bluuebunny/crossref_metadata_2025 huggingface dataset:
DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
Authors: Huang Yifan , Huang Zhiwen , Jin Tao
Published: May 2024

Surface defect detection is a critical task in the manufacturing industry to ensure product quality and machining efficiency. Image-based precise defect detection faces significant challenges due to defects lacking fixed shapes and the detection being heavily influenced by lighting conditions. Addressing the efficiency demands of defect detection algorithms, often deployed on embedded devices, and the highly imbalanced pixel ratio between foreground and background images, this paper introduces a multi-scale fusion staged U-shaped convolutional neural network (DEU-Net). The network provides segmentation results for defect anomalies while indicating the probability of defect presence. It enables the model to train with fewer parameters, a crucial requirement for practical applications. The proposed model achieves an MIoU of 66.94 and an F1 score of 74.89 with lower Params (36.675) and Flops (19.714). Comparative analysis with FCN, U-Net, Deeplab v3+, U-Net++, Attention U-Net, and Trans U-Net demonstrates the superiority of the proposed approach in surface defect detection.
Thermo-Regulated Cotton: Enhanced Insulation through PVA Nanofiber-Coated PCM Microcapsules
Authors: Dirlik-Uysal Dilara , Mínguez-García David , Bou-Belda Eva , Gisbert-Payá Jaime , Bonet-Aracil Marilés
Published: May 2024

The innovative integration of phase change materials (PCMs) into textiles through microencapsulation presents a transformative approach to developing thermally regulated fabrics. This study explores the synthesis and characterization of microcapsules containing a coconut oil core and an ethylcellulose shell, and their application on cotton fabrics coated with polyvinyl alcohol (PVA) nanofibers. The dual-layer system involving microcapsules and nanofibers is designed to enhance the thermal insulation properties of textiles by regulating heat through the absorption and release of thermal energy. The microencapsulation of PCMs allows for the effective incorporation of these materials into textiles without altering the fabric’s inherent properties. In this study, the coconut oil serves as the PCM, known for its suitable phase change temperature range, while ethylcellulose provides a robust shell, enhancing the microcapsules’ structural integrity. The application of a PVA nanofibers layer not only strengthens the thermal regulation properties but also protects the microcapsules from release while the fabric is manipulated, thereby prolonging the functional life of the fabric. Comprehensive testing, including scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR), confirms the successful application and durability of the microcapsules on the textiles. Thermal imaging studies demonstrate the fabric’s enhanced capability to maintain a consistent temperature, highlighting the potential of this technology in applications ranging from smart clothing to energy-efficient building materials or automotive isolation. The integration of PCMs in textiles via microencapsulation and nanofiber technology marks a significant advancement in textile engineering, offering new opportunities for the development of smart and sustainable materials. The study demonstrates the promising potential of integrating PCMs into textiles using microencapsulation and nanofiber technologies. Despite the initially modest insulation improvements, the methodology provides a robust foundation for further research and development.
A Deep Learning-Based Framework for Strengthening Cybersecurity in Internet of Health Things (IoHT) Environments
Authors: Algethami Sarah A. , Alshamrani Sultan S.
Published: May 2024

The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it has also exposed some critical vulnerabilities, particularly in cybersecurity. IoHT is characterized by interconnected medical devices sharing sensitive patient data, which amplifies the risk of cyber threats. Therefore, ensuring healthcare data’s integrity, confidentiality, and availability is essential. This study proposes a hybrid deep learning-based intrusion detection system that uses an Artificial Neural Network (ANN) with Bidirectional Long Short-Term Memory (BLSTM) and Gated Recurrent Unit (GRU) architectures to address critical cybersecurity threats in IoHT. The model was tailored to meet the complex security demands of IoHT and was rigorously tested using the Electronic Control Unit ECU-IoHT dataset. The results are impressive, with the system achieving 100% accuracy, precision, recall, and F1-Score in binary classifications and maintaining exceptional performance in multiclass scenarios. These findings demonstrate the potential of advanced AI methodologies in safeguarding IoHT environments, providing high-fidelity detection while minimizing false positives.
Improving Supply Chain Management Processes Using Smart Contracts in the Ethereum Network Written in Solidity
Authors: Yigit Eren , Dag Tamer
Published: May 2024

This paper investigates the potential of integrating supply chain management with blockchain technology, specifically by implementing smart contracts on the Ethereum network using Solidity. The paper explores supply chain management concepts, blockchain, distributed ledger technology, and smart contracts in the context of their integration into supply chains to increase traceability, transparency, and accountability with faster processing times. After investigating these technologies’ applications and potential use cases, a framework for smart contract implementation for supply chain management is constructed. Potential data models and functions of a smart contract implementation improving supply chain management processes are discussed. After constructing a framework, the effects of the proposed system on supply chain processes are explained. The proposed framework increases the reliability of the supply chain history due to the usage of DLT (distributed ledger technology). It utilizes smart contracts to increase the manageability and traceability of the supply chain. The proposed framework also eliminates the SPoF (Single Point of Failure) vulnerabilities and external alteration of the transactional data. However, due to the ever-changing and variable nature of the supply chains, the proposed architecture might not be a one-size-fits-all solution, and tailor-made solutions might be necessary for different supply chain management implementations.
Estimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network
Authors: da Silva Giovana Gonçalves , de Queiroz Alexandre , Garbelini Enio , dos Santos Wesley Prado Leão , Minussi Carlos Roberto , Bonini Neto Alfredo
Published: May 2024

