Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Dimensional Reconstruction of Vascular Model in Intravascular Ultrasound Images Using Semantic Segmentation

Coronary atherosclerotic disease is a major cause of myocardial infarction and usually causes partial or complete coronary artery disorders, which can be life-threatening in severe cases. Currently, the diagnosis of coronary atherosclerosis is mainly achieved through intravascular ultrasound (IVUS). By using intravascular ultrasound, the location and morphology of lesions can be identified early, which is crucial for the early detection and accurate diagnosis of coronary artery disease. Intravascular ultrasound is a widely used imaging technique for diagnosing and treating cardiovascular diseases. In this paper, we propose a new method for three-dimensional (3D) reconstruction of IVUS vascular models based on semantic segmentation. The proposed method utilizes state-of-the-art deep learning techniques to accurately segment the blood vessels in IVUS images. The proposed method utilizes state-of-the-art deep learning techniques to accurately segment the blood vessels in IVUS images. Subsequently, the segmented vessels are used to generate 3D reconstructions of the vascular models. Our method achieves high accuracy and robustness, and it has the potential to enhance the accuracy of IVUS-based diagnosis and treatment planning. Experimental results on a dataset of real-world IVUS images demonstrate the effectiveness of our method.
Structured Pruning in Deep Neural Networks with Trainable Probability Masks

The current trend of over-parameterized Deep Neural Networks makes the deployment on resource constrained systems challenging. To deal with this, optimization techniques, such as network pruning, can be adopted. We propose a novel pruning technique based on trainable probability masks that, when binarized, select the elements of the network to prune. Our method features i) an automatic selections of the elements to prune by jointly training the binary masks with the model, ii) the capability of controlling the pruning level through hyper-parameters of a novel regularization term. We assess the effectiveness of our method by employing it in the structured pruning of the fully connected layers of shallow and deep neural networks where it outperforms the magnitude-based pruning approaches.
ARA-GAN: Adaptive Residual Attention Generative Adversarial Network for Retinal Vessel Segmentation

Automatic segmentation of retinal vessels is a critical task in fundoscopic image analysis. The emergence of deep learning has shown promising abilities of feature representation, particularly with Convolutional Neural Networks (CNNs). However, the fixed receptive field in CNNs limits their ability to adapt to the scale variation of natural vascular networks and capture nonlocal context dependencies across feature maps. To address these limitations, we propose a novel model called ARA-GAN that can adaptively extract nonlocal feature contexts and aggregate multi-scale information for retinal vessel segmentation. The proposed model comprises a novel Generative Adversarial Network (GAN) as the overall framework to obtain global information and strong robustness. Additionally, we integrate Residual Nonlocal Attention (RNA) Module into the framework to adaptively capture nonlocal context dependencies across the input features. Finally, we add a Pyramid Pooling Module (PPM) to extract the morphological characteristics of natural retinal vessels at multiple scales. Our experimental results demonstrate that our method outperforms state-of-the-art approaches on both the DRIVE and STARE datasets.
Inter-organizational network in Indonesia during disasters: Examples and research agenda on disaster management

Indonesia is facing various type of disaster risks, each with its own nature (sudden or slow onset, purely natural or man-made) and coverage of affected areas. Whereas science, technology and engineering intervention requires different modalities for each hazard, little has been known on whether the institutional setup and organizations involvement requires a different or similar types of intervention. Under a decentralized disaster management system, potential involvement of international organizations in response and growing diversified organizations involved in responding to disaster, it is important to understand the nature of inter-organizational network during various type of disasters in Indonesia. This paper is mixture of in-depth literature review and multiple case studies on utilization of social network analysis (SNA) in modelling inter-organizational network during various disasters in Indonesia.
Performance of maize genotypes of Rio Grande do Sul using mixed models

The initial process for genetic gain in maize is obtained by identifying sources of variability in open pollinated populations. For the evaluation of these populations, statistical procedures as REML/BLUP analysis, has been increasingly employed by maize breeders. This study aimed to use the method of mixed models (REML / BLUP) for the estimation of genetic parameters and evaluation to a set of agronomic characters in hybrid genotypes, synthetic and landraces and verify its potential for breeding purposes and/or indicate their crop for small farmers in the Pelotas region. The characters evaluated were: tassel length (TL), tassel branch number (TBN), ear insertion height (EI), grain yield (GY), ear row number (ERN), kernels per row (KPR) and weight of a hundred grains (WHG). Were estimated by the mixed models of variance components by restricted maximum likelihood (REML) and the components of the prediction of the average, best linear pre­dictor unbiased (BLUP). The best genotypes which presented final genotypic values superior to the general mean were the double hybrid COODETEC 308 for the characters TL, TBN, EI, GY and ERN; the single hybrid AS1551, triple hybrid AS3466 and synthetic BRS Planalto for TBN, EH, GY and ERN. Also, the pop­ulations Caiano Branco for TL, ERN and EI; Caiano Rajado for GY and ERN; Criolao for TBN and WHF; Dente de ouro for TL and ERN; Branco Roxo Indio for TL and WHG and Argentino Branco for TL and TBN. The genotypes BRS Planalto and Caiano Rajado show grain yield above the overall average and show promise for use by small and breeding programs.
Characterization of Radiation Hardness of Organic Transistors for a Flexible X-Ray Imager

