bluuebunny/crossref_metadata_2025 huggingface dataset:
Green Synthesis of LaMnO3-Ag Nanocomposites Using Citrus limon (L.) Burm Peel Aqueous Extract: Photocatalytic Degradation of Rose Bengal Dye and Antibacterial Applications
Authors: Hasan Nazim
Published: September 2024

Perovskites can absorb solar energy and are extensively used in various catalytic and photocatalytic reactions. However, noble metal particles may enhance the catalytic, photocatalytic, and antibacterial activities. This study demonstrates the cost-effective green synthesis of the photocatalyst perovskite LaMnO3 and its modification with noble metal Ag nanoparticles. The green synthesis of nanocomposite was achieved through a hydrothermal method employing aqueous extract derived from Citrus limon (L.) Burm peels. The properties of fabricated perovskites LaMnO3 and LaMnO3-Ag nanocomposites were evaluated and characterized by Ultraviolet-Visible spectroscopy (UV-Vis), Diffuse Reflectance Spectroscopy (DRS), X-ray diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FT-IR), High-Resolution Transmission Electron Microscopy (HRTEM), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX) and Brunauer–Emmett–Teller (BET) surface area techniques. The particle size distribution % of LaMnO3 and LaMnO3-Ag was observed to be 20 to 60 nm after using TEM images. The maximum percentage size distribution was 37 nm for LaMnO3 and 43 nm for LaMnO3-Ag. In addition, LaMnO3-Ag nanocomposite was utilized as a photocatalyst for the degradation of Rose Bengal (RB) dye and its antibacterial activities against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). The surface area and band gap for perovskite LaMnO3 nanoparticles were calculated as 12.642 m2/g and 3.44 eV, respectively. The presence of noble metal and hydrothermal-bio reduction significantly impacted the crystallinity. The BET surface area was found to be 16.209 m2/g, and band gap energy was calculated at 2.94 eV. The LaMnO3 nanocomposite with noble metal shows enhanced photocatalytic effectiveness against RB dye (20 PPM) degradation (92%, R2 = 0.995) with pseudo-first-order chemical kinetics (rate constant, k = 0.05057 min−1) within 50 min due to the ultimate combination of the hydrothermal and bio-reduction technique. The photocatalytic activity of the LaMnO3-Ag nanocomposite was optimized at different reaction times, photocatalyst doses (0.2, 0.4, 0.6, and 0.8 g/L), and various RB dye concentrations (20, 30, 40, and 50 ppm). The antibacterial activities of green synthesized LaMnO3 and LaMnO3-Ag nanoparticles were explored based on colony-forming unit (cfu) reduction and TEM images of bacterial and nanoparticle interactions for S. aureus and E. coli. An amount of 50 µg/mL LaMnO3-Ag nanocomposite was sufficient to work as the highest antibacterial activity for both bacteria. The perovskite LaMnO3-Ag nanocomposite synthesis process is economically and environmentally friendly. Additionally, it has a wide range of effective and exclusive applications for remediating pollutants.
Highly Efficient 1-Iodination of Terminal Alkynes Catalyzed by Inorganic or Organic Bases
Authors: Yang Yang , Zhang Jian , Wan Fang , Liu E , Zhang Huaxin
Published: September 2024

1-Iodoalkynes are one type of the most reactive and the most practical intermediates in organic synthesis. Here, some facile and efficient methods for the 1-iodination of terminal alkynes are developed using N-iodosuccinimide (NIS) as an iodination reagent and some inexpensive mild bases as catalysts. K2CO3 and 4-dimethylaminopyridine (DMAP) have been proven to be the most suitable inorganic and organic bases, succeeding in 17 examples with excellent yields (up to 99%).
Comparative Assessment of First-Row 3d Transition Metals (Ti-Zn) Supported on CeO2 Nanorods for CO2 Hydrogenation
Authors: Lykaki Maria , Stefa Sofia , Varvoutis Georgios , Binas Vassilios D. , Marnellos George E. , Konsolakis Michalis
Published: September 2024

Herein, motivated by the excellent redox properties of rod-shaped ceria (CeO2-NR), a series of TM/CeO2 catalysts, employing the first-row 3d transition metals (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn) as active metal phases, were comparatively assessed under identical synthesis and reaction conditions to decipher the role of active metal in the CO2 hydrogenation process. Notably, a volcano-type dependence of CO2 hydrogenation activity/selectivity was disclosed as a function of metal entity revealing a maximum for the Ni-based sample. Ni/CeO2 is extremely active and fully selective to methane (YCH4 = 90.8% at 350 °C), followed by Co/CeO2 (YCH4 = 45.2%), whereas the rest of the metals present an inferior performance. No straightforward relationship was disclosed between the CO2 hydrogenation performance and the textural, structural, and redox properties, whereas, on the other hand, a volcano-shaped trend was established with the relative concentration of oxygen vacancies and partially reduced Ce3+ species. The observed trend is also perfectly aligned with the previously reported volcano-type dependence of atomic hydrogen adsorption energy and CO2 activation as a function of 3d-orbital electron number, revealing the key role of intrinsic electronic features of each metal in conjunction to metal–support interactions.
Treg Cell Therapeutic Strategies for Breast Cancer: Holistic to Local Aspects
Authors: Zhang Hanwen , Felthaus Oliver , Eigenberger Andreas , Klein Silvan , Prantl Lukas
Published: September 2024