Total real and reactive power losses in electrical power systems are an inevitable phenomenon and occur due to several factors, such as conductor resistance, transformer impedance, line reactance, equipment losses, and phase unbalance. Minimizing them is crucial to the system’s efficiency. In this study, an artificial neural network, specifically a Multi-layer Perceptron, was employed to predict total real and reactive power losses in electrical systems. The network is composed of three layers: an input layer consisting of the variables loading factor, real and reactive power generated on the slack bus, a hidden layer, and an output layer representing the total real and reactive power losses. The training method used was backpropagation, adjusting the weights based on the desired output. The results obtained, using datasets from IEEE systems with 14, 30, and 57 buses, showed satisfactory performance, with a mean squared error of around 10−4 and a coefficient of determination (R2) of 0.998. In validation with 20% of the data that was not part of the training, the network demonstrated effectiveness, with a mean squared error around 10−3. This indicates that the network was able to accurately predict total power losses based on loads, generating estimates close to the desired values.
Certain New Applications of Symmetric q-Calculus for New Subclasses of Multivalent Functions Associated with the Cardioid Domain
Authors: Srivastava Hari M. , Breaz Daniel , Khan Shahid , Tchier Fairouz
Published: May 2024

In this work, we study some new applications of symmetric quantum calculus in the field of Geometric Function Theory. We use the cardioid domain and the symmetric quantum difference operator to generate new classes of multivalent q-starlike and q-convex functions. We examine a wide range of interesting properties for functions that can be classified into these newly defined classes, such as estimates for the bounds for the first two coefficients, Fekete–Szego-type functional and coefficient inequalities. All the results found in this research are sharp. A number of well-known corollaries are additionally taken into consideration to show how the findings of this research relate to those of earlier studies.
A Cross-Stage Partial Network and a Cross-Attention-Based Transformer for an Electrocardiogram-Based Cardiovascular Disease Decision System
Authors: Lee Chien-Ching , Chuang Chia-Chun , Yeng Chia-Hong , So Edmund-Cheung , Chen Yeou-Jiunn
Published: May 2024

Cardiovascular disease (CVD) is one of the leading causes of death globally. Currently, clinical diagnosis of CVD primarily relies on electrocardiograms (ECG), which are relatively easier to identify compared to other diagnostic methods. However, ensuring the accuracy of ECG readings requires specialized training for healthcare professionals. Therefore, developing a CVD diagnostic system based on ECGs can provide preliminary diagnostic results, effectively reducing the workload of healthcare staff and enhancing the accuracy of CVD diagnosis. In this study, a deep neural network with a cross-stage partial network and a cross-attention-based transformer is used to develop an ECG-based CVD decision system. To accurately represent the characteristics of ECG, the cross-stage partial network is employed to extract embedding features. This network can effectively capture and leverage partial information from different stages, enhancing the feature extraction process. To effectively distill the embedding features, a cross-attention-based transformer model, known for its robust scalability that enables it to process data sequences with different lengths and complexities, is employed to extract meaningful embedding features, resulting in more accurate outcomes. The experimental results showed that the challenge scoring metric of the proposed approach is 0.6112, which outperforms others. Therefore, the proposed ECG-based CVD decision system is useful for clinical diagnosis.
Liraglutide Protects Cardiomyocytes against Isoprenaline-Induced Apoptosis in Experimental Takotsubo Syndrome
Authors: Bajic Zorislava , Sobot Tanja , Amidzic Ljiljana , Vojinovic Natasa , Jovicic Sanja , Gajic Bojic Milica , Djuric Dragan M. , Stojiljkovic Milos P. , Bolevich Sergey , Skrbic Ranko
Published: May 2024

Takotsubo syndrome (TTS) is a stress-induced cardiomyopathy, characterized by an increased concentration of catecholamines, free radicals, and inflammatory cytokines, endothelial dysfunction, and increased apoptotic activity. High doses of isoprenaline are used in animal models to induce Takotsubo (TT)-like myocardial injury. The aim of the study was to investigate the antiapoptotic effects of liraglutide in experimental TTS and its role in the NF-κB pathway. Wistar rats were pretreated with liraglutide for 10 days, and on days 9 and 10, TT-like myocardial injury was induced with isoprenaline. After the sacrifice on day 11, hearts were isolated for histopathological and immunohistochemical analysis. Liraglutide reduced isoprenaline-induced cardiomyocyte apoptosis by decreasing cleaved caspase-3 (CC3), BCL-2-associated X protein (BAX), and NF-κB and increasing B-cell lymphoma/leukemia-2 (BCL-2). An increase in NF-κB in isoprenaline-treated rats was in positive correlation with proapoptotic markers (BAX and CC3) and in negative correlation with antiapoptotic marker BCL-2. Liraglutide increased BCL-2 and decreased NF-κB, BAX, and CC3, preserving the same correlations of NF-κB to apoptotic markers. It is concluded that liraglutide protects cardiomyocytes against isoprenaline-induced apoptosis in experimental TT-like myocardial injury through downregulation of the NF-κB pathway.
Prognostic Factors for Survival in Glioblastoma: A Retrospective Analysis Focused on the Role of Hemoglobin
Authors: Pleskacova Zuzana , Bartos Michael , Vosmikova Hana , Dolezal Rafael , Krupa Petr , Vitovcova Barbora , Kasparova Petra , Rudolf Emil , Skarkova Veronika , Pohankova Denisa , Novotna Veronika , Petera Jiri
Published: May 2024

