bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping the Neural Basis of Neuroeconomics with Functional Magnetic Resonance Imaging: A Narrative Literature Review
Authors: Mallio Carlo A. , Buoso Andrea , Stiffi Massimo , Cea Laura , Vertulli Daniele , Bernetti Caterina , Di Gennaro Gianfranco , van den Heuvel Martijn P. , Beomonte Zobel Bruno
Published: May 2024

Neuroeconomics merges neuroscience, economics, and psychology to investigate the neural basis of decision making. Decision making involves assessing outcomes with subjective value, shaped by emotions and experiences, which are crucial in economic decisions. Functional MRI (fMRI) reveals key areas of the brain, including the ventro-medial prefrontal cortex, that are involved in subjective value representation. Collaborative interdisciplinary efforts are essential for advancing the field of neuroeconomics, with implications for clinical interventions and policy design. This review explores subjective value in neuroeconomics, highlighting brain regions identified through fMRI studies.
Outcomes of Budesonide as a Treatment Option for Immune Checkpoint Inhibitor-Related Colitis in Patients with Cancer
Authors: Machado Antonio Pizuorno , Shaikh Abdullah Salim , Saji Alice , Shatila Malek , Oliva Isabella Glitza , Wang Yinghong , Shirwaikar Thomas Anusha
Published: May 2024

Background: Current treatment guidelines for moderate to severe colitis (IMC) secondary to immune checkpoint inhibitors (ICI) recommend systemic corticosteroids as the primary therapy in conjunction with biologics, namely infliximab and/or vedolizumab. We aimed to explore the efficacy and safety of oral budesonide in the treatment of IMC. Methods: We performed a retrospective analysis at MD Anderson Cancer Center of adult cancer patients with a confirmed (based on clinical, radiographic and laboratory assessment) diagnosis of IMC between 1 January 2015 and 31 November 2022, treated with budesonide. Data collection included demographics, oncologic history, IMC-related information and outcomes up to 6 months after the last dose of ICI. Results: Our sample (n = 69) comprised primarily of Caucasian (76.8%) females (55.1%). The majority of patients received combination therapy with anti-PD-1/L1 and anti-CTLA-4 (49.3%), and the most common malignancy treated was melanoma (37.6%). The median grade of diarrhea was 3 and of colitis was 2. Of the 50 patients who underwent endoscopic evaluation, a majority had non-ulcerative inflammation (64%) and active colitis on histology (78%). Budesonide was used as primary treatment at onset of IMC in 56.5% patients, as well as a bridging therapy from systemic corticosteroids in 33.3%. Less than half of the patients (44.9%) required additional therapies such as biologics or fecal microbiota transplant. Additionally, 75.3% of patients achieved full remission of IMC and 24.6% had a recurrence of IMC. ICI was resumed in 31.9% of patients and 17.4% received other forms of cancer therapies. Conclusions: Budesonide may be an effective strategy to treat and prevent the recurrence of IMC. The remission rates observed in our analysis with budesonide alone are comparable to systemic corticosteroids. Patients that require an extended duration of steroid exposure and those with moderate to severe colitis may benefit from budesonide given its lower risk of infection and complications. Furthermore, we observe that budesonide may serve as a successful bridge from systemic corticosteroids with subsequent biologic treatment. Larger prospective studies are necessary to determine the role of budesonide as well as its safety profile.
Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches
Authors: Bellin Marie-France , Valente Catarina , Bekdache Omar , Maxwell Florian , Balasa Cristina , Savignac Alexia , Meyrignac Olivier
Published: May 2024

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.
Targeted Therapies in the Treatment of Mantle Cell Lymphoma
Authors: Thomas Colin J. , Carvajal Veronica , Barta Stefan K.
Published: May 2024