Regulatory T cells (Tregs) play a key role in maintaining immune homeostasis and preventing autoimmunity through their immunosuppressive function. There have been numerous reports confirming that high levels of Tregs in the tumor microenvironment (TME) are associated with a poor prognosis, highlighting their role in promoting an immunosuppressive environment. In breast cancer (BC), Tregs interact with cancer cells, ultimately leading to the suppression of immune surveillance and promoting tumor progression. This review discusses the dual role of Tregs in breast cancer, and explores the controversies and therapeutic potential associated with targeting these cells. Researchers are investigating various strategies to deplete or inhibit Tregs, such as immune checkpoint inhibitors, cytokine antagonists, and metabolic inhibition. However, the heterogeneity of Tregs and the variable precision of treatments pose significant challenges. Understanding the functional diversity of Tregs and the latest advances in targeted therapies is critical for the development of effective therapies. This review highlights the latest approaches to Tregs for BC treatment that both attenuate Treg-mediated immunosuppression in tumors and maintain immune tolerance, and advocates precise combination therapy strategies to optimize breast cancer outcomes.
Glass Wool Recycling by Water-Based Solvolysis
Authors: Kántor Petra , Béri János , Képes Bence , Székely Edit
Published: September 2024

Glass wool is an insulation material used in large quantities; despite its popularity, it is still surprising that around 2.5 million tons of mineral wool waste is generated every year in Europe. Waste management faces numerous challenges because, with current technologies, waste cannot be cleaned and melted again for repurposing, so even the recollected glass wool ends up in landfill. Herein, we present a hydrolysis technology that decomposes the binder applied to glass fibers using exclusively water. We succeeded in decomposing the resin from the surfaces of the end-of-life fibers, originating from different sources like industrial furnace insulation and two demolition-sourced building insulation wastes. The effects of temperature, pressure, the mass flow of the water, and the applied fluid ratio were investigated. The most important parameters are the temperature and the time for the decomposition, but to minimize glass loss through the solubility of glass, the fluid ratio plays an important role as well. The fibers were efficiently cleaned after only 20 min at 300 °C by the optimized parameters, with a mass recovery of 70 wt.%.
Accurate Prediction of Tea Catechin Content with Near-Infrared Spectroscopy by Deep Learning Based on Channel and Spatial Attention Mechanisms
Authors: Zhang Mingzan , Zhang Tuo , Wang Yuan , Duan Xueyi , Pu Lulu , Zhang Yuan , Li Qin , Liu Yabing
Published: September 2024

The assessment of catechin content stands as a pivotal determinant of tea quality. In tea production and quality grading, the development of accurate and non-destructive techniques for the accurate prediction of various catechin content is paramount. Near-infrared spectroscopy (NIRS) has emerged as a widely employed tool for analyzing the chemical composition of tea. Nevertheless, the spectral information obtained from NIRS faces challenges when discerning different types of catechins in black tea, owing to their similar physical and chemical properties. Moreover, the vast number of NIRS wavelengths exceeds the available tea samples, further complicating the accurate assessment of catechin content. This study introduces a novel deep learning approach that integrates specific wavelength selection and attention mechanisms to accurately predict the content of various catechins in black tea simultaneously. First, a wavelength selection algorithm is proposed based on feature interval combination sensitivity segmentation, which effectively extracts the NIRS feature information of tea. Subsequently, a one-dimensional convolutional neural network (CNN) incorporating channel and spatial–sequential attention mechanisms is devised to independently extract the key features from the selected wavelength variables. Finally, a multi-output predictor is employed to accurately predict the four main catechins in tea. The experimental results demonstrate the superiority of the proposed model over existing methods in terms of prediction accuracy and stability (R2 = 0.92, RMSE = 0.018 for epicatechin; R2 = 0.96, RMSE = 0.11 for epicatechin gallate; R2 = 0.97, RMSE = 0.14 for epigallocatechin; R2 = 0.97, RMSE = 0.32 for epigallocatechin gallate). This innovative deep learning approach amalgamates wavelength selection with attention mechanisms, provides a new perspective for the simultaneous assessment of the major components in tea, and contributes to the advancement of precision management in the tea industry’s production and grading processes.
Development and Validation of a Realistic Neonatal Intestinal Jejunoileal Atresia Simulator for the Training of Pediatric Surgeons
Authors: Arredondo Montero Javier , Pérez Riveros Blanca Paola , Bueso Asfura Oscar Emilio , Martín Calvo Nerea , Pueyo Francisco Javier , López de Aguileta Castaño Nicolás
Published: September 2024

