bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning Based Breast Cancer Detection Using Decision Fusion
Authors: Manalı Doğu , Demirel Hasan , Eleyan Alaa
Published: November 2024

Breast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early breast cancer detection. Convolutional neural networks (CNNs) and support vector machines (SVMs) have been used in computer-aided diagnosis (CAD) systems to identify breast tumors from mammograms. However, existing methods often face challenges in accuracy and reliability across diverse diagnostic scenarios. This paper proposes a three parallel channel artificial intelligence-based system. First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. Second, a pre-trained CNN extracts features, and SVM identifies potential tumors. Third, a newly developed CNN is trained and used to classify mammogram images. Finally, a decision fusion that combines results from the three channels to enhance system performance is implemented using different rules. The proposed decision fusion-based system outperforms state-of-the-art alternatives with an overall accuracy of 99.1% using the product rule.
Reinforcement-Learning-Based Edge Offloading Orchestration in Computing Continuum
Authors: Martin Ioana Ramona , Arcas Gabriel Ioan , Cioara Tudor
Published: November 2024

The AI-driven applications and large data generated by IoT devices connected to large-scale utility infrastructures pose significant operational challenges, including increased latency, communication overhead, and computational imbalances. Addressing these is essential to shift the workloads from the cloud to the edge and across the entire computing continuum. However, to achieve this, significant challenges must still be addressed, particularly in decision making to manage the trade-offs associated with workload offloading. In this paper, we propose a task-offloading solution using Reinforcement Learning (RL) to dynamically balance workloads and reduce overloads. We have chosen the Deep Q-Learning algorithm and adapted it to our workload offloading problem. The reward system considers the node’s computational state and type to increase the utilization of the computational resources while minimizing latency and bandwidth utilization. A knowledge graph model of the computing continuum infrastructure is used to address environment modeling challenges and facilitate RL. The learning agent’s performance was evaluated using different hyperparameter configurations and varying episode lengths or knowledge graph model sizes. Results show that for a better learning experience, a low, steady learning rate and a large buffer size are important. Additionally, it offers strong convergence features, with relevant workload tasks and node pairs identified after each learning episode. It also demonstrates good scalability, as the number of offloading pairs and actions increases with the size of the knowledge graph and the episode count.
Current Understanding of Microneedling Procedures for Acne Skin: A Narrative Review
Authors: Măgerușan Șoimița Emiliana , Hancu Gabriel , Rusu Aura
Published: November 2024

Acne vulgaris, the most prevalent skin disorder among teenagers and young adults, is often perceived as a moderate skin condition; however, it imposes a substantial economic and psychological burden on individuals and society. Microneedling emerges as a valuable therapeutic option for acne vulgaris. The procedure involves using fine needles to create controlled micro-injuries in the skin, stimulating collagen production and enhancing the skin’s natural healing processes. This minimally invasive technique effectively reduces acne lesions, improves skin texture, and increases collagen production with minimal adverse effects and downtime. This narrative review evaluates the efficacy and safety of microneedling procedures in treating acne vulgaris. A comprehensive research strategy was employed across various databases (PubMed, Google Scholar, Cochrane Library) to identify relevant studies. Inclusion criteria encompassed studies investigating microneedling procedures for acne, including controlled trials and case studies. Outcomes such as reduced acne lesions, improved skin texture, adverse effects, and patient satisfaction were analyzed. While further well-designed studies are warranted to elucidate optimal treatment protocols and long-term outcomes, current evidence supports the integration of microneedling into managing acne-prone skin. Moreover, the long-term consequences of microneedling in acne management remain an area for future research.
Facilitation of Enrollment onto Cancer Clinical Trials Using a Novel Navigator-Assisted Program: A Cross-Sectional Study
Authors: Hossami Mahmoud , Abdel-Nabi Rhonda , Zaib Farwa , Touma Kayla , Nassar Renee , Rim Sanghyuk Claire , Paunic Milica , Hilal Olla , Gupta Pratham , Hirmiz Roaa , Touma Michael , Sadik Govana , Akingbade Emmanuel , Sharma Depen , Kalia Swati , Fatima Rija , Luginaah Anthony , Mohamed Ibrahim , Luo Rong , Delisle Megan , Hamm Caroline
Published: November 2024

