for the enhancing tumor region, whole tumor region, and tumor core region, respectively, with the multimodal brain tumor segmentation (BraTS)-2020 open database, surpassing the performance of existing state-of-the-art methods. Additionally, PFA-Net is validated with another open database of brain tumor progression and achieves a DS of 64.58% for heterogeneous dataset analysis, surpassing the performance of existing state-of-the-art methods.
Extracellular Vesicles in Multiple Sclerosis: Their Significance in the Development and Possible Applications as Therapeutic Agents and Biomarkers
Authors: Manna Ida , De Benedittis Selene , Porro Danilo
Published: June 2024

Extracellular vesicles (EVs) are “micro-shuttles” that play a role as mediators of intercellular communication. Cells release EVs into the extracellular environment in both physiological and pathological conditions and are involved in intercellular communication, due to their ability to transfer proteins, lipids, and nucleic acids, and in the modulation of the immune system and neuroinflammation. Because EVs can penetrate the blood–brain barrier and move from the central nervous system to the peripheral circulation, and vice versa, recent studies have shown a substantial role for EVs in several neurological diseases, including multiple sclerosis (MS). MS is a demyelinating disease where the main event is caused by T and B cells triggering an autoimmune reaction against myelin constituents. Recent research has elucidate the potential involvement of extracellular vesicles (EVs) in the pathophysiology of MS, although, to date, their potential role both as agents and therapeutic targets in MS is not fully defined. We present in this review a summary and comprehensive examination of EVs’ involvement in the pathophysiology of multiple sclerosis, exploring their potential applications as biomarkers and indicators of therapy response.