bluuebunny/crossref_metadata_2025 huggingface dataset:
Two-Dimensional Electron Gas in Thin N-Polar GaN Channels on AlN on Sapphire Templates
Authors: Pristovsek Markus , Furuhashi Itsuki , Yang Xu , Zhang Chengzhi , Smith Matthew D.
Published: September 2024

We report on 2-dimensional electron gases realized in binary N-polar GaN channels on AlN on sapphire templates grown by metal–organic vapor phase epitaxy. The measured sheet carrier density of 3.8×1013 cm−2 is very close to the theoretical value of 3.95×1013 cm−2 due to the low carbon and oxygen background doping in the N-polar GaN if grown with triethyl-gallium. By inserting an intermediate AlN transition layer, room temperature mobilities in 5 nm channels up to 100 cm2/Vs were realized, probably limited by dislocations and oxygen background in N-polar AlN. Thicker channels of 8 nm or more showed relaxation and thus much lower mobilities.
Multiaxial Fatigue Lifetime Estimation Based on New Equivalent Strain Energy Damage Model under Variable Amplitude Loading
Authors: Tao Zhi-Qiang , Pan Xiangnan , Zhang Zi-Ling , Chen Hong , Li Li-Xia
Published: September 2024

The largest normal stress excursion during contiguous turn time instants of the maximum torsional stress is presented as an innovative path-independent fatigue damage quantity upon the critical plane, which is further employed for characterizing fatigue damage under multiaxial loading. Via using the von Mises equivalent stress formula, an axial stress amplitude with equivalent value is proposed, incorporating the largest torsional stress range and largest normal stress excursion upon the critical plane. The influence of non-proportional cyclic hardening is considered within the presented axial equivalent stress range. Moreover, according to proposed axial equivalent stress amplitude, an energy-based damage model is presented to estimate multiaxial fatigue lifetime upon the critical plane. In order to verify the availability of the proposed approach, the empirical results of a 7050-T7451 aluminum alloy and En15R steel are used, and the predictions indicated that estimated fatigue lives correlate with the experimentally observed fatigue results well for variable amplitude multiaxial loadings.
Glucose and Lipid Metabolism Disorders in Adults with Spinal Muscular Atrophy Type 3
Authors: Miletić Marija , Stević Zorica , Vujović Svetlana , Rakočević Jelena , Tomić Ana , Tančić Gajić Milina , Stojanović Miloš , Palibrk Aleksa , Žarković Miloš
Published: September 2024

Background: Spinal muscular atrophy type 3 (juvenile SMA, Kugelberg–Welander disease) is a genetic disease caused by changes in the survival motor neuron 1 (SMN) gene. However, there is increasing evidence of metabolic abnormalities in SMA patients, such as altered fatty acid metabolism, impaired glucose tolerance, and defects in the functioning of muscle mitochondria. Given that data in the literature are scarce regarding this subject, the purpose of this study was to estimate the prevalence of glucose and lipid metabolism disorders in adult patients with SMA type 3. Methods: We conducted a cross-sectional study of 23 adult patients with SMA type 3 who underwent a comprehensive evaluation, including a physical examination, biochemical analysis, and an oral glucose tolerance test during 2020–2023. Results: At least one lipid abnormality was observed in 60.8% of patients. All four lipid parameters were atypical in 4.3% of patients, three lipid parameters were abnormal in 21.7% of patients, and two lipid parameters were altered in 8.7% patients. A total of 91.3% of SMA3 patients met the HOMA-IR criteria for insulin resistance, with 30.43% having impaired glucose tolerance. None of the patients met the criteria for a diagnosis of overt DM2. Conclusions: The prevalence of dyslipidemia and altered glucose metabolism in our study sets apart the adult population with SMA3 from the general population, confirming a significant interplay between muscle, liver, and adipose tissue. Ensuring metabolic care for aging patients with SMA 3 is crucial, as they are vulnerable to metabolic derangements and cardiovascular risks.
Influence of Right Atrial Pressure on the Prognosis of Patients with Rheumatic Mitral Stenosis Undergoing Percutaneous Mitral Balloon Valvuloplasty
Authors: Nazzetta Daniella Cian , de Sousa Larissa Christine Gomes , Rosa Vitor Emer Egypto , Tessari Fernanda Castiglioni , Campos Carlos M. , Lopes Maria Antonieta Albanez Medeiros , Jardim Carlos Viana Poyares , Mapa Luís Gustavo , Lipari Layara Fernanda Vicente Pereira , Lopes Mariana Pezzute , Fernandes João Ricardo Cordeiro , de Santis Antonio , Pires Lucas José Neves Tachotti , Sampaio Roney Orismar , Tarasoutchi Flávio
Published: September 2024