Introduction: Clinical trials are essential to the advancement of clinical therapies that improve the outcomes of people with cancer. However, enrollment in clinical trials remains a challenge. The Clinical Trial Navigator [CTN] Program was designed to address the current gap in the cancer care journey by assisting with the clinical trials search process. Methods: Between March 2019 and July 2024, applicants of the CTN program included people with cancer, their family members, and/or their care team. Applicants entered the CTN program through a REDCap® survey that collected the patient’s medical history. A final curated list of potential clinical trials was provided to the applicant. Metrics of success included clinical trial referral and enrollment, and we examined the factors that impacted these outcomes. Results: A total of 445 people with cancer applied to the CTN program during the study. Of the 262 patients with referral and enrollment information, a trial referral occurred in 27.5% [n = 72]. Of the 72 patients who were referred to a clinical trial, 13 [18.1%] were enrolled, 9 [12.5%] are pending enrollment, and 50 [69.4%] were not enrolled. We identified a potential trial for 88% of applicants, with a median of one potential trial per patient. Physicians were highly involved as applicants. Interpretation: The CTN program is successful in searching for clinical trials for people with cancer. Ongoing implementation into other Canadian sites, assessments of patient-reported outcomes, website and social media campaigns, and research into the factors that impact referral and enrollment are underway.
Extraction and Identification of Flavonoids from the Leaves of Pilocarpus microphyllus: Focus on Antioxidant Activity and Neuroprotective Profile
Authors: Perfeito Márcia Luana Gomes , Ribeiro Fábio de Oliveira Silva , Jesus Joilson Ramos de , Véras Leiz Maria Costa , Nobre Alyne Rodrigues de Araújo , Lopes Everton Moraes , Queiroz José Carlos Eloi de , Vasconcelos Andreanne Gomes , Cardoso Miguel Gomes , Gonçalves João , Almeida Fernanda Regina de Castro , Arcanjo Daniel Dias Rufino , Leite José Roberto de Souza de Almeida
Published: November 2024

This work is based on research aiming to extract and identify flavonoids from jaborandi (Pilocarpus microphyllus) leaves and investigate their antioxidant and acute antinociceptive capacity. Characterization of the constituents of the ethyl acetate fraction (EtOAcF) obtained from the methanolic extract (ME) was performed by UV-Vis spectrophotometry, infrared spectroscopy, high-performance liquid chromatography (HPLC), mass spectrometry (MS), and cyclic voltammetry, demonstrating the possible majority component of this fraction, the flavone chrysin. Its solubility properties in HPLC are very close to those of the flavonol quercetin, revealing the characteristic presence of this group. An MS spectrum of the fraction revealed a major protonated molecule of m/z 254.9 [M+H]+. The EtOAcF fraction showed three oxidation processes at 0.32 V, 0.54 V, and 0.73 V vs. Ag/AgCl. Three reduction processes at the respective potentials: 0.60 V, −0.03 V, and -0.24 V vs. Ag/AgCl, indicating potential antioxidant activity. At DPPH and ABTS antioxidant radical capture assay, The IC50 obtained was 0.5 mg/mL and 0.81 mg/mL, respectively. In vivo test to determine the mechanical nociceptive threshold in the von Frey test, the dose of 100 mg/kg of the EtOAcF was able to cause inhibition of behavioral changes in neuropathy. The results obtained in this study demonstrate the biological potential of an EtOAcF derived from jaborandi leaves.
Enhanced Non-Invasive Diagnosis of Female Urinary Incontinence Using Static and Functional Transperineal Ultrasonography
Authors: Pietrus Milosz , Pityński Kazimierz , Socha Maciej W. , Gawron Iwona , Biskupski-Brawura-Samaha Robert , Waligóra Marcin
Published: November 2024

Background/Objectives: To investigate the utility of transperineal ultrasound in detecting stress urinary incontinence (SUI) and identify optimal anatomical and functional parameters. Methods: Thirty-four women presenting with SUI with or without pelvic organ prolapse between 2012 and 2016 were studied. The control group included patients without SUI who underwent surgery for mild gynecologic disorders or pelvic organ prolapse. The relationship between selected ultrasound parameters and SUI was determined. Results: Among the 20 variables measured in ultrasonography using 4 angles and the bladder–symphysis distance (BSD) values, we found that the difference in the BSD obtained at rest and during the Valsalva maneuver (odds ratio [OR]: 1.15, 95% confidence interval [CI]: 1.05–1.27, p = 0.004), the mean urethral diameter (UD; OR: 4.29, 95% CI: 2.07–8.83, p = 0.0001), and the occurrence of the funneling sign during the Valsalva maneuver (OR: 21; 95% CI: 6.1–71.9, p < 0.0001) were associated with urinary incontinence in the logistic regression analysis. The optimal cut-off point for BSD was >8 mm (area under the curve (AUC), 0.71; sensitivity, 91.2%; specificity, 56.8%; p = 0.001) and that for UD was >6 mm (AUC, 0.84; sensitivity, 82.1%; specificity, 73%; p < 0.001). Conclusions: Transperineal ultrasonography is a useful tool for detecting SUI. Our findings highlighted the utility of several sonographic parameters, mainly the urethral diameter, in the diagnosis of urinary incontinence.
Remote Management of Heart Failure in Patients with Implantable Devices
Authors: Santini Luca , Adamo Francesco , Mahfouz Karim , Colaiaco Carlo , Finamora Ilaria , De Lucia Carmine , Danisi Nicola , Gentile Stefania , Sorrentino Claudia , Romano Maria Grazia , Sangiovanni Luca , Nardini Alessio , Ammirati Fabrizio
Published: November 2024