Background: Although several prognostic factors for survival have been identified in glioblastoma, there are numerous other potential markers (such as hemoglobin) whose role has not yet been confirmed. The aim of this study was to evaluate a wide range of potential prognostic factors, including HIF-1α and hemoglobin levels, for survival in glioblastoma. A secondary aim was to determine whether hemoglobin levels were associated with HIF-1α expression. Methods: A retrospective study of 136 patients treated for glioblastoma at our institution between 2012 and 2021 was performed. Cox univariate and multivariate analyses were carried out. Kaplan–Meier survival curves were generated. In addition, bivariate non-parametric correlation analyses were performed for key variables. Results: Median survival was 11.9 months (range: 0–119.4). According to the univariate analysis, 13 variables were significantly associated with survival: age, performance status, extent of surgery, tumor depth, tumor size, epilepsy, postoperative chemoradiotherapy, IDH mutations, CD44, HIF-1α, HIF-1β, vimentin, and PDFGR. According to the multivariate regression analysis, only four variables remained significantly associated with survival: age, extent of surgery, epilepsy, and HIF-1α expression. No significant association was observed between hemoglobin levels (low <120 g/L in females or <140 g/L in males vs. high ≥120 or ≥140 g/L) and survival or HIF-1α/HIF-1β expression. Conclusions: In this retrospective study of patients with glioblastoma, four variables—age, extent of surgery, HIF-1α expression, and epilepsy—were significant prognostic factors for survival. Hemoglobin levels were not significantly associated with survival or HIF-1α expression. Although hypoxia is a well-recognized component of the glioblastoma microenvironment, more research is needed to understand the pathogenesis of onset tumor hypoxia and treatment implication.
Unveiling Novel Structural Biomarkers for the Diagnosis of Glaucoma
Authors: Tsai Yu-Chien , Lee Hsin-Pei , Tsung Ta-Hsin , Chen Yi-Hao , Lu Da-Wen
Published: May 2024

Glaucoma, a leading cause of irreversible blindness, poses a significant global health burden. Early detection is crucial for effective management and prevention of vision loss. This study presents a collection of novel structural biomarkers in glaucoma diagnosis. By employing advanced imaging techniques and data analysis algorithms, we now can recognize indicators of glaucomatous progression. Many research studies have revealed a correlation between the structural changes in the eye or brain, particularly in the optic nerve head and retinal nerve fiber layer, and the progression of glaucoma. These biomarkers demonstrate value in distinguishing glaucomatous eyes from healthy ones, even in the early stages of the disease. By facilitating timely detection and monitoring, they hold the potential to mitigate vision impairment and improve patient outcomes. This study marks an advancement in the field of glaucoma, offering a promising avenue for enhancing the diagnosis and possible management.
Chronic Mexiletine Administration Increases Sodium Current in Non-Diseased Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes
Authors: Nasilli Giovanna , Verkerk Arie O. , O’Reilly Molly , Yiangou Loukia , Davis Richard P. , Casini Simona , Remme Carol Ann
Published: May 2024

A sodium current (INa) reduction occurs in the setting of many acquired and inherited conditions and is associated with cardiac conduction slowing and increased arrhythmia risks. The sodium channel blocker mexiletine has been shown to restore the trafficking of mutant sodium channels to the membrane. However, these studies were mostly performed in heterologous expression systems using high mexiletine concentrations. Moreover, the chronic effects on INa in a non-diseased cardiomyocyte environment remain unknown. In this paper, we investigated the chronic and acute effects of a therapeutic dose of mexiletine on INa and the action potential (AP) characteristics in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) of a healthy individual. Control hiPSC-CMs were incubated for 48 h with 10 µM mexiletine or vehicle. Following the wash-out of mexiletine, patch clamp analysis and immunocytochemistry experiments were performed. The incubation of hiPSC-CMs for 48 h with mexiletine (followed by wash-out) induced a significant increase in peak INa of ~75%, without any significant change in the voltage dependence of (in)activation. This was accompanied by a significant increase in AP upstroke velocity, without changes in other AP parameters. The immunocytochemistry experiments showed a significant increase in membrane Nav1.5 fluorescence following a 48 h incubation with mexiletine. The acute re-exposure of hiPSC-CMs to 10 µM mexiletine resulted in a small but significant increase in AP duration, without changes in AP upstroke velocity, peak INa density, or the INa voltage dependence of (in)activation. Importantly, the increase in the peak INa density and resulting AP upstroke velocity induced by chronic mexiletine incubation was not counteracted by the acute re-administration of the drug. In conclusion, the chronic administration of a clinically relevant concentration of mexiletine increases INa density in non-diseased hiPSC-CMs, likely by enhancing the membrane trafficking of sodium channels. Our findings identify mexiletine as a potential therapeutic strategy to enhance and/or restore INa and cardiac conduction.
Interleukin-1α as a Potential Prognostic Biomarker in Pancreatic Cancer
Authors: Gigante Leonardo , Gaudillière-Le Dain Gwladys , Bertaut Aurélie , Truntzer Caroline , Ghiringhelli François
Published: May 2024