Background: Pulmonary hypertension (PH) often complicates mitral stenosis (MS). The prognostic impact of pulmonary vascular resistance (PVR) in MS patients remains unclear. Previous study has demonstrated the prognostic impact of right atrial pressure (RAP) in patients with primary PH. We aim to determine the prognostic impact of PVR and RAP in patients with rheumatic MS undergoing percutaneous mitral balloon valvuloplasty (PMBV). Methods: A total of 58 patients with symptomatic severe rheumatic MS who underwent PMBV between 2016 and 2020 were included. Patients were divided into two groups: PVR ≤ 2WU (N = 26) and PVR > 2WU (N = 32). The composite endpoint included death, reintervention or persistent NYHA functional class III-IV during follow-up. Results: The median age was 50 (42–60) years, with 82.8% being female. Median pulmonary artery systolic pressure (PASP) was 42 (35–50.5) mmHg. Patients with PVR ≤ 2WU had lower PASP on both echocardiogram and catheterization. The PMBV success rate was 75.9%. Multivariate analysis, adjusted for PVR, showed RAP as the only independent predictor of the composite endpoint (HR:1.507, 95% CI:1.015–2.237, p = 0.042). The optimal RAP cutoff was 9.5 mmHg (HR:3.481, 95% CI:1.041–11.641; p = 0.043). Conclusions: RAP was an independent predictor of adverse outcomes in patients with rheumatic MS undergoing PMBV, while PVR did not show prognostic significance. These findings suggest that the prognostic value of PVR may be lower than expected.
Distal Transradial Access Optimization: A Prospective Trial of Ultrasound-Guided Radial Artery Characterization for the Anatomical Snuffbox
Authors: Koziński Łukasz , Orzałkiewicz Zbigniew , Zagożdżon Paweł , Dąbrowska-Kugacka Alicja
Published: September 2024

Background/Objectives: The distal transradial approach (dTRA) is increasingly used in interventional cardiology. Doppler Ultrasound (DUS) effectively assesses radial artery (RA) characteristics. This study aims to identify specific RA DUS characteristics in patients undergoing coronary procedures via dTRA. Methods: Participants from the ANTARES trial who completed the intervention per-protocol and retained RA patency were included. DUS was performed at baseline, 1 day, and 60 days post-procedure. Results: Among 400 participants, 348 had either dTRA (n = 169) or conventional transradial access (cTRA) (n = 179). Distal RA lumen diameter was 12% smaller than that of the proximal RA (p < 0.001). Men had a 14% larger distal RA diameter than women (2.33 ± 0.31 mm vs. 2.04 ± 0.27 mm, p < 0.0001), similar to the proximal RA relationship. Peak flow velocities were similar between the sexes. Univariate linear regression showed that height, weight, body mass index, and body surface area (BSA) predicted arterial size, with BSA remaining significant in multivariate analysis (beta coefficient 0.62; confidence interval 0.49–0.75; p < 0.0001). Distal RA diameter correlated positively with palpable pulse at the snuffbox and wrist. The dTRA resulted in an immediate 14% and 11% increase in distal and proximal RA diameter, respectively (both p < 0.05). Sixty days after dTRA, the distal RA remained slightly dilated (p < 0.05), while the proximal RA returned to baseline. Conclusions: Distal RA diameter is significantly associated with sex, measuring smaller than the forearm segment. A strong palpable pulse correlates with larger distal RA size. The dTRA induces RA lumen expansion. A thorough understanding of distal RA anatomy is essential for optimizing patient selection and refining techniques for transradial procedures.
“Lazarus Response” When Feto-Maternal Microchimerism Kicks in: Spontaneous Remission in Refractory Primary Mediastinal B Cell Lymphoma Following Twin Pregnancy
Authors: Tomai Radu Andrei , Iluta Sabina , Tigu Adrian Bogdan , Nistor Madalina , Bancos Anamaria , Cenariu Diana , Jitaru Ciprian , Patcas Sergiu , Dima Delia , Kegyes David , Buruiana Sanda , Zdrenghea Mihnea , Tanase Alina Daniela , Tomuleasa Ciprian , Micu Romeo
Published: September 2024

Background: Spontaneous remission of cancer is a rare and poorly understood phenomenon characterized by complete or partial remission of a malignancy in the absence of or with inadequate treatment. The underlying mechanism for such occurrences is poorly understood, however, immune mechanisms seem to play an important role in such cases. In recent years increasingly more data have become available in favor of the clinical benefit of low levels of chimerism in hematologic malignancies. One such instance of naturally occurring low-level chimerism is feto-maternal microchimerism which has been shown to influence cancer progression and, in some instances, to be a protective factor against malignancy. Case report: We report a case of a young female patient with aggressive primary mediastinal large B cell lymphoma refractory to two lines of chemo-immunotherapy achieving sustained complete metabolic remission of tumor while pregnant with twins. Results: A focus on feto-maternal microchimerism during and after pregnancy revealed transient levels of feto-maternal microchimerism in the peripheral blood of the patient as measured by quantifying the Y-chromosome-linked SRY gene. Conclusions: Microchimerism presents significant potential for enhancing our comprehension of disease mechanisms, uncovering novel therapeutic targets, and refining diagnostic and treatment approaches, especially concerning cancer.
Osteosarcoma Metastasis to the Thorax: A Pictorial Review of Chest Computed Tomography Findings
Authors: Alduraibi Khalid Abdulaziz , Towhari Jawaher Ali , Alebdi Hatim Abdullah , Alfadhel Bader Zaid , Alotaibi Ghazi S. , Ghosh Subha , Bin Saeedan Mnahi
Published: September 2024