Background: Heart failure (HF) is a chronic disease with a steadily increasing prevalence, high mortality, and social and economic costs. Furthermore, every hospitalization for acute HF is associated with worsening prognosis and reduced life expectancy. In order to prevent hospitalizations, it would be useful to have instruments that can predict them well in advance. Methods: We performed a review on remote monitoring of heart failure through implantable devices. Results: Precise multi-parameter algorithms, available for ICD and CRT-D patients, have been created, which also use artificial intelligence and are able to predict a new heart failure event more than 30 days in advance. There are also implantable pulmonary artery devices that can predict hospitalizations and reduce the impact of heart failure. The proper organization of transmission and alert management is crucial for clinical success in using these tools. Conclusions: The full implementation of remote monitoring of implantable devices, and in particular, the use of new algorithms for the prediction of acute heart failure episodes, represents a huge challenge but also a huge opportunity.
Salivary Chromium and Cobalt Concentrations in Patients with Dental Metallic Restorations—A Pilot Study
Authors: Tomova Zlatina , Tomov Desislav , Davcheva Delyana , Uzunova Yordanka
Published: November 2024

Introduction: Metal ions, released from dental alloys due to corrosion, come in contact with the cells of the surrounding tissues and may spread throughout the body via the gastrointestinal system, thus inducing dose-dependent cytopathological effects. This study aimed to assess and compare the salivary cobalt and chromium concentrations in individuals aged 18–65 years with and without dental restorations containing metal alloys. Methods: Participants were divided into two main groups according to the existence of metal alloys in the oral cavity—18 patients had fixed prosthetic restorations made of metal alloys, and 17 patients had no metal objects in their oral cavity. Each main group was subdivided into two subgroups according to the type of saliva sample—with or without additional stimulation. Salivary cobalt and chromium concentrations were measured by inductively coupled plasma mass spectrometry. A non-parametric Mann–Whitney test and Spearman’s rank correlation coefficient were applied, and the level of significance was set to p < 0.05. Results: The results showed that the chromium level in non-stimulated saliva was higher in the group of patients with metal dental restorations. No statistical difference was found in cobalt levels. There was no statistical difference in Co or Cr concentrations in stimulated saliva between the studied groups. A positive correlation was found between Cr and Co concentrations in non-stimulated saliva and between cobalt concentrations in stimulated and non-stimulated saliva. Conclusions: Metal alloys in the oral cavity induced elevated chromium levels in non-stimulated saliva, and a correlation between chromium and cobalt ion concentration was found. A detailed examination of patients and their medical history prior to prosthetic treatment is advisable in order to avoid any undesired health effects.
Evaluation and Comparison of Manual and Mechanical Endodontic Instrumentation Completed by Undergraduate Dental Students on Endodontic Blocks
Authors: Ginjeira António , Baruwa Abayomi O. , Baumotte Karla
Published: November 2024

Background: The shaping of root canal space was completed using manual stainless steel files in earlier decades and with the advent of mechanical nickel–titanium (NiTi) instruments, there is potential for more efficient root canal preparation. Despite the advantages of NiTi instruments, their adoption in undergraduate dental education remains limited. The aim of this study was to evaluate three root canal instrumentation techniques, manual instrumentation using stainless steel hand files, continuous rotation employing ProTaper Gold (PTG) files, and reciprocation with WaveOne Gold (WOG) files, on endodontic resin blocks to assess the quality of preparation and the time required for instrumentation. Methods: A total of 36 third-year dental students, all lacking prior experience in root canal procedures, were divided into six groups to prepare 108 resin endodontic blocks with each student preparing 3 blocks. Images were captured at the preoperative, intraoperative, and postoperative stages to facilitate comparisons and measurements of the prepared blocks to assess the degree of resin removal, apical deviation, and mid-cervical wear. Furthermore, questionnaires were distributed to assess the students’ experiences and satisfaction with the techniques. The Friedman test, Wilcoxon test with Bonferroni correction, and Kruskal–Wallis test with Mann–Whitney U test were used to analyse and compare techniques, with the level of significance set at p < 0.05. Results: Instrumentation with PTG exhibited significantly reduced apical deviation (0.073 ± 0.003) compared to both the WOG and manual instrumentations (p < 0.001). Significant differences in mid-cervical wear were observed only between PTG and the manual instrumentation. In terms of resin removal, the manual instrumentation displayed greater variability and was five times slower to complete the instrumentation. In total, 90% of students favoured mechanical instrumentation, with substantial preferences for them over manual techniques. Conclusions: Mechanical instrumentation techniques, notably with the PTG system, were significantly faster and more effective in preparation quality. This highlights the potential for the inclusion of mechanical instrumentation in undergraduate dental curricula.
A Review of Drone Technology and Operation Processes in Agricultural Crop Spraying
Authors: García-Munguía Argelia , Guerra-Ávila Paloma Lucía , Islas-Ojeda Efraín , Flores-Sánchez Jorge Luis , Vázquez-Martínez Otilio , García-Munguía Alberto Margarito , García-Munguía Otilio
Published: November 2024