Mantle cell lymphoma (MCL) is a rare, heterogeneous B-cell non-Hodgkin’s lymphoma. The standard front-line treatment utilizes chemotherapy, often followed by consolidation with an autologous hematopoietic cell transplant; however, in most patients, the lymphoma will recur and require subsequent treatments. Additionally, mantle cell lymphoma primarily affects older patients and is frequently chemotherapy-resistant, which has further fostered the necessity for new, chemotherapy-free treatment options. In the past decade, targeted therapies in mantle cell lymphoma have been practice-changing as the treatment paradigm shifts further away from relying primarily on cytotoxic agents. Here, we will review the pathophysiology of mantle cell lymphoma and discuss the emergence of targeted, chemotherapy-free treatments aimed at disrupting the abnormal biology driving its lymphomagenesis. Treatments targeting the constitutive activation of NF-kB, Bruton’s Tyrosine Kinase signaling, and anti-apoptosis will be the primary focus as we discuss their clinical data and toxicities. Our review will also focus primarily on the emergence and use of targeted therapies in the relapsed/refractory setting but will also discuss the emergence of their use in front-line therapy and in combination with other agents.
Significance of Prediction Models for Post-Hepatectomy Liver Failure Based on Type IV Collagen 7s Domain in Patients with Hepatocellular Carcinoma
Authors: Okada Takuma , Shinkawa Hiroji , Taniuchi Satsuki , Kinoshita Masahiko , Nishio Kohei , Ohira Go , Kimura Kenjiro , Tanaka Shogo , Shintani Ayumi , Kubo Shoji , Ishizawa Takeaki
Published: May 2024

Background: Previous studies have attempted to establish predictive models for post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) undergoing liver resection. However, a versatile and useful predictive model for PHLF remains to be developed. Therefore, we aimed to develop predictive models for PHLF based on type IV collagen 7s domain (7s collagen) in patients with HCC. Methods: We retrospectively collected data from 972 patients with HCC who had undergone initial curative liver resection between February 2000 and December 2020 at our hospital. Multivariate logistic regression analysis using a restricted cubic spline was performed to evaluate the effect of 7s collagen on the incidence of PHLF. A nomogram was developed based on 7s collagen. Results: PHLF grades B or C were identified in 104 patients (11%): 98 (10%) and 6 (1%) PHLF grades B and C, respectively. Multivariate logistic regression analysis revealed that the preoperative serum level of 7s collagen was significantly associated with a proportional increase in the risk of PHLF, which was confirmed in both laparoscopic and open liver resections. A nomogram was developed based on 7s collagen, with a concordance index of 0.768. The inclusion of 7s collagen values in the predictive model increased the predictive accuracy. Conclusion: The findings highlight the efficacy of the serum level of 7s collagen as a predictive factor for PHLF. Our novel nomogram using 7s collagen may be useful for predicting the risk of PHLF.
Epigenetics Meets CAR-T-Cell Therapy to Fight Cancer
Authors: Santourlidis Simeon , Araúzo-Bravo Marcos J. , Erichsen Lars , Bendhack Marcelo L.
Published: May 2024

Based on the impressive success of Car-T-cell therapy in the treatment of hematological malignancies, a broad application for solid tumors also appears promising. However, some important hurdles need to be overcome. One of these is certainly the identification of specific target antigens on cancer cells. Hypomethylation is a characteristic epigenetic aberration in many tumor entities. Genome-wide screenings for consistent DNA hypomethylations in tumors enable the identification of aberrantly upregulated transcripts, which might result in cell surface proteins. Thus, this approach provides a new perspective for the discovery of potential new Car-T-cell target antigens for almost every tumor entity. First, we focus on this approach as a possible treatment for prostate cancer.
Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review
Authors: Bleaney Christopher W. , Abdelaal Hebatalla , Reardon Mark , Anandadas Carmel , Hoskin Peter , Choudhury Ananya , Forker Laura
Published: May 2024

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were ‘radiosensitivity’ signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 ‘radiosensitivity’ signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.
Immunohistochemical Investigation into Protein Expression Patterns of FOXO4, IRF8 and LEF1 in Canine Osteosarcoma
Authors: Brot Simone de , Cobb Jack , Alibhai Aziza A. , Jackson-Oxley Jorja , Haque Maria , Patke Rodhan , Harris Anna E. , Woodcock Corinne L. , Lothion-Roy Jennifer , Varun Dhruvika , Thompson Rachel , Gomes Claudia , Kubale Valentina , Dunning Mark D. , Jeyapalan Jennie N. , Mongan Nigel P. , Rutland Catrin S.
Published: May 2024