Purpose: We assessed the prognostic role of pro-inflammatory cytokines of the IL-1 superfamily in patients with pancreatic cancer. Methods: This retrospective study was performed using two independent cohorts of patients with pancreatic cancer: the International Cancer Genome Consortium (ICGC, N = 267) cohort and The Cancer Genome Atlas (TCGA, N = 178) cohort. Univariate Cox regressions were used to identify prognosis-related pro-inflammatory cytokines of the IL-1 superfamily. Cytokines associated with outcome were included in a multivariate Cox model with relevant clinicopathological variables to identify prognostic biomarkers. Results: IL-1α was the only pro-inflammatory cytokine of the IL-1 superfamily that was significantly associated with prognosis in both cohorts. In the training cohort (ICGC), the decile of patients with the lowest IL1A expression had better overall survival (HR = 1.99 [1.01–3.93], p = 0.05) and better relapse-free survival (HR = 1.85 [1.02–3.34], p = 0.04) than the group with the highest IL1A expression. The validation cohort (TCGA) confirmed these results: the decile with the lowest IL1A expression had better overall survival (HR = 3.00 [1.14–7.90], p = 0.03) and a lower risk of progression (HR = 3.11 [1.24–7.80], p = 0.01). Conclusions: IL1A is an independent prognostic marker and could be considered a potential therapeutic target in pancreatic cancer patients.
Evolutionary Approach to Optimal Oil Skimmer Assignment for Oil Spill Response: A Case Study
Authors: Kim Yong-Hyuk , Kim Hye-Jin , Cho Dong-Hee , Yoon Yourim
Published: May 2024

We propose a genetic algorithm for optimizing oil skimmer assignments, introducing a tailored repair operation for constrained assignments. Methods essentially involve simulation-based evaluation to ensure adherence to South Korea’s regulations. Results show that the optimized assignments, compared to current ones, reduced work time on average and led to a significant reduction in total skimmer capacity. Additionally, we present a deep neural network-based surrogate model, greatly enhancing efficiency compared to simulation-based optimization. Addressing inefficiencies in mobilizing locations that store oil skimmers, further optimization aimed to minimize mobilized locations and was validated through scenario-based simulations resembling actual situations. Based on major oil spills in South Korea, this strategy significantly reduced work time and required locations. These findings demonstrate the effectiveness of the proposed genetic algorithm and mobilized location minimization strategy in enhancing oil spill response operations.
How Does Individual Psychotherapy Promote Recovery for Persons with Psychosis? A Systematic Review of Qualitative Studies to Understand the Patient’s Experience
Authors: Faith Laura A. , Hillis-Mascia Jaclyn D. , Wiesepape Courtney N.
Published: May 2024

Psychotherapy for individuals with psychosis is an effective treatment that promotes recovery in various ways. While there is strong quantitative evidence across modalities, less is known from the patient’s perspective. There are many varied forms of psychotherapy, and gaining the patient’s perspective can improve understanding of salient elements of psychotherapy and increase engagement, ultimately improving recovery rates. The purpose of this review is to identify and integrate data from published studies of patient perspectives of psychotherapy for psychosis to understand essential elements across approaches, differences between approaches, and how psychotherapy impacts recovery. We aimed to understand further: what are the perceptions about individual psychotherapy from the perspective of individuals with psychosis? The current study was a systematic review using PRISMA guidelines of studies that included qualitative interviews with persons with experiences of psychosis who participated in psychotherapy. All three authors participated in the literature search using Pubmed, APA PsycInfo, and Psychiatry Online. We identified N = 33 studies. Studies included cognitive therapies, acceptance and mindfulness approaches, trauma therapies, metacognitive therapy, and music therapy. All studies reported participants’ perceived benefit with the therapeutic relationship as especially salient. Participants described diverse aspects of objective (e.g., symptoms, functioning) and subjective (e.g., self-experience or quality of life) recovery improvements, with perceived mechanisms of change, and with music therapy having some unique benefits. Participants also reported challenges and suggestions for improvement. Study findings highlight the salient aspects of psychotherapy identified by patients that may help therapists to individualize and improve approaches to psychotherapy when working with individuals experiencing psychosis. Overall, findings support the potential for integrative psychotherapy approaches for maximal treatment personalization.
Architectural Physical Acoustic Environmental Design Transformation of Academic Lecture Halls in the Universities—Taking the Hall of School of Architecture and Urban Planning of Yunnan University as an Example
Authors: Yang Yao-Ning , Zhou Jie , Song Jing-Ran , Wang Xin-Ping , Xu Xiao-Huan , Li Yuan-Xi , Zeng Jun-Cheng , Sa Ying , Jiang Wei
Published: May 2024

