bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Configuration of Multi-Component Solid Waste Cementitious Materials and the Strength Characteristics of Consolidated Aeolian Sand
Authors: Maimait Akelamjiang , Wang Yaqiang , Cheng Jianjun , Duan Yanfu , Pan Zhouyang
Published: September 2024

Developing green, low-carbon building materials has become a viable option for managing bulk industrial solid waste. This paper presents a kind of all solid waste cementitious material (SWCM), which is made entirely from six common industrial wastes, including carbide slag and silica fume, that demonstrate strong mechanical properties and effectively stabilize aeolian sand (AS). Initially, we investigated the mechanical strength of waste-based cementitious materials in various mix ratios, focusing on their ability to stabilize river sand (RS) and aeolian sand. The results show that it is necessary to use alkaline solid waste carbide slag to provide a suitable reaction environment to achieve the desired strength. In contrast, the low reactivity of coal gangue powder did not contribute effectively to the strength of the cementitious material. Further orthogonal experiments determined the impact of different waste dosages on the strength of stabilized AS. It was found that increasing the amounts of carbide slag, silica fume, and blast furnace slag powder improved strength, while increasing fly ash first increased and then decreased strength. In contrast, higher additions of desulfurization gypsum and coal gangue powder led to a continuous decrease in strength. The optimized mix is carbide slag—desulfurization gypsum—fly ash—silica fume—blast furnace slag powder in a ratio of 4:2:2:3:3. The experimental results using SWCM to stabilize AS indicated a proportional relationship between strength and SWCM content. When the content is ≥20%, it meets the strength requirements for road subbases. The primary hydration products of stabilized AS are C-(A)-S-H, AFt, and CaCO3. Increasing the SWCM content enhances the reaction degree of the materials, thereby improving mechanical strength. This study highlights the mechanical properties of cementitious materials made entirely from waste for stabilizing AS. It provides a reference for the large-scale utilization of industrial solid waste and practical applications in desert road construction.
Introducing the Index of Caries Risk (ICR): A Comparative Study on a Novel Tool for Caries Risk Assessment in Pediatric Patients
Authors: Lucchi Patrizia , Nasuti Alice Dina , Franciosi Giovanni , Gaeta Carlo , Grandini Simone , Ludovichetti Francesco Saverio , Mazzoleni Sergio , Malvicini Giulia
Published: September 2024

Background: The prevalence of dental caries presents a global public challenge, particularly in children. Traditional caries risk assessment tools like Cariogram are effective but often complex and resource intensive. The aim of the present study was to introduce, for the first time, a newly designed caries risk assessment (CRA) tool named Index of Caries Risk (ICR) and to evaluate its efficacy for pediatric patients. Methods: This observational study was conducted at the University Hospital of Siena (Italy), involving 55 children aged 6 to 12 years. Participants were assessed using both the newly developed ICR and the well-known Cariogram tool. The data were collected by two calibrated operators. The ICR was compared with the Cariogram tool, and a descriptive analysis and a Pearson correlation coefficient were performed. Results: Results indicated a strong positive correlation (R = 0.88, p < 0.01) between the two methods, with ICR simplifying the assessment process while maintaining efficacy. Conclusions: This study highlights the ICR’s potential to provide a practical, cost-effective alternative for routine caries risk assessment in pediatric dentistry. Despite its limitations, this research marks a preliminary investigation of a promising new CRA tool. Further research should focus on validating the ICR in the larger and more diverse pediatric population, as well as exploring its long-term effectiveness and its application in different clinical settings.
Application of Deep Learning for Heart Attack Prediction with Explainable Artificial Intelligence
Authors: Dritsas Elias , Trigka Maria
Published: September 2024

Heart disease remains a leading cause of mortality worldwide, and the timely and accurate prediction of heart attack is crucial yet challenging due to the complexity of the condition and the limitations of traditional diagnostic methods. These challenges include the need for resource-intensive diagnostics and the difficulty in interpreting complex predictive models in clinical settings. In this study, we apply and compare the performance of five well-known Deep Learning (DL) models, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a Hybrid model, to a heart attack prediction dataset. Each model was properly tuned and evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC) as performance metrics. Additionally, by integrating an Explainable Artificial intelligence (XAI) technique, specifically Shapley Additive Explanations (SHAP), we enhance the interpretability of the predictions, making them actionable for healthcare professionals and thereby enhancing clinical applicability. The experimental results revealed that the Hybrid model prevailed, achieving the highest performance across all metrics. Specifically, the Hybrid model attained an accuracy of 91%, precision of 89%, recall of 90%, F1-score of 89%, and an AUC of 0.95. These results highlighted the Hybrid model’s superior ability to predict heart attacks, attributed to its efficient handling of sequential data and long-term dependencies.
Construction of the Invoicing Process through Process Mining and Business Intelligence in the Colombian Pharmaceutical Sector
Authors: Sanchez-Obando Jhon Wilder , Duque-Méndez Néstor Darío , Bedoya Herrera Oscar Mauricio
Published: September 2024