Osteosarcoma (OSA) is the most common type of primary bone malignancy in people and dogs. Our previous molecular comparisons of canine OSA against healthy bone resulted in the identification of differentially expressed protein-expressing genes (forkhead box protein O4 (FOXO4), interferon regulatory factor 8 (IRF8), and lymphoid enhancer binding factor 1 (LEF1)). Immunohistochemistry (IHC) and H-scoring provided semi-quantitative assessment of nuclear and cytoplasmic staining alongside qualitative data to contextualise staining (n = 26 patients). FOXO4 was expressed predominantly in the cytoplasm with significantly lower nuclear H-scores. IRF8 H-scores ranged from 0 to 3 throughout the cohort in the nucleus and cytoplasm. LEF1 was expressed in all patients with significantly lower cytoplasmic staining compared to nuclear. No sex or anatomical location differences were observed. While reduced levels of FOXO4 might indicate malignancy, the weak or absent protein expression limits its primary use as diagnostic tumour marker. IRF8 and LEF1 have more potential for prognostic and diagnostic uses and facilitate further understanding of their roles within their respective molecular pathways, including Wnt/beta-catenin/LEF1 signalling and differential regulation of tumour suppressor genes. Deeper understanding of the mechanisms involved in OSA are essential contributions towards the development of novel diagnostic, prognostic, and treatment options in human and veterinary medicine contexts.
Accelerating Conjugate Heat Transfer Simulations in Squared Heated Cavities through Graphics Processing Unit (GPU) Computing
Authors: da Silva Reis César Augusto Borges , Botezelli Daniel , de Azevedo Arthur Mendonça , dos Santos Magalhães Elisan , da Silveira Neto Aristeu
Published: May 2024

This research develops an innovative framework for accelerating Conjugate Heat Transfer (CHT) simulations within squared heated cavities through the application of Graphics Processing Units (GPUs). Although leveraging GPUs for computational speed improvements is well recognized, this study distinguishes itself by formulating a tailored optimization strategy utilizing the CUDA-C programming language. This approach is specifically designed to tackle the inherent challenges of modeling squared cavity configurations in thermal simulations. Comparative performance evaluations reveal that our GPU-accelerated framework reduces computation times by up to 99.7% relative to traditional mono-core CPU processing. More importantly, it demonstrates an increase in accuracy in heat transfer predictions compared to existing CPU-based models. These results highlight not only the technical feasibility but also the substantial enhancements in simulation efficiency and accuracy, which are crucial for critical engineering applications such as aerospace component design, electronic device cooling, and energy system optimization. By advancing GPU computational techniques, this work contributes significantly to the field of thermal management, offering a potential for broader application and paving the way for more efficient, sustainable engineering solutions.
Chiral 4f and 3d-4f Complexes from Enantiopure Salen-Type Schiff Base Ligands
Authors: Raptopoulou Catherine P.
Published: May 2024

This review summarizes the structural characteristics and physicochemical properties of chiral 4f and 3d-4f complexes based on enantiopure salen-type Schiff base ligands. The chirality originates from the enantiopure diamines and is imparted to the Schiff base ligands and complexes and finally to the crystal structures. The reported enantiopure Schiff base ligands derive from the condensation of aromatic aldehydes, such as salicylaldehyde and its various derivatives, and the enantiopure diamines, (1R,2R) or (1S,2S)-1,2-diamino-cyclohexane, (1R,2R) or (1S,2S)-1,2-diamino-1,2-diphenylethane, (R) or (S)-2,2′-diamino-1,1′-binaphthalene, and 1,2-diaminopropane.
Study of the Microstructure and Mechanical Property Relationships of Gas Metal Arc Welded Dissimilar Protection 600T, DP450 and S275JR Steel Joints
Authors: Elmas Mustafa , Koçar Oğuz , Anaç Nergizhan
Published: May 2024

The need for combining dissimilar materials is steadily increasing in the manufacturing industry, and the resulting products are expected to always have high performance. While there are various methods available for joining such material pairs, one of the commonly preferred techniques is fusion welding. In this study, three different steel materials (Protection 600T, DP450, and S275JR) were joined using gas metal arc welding (GMAW) in different combinations (similar/dissimilar). The microstructure and mechanical properties of the joints were evaluated. Tensile test, Vickers microhardness (HV 0.1), bending, Charpy V-notch impact testing, and microstructure examinations were conducted to analyze the weld and heat-affected zone. The tensile strengths of the base metal materials Protection 600T, DP450, and S275JR were found to be 1524.73 ± 18.7, 500.8 ± 10.4, and 508.5 ± 9.5 MPa, respectively. In welded samples of similar materials, the highest efficiency was found to be 103.05% for DP450/DP450, while in dissimilar welded joints, it was 105.5% for the DP450/S275JR pair. Hardness values for the base materials Protection 600T, DP450, and S275JR were measured as 526.5 ± 10.5, 153.8 ± 1.8, and 162.5 ± 5.2, respectively. In all welded samples, there was an increase in hardness in the weld zone (due to the welding wire) and the heat-affected zone (due to grain size refinement). While the impact energy values of similar material pairs were close to the base material impact energy values, the impact energy values of dissimilar material pairs varied according to the base materials. In addition, in joints made with similar materials, the bending force was close to the base materials, while a decrease in bending force was observed in joints formed with dissimilar materials. As a result, the welding of DP450 and S275JR materials was carried out efficiently. Protection 600T was welded with other materials, but its welding strength was limited to the strength of the material with low mechanical properties.
Multi-Scale Habitat Selection by the Wintering Whooper Swan (Cygnus cygnus) in Manas National Wetland Park, Northwestern China
Authors: Yan Han , Ma Xuejun , Yang Weikang , Xu Feng
Published: May 2024

