Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Ranking-Based Preference Completion via Graph Pattern Matching and Rematching

As an emerging topic on preference learning, aiming at deducting the linear order of alternatives from the partial ranking, preference completion is to complete the preference of the target agent to form a linear order from the preferences of other agents under certain complex requirements. In order to improve the effectiveness and efficiency of preference completion in Big Data environments, firstly the preference graph is introduced to represent the collective preference of the agents over the alternatives with a certain consensus algorithm following the preference of the target agent. This preference graph can preserve rich information between agents. In addition, with the introduction of fuzzy ranking, it can illustrate the fuzziness of the target agent that can include several ranking options of the target agent over alternatives. Then, the satisfied preference can be matched from the preference graph with the fuzzy ranking requested by the target agent via isomorphism-based graph pattern matching. With the matched preference, the preference of the target agent can be completed. If the completed preference is not satisfied, the target agent can modify the fuzzy ranking, process the graph pattern rematching and complete the preference again. The experimental results show that with several real datasets the effectiveness and efficiency of the fuzzy ranking-based preference completion via graph pattern matching can be validated.
Carbon–TiO2 Hybrid Quantum Dots for Photocatalytic Inactivation of Gram-Positive and Gram-Negative Bacteria

Carbon–semiconductor hybrid quantum dots are classical carbon dots with core carbon nanoparticles doped with a selected nanoscale semiconductor. Specifically, on those with the nanoscale TiO2 doping, denoted as CTiO2-Dots, their synthesis and thorough characterization were reported previously. In this work, the CTiO2-Dots were evaluated for their visible light-activated antibacterial function, with the results showing the effective killing of not only Gram-positive but also the generally more resistant Gram-negative bacteria. The hybrid dots are clearly more potent antibacterial agents than their neat carbon dot counterparts. Mechanistically, the higher antibacterial performance of the CTiO2-Dots is attributed to their superior photoexcited state properties, which are reflected by the observed much brighter fluorescence emissions. Also considered and discussed is the possibility of additional contributions to the antibacterial activities due to the photosensitization of the nanoscale TiO2 by its doped core carbon nanoparticles.
Abnormal Activity Detection and Classification of Bus Passengers With In-Vehicle Image Sensing

As the self-driving technology is getting mature for public transportation applications, the safety concern of onboard passengers has become an important issue. It is essential to identify inappropriate or hazardous behaviors of passengers for the vehicles without human operators. In this work, we propose a technique to detect and classify the abnormal activities of passengers in a bus environment. Different from the existing human activity classification algorithms, our approach reduces the occlusion and increases the recognition rate by acquiring images from an overhead vision system. To overcome the increased complexity on feature extraction and classification, an action recognition network for top-view images are proposed by incorporating both spatial and temporal information. An image dataset, BUS-HAR, is generated for practical application scenarios with bus passengers. Experiments using real-world scene images have demonstrated the feasibility of our technique compared to existing approaches. The codes and image dataset are made available publicly at https://github.com/richardkuo1999/passenger-action-recognition.
Sinhala Sign Language Translation through Immersive 3D Avatars and Adaptive Learning

This research paper addresses the challenge of bridging the communication gap between sign language users and non-users through a comprehensive study on sign language translation. The focus is on Sinhala Sign Language (SSL), and the research problem encompasses various innovative components. These include the conversion of Sinhala Sign Language (SSL) into text, emotion recognition and representation, personalized sign language learning, and accurate identification and translation of sign language gestures. By leveraging machine learning, deep learning, 3D modeling, and image/video processing techniques, the system enhances accessibility, communication, and education for the deaf and hard-of-hearing community. The outcomes of this research encompass an accurate and visually appealing sign language translation system, personalized learning interfaces, and the integration of emotions into sign language communication. The research contributes by providing insights into SSL grammar and lexicon, developing a tailored sign language learning curriculum, implementing reinforcement learning algorithms for personalized feedback, utilizing 3D components for enhanced visual quality, and optimizing deployments and models for improved performance. The proposed system has significant implications for improved accessibility, education, and inclusivity, fostering effective communication and understanding between sign language users and non-users.
Modeling of Human Rabies Cases in Brazil in Different Future Global Warming Scenarios