Precision agriculture is revolutionizing the management and production of agricultural crops. The development of new technologies in agriculture, such as unmanned aerial vehicles (UAVs), has proven to be an efficient option for spraying various compounds on crops. UAVs significantly contribute to enhancing precision agriculture. This review aims to determine whether integrating advanced precision technologies into drones for crop spraying enhances spraying accuracy compared to drones utilizing standard spraying technologies. To achieve this, 100 articles published between 2019 and 2024 were selected and analyzed. The information was summarized into five main areas: (1) improved spraying with agricultural drone technologies, (2) operational parameters, (3) spraying applications of chemical and natural compounds with agricultural drones, (4) evaluations of control pest efficacy, and (5) considerable limitations. Finally, considerations are presented on the advantages of drone technology with artificial intelligence (AI); the practical effects of reducing pesticides, which, in some cases, have reached a reduction of 30% compared to the recommended dose; and future directions for improving precision agriculture. The use of drones in precision agriculture presents technical and scientific challenges for the maximization of spraying efficiency and the minimization of agrochemical use.
Exploring the Impact of Additive Shortcuts in Neural Networks via Information Bottleneck-like Dynamics: From ResNet to Transformer
Authors: Lyu Zhaoyan , Rodrigues Miguel R. D.
Published: November 2024

Deep learning has made significant strides, driving advances in areas like computer vision, natural language processing, and autonomous systems. In this paper, we further investigate the implications of the role of additive shortcut connections, focusing on models such as ResNet, Vision Transformers (ViTs), and MLP-Mixers, given that they are essential in enabling efficient information flow and mitigating optimization challenges such as vanishing gradients. In particular, capitalizing on our recent information bottleneck approach, we analyze how additive shortcuts influence the fitting and compression phases of training, crucial for generalization. We leverage Z-X and Z-Y measures as practical alternatives to mutual information for observing these dynamics in high-dimensional spaces. Our empirical results demonstrate that models with identity shortcuts (ISs) often skip the initial fitting phase and move directly into the compression phase, while non-identity shortcut (NIS) models follow the conventional two-phase process. Furthermore, we explore how IS models are still able to compress effectively, maintaining their generalization capacity despite bypassing the early fitting stages. These findings offer new insights into the dynamics of shortcut connections in neural networks, contributing to the optimization of modern deep learning architectures.
Sensitivity of Bayesian Networks to Errors in Their Structure
Authors: Onisko Agnieszka , Druzdzel Marek J.
Published: November 2024

There is a widespread belief in the Bayesian network (BN) community that while the overall accuracy of the results of BN inference is not sensitive to the precision of parameters, it is sensitive to the structure. We report on the results of a study focusing on the parameters in a companion paper, while this paper focuses on the BN graphical structure. We present the results of several experiments in which we test the impact of errors in the BN structure on its accuracy in the context of medical diagnostic models. We study the deterioration in model accuracy under structural changes that systematically modify the original gold standard model, notably the node and edge removal and edge reversal. Our results confirm the popular belief that the BN structure is important, and we show that structural errors may lead to a serious deterioration in the diagnostic accuracy. At the same time, most BN models are forgiving to single errors. In light of these results and the results of the companion paper, we recommend that knowledge engineers focus their efforts on obtaining a correct model structure and worry less about the overall precision of parameters.
Robustness Analysis of Multilayer Infrastructure Networks Based on Incomplete Information Stackelberg Game: Considering Cascading Failures
Authors: Li Haitao , Ji Lixin , Li Yingle , Liu Shuxin
Published: November 2024

The growing importance of critical infrastructure systems (CIS) makes maintaining their normal operation against deliberate attacks such as terrorism a significant challenge. Combining game theory and complex network theory provides a framework for analyzing CIS robustness in adversarial scenarios. Most existing studies focus on single-layer networks, while CIS are better modeled as multilayer networks. Research on multilayer network games is limited, lacking methods for constructing incomplete information through link hiding and neglecting the impact of cascading failures. We propose a multilayer network Stackelberg game model with incomplete information considering cascading failures (MSGM-IICF). First, we describe the multilayer network model and define the multilayer node-weighted degree. Then, we present link hiding rules and a cascading failure model. Finally, we construct MSGM-IICF, providing methods for calculating payoff functions from the different perspectives of attackers and defenders. Experiments on synthetic and real-world networks demonstrate that link hiding improves network robustness without considering cascading failures. However, when cascading failures are considered, they become the primary factor determining network robustness. Dynamic capacity allocation enhances network robustness, while changes in dynamic costs make the network more vulnerable. The proposed method provides a new way of analyzing the robustness of diverse CIS, supporting resilient CIS design.
Does Institutional Quality Enhance the Effect of Health Outcomes on Economic Growth? Insights from Sub-Saharan African Countries
Authors: Gebrihet Hafte Gebreselassie , Gebresilassie Yibrah Hagos , Woldu Gabriel Temesgen
Published: November 2024