Habitat selection has been a central focus of animal ecology, with research primarily concentrating on habitat choice, utilization, and evaluation. However, studies confined to a single scale often fail to reveal the habitat selection needs of animals fully and accurately. This paper investigates the wintering whooper swan (Cygnus cygnus) in Manas National Wetland Park, Xinjiang, using satellite tracking to determine their locations. The Maximum Entropy model (MaxEnt) was applied to explore the multi-scales habitat selection needs of Manas National Wetland Park’s wintering whooper swans across nighttime, daytime, and landscape scales. This study showed that the habitat selection of the wintering whooper swans varied in different scales. At the landscape scale, wintering whooper swans prefer habitats with average winter precipitations of 6.9 mm and average temperatures of −6 °C, including water bodies and wetlands, indicating that climate (precipitation and temperature) and land type (wetlands and water bodies) influence their winter habitat selection. During daytime, whooper swans prefer areas close to wetlands, water bodies, and bare land, with a more dispersed distribution of water bodies. For nighttime, they tend to choose areas within the wetland park where human disturbance is minimal and safety is higher. This study can provide scientific basis and data support for habitat conservation and management of wintering waterbirds like whooper swans, recommending targeted conservation measures to effectively manage and protect the wintering grounds of whooper swans.
Comparative Analysis of the Predictive Performance of an ANN and Logistic Regression for the Acceptability of Eco-Mobility Using the Belgrade Data Set
Authors: Komarica Jelica , Glavić Draženko , Kaplanović Snežana
Published: May 2024

To solve the problem of environmental pollution caused by road traffic, alternatives to vehicles with internal combustion engines are often proposed. As such, eco-mobility microvehicles have significant potential in the fight against environmental pollution, but only on the condition that they are widely accepted and that they replace the vehicles that predominantly pollute the environment. With this in mind, this study aims to elucidate the main variables that influence the acceptability of these vehicles, using prediction models based on binary logistic regression and a multilayer artificial neural network—a multilayer perceptron (ANN). The data of a random sample obtained via an online questionnaire, answered by 503 inhabitants of Belgrade (Serbia), were used for training and testing the model. A multilayer perceptron with 9 and 7 neurons in two hidden layers, a hyperbolic tangent activation function in the hidden layer, and an identity function in the output layer performed slightly better than the binary logistic regression model. With an accuracy of 85%, a precision of 79%, a recall of 81%, and an area under the ROC curve of 0.9, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that a respondent’s relationship to their current environmental pollution, the frequency of their use of modes of transport such as bicycles and motorcycles, their mileage for commuting, and their personal income have the greatest influence on the acceptability of using eco-mobility vehicles.
Eating Attitudes, Stress, Anxiety, and Depression in Dietetic Students and Association with Body Mass Index and Body Fat Percent: A Cross-Sectional Study
Authors: Magni Olga , Detopoulou Paraskevi , Fappa Evangelia , Perrea Anastasia , Levidi Despoina , Dedes Vasilios , Tzoutzou Milia , Gioxari Aristea , Panoutsopoulos Georgios
Published: May 2024