In recent years, multi-functional lecture halls have developed rapidly and become a symbol of contemporary public spaces and places. This kind of spatial facility that brings together the advantages of land intensiveness and multi-functional integration also faces feedback such as poor acoustic effects. However, current research rarely involves the architectural design perspective, which is actually the root consideration of this problem; that is, how to set up corresponding spatial layout measures to optimize acoustic performance in a relatively economical and simple way. This study uses the academic lecture hall of the School of Architecture and Planning of Yunnan University as a case to try to solve these problems. The research is based on holistic considerations, starting from site selection, architectural design, aesthetic considerations, and environmental noise assessment, and combining simulation results with actual measurement results. Using a prediction–comparison–verification method, key acoustic parameters such as speech intelligibility, loudness, and reverberation time were calculated and evaluated to understand the acoustic design problems of the hall. The study found that the out-of-control reverberation time was the main cause of poor acoustic feedback, and based on this, optimization and transformation were carried out from an architectural perspective. Finally, a renovation suggestion was made that the application of sound-absorbing materials on the rear wall can achieve better acoustic effects inside the hall. Among the space combination methods, the combination of “rear wall, central ceiling, and front ceiling” has the best effect. Practical insights are provided for improving the acoustic performance of the multi-functional lecture halls while taking into account the acoustic design and feasible requirements.
Promotion Effect of H2S at High Concentrations on Catalytic Dry Reforming of Methane in Sour Natural Gas
Authors: Ni Hengchang , Jia Xiaoyu , Yu Li , Li Yuyang , Li Ping
Published: May 2024

The effect of high concentrations of H2S in sour natural gas on the catalytic dry reforming of methane (DRM) process has seldom been studied previously in the literature. Herein, several types of catalysts, including MgO, NiO/MgO, and LaNiO3 in different states, were prepared for conducting DRM at 800 °C and 0.1 MPa in a feed of 20 vol% CO2 and 20 vol% CH4, and their catalytic performance under conditions of the absence and presence of H2S was compared. A promotion effect of increasing H2S concentration on both the conversions of CO2 and CH4 and the molar yields of CO and H2 was observed on all the catalysts and was particularly remarkable on the MgO and the pristine NiO/MgO. For NiO/MgO, the addition of 15 vol% H2S increased the conversion of CH4 from 6.92% to 26.86% and CO2 from 9.15% to 42.10%. While there was a significant decline in the catalytic activity of the reduced NiO/MgO and LaNiO3 catalysts after adding H2S, moderate reactant conversions were still sustained. The results of process analysis and catalyst structure characterization suggest that H2S participation can contribute to the increment in CO2 and CH4 conversion, and active S-adsorbed species may play the key role of catalysis in reactions involving H2S.
Nano-Magnetic Sugarcane Bagasse Cellulosic Composite as a Sustainable Photocatalyst for Textile Industrial Effluent Remediation
Authors: Tony Maha , El-Gendy Nour , Hussien Mohamed , Ahmed Abdullah , Xin Jiayu , Lu Xingmei , El-Sayed Ibrahim
Published: May 2024

Researchers have focused on deriving environmentally benign materials from biomass waste and converting them into value-added materials. In this study, cellulosic crystals derived from sugarcane bagasse (SCB) are augmented with magnetite (M) nanoparticles. Following the co-precipitation route, the composite was prepared, and then the mixture was subjected to a green microwave solvent-less technique. Various mass ratios of SCB:M (1:1, 2:1, 3:1, 5:1, and 1:2) were prepared and efficiently utilized as photocatalysts. To look at the structural and morphological properties of the prepared samples, X-ray diffraction pattern (XRD), scanning electron microscopy (SEM), and elemental analysis were used to describe the composite fibers. SCB:M augmented with H2O2 as a Fenton reaction was used to eliminate Reactive blue 19 (RB19) from polluted water and was compared with pristine SCB and M. Additionally, the response surface methodology (RSM) statistically located and assessed the optimized parameters. The optimal operating conditions were recorded at pH 2.0 and 3:1 SCB: M with 40 mg/L and 100 mg/L of hydrogen peroxide. However, the temperature increase inhibits the oxidation reaction. The kinetic modeling fit showed the reaction following the second-order kinetic model with an energy barrier of 98.66 kJ/mol. The results show that such photocatalyst behavior is a promising candidate for treating textile effluent in practical applications.
Performance of Ni-Based Catalysts with La Promoter for the Reforming of Methane in Gasification Process
Authors: Chen Meng , Wang Lei
Published: May 2024