Background: Osteosarcoma, a primary bone malignancy in children and adolescents, frequently metastasizes to the lungs, contributing significantly to morbidity and mortality. Lung Metastases: At diagnosis, 15–20% of patients present with detectable lung metastases. Chest computed tomography (CT) is vital for the early detection and monitoring of these metastases. Lung involvement typically presents as multiple nodules of varying sizes and can include atypical features such as cavitation, cystic lesions, ground-glass halos, intravascular tumor thrombi, and endobronchial disease. Additional Findings: Pleural metastasis often occurs alongside pulmonary disease, and complications like spontaneous pneumothorax may arise. Additional findings may include thoracic lymphadenopathy, cardiac tumor thrombus, and chest wall deposits. Conclusion: Familiarity with these imaging patterns is essential for radiologists to ensure timely diagnosis and effective management. This review highlights the critical role of chest CT in detecting and characterizing osteosarcoma metastasis.
Endoscopic Ultrasound and Intraductal Ultrasound in the Diagnosis of Biliary Tract Diseases: A Narrative Review
Authors: Nakahata Akiya , Yamashita Yasunobu , Kitano Masayuki
Published: September 2024

Endoscopic ultrasound (EUS) and intraductal ultrasound (IDUS) play very important roles in the field of biliary tract disease. Because of their excellent spatial resolution, the detection of small lesions and T- or N-staging of tumors have become possible. Additionally, contrast-enhanced EUS and the new imaging technique of detective flow imaging are reported to be useful for differential diagnosis. Furthermore, EUS-guided tissue acquisition is used not only for pathological diagnosis but also to collect tissue samples for cancer genome profiling. This review provides an overview of diagnosis utilizing the features and techniques of EUS and IDUS.
Performance Analysis and Conceptual Design of Lightweight UAV for Urban Air Mobility
Authors: Mazzeo Francesco , de Angelis Emanuele L. , Giulietti Fabrizio , Talamelli Alessandro , Leali Francesco
Published: September 2024

In the present study, a performance analysis of three different VTOL configurations is presented within an urban air mobility context. A classical lightweight helicopter was employed as a reference configuration to design a dual-rotor side-by-side helicopter and a hexacopter drone layout. An analytical model based on general momentum and blade element theories was developed for single- and multiple-rotor configurations in horizontal and vertical flight conditions. Suitable battery pack and electric motor designs were produced to evaluate the endurance and range of the different configurations for a specific mission. This paper provides fundamental insights into the endurance and range capabilities of multiple-rotor unmanned aerial vehicles (UAVs) and a qualitative discussion on the safety and acceptability features of each configuration implemented in an advanced air mobility context. As a result, the side-by-side helicopter configuration was identified as the best solution to be introduced within urban environments, fulfilling all the performance and mission requirements.
Inclusive Education Virtual Professional Development: School-Based Professionals’ Knowledge of Best Practices
Authors: Montalbano Cristin , Lang Julie , Coviello James C. , McQueston Jessica A. , Hogan Joseph A. , Nissley-Tsiopinis Jenelle , Ciotoli Francesca , Buglione Fred
Published: September 2024

This study investigated the effectiveness of a five-session virtual professional development program designed to increase the knowledge of inclusive education practices among school-based professionals from 26 schools on a topic of their choice. Participants, including administrators, general and special education teachers, child study team members (i.e., school psychologists, school counselors, and social workers), and others, demonstrated statistically significant increases in content knowledge on Accommodations and Modifications, Universal Design for Learning (UDL), Co-teaching, and Differentiation, as measured by pre- and post-assessments. Utilizing multilevel modeling and dependent samples t-tests, the results confirm the usefulness of virtual professional development in building knowledge of inclusive education practices. The findings provide empirical support for virtual training and offer insights into best practices for delivering professional development in inclusive education, suggesting future research should investigate the long-term impacts on classroom practices and student outcomes.
Measuring Anxiety in Autistic Children: Assessing the Validity of the Anxiety Scale for Children with Autism Spectrum Disorder
Authors: Goulding Keira , Campbell Linda , Freeman Emily
Published: September 2024

The present study assessed the validity of one of the first autism-specific anxiety measures, the Anxiety Scale for Children with Autism Spectrum Disorder (ASC-ASD), and compared its ability to predict parent-reported clinical anxiety to a ‘traditional’ anxiety measure, the Spence Children’s Anxiety Scale (SCAS). Whether the inclusion of the child form for each measure improved the predictive ability of the parent forms was also examined. Eighty-seven parents of autistic children, aged 8–12 years, completed the ASC-ASD, the SCAS, and the Social Communication Questionnaire (SCQ), a screener for autism characteristics. Of these parents, 56 had their child complete the ASC-ASD and SCAS. The children with a reported anxiety diagnosis were rated significantly higher by their parents on both the SCAS and the ASC-ASD compared to the non-anxious children. Pearson’s correlation coefficients indicated that the ASC-ASD had good divergent and convergent validity, as demonstrated by a poor, non-significant correlation with the SCQ and a strong, significant correlation with the SCAS. Regression analyses indicated that while the ASC-ASD was a significant predictor of parent-reported clinical anxiety in autistic children, the SCAS was not. Neither model was improved with the inclusion of the respective child form. This study is the first to demonstrate the ability of the ASC-ASD to predict child clinical anxiety disorder status and adds to the growing body of evidence for the validity of this measure. The findings also suggest that parent reports of anxiety may be sufficient to identify autistic children warranting further clinical investigation of anxiety in this age group.
A Hybrid Approach to Dimensional Aspect-Based Sentiment Analysis Using BERT and Large Language Models
Authors: Zhang Yice , Xu Hongling , Zhang Delong , Xu Ruifeng
Published: September 2024