The invoicing process is critical to the financial management of organizations. However, modeling this process presents challenges such as data updating, information availability, and aligning planned activities with the actual execution of the process. One difficulty is that designing the invoicing process requires extensive knowledge of the activities involved, and process representations based on organizational repositories are not necessarily aligned with the actual invoicing processes in the organization. Process Mining is complemented by the use of dashboards, which are inherent to business intelligence and allow for visual tracking of process behavior. This paper explores how the combination of process mining and business intelligence can enable a new level of process modeling that addresses specific issues in constructing processes that are aligned with real-world activities. To accomplish this, we first propose the Design Science Research (DSR) methodology, which outlines how a researcher or practitioner should approach the task of modeling a specific process using Process Mining augmented with dashboard resources. The research strategy was to identify the most appropriate methodology to construct the actual billing process, which led to the identification of the DSR methodology. This methodology, with its 12-step plan, allowed the construction of an artifact representing the actual invoicing process. Ultimately, the objective of constructing a real invoicing process in the Colombian pharmaceutical sector is achieved through the development of an artifact, complemented by business intelligence dashboards that ensure the alignment of the execution of activities within the process.
Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor–Recipient Matching
Authors: Alowidi Nahed , Ali Razan , Sadaqah Munera , Naemi Fatmah M. A.
Published: September 2024

(1) Background: Globally, the kidney donor shortage has made the allocation process critical for patients awaiting a kidney transplant. Adopting Machine Learning (ML) models for donor–recipient matching can potentially improve kidney allocation processes when compared with traditional points-based systems. (2) Methods: This study developed an ML-based approach for donor–recipient matching. A comprehensive evaluation was conducted using ten widely used classifiers (logistic regression, decision tree, random forest, support vector machine, gradient boosting, boost, CatBoost, LightGBM, naive Bayes, and neural networks) across three experimental scenarios to ensure a robust approach. The first scenario used the original dataset, the second used a merged version of the dataset, and the last scenario used a hierarchical architecture model. Additionally, a custom ranking algorithm was designed to identify the most suitable recipients. Finally, the ML-based donor–recipient matching model was integrated into a web-based platform called Nephron. (3) Results: The gradient boost model was the top performer, achieving a remarkable and consistent accuracy rate of 98% across the three experimental scenarios. Furthermore, the custom ranking algorithm outperformed the conventional cosine and Jaccard similarity methods in identifying the most suitable recipients. Importantly, the platform not only facilitated efficient patient selection and prioritisation for kidney allocation but can be flexibly adapted for other solid organ allocation systems built on similar criteria. (4) Conclusions: This study proposes an ML-based approach to optimize donor-recipient matching within the kidney allocation process. Successful implementation of this methodology demonstrates significant potential to enhance both efficiency and fairness in kidney transplantation.
Carpal Tunnel Syndrome in Elite Female Tug-of-War Athletes: Prevalence and Risk Factor Analysis
Authors: Huang Chiang-Hui , Liu Kuo-Cheng , Cheng Ju-Wen , Hsu Shao-Chih , Chen Chih-Kuang
Published: September 2024

Background: Tug-of-War (TOW) games involve repetitive hand movements and vigorous force, raising the risk of peripheral neuropathy in the upper extremities. The prevalence of carpal tunnel syndrome (CTS) in TOW athletes remains unclear. We hypothesize that elite female TOW athletes have a higher prevalence of CTS than the general population. Methods: Twenty-nine female TOW athletes were recruited from a national team and participated in the study. CTS was clinically diagnosed by history taking and physical examination. Nerve conduction studies (NCS) were additionally performed to confirm CTS. Results: Twelve athletes were clinically diagnosed with CTS; however, only nine were confirmed by NCS. Ten athletes were diagnosed with subclinical CTS by NCS, while seven were classified as truly-non-CTS by both clinical assessment and NCS. The prevalence of CTS and subclinical CTS among the athletes was found to be 33.3% and 37.0%, respectively, significantly higher than 2.7% in the general population by electrodiagnosis. The body weight (p = 0.025) of the athletes with CTS and subclinical CTS was significantly different from those of the athletes without CTS. Conclusions: Our observations revealed a higher prevalence of CTS among elite female TOW athletes, with body weight being a risk factor. The forceful grasping and pulling of the rope may contribute to the development of CTS.
The Effect of Image Count on Accuracy in Digital Measurements in Dentistry
Authors: Güntekin Neslihan , Çiftçi Aslı , Gözen Mehmet , İleri Sema Ateşalp
Published: September 2024