The deactivation of active sites caused by high-temperature sintering and the deposition of a large amount of carbon are the main difficulties in the reforming of methane using Ni-based catalysts. La, as a promoter, has an ameliorating effect on the defects of Ni-based catalysts. In this article, the mechanism of action of Ni-based catalysts with the introduction of the rare-earth metal additive La was reviewed, and the effects of La on the methane-reforming performance of Ni-based catalysts were examined. The physical properties, alkalinity, and activity of Ni-based catalysts can be enhanced by the use of the auxiliary agent La, which promotes the conversion of CH4 and CO2 as well as the selectivity towards H2 and CO formation in the reforming of methane. The reason why the Ni-based catalysts could maintain long-term stability in the presence of La was discussed. Furthermore, the current state of research on the introduction of different amounts of La in the reforming of methane at home and abroad was analyzed. It was found that 2–5 wt.% La is the most optimal quantity for improving the catalyst activity and stability, as well as the CO2 chemisorption. The limitations and directions for future research in the reforming of methane were discussed.
Development of a Subtraction Processing Technology for Assistance in the Comparative Interpretation of Mammograms
Authors: Kai Chiharu , Kondo Satoshi , Otsuka Tsunehiro , Yoshida Akifumi , Sato Ikumi , Futamura Hitoshi , Kodama Naoki , Kasai Satoshi
Published: May 2024

A comparative interpretation of mammograms has become increasingly important, and it is crucial to develop subtraction processing and registration methods for mammograms. However, nonrigid image registration has seldom been applied to subjects constructed with soft tissue only, such as mammograms. We examined whether subtraction processing for the comparative interpretation of mammograms can be performed using nonrigid image registration. As a preliminary study, we evaluated the results of subtraction processing by applying nonrigid image registration to normal mammograms, assuming a comparative interpretation between the left and right breasts. Mediolateral-oblique-view mammograms were taken from noncancer patients and divided into 1000 cases for training, 100 cases for validation, and 500 cases for testing. Nonrigid image registration was applied to align the horizontally flipped left-breast mammogram with the right one. We compared the sum of absolute differences (SAD) of the difference of bilateral images (Difference Image) with and without the application of nonrigid image registration. Statistically, the average SAD was significantly lower with the application of nonrigid image registration than without it (without: 0.0692; with: 0.0549 (p < 0.001)). In four subgroups using the breast area, breast density, compressed breast thickness, and Difference Image without nonrigid image registration, the average SAD of the Difference Image was also significantly lower with nonrigid image registration than without it (p < 0.001). Nonrigid image registration was found to be sufficiently useful in aligning bilateral mammograms, and it is expected to be an important tool in the development of a support system for the comparative interpretation of mammograms.
Gender-Specific Differences in Spinal Alignment and Muscle Power in Patients with Parkinson’s Disease
Authors: Bissolotti Luciano , Rota Matteo , Calza Stefano , Romero-Morales Carlos , Alonso-Pérez José Luís , López-Bueno Rubén , Villafañe Jorge Hugo
Published: May 2024

Background: Parkinson’s disease (PD) is an advancing neurodegenerative disorder characterized by spinal anomalies and muscular weakness, which may restrict daily functional capacities. A gender-focused examination of these effects could provide valuable insights into customized rehabilitation strategies for both sexes. Purpose: This study investigates the influence of spinal alignment on lower-limb function during the sit-to-stand (STS) movement in patients with Parkinson’s disease compared to healthy individuals. Methods: A cross-sectional study was conducted with 43 consecutive patients with PD (25 males and 18 females; average age 73.7 ± 7.1 years) and 42 healthy controls (22 males and 20 females; average age 69.8 ± 6.0 years). Assessments included the International Physical Activity Questionnaire (IPAQ), Hoehn and Yahr staging, and measurements of vertical deviations from several spinal landmarks. Lower-limb muscle power during the STS task was evaluated using the Muscle Quality Index (MQI). Results: Both absolute (Watts) and relative (Watts/Kg) muscle power in the lower limbs were notably decreased in the PD group compared to the control group. Within the PD cohort, muscle power showed a negative relationship with age and a positive association with the degree of lumbar lordosis (PL-L3). Importantly, gender-specific analysis revealed that male patients with PD had significantly higher lower-limb muscle power compared to female patients with PD, highlighting the need for gender-tailored therapeutic approaches. Conclusions: The findings suggest that preserving lumbar lordosis is crucial for maintaining effective lower-limb muscle biomechanics in individuals with Parkinson’s disease.
CooPercept: Cooperative Perception for 3D Object Detection of Autonomous Vehicles
Authors: Zhang Yuxuan , Chen Bing , Qin Jie , Hu Feng , Hao Jie
Published: May 2024