Background: Neonatal surgical pathology presents highly technical complexity and few opportunities for training. Many of the neonatal surgical entities are not replicable in animal models. Realistic 3D models are a cost-effective and efficient alternative for training new generations of pediatric surgeons. Methods: We conceptualized, designed, and produced an anatomically realistic model for the open correction of jejunoileal atresia. We validated it with two groups of participants (experts and non-experts) through face, construct, and content validity questionnaires. Results: The model was validated by eleven experts and nine non-experts. The mean procedure time for the experts and non-experts groups was 41 and 42 min, respectively. Six non-experts and one expert did not complete the procedure by the designed time (45 min) (p = 0.02). The mean score of face validity was 3.1 out of 4. Regarding construct validity, we found statistically significant differences between groups for the correct calculation of the section length of the antimesenteric border (Nixon’s technique) (p < 0.01). Concerning content validity, the mean score was 3.3 out of 4 in the experts group and 3.4 out of 4 in the non-experts group. Conclusions: The present model is a realistic and low-cost valid option for training for open correction of jejunoileal atresia. Before drawing definitive conclusions, future studies with larger sample sizes and blinded validators are needed.
Metallization of Carbon Fiber-Reinforced Plastics (CFRP): Influence of Plasma Pretreatment on Mechanical Properties and Splat Formation of Atmospheric Plasma-Sprayed Aluminum Coatings
Authors: Semmler Christian , Schwan Willi , Killinger Andreas
Published: September 2024

Carbon fiber-reinforced plastics (CFRPs) have broad applications as lightweight structural materials due to their remarkable strength-to-weight ratio. Aluminum is often used as a bond coating to ensure adhesion between CFRPs and further coatings with a higher melting temperature. However, challenges persist in optimizing their surface properties and adhesion attributes for diverse applications. This investigation explores the impact of sandblasting and plasma pretreatment on CFRP surfaces and their influence on plasma-sprayed aluminum coatings. Two distinct CFRP substrates, distinguished by their cyanate ester and epoxy resin matrices, and two different aluminum powder feedstocks were employed. Plasma pretreatment induced micro-surface roughening in the range of 0.5 µm and significantly reduced the contact angles on polished specimens. Notably, on sandblasted specimens, plasma-activated surfaces displayed improved wetting behavior, which is attributed to the removal of polymeric fragments and augmented fiber exposure. Aluminum splats show a better interaction with carbon fibers compared to a polymeric matrix material. The impact of plasma activation on the coating adhesion proved relatively limited. All samples with plasma activation had deposition efficiencies that increased by 12.5% to 34.4%. These findings were supported by SEM single-splat analysis and contribute to a deeper comprehension of surface modification strategies tailored to CFRPs.
Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm
Authors: Tsagris Michail , Papadakis Manos , Alenazi Abdulaziz , Alzeley Omar
Published: September 2024

Outlier detection, or anomaly detection as it is known in the machine learning community, has gained interest in recent years, and it is commonly used when the sample size is smaller than the number of variables. In 2015, an outlier detection procedure was proposed 7 for this high-dimensional setting, replacing the classic minimum covariance determinant estimator with the minimum diagonal product estimator. Computationally speaking, their method has two drawbacks: (a) it is not computationally efficient and does not scale up, and (b) it is not memory efficient and, in some cases, it is not possible to apply due to memory limits. We address the first issue via efficient code written in both R and C++, whereas for the second issue, we utilize the eigen decomposition and its properties. Experiments are conducted using simulated data to showcase the time improvement, while gene expression data are used to further examine some extra practicalities associated with the algorithm. The simulation studies yield a speed-up factor that ranges between 17 and 1800, implying a successful reduction in the estimator’s computational burden.
Exploring Soliton Solutions for Fractional Nonlinear Evolution Equations: A Focus on Regularized Long Wave and Shallow Water Wave Models with Beta Derivative
Authors: Devnath Sujoy , Helmi Maha M. , Akbar M. Ali
Published: September 2024

The fractional regularized long wave equation and the fractional nonlinear shallow-water wave equation are the noteworthy models in the domains of fluid dynamics, ocean engineering, plasma physics, and microtubules in living cells. In this study, a reliable and efficient improved F-expansion technique, along with the fractional beta derivative, has been utilized to explore novel soliton solutions to the stated wave equations. Consequently, the study establishes a variety of reliable and novel soliton solutions involving trigonometric, hyperbolic, rational, and algebraic functions. By setting appropriate values for the parameters, we obtained peakons, anti-peakon, kink, bell, anti-bell, singular periodic, and flat kink solitons. The physical behavior of these solitons is demonstrated in detail through three-dimensional, two-dimensional, and contour representations. The impact of the fractional-order derivative on the wave profile is notable and is illustrated through two-dimensional graphs. It can be stated that the newly established solutions might be further useful for the aforementioned domains.
Experimental Study of Reaming Sizes on Fatigue Life of Cold-Expanded 7050-T7451 Aluminum Alloy
Authors: Guan Muyu , Xue Qichao , Zhuang Zixin , Hu Quansheng , Qi Hui
Published: September 2024

The split-sleeve cold expansion technology is widely used in the aerospace industry, particularly for fastening holes, to enhance the fatigue life of components. However, to ensure proper assembly and improve surface integrity, reaming of the cold-expanded holes is necessary. This study investigates the effects of cold expansion and reaming processes on the fatigue performance of 7050-T7451 aluminum alloy. Fatigue tests, residual stress measurements, and microstructural analyses of the hole edges were conducted on specimens with four different hole diameters after cold expansion and reaming. It was found that the depth of reaming significantly affects fatigue life. During the cold expansion process, the compressive residual stress formed around the hole effectively improves fatigue performance. The experiments demonstrated that reaming by 0.2 mm to 0.4 mm helps eliminate minor defects, thereby improving fatigue life. However, reaming beyond 0.5 mm may lead to stress relief and the removal of dense grains at the hole edges, reducing fatigue life. Therefore, determining the optimal reaming size is crucial for enhancing the reliability of aerospace fasteners.
Disparities in Clinical Trial Participation: A Cross-Sectional Survey of Cancer Patients at a Midwest Academic Medical Center
Authors: Moreland Katie , Butsch Kovacic Melinda , Rai Shesh , Sohal Davendra
Published: September 2024

Research conducted on homogenous populations can lead to biased and misleading findings, impeding the development of effective interventions and treatments for diverse populations. Low participation among minority groups further leads to disparities in access to innovative cancer care and treatment outcomes associated with trial participation. To better understand cancer patients’ attitudes and willingness to participate in clinical trials, solid tumor patients attending their clinic visits were invited to complete a survey. The survey included questions on demographics, previous trial participation, and future trial interest. Responses were analyzed with frequency tables and chi-square tests. Of 300 participants, only 96 (32%) were asked to participate in a clinical trial. Of these, 81 (84%) chose to participate and 15 (16%) did not. There were notable differences by race but not gender or education level. Of the 204 who had never been asked to participate, 70% indicated that they would be willing to participate in future, and there was a strong sex–race interaction. Non-White males were the most hesitant group. Of 204, 99% indicated that they would participate to access new treatments, and 57% would participate to contribute to research overall. This study shows that many solid tumor patients undergoing treatment are not offered clinical trials. Racial differences in attitudes toward trial participation are evident. Nonetheless, many patients are willing to participate in trials to access innovative treatments and to support research. Culturally relevant outreach to build trust with minority groups is needed to increase overall participation in clinical trials.
Advances in Ultrasound-Guided Surgery and Artificial Intelligence Applications in Musculoskeletal Diseases
Authors: Hattori Soichi , Saggar Rachit , Heidinger Eva , Qi Andrew , Mullen Joseph , Fee Brianna , Brown Cortez L. , Canton Stephen P. , Scott Devon , Hogan MaCalus V.
Published: September 2024

Ultrasound imaging is a vital imaging tool in musculoskeletal medicine, with the number of publications on ultrasound-guided surgery increasing in recent years, especially in minimally invasive procedures of sports, foot and ankle, and hand surgery. However, ultrasound imaging has drawbacks, such as operator dependency and image obscurity. Artificial intelligence (AI) and deep learning (DL), a subset of AI, can address these issues. AI/DL can enhance screening practices for hip dysplasia and osteochondritis dissecans (OCD) of the humeral capitellum, improve diagnostic accuracy for carpal tunnel syndrome (CTS), and provide physicians with better prognostic prediction tools for patients with knee osteoarthritis. Building on these advancements, DL methods, including segmentation, detection, and localization of target tissues and medical instruments, also have the potential to allow physicians and surgeons to perform ultrasound-guided procedures more accurately and efficiently. This review summarizes recent advances in ultrasound-guided procedures for musculoskeletal diseases and provides a comprehensive overview of the utilization of AI/DL in ultrasound for musculoskeletal medicine, particularly focusing on ultrasound-guided surgery.
A Deep Learning Model for Cervical Optical Coherence Tomography Image Classification
Authors: Zuo Xiaohu , Liu Jianfeng , Hu Ming , He Yong , Hong Li
Published: September 2024