Objective: This study investigated how the number of images collected for digital measurements in dentistry affects accuracy compared with traditional methods. Methods: A Frasaco maxillary model was scanned using a SHINING 3D AutoScan-DS-MIX dental 3D scanner to create an STL file. The maxilla was molded 10 times using polyvinyl siloxane (Zhermack Elite HD+) to produce plaster models, which were scanned with the same reference scanner to generate 10 STL files. The Frasaco model was scanned 10 times, capturing images in intervals of 800–1000, 1000–1200, and 1200–1500 using a 3Shape TRIOS 3 intraoral scanner, creating additional STL files. These were analyzed with reverse engineering software. Results: The most accurate measurements were obtained using 1200–1500 images. Conventional impression techniques performed significantly worse. There was a significant difference between the groups Digital 1200–1500 and Plaster (p < 0.001) and between Digital 800–1000 and Plaster (p = 0.007). No significant difference was found when the digital groups were compared among themselves. There was also no significant difference between the Plaster and Digital 1000–1200 groups. To compare precision values that were normally distributed across three or more methods, a one-way ANOVA was used. Trueness values that were not normally distributed with three or more methods were compared employing the Kruskal–Wallis test. Conclusions: Different image counts affect digital measurement accuracy. The most accurate measurements were obtained when collecting 1200–1500 images. Conventional impression techniques were shown to perform significantly worse than digital impression.
Best-Corrected Visual Acuity Quantitative Prediction for Cataract Patients: AI-Assisted Clinical Diagnostics Facilitation via the Inverse Problem Algorithm
Authors: Lin Ya-Hui , Liang Chun-Chieh , Chou Ying-Liang , Lin Chih-Sheng , Chen Ke-Lin , Pan Lung-Kwang , Cheng Kai-Yuan , Ke Ching-Hsiu
Published: September 2024

Objective: This study provided a quantitative prediction of best-corrected visual acuity (BCVA) for cataract patients using the inverse problem algorithm (IPA) technique earlier proposed by the authors. Methods: To this end, seven risk factors (age, BMI, MAP, IOP, HbA1c, LDL-C, and gender) were linked by a semi-empirical formula by normalizing each factor into a dimensionless range of −1.0 to +1.0. The adopted inverse problem algorithm (IPA) technique was run via a self-developed program in STATISTICA 7.0, featuring a 29-term nonlinear equation considering seven risk factors, cross-interaction between various pairs of factors, and one constant term [7 + (7 × 6)/2 + 1 = 29]. The IPA neglected quadratic, triple, or quadruple factors′ cross-interactions. This study used a dataset of 632 cataract patients to attain a reliable BCVA prediction with a variance of 0.929. A verification dataset of 160 patients with similar symptoms was used to verify this approach′s feasibility, reaching a good correlation with R2 = 0.909. Results: The verification group′s derived average AT (agreement) (9.12 ± 27.00%) indicated a slight deviation between the theoretical prediction and practical BCVA. The significant factors were age, body mass index (BMI), and intraocular pressure (IOP), whereas mean arterial pressure (MAP), hemoglobin A1c (HbA1c), low-density-lipoprotein cholesterol (LDL-C), and gender insignificantly contributed to BCVA. Conclusions: The proposed approach is instrumental in AI-assisted clinical diagnosis, yielding robust BCVA predictions for individual cataract patients based on their biological indices before the ophthalmological examination procedure.
Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review
Authors: Khalid Usman , Gurung Jasmin , Doykov Mladen , Kostov Gancho , Hristov Bozhidar , Uchikov Petar , Kraeva Maria , Kraev Krasimir , Doykov Daniel , Doykova Katya , Valova Siyana , Chervenkov Lyubomir , Tilkiyan Eduard , Eneva Krasimira
Published: September 2024

The development of the Gleason grading system has proven to be an irreplaceable tool in prostate cancer diagnostics within urology. Despite the advancements and developments in diagnostics, there remains a discrepancy in the grading process among even the most experienced pathologists. AI algorithms have demonstrated potential in detecting cancer and assigning Gleason grades, offering a solution to the issue of significant variability among pathologists’ evaluations. Our paper explores the evolving role of AI in prostate cancer histopathology, with a key focus on outcomes and the reliability of various AI algorithms for Gleason pattern assessment. We conducted a non-systematic review of the published literature to examine the role of artificial intelligence in Gleason pattern diagnostics. The PubMed and Google Scholar databases were searched to gather pertinent information about recent advancements in artificial intelligence and their impact on Gleason patterns. We found that AI algorithms are increasingly being used to identify Gleason patterns in prostate cancer, with recent studies showing promising advancements that surpass traditional diagnostic methods. These findings highlight AI’s potential to be integrated into clinical practice, enhancing pathologists’ workflows and improving patient outcomes. The inter-observer variability in Gleason grading has seen an improvement in efficiency with the implementation of AI. Pathologists using AI have reported successful outcomes, demonstrating its effectiveness as a supplementary tool. While some refinements are still needed before AI can be fully implemented in clinical practice, its positive impact is anticipated soon.
Can Rhomboid Intercostal Block Be an Alternative to Paravertebral Block in Video-Assisted Thoracoscopic Surgery? A Randomized Prospective Study
Authors: Manici Mete , Salgın Belitsu , Söğüt Muhammet Selman , Tanju Serhan , Dilege Şükrü , Gürkan Yavuz , Elsharkawy Hesham
Published: September 2024