Bat species have been observed to have the potential to expand their distribution in response to climate change, thereby influencing shifts in the spatial distribution and population dynamics of human rabies cases. In this study, we applied an ensemble niche modeling approach to project climatic suitability under different future global warming scenarios for human rabies cases in Brazil, and assessed the impact on the probability of emergence of new cases. We obtained notification records of human rabies cases in all Brazilian cities from January 2001 to August 2023, as reported by the State and Municipal Health Departments. The current and future climate data were sourced from a digital repository on the WorldClim website. The future bioclimatic variables provided were downscaled climate projections from CMIP6 (a global model ensemble) and extracted from the regionalized climate model HadGEM3-GC31-LL for three future socioeconomic scenarios over four periods (2021–2100). Seven statistical algorithms (MAXENT, MARS, RF, FDA, CTA, GAM, and GLM) were selected for modeling human rabies. Temperature seasonality was the bioclimatic variable with the highest relative contribution to both current and future consensus models. Future scenario modeling for human rabies indicated a trend of changes in the areas of occurrence, maintaining the current pace of global warming, population growth, socioeconomic instability, and the loss of natural areas. In Brazil, there are areas with a higher likelihood of climatic factors contributing to the emergence of cases. When assessing future scenarios, a change in the local climatic suitability is observed that may lead to a reduction or increase in cases, depending on the region.
Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Introduction The utilization of social media presents a promising avenue for the prevention and management of diabetes. To effectively cater to the diabetes-related knowledge, support, and intervention needs of the community, it is imperative to attain a deeper understanding of the extent and content of discussions pertaining to this health issue. This study aims to assess and compare various topic modeling techniques to determine the most effective model for identifying the core themes in diabetes-related tweets, the sources responsible for disseminating this information, the reach of these themes, and the influential individuals within the Twitter community in India. Methods Twitter messages from India, dated between 7 November 2022 and 28 February 2023, were collected using the Twitter API. The unsupervised machine learning topic models, namely, Latent Dirichlet Allocation (LDA), non-negative matrix factorization (NMF), BERTopic, and Top2Vec, were compared, and the best-performing model was used to identify common diabetes-related topics. Influential users were identified through social network analysis. Results The NMF model outperformed the LDA model, whereas BERTopic performed better than Top2Vec. Diabetes-related conversations revolved around eight topics, namely, promotion, management, drug and personal story, consequences, risk factors and research, raising awareness and providing support, diet, and opinion and lifestyle changes. The influential nodes identified were mainly health professionals and healthcare organizations. Discussion The study identified important topics of discussion along with health professionals and healthcare organizations involved in sharing diabetes-related information with the public. Collaborations among influential healthcare organizations, health professionals, and the government can foster awareness and prevent noncommunicable diseases.
Studies on the Effect of Piperine on Hepatocyte Nuclear Factor 1 Alpha (HNF-1α) and Sterol Regulatory Element-Binding Protein 1c (SREBP-1c) Levels in High-Fat-Diet and Sucrose-Induced Type 2 Diabetes Mellitus Rats

Background: Piperine, a naturally occurring compound in black pepper (Piper nigrum), is known for its potential health benefits, including its reported enhancement of insulin sensitivity. However, the precise impact of piperine on hepatocyte nuclear factor 1 alpha (HNF-1α) and sterol regulatory element-binding protein 1c (SREBP-1c), transcription factors for insulin signaling and glucose metabolism in hepatocytes, remains unclear. Objective: This study aims to investigate the effect of piperine, compared to metformin, on blood glucose and insulin levels by modifying the expression of hepatic HNF-1α and SREBP-1c in high-fat-diet (HFD) and sucrose-induced type 2 diabetes mellitus (T2DM) rats and in human Chang liver cells. Methods: Adult male albino rats were categorized into four groups: group 1 as the control, group 2 as T2DM, group 3 as T2DM rats treated with piperine (40 mg), and group 4 as T2DM rats treated with metformin (50 mg). Fasting blood glucose (FBG) and serum insulin levels were measured using enzyme-linked immunosorbent assay (ELISA), while real-time polymerase chain reaction (RT-PCR) analysis was conducted to assess the mRNA expression of HNF-1α and SREBP-1c. Further, piperine was treated with normal and high glucose-induced Chang liver cells, and gene expression was analysed. Data analysis was performed using one-way analysis of variance (ANOVA), with a significance set at p<0.05. Results: Treatment with piperine led to a notable decrease in blood glucose levels and circulating insulin when compared with T2DM rats (group 2). Additionally, piperine administration resulted in the upregulation of HNF-1α mRNA expression and downregulation of SREBP-1c mRNA levels whose effects were found to be near that of the control and standard drug metformin's effects. In vitro study also confirmed that piperine improved the HNF-1α expression and reduced the expression of SREBP-1c in Chang liver cells. Conclusion: Our findings suggest that piperine treatment effectively regulates hyperglycemic and hyperinsulinemic insulin resistance in the liver by modulating the expression of HNF-1α and SREBP-1c. Consequently, piperine emerges as a promising candidate for therapeutic intervention in managing T2DM.
Improved Personnel Statistics Algorithm for Construction Main Working Face Based on YOLOv5