Autonomous vehicles rely extensively on onboard sensors to perceive their surrounding environments for motion planning and vehicle control. Despite recent advancements, prevalent perception algorithms typically utilize data acquired from the single host vehicle, which can lead to challenges such as sensor data sparsity, field-of-view limitations, and occlusion. To address these issues and enhance the perception capabilities of autonomous driving systems, we explore the concept of multi-vehicle multimedia cooperative perception by investigating the fusion of LiDAR point clouds and camera images from multiple interconnected vehicles with different positions and viewing angles. Specifically, we introduce a semantic point cloud feature-level cooperative perception framework, termed CooPercept, designed to mitigate computing complexity and reduce turnaround time. This is crucial, as the volume of raw sensor data traffic generally far exceeds the bandwidth of existing vehicular networks. Our approach is validated through experiments conducted on synthetic datasets from KITTI and OPV2V. The results demonstrate that our proposed CooPercept model surpasses comparable perception models, achieving enhanced detection accuracy and greater detection robustness.
Enhancing Active Learning through a Holistic Approach: A Case Study of Primary Education in Lithuania
Authors: Miseliūnaitė Brigita , Cibulskas Gintautas
Published: May 2024

The renewed Lithuanian General Framework for Primary Education emphasises an integrated and holistic approach, advocating for active, experiential learning methods without compartmentalisation (2022). This study aims to determine which active, holistic educational methods are prevalent in the Lithuanian primary education system. In holistic education theory, active learning methodologies are divided into transactional and transformational strands. To accomplish the research goal, we carried out a two-phase quantitative study. This study involved 365 Lithuanian primary school teachers and seven public primary schools. The researcher observed 30 lessons conducted by different teachers. The collected data underwent quantitative analysis and qualitative interpretation. The results indicate that transaction-oriented active learning methods, such as group work, discussion, and play-based learning, are standard in Lithuanian primary education settings. The study identifies a lack of transformative educational approaches at the primary level.
Inquiry vs. Inquiry-Creative: Emphasizing Critical Thinking Skills of Prospective STEM Teachers in the Context of STEM Learning in Indonesia
Authors: Prayogi Saiful , Bilad Muhammad Roil , Verawati Ni Nyoman Sri Putu , Asy’ari Muhammad
Published: May 2024

In an evolving perspective, lecturers consider that inquiry is one of the best forms of learning to drill critical thinking. This study assesses the practice of inquiry to develop the critical thinking skills of prospective science, technology, engineering, and mathematics (STEM) teachers in Indonesia, which is a suitable way to address the problems in the country. Through the experimental design, three groups were formed, which were intervened with inquiry learning, inquiry-creative, and traditional teaching. The learning intervention was carried out within one month using a pre-validated instrument. The critical thinking data were analyzed descriptively based on the pre-test and post-test mean parameters and n-gain on critical thinking indicators, as well as individual critical thinking performance. Statistical analyses (paired-t test, ANOVA, and least significant difference test) were employed to provide confidence in the differences in critical thinking skills across the three learning treatments (p < 0.05). The prospective STEM teachers’ critical thinking skills showed varied performances among the three groups. The inquiry-creative group had the strongest impact, followed by inquiry and traditional teaching, all differing significantly. In summary, the findings suggest that current teaching practices in STEM education need to be reconsidered, showing the advantage of the inquiry-creative model in developing the critical thinking skills essential for future teachers and creators in the STEM fields.
Vulnerability Assessment and Topology Reconstruction of Task Chains in UAV Networks
Authors: Yue Qingfeng , Li Jinglei , Huang Zijia , Xie Xiaoyu , Yang Qinghai
Published: May 2024

With the increasing complexity of environments and the diversity of task chains, individual unmanned aerial vehicles (UAVs) often struggle to satisfy the demands of task chains, including load capacity improvement, information perception, and information procession. In complex task chains involving various UAVs, such as area reconnaissance and fire rescue, any attack on critical UAVs can greatly disrupt the execution of the entire task chain by causing equipment damage or connectivity disruption. To ensure network resilience post attack, identifying vulnerable nodes in the UAV network becomes crucial. In this paper, a Vulnerability-based Topology Reconstruction Mechanism (VUTRM) is proposed to rank the importance of nodes in task chains and formulate a topology reconstruction. It consists of two parts: the first part is a Multi-metric Node Vulnerability Assessment Algorithm (MENVAL) used to rank the importance of nodes in task chains, and the second part is a Node Importance-based Topology Reconstruction Algorithm (NITRA) used to reconstruct the UAV network with the obtained node ranking. Finally, simulations carried out with simulation software demonstrate that our proposed method accurately identifies network vulnerabilities and promptly implements effective reconstruction measures to minimize network damage.
Exploring Neighbor Spatial Relationships for Enhanced Lumbar Vertebrae Detection in X-ray Images
Authors: Zeng Yu , Wang Kun , Dai Lai , Wang Changqing , Xiong Chi , Xiao Peng , Cai Bin , Zhang Qiang , Sun Zhiyong , Cheng Erkang , Song Bo
Published: May 2024