We present the characterization of complementary p-type and n-type organic transistors in terms of X-ray radiation hardness. These transistors are fabricated as the backplane of an array of photo-conductive pixels based on perovskites for direct X-ray imaging on flexible substrates. Experimental results show that irradiation up to 1 kGy produces negligible effects (a tolerable maximum shift of the transistor threshold voltage of about 40% right after exposure, which is almost completely recovered after 24 hours at rest).
Media for Enhanced Phosphorus Removal from Secondary Wastewater Effluent

Constructed subsurface flow wetlands have the potential for high phosphorus removal through chemical adsorption onto the media. Over the past decade researchers at Griffith University, Australia have been using 240L mesocosms to investigate the long term sustainability and performance efficiency of using different soil media and plant species to maximise nutrient removal from secondary wastewater effluent. The performance efficiency of fifteen different media mixes including amendments for enhanced phosphorus adsorption, and ten different plant species have been investigated. Total Phosphorus removal was highest (94-99%) in sand media amended with Water Treatment Residuals (WTR), followed by ‘brickies loam’ (92%), then sand amended with Red Mud or Krasnozems (86-89%) and lowest in gravel (44%). Phosphorus removal using sand with WTR had the greatest efficiency when treating influent with P concentrations ranging from 0.5-8mg/L (90-99%). The addition of biochar to sand did not enhance P removal during a 2 year trial. Leaching and export of P occurred from the media either after heavy rain or loading with stormwater having low P concentrations (< 0.06mg/L). However, following such ‘flushing events’ P sorption and hence P removal efficiency were enhanced. Mature plant assemblages could uptake up to 16gP/m2/year. Of the herbaceous plants the perennial grasses Pennisetum alopecuroides and Vetiver -Chrysopogon zizanioides, and the sedge Carex appressa had the highest biomass. The woody species Callistemon pachyphyllus and Melaleuca quinquenervia had the highest carbon sequestration (1000g C/m2/y). Thus various sandy loam media can be used to enhance long term effective phosphorus removal, and plants can be used for carbon credits.
Potential för ökad tillförsel och avsättning av inhemsk biomassa i en växande svensk bioekonomi

I denna studie analyseras potentialen for en okad tillforsel av biomassa fran skog, jordbruk och akvatiska system i Sverige baserat pa en sammanstallning och bearbetning av aktuella potentialstudier. Analysen inkluderar tekno-ekonomiska begransningar och minimering av risker for miljomalskonflikter vid en okad produktion av biomassa. Dessutom analyseras potentialen for en okad avsattning av biomassa for att ersatta fossil energi och ravara inom olika sektorer. Tidsperspektivet stracker sig fran idag till 2050. Resultaten visar att det finns en relativt god balans mellan de uppskattade okade tillforsel- och avsattningspotentialerna inom tidsperspektivet idag till 2030 och att dessa uppgar till cirka 40-50 TWh biomassa per ar. Kring 2050 bedoms tillforselpotentialen kunna oka till cirka 70-90 TWh per ar medan avsattningspotentialen bedoms kunna oka till cirka 60-70 TWh per ar. Tillforseln av skogsbaserad biomassa ar nagot hogre an jordbruksbaserad medan akvatisk biomassa bedoms bli marginell. Det finns dock ett stort osakerhetsintervall bade vad galler okad tillforselpotential och avsattningspotential, d v s beroende pa vilka utvecklingsvagar som valjs kan teoretiskt saval betydande underskott som overskott av biomassa fas. For att minimera risker for miljomalskonflikter kravs langsiktigt uthalliga produktionssystem som bl a har ett okat landskapsperspektiv, i kombination med atgarder pa anvandarsidan for att begransa okningen av biomassabehovet genom energieffektivisering och elektrifiering inom transport-, industri- och varmesektorn. For att de langsiktigt uthalliga okade tillforsel- och avsattningspotentialerna ska kunna realiseras kravs skarpta styrmedel och incitament inom alla berorda sektorer. (Less)
Enhancing Electric Vehicle Comfort with Magnetostrictive Energy Harvesting