University students face challenges impacting psychology and dietary choices. The present work examined the association between eating attitudes, stress, anxiety, and depression to body mass index (BMI) and body fat percentage in Dietetics students. Respondents completed the Eating Attitudes Test-26 (EAT-26), the Depression Anxiety Stress Scales (DASS), a validated Food Frequency Questionnaire, and the Hellenic Physical Activity Questionnaire (HPAQ). Anthropometry and a bioimpedance analysis were performed. The EAT-26 score was 11 (8–16) and the DASS score was 20 (11–36) (medians and interquartile ranges). Disordered eating was detected in 20% of men and 15% of women. Depressive symptomatology was detected in 30% of males and 23% of females, anxiety in 35% of males and 40% of females, and stress in 29% of males and 35% of females. EAT-26 and DASS scores were highly correlated (r = 0. 0.221, p = 0.001). The EAT-26 oral control subscale (B = 0.430, SE = 0.184, p = 0.026) was positively correlated with BMI in men in the models, adjusted for age, physical activity, and Mediterranean Diet Score, while no association was documented for % body fat. The DASS depression score was not related to BMI in multi-adjusted models. In conclusion, disordered eating, depression, stress, and anxiety are present in this sample of university students. The relationship between disordered eating and BMI needs consideration in programs targeting overweight or underweight in Dietetics students.
The Treatment and Management of Oroantral Communications and Fistulas: A Systematic Review and Network Metanalysis
Authors: Oliva Stefano , Lorusso Felice , Scarano Antonio , D’Amario Maurizio , Murmura Giovanna
Published: May 2024

Objectives: The aim of this work was to systematically review and carry out a statistical metanalysis to identify the best treatment for close oroantral communications and fistulas and to avoid the risk of recurrence. Materials and Methods: An electronic search was conducted on the MEDLINE database (Pubmed), Scopus, and Google scholar using the following keywords: “oro antral communication (OAC)” OR “oro antral fistula (OAF)” OR “antro-oral communication” OR “communication between maxillary sinus and oral cavity” OR “oro-sinusal communication” OR “oro-sinusal fistula” OR “sinus communication” OR “sinus fistula” OR “antral communication” AND “treatment” OR “management” OR “surgical treatment” OR “surgical interventions”. This work was performed in accordance with the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses). After article screening, 9 RCTs (randomized controlled trials), comparing two or more techniques, were included in this review. Results: A statistically significant difference was detected in favor of the buccal fat pad compared to the buccal advancement flap and palatal rotational flap. Conclusions: With the limitations of this study, the buccal fat pad showed the best results in terms of communication closure and reducing the risk of relapse.
Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen–Shannon Distance
Authors: Zunino Luciano
Published: May 2024

Taking into account the complexity of the human brain dynamics, the appropriate characterization of any brain state is a challenge not easily met. Actually, even the discrimination of simple behavioral tasks, such as resting with eyes closed or eyes open, represents an intricate problem and many efforts have been and are being made to overcome it. In this work, the aforementioned issue is carefully addressed by performing multiscale analyses of electroencephalogram records with the permutation Jensen–Shannon distance. The influence that linear and nonlinear temporal correlations have on the discrimination is unveiled. Results obtained lead to significant conclusions that help to achieve an improved distinction between these resting brain states.
Using Synchronized Eye Movements to Predict Attention in Online Video Learning
Authors: Su Caizhen , Liu Xingyu , Gan Xinru , Zeng Hang
Published: May 2024

Concerns persist about attentional engagement in online learning. The inter-subject correlation of eye movements (ISC) has shown promise as an accessible and effective method for attention assessment in online learning. This study extends previous studies investigating ISC of eye movements in online learning by addressing two research questions. Firstly, can ISC predict students’ attentional states at a finer level beyond a simple dichotomy of attention states (e.g., attending and distracted states)? Secondly, whether learners’ learning styles affect ISC’s prediction rate of attention assessment in video learning? Previous studies have shown that learners of different learning styles have different eye movement patterns when viewing static materials. However, limited research has explored the impact of learning styles on viewing patterns in video learning. An eye tracking experiment with participants watching lecture videos demonstrated a connection between ISC and self-reported attention states at a finer level. We also demonstrated that learning styles did not significantly affect ISC’s prediction rate of attention assessment in video learning, suggesting that ISC of eye movements can be effectively used without considering learners’ learning styles. These findings contribute to the ongoing discourse on optimizing attention assessment in the evolving landscape of online education.
A New Method for Anti-Interference Measurement of Capacitance Parameters of Long-Distance Transmission Lines Based on Harmonic Components
Authors: Wang Kaibai , Zhang Zihao , Xu Xingwei , Hu Zhijian , Sun Zhengwei , Tan Jiahao , Yao Xiang , Tian Jingfu
Published: May 2024