Dimensional aspect-based sentiment analysis (dimABSA) aims to recognize aspect-level quadruples from reviews, offering a fine-grained sentiment description for user opinions. A quadruple consists of aspect, category, opinion, and sentiment intensity, which is represented using continuous real-valued scores in the valence–arousal dimensions. To address this task, we propose a hybrid approach that integrates the BERT model with a large language model (LLM). Firstly, we develop both the BERT-based and LLM-based methods for dimABSA. The BERT-based method employs a pipeline approach, while the LLM-based method transforms the dimABSA task into a text generation task. Secondly, we evaluate their performance in entity extraction, relation classification, and intensity prediction to determine their advantages. Finally, we devise a hybrid approach to fully utilize their advantages across different scenarios. Experiments demonstrate that the hybrid approach outperforms BERT-based and LLM-based methods, achieving state-of-the-art performance with an F1-score of 41.7% on the quadruple extraction.
AbFTNet: An Efficient Transformer Network with Alignment before Fusion for Multimodal Automatic Modulation Recognition
Authors: Ning Meng , Zhou Fan , Wang Wei , Wang Shaoqiang , Zhang Peiying , Wang Jian
Published: September 2024

Multimodal automatic modulation recognition (MAMR) has emerged as a prominent research area. The effective fusion of features from different modalities is crucial for MAMR tasks. An effective multimodal fusion mechanism should maximize the extraction and integration of complementary information. Recently, fusion methods based on cross-modal attention have shown high performance. However, they overlook the differences in information intensity between different modalities, suffering from quadratic complexity. To this end, we propose an efficient Alignment before Fusion Transformer Network (AbFTNet) based on an in-phase quadrature (I/Q) and Fractional Fourier Transform (FRFT). Specifically, we first align and correlate the feature representations of different single modalities to achieve mutual information maximization. The single modality feature representations are obtained using the self-attention mechanism of the Transformer. Then, we design an efficient cross-modal aggregation promoting (CAP) module. By designing the aggregation center, we integrate two modalities to achieve the adaptive complementary learning of modal features. This operation bridges the gap in information intensity between different modalities, enabling fair interaction. To verify the effectiveness of the proposed methods, we conduct experiments on the RML2016.10a dataset. The experimental results show that multimodal fusion features significantly outperform single-modal features in classification accuracy across different signal-to-noise ratios (SNRs). Compared to other methods, AbFTNet achieves an average accuracy of 64.59%, with a 1.36% improvement over the TLDNN method, reaching the state of the art.
Improving YOLOv7 for Large Target Classroom Behavior Recognition of Teachers in Smart Classroom Scenarios
Authors: Ma Long , Zhou Tao , Yu Baohua , Li Zhigang , Fang Rencheng , Liu Xinqi
Published: September 2024

Deep learning technology has recently become increasingly prevalent in the field of education due to the rapid growth of artificial intelligence. Teachers’ teaching behavior is a crucial component of classroom teaching activities, and identifying and examining teachers’ classroom teaching behavior is an important way to assess teaching. However, the traditional teaching evaluation method involves evaluating by either listening to the class on-site or playing back the teaching video afterward, which is a time-consuming and inefficient manual method. Therefore, this paper obtained teaching behavior data from a real smart classroom scenario and observed and analyzed the teacher behavior characteristics in this scenario. Aiming at the problems of complex classroom environments and the high similarity between teaching behavior classes, a method to improve YOLOv7 for large target classroom behavior recognition in smart classroom scenarios is proposed. First, we constructed the Teacher Classroom Behavior Data Set (TCBDS), which contains 6660 images covering six types of teaching behaviors: facing the board (to_blackboard, tb), facing the students (to_student, ts), writing on the board (writing, w), teaching while facing the board (black_teach, bt), teaching while facing the students (student_teach, st), and interactive (interact, i). This research adds a large target detection layer to the backbone network so that teachers’ instructional behaviors can be efficiently identified in complex classroom circumstances. Second, the original model’s backbone was extended with an effective multiscale attention module (EMA) to construct cross-scale feature dependencies under various branches. Finally, the bounding box loss function of the original model was replaced with MPDIoU, and a bounding box scaling factor was introduced to propose the Inner_MPDIoU loss function. Experiments were conducted using the TCBDS dataset. The method proposed in this study achieved mAP@.50, mAP@.50:.95, and recall values of 96.2%, 82.5%, and 92.9%, respectively—improvements of 1.1%, 2.0%, and 2.3% over the original model. This method outperformed other mainstream models compared to the current state of the art. The experimental results demonstrate the method’s excellent performance, its ability to identify various classroom behaviors of teachers in realistic scenarios, and its potential to facilitate the analysis and visualization of teacher classroom behaviors.
Toward Automated Fabric Defect Detection: A Survey of Recent Computer Vision Approaches
Authors: Carrilho Rui , Yaghoubi Ehsan , Lindo José , Hambarde Kailash , Proença Hugo
Published: September 2024

Defect detection is a crucial part of the pipeline in many industries. In the textile industry, it is especially important, as it will affect the quality and price of the final product. However, it is mostly performed by human agents, who have been reported to have poor performance, along with requiring a costly and time-consuming training process. As such, methods to automate the process have been increasingly explored throughout the last 20 years. While there are many traditional approaches to this problem, with the advent of deep learning, machine learning-based approaches now constitute the majority of all possible approaches. Other articles have explored traditional approaches and machine learning approaches in a more general way, detailing their evolution over time. In this review, we summarize the most important advancements in the last 5 years and focus mostly on machine learning-based approaches. We also outline the most promising avenues of research in the future.
Robust Reversible Watermarking Scheme in Video Compression Domain Based on Multi-Layer Embedding
Authors: Meng Yifei , Niu Ke , Zhang Yingnan , Liang Yucheng , Hu Fangmeng
Published: September 2024

Most of the existing research on video watermarking schemes focus on improving the robustness of watermarking. However, in application scenarios such as judicial forensics and telemedicine, the distortion caused by watermark embedding on the original video is unacceptable. To solve this problem, this paper proposes a robust reversible watermarking (RRW)scheme based on multi-layer embedding in the video compression domain. Firstly, the watermarking data are divided into several sub-secrets by using Shamir’s (t, n)-threshold secret sharing. After that, the chroma sub-block with more complex texture information is filtered out in the I-frame of each group of pictures (GOP), and the sub-secret is embedded in that frame by modifying the discrete cosine transform (DCT) coefficients within the sub-block. Finally, the auxiliary information required to recover the coefficients is embedded into the motion vector of the P-frame of each GOP by a reversible steganography algorithm. In the absence of an attack, the receiver can recover the DCT coefficients by extracting the auxiliary information in the vectors, ultimately recovering the video correctly. The watermarking scheme demonstrates strong robustness even when it suffers from malicious attacks such as recompression attacks and requantization attacks. The experimental results demonstrate that the watermarking scheme proposed in this paper exhibits reversibility and high visual quality. Moreover, the scheme surpasses other comparable methods in the robustness test session.
FusionNetV2: Explicit Enhancement of Edge Features for 6D Object Pose Estimation
Authors: Ye Yuning , Park Hanhoon
Published: September 2024

FusionNet is a hybrid model that incorporates convolutional neural networks and Transformers, achieving state-of-the-art performance in 6D object pose estimation while significantly reducing the number of model parameters. Our study reveals that FusionNet has local and global attention mechanisms for enhancing deep features in two paths and the attention mechanisms play a role in implicitly enhancing features around object edges. We found that enhancing the features around object edges was the main reason for the performance improvement in 6D object pose estimation. Therefore, in this study, we attempt to enhance the features around object edges explicitly and intuitively. To this end, an edge boosting block (EBB) is introduced that replaces the attention blocks responsible for local attention in FusionNet. EBB is lightweight and can be directly applied to FusionNet with minimal modifications. EBB significantly improved the performance of FusionNet in 6D object pose estimation in experiments on the LINEMOD dataset.
Augmented Reality Interface for Adverse-Visibility Conditions Validated by First Responders in Rescue Training Scenarios
Authors: Oregui Xabier , Fernández García Anaida , Azpiroz Izar , Larraga-García Blanca , Ruiz Verónica , García Olaizola Igor , Gutiérrez Álvaro
Published: September 2024

Updating the equipment of the first responder (FR) by providing them with new capabilities and useful information will inevitably lead to better mission success rates and, therefore, more lives saved. This paper describes the design and implementation of a modular interface for augmented reality displays integrated into standard FR equipment that will provide support during the adverse-visibility situations that the rescuers find during their missions. This interface includes assistance based on the machine learning module denoted as Robust Vision Module, which detects relevant objects in a rescue scenario, particularly victims, using the feed from a thermal camera. This feed can be displayed directly alongside the detected objects, helping FRs to avoid missing anything during their operations. Additionally, the information exposition in the interface is organized according to the biometrical parameters of FRs during the operations. The main novelty of the project is its orientation towards useful solutions for FRs focusing on something occasionally ignored during research projects: the point of view of the final user. The functionalities have been designed after multiple iterations between researchers and FRs, involving testing and evaluation through realistic situations in training scenarios. Thanks to this feedback, the overall satisfaction according to the evaluations of 18 FRs is 3.84 out of 5 for the Robust Vision Module and 3.99 out of 5 for the complete AR interface. These functionalities and the different display modes available for the FRs to adapt to each situation are detailed in this paper.
IoT-GChain: Internet of Things-Assisted Secure and Tractable Grain Supply Chain Framework Leveraging Blockchain
Authors: Thakur Karan Singh , Ahuja Rohit , Singh Raman
Published: September 2024

The grain supply chain is crucial for any nation’s self-sustainability due to its huge impact on food security, economic stability, and the livelihoods of several people. The path grain takes from farmers to consumers is opaque and complicated, due to which consumers cannot trust grain quality and its origin. Although blockchain is widely used for fair and secure transactions between farmers and buyers, issues related to transparency and traceability in the grain supply chain, such as counterfeiting and middlemen involvement, have not been adequately addressed. To tackle these issues, a blockchain-based solution is proposed that unites farmers, warehouses, government central and state agencies, transporters, and food corporations on a single platform to enhance transparency, traceability, and trust among all parties. This system involves minting a non-fungible token (NFT) corresponding to each lot of grain approved by government officials. The NFT comprises grain quality, type, temperature data from sensors, weight, and ownership information, which updates as the grain lot moves across the supply chain from central agencies to state agencies and so on. NFTs enable stakeholders to track the grain lot from cultivation to end-users, providing insights into grain conditions and quality. An Internet of Things-based circuit is designed using a Digital-output relative humidity & temperature (DHT22) sensor, which offers real-time temperature and humidity readings, and geolocation coordinates are gathered from the GPS module across the supply chain. Farmers can directly interact with warehouses to sell grains, eliminating the need for middlemen and fostering trust among all parties. The proposed four-tier framework is implemented and deployed on the Ethereum network, with smart contracts interacting with React-based web pages. Analysis and results of the proposed model illustrate that it is viable, secure, and superior to the existing grain supply chain system.
YOLO-TSF: A Small Traffic Sign Detection Algorithm for Foggy Road Scenes
Authors: Li Rongzhen , Chen Yajun , Wang Yu , Sun Chaoyue
Published: September 2024

The accurate and rapid detection of traffic signs is crucial for intelligent transportation systems. Aiming at the problems that traffic signs have including more small targets in road scenes as well as misdetection, omission, and low recognition accuracy under the influence of fog, we propose a model for detecting traffic signs in foggy road scenes—YOLO-TSF. Firstly, we design the CCAM attention module and combine it with the idea of local–global residual learning thus proposing the LGFFM to enhance the model recognition capabilities in foggy weather. Secondly, we design MASFFHead by introducing the idea of ASFF to solve the feature loss problem of cross-scale fusion and perform a secondary extraction of small targets. Additionally, we design the NWD-CIoU by combining NWD and CIoU to solve the issue of inadequate learning capacity of IoU for diminutive target features. Finally, to address the dearth of foggy traffic signs datasets, we construct a new foggy traffic signs dataset, Foggy-TT100k. The experimental results show that the mAP@0.5, mAP@0.5:0.95, Precision, and F1-score of YOLO-TSF are improved by 8.8%, 7.8%, 7.1%, and 8.0%, respectively, compared with YOLOv8s, which proves its effectiveness in detecting small traffic signs in foggy scenes with visibility between 50 and 200 m.
Multi-Stage ANN Model for Optimizing the Configuration of External Lightning Protection and Grounding Systems
Authors: Rohana Rohana , Hardi Surya , Nasaruddin Nasaruddin , Away Yuwaldi , Novandri Andri
Published: September 2024

This paper proposes an Artificial Neural Network (ANN) model using a Multi-Stage method to optimize the configuration of an External Lightning Protection System (ELPS) and grounding system. ELPS is a system designed to protect an area from damage caused by lightning strikes. Meanwhile, the grounding system functions to direct excess electric current from lightning strikes into the ground. This study identifies the optimal protection system configuration, reducing the need for excessive components. The ELPS configuration includes the number of protection pole units and the height of the protection poles. In contrast, the grounding system configuration consists of the number of electrode units and the length of the electrodes. This study focuses on the protection system configuration at a Photovoltaic Power Station, where the area is highly vulnerable to lightning strikes. Several aspects need to be considered in determining the appropriate configuration, such as average thunderstorm days per year, ELPS efficiency, total area of photovoltaic module, area to be protected, soil resistivity, electrode spacing factor, and the total required electrode resistance. The proposed multi-stage ANN model consists of three processing stages, each responsible for handling a portion of the overall system tasks. The first stage is responsible for determining the protection pole configuration. In the second stage, the Lightning Protection Level (LPL) classification is performed. Then, in the third stage, the process of determining the grounding configuration is handled. The analysis results show that the Multi-Stage ANN model can effectively determine the configuration with a low error rate: MAE of 0.265, RMSE of 0.314, and MPE of 9.533%. This model can also explain data variation well, as indicated by the high R2 value of 0.961. The comparison results conducted with ATP/EMTP software show that the configuration produced by ANN results in fewer protection pole units but with greater height. Meanwhile, ANN produces a configuration with shorter electrode lengths but fewer units in the grounding system.
Engineered Biochar for Metal Recycling and Repurposed Applications
Authors: Hasan Mehedi , Chakma Soumik , Liang Xunjia , Sutradhar Shrikanta , Kozinski Janusz , Kang Kang
Published: September 2024