Objectives: Optical coherence tomography (OCT) has recently been used in gynecology to detect cervical lesions in vivo and proven more effective than colposcopy in clinical trials. However, most gynecologists are unfamiliar with this new imaging technique, requiring intelligent computer-aided diagnosis approaches to help them interpret cervical OCT images efficiently. This study aims to (1) develop a clinically-usable deep learning (DL)-based classification model of 3D OCT volumes from cervical tissue and (2) validate the DL model’s effectiveness in detecting high-risk cervical lesions, including high-grade squamous intraepithelial lesions and cervical cancer. Method: The proposed DL model, designed based on the convolutional neural network architecture, combines a feature pyramid network (FPN) with texture encoding and deep supervision. We extracted, represent, and fused four-scale texture features to improve classification performance on high-risk local lesions. We also designed an auxiliary classification mechanism based on deep supervision to adjust the weight of each scale in FPN adaptively, enabling low-cost training of the whole model. Results: In the binary classification task detecting positive subjects with high-risk cervical lesions, our DL model achieved an 81.55% (95% CI, 72.70–88.51%) F1-score with 82.35% (95% CI, 69.13–91.60%) sensitivity and 81.48% (95% CI, 68.57–90.75%) specificity on the Renmin dataset, outperforming five experienced medical experts. It also achieved an 84.34% (95% CI, 74.71–91.39%) F1-score with 87.50% (95% CI, 73.20–95.81%) sensitivity and 90.59% (95% CI, 82.29–95.85%) specificity on the Huaxi dataset, comparable to the overall level of the best investigator. Moreover, our DL model provides visual diagnostic evidence of histomorphological and texture features learned in OCT images to assist gynecologists in making clinical decisions quickly. Conclusions: Our DL model holds great promise to be used in cervical lesion screening with OCT efficiently and effectively.
Empirical Treatments for Male Infertility: A Focus on Lifestyle Modifications and Medicines
Authors: Kaltsas Aris , Zachariou Athanasios , Dimitriadis Fotios , Chrisofos Michael , Sofikitis Nikolaos
Published: September 2024

Background/Objectives: Male infertility is a prevalent issue impacting numerous couples worldwide. This review aims to evaluate the effectiveness of empirical therapies for male infertility, focusing on both lifestyle modifications and medical treatments. This study provides a comprehensive overview of interventions aimed at improving male fertility outcomes. Methods: A thorough review of the existing literature was conducted, encompassing studies on lifestyle changes such as dietary changes, smoking cessation, alcohol moderation, and exercise. Additionally, medical treatments including selective estrogen receptor modulators, gonadotropins, aromatase inhibitors, phosphodiesterase-5 inhibitors, antioxidants, dopamine agonists, kallikrein, indomethacin, low-dose corticosteroids, alpha-blockers, and nitric oxide donors were evaluated. The study population included males diagnosed with infertility, focusing on various underlying causes. Results: Lifestyle modifications were found to have a positive impact on sperm quality. Evidence shows that a healthy diet, smoking cessation, moderate alcohol consumption, and regular exercise improve fertility outcomes. Medical treatments demonstrated significant improvements in sperm production and quality. Selective estrogen receptor modulators and gonadotropins enhanced sperm parameters. Aromatase inhibitors and phosphodiesterase-5 inhibitors specifically improved sperm motility and increased pregnancy rates. Antioxidants, such as vitamins E and C and coenzyme Q10, reduced oxidative stress and enhanced sperm counts, motility, and morphology. Dopamine agonists, particularly cabergoline, normalized prolactin levels and improved fertility outcomes. Kallikrein therapy improved sperm parameters and increased pregnancy rates. Indomethacin treatment was associated with increased sperm concentrations and motility. Low-dose corticosteroids and alpha-blockers showed variable results, and nitric oxide donors like L-arginine enhanced sperm counts and motility. Conclusions: Empirical therapies, including lifestyle modifications and medical treatments, significantly enhance sperm quality and reproductive potential. These integrated approaches are essential in improving fertility outcomes in males. However, further extensive randomized trials are necessary to definitively establish the most effective treatments.
Levy Sooty Tern Optimization Algorithm Builds DNA Storage Coding Sets for Random Access
Authors: Zhang Jianxia
Published: September 2024

DNA molecules, as a storage medium, possess unique advantages. Not only does DNA storage exhibit significantly higher storage density compared to electromagnetic storage media, but it also features low energy consumption and extremely long storage times. However, the integration of DNA storage into daily life remains distant due to challenges such as low storage density, high latency, and inevitable errors during the storage process. Therefore, this paper proposes constructing a DNA storage coding set based on the Levy Sooty Tern Optimization Algorithm (LSTOA) to achieve an efficient random-access DNA storage system. Firstly, addressing the slow iteration speed and susceptibility to local optima of the Sooty Tern Optimization Algorithm (STOA), this paper introduces Levy flight operations and propose the LSTOA. Secondly, utilizing the LSTOA, this paper constructs a DNA storage encoding set to facilitate random access while meeting combinatorial constraints. To demonstrate the coding performance of the LSTOA, this paper consists of analyses on 13 benchmark test functions, showcasing its superior performance. Furthermore, under the same combinatorial constraints, the LSTOA constructs larger DNA storage coding sets, effectively reducing the read–write latency and error rate of DNA storage.
The Structure and Nature of Social Capital in the Relationship between Spin-Offs and Parent Companies in Information Technology Clusters in Brazil and Spain
Authors: Cardoso Flávio Manoel Coelho Borges , Martínez-Fernández Maria Teresa , Sousa Marcos de Moraes , Hoffmann Valmir Emil
Published: September 2024

