bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Equivalent Stiffness of a Local Resonance Metamaterial Concrete Unit Cell
Authors: Zhao Haixiang , Zhang En , Lu Guoyun
Published: April 2024

This paper addresses the pressing scientific problem of accurately predicting the equivalent stiffness of local resonance metamaterial concrete unit cells. Existing theoretical models often fail to capture the nuanced dynamics of these complex systems, resulting in suboptimal predictions and hindering advancements in engineering applications. To address this deficit, this paper proposes a novel two-dimensional theoretical vibration model that incorporates shear stiffness, a crucial yet often overlooked parameter in previous formulations. Motivated by the need for improved predictive accuracy, this paper rigorously validates a new theoretical model through numerical simulations, considering variations in material parameters and geometric dimensions. The analysis reveals several key findings: firstly, the equivalent stiffness increases with elastic modulus while the error rate decreases, holding geometric parameters and Poisson’s ratio constant. Secondly, under fixed geometric parameters and coating elastic modulus, the equivalent stiffness rises with an increasing Poisson’s ratio, accompanied by a decrease in error rate. Importantly, this paper demonstrates that the proposed model exhibits the lowest error rate across all parameter conditions, facilitating superior prediction of equivalent stiffness. This advancement holds significant implications for the design and optimization of metamaterial structures in various engineering applications for vibration isolation, with promising enhancements of performance and efficiency.
Experimental Study on Shear Strength of Roof–Snow Interfaces for Prediction of Roof Snow Sliding
Authors: Cao Xinli , Mo Huamei , Zhang Guolong , Zhang Qingwen , Fan Feng
Published: April 2024

The sliding of roof snow may result in surcharges of snow load on lower roofs or the injury of pedestrians on the ground. It is therefore of great significance to study the mechanism of roof snow sliding, such that prevention or control measures can be developed to manage the risk. Considering four commonly used roofing materials, glass, steel, membrane, and concrete, two types of experiments were carried out in this study to possibly reveal the influence of roofing materials on the shear strength of the roof–snow interface: one is the critical angle tests where the angle at which the snow starts to slide off from the roof is tested, and the other is the shearing tests which aim to test the shear strength of the roof–snow interfaces at specific temperatures. The results showed that the critical angle for roof snow sliding, as well as the shear strength of the roof–snow interface for the four considered roofing materials, show a U-shape trend with the increase in surface roughness and that the shear strength of the roof–snow surface ranges from 0.15 kPa to 2 kPa for the cases considered, while the strength reaches its maximum at certain temperatures near −5 °C for a specific roofing material and snow thickness. These findings could be a useful reference for future experimental or simulation studies on roof snow sliding.
Advances and Challenges in Oxygen Carriers for Chemical Looping Partial Oxidation of Methane
Authors: Zhang Jinnan , Cui Yuxuan , Si-ma Wang , Zhang Yanqi , Gao Yuming , Wang Pengxuan , Zhang Qian
Published: April 2024

To cope with global warming and increasing carbon emissions, the chemical looping process has attracted attention due to its excellent ability to convert fossil fuel and capture CO2. In this case, chemical looping partial oxidation technology has become the focus of attention due to its advantages in the production of syngas and hydrogen, especially with respect to the design and selection of oxygen carriers, which directly affect the efficiency of the production of syngas and hydrogen. In particular, the conversion of methane can reach 95% in the chemical looping partial oxidation of methane, and the selectivity of syngas, in the range of 700 °C to 900 °C at atmospheric pressure, can reach 99% for twenty or more cycles. In this review, from the perspective of metal oxide selection and structure regarding the chemical looping partial oxidation process, we discuss the role of oxygen carriers in the chemical looping partial oxidation cycle, in which the specific surface area, the lattice oxygen mobility, and the thermal stability are understood as the important factors affecting reactivity. We hope to summarize the design and development of efficient oxygen carriers with high oxygen-carrying capacity and syngas selectivity, as well as contribute to the selection, design, optimization, and redox reaction mechanism of redox catalysts.
Production of 14α-Hydroxy Progesterone Using a Steroidal Hydroxylase from Cochliobolus lunatus Expressed in Escherichia coli
Authors: Chang Yaowen , Liu Han , Tian Wei , Chang Zunxue
Published: April 2024

Steroids with hydroxylation at C14 are drawing increased attention because of their diverse biological activities and applications. P-450lun from Cochliobolus lunatus is the first fungal cytochrome P450 reported to have 14α-hydroxylase activity. Studies have shown that P-450lun catalyzes the hydroxylation of progesterone (PROG) at C14α with low regiospecificity and activity. To improve its regiospecificity and activity for PROG, truncated forms of P-450lun and its cognate redox partner CPRlun were functionally co-expressed in Escherichia coli. Then, a semi-rational protein engineering approach was applied to P-450lun, resulting in a double-site mutant E109A/F297W with enhanced 14α-position selectivity for PROG compared with the wild-type P-450lun (97% vs. 28%). Protein structure analysis revealed that the F297W substitution can hinder the binding pose for 11β-hydroxylation product formation. Finally, whole-cell catalysis was optimized, and the final titer of 14α-OH-PROG reached 16.0 mg/L. This is the first report where a fungal 14α-hydroxylase was functionally expressed in Escherichia coli. The steroid hydroxylation system obtained in this study can serve as a basis for the synthesis of 14α-hydroxylated PROG and the rapid evolution of eukaryotic cytochrome P-450lun.
Cell Membrane Fragment-Wrapped Parenteral Nanoemulsions: A New Drug Delivery Tool to Target Gliomas
Authors: Dianzani Chiara , Bozza Annalisa , Bordano Valentina , Cangemi Luigi , Ferraris Chiara , Foglietta Federica , Monge Chiara , Gallicchio Margherita , Pizzimenti Stefania , Marini Elisabetta , Muntoni Elisabetta , Valsania Maria Carmen , Battaglia Luigi
Published: April 2024

Poor prognosis in high-grade gliomas is mainly due to fatal relapse after surgical resection in the absence of efficient chemotherapy, which is severely hampered by the blood–brain barrier. However, the leaky blood–brain–tumour barrier forms upon tumour growth and vascularization, allowing targeted nanocarrier-mediated drug delivery. The homotypic targeting ability of cell-membrane fragments obtained from cancer cells means that these fragments can be exploited to this aim. In this experimental work, injectable nanoemulsions, which have a long history of safe clinic usage, have been wrapped in glioma-cell membrane fragments via co-extrusion to give targeted, homogeneously sized, sterile formulations. These systems were then loaded with three different chemotherapeutics, in the form of hydrophobic ion pairs that can be released into the target site thanks to interactions with physiological components. The numerous assays performed in two-dimensional (2D) and three-dimensional (3D) cell models demonstrate that the proposed approach is a versatile drug-delivery platform with chemo-tactic properties towards glioma cells, with adhesive interactions between the target cell and the cell membrane fragments most likely being responsible for the effect. This approach’s promising translational perspectives towards personalized nanomedicine mean that further in vivo studies are foreseen for the future.
ERβ Regulation of Indian Hedgehog Expression in the First Wave of Ovarian Follicles
Authors: Chakravarthi V. Praveen , Dilower Iman , Ghosh Subhra , Borosha Shaon , Mohamadi Ryan , Dahiya Vinesh , Vo Kevin , Lee Eun B. , Ratri Anamika , Kumar Vishnu , Marsh Courtney A. , Fields Patrick E. , Rumi M. A. Karim
Published: April 2024

Increased activation of ovarian primordial follicles in Erβ knockout (ErβKO) rats becomes evident as early as postnatal day 8.5. To identify the ERβ-regulated genes that may control ovarian primordial follicle activation, we analyzed the transcriptome profiles of ErβKO rat ovaries collected on postnatal days 4.5, 6.5, and 8.5. Compared to wildtype ovaries, ErβKO ovaries displayed dramatic downregulation of Indian hedgehog (Ihh) expression. IHH-regulated genes, including Hhip, Gli1, and Ptch1, were also downregulated in ErβKO ovaries. This was associated with a downregulation of steroidogenic enzymes Cyp11a1, Cyp19a1, and Hsd17b1. The expression of Ihh remained very low in ErβKO ovaries despite the high levels of Gdf9 and Bmp15, which are known upregulators of Ihh expression in the granulosa cells of activated ovarian follicles. Strikingly, the downregulation of the Ihh gene in ErβKO ovaries began to disappear on postnatal day 16.5 and recovered on postnatal day 21.5. In rat ovaries, the first wave of primordial follicles is rapidly activated after their formation, whereas the second wave of primordial follicles remains dormant in the ovarian cortex and slowly starts activating after postnatal day 12.5. We localized the expression of Ihh mRNA in postnatal day 8.5 wildtype rat ovaries but not in the age-matched ErβKO ovaries. In postnatal day 21.5 ErβKO rat ovaries, we detected Ihh mRNA mainly in the activated follicles in the ovaries’ peripheral regions. Our findings indicate that the expression of Ihh in the granulosa cells of the activated first wave of ovarian follicles depends on ERβ.
Antagonizing Activin A/p15INK4b Signaling as Therapeutic Strategy for Liver Disease
Authors: Mekala Sowmya , Rai Ravi , Reed Samantha Loretta , Bowen Bill , Michalopoulos George K. , Locker Joseph , Raeman Reben , Oertel Michael
Published: April 2024