In the context of strong electromagnetic interference environments, the measurement accuracy of the capacitance parameters of transmission lines under power frequency measurement methods is not high. In this paper, a capacitance parameter anti-interference measurement method for transmission lines based on harmonic components is proposed to overcome the impact of power frequency interference. When applying this method, it is first necessary to open-circuit the end of the line under test. Subsequently, apply voltage to the head end of the tested line through a step-up transformer. Due to the saturation of the transformer during no-load conditions, a large number of harmonics are generated, primarily third harmonic. The third harmonic components of voltage and current on the tested transmission line are extracted using the Fourier transform. The proposed method addresses the influence of line distribution effects by establishing a distributed parameter model for long-distance transmission lines. The relevant transmission matrix for the zero-sequence distributed parameters is obtained by combining Laplace transform and similarity transform to solve the transmission line equations. Using synchronous measurement data from the third harmonic components of voltage and current at both ends of the transmission line, combined with the transmission matrix, this method accurately measures the zero-sequence capacitance parameters. The PSCAD/EMTDC simulation results and field test outcomes have demonstrated the feasibility and accuracy of the proposed method for measuring line capacitance parameters under strong electromagnetic interference.
GraM: Geometric Structure Embedding into Attention Mechanisms for 3D Point Cloud Registration
Authors: Liu Pin , Zhong Lin , Wang Rui , Zhu Jianyong , Zhai Xiang , Zhang Juan
Published: May 2024

3D point cloud registration is a crucial technology for 3D scene reconstruction and has been successfully applied in various domains, such as smart healthcare and intelligent transportation. With theoretical analysis, we find that geometric structural relationships are essential for 3D point cloud registration. The 3D point cloud registration method achieves excellent performance only when fusing local and global features with geometric structure information. Based on these discoveries, we propose a 3D point cloud registration method based on geometric structure embedding into the attention mechanism (GraM), which can extract the local features of the non-critical point and global features of the corresponding point containing geometric structure information. According to the local and global features, the simple regression operation can obtain the transformation matrix of point cloud pairs, thereby eliminating the semantics that ignores the geometric structure relationship. GraM surpasses the state-of-the-art results by 0.548° and 0.915° regarding the relative rotation error on ModelNet40 and LowModelNet40, respectively.
The Resilience of Electrical Support in UAV Swarms in Special Missions
Authors: Kabashkin Igor
Published: May 2024

Unmanned aerial vehicle (UAV) swarms serve as a dynamic platform for diverse missions, including communication relays, search and rescue operations, and environmental monitoring. The success of these operations crucially depends on the resilience of their electrical support systems, especially in terms of battery management. This paper examines the reliability of electrical support for UAV swarms engaged in missions that require prioritization into high and low categories. The paper proposes a dynamic resource allocation strategy that permits the flexible reassignment of drones across different-priority tasks, ensuring continuous operation while optimizing resource use. By leveraging the Markov chain theory, an analytical model for the evaluation of the resilience of the battery management system under different operational scenarios was developed. The paper quantitatively assesses the impact of different operational strategies and battery management approaches on the overall system resilience and mission efficacy. This approach aims to ensure uninterrupted service delivery for critical tasks while optimizing the overall utilization of available electrical resources. Through modeling and analytical evaluations, the paper quantifies the impact of various parameters and operating strategies on overall system resilience and mission availability, considering the utilization strategies of batteries and their reliability and maintenance metrics. The developed models and strategies can inform the development of robust battery management protocols, resource allocation algorithms, and mission planning frameworks, ultimately enhancing the operational availability and effectiveness of UAV swarms in critical special missions.
Analysis of Ferroresonance Mitigation Effectiveness in Auxiliary Power Systems of High-Voltage Substations
Authors: Tarko Rafał , Nowak Wiesław , Gajdzica Jakub , Czapp Stanislaw
Published: May 2024

Ferroresonance in power networks is a dangerous phenomenon, which may result in overcurrents and overvoltages, causing damage to power equipment and the faulty operation of protection systems. For this reason, the possibility of the occurrence of ferroresonance has to be identified, and adequate methods need to be incorporated to eliminate or reduce its effects. The aim of this paper is to evaluate the effectiveness of ferroresonance damping in auxiliary power systems of high-voltage substations by selected damping devices. Laboratory experiments, the results of which created bases for the development of models of selected damping devices, are presented. These models were used to simulate the effectiveness of ferroresonance damping in an auxiliary power system of a 220/110 kV substation in the EMTP-ATP program. The analyses showed that control systems with different algorithms of operation are used in damping devices. This knowledge is important when selecting parameters and settings of the applied damping devices for a given network and the disturbances in it. The presented research results have proved the effectiveness of commercially available damping devices, provided their parameters are correctly coordinated with the settings of the power system protection.
Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant
Authors: Al-Dahidi Sameer , Baraldi Piero , Fresc Miriam , Zio Enrico , Montelatici Lorenzo
Published: May 2024