The objective of this research is to determine how proximity between organizations promotes the intensity of relationships and facilitates the exchange of information and knowledge in the relationship between the parent firm and the spin-off and its influence on organizational performance. Therefore, four constructs related to business competitiveness are integrated: networks, social capital, spin-offs, and innovation. The loci of the research were two clusters of Information and Communication Technology, with a sample of 166 companies in Brazil and 66 in Spain. Structural Equation Modeling was applied. The results indicate that geographical proximity between organizations promotes the intensity of relationships and facilitates the exchange of knowledge between the parent firm and the spin-off, but it cannot be said that geographical proximity impacts innovation for the parent firm. Furthermore, because the parent firm relates to its spin-off, the parent firm did not perform better than the nonparent companies with other companies. This study improves the understanding of companies that are in a business cluster, and its results have both institutional and business implications for the Information and Communication Technology (ICT) sector.
Economic Policy Uncertainty, Managerial Ability, and Cost of Equity Capital: Evidence from a Developing Country
Authors: Hamdy Arafat , Eissa Aref M. , Diab Ahmed
Published: September 2024

This study investigates the relationship between economic policy uncertainty (EPU) and the cost of equity capital (CoEC). It also reveals the moderating role of managerial ability (MA) in the relationship between EPU and CoEC in Saudi Arabia. The study sample consists of listed non-financial firms in Tadawul from 2008 to 2019. We analyzed data using STATA, depending on Pearson correlation analysis, two independent sample t-tests, OLS regression, and OLS with robust standard errors clustered by firm. Our study shows a positive effect of EPU on the CoEC. In addition, the results confirm that MA mitigates the positive effect of EPU on the CoEC. This is the first research to investigate the influence of the relationship between EPU on CoEC in Saudi Arabia, one of the largest emerging economies in the Middle East and Gulf countries. Our findings motivate decision-makers to strengthen their MA and establish a safe and stable investment environment to ensure better financing and investment decisions during uncertain times. Lending agencies, investors, and other stakeholders should consider the MA of corporations when making investment decisions.
Advancing Middle Grade Research on Critical Pedagogy: Research Synthesis
Authors: Walker Amy , Yoon Bogum , Pankowski Jennifer
Published: September 2024

In this critical literature review, we examine how middle-level pedagogies, specifically critical pedagogies, impact students’ academic, physical, and socioemotional development. This literature review examines critical pedagogies research in middle-level education, focusing on methods that address systemic inequities and center diverse and historically marginalized student populations. Examining the literature from 2013 to 2023, key findings indicate that critical pedagogies, including culturally responsive teaching practices, diverse instructional approaches, and decolonial and antiracist strategies can impact student engagement and development. When used purposefully to foster equity, these approaches disrupt institutional power dynamics, promote social justice, and support diverse student needs. However, there is a lack of research on these pedagogies when in practice for a sustained amount of time, as well as a lack of inclusion of literature with student voices. Because of this, we call for future research to include the perspectives and voices of middle-level students to better understand and refine critical pedagogical practices.
Research on Data Quality Governance for Federated Cooperation Scenarios
Authors: Shen Junxin , Zhou Shuilan , Xiao Fanghao
Published: September 2024

Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a “Two-stage” data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved.
Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets with ACD-NSGA-II Algorithm
Authors: Zhang Hong , Miao Kunzhong , Yu Huangzhi , Niu Yifeng
Published: September 2024

The existing task assignment algorithms usually solve only a point-based model. This paper proposes a novel algorithm for task assignment in detection search tasks. Firstly, the optimal reconnaissance path is generated by considering the drone’s position and attitude information, as well as the type of heterogeneous targets present in the actual scene. Subsequently, an adaptive crowding distance calculation (ACD-NSGA-II) is proposed based on the relative position of solutions in space, taking into account the spatial distribution of parent solutions and constraints imposed by uncertain targets and terrain. Finally, comparative experiments using digital simulation are conducted under two different target probability scenarios. Moreover, the improved algorithm is further evaluated across 100 cases, and a comparison of the Pareto solution set with other algorithms is conducted to demonstrate the algorithm’s overall adaptability.
Efficient Human Activity Recognition on Wearable Devices Using Knowledge Distillation Techniques
Authors: Gonçalves Paulo H. N. , Bragança Hendrio , Souto Eduardo
Published: September 2024

Mobile and wearable devices have revolutionized the field of continuous user activity monitoring. However, analyzing the vast and intricate data captured by the sensors of these devices poses significant challenges. Deep neural networks have shown remarkable accuracy in Human Activity Recognition (HAR), but their application on mobile and wearable devices is constrained by limited computational resources. To address this limitation, we propose a novel method called Knowledge Distillation for Human Activity Recognition (KD-HAR) that leverages the knowledge distillation technique to compress deep neural network models for HAR using inertial sensor data. Our approach transfers the acquired knowledge from high-complexity teacher models (state-of-the-art models) to student models with reduced complexity. This compression strategy allows us to maintain performance while keeping computational costs low. To assess the compression capabilities of our approach, we evaluate it using two popular databases (UCI-HAR and WISDM) comprising inertial sensor data from smartphones. Our results demonstrate that our method achieves competitive accuracy, even at compression rates ranging from 18 to 42 times the number of parameters compared to the original teacher model.
AiMap+: Guiding Technology Mapping for ASICs via Learning Delay Prediction
Authors: Liu Junfeng , Zhao Qinghua
Published: September 2024