Background/Objectives: Rhomboid intercostal block (RIB) is a new interfascial plane block. RIB is a simple and clinically effective technique. Paravertebral block (PVB) is offered as a first-line regional anesthesia technique for thoracoscopic surgeries. In this study, we aim to compare the analgesic efficacy of RIB to PVB in video-assisted thoracoscopic surgeries (VATSs). Methods: This is a prospective randomized study with 84 patients aged 18–75 and ASA I–III, undergoing VATS for primary lung cancer. The study was approved by an ethical committee and registered under clinicaltrials.org. With informed consent, patients were randomized to receive ultrasound-guided RIB or PVB at T5-level with 20 mL of %0.25 bupivacaine preoperatively. Surgeries were performed under general anesthesia. Postoperatively, patient-controlled IV fentanyl analgesia was prescribed, delivering 10 μg boluses upon request with 10 min of a lock-out period. Patients received paracetamol 1 g IV three times a day and tramadol 50 mg IV for breakthrough pain. The postoperative Numeric Rating Scale (NRS) for pain, total opioid consumption, and rescue analgesic requirements were recorded postoperatively at 1, 3, 6, 12, and 24 h. Results: There were no significant differences in 24 h total opioid consumption between the RIB and PVB groups [PVB: 48.5 (39.5–55) mcg; RIB: 48.6 (40.2–65) mcg; p = 0.258], nor in rescue analgesic requirements [PVB: seven patients (20%); RIB: seven patients (17.1%); p = 0.570]. NRS pain scores were also similar between the groups, with no significant difference in overall pain control efficacy (p = 0.833). Conclusions: RIB is comparable to PVB in analgesic efficacy for VATS and can be considered as an alternative analgesic modality.
The Interlinking Metabolic Association between Type 2 Diabetes Mellitus and Cancer: Molecular Mechanisms and Therapeutic Insights
Authors: Asiri Abutaleb , Al Qarni Ali , Bakillah Ahmed
Published: September 2024

Type 2 diabetes mellitus (T2DM) and cancer share common risk factors including obesity, inflammation, hyperglycemia, and hyperinsulinemia. High insulin levels activate the PI3K/Akt/mTOR signaling pathway promoting cancer cell growth, survival, proliferation, metastasis, and anti-apoptosis. The inhibition of the PI3K/Akt/mTOR signaling pathway for cancer remains a promising therapy; however, drug resistance poses a major problem in clinical settings resulting in limited efficacy of agents; thus, combination treatments with therapeutic inhibitors may solve the resistance to such agents. Understanding the metabolic link between diabetes and cancer can assist in improving the therapeutic strategies used for the management of cancer patients with diabetes and vice versa. This review provides an overview of shared molecular mechanisms between diabetes and cancer as well as discusses established and emerging therapeutic anti-cancer agents targeting the PI3K/Akt/mTOR pathway in cancer management.
Customized 3D-Printed Mesh, Membrane, Bone Substitute, and Dental Implant Applied to Guided Bone Regeneration in Oral Implantology: A Narrative Review
Authors: Di Spirito Federica , Giordano Francesco , Di Palo Maria Pia , Ferraro Cosimo , Cecere Luigi , Frucci Eugenio , Caggiano Mario , Lo Giudice Roberto
Published: September 2024

Background: The new frontiers of computer-based surgery, technology, and material advances, have allowed for customized 3D printed manufacturing to become widespread in guided bone regeneration (GBR) in oral implantology. The shape, structural, mechanical, and biological manufacturing characteristics achieved through 3D printing technologies allow for the customization of implant-prosthetic rehabilitations and GBR procedures according to patient-specific needs, reducing complications and surgery time. Therefore, the present narrative review aims to elucidate the 3D-printing digital radiographic process, materials, indications, 3D printed manufacturing-controlled characteristics, histological findings, complications, patient-reported outcomes, and short- and long-term clinical considerations of customized 3D printed mesh, membranes, bone substitutes, and dental implants applied to GBR in oral implantology. Methods: An electronic search was performed through MEDLINE/PubMed, Scopus, BioMed Central, and Web of Science until 30 June 2024. Results: Three-dimensionally printed titanium meshes and bone substitutes registered successful outcomes in vertical/horizontal bone defect regeneration. Three-dimensionally printed polymeric membranes could link the advantages of conventional resorbable and non-resorbable membranes. Few data on customized 3D printed dental implants and abutments are available, but in vitro and animal studies have shown new promising designs that could improve their mechanical properties and tribocorrosion-associated complications. Conclusions: While 3D printing technology has demonstrated potential in GBR, additional human studies are needed to evaluate the short- and long-term follow-up of peri-implant bone levels and volumes following prosthetic functional loading.
Design and Implementation of a Novel UAV-Assisted LoRaWAN Network
Authors: Zhao Honggang , Tang Wenxin , Chen Sitong , Li Aoyang , Li Yong , Cheng Wei
Published: September 2024

