Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement of Tiny Object Segmentation Accuracy in Aerial Images for Asphalt Pavement Pothole Detection

In this study, we propose an algorithm to improve the accuracy of tiny object segmentation for precise pothole detection on asphalt pavements. The approach comprises a three-step process: MOED, VAPOR, and Exception Processing, designed to extract pothole edges, validate the results, and manage detected abnormalities. The proposed algorithm addresses the limitations of previous methods and offers several advantages, including wider coverage. We experimentally evaluated the performance of the proposed algorithm by filming roads in various regions of South Korea using a UAV at high altitudes of 30–70 m. The results show that our algorithm outperforms previous methods in terms of instance segmentation performance for small objects such as potholes. Our study offers a practical and efficient solution for pothole detection and contributes to road safety maintenance and monitoring.
Characterization of the Synergistic Antioxidant Activity of Epigallocatechin Gallate (EGCG) and Kaempferol

Epigallocatechin gallate (EGCG) and kaempferol exhibit cellular antioxidant activity; however, their interactive effects in terms of antioxidant actions and underlying mechanisms remain unclear. In this study, their cytoprotective effects were examined against 2,2-azobis (2-amidinopropane) dihydrochloride solution (ABAP)-induced oxidative stress in HepG2 cells. The results showed that the median effective dose (EC50) of the EGCG and kaempferol (6:1.5, c/c) combination was 3.4 ± 0.1 μg/mL, with a combination index (CIavg) value of 0.54, which represented a significant synergistic effect. Further experiments proved that the combined pretreatment with EGCG and kaempferol exerted protective effects by suppressing reactive oxygen species (ROS) generation, upregulating cellular antioxidant enzyme activities (superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px)) in a dose-dependent manner. The mechanism of synergistic antioxidant effects of EGCG combined with kaempferol may be due to the up-regulation of higher antioxidant enzyme activities that improve the antioxidant capacities and balance the cell oxidative stress. The synergistic antioxidant effect of EGCG and kaempferol can provide a theoretical basis for the development of formulas of functional food ingredients.
Novel Techniques Applied to MIMO Antenna Array for 5G Sub-7 GHz Smartphone Applications

As many modern cities have recently embraced the 5G communication system, its related antenna designs for smartphones operating in the 5G New Radio (NR) band (n77/n78/n79) are vital to the 5G user experience. In this Invited Talk, dualband/multiband and wideband 5G antenna designs with MIMO array technology that can cover the entire 5G NR band n77/n78/n79 (3300–5000 MHz) are initially introduced. To further fulfill the new 5G specifications NR-U (New Radio Unlicensed) Band n46 (5150–5925 MHz) and n96 (5925–7125 MHz), various novel MIMO array antenna design techniques for achieving the entire Sub-7 GHz are uncovered with detailed diagrams to reveal the logic behind each design technique.
From pixels to people: graph based methods for grouping problems in computer vision

In this dissertation, we study grouping problems in computer vision using graph-based machine learning techniques. Grouping problems abound in computer vision and are typically challenging ones in order to generate perceptually and semantically consistent results. In the context of this dissertation, we strive to (1) group image pixels into meaningful objects and backgrounds; (2) group interacting people present in a video into sound social communities. Traditionally, in a graph-based formulation, the entities (e.g. image pixels) are treated as graph vertices and their interrelations are encoded in a weighted adjacency matrix of the graph. In this dissertation, we go beyond standard graph construction methods by building on probabilistic image hypergraphs and learned social graphs (or social networks) for the two parts of work respectively. Learning on graphs results in labeling of entities. In our work, graph based smoothness and modularity measures are examined and adapted to the problems under study. Under this general graph-based framework, the first pursued direction is interactive image segmentation, or the problem of grouping image pixels into meaningful objects and their backgrounds, given a limited number of user-supplied seeds. Our contributions in this direction include the probabilistic hypergraph image model (PHIM) to address higher-order relations among pixels in segment labels, which are commonly ignored in competing approaches. To further alleviate the dependence of interactive segmentation on user-supplied seeds, we introduce diffusion signatures derived from salient boundaries and present a framework for automatically introducing new seeds at critical image locations, in order to enhance segmentation results. Both proposed frameworks are extensively tested on a standard image dataset and achieved excellent quantitative and qualitative results in segmentation. In the second direction, we contribute an automatic framework to infer relations among actors from videos. In particular, we propose a principled graph-based affinity learning method, which synthesizes both co-occurrence information among actors and local grouping cue estimates at the scene level in order to make informed decisions. Once the pairwise affinities between actors are learned from the video content using visual and auditory features, we perform social network analysis based on modularity measures to detect communities, which are groups of actors. Experiments on a dataset of ten movies that we collected have shown promising results. Moreover, the proposed framework has considerably outperformed baseline methods not using visual or auditory features, suggesting the importance of audiovisual cues in high-level relational understanding tasks. In summary, built on a graph-based learning framework, this dissertation makes contributions to grouping problems in computer vision. Specifically, we have proposed effective techniques to solve problems in both low-level analysis of images (segmentation) and highlevel understanding of videos (relational inference).
Agricultural fungicidal effect of floral extracts of Bauhinia variegata, B. forficata and B. purpurea