Heavy metal pollution is posing significant threats to the environment and human health. Engineered biochar, derived from various biomass sources through thermochemical processes, has emerged as a promising solution for metal pollutant remediation and metal recovery. This review explores the latest advancements in the preparation, characterization, and application of engineered biochar for metal adsorption, recycling, and utilization. It begins by discussing the significance of metal adsorption and providing an overview of biochar properties. The review examines the preparation and characterization techniques, emphasizing feedstock selection, thermochemical conversion methods, and surface modifications. Mechanisms of metal adsorption, such as physical and chemical adsorption, ion exchange, and surface complexation, are critically discussed. Moreover, factors influencing metal adsorption capacity, including biochar properties, metal characteristics, and environmental conditions, are critically analyzed. The efficacy of engineered biochar in adsorbing specific metals, including heavy metals, transition metals, and rare earth elements, is reviewed with recent studies and key findings. Furthermore, the recycling and regeneration of metal-loaded biochar are discussed, focusing on recycling and repurposed application techniques alongside challenges and economic considerations. Finally, future perspectives are provided for the enlightening of future research. This review is unique in addressing the potential of metal-adsorbed biochar as a novel precursor to produce catalytical and electrochemical materials.
Smart City and Sustainable Energy—Evidence from the European Union Capital Cities
Authors: Tundys Blanka , Wiśniewski Tomasz
Published: September 2024

The aim of the paper was to identify which European capitals are sustainable and smart, why, and what influences the ranking. The main research hypothesis was to indicate that cities in the ‘old’ E.U. countries (richer and with higher levels of economic development) are more sustainable and smart. Furthermore, sustainable smart cities, by definition, through the use of advanced and modern management tools and technological support, should contribute to community resilience. Sustainable energy plays a significant role in the measurement system. The study’s results showed the differences that exist across countries, as well as the leaders in each smart category and area. This is interesting and new; from a research point of view, there has been no study based on OECD research and data confronting and correlating the range of data with indicators found in the literature. The study results show that the concept of a smart city is comprehensive and that it is necessary to analyze in depth the various sub-categories included in the measurement and assessment of smartness offered by different indicators. This is because it turns out that an overall score and ranking do not always mean that a city is smart in every area and every element included in smart. Statistical methods and literature analysis are used for the study. The results represent a novel development and contribution to the science discipline and can be the basis for further scientific exploration in this area. The research gap and challenge indicate whether there is a link and correlation between the use of sustainable energy in E.U. countries and the implementation of smart concepts in European capitals in the context of the division into ‘new’ and ‘old’ E.U. capitals. An important element is the verification of the thesis that ‘old’ capitals are more advanced in the implementation of smart cities and make greater use of sustainable energy to meet social and economic needs. The thesis has been partly falsified and confirmed negatively; the results are not obvious. It means that the ‘new’ E.U. countries are very skillful in using financial, organizational, and common development policy opportunities to make their cities modern, intelligent, and friendly to their inhabitants.
Forecasting of Residential Energy Utilisation Based on Regression Machine Learning Schemes
Authors: Mosetlhe Thapelo , Yusuff Adedayo Ademola
Published: September 2024

Energy utilisation in residential dwellings is stochastic and can worsen the issue of operational planning for energy provisioning. Additionally, planning with intermittent energy sources exacerbates the challenges posed by the uncertainties in energy utilisation. In this work, machine learning regression schemes (random forest and decision tree) are used to train a forecasting model. The model is based on a yearly dataset and its subset seasonal partitions. The dataset is first preprocessed to remove inconsistencies and outliers. The performance measures of mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) are used to evaluate the accuracy of the model. The results show that the performance of the model can be enhanced with hyperparameter tuning. This is shown with an observed improvement of about 44% in accuracy after tuning the hyperparameters of the decision tree regressor. The results further show that the decision tree model can be more suitable for utilisation in forecasting the partitioned dataset.
Analysis of the Potential for Thermal Flexibility of Cooling Applications
Authors: Laband Dana Laureen , Esken Henning , Pollerberg Clemens , Joemann Michael , Doetsch Christian
Published: September 2024

The feed-in of electricity from renewable energies, such as wind or solar power, fluctuates based on weather conditions. This unpredictability due to volatile feed-in can lead to sudden changes in energy generation so that solutions ensuring grid stability need to be implemented. The cooling sector offers the opportunity to create flexibilities for such balancing, with this study focusing on the thermal flexibilities that can be provided by cooling applications. Various cooling-demand profiles are investigated with respect to their load profile and their impact on flexibility is analysed. In addition to the cooling demand, scenarios of different storage dimensions are considered. As a result, it shows that an increasing base-load level and increasing operating-load duration have a negative effect on flexibility, while an increasing full-load duration is beneficial for flexibility. Storage size also has a strong impact as higher storage capacity and storage performance indicate higher flexibility, whereas above a certain size they only provide little added value.
The Optimization of Supply–Demand Balance Dispatching and Economic Benefit Improvement in a Multi-Energy Virtual Power Plant within the Jiangxi Power Market
Authors: Xinfa Tang , Jingjing Wang , Yonghua Wang , Youwei Wan
Published: September 2024