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.
Performance Improvement of a Limaçon Gas Expander Using an Inlet Control Valve: Two Case Studies
Authors: Hossain Md Shazzad , Sultan Ibrahim , Phung Truong , Kumar Apurv
Published: May 2024

Renewable energy-based compact energy-generation systems based on the organic Rankine cycle (ORC) can be employed to meet the ever-growing thirst for affordable and clean energy. The overall performance and effectiveness of ORC systems are constrained by the low efficiency of the gas expander, specifically the positive displacement expander, which is responsible for energy conversion from the working fluid. This low-efficiency scenario can be significantly improved by employing a control valve to regulate and restrict the flow of the working fluid into the expander. A control valve can effectively curve the loss of costly compressed and energized working fluids by allowing them to expand in the expander chamber before discharging through the outlet port. They can thus be used to regulate the amount of energy yield and output power. In this work, two direct drive rotary valves (DDRVs) operated by a stepper motor (SM-DDRV) and rotary solenoid (RS-DDRV) are suggested, and the behavior of the valves is examined. The effect of friction and temperature on the valve response is also studied. Additionally, the effect of inlet control valves on the overall system performance of the limaçon expander is assessed. Thermodynamic properties such as the isentropic efficiency and filling factor are also computed. The effect of leakage due to valve response delay is analyzed at different inlet pressures. The performance indices are compared to the expander performance without any inlet valve. The SM-DDRV setup results in a 14.86% increase in isentropic efficiency and a 220% increase in the filling factor, whereas the RS-DDRV performs moderately with a 2.58% increase in isentropic efficiency and an 80% increase in the filling factor compared to a ported expander. The SM-DDRV provides better performance indices compared to the RS-DDRV and without valve setups. However, the performance of the limaçon expander with the SM-DDRV is sensitive to the inlet pressure and degrades at higher pressure. Overall, the valves proposed in this work present key insights into improving the performance characteristics of gas expanders of ORC systems.
Numerical Investigation of Innovative Photovoltaic–Thermal (PVT) Collector Designs for Electrical and Thermal Enhancement
Authors: Wang Ziqiang , Hou Gaoyang , Taherian Hessam , Song Ying
Published: May 2024

Photovoltaic–thermal (PVT) technology is gaining popularity due to the diminishing availability of traditional fossil fuels and escalating environmental concerns. Enhancing the heat dissipation of PVT to improve its electrical and thermal performance remains a significant task. This study simulates the thermodynamic and heat transfer characteristics in multiple novel PVT structures by examining the impact of various factors such as collector materials, radiation intensity, mass flow rate, and inlet temperature. This work also identifies the optimal mass flow rate for locations with different solar radiation. The numerical results indicate that the electrical efficiency of a designed cylindrical structure has increased by 1.73% while the thermal efficiency has increased by 8.29%. Aluminum is identified as the most cost-effective material for the collector. The optimal mass flow rates in selected locations of Xining, Taiyuan, and Turpan are 0.36 kg/s, 0.35 kg/s, and 0.30 kg/s, respectively. The numerical results provide valuable insight into optimizing the design and operating conditions of PVT systems.
Viability of an Open-Loop Heat Pump Drying System in South African Climatic Conditions
Authors: Ngalonkulu Solomzi Marco , Huan Zhongjie
Published: May 2024

Drying agricultural produce consumes a considerable amount of energy. As an energy-efficient system, a heat pump can improve the energy efficiency of the drying process and hence reduce the energy consumption, especially in South Africa, where both sub-tropical and temperate weather conditions dominate. The objective of this research is to experimentally investigate the impacts of weather conditions on the operational conditions and thermal performance of an open-loop air-source heat pump drying system. The experimental investigation was conducted in a climate chamber where the climate conditions were simulated from −10 °C to 20 °C with an interval of 10 °C for the typical temperature range of the harvesting season in South Africa. The findings indicate that ambient temperatures have a significant impact on both the operating conditions and thermal performance of an open-loop heat pump system; the change in ambient temperatures from −10 °C to 20 °C leads to a 141.6% improvement in the suction pressure, a 214.2% increase in the discharge pressure, and 30.1% increase in the compression ratio, as well as a consequent increase of 130.6% in the refrigerant mass flow rate (from 0.0067 to 0.0155 kg/s), resulting in a corresponding increase in the coefficient of performance (COP) of the heat pump drying system by about 42.1%. Therefore, this study suggests that, while using an open-loop air-source heat pump drying system utilising R134a refrigerant is feasible in South Africa, it may be practically limited to regions with warm climates or during warmer seasons.
Study on Emulsification Effect of Crude Oil in Brine Emulsions by Automated Demulsibility Tester
Authors: Hartyányi Máté , Nagy Roland , Bartha László , Puskás Sándor
Published: May 2024

The purpose of the surfactants used is to greatly reduce the interfacial tension between the crude oil and brine, thereby decreasing the capillary number. The resulting oil-in-water emulsions are often grouped according to the Winsor theory. Oil recovery aims to produce Winsor type-III emulsions because they have the lowest interfacial tension values and the most favorable flow properties. The sensitivity of oil–water–surfactant systems to environmental influences (e.g., mixing speed and equilibration time) increases close to the favorable environmental range (temperature, brine total salt concentration, pressure, etc.) of the Winsor III type, the middle microemulsion phase, which is favorable for crude oil recovery. The tests aimed to investigate the quality and quantity of emulsions prepared with surfactants used in enhanced oil recovery (EOR) using an automatic device to characterize and select surfactants for industrial petroleum applications. An essential method for surfactant selection is to study the emulsifying effect and phase behavior. Phase behavior tests and emulsifying effect tests were performed on surfactants and surfactant packages as a function of mixing parameters. The mixing speed and mixing time can influence the results of the phase behavior and emulsifying effect tests, although during the investigations, other parameters were unchanged.
A Microplastic Pollution Hotspot: Elevated Levels in Sediments from the San Francisco Bay Area
Authors: Dronjak Lara , Rovira Joaquim , Lin Diana , Park June-Soo , Ghosal Sutapa , Expósito Nora , Schuhmacher Marta , Sierra Jordi
Published: May 2024

San Francisco Bay’s sediment is currently monitored for a variety of contaminants; however, data regarding the microplastics (MPs) in the area are still scarce. MPs’ occurrence in sediment samples has gained recognition as a reservoir for MP accumulation. Moreover, Bay sediment is also an important matrix for monitoring because sediment tends to accumulate certain contaminants and act as a source of contaminants in the Bay food web. This study analyzed MPs ranging from 25 µm to 5 mm in surface sediment grab samples (n = 8) and two sediment core samples (n = 2 cores analyzed with 11 samples from different depths). Our findings provide an evaluation of MP levels in different regions of the bay. The MP levels detected in Bay surface grab samples ranged from 2.1 to 11.9 MPs/g dry weight (n = 8), with a mean value of 6.2 MPs/g. The most abundant morphology was fibers, followed by fragments and films.
Advanced Image Analysis and Machine Learning Models for Accurate Cover Factor and Porosity Prediction in Knitted Fabrics: Tailored Applications in Sportswear, Swimwear, and Casual Wear
Authors: Rolich Tomislav , Domović Daniel , Čubrić Goran , Salopek Čubrić Ivana
Published: May 2024

This paper presents a study focused on developing robust algorithms for cover factor and porosity calculation through digital image analysis. Computational models based on machine learning for efficient cover factor prediction based on fabric parameters have also been developed. Five algorithms were devised and implemented in MATLAB: the single threshold algorithm (ST); multiple linear threshold algorithms, ML-1 and ML-2; and algorithms with multiple thresholds obtained by the Otzu method, MT-1 and MT-2. These algorithms were applied to knitted fabrics used for football, swimming, and leisure. Algorithms ML-1 and MT-1, employing multiple thresholds, outperformed the single threshold algorithm. The ML-1 variant yielded the highest average porosity value at 95.24%, indicating the importance of adaptable thresholding in image analysis. Comparative analysis revealed that algorithm variants ML-2 and MT-2 obtain lower cover factors compared to ML-1 and MT-1 but can detect potential void areas in fabrics with higher reliability. Algorithm MT-1 proved to be the most sensitive when it came to distinguishing between different fabric samples. Computational models that were developed based on random tree, random forest, and SMOreg machine learning algorithms predicted cover factor based on fabric parameters with up to 95% accuracy.