When LoRaWAN networks are deployed in complex environments with buildings, jungles, and other obstacles, the communication range of LoRa signals experiences a notable reduction, primarily due to multipath propagation, fading, and interference. With the flight advantage of height, mobility, and flexibility, UAV can provide line-of-sight (LOS) communication or more reliable communication in many scenarios, which can be used to enhance the LoRaWAN network’s performance. In this paper, a novel UAV-assisted LoRaWAN network is designed and implemented. Specifically, a UAV-assisted LoRaWAN network system architecture is proposed to improve the LoRaWAN network coverage and communication reliability, in which the UAV architecture of “UAV + Remote Controller + Server” is combined with the traditional LoRaWAN architecture of “End-Device + Gateway + Server”. Then, the implementation of the UAV gateway and the remote controller relay is presented, which play the important role of forwarding LoRaWAN frames transparently in our proposed architecture. In detail, the UAV gateway is developed based on the UAV’s PSDK and classical LoRa packet forwarder, and the remote controller relay is developed based on UAV’s MSDK. The experimental results show that the network coverage and communication reliability of our proposed LoRaWAN network have been significantly improved, effectively supporting a wide range of LoRaWAN applications. Specifically, when the end-device is deployed 1.3 km away with numerous obstacles in the propagation environment, with the UAV altitude advantage and the remote controller’s relay capability, the proposed system achieved an SNR of 5 db and an RSSI of −80 dbm with a packet loss rate of 3%. In comparison, the ground gateway only achieved an SNR of −16 db and an RSSI of −113 dbm with a packet loss rate of 73%.
Double-Layer Distributed and Integrated Fault Detection Strategy for Non-Gaussian Dynamic Industrial Systems
Authors: Dong Shengli , Xu Xinghan , Chen Yuhang , Zhang Yifang , Wang Shengzheng
Published: September 2024

Currently, with the increasing scale of industrial systems, multisensor monitoring data exhibit large-scale dynamic Gaussian and non-Gaussian concurrent complex characteristics. However, the traditional principal component analysis method is based on Gaussian distribution and uncorrelated assumptions, which are greatly limited in practice. Therefore, developing a new fault detection method for large-scale Gaussian and non-Gaussian concurrent dynamic systems is one of the urgent challenges to be addressed. To this end, a double-layer distributed and integrated data-driven strategy based on Laplacian score weighting and integrated Bayesian inference is proposed. Specifically, in the first layer of the distributed strategy, we design a Jarque–Bera test module to divide all multisensor monitoring variables into Gaussian and non-Gaussian blocks, successfully solving the problem of different data distributions. In the second layer of the distributed strategy, we design a dynamic augmentation module to solve dynamic problems, a K-means clustering module to mine local similarity information of variables, and a Laplace scoring module to quantitatively evaluate the structural retention ability of variables. Therefore, this double-layer distributed strategy can simultaneously combine the different distribution characteristics, dynamism, local similarity, and importance of variables, comprehensively mining the local information of the multisensor data. In addition, we develop an integrated Bayesian inference strategy based on detection performance weighting, which can emphasize the differential contribution of local models. Finally, the fault detection results for the Tennessee Eastman production system and a diesel engine working system validate the superiority of the proposed method.
Energy Cost Reduction in the Administrative Building by the Implementation of Technical Innovations in Slovakia
Authors: Teplická Katarína , Khouri Samer , Mehana Ibrahim , Petrovská Ivana
Published: September 2024

The presented article investigates the effects of technical innovations in administrative buildings on the financial side of the business, with a main focus on reducing energy costs and energy consumption. The administrative buildings in a business contribute significantly to the business’s total energy consumption. The basic pillar of Industry 4.0 is the optimization of resources such as energy, which, in production enterprises, represents economic value—costs. The article is orientated to investigate the effect of the technical innovations of administrative buildings on the financial side of the enterprise, with a main focus on reducing energy costs and energy consumption. This research was conducted in Slovakia’s manufacturing sector. In this research, we used economic and financial analysis and economic indicators. This research was conducted between 2019 and 2023. In this period, the results were positive. The results indicated a reduction in energy consumption of 143 GJ (39,722 kWh), reduced energy costs of EUR 6356, reduction in the energy cost structure of 1.3%, and the indicator, the payback period, was determined to be in the range of 6 to 12 years for individual technical innovations. The new design of administrative buildings is an advantage for manufacturing enterprises and can be used as a marketing tool to attract both customers and suppliers. The suggested energy, environmental, and economic sustainability model will allow production enterprises to improve their energy use in administrative buildings.
The Moderating Role of Interest in the Relationship between Perceived Task Difficulty and Invested Mental Effort
Authors: Schuessler Katrin , Fischer Vanessa , Walpuski Maik , Leutner Detlev
Published: September 2024