The statistics of the number of personnel on the construction main working face is one of the important indicators for identifying construction progress, and is of great significance for the refined management and resource allocation of construction projects. The deep learning object detection algorithm can effectively replace the existing manual statistics method, reduce mechanical and time-consuming statistical work, and improve statistical accuracy. However, existing object detection algorithms have technical shortcomings such as information loss, insufficient features, and scale imbalance in detecting such small-sized entities. Therefore, this study improves the YOLOv5 object detection algorithm through methods such as image enhancement, multi-scale samples, data enhancement, and model improvement. The experimental results show that our improved algorithm achieves a mean average precision of 83.3%, significantly improving accuracy and robustness, and effectively solving the problem of detecting small and medium-sized personnel in the captured images of the construction main working face.
Intelligent Surface Defect Detection of Hot Rolled Strip Steel Using YOLOv8 Framework

Hot-rolled strip steel is a common metal product widely used in intelligent industries such as construction, machinery manufacturing, and automotive production. With the development of industrial modernization, the quality inspection of hot-rolled strip steel is of great significance. Previously, deep learning-based computer vision technology has proven to be highly effective in defect detection. In this paper, we propose a method for detecting surface defects in hot-rolled strip steel using the You Only Look Once v8m (YOLOv8m) framework. We validate the performance of the YOLOv8m model using the open-source NEU Surface Defect Dataset from Northeastern University. Furthermore, we train the dataset using the Single Shot MultiBox Detector (SSD) and Faster R-CNN for comparison. The results in terms of accuracy and lightweight design indicate that YOLOv8m outperforms the others and is better suited for practical applications. Additionally, due to the lightweight design of YOLOv8m, our method can run in real-time on embedded devices, enabling automated detection on production lines. The surface defect detection method based on YOLOv8 holds significant application potential, marking an important step for the manufacturing industry in quality inspection and supporting intelligent production in the era of Industry 4.0.
Processes of rainfall infiltration and sediment yield in soils containing different rock fragment contents

The presence of rock fragment has effects on some physical properties for homogeneous soil. As a result the processes of rainfall infiltration and sediment yield are changed. Knowledge of the processes affected by rock fragment is benefit to simulate and predict soil and water processes in stony-soil media. On the conditions of rainfall simulation, laboratory soils with four different rock fragment contents (gravimetric contents were 0%, 10%, 20%, and 30%, respectively) were constructed to study the processes of rainfall infiltration and sediment yield as affected by rock fragment. Results show that the maximum of infiltration rate presented at the rock fragment content was 10%, while the rock fragment content exceeded 10% the infiltration rate reduced on the contrary. The initial 0-20 min was the peak of sediment yields for four laboratory soils. After this sediment yields kept relatively steady and had hardly difference among four laboratory soils. During rainfall sediment concentration for soil with 10% rock fragment content held steady and at low level, and other laboratory soils were high at the beginning of rainfall and reduced remarkably within 0-10 min and then close to that of soil with 10% rock fragment content.
The Role of Interleukin 6 in Periodontitis and Its Complications

Interleukin 6 (IL-6) is a pleomorphic pro-inflammatory cytokine that is strongly associated with local as well as systemic inflammatory processes. Its role in physiological and pathogenic processes throughout the human body has been the subject of numerous studies in recent years. Measurements of the IL-6 levels in gingival crevicular fluid (GFC), as well as in serum, can be important diagnostic and prognostic factors in periodontal diseases (PD) and in assessing their impact on a range of related inflammatory diseases. This narrative review explores the significant role of IL-6 in patients with periodontitis and its association with other widespread inflammatory pathologies.
Research of a High Frequency Contactless Battery Charger for the Marine Industry

This article will consider the introduction of a hybrid battery power system into the ship's electric power grid, as well as a contactless charging system for these batteries. As a solution, it is proposed to use ionisters together with lithium-ion batteries. It is proposed to use a high-frequency contactless power supply with stabilized output voltage as a charger.
Evaluating the Effects of Kidney Preservation at 10 °C with Hemopure and Sodium Thiosulfate in a Rat Model of Syngeneic Orthotopic Kidney Transplantation

Kidney transplantation is preferred for end-stage renal disease. The current gold standard for kidney preservation is static cold storage (SCS) at 4 °C. However, SCS contributes to renal graft damage through ischemia–reperfusion injury (IRI). We previously reported renal graft protection after SCS with a hydrogen sulfide donor, sodium thiosulfate (STS), at 4 °C. Therefore, this study aims to investigate whether SCS at 10 °C with STS and Hemopure (blood substitute), will provide similar protection. Using in vitro model of IRI, we subjected rat renal proximal tubular epithelial cells to hypoxia–reoxygenation for 24 h at 10 °C with or without STS and measured cell viability. In vivo, we preserved 36 donor kidneys of Lewis rats for 24 h in a preservation solution at 10 °C supplemented with STS, Hemopure, or both followed by transplantation. Tissue damage and recipient graft function parameters, including serum creatinine, blood urea nitrogen, urine osmolality, and glomerular filtration rate (GFR), were evaluated. STS-treated proximal tubular epithelial cells exhibited enhanced viability at 10 °C compared with untreated control cells (p < 0.05). Also, STS and Hemopure improved renal graft function compared with control grafts (p < 0.05) in the early time period after the transplant, but long-term function did not reach significance. Overall, renal graft preservation at 10 °C with STS and Hemopure supplementation has the potential to enhance graft function and reduce kidney damage, suggesting a novel approach to reducing IRI and post-transplant complications.
Limitations of Results-Based Management on the Example of the Police Force of North-Rhine Westphalia

Results-based management is a key feature of the reforms of New Public Management and a well-known tool in project management as the success of a project is more than the compliance with time, budget, and quality objectives. Although the advantages of results-based management are recognized and impact orientation is successfully applied in many non-governmental organizations, public administration finds it difficult to get grip on this method. The purpose of this paper is to identify essential limitations of result-based management when applied in public administration and to validate the selected findings. Three limitations derived from literature will be analysed: multicausality, contract management, and controlling cycle. The results are then put into the context of the police force of North-Rhine Westphalia who serves as an example of public administration in practice.
Safety evaluation of a buffer used in the lyophilization of canine platelets: type I hypersensitivity reaction in dogs caused by bovine serum albumin

Introduction The present study was designed to evaluate the safety of substances generally used in the preparation of lyophilized platelet products (LPPs) because the possibility of an immune response to bovine serum albumin (BSA) was considered high when using previously described technology. Methods An intradermal skin test, followed by a drug provocation test, was conducted to observe adverse events and identify the substances responsible for an immune response. Five male beagles (2 years old) weighing 12–14 kg were used. The dogs were clinically healthy and had no history of medication use. An intradermal skin test was conducted with each substance [i.e., 4-(2-hydroxyethyl)-1-piperazine ethanesulfonic acid, sodium chloride, potassium chloride, sodium bicarbonate, theophylline, trehalose, and BSA] used in the conventional freeze-dry method. Results In the intradermal skin test, three dogs tested positive at the BSA injection site and showed clinical signs after the intradermal injection, including nausea and vomiting. For the drug provocation test, all dogs received two intravenous injections of an LPP buffer solution. The initial injection was devoid of BSA, whereas the subsequent injection contained BSA. The three dogs that had reacted to BSA in the intradermal skin test exhibited adverse events such as lethargy, vomiting, and nausea immediately after intravenous injection of the LPP buffer containing BSA. All dogs recovered uneventfully after symptomatic treatment in both tests. Discussion The high incidence and severity of type I hypersensitivity reactions observed in this study suggested that BSA is unsuitable as a component of canine LPP.
Application of image-based phenotyping for assessing tolerance of rice varieties to combined water and salt stress

This study aims to evaluate the feasible application of image-based phenotyping in evaluating the morphological responses of rice plants to combined abiotic stresses. The study was based on plants of five rice cultivars, namely Ciherang (CH), Inpari 29 (IN29), Inpari 34 (IN34), IR29 (IR29) and Jeliteng (JL). Stress condition factors consist of control (C), drought stress (D), salinity (S), and their combinations (DS). Observations were made on 3 image-derived traits implicated in rice responses to drought and salinity, including the ratio of the area perimeter (PARa), the ratio of plant greenness (GPARa), and the index of atmospheric resistance (VARI). The ANOVA result showed that interaction between genotypes and treatments is affected in GPARa at 105 days after transplantation (DAT; 0.0023) and significantly differentiates control and stress conditions by t-test analysis. Moreover, this trait enabled us to distinguish tolerant (CH, IN29, IN34) and sensitive varieties (IR29 and JL) from stress conditions with exhibits values 46.43% greater in IN29 concerning IR 29. Consequently, the results of this study could pave the way for the application of image-based phenotyping aimed to speed up breeding programs of more resilient rice varieties for individual and/or combined abiotic stresses that are expected to occur more frequently soon due to climate change.