Technology mapping is an essential process in the Electronic Design Automation (EDA) flow which aims to find an optimal implementation of a logic network from a technology library. In application-specific integrated circuit (ASIC) designs, the non-linear delay behaviors of cells in the library essentially guide the search direction of technology mappers. Existing methods for cell delay estimation, however, rely on approximate simplifications that significantly compromise accuracy, thereby limiting the achievement of better Quality-of-Result (QoR). To address this challenge, we propose formulating cell delay estimation as a regression learning task by incorporating multiple perspective features, such as the structure of logic networks and non-linear cell delays, to guide the mapper search. We design a learning model that incorporates a customized attention mechanism to be aware of the pin delay and jointly learns the hierarchy between the logic network and library through a Neural Tensor Network, with the help of proposed parameterizable strategies to generate learning labels. Experimental results show that (i) our proposed method noticeably improves area by 9.3% and delay by 1.5%, and (ii) improves area by 12.0% for delay-oriented mapping, compared with the well-known mapper.
An Ensemble Method for Non-Intrusive Load Monitoring (NILM) Applied to Deep Learning Approaches
Authors: Moreno Silvia , Teran Hector , Villarreal Reynaldo , Vega-Sampayo Yolanda , Paez Jheifer , Ochoa Carlos , Espejo Carlos Alejandro , Chamorro-Solano Sindy , Montoya Camilo
Published: September 2024

Climate change, primarily driven by human activities such as burning fossil fuels, is causing significant long-term changes in temperature and weather patterns. To mitigate these impacts, there is an increased focus on renewable energy sources. However, optimizing power consumption through effective usage control and waste recycling also offers substantial potential for reducing energy demands. This study explores non-intrusive load monitoring (NILM) to estimate disaggregated energy consumption from a single household meter, leveraging advancements in deep learning such as convolutional neural networks. The study uses the UK-DALE dataset to extract and plot power consumption data from the main meter and identify five household appliances. Convolutional neural networks (CNNs) are trained with transfer learning using VGG16 and MobileNet. The models are validated, tested on split datasets, and combined using ensemble methods for improved performance. A new voting scheme for ensembles is proposed, named weighted average confidence voting (WeCV), and it is used to create combinations of the best 3 and 5 models and applied to NILM. The base models achieve up to 97% accuracy. The ensemble methods applying WeCV show an increased accuracy of 98%, surpassing previous state-of-the-art results. This study shows that CNNs with transfer learning effectively disaggregate household energy use, achieving high accuracy. Ensemble methods further improve performance, offering a promising approach for optimizing energy use and mitigating climate change.
Solar Irradiance Stability Factors
Authors: Jereb Borut
Published: September 2024

In the field of renewable energies, the logistical intricacies of production, as well as the use and storage of photovoltaic energy, have become critical issues. In addition to sheer quantity, the stability of this type of energy is a crucial factor in ensuring the reliability and consistency of power generation. This paper defines Solar Irradiance Stability Factors (SISFs) as indicators complementing electricity production. When planning solar energy production in each geographical area, both the quantity and stability of solar irradiance are necessary for exploitation and determining the quality of solar irradiance. While the average production of solar energy per unit area in each time interval is a widely used parameter in daily practice, the observation of the amplitude of solar irradiance and its influence on energy production in the observed time interval is currently still rare. The SISFs defined in this article are new metrics that mainly depend on the meteorological variability in an area, and the observed time intervals should be in the range of seconds, minutes, or even hours. Larger time intervals are not helpful for the stability of solar irradiance in energy production and logistics from the source to the destination. They provide a complementary and more accurate measure of how suitable a particular geographical area is for producing solar energy.
Lightweight Anonymous Authentication and Key Agreement Protocol for a Smart Grid
Authors: Zhang Ya , Chen Junhua , Wang Shenjin , Ma Kaixuan , Hu Shunfang
Published: September 2024

The smart grid (SG) is an efficient and reliable framework capable of controlling computers, automation, new technologies, and devices. Advanced metering infrastructure (AMI) is a crucial part of the SG, facilitating two-way communication between users and service providers (SPs). Computation, storage, and communication are extremely limited as the AMI’s device is typically deployed outdoors and connected to an open network. Therefore, an authentication and key agreement protocol is necessary to ensure the security and confidentiality of communications. Existing research still does not meet the anonymity, perfect forward secrecy, and resource-limited requirements of the SG environment. To address this issue, we advance a lightweight authentication and key agreement scheme based on elliptic curve cryptography (ECC). The security of the proposed protocol is rigorously proven under the random oracle model (ROM), and was verified by a ProVerif tool. Additionally, performance comparisons validate that the proposed protocol provides enhanced security features at the lowest computation and communication costs.
A Comprehensive Review of Integrated Energy Systems Considering Power-to-Gas Technology
Authors: Faisal Shah , Gao Ciwei
Published: September 2024