Institutional quality (InQ) plays an important role in shaping economic growth (ECG), influencing how economies develop and perform. The literature addresses the nexus between InQ and ECG and the link between health and ECG; findings are often contradictory, creating knowledge gaps. Importantly, research on the interplay between InQ, health, and ECG in Sub-Saharan African (SSA) countries is particularly limited. This study aims to address this gap by evaluating how health impacts ECG, with an emphasis on the mediating role of InQ in the health–growth nexus in SSA. This study examines these interplays across 35 SSA countries from 2012 to 2022. The life expectancy at birth (LEX) and real gross domestic product per capita (GDP) are used as proxies for health outcomes and ECG, respectively. The system generalised method of moments estimator is employed to analyse data. Results show that the LEX has a strong positive effect on economic growth in SSA countries. Furthermore, the InQ indicators (such as control of corruption, government effectiveness, rule of law and political stability, and absence of violence) are positively correlated with ECG. When the LEX interacts with InQ indicators, InQ is identified as a key channel through which LEX influences ECG. The findings confirm that InQ plays a crucial role in the health–growth nexus, with the positive impact of LEX on ECG being more pronounced in countries with higher levels of InQ, while the effect is weaker in countries with lower levels of InQ. The findings of this study have crucial policy implications, highlighting the intricate link among institutional quality, health outcomes, and economic growth. This study’s findings provide essential insights for policymakers to design focused strategies that improve InQ and health outcomes to achieve sustained ECG in SSA.
Children and Pre-Adolescents’ Moral Disengagement and Cooperation in the Context of Deprived Neighborhoods: An Action Research Project
Authors: Marcone Roberto , Brancaccio Deborah , Borghese Vincenza , Ariemma Lucia , Ritella Giuseppe
Published: November 2024

The purpose of this study is to examine family-level and community-level factors contributing to children and pre-adolescents’ moral disengagement and cooperation in the context of deprived neighborhoods. A set of validated measures has been administered to 374 participants, who were involved in an action research project aimed at reducing cultural disadvantage in a deprived Italian neighborhood. The analysis was conducted through descriptive and correlational analyses. In addition, a model was tested through a path analysis. The findings are consistent with the hypothesized model and allow us to discuss the role of parental rejection, age and socioeconomic status in the examined developmental processes.
Teacher Practices for Formatively Assessing Computational Thinking with Early Elementary Learners
Authors: Sherwood Heather , Culp Katherine McMillan , Ferguson Camille , Kaiser Alice , Henry Meagan , Negron Anthony
Published: November 2024

Few studies of computational thinking (CT) integration in elementary curricula have yet focused on supporting early elementary educators with implementing and assessing their young students’ application of these practices to content area work. This paper summarizes a collaborative research project that engaged researchers, K-second grade teachers, and professional development (PD) providers in implementing a hybrid PD model to answer the following research questions: (1) What kind of PD and guidance do teachers need to identify and support emergent computational thinking development in young students’ language and work process? (2) What kind of PD and guidance do teachers need to identify emergent computational thinking development in young students’ work products? This project employed a mixed-methods research design that included pre- and post-surveys and interviews with teachers to measure and understand how growth in teachers’ confidence, knowledge, and self-efficacy with CT prepared them to identify and support these concepts with young learners. Additionally, analysis was able to identify the key formative assessment strategies these teachers employed to generate insight into students’ understanding and application of CT during problem-solving.
RAP-Optimizer: Resource-Aware Predictive Model for Cost Optimization of Cloud AIaaS Applications
Authors: Sathupadi Kaushik , Avula Ramya , Velayutham Arunkumar , Achar Sandesh
Published: November 2024

Artificial Intelligence (AI) applications are rapidly growing, and more applications are joining the market competition. As a result, the AI-as-a-service (AIaaS) model is experiencing rapid growth. Many of these AIaaS-based applications are not properly optimized initially. Once they start experiencing a large volume of traffic, different challenges start revealing themselves. One of these challenges is maintaining a profit margin for the sustainability of the AIaaS application-based business model, which depends on the proper utilization of computing resources. This paper introduces the resource award predictive (RAP) model for AIaaS cost optimization called RAP-Optimizer. It is developed by combining a deep neural network (DNN) with the simulated annealing optimization algorithm. It is designed to reduce resource underutilization and minimize the number of active hosts in cloud environments. It dynamically allocates resources and handles API requests efficiently. The RAP-Optimizer reduces the number of active physical hosts by an average of 5 per day, leading to a 45% decrease in server costs. The impact of the RAP-Optimizer was observed over a 12-month period. The observational data show a significant improvement in resource utilization. It effectively reduces operational costs from USD 2600 to USD 1250 per month. Furthermore, the RAP-Optimizer increases the profit margin by 179%, from USD 600 to USD 1675 per month. The inclusion of the dynamic dropout control (DDC) algorithm in the DNN training process mitigates overfitting, achieving a 97.48% validation accuracy and a validation loss of 2.82%. These results indicate that the RAP-Optimizer effectively enhances resource management and cost-efficiency in AIaaS applications, making it a valuable solution for modern cloud environments.
Correction: Xu et al. Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks. Electronics 2023, 12, 4817
Authors: Xu Qingling , Zhao Dawei , Yang Shumian , Xu Lijuan , Li Xin
Published: November 2024