Background/Aim: Activin A is involved in the pathogenesis of human liver diseases, but its therapeutic targeting is not fully explored. Here, we tested the effect of novel, highly specific small-molecule-based activin A antagonists (NUCC-474/555) in improving liver regeneration following partial hepatectomy and halting fibrosis progression in models of chronic liver diseases (CLDs). Methods: Cell toxicity of antagonists was determined in rat hepatocytes and Huh-7 cells using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide assay. Hepatocytes and hepatic stellate cells (HSCs) were treated with activin A and NUCC-555 and analyzed by reverse transcription–polymerase chain reaction and immunohistochemistry. Partial hepatectomized Fisher (F)344 rats were treated with NUCC-555, and bromodeoxyuridine (BrdU) incorporation was determined at 18/24/36/120/240 h. NUCC-555 was administered into thioacetamide- or carbon tetrachloride-treated F344 rats or C57BL/6 mice, and the fibrosis progression was studied. Results: NUCC-474 showed higher cytotoxicity in cultured hepatic cells; therefore, NUCC-555 was used in subsequent studies. Activin A-stimulated overexpression of cell cycle-/senescence-related genes (e.g., p15INK4b, DEC1, Glb1) was near-completely reversed by NUCC-555 in hepatocytes. Activin A-mediated HSC activation was blocked by NUCC-555. In partial hepatectomized rats, antagonizing activin A signaling resulted in a 1.9-fold and 2.3-fold increase in BrdU+ cells at 18 and 24 h, respectively. Administration of NUCC-555 in rats and mice with progressing fibrosis significantly reduced collagen accumulation (7.9-fold), HSC activation indicated by reduced alpha smooth muscle actin+ and vimentin+ cells, and serum aminotransferase activity. Conclusions: Our studies demonstrate that activin A antagonist NUCC-555 promotes liver regeneration and halts fibrosis progression in CLD models, suggesting that blocking activin A signaling may represent a new approach to treating people with CLD.
Application of Machine Learning Models in Coaxial Bioreactors: Classification and Torque Prediction
Authors: Rahimzadeh Ali , Ranjbarrad Samira , Ein-Mozaffari Farhad , Lohi Ali
Published: April 2024

Coaxial bioreactors are known for effectively dispersing gas inside non-Newtonian fluids. However, due to their design complexity, many aspects of their design and function, including the relationship between hydrodynamics and bioreactor efficiency, remain unexplored. Nowadays, various numerical models, such as computational fluid dynamics (CFD) and artificial intelligence models, provide exceptional opportunities to investigate the performance of coaxial bioreactors. For the first time, this study applied various machine learning models, both classifiers and regressors, to predict the torque generated by a coaxial bioreactor. In this regard, 500 CFD simulations at different aeration rates, central impeller speeds, anchor impeller speeds, and rotating modes were conducted. The results obtained from the CFD simulations were used to train and test the machine learning models. Careful feature scaling and k-fold cross-validation were performed to enhance all models’ performance and prevent overfitting. A key finding of the study was the importance of selecting the right features for the model. It turns out that just by knowing the speed of the central impeller and the torque generated by the coaxial bioreactor, the rotating mode can be labelled with perfect accuracy using k-nearest neighbors (kNN) or support vector machine models. Moreover, regression models, including multi-layer perceptron, kNN, and random forest, were examined to predict the torque of the coaxial impellers. The results showed that the random forest model outperformed all other models. Finally, the feature importance analysis indicated that the rotating mode was the most significant parameter in determining the torque value.
Parental Involvement and Stress in Children’s Quality of Life: A Longitudinal Study with Portuguese Parents during the COVID-19 Pandemic Period
Authors: Mocho Helena , Martins Cátia , dos Santos Rita , Nunes Cristina
Published: April 2024

Parental involvement (PI) has particular relevance on children’s academic adjustment and on children’s general quality of life (QoL). QoL can be influenced by parental stress, specifically the stress suffered during the COVID-19 pandemic. Thus, this study aimed to analyze the differences during the pandemic period (before, after and with no lockdown), comparing these constructs with parental educational level to provide predictors of their children’s quality of life. Data collection was performed with a non-probability convenience sampling procedure. It was composed of 129 parents, mainly women (71.8%), with children aged between 6 and 12 years. The family’s sociodemographic characteristics were assessed, as well as the PI, parental stress, and children’s QoL. The comparison between the three times revealed an increase in the children’s QoL, but no differences were found in PI. Based on the parental educational level, as defined by academic qualifications and split into superior and non-superior levels, it is possible conclude that engagement in school activities and parents’ meetings increased in the parents with superior education levels and decreased in the parents with lower education levels. This study concluded that despite this difficult and uncertain pandemic period, these parents were able to maintain important aspects of their children’s lives.
Machine Learning Identification of Attributes and Predictors for a Flash Drought in Eastern Australia
Authors: Speer Milton , Hartigan Joshua , Leslie Lance M.
Published: April 2024

Flash droughts (FDs) are natural disasters that strike suddenly and intensify quickly. They occur almost anywhere, anytime of the year, and can have severe socio-economic, health and environmental impacts. This study focuses on a recent FD that began in the cool season of the Upper Hunter region of Eastern Australia, an important energy and agricultural local and global exporter that is both flood- and drought-prone. Here, the authors investigate the FD that started abruptly in May 2023 and extended to October 2023. The FD followed floods in November 2021 and much above-average May–October 2022 rainfall. Eight machine learning (ML) regression techniques were applied to the 60 May–October periods from 1963–2022, using a rolling windows attribution search from 45 possible climate drivers, both individually and in combination. The six most prominent climate drivers, and likely predictors, provide an understanding of the major contributors to the FD. Next, the 1963–2022 data were divided into two shorter timespans, 1963–1992 and 1993–2022, generally accepted as representing the early and accelerated global warming periods, respectively. The key attributes were markedly different for the two timespans. These differences are readily explained by the impacts of global warming on hemispheric and synoptic-scale atmospheric circulations.
Macroscopic Characteristic and Properties of Inconel 625 Cladding Layers on a Cylinder Liner Based on Laser Cladding Assisted by a Steady-State Magnetic Field
Authors: Wang Xinlin , Zhu Jing , Tian Yongchang , Jiang Jinkun , Guan Tianmin
Published: April 2024

Cylinder liners, which are an vital part of marine diesel engines, are prone to damage owing to the pool working conditions of reciprocating friction and electrochemical corrosion. As a burgeoning manufacturing technology, laser cladding has a prospective application on repairing and performance enhancement of cylinder liners. The performance of cladding layers on cylinder liners reported by current studies is not satisfactory. The laser cladding, assisted by the steady state magnetic field on the cylinder liner, is an effectual method to cover the shortage. However, there are few studies about that. In this study, single-track Inconel 625 cladding layers were carried out on a cylinder liner, assisted by a steady-state magnetic field. The effects of the magnetic field intensity and direction on the geometrical characteristics (width, height, penetration, and dilution ratio), microstructure, phase composition, microhardness, wear resistance, and corrosion resistance were investigated. According to the results obtained, adding a magnetic field with a small magnetic field intensity can significantly enhance the flatness, hardness, friction, wear resistance, and corrosion resistance of the cladding layer. Applying a magnetic field in the horizontal direction was conducive to improving the corrosion resistance of the sample. With the application of a vertical magnetic field, the microhardness increased, and wear resistance, as well as the flatness of the cladding layer, were improved.
Realizing Dual Functions through Y2O3 Modification to Enhance the Electrochemical Performance of LiNi0.8Co0.1Mn0.1O2 Material
Authors: Wang Xintao , Wang Feng , Zheng Meiqi , Rong Maohua , Wang Jiang , Deng Jianqiu , Liu Peng , Liu Daosheng
Published: April 2024

In recent years, the remarkable energy density of high-nickel ternary materials has captured considerable attention. Nevertheless, the high-nickel ternary cathode material encounters several challenges, including cationic mixing, microcrack formation, poor cycling capability, and limited thermal stability. Coating, as a viable approach, proves to be effective in enhancing the material properties. In this study, the LiNi0.8Co0.1Mn0.1O2 (NCM811) sample underwent a dry grinding process, followed by Y2O3 coating and subsequent sintering at varying temperatures. The microstructure, morphology, and electrochemical properties of the materials were meticulously examined, and the underlying mechanism of coating modification was meticulously explored. The outcomes demonstrate the attainment of dual coating and doping effects through Y2O3 modification. Y2O3 coating mitigates the direct interaction between the NCM811 surface and the electrolyte, thereby inhibiting undesired side reactions at the interface. Moreover, the Y element infiltrates the crystal structure, imparting stability at elevated sintering temperatures. Remarkably, the Y2O3-coated cathode materials exhibit significantly enhanced cycling stability, discharge capacity, and rate performance. These findings can provide novel insights that can be harnessed to improve the energy density cathode material of NCM811.
GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification
Authors: Bir-Jmel Ahmed , Douiri Sidi Mohamed , Bernoussi Souad El , Maafiri Ayyad , Himeur Yassine , Atalla Shadi , Mansoor Wathiq , Al-Ahmad Hussain
Published: April 2024

Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitating efficient dimension reduction and gene selection methods to accurately identify cancerous tumors. In response to this challenge, this study introduces an innovative strategy for microarray data dimension reduction and crucial gene set selection, aiming to enhance the accuracy of cancerous tumor identification. Leveraging DNA microarray technology, our method focuses on pinpointing significant genes implicated in tumor development, aiding the development of sophisticated computerized diagnostic tools. Our technique synergizes gene selection with classifier training within a logistic regression framework, utilizing a generalized Fused LASSO (GFLASSO-LR) regularizer. This regularization incorporates two penalties: one for selecting pertinent genes and another for emphasizing adjacent genes of importance to the target class, thus achieving an optimal trade-off between gene relevance and redundancy. The optimization challenge posed by our approach is tackled using a sub-gradient algorithm, designed to meet specific convergence prerequisites. We establish that our algorithm’s objective function is convex, Lipschitz continuous, and possesses a global minimum, ensuring reliability in the gene selection process. A numerical evaluation of the method’s parameters further substantiates its effectiveness. Experimental outcomes affirm the GFLASSO-LR methodology’s high efficiency in processing high-dimensional microarray data for cancer classification. It effectively identifies compact gene subsets, significantly enhancing classification performance and demonstrating its potential as a powerful tool in cancer research and diagnostics.
A Diagnostic-Driven Prospective Clinical Study Evaluating the Combination of an Antibiofilm Agent and Negative Pressure Wound Therapy
Authors: Serena Thomas E. , King Emily , Serena Laura , Breisinger Kristy , Al-Jalodi Omar , Myntti Matthew F.
Published: April 2024