Bauhinia is a botanical genus widely distributed in parts of Asia and South America. The plant bears beautiful and aromatic flowers that add ornamental beauty to parks and gardens, in addition to being used in medicine and ayurverda. This study aimed to evaluate the floral ethanolic extract of B. variegata, B. forficata and B. purpurea in fungal inhibition against Sclerotinia sclerotiorum, Colletotrichum gloeosporioides and Colletotrichum acutatum. Flowers were harvested in 2021 and 2022. The floral ethanolic extract was produced by the static maceration method. The antifungal activity was performed by the agar diffusion method at different extract s;concentrations. Bauhinia floral extracts showed a potential fungicidal effect on fungal species evaluated in particular for Sclerotinia sclerotiorum, followed by Colletotrichum gloeosporioides and Colletotrichum acutatum.
Using manifold structure for automatic image annotation by fusion of multiple feature spaces

Automatic image annotation has been an active research topic in recent years. Low level features like as color, texture, shape as well as object spatial relations are extracted to represent images in general. These syntaxes are further used to retrieve images from large image data sets. However, the similarity of images could not be found correctly by similarity measures such as Euclidean distance in many situations. On the other hand, graph models have been shown powerful in solving many machine learning problems in recent years. In this paper, we propose a graph-based learning approach, named Conceptual Manifold Structure (CMS), based on transition from conceptual to observation space. In the proposed method, a graph including both the trained and tested samples is constructed by fusion of multiple feature spaces. Conceptual transition in graph structure is found by altering the edge values in an innovative manner. This is caused to learn the manifold structure where the samples dissimilarity is closer to the conceptual distance. Furthermore, the continuity between the instances of a semantic in the conceptual space is kept in feature space. Keeping the continuity in manifold structure is the main idea to decrease the semantic gap in this study. The experiments on different image data sets indicated that the geometrical distances between the samples on the manifold space are closer to their conceptual distance. The proposed method has been compared to other well-known approaches. The results confirmed the effectiveness and validity of the proposed method.
An Automated Sitting Posture Recognition System Utilizing Pressure Sensors

Prolonged sitting with poor posture can lead to various health problems, including upper back pain, lower back pain, and cervical pain. Maintaining proper sitting posture is crucial for individuals while working or studying. Existing pressure sensor-based systems have been proposed to recognize sitting postures, but their accuracy ranges from 80% to 90%, leaving room for improvement. In this study, we developed a sitting posture recognition system called SPRS. We identified key areas on the chair surface that capture essential characteristics of sitting postures and employed diverse machine learning technologies to recognize ten common sitting postures. To evaluate the accuracy and usability of SPRS, we conducted a ten-minute sitting session with arbitrary postures involving 20 volunteers. The experimental results demonstrated that SPRS achieved an impressive accuracy rate of up to 99.1% in recognizing sitting postures. Additionally, we performed a usability survey using two standard questionnaires, the System Usability Scale (SUS) and the Questionnaire for User Interface Satisfaction (QUIS). The analysis of survey results indicated that SPRS is user-friendly, easy to use, and responsive.
Staff Members’ Experience of Italian Shelters for LGBTQIA+ Homeless and Runaway People: An Exploratory Study

Background: Some LGBTQIA+ people, after coming out, experience marginalization and homelessness due to rejection and discrimination from their family and community. The increase in support requests led to the creation of LGBTQIA+ temporary shelter homes worldwide. This study aims to explore the functioning and effectiveness of shelters, analyzing the experiences of staff members in Italy. Methods: Focus groups were held with a total of 15 staff members (age range: 32–53) working in three shelters for LGBTQIA+ people. Data were analyzed qualitatively through the grounded theory methodology. Results: Data coding showed five final core categories: (1) user characteristics; (2) staff characteristics; (3) community relations; (4) activities carried out by services; (5) criteria for intervention assessment and staff satisfaction. Results revealed some criticalities in the effectiveness of these services, particularly the difficulty in achieving autonomy for users, a weakness attributable to the non-exhaustive training of staff members and the funding discontinuity. Conclusion: To improve the efficacy of shelters, this study emphasizes the necessity to (a) carry out an analysis of the vulnerability of the local LGBTQIA+ community, (b) establish a stable network with local services (NHS system), and (c) implement staff members’ psychological training.
Discrepancies with satellite observations in the spatial structure of global precipitation as derived from global climate models