Accurately detecting spine vertebrae plays a crucial role in successful orthopedic surgery. However, identifying and classifying lumbar vertebrae from arbitrary spine X-ray images remains challenging due to their similar appearance and varying sizes among individuals. In this paper, we propose a novel approach to enhance vertebrae detection accuracy by leveraging both global and local spatial relationships between neighboring vertebrae. Our method incorporates a two-stage detector architecture that captures global contextual information using an intermediate heatmap from the first stage. Additionally, we introduce a detection head in the second stage to capture local spatial information, enabling each vertebra to learn neighboring spatial details, visibility, and relative offset. During inference, we employ a fusion strategy that combines spatial offsets of neighboring vertebrae and heatmap from a conventional detection head. This enables the model to better understand relationships and dependencies between neighboring vertebrae. Furthermore, we introduce a new representation of object centers that emphasizes critical regions and strengthens the spatial priors of human spine vertebrae, resulting in an improved detection accuracy. We evaluate our method using two lumbar spine image datasets and achieve promising detection performance. Compared to the baseline, our algorithm achieves a significant improvement of 13.6% AP in the CM dataset and surpasses 6.5% and 4.8% AP in the anterior and lateral views of the BUU dataset, respectively.
Research Progress on the Fire Characteristics of Electric Cables and Wires
Authors: Yu Feiyang , Wang Shijie , Tang Kaixuan , Lin Yifan , Wang Shasha , Zhang Ying
Published: May 2024

With the development of the social economy and the improvement of electrification, cables and wires play an important role in people’s lives and industrial development. Meanwhile, the large-scale laying of cables has also made them a fire hazard that cannot be ignored in land construction such as residential buildings, utility tunnels, nuclear power plants, refineries, marine systems such as submarines and ships, and airborne systems such as spacecrafts and aircrafts. In this work, studies on fire the characteristics of cables and wires over the last decades have been reviewed. Based on different experimental forms and objects (laboratory wires and commercial cables), this paper summarizes the theories of the fire dynamics in wire combustion, including the models of ignition and flame propagation, the criteria for blowing off and quenching, and the critical conditions for dripping behavior. The effects of materials, layouts, and environments on wire combustion phenomena such as airflow, ambient pressure, oxygen, gravity, and orientation angle have been discussed in detail according to the theories of heat transfer and combustion. In addition, test standards and studies on the fire behavior and release of toxic gases of commercial cables have also been fully described. Through the summary of the above content, it is expected to build a preliminary theoretical framework and future research directions for researchers in the field of cable fires.
Measuring Turbulent Flows: Analyzing a Stochastic Process with Stochastic Tools
Authors: Rozos Evangelos , Wieland Jörg , Leandro Jorge
Published: May 2024

Assessing drag force and Reynolds stresses in turbulent flows is crucial for evaluating the stability and longevity of hydraulic structures. Yet, this task is challenging due to the complex nature of turbulent flows. To address this, physical models are often employed. Nonetheless, this practice is associated with difficulties, especially in the case of high sampling frequency where the inherent randomness of velocity fluctuations becomes mixed with the measurement noise. This study introduces a stochastic approach, which aims to mitigate bias from measurement errors and provide a probabilistic estimate of extreme stress values. To accomplish this, a simple experimental setup with a hydraulic jump was employed to acquire long-duration velocity measurements. Subsequently, a modified first-order autoregressive model was applied through ensemble simulations, demonstrating the benefits of the stochastic approach. The analysis highlights its effectiveness in estimating the uncertainty of extreme events frequency and minimizing the bias induced by the noise in the high-magnitude velocity measurements and by the limited length of observations. These findings contribute to advancing our understanding of turbulent flow analysis and have implications for the design and assessment of hydraulic structures.
Hartmann Flow of Two-Layered Fluids in Horizontal and Inclined Channels
Authors: Parfenov Arseniy , Gelfgat Alexander , Ullmann Amos , Brauner Neima
Published: May 2024

The effect of a transverse magnetic field on two-phase stratified flow in horizontal and inclined channels is studied. The lower heavier phase is assumed to be an electrical conductor (e.g., liquid metal), while the upper lighter phase is fully dielectric (e.g., gas). The flow is defined by prescribed flow rates in each phase, so the unknown frictional pressure gradient and location of the interface separating the phases (holdup) are found as part of the whole solution. It is shown that the solution of such a two-phase Hartmann flow is determined by four dimensionless parameters: the phases’ viscosity and flow-rate ratios, the inclination parameter, and the Hartmann number. The changes in velocity profiles, holdups, and pressure gradients with variations in the magnetic field and the phases’ flow-rate ratio are reported. The potential lubrication effect of the gas layer and pumping power reduction are found to be limited to low magnetic field strength. The effect of the magnetic field strength on the possibility of obtaining countercurrent flow and multiple flow states in concurrent upward and downward flows, and the associated flow characteristics, such as velocity profiles, back-flow phenomena, and pressure gradient, are explored. It is shown that increasing the magnetic field strength reduces the flow-rate range for which multiple solutions are obtained in concurrent flows and the flow-rate range where countercurrent flow is feasible.
A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing
Authors: Han Zhang , Ahmad Waqas , Rong Yanna , Chen Xuanyu , Zhao Songguang , Yu Jinghao , Zheng Pengfei , Huang Chunchi , Li Huanhuan
Published: May 2024

The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3–4–1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process.