This paper presents an optimization method for scheduling a multi-energy VPP (Virtual Power Plant) supply–demand balance in the power market environment of Jiangxi Province. The primary objective of this method is to improve the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method takes into account the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Furthermore, it integrates real-time market price feedback to achieve the accurate allocation of power supply and demand. Through a case study of a multi-energy VPP in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply–demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.
Experimental Investigation on Heat Transfer in Two-Phase Closed Thermosyphon Containing Non-Condensable Gas
Authors: Song Binglin , Meng Guoying , Huang Wei , Wang Aiming , Cheng Xiaohan , Yang Jie
Published: September 2024

Given that two-phase closed thermosiphons (TPCT) are a prevalent and efficacious means of heat transfer. However, non-condensable gas (NCG) may potentially impact the heat transfer performance of the aforementioned devices. Nevertheless, the theoretical analysis of heat transfer in TPCT containing NCG is not fully comprehensive and therefore requires further supplementation by means of relevant experiments. This paper presents the development and experimental investigation of a theoretical heat transfer model for a TPCT containing NCG. The research encompasses the optimal fluid-filling ratio of R22 and R410a working fluid and the impact of NCG on heat transfer in the condenser section of TPCT. Experimental findings indicate that TPCT with R22 and R410a working fluids at a fluid-filling ratio of 60% and 50%, respectively, demonstrate excellent isotherm and heat transfer efficiency. The presence of NCG affects the condenser section heat transfer process of the vapor, leading to a 2 °C decrease in the average temperature of the condenser section of the TPCT (Tca). In comparison with the TPCT without NCG, it was observed that an increase in the mass of NCG from 0.0097 to 0.0197 mol resulted in a reduction in the effective length of the condenser section (La) and effective heat transfer rate (κ) of R22 TPCT. The decrease in La was 75.1 mm, while the decrease in κ was 15.02%. Furthermore, at the same NCG mass, the effective heat transfer rate of R410a TPCT is evidently superior to that of R22 TPCT. The NCG in the TPCT can be removed by using a check valve. Nevertheless, this will result in a reduction in the fluid-filling ratio of the TPCT. The temperature of the R410a TPCT containing 0.0197 mol NCG with a fluid-filling ratio of 50% is comparable to that observed in fluid-filling ratio of 40% after the NCG is exhausted.
Equalized Distributed Control Strategy for AC Microgrid Energy Storage SOCs
Authors: Li Qingsheng , Lu Kaicheng , Zhu Yongqing , Zhang Zhaofeng , Li Zhen
Published: September 2024

Most of the previous SOC equalization methods for microgrid energy storage target DC microgrids and use centralized control structures, while in recent years many researchers have begun to focus on a decentralized, communication-based implementation of distributed control structures. In this paper, based on the existing research, we use the multi-agent system (MAS) structure and dynamic consistency algorithm (DCA) to realize the estimation of the SOC mean value of microgrid energy storage obtained by each agent in the system. For the problems of the iterative convergence that exists in the distributed control, such as being too slow, too frequent communication, stability, etc., we optimally select the parameters of the consistency iterative summation to improve convergence. In addition, we use the event-driven method to further reduce the unnecessary communication frequency. Finally, a numerical simulation model of the AC microgrid is established by Matlab to verify the effectiveness of the method, which reduces the communication volume by about 50% while maintaining the effect of the control strategy.
CFD Simulation of Pre-Chamber Spark-Ignition Engines—A Perspective Review
Authors: Jeong Soo-Jin
Published: September 2024

The growing demand to reduce emissions of pollutants and CO2 from internal combustion engines has led to a critical need for the development of ultra-lean burn engines that can maintain combustion stability while mitigating the risk of knock. One of the most effective techniques is the pre-chamber spark-ignition (PCSI) system, where the primary combustion within the cylinder is initiated by high-energy reactive gas jets generated by pilot combustion in the pre-chamber. Due to the complex physical and chemical processes involved in PCSI systems, performing 3D CFD simulations is crucial for in-depth analysis and achieving optimal design parameters. Moreover, combining a detailed CFDs model with a calibrated 0D/1D model is expected to provide a wealth of new insights that are difficult to gather through experimental methods alone, making it an indispensable tool for improving the understanding and optimization of these advanced engine systems. In this context, numerous previous studies have utilized CFD models to optimize key design parameters, including the geometric configuration of the pre-chamber, and to study combustion characteristics under various operating conditions in PCSI engines. Recent studies indicate that several advanced models designed for conventional spark-ignition (SI) engines may not accurately predict performance under the demanding conditions of Turbulent Jet Ignition (TJI) systems, particularly when operating in lean mixtures and environments with strong turbulence–chemistry interactions. This review highlights the pivotal role of Computational Fluid Dynamics (CFDs) in optimizing the design of pre-chamber spark-ignition (PCSI) engines. It explores key case studies and examines both the advantages and challenges of utilizing CFDs, not only as a predictive tool but also as a critical component in the design process for improving PCSI engine performance.