The journal has corrected the article “Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks” [...]
TOLGAN: An End-To-End Framework for Producing Traditional Orient Landscape
Authors: Kim Booyong , Yang Heekyung , Min Kyungha
Published: November 2024

We present TOLGAN that generates traditional oriental landscape (TOL) image from a map that specifies the locations and shapes of the elements composing TOL. Users can create a TOL map by using a user interface or a segmentation scheme from a photograph. We design the generator of TOLGAN as a series of decoding layers where the map is applied between the layers. The generated TOL image is further enhanced through an AdaIN architecture. The discriminator of TOLGAN processes a generated image and its groundtruth TOL artwork image. TOLGAN is trained through a dataset composed of paired TOL artwork images and their TOL maps. We present a tool through which users can produce a TOL map by specifying and organizing the elements of TOL artworks. TOLGAN successfully generates a series of TOL images from the TOL map. We evaluate our approach using a quantitative way by estimating FID and ArtFID scores and a qualitative way by executing two user studies. Through these studies, we prove the excellence of our approach by comparing our results with those from several important existing works.
HCA-RFLA: A SAR Remote Sensing Ship Detection Based on Hierarchical Collaborative Attention Method and Gaussian Receptive Field-Driven Label Assignment Strategy
Authors: Xue Tao , Zhang Jiayi , Lv Wen , Xi Long , Li Xiang
Published: November 2024

Ensuring safety at sea has become a primary focus of marine monitoring, driving the increasing adoption of ship detection technology in the maritime industry. Detecting small ship targets in SAR images presents challenges, as they occupy only a small portion of the image and exhibit subtle features, reducing detection efficiency. To address these challenges, we propose the HCA-RFLA algorithm for ship detection in SAR remote sensing. To better capture small targets, we design a hierarchical collaborative attention (HCA) mechanism that enhances feature representation by integrating multi-level features with contextual information. Additionally, due to the scarcity of positive samples for small targets under IoU and center sampling strategies, we propose a label assignment strategy based on Gaussian receptive fields, known as RFLA. RFLA assigns positive samples to small targets based on the Gaussian distribution between feature points and ground truth, increasing the model’s sensitivity to small samples. The HCA-RFLA was experimentally validated using the SSDD, HRSID, and SSD datasets. Compared to other state-of-the-art methods, HCA-RFLA improves detection accuracy by 6.2%, 4.4%, and 3.6%, respectively. These results demonstrate that HCA-RFLA outperforms existing algorithms in SAR remote sensing ship detection.
A Novel 3D 2TnC FeRAM Architecture and Operation Scheme with Improved Disturbance for High-Bit-Density Dynamic Random-Access Memory
Authors: Lee Ji-yeon , Song Jiho , Choi Seonjun , Sim Jae-min , Song Yun-Heub
Published: November 2024

In this paper, we proposed the development of stackable 3D ferroelectric random-access memory (FeRAM), with two select transistors and n capacitors (2TnC), to address scaling limitations for bit density growth and the complicated manufacturing of 3D dynamic random-access memory (DRAM). The proposed 3D FeRAM has a 3D NAND-like architecture, with stacked metal–ferroelectric–metal (MFM) capacitors serving as memory cells in a unit string. A similar manufacturing process is used to achieve a cost-effective process and high bit density for next-generation DRAM applications. The two access transistors, string–select–line (SSL) and ground–select–line (GSL), are perfect string selections. We confirmed that the grounded back gate (GBG) of the proposed architecture can significantly improve the worst disturbance case compared to a floating back gate (FBG) like the 1TnC structure. Also, we confirmed the feasibility of performing the random-access operation during the read operation regardless of the data pattern of the selected string.
Standard Cell Sizing for Worst-Case Performance Optimization Considering Process Variation in Subthreshold Region
Authors: Cao Peng , Guo Jingjing
Published: November 2024

Ultra-low-voltage design brings considerable outcomes in power reduction and energy efficiency improvement at the cost of performance degradation and uncertainty. Conventional standard cell design methodology cannot guarantee optimal performance for subthreshold operations due to the lack of consideration of process variation. In this paper, an effective subthreshold cell sizing method is proposed to minimize the worst-case propagation delay by deriving the optimal pMOS-to-nMOS width ratio (β) analytically, which reveals the relation between the minimal worst-case delay and the process parameters and provides distinct guidance for standard cell library design. The proposed method demonstrated good agreement with the Monte Carlo SPICE simulation results and was validated at the cell level and the circuit level. At the cell level, the logic cells designed with the proposed method show at least 8.6% and 7.4% improvement, on average, for worst-case delay and energy-delay product (EDP), respectively, with an additional 3.2% energy overhead compared to the prior approaches. At the circuit level, the proposed method improves the worst-case performance and worst-case EDP of the ring oscillator by at least 15.5% and 15.0%, respectively, with a 0.9% energy penalty. Moreover, the ISCAS’89 and OpenCores circuits synthesized with the optimized cells achieve at least 6.6% worst-case performance enhancement, 6.9% power reduction, and 9.4% area saving.
Mixed Reality-Based Inspection Method for Underground Water Supply Network with Multi-Source Information Integration
Authors: Zhao Xuefeng , Tao Yibing , Bao Yan , Sun Zhe , Wu Shan , Li Wangbing , Fan Xiongtao
Published: November 2024