Background: Each year, millions of Americans develop truncal pressure ulcers (PUs) which can persist for months, years, or until the end of life. Despite the negative impact on quality of life and escalating costs associated with PUs, there is sparse evidence supporting validated and efficacious treatment options. As a result, treatment is based on opinion and extrapolation from other wound etiologies. The ideal reconstructive plan maximizes the patient’s nutritional status, incorporates the basic tenets of wound bed preparation (debridement, offloading, proper moisture balance, reduction of bacterial burden), and employs diagnostics to guide therapeutic intervention. The use of combination therapies can potentially overcome several of the barriers to wound healing. Negative pressure wound therapy (NPWT), a commonly used modality in the management of PUs, facilitates healing by stimulating the formation of granulation tissue and promoting wound contraction; however, NPWT alone is not always effective. Clinical studies examining microbial bioburden in PUs determined that most ulcers contain bacteria at levels that impede wound healing (>104 CFU/g). Objective: Thus, we hypothesized that adding an anti-microbial agent to decrease both planktonic and biofilm bacteria in the wound would increase the efficacy of NPWT. Method: In this prospective study, twenty patients with recalcitrant PUs that previously failed NPWT were treated with a biofilm-disrupting agent (Blast-X, Next Science, Jacksonville, FL, USA) in combination with NPWT. Fluorescence imaging was used to follow bacterial burden and guide therapy. Results: In total, 45% of the PUs reduced in size over the course of the four-week study, with a resolution of bacterial fluorescence in the NPWT dressing and wound bed seen in an average of three weeks. Conclusion: The combination of an antibiofilm agent and NPWT reduced bacterial levels and improved wound healing in recalcitrant PUs.
An Efficient Adjacent Frame Fusion Mechanism for Airborne Visual Object Detection
Authors: Ye Zecong , Peng Yueping , Liu Wenchao , Yin Wenji , Hao Hexiang , Han Baixuan , Zhu Yanfei , Xiao Dong
Published: April 2024

With the continuous advancement of drone technology, drones are demonstrating a trend toward autonomy and clustering. The detection of airborne objects from the perspective of drones is critical for addressing threats posed by aerial targets and ensuring the safety of drones in the flight process. Despite the rapid advancements in general object detection technology in recent years, the task of object detection from the unique perspective of drones remains a formidable challenge. In order to tackle this issue, our research presents a novel and efficient mechanism for adjacent frame fusion to enhance the performance of visual object detection in airborne scenarios. The proposed mechanism primarily consists of two modules: a feature alignment fusion module and a background subtraction module. The feature alignment fusion module aims to fuse features from aligned adjacent frames and key frames based on their similarity weights. The background subtraction module is designed to compute the difference between the foreground features extracted from the key frame and the background features obtained from the adjacent frames. This process enables a more effective enhancement of the target features. Given that this method can significantly enhance performance without a substantial increase in parameters and computational complexity, by effectively leveraging the feature information from adjacent frames, we refer to it as an efficient adjacent frame fusion mechanism. Experiments conducted on two challenging datasets demonstrate that the proposed method achieves superior performance compared to existing algorithms.
Green Shocks: The Spillover Effects of Green Equity Indices on Global Market Dynamics
Authors: Trancoso Tiago , Gomes Sofia
Published: April 2024

This study investigates the impact of green equity indices on global market dynamics using a time-varying parameter vector autoregression (TVP-VAR) model. We uncover a significant shift in the role of the global market, transitioning from a shock transmitter to a shock receiver, as the influence of green finance grows. By directly comparing green equity indices with their corresponding global parent indices, we adopt a global perspective that transcends the limitations of studies focusing on specific regions, such as the USA, China, or Europe. This novel approach minimizes the potential biases in the transmission channels within regional markets, enabling a more comprehensive understanding of the relationship between green finance and global market dynamics. Moreover, by focusing on equity indices we ensure a consistent comparison of financial instruments, avoiding the complexities that arise when comparing different asset classes such as green bonds and conventional equities. For global investors, our results highlight the importance of dynamic and flexible hedging strategies that adapt to the distinct characteristics of green assets and their growing influence on the global market. Risk managers should incorporate these time-varying spillover effects into their models to better assess and mitigate potential risks. Policymakers should consider the growing influence of green finance on the broader market when formulating regulations and incentives to support sustainable investing, as our findings underscore the increasing importance of this sector in shaping market dynamics.
Instrument to Evaluate Intercultural Competence in Pedagogy Students
Authors: Mardones Tricia Del Carmen , Paulet Michelle Francois , Ortiz Juan Eduardo , Díaz Elisabet , Romero Marcela
Published: April 2024