One of the applications of satellite‐derived precipitation datasets, such as Global Precipitation Climatology Project (GPCP) or Climate Prediction Centre Merged Analysis of Precipitation (CMAP), is to validate output from numerical models, either numerical weather prediction (NWP) models, Regional Climate Models (RCMs), Global Climate Models (GCMs) or Earth System Models (ESMs). A qualitative comparison of total annual precipitation and climatology is the first step in detecting model deficiencies and thus improving our understanding of the world's climate. However, spatial (or association) analysis of the precipitation fields offers new insights into model performance and their ability to provide realistic predictions of rain and snowfall in both current and future climates. Here we analyse the spatial structure of precipitation according to 40 GCMs for 20 years (January 1980 to December 1999), quantitatively comparing the modelled precipitation against five observational datasets: three land‐only (CRU, PRECL and GPCC) and two global (GPCP and CMAP). We found discrepancies between the GCMs' predictions and the observational datasets and noted that satellite‐derived datasets are essential for pinpointing areas that require attention. The analyses also revealed a consistent trend towards less spatially correlated fields. This trend is not apparent in aggregated, traditional validation exercises but arises when spatial association indices are applied. So long as the trend is not an artefact in the observational datasets, then we suggest the tendency could be attributed to changes in stratiform/convective partitioning or the result of increasingly intense convection.
Mobile Photography and Open, Networked Learning

This paper explores the intersection of photography and education at a time when both are facing significant changes, partly as a result of the increasing use of digital, mobile devices. Mobile smart phones not only include increasingly good quality built-in cameras, they also enable the immediate editing and sharing of photos through social networks. This reduces the importance of the image as an artifact and increases the social aspect of photography. Similarly, Bring Your Own Device (BYOD) policies in schools and universities provide opportunities to reconsider how and where learning happens. Mobile digital devices allow students to explore, create, communicate and collaborate with their peers in the classroom and with others beyond. Following a review of how networked smart phones are used for photography and in education, I report on two innovative photography courses that make effective use of mobile devices and social networks to empower students and enhance the learning experience. Phonar (photography and narrative) is a hybrid course that is offered to fee-paying place-based students at the University of Coventry and is also open for others to access for free. Phonar Nation is a free, non-credit course designed for teenagers and delivered through a mobile Web app with the help of community mentors. I discuss how their use of mobile media and social networks, together with collaboration and open strategies, serve as examples of how mobile devices can be effectively used to serve creative practice and extend learning.
Advancement in Paper-Based Electrochemical Biosensing and Emerging Diagnostic Methods

The utilization of electrochemical detection techniques in paper-based analytical devices (PADs) has revolutionized point-of-care (POC) testing, enabling the precise and discerning measurement of a diverse array of (bio)chemical analytes. The application of electrochemical sensing and paper as a suitable substrate for point-of-care testing platforms has led to the emergence of electrochemical paper-based analytical devices (ePADs). The inherent advantages of these modified paper-based analytical devices have gained significant recognition in the POC field. In response, electrochemical biosensors assembled from paper-based materials have shown great promise for enhancing sensitivity and improving their range of use. In addition, paper-based platforms have numerous advantageous characteristics, including the self-sufficient conveyance of liquids, reduced resistance, minimal fabrication cost, and environmental friendliness. This study seeks to provide a concise summary of the present state and uses of ePADs with insightful commentary on their practicality in the field. Future developments in ePADs biosensors include developing novel paper-based systems, improving system performance with a novel biocatalyst, and combining the biosensor system with other cutting-edge tools such as machine learning and 3D printing.
Functional Magnetic Resonance - and Diffusion Tensor Imaging Investigations of Pure Adult Gilles de la Tourette Syndrome

Gilles de la Tourette syndrome (GTS) is a chronic neuropsychiatric disorder characterized by multiple motor and vocal tics, affecting approximately 1% of the population. The precise neuropathology of GTS has not yet been delineated, but current models implicate subcortical and cortical areas the cortico-striato-thalamo-cortical (CSTC) circuit. The majority of studies in the literature have either dealt with GTS with comorbid conditions and/or children with GTS. As these factors are known to affect brain structure and function, it unknown what the neurobiological underpinnings of pure adult GTS are. The objective of this body of work was to use functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) to characterize differences in brain function and structure in pure adult GTS patients versus ageand sex-matched controls. I employed a series of three distinct analyses for this purpose, based upon current models of CSTC circuit-related dysfunction in GTS. In the first, GTS patients and control participants executed three finger-tapping paradigms that varied in both complexity and memory requirements. These finger-tapping tasks were modeled after previous studies that showed CSTC circuit-related activity in healthy individuals. Using a multivariate statistical technique to assess task-related patterns of activation across the
Accuracy of Manual Snow Sampling, Depending on the Sampler’s Cross-Section—A Comparative Study