Regular on-site inspection is crucial for promptly detecting faults in water supply networks (WSNs) and auxiliary facilities, significantly reducing leakage risks. However, the fragmentation of information and the separation between virtual and physical networks pose challenges, increasing the cognitive load on inspectors. Furthermore, due to the lack of real-time computation in current research, the effectiveness in detecting anomalies, such as leaks, is limited, hindering its ability to provide immediate and direct-decision support for inspectors. To address these issues, this research proposes a mixed reality (MR) inspection method that integrates multi-source information, combining building information modeling (BIM), Internet of Things (IoT), monitoring data, and numerical simulation technologies. This approach aims to achieve in situ visualization and real-time computational capabilities. The effectiveness of the proposed method is demonstrated through case studies, with user feedback confirming its feasibility. The results indicate improvements in inspection task performance, work efficiency, and standardization compared to traditional mobile terminal-based methods.
Theoretical Analysis Based on Experimental Studies of Heat and Moisture Fluxes Penetrating Through a Masonry Wall Above Ground Level in an Annual Cycle
Authors: Owczarek Mariusz , Nasiłowska Barbara
Published: November 2024

This article calculates horizontal and vertical heat and moisture fluxes in the wall based on measurements of temperature and relative humidity in the building wall. It was a basement wall that was close to the ground on one side and the basement ceiling on the other, which increased the difficulty in problem simulation. The brick material from the wall was also analyzed under an electron microscope and its elemental composition was determined using the EDX (Energy Dispersive X-ray spectroscopy) method. The brick had a relatively uniform elemental composition apart from several variations in calcium content. Monthly, daily, and hourly heat and moisture fluxes were determined. The tested wall was characterized by low humidity, and the values obtained of the moisture fluxes confirmed this. The maximum recorded relative humidity inside the wall is 57.89%, and the minimum is 43.99%. The effect of buffering moisture by brick material was noticed. Vertical streams of water vapor were found to be important in the moisture balance of the tested partition. The maximum heat flux through the tested wall area in August was 0.06 W, and the minimum in January was −0.2 W. The maximum moisture flux in August was 5 × 10−11 kg/s, and the minimum in January was −5 × 10−11 kg/s.
Effect of Indoor Green Walls on Environment Perception and Well-Being of Occupants in Office Buildings
Authors: Lipczynska Aleksandra , Kaczmarczyk Jan , Dziedzic Beata
Published: November 2024

Office employees often face discomfort due to dry air, impacting their productivity. Existing solutions like standalone humidifiers or HVAC-integrated systems are either inefficient or costly. Thus, maintaining optimal air humidity remains a challenge. Green walls offer a potential solution by influencing humidity and air quality. A survey was conducted in five Warsaw office buildings involving 85 participants to investigate this issue. Measurements were taken in three stages: without humidifiers, with standalone humidifiers, and with green walls. The results showed a high acceptance of humidification systems, with green walls being perceived as the most effective in neutralizing air humidity perception. The air quality was rated highest with both humidifiers and green walls. Notably, well-being indicators significantly improved with humidifiers, particularly with the presence of green walls. This study underscores the potential of green walls in improving indoor environmental conditions and occupants’ well-being, offering a promising avenue for enhancing office comfort and efficiency.
Correction: Hasanbeigi, A.; Zuberi, M.J.S. Electrified Process Heating in Textile Wet-Processing Industry: A Techno-Economic Analysis for China, Japan, and Taiwan. Energies 2022, 15, 8939
Authors: Hasanbeigi Ali , Zuberi M. Jibran S.
Published: November 2024

There was an error in the original publication [...]
The Effect of the Number of Parallel Winding Paths on the Fault Tolerance of a Switched Reluctance Motor
Authors: Korkosz Mariusz , Prokop Jan , Bogusz Piotr
Published: November 2024

Achieving increased fault tolerance in an electric motor requires decisions to be made about the type and specifications of the motor machine and its appropriate design. Depending on the type of motor, there are generally three ways to achieve an increased resistance of the drive system to tolerate resulting faults. The simplest way is to select the right motor and design it appropriately. Switched reluctance motors (SRMs) have a high tolerance for internal faults (in the motor windings). Failure tolerance can be improved by using parallel paths. The SRM 24/16 solution has been proposed, which allows for operation with four parallel paths. In this paper, a mathematical model designed to analyse the problem under consideration is provided. Based on numerical calculations, the influence of typical faults (open and partial short circuit in one of the paths) on the electromagnetic torque generated as well as its ripple and (source and phase) currents were determined. The higher harmonics of the source current (diagnostic signal) were determined. Laboratory tests were performed to verify the various configurations for the symmetric and emergency operating states. The feasibility of SRM correct operation monitoring was determined from an FFT analysis of the source current.
Environmental Policy vs. the Reality of Greenhouse Gas Emissions from Top Emitting Countries
Authors: Portillo Juan Nerea , Negro Valdecantos Vicente , Olalde Rodríguez Javier , Iglesias Gregorio
Published: November 2024

The 21st century climate crisis has been compounded by the COVID-19 health crisis and the Russian war. What at first appeared to be an opportunity to move towards sustainable growth and development has turned into the opposite. In this context, it seems necessary to pause and analyze what countries are doing and where they are heading in order to ensure that their environmental efforts are not in vain. This article analyzes the environmental policies of the seven countries emitting the most GHGs from 1990 to the present day and compares them with the reality of their emissions. These behaviors are extrapolated into the future and, finally, conclusions are drawn as to which countries are not fully living up to their commitments, which have implemented the most effective measures, and where particular attention needs to be directed for maximum efficiency in decarbonizing the energy mix.
Comparative Analysis of Market Clearing Mechanisms for Peer-to-Peer Energy Market Based on Double Auction
Authors: Liyanage Kisal Kawshika Gunawardana Hathamune , Islam Shama Naz
Published: November 2024

This paper aims to develop an optimisation-based price bid generation mechanism for the sellers and buyers in a double-auction-aided peer-to-peer (P2P) energy trading market. With consumers being prosumers through the continuous adoption of distributed energy resources, P2P energy trading models offer a paradigm shift in energy market operation. Thus, it is essential to develop market models and mechanisms that can maximise the incentives for participation in the P2P energy market. In this sense, the proposed approach focuses on maximising profit at the sellers, as well as maximising cost savings at the buyers. The bids generated from the proposed approach are integrated with three different market clearing mechanisms, and the corresponding market clearing prices are compared. A numerical analysis is performed on a real-life dataset from Ausgrid to demonstrate the bids generated from sellers/buyers, as well as the associated market clearing prices throughout different months of the year. It can be observed that the market clearing prices are lower when the solar generation is higher. The statistical analysis demonstrates that all three market clearing mechanisms can achieve a consistent market clearing price within a range of 5 cents/kWh for 50% of the time when trading takes place.
Targeted Study of the Effect of Yeast Strain on Volatile Compounds Produced in Sorghum Beer
Authors: Budner Drew , Carr Joseph , Serafini Brett , Tucker Samantha , Dieckman-Meyer Elisabeth , Bell Lindsey , Thompson-Witrick Katherine A.
Published: November 2024

An increase in the consumer demand and the availability of gluten-free products has led to several brewers investigating brewing with grains other than barley. The primary grain of choice has been sorghum. These new gluten-free beers have a unique flavor and aroma, which previous research has shown is the result of differences in concentration for key chemical compounds, including ethyl butyrate, butyl acetate, isoamyl acetate, ethyl caproate, hexyl acetate, 1-octanol, nonanal, ethyl octanoate, and ethyl decanoate. This study focused looked at the influence different strains of yeast had on the concentration of these key compounds. Beer was brewed using either barley or sorghum malt extract. The concentrations of these key volatile compounds were determined using Solid Phase Microextraction (SPME) with Gas Chromatography and Mass Spectral (GC-MS) detection. Overall, it was found that the concentrations of these compounds were statistically different in the beers brewed from these two grain types. However, the yeast strain had no significant impact on the concentrations.
Effect of Various Carbohydrates in Aqueous Solutions on Color Stability and Degradation Kinetics of Selected Anthocyanins During Storage
Authors: Tobolka Adam , Škorpilová Tereza , Beňo Filip , Podskalská Tereza , Rajchl Aleš
Published: November 2024

Anthocyanins are flavonoid substances of plant origin with potential antioxidant effects. Because of their intense colors, they are used as natural dyes in food. However, their stability in food matrices is limited. This study aimed to verify the effect of selected carbohydrates on the stability of anthocyanins (cyanidin-3-O-β-glucopyranoside, cyanidin-3-O-β-galactopyranoside, cyanidin-3-O-β-rutinoside and delphinidin-3-O-β-rutinoside) during the accelerated storage test, since carbohydrates help to preserve the typical color of anthocyanins, increase their shelf-life and availability in the organism, and reduce losses during processing. Moreover, the kinetic parameters of anthocyanin degradation (Ea, k, t1/2) were determined. Sucrose was found to have the greatest potential for retarding anthocyanin degradation during storage, whereas fructose exerted an accelerating effect. Glycosidation of anthocyanin aglycone had no significant effect in terms of their stability. Anthocyanin degradation was significantly positively correlated with the change in the a* parameter (redness), and subsequently, a significant positive correlation was observed in the determination of the kinetic parameters for anthocyanins and the a* parameter. The highest stability of anthocyanins was observed in the presence of sucrose and their degradation can be predicted by the value of the a* parameter, which would also be a very fast and non-destructive method for food processing companies.