Confronting the current educational reality involves developing intercultural competence during and after training to produce an impact on educational practices that promote positive classroom interactions with culturally and linguistically diverse students. In this context, this article presents the results of the construction and validation of the Intercultural Competence for Pedagogy Students (ICPE) instrument in a Chilean university. Taking into account the focus of interest, intercultural education in initial teacher training, the validation sample of the survey included 521 students in initial teacher training. This research aimed to establish the goodness of fit of the scale by analyzing the reliability and validity of the construct through confirmatory factor analysis. The scale presented adequate psychometric properties, contributing to the measurement of intercultural competence in initial teacher training to support attention to students in their cultural diversity.
Schools’ Challenges in Distance Learning during Emergency Education: Focus Group Methodology
Authors: Daher Wajeeh
Published: April 2024

The present research uses the focus group discussion methodology to report the challenges met by the educational system in distance education during emergency education. It shows the different potentialities of this use. In doing this, it studies the practices used by schools during emergency education. Furthermore, the research verifies the suggestions given by a group of educationists to cope with challenges in emergency education. A focus group of nine participants met in the frame of a Ph.D. course to discuss the issues in which the present research is interested, and the number of discussion sessions was four. The research results indicated that the reasons varied for agreement and disagreement in the FGDs, where these reasons were mostly experience-based, perception-based, affiliation-based, inconsideration-based, compromise-based, and suggestion-based. Moreover, the types of agreement and disagreement were mostly complementary, vis-à-vis agreement-with-objection and agreement-with-advancement. The facilitator performed the following different functions: (1) initiator, (2) caring about the different voices in the FGD, and thus encouraging equity in the FGD, (3) making the discussion smooth, (4) advancing the discussion, (5) and orchestrating the discussion. In addition, the discussion of the participants addressed challenges related to the teachers, to the Ministry of Education, and to the resources. The schools’ and the Ministry of Education’s practices included different actions in an attempt to overcome the challenges of distance education: holding workshops related to distance education, suggesting the schools as places for the teachers to teach their online lessons, and making declarations for the public and families. The suggestions given by the participants to maintain and improve online learning included communicating with the students and holding workshops for the professional development of teachers at regular times. Close relationships between the Ministry of Education, the schools, and the parents were recommended in order to maintain an acceptable level of distance education.
Examining the Science Design Skills Competency among Science Preservice Teachers in the Post-COVID-19 Pandemic Period
Authors: Mafugu Tafirenyika
Published: April 2024

The study aimed to investigate the competencies of 42 preservice science teachers from a rural university in crafting scientific investigations while utilising the constructivist learning theory as its theoretical foundation. Employing an explanatory sequential design, the research initially collected quantitative data through a pre-test, followed by an intervention in the experimental group, succeeded by a post-test phase. In order to enrich the quantitative findings, qualitative data in the form of student responses were analysed, offering a more comprehensive understanding of the preservice teachers’ proficiency in experimental design. Within the control group, no statistically significant variance emerged between the pre-test and post-test rankings, Z = −1.3, p = 0.190. In contrast, the experimental group exhibited noteworthy divergence. The Wilcoxon signed-rank test unveiled a substantial upsurge in post-test rankings when juxtaposed with the pre-test standings, Z = −4, p < 0.001. The qualitative data revealed that preservice teachers manifested a lack of familiarity with comprehension of the variables, strategies for ensuring investigation validity and reliability, and a coherent approach to gauging intervention impact. Emphasising the significance of these revelations, the study suggests plausible pathways for addressing these knowledge gaps via collaborative interventions, thus striving to effectively bridge the identified disparities.
Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements
Authors: Samadhiya Sadhna , Ku Cooper Cheng-Yuan
Published: April 2024

Cloud-service recommendation systems make suggestions based on ratings provided by cloud users. These ratings may contain sparse data, which makes it difficult to speculate on suitable cloud services. Moreover, new cloud users often suffer from cold-start difficulties. Therefore, in this study, we attempt to better overcome these two challenges, i.e., cold start and data sparsity, using a hybrid approach incorporating neural matrix factorization, deep autoencoders, and suitable questionnaires. The proposed approach provides a list of the top N cloud service providers for old cloud users based on the predicted preferences using quality of service data and asymmetrically weighted cosine similarity. To address the cold start problem, we design a questionnaire to survey new user preferences and suggest personalized cloud providers accordingly. The experiments based on the Cloud Armor database demonstrate that our approach outperforms other models. The proposed approach has a precision of 85% and achieves a mean absolute error (MAE) of 0.05 and root-mean-square error (RMSE) of 0.14 for the differences between the input and predicted values. We also receive a satisfaction level of nearly 78.5% for recommendation lists provided to new cloud service customers.
Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models
Authors: Zhang Yichong , Hao Yongtao
Published: April 2024

This study explores the use of large language models in constructing a knowledge graph for Traditional Chinese Medicine (TCM) to improve the representation, storage, and application of TCM knowledge. The knowledge graph, based on a graph structure, effectively organizes entities, attributes, and relationships within the TCM domain. By leveraging large language models, we collected and embedded substantial TCM–related data, generating precise representations transformed into a knowledge graph format. Experimental evaluations confirmed the accuracy and effectiveness of the constructed graph, extracting various entities and their relationships, providing a solid foundation for TCM learning, research, and application. The knowledge graph has significant potential in TCM, aiding in teaching, disease diagnosis, treatment decisions, and contributing to TCM modernization. In conclusion, this paper utilizes large language models to construct a knowledge graph for TCM, offering a vital foundation for knowledge representation and application in the field, with potential for future expansion and refinement.
Energy-Efficient Access Point Selection Scheme for Reconfigurable-Intelligent-Surface-Assisted Cell-Free Massive MIMO Systems
Authors: Wang Weiran , Song Jiaqi , Zhou Jianguo
Published: April 2024

Reconfigurable intelligent surface (RIS)-assisted cell-free (CF) massive Multiple-Input Multiple-Output (MIMO) technology exhibits significant potential in enhancing the energy efficiency of 6G mobile communications. Nevertheless, recent studies suggest that both access points (APs) and RISs encounter challenges related to a high energy consumption during operation. To address this issue, strategies involving AP hibernation and RIS shut-off are proposed. Subsequently, an optimization problem is formulated to jointly optimize RISs, beamforming vectors, and AP selection with the aim of maximizing the energy efficiency (EE). Initially, the non-convex optimization problem for maximizing energy efficiency is decomposed into three sub-problems. These sub-problems are subsequently reformulated using fractional programming and variational programming techniques and then solved using the successive convex approximation (SCA) algorithm, Dinkelbach algorithm, and greedy algorithm, respectively. Subsequently, an alternate optimization algorithm based on block gradient descent is introduced to iteratively solve the four-variable optimization problem, thereby obtaining an approximate solution to the original problem. The simulation results demonstrate that the algorithm significantly reduces energy consumption. Specifically, compared to the scheme without the hibernation strategy, the energy efficiency (EE) is enhanced by 35%.
Proactive Return Prediction in Online Fashion Retail Using Heterogeneous Graph Neural Networks
Authors: Ma Shaohui , Wang Weichen
Published: April 2024

Online fashion retailers face enormous challenges due to high return rates that significantly affect their operational performance. Proactively predicting returns at the point of order placement allows for preemptive interventions to reduce potentially problematic transactions. We propose an innovative inductive Heterogeneous Graph Neural Network tailored for proactive return prediction within the realm of online fashion retail. Our model intricately encapsulates customer preferences, product attributes, and order characteristics, providing a holistic approach to return prediction. Through evaluation using real-world data sourced from an online fashion retail platform, our methodology demonstrates superior predictive accuracy on the return behavior of repeat customers, compared to conventional machine learning techniques. Furthermore, through ablation analysis, we underscore the importance of simultaneously capturing customer, order, and product characteristics for an effective proactive return prediction model.
Enhancing the Performance of GaN-Based Light-Emitting Diodes by Incorporating a Junction-Type Last Quantum Barrier
Authors: Wang Jun , Xu Yiman , Wang Xiaofei , Xu Zuyu , Gong Maogao
Published: April 2024

In this paper, an n-i-p-type GaN barrier for the final quantum well, which is closest to the p-type GaN cap layer, is proposed for nitride light-emitting diodes (LEDs) to enhance the confinement of electrons and to improve the efficiency of hole injection. The performances of GaN-based LEDs with a traditional GaN barrier and with our proposed n-i-p GaN barrier were simulated and analyzed in detail. It was observed that, with our newly designed n-i-p GaN barrier, the performances of the LEDs were improved, including a higher light output power, a lower threshold voltage, and a stronger electroluminescence emission intensity. The light output power can be remarkably boosted by 105% at an injection current density of 100 A/cm2 in comparison with a traditional LED. These improvements originated from the proposed n-i-p GaN barrier, which induces a strong reverse electrostatic field in the n-i-p GaN barrier. This field not only enhances the confinement of electrons but also improves the efficiency of hole injection.
USES-Net: An Infrared Dim and Small Target Detection Network with Embedded Knowledge Priors
Authors: Li Lingxiao , Liu Linlin , He Yunan , Zhong Zhuqiang
Published: April 2024

Detecting and identifying small infrared targets has always been a crucial technology for many applications. To address the low accuracy, high false-alarm rate, and poor environmental adaptability that commonly exist in infrared target detection methods, this paper proposes a composite infrared dim and small target detection model called USES-Net, which combines the target prior knowledge and conventional data-driven deep learning networks to make use of both labeled data and the domain knowledge. Based on the typical encoder–decoder structure, USES-Net firstly introduces the self-attention mechanism of Swin Transformer to replace the universal convolution kernel at the encoder end. This helps to extract potential features related to dim, small targets in a larger receptive field. In addition, USES-Net includes an embedded patch-based contrast learning module (EPCLM) to integrate the spatial distribution of the target as a knowledge prior in the training network model. This guides the training process of the constrained network model with clear physical interpretability. Finally, USES-Net also designs a bottom-up cross-layer feature fusion module (AFM) as the decoder of the network, and a data-slicing-aided enhancement and inference method based on Slicing Aided Hyper Inference (SAHI) is utilized to further improve the model’s detection accuracy. An experimental comparative analysis shows that USES-Net achieves the best results on three typical infrared weak-target datasets: NUAA-SIRST, NUDT-SIRST, and IRSTD-1K. The results of the target segmentation are complete and sufficient, which demonstrates the validity and practicality of the proposed method in comparison to others.
Enhancing Throughput in IoT Networks: The Impact of Active RIS on Wireless Powered Communication Systems
Authors: Hameed Iqra , Koo Insoo
Published: April 2024

This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity.
Tensor Product Alternatives for Nonlinear Field-Oriented Control of Induction Machines
Authors: Kuczmann Miklós , Horváth Krisztián
Published: April 2024

The paper presents a nonlinear field-oriented control technique based on the tensor product representation of the nonlinear induction machine model and the solvability of linear matrix inequalities. The nonlinear model has 32 quasi linear parameter-varying equivalent variants, and it is shown that only half of the models result in feasible controller. Two control goals are realized: torque control and speed control. The controller is a nonlinear state feedback controller completed by integral action. A new block diagram is investigated for speed control. The controller gains are designed by the solution of linear matrix inequalities to solve the Lyapunov inequality to obtain a stable and fast response and constraints on the control signal. The presented methods are verified and compared by simulations.
Anti-Anemic and Anti-Dyspepsia Potential of Yogurt with Carao (Cassia grandis) in Rat Model
Authors: Marcia Jhunior , de Jesús Álvarez Gil Manuel , Fernández Héctor Zumbado , Montero-Fernández Ismael , Martín-Vertedor Daniel , Yadav Ajitesh , Aleman Ricardo S.
Published: April 2024

Iron deficiency anemia is a significant health problem in developing countries and this is rising, particularly in children and pregnant women. Several therapeutic properties have been attributed to Cassia grandis (carao), including the treatment against anemia, a laxative effect, and the reduction of bleeding. Yogurt is a vehicle for functional ingredients. As a result, this investigation aims to examine the application of Cassia grandis pulp as an anti-anemic and anti-dyspepsia agent in enriched yogurt. Carao pulp powder was added to milk at 0%, 0.5%, 1%, and 3% to produce yogurt. The bioavailability characteristics of iron deficiency anemia were analyzed in albino rats, which were studied for 4 weeks. Other groups of rats were used to set up the dyspepsia model by being fed a high-fat and high-calorie diet. Intestinal propulsion rate, gastric emptying rate, small intestinal contraction, motilin levels, and intestinal muscle tension were analyzed in rats with dyspepsia. Yogurt with 3% carao pulp powder restored ferritin, hemoglobin, total protein and iron at the end of the 4-week feeding period, with significant competition revealed in calcium and zinc absorbance. Furthermore, yogurt with 3% carao pulp powder improved intestinal propulsion rate, gastric emptying rate, small intestinal contraction, motilin levels, and intestinal muscle tension in dyspepsia rats. Carao can be recommended as an anti-anemia supplement in yogurt fortification.
Aged to Perfection: The Scientific Symphony behind Port Wine, Vinegar, and Acetic Acid Bacteria
Authors: Mota João , Vilela Alice
Published: April 2024

This review critically examines the multifaceted role of acetic acid bacteria (AAB) in the intricate production process of port wine vinegar, particularly in its transformative process from port wine. With the emergence of port wine vinegar as a distinctive agricultural product in 2018, producers have been faced with a diverse array of challenges, ranging from reducing the high alcohol content to preserving the inherent sweetness. Through an exhaustive exploration of acetic fermentation processes and the indispensable role of AAB, this review meticulously elucidates the complex biochemistry underlying vinegar formation, delving into the nuanced interactions between microbial activity and chemical composition. Furthermore, this review underscores the importance of sensory characteristics and consumer perception derived from vinegar production, providing invaluable insights into these fermented products’ sensory profiles and marketability. In summary, this study offers valuable insights into the evolution of port wine into vinegar, highlighting its significance in agricultural and culinary contexts.