Snow sampling, either by inserting a tube through the entire snowpack or by taking samples from the vertical profile, is widely applied to measure the snow depth, density, and snow water equivalent (SWE). A comparative study of snow-sampling methods was carried out on 24 March 2022 in Sodankylä, Finland. Six groups from five countries (Estonia, Finland, Greenland, Iceland, and Sweden) participated, using 12 different snow samplers, including 9 bulk tube samplers and 3 density cutters. The cross-sectional area of the SWE samplers varied from 11 to 100 cm2, while tube length varied from 30 cm to 100 cm. The cross-sectional area of the density profile cutters varied from 100 cm2 to 200 cm2 and the vertical sampling step varied from 5 cm to 10 cm. The samples were taken from snow pits in 55–65-centimeter-deep snow cover in a flat area with sparse pine trees, with the pits at a maximum distance of 10 m from each other. Each tube sampling series consisted of 3–10 vertical columns to ensure statistical validation. The snowpack was relatively soft, with two moderately hard crust layers. The density recorded in the tube sample measurements varied from 218 to 265 kgm−3. The measurement results of SWE, however, varied depending on the sampling equipment used, ranging from 148 to 180 kgm−2, with two outliers of 77 and 106 kgm−2, both with 11 cm2 samplers.
Exploring the Capability of NASNet Model for Plant Classification on GRASP-125 Dataset

Plant classification and identification, which has strong ties to both computer science and taxonomy, is becoming an increasingly dynamic area of research within the field of computer recognition. Researchers have utilized different techniques including image processing, pattern recognition, and machine learning to develop systems capable of accurately identifying and classifying plants. Access to large image databases and advances in image processing and artificial intelligence have led to the development of highly accurate plant identification systems. These systems have the potential to greatly reduce the amount of time and proficiency required for plant identification, which are only achievable by skilled taxonomists. In this study, we experimented with the Greek vAScular Plants (GRASP) dataset, consisting of images across 125 different plant species native to the mountains belonging Oiti and Parnassus in central Greece, to perform automatic plant identification using a deep learning approach namely NASNet. These plant species, which include both rare and aesthetically pleasing varieties, are often found along popular hiking routes in the region. Our method for plant classification utilizes deep learning techniques on the GRASP-125 dataset, with the goal of improving the accuracy and efficiency of plant identification.
In2O3 Nanofiber Neuromorphic Transistors for Reservoir Computing

In this letter, we propose neuromorphic transistors employing indium oxide (In2O3) nanofibers as the channel layers. Basic synaptic function, such as short-term memory can be emulated by one nanofiber neuromorphic transistor. Nonlinear synaptic function and short-term memory characteristic of such neuromorphic transistors are favorable for reservoir computing (RC) system with high energy-efficiency. Ultra-low energy consumption (15 pJ per reservoir state) and ultra-high accuracy (100%) of speech digital recognition are realized based on such nanofiber neuromorphic transistors, proving a great potential of the RC system for intelligent processing tasks.
Performance of cross-bred (White Pekin × Khaki Campbell) ducks under intensive system of rearing

In the present investigation, the performance of a crossbred (White pekin × Khaki Campbell) duck of meat and egg variety produced through artificial insemination were studied under intensive system of management. A total of 157 day old ducklings were reared in the experimental duck farm with standard management practice of brooding, and growing with balanced feed and health management. However, at 16th wk of age, 30 female ducks were confined to individual cage to record their egg laying performances. The growth performance revealed the significant difference between male and female from 8th to 24th week of age. Most of the body measurement parameters found higher than that of Khaki Campbell and less than that of White Pekin ducks of similar age. The blood biochemistry estimates revealed no variation between male and female ducks except for albumin and calcium wherein the value is highly significant for females. The carcass quality estimates of 20th week male ducks were at par with their age. The average age of sexual maturity was recorded as 131.83±1.65 days and age at 50% egg production in the flock was 139 days. The duck day egg production per cent up to 45th week of age was 80.31±3.44 and it was also recorded that in 108 occasions, one duck laid more than one egg. The egg estimates study of the experimental duck eggs (100 nos) at 40 wks age revealed 62.31± 0.44 g egg weight and 92.378 ± 0.453 as Hague unit scale. The study revealed that the production and reproduction parameters of the cross-bred (WP × KC) ducks may meet the expectation of farmers to be benefitted through both meat and egg production.