Integrated energy systems (IESs) considering power-to-gas (PtG) technology are an encouraging approach to improve the efficiency, reliability, and elasticity of the system. As the evolution towards decarbonization is increasing, the unified coordination between IESs and PtG technology is also increasing. PtG technology is an option for long-term energy storage in the form of gas, but, compared to other technologies, it is economically expensive at the present time to optimize the technology. This article presents a comprehensive review of the state-of-the-art research and of the developments regarding integrated energy systems considering PtG technology. This presented review emphasizes planning and economic analysis, including system integration enhancements focusing on optimization, conversion technologies, and energy storage to improve the operation and stability and to enhance the facilities for consumers. The role of a PtG system in generation, transmission, distribution, and consumption is discussed. By emphasizing planning, integration, and the role, this paper aims to guide researchers, scientists, engineers, and policy makers towards effective research and broad strategies that sustain an IES-PtG.
Packing Characteristics and Heat Transfer Performance of Non-Spherical Particles for Concentrated Solar Power Applications
Authors: Boribayeva Aidana , Gvozdeva Xeniya , Golman Boris
Published: September 2024

Concentrated solar power (CSP) technology relies on thermal energy storage to extend operating hours, making the selection of heat storage media crucial for system efficiency. Bauxite powder, known for its availability and high-temperature stability, emerges as a potential alternative to conventional materials in CSP systems. This study employed the discrete element method to investigate the influence of particle shape on the packing and heat transfer characteristics of non-spherical particles. The research focused on assessing the impact of particle sphericity by comparing spherical particles with non-spherical shapes, including ellipsoids and cylinders, and exploring the effect of varying the aspect ratio (AR) of the cylindrical particles. Particle sphericity significantly influenced packing morphology, with the cylindrical particles exhibiting distinct structural patterns that were absent in the ellipsoidal particles, and strongly affected heat transfer, as observed in the average temperature variations within the packed bed over time. The cylinders with higher aspect ratios demonstrated enhanced heat transfer rates, driven by the increased contact area and coordination numbers, despite their predominant misalignment with the heat flux direction. These insights are valuable for optimizing thermal energy storage media in CSP systems.
Research on the Multi-Agent Evolutionary Game Behavior of Joint Operation between Coal Power Enterprises and New Energy Power Enterprises under Government Supervision
Authors: Chen Jingxiao , Zhang Lei , Deng Gaodan
Published: September 2024

Based on the perspective of joint operation between coal power enterprises and new energy power enterprises (JOCN), this paper constructs an evolutionary game model among coal power enterprises (CPEs), new energy power enterprises (NEPEs) and the government, and analyzes the influencing factors of the final strategy choices of various subjects through evolutionary simulation. The results show that: (1) The game among CPEs, NEPEs and the government in JOCN is a win–win game. (2) The probability of CPEs choosing to participate in the joint operation is positively correlated with additional operating income, government subsidies and environmental costs in coal power generation, while negatively correlated with additional operating costs and spillover effects. (3) The probability of NEPEs choosing to participate in the joint operation is positively correlated with additional operating income, government subsidies and the cost of power loss in new energy power generation, while negatively correlated with additional operating costs and spillover effects. (4) The probability of the government choosing to support joint operation is positively correlated with social welfare, and negatively correlated with the cost of governmental strict supervision. The research results can provide theoretical reference for enterprises and the government in the future transformations and policy formulation.
Reinforcement Learning for Fair and Efficient Charging Coordination for Smart Grid
Authors: Elshazly Amr A. , Badr Mahmoud M. , Mahmoud Mohamed , Eberle William , Alsabaan Maazen , Ibrahem Mohamed I.
Published: September 2024

The integration of renewable energy sources, such as rooftop solar panels, into smart grids poses significant challenges for managing customer-side battery storage. In response, this paper introduces a novel reinforcement learning (RL) approach aimed at optimizing the coordination of these batteries. Our approach utilizes a single-agent, multi-environment RL system designed to balance power saving, customer satisfaction, and fairness in power distribution. The RL agent dynamically allocates charging power while accounting for individual battery levels and grid constraints, employing an actor–critic algorithm. The actor determines the optimal charging power based on real-time conditions, while the critic iteratively refines the policy to enhance overall performance. The key advantages of our approach include: (1) Adaptive Power Allocation: The RL agent effectively reduces overall power consumption by optimizing grid power allocation, leading to more efficient energy use. (2) Enhanced Customer Satisfaction: By increasing the total available power from the grid, our approach significantly reduces instances of battery levels falling below the critical state of charge (SoC), thereby improving customer satisfaction. (3) Fair Power Distribution: Fairness improvements are notable, with the highest fair reward rising by 173.7% across different scenarios, demonstrating the effectiveness of our method in minimizing discrepancies in power distribution. (4) Improved Total Reward: The total reward also shows a significant increase, up by 94.1%, highlighting the efficiency of our RL-based approach. Experimental results using a real-world dataset confirm that our RL approach markedly improves fairness, power efficiency, and customer satisfaction, underscoring its potential for optimizing smart grid operations and energy management systems.