Including motivational variables such as interest in the cognitive load framework is an ongoing process. Of particular interest is the question of how motivational variables influence the investment of mental effort. In this study, we investigated how topic interest affects the investment of mental effort in simple tasks. A total of 1543 students’ judgments regarding invested mental effort, perceived task difficulty, and topic interest for 32 tasks of a chemistry test were analyzed at the task level based on item response theory parameters. Additionally, objective task difficulty was calculated. The Rasch parameters were used for correlation and moderated regression analyses. The results indicated that when perceived task difficulty was low, students invested more mental effort in solving tasks of low topic interest compared to tasks of high topic interest. With increasing perceived task difficulty, the amount of invested mental effort rose for tasks of low as well as high topic interest. However, the difference between tasks of low and high topic interest in the amount of invested mental effort decreased as perceived task difficulty increased and even vanished when perceived task difficulty roughly corresponded to students’ performance capability. These results are in line with flow theory and the expectancy-value-cost model of motivation. When solving tasks that match their performance capability, students can experience a flow situation. However, when solving rather easy tasks of low interest, students can experience motivational costs in terms of additional effort, such as an increased need for motivational self-regulation. The results of this study provide a basis for systematically investigating and better understanding the relationship between interest, task difficulty, invested mental effort, flow experience, and emotional costs.
Building Bridges in Teacher Education to Enhance Teachers for Students’ Diversity in Physical Education
Authors: Batista Paula , Graça Amândio , Moura André , Ribeiro-Silva Elsa , Estriga Luisa
Published: September 2024

Following the UNESCO guidelines for Quality Physical Education, this participatory action research study aimed to explore the potential of a Community of Practice (CoP) to empower both schoolteacher educators and preservice teachers (PSTs) to become practitioners–researchers in order to achieve inclusive practices in Physical Education (PE). This study involved a partnership between a university in North Portugal and four cooperating schools. CoP participants were six teacher educators and four cooperating teachers (CTs) who work with twelve PSTs in the schools where they developed their teaching practices. Data collection included focus groups with CoP members and PSTs, CoP meetings, and school placement meetings. A thematic inductive–deductive analysis revealed that CTs were actively engaged in the CoP, achieving mutual involvement and a shared repertoire, and making friendships beyond the CoP. Becoming practitioners–researchers made CTs more critical of their teaching and supervisory roles. Supporting PSTs to be practitioners–researchers was challenging, mostly due to PSTs’ lack of content knowledge, which made it difficult to identify the students’ problems in engaging with the proposed activities and how to solve them. Both CTs and PSTs highlighted the value of the CoP in gaining confidence and knowledge to improve their teaching to become more inclusive.
A Graph Similarity Algorithm Based on Graph Partitioning and Attention Mechanism
Authors: Miao Fengyu , Zhou Xiuzhuang , Xiao Shungen , Zhang Shiliang
Published: September 2024

In recent years, graph similarity algorithms have been extensively developed based on neural networks. However, with an increase in the node count in graphs, these models either suffer from a reduced representation ability or face a significant increase in the computational cost. To address this issue, a graph similarity algorithm based on graph partitioning and attention mechanisms was proposed in this study. Our method first divided each input graph into the subgraphs to directly extract the local structural features. The residual graph convolution and multihead self-attention mechanisms were employed to generate node embeddings for each subgraph, extract the feature information from the nodes, and regenerate the subgraph embeddings using varying attention weights. Initially, rough cosine similarity calculations were performed on all subgraph pairs from the two sets of subgraphs, with highly similar pairs selected for precise similarity computation. These results were then integrated into the similarity score for the input graph. The experimental results indicated that the proposed learning algorithm outperformed the traditional algorithms and similar computing models in terms of graph similarity computation performance.
An Improved Product Defect Detection Method Combining Centroid Distance and Textural Information
Authors: Wu Haorong , Li Xiaoxiao , Sun Fuchun , Huang Limin , Yang Tao , Bian Yuechao , Lv Qiurong
Published: September 2024

In order to solve the problems of a high mismatching rate and being easily affected by noise and gray transformation, an improved product defect detection method combining centroid distance and textural information is proposed in this paper. Based on image preprocessing, the improved fuzzy C-means clustering method is used to extract the closed contour features. Then, the contour center distance description operator is used for bidirectional matching, and a robust coarse matching contour pair is obtained. After the coarse matching contour pair is screened, the refined matching result is obtained by using the improved local binary pattern operator. Finally, by comparing whether the number of fine matching pairs is consistent with the number of template outlines, the detection of good and bad industrial products is realized, and the closed contour extraction experiment, the anti-rotation matching experiment, the anti-gray difference matching experiment, and the defect detection experiment of three different products are designed. The experimental results show that the improved product defect detection method has good performance in relation to anti-rotation transformation and anti-gray difference, the detection accuracy can reach more than 90%, and the detection time is up to 362.6 ms, which can meet the requirements of industrial real-time detection.
Cost of Capital in the Energy Sector, in Emerging Markets, the Case of a Dollarized Economy
Authors: Aguilar Victor , Naula Freddy , Cabrera Fanny
Published: September 2024

This article estimates the weighted average cost of capital (WACC) for the energy sector in Ecuador, a country with a dollarized economy and illiquid stock markets. Thus, reference companies in the region were taken, and at the same time combined with characteristics of national companies, establishing a useful methodology, which makes sense with the acceptable discount rates in the Ecuadorian economy. For the above, four estimation alternatives were used. In method one, the traditional WACC formula was applied using interest rates and risk premiums from the U.S. market, which resulted in an overestimation due to the double penalty of the country risk and the U.S. market premium. Method two adjusted the market risk premium to consider only the Ecuador-specific risk premium, thus avoiding the double penalty. In method three, the credit default swap (CDS) was used to calculate the country risk premium, and the CDS was excluded from the nominal interest rate, avoiding redundancies. Finally, method four combined the U.S. interest rate with the CDS directly to calculate the market risk premium, more accurately reflecting local economic conditions in a dollarized economy. The WACC results range from 12.63% to 29.70%. In addition, a dummy variable was controlled for during the pandemic period. This article highlights the need for methodologies adapted to emerging markets, since traditional approaches would overestimate the WACC.
Pore-Scale Mechanism Analysis of Enhanced Oil Recovery by Horizontal Well, Dissolver, Nitrogen, and Steam Combined Flooding in Reducer Systems with Different Viscosities for Heavy Oil Thermal Recovery
Authors: Zhang Bowen , Song Zhiyong , Zhang Yang
Published: September 2024

Horizontal well, dissolver, nitrogen, and steam (HDNS) combined flooding is mainly applied to shallow and thin heavy oil reservoirs to enhance oil recovery. Due to the lack of pore-scale mechanism studies, it is impossible to clarify the oil displacement mechanism of each slug in the process combination and the influence of their interaction on enhanced oil recovery (EOR). Therefore, in this study, HDNS combined flooding technology was simulated in a two-dimensional visualization microscopic model, and three viscosity reducer systems and multi-cycle combined flooding processes were considered. In combination with an emulsification and viscosity reduction experiment, two-dimensional microscopic multiphase seepage experiments were carried out to compare the dynamic seepage law and microscopic occurrence state of multiphase fluids in different systems. The results showed that the ability of three viscosity reducers to improve viscosity reduction efficiency in HDNS combined flooding was A > B > C, and their contributions to the recovery reached 65%, 41%, and 30%, respectively. In the system where a high viscosity reduction efficiency was shown by the viscosity reducer, the enhancements of both sweeping efficiency and displacement efficiency were primarily influenced by the viscosity reducer flooding. Steam flooding collaborated to improve displacement efficiency. The thermal insulation characteristics of N2 flooding may not provide a gain effect. In the system where a low viscosity reduction efficiency was shown by the viscosity reducer, the steam flooding was more important, contributing to 57% of the sweeping efficiency. Nitrogen was helpful for expanding the sweep area of the subsequent steam and viscosity reducer, and the gain effect of the thermal insulation steam chamber significantly improved the displacement efficiency of the subsequent steam flooding by 25%. The interaction of each slug in HDNS combined flooding resulted in the additive effect of increasing production. In actual production, it is necessary to optimize the process and screen the viscosity reducer according to the actual conditions of the reservoir and the characteristics of different viscosity reducers.
A Novel PI-Based Control Structure with Additional Feedback from Torsional Torque and Its Derivative for Damping Torsional Vibrations
Authors: Derugo Piotr , Kahsay Amanuel Haftu , Szabat Krzysztof , Shikata Kosuke , Katsura Seiichiro
Published: September 2024

This paper presents issues related to the damping of torsional vibrations in a system with elastic coupling. A novel PI-based control structure with additional feedback from torsional torque and its derivative is proposed. For the estimation of the required variables, the integral observer is proposed. Analytical expressions are presented to enable the selection of parameters of the control system. The relationship between the considered system and popular structures with a PI controller and one additional feedback from torsional torque and the derivative of torsional torque is pointed out. The proposed control structure is tested under simulation and experimental studies.
On the Different Fair Allocations of Economic Benefits for Energy Communities
Authors: Volpato Gabriele , Carraro Gianluca , Dal Cin Enrico , Rech Sergio
Published: September 2024

Energy Communities (ECs) are aggregations of users that cooperate to achieve economic benefits by sharing energy instead of operating individually in the so-called “disagreement” case. As there is no unique notion of fairness for the cost/profit allocation of ECs, this paper aims to identify an allocation method that allows for an appropriate weighting of both the interests of an EC as a whole and those of all its members. The novelty is in comparing different optimization approaches and cooperative allocation criteria, satisfying different notions of fairness, to assess which one may be best suited for an EC. Thus, a cooperative model is used to optimize the operation of an EC that includes two consumers and two solar PV prosumers. The model is solved by the “Social Welfare” approach to maximizing the total “incremental” economic benefit (i.e., cost saving and/or profit increase) and by the “Nash Bargaining” approach to simultaneously maximize the total and individual incremental economic benefits, with respect to the “disagreement” case. Since the “Social Welfare” approach could lead to an unbalanced benefit distribution, the Shapley value and Nucleolus criteria are applied to re-distribute the total incremental economic benefit, leading to higher annual cost savings for consumers with lower electricity demand. Compared to “Social Welfare” without re-distribution, the Nash Bargaining distributes 39–49% and 9–17% higher annual cost savings to consumers with lower demand and to prosumers promoting the energy sharing within the EC, respectively. However, total annual cost savings drop by a maximum of 5.5%, which is the “Price of Fairness”.
Minimum Risk Quantification Method for Error Threshold of Wind Farm Equivalent Model Based on Bayes Discriminant Criterion
Authors: Shen Yuming , Yang Hao , Xu Jiayin , Li Kun , Wang Jiaqing , Zhu Qianlong
Published: September 2024

The error threshold is the cornerstone to balance the mathematical complexity and simulation speed of wind farm (WF) equivalent models, and can promote the standardization process of equivalent methodology. Due to differences in power system conditions and model evaluation standards in different countries, the form and indexes of error thresholds of WF equivalent models have not been unified yet. This paper proposes a theoretical method for quantifying the minimum risk of error threshold of WF equivalent models based on the Bayes discriminant criterion. Firstly, the Euclidean errors of WF equivalent models in different periods are quantified, and the probability density distributions of the errors are fitted by kernel density estimation. Secondly, the real-time weighted prior probability algorithm is used to obtain the prior probability of a valid WF equivalent model, and the different losses caused by the missed judgment and misjudgment of the model validity to power systems are taken into account. Thirdly, the minimum risk quantification model of error threshold is established based on the Bayes discriminant criterion, and the feasibility of the proposed method is verified by an actual WF with numerical examples. Compared with the existing error thresholds, the proposed error threshold can more quickly and accurately determine the validity of WF equivalent models.
Responses of Climatic Drought to Vegetation Cover Dynamics: A Case Study in Yunnan, China
Authors: Wan Yangtao , Han Han , Mao Yao , He Bao-Jie
Published: September 2024

Vegetation cover can regulate regional climate and associated dry–wet variations. However, the effects of the quantitative structure and landscape pattern of vegetation cover on climatic drought remain unclear. Yunnan Province in China, with its abundant vegetation resources, provides a good setting for addressing this research gap. Our objective is to provide guiding recommendations for climate-warming mitigation through the study of the topic. This study adopted four periods of vegetation cover data, from 1992 to 2020, and explored their dynamics. Monthly average precipitation and temperature data from 125 meteorological stations in Yunnan were used to calculate standardized precipitation–evapotranspiration index (SPEI) for 1992–2020 to understand the responses of climatic drought to vegetation cover dynamics. The correlations between quantitative structure, landscape pattern, and climatic drought were investigated by Pearson’s correlation coefficient in 10 km, 20 km, 30 km, and 40 km grid cells, respectively. The results indicate that changes in the quantitative structure of vegetation could influence regional climates, with the contributions to climatic drought mitigation ranked in the following order: broad-leaved forest > shrubland > needle-leaved forest > cropland > grassland. Landscape patterns significantly affected local climates, where broad-leaved and needle-leaved forests had the strongest and most stable correlations with climatic drought, whereas shrubland and grassland showed weaker correlations. The correlations between landscape patterns and climatic drought were stronger during the dry season than the rainy season. Factors such as the landscape dominance index, fragmentation index, and aggregation index had a significant impact on climatic drought. The dominant and aggregated-distribution broad-leaved forests were conducive to climatic drought mitigation, while needle-leaved forests, croplands, and grasslands might exacerbate climatic drought.
Construction and Comparison of Machine-Learning Forecast Models of Albacore Thunnus alalunga Fishing Grounds in the South Pacific Ocean
Authors: Li Jianxiong , Chen Feng , Dai Qian , Zhu Wenbin , Li Dewei , Yu Wei , Zhou Weifeng
Published: September 2024

The traditional methods for predicting the distribution of albacore (Thunnus alalunga) fishing grounds have low performance and accuracy. Uneven sampling can result in unreasonable evaluation indicators. To address these issues, three methods, equi-frequency, K-means clustering algorithm, and 1-R split, were applied to discretize the catch per unit effort (CPUE) of albacore in the South Pacific from 2016 to 2021 and partition the fishing grounds into abundance levels. Eight machine learning models were used to predict the fishing grounds. In addition to the traditional evaluation index based on confusion matrix, top-k index was also used to evaluate the accuracy of fishery abundance predictions. The results showed that (1) When sampling is unbalanced, the reported accuracy does not fully represent the actual performance of the model in predicting the abundance of albacore in the fishing ground. F1 value can be used as the index of the model effect and stability. (2) In binary classification, the quartile stacking algorithm has the best stacking performance, with F1 0.89. (3) The top-1 prediction accuracy of three-category fishery forecasting is the highest at 0.74, and the top-1 prediction accuracy of five-category fishery forecasting is the highest at 0.54. (4) The top-k accuracy of classification of fisheries with multiple abundance using K-means is significantly better than that of equal frequency discretization (p < 0.001). The top-k evaluation index was used to predict the fishing grounds of albacore across multiple abundance levels for the first time in this study, which is significant for pioneering a new method for this application and which provides a demonstration of the development of artificial intelligence techniques for fisheries in the future.