The motion of electric vehicles on roads is commonly affected by vibrations arising from the unevenness of the ground and, partially, from the electric motor. This paper proposes a novel approach to harness these vibrations using an Energy Harvesting device based on magnetostrictive materials, with the primary objective of recovering otherwise wasted energy and enhancing passenger comfort. The key concept underlying this idea involves integrating the harvester between the car's body and suspensions. By doing so, the initial energy generated by the car body's vibrations is partially converted into electrical energy before reaching the suspensions, thereby leading to improved comfort. The same concept applies between the car body and the motor mounts. The performance of a prototype device has been experimentally tested under realistic dynamic mechanical stresses and the main results are presented herein.
Towards IoT Ambient Intelligence for Industry 4.0

The advances in Internet of Things (IoT), sensors, communications, artificial intelligence, and augmented reality technologies make it possible to conceive smart spaces able to monitor the environment, generate enhanced context information (for example, accurate positioning, situational awareness), take logical decisions, and adapt to the environment’s characteristics and user needs intelligently. These developments fit under the Ambient Intelligence idea, which strives to close the gap between the virtual and real worlds. This paper’s main goal is to illustrate how these technologies might be applied in an Industry 4.0 environment to boost IoT ambient intelligence and raise worker safety. Ultra-Wideband (UWB), Computer Vision (CV), Visible Light Positioning (VLP), Optical Camera Communications (OCC), and Augmented Reality (AR) are some of the technologies used. The paper reports on a practical application of these technologies to provide the position and related information from humans, moving autonomous vehicles and other IoT sensors, by using a diverse range of detection methods and user-friendly AR presentation and interaction features, with can contribute to enhance the productivity and safety in Industry 4.0 environments.
Auditory and semantic cues facilitate decoding of visual object category in MEG

Sounds (e.g., barking) help us to visually identify objects (e.g., a dog) that are distant or ambiguous. While neuroimaging studies have revealed neuroanatomical sites of audiovisual interactions, little is known about the time-course by which sounds facilitate visual object processing. Here we used magnetoencephalography (MEG) to reveal the time-course of the facilitatory influence of natural sounds (e.g., barking) on visual object processing, and compared this to the facilitatory influence of spoken words (e.g., “dog”). Participants viewed images of blurred objects preceded by a task-irrelevant natural sound, a spoken word, or uninformative noise. A classifier was trained to discriminate multivariate sensor patterns evoked by animate and inanimate intact objects with no sounds, presented in a separate experiment, and tested on sensor patterns evoked by the blurred objects in the three auditory conditions. Results revealed that both sounds and words, relative to uninformative noise, significantly facilitated visual object category decoding between 300-500 ms after visual onset. We found no evidence for earlier facilitation by sounds than by words. These findings provide evidence for a semantic route of facilitation by both natural sounds and spoken words, whereby the auditory input first activates semantic object representations, which then modulate the visual processing of objects.
Simulation of Rwa Bhineda's Integrated Weighted Product Method Calculation in Alkin-Provus Amalgamation Evaluation Application

The aim of this study was to identify the dominant indicators influencing the success of implementing asynchronous-synchronous learning at IT vocational schools in Bali. This was accomplished by showcasing the phases and level of simulation for the Weighted Product method's calculation that was combined with the Rwa Bhineda idea. This research employed a quantitative approach. Ten experts participated in the evaluation of the quality of the results from the Weighted Product computation simulation. To evaluate the caliber of the simulation results, a questionnaire was employed as the assessment method. The results analysis of the quality assessment of the Weighted Product simulation computing method combined with Rwa Bhineda was conducted using a quantitative descriptive technique. This analysis approach evaluated the simulation results of the Weighted Product method's calculation to quality requirements using a five-point grading system as a reference. The results of the study show that 88.67% of respondents evaluated the stages and quality of the Weighted Product calculation simulation method integrated with Rwa Bhineda as good in the Alkin- Provus Amalgamation evaluation application.
Performance Evaluation of OPC UA and MQTT for ETAT Smart Lab (ESL)

The Eastern Economic Corridor (EEC) serves as a key and central strategic initiative within the broader Thailand 4.0 framework. The ETAT project aims to establish dedicated engineering education and training centers for participating universities, providing essential support and skills development opportunities for engineers and young professionals. As one of the project partners, Rajabhat Rajanagarindra University (RRU) has undertaken the development of the ETAT Smart Lab to design a smart agricultural system that incorporates the Internet of Things (IoT), programmable logic controllers (PLCs), and Raspberry Pi. In this study, we focus on the ETAT Smartlab environment, specifically on the task of transferring data from RS-485 Modbus sensors to the PLCnext platform using both OPC UA and MQTT protocols. Our method for evaluating performance in this context focuses on measuring the time stamp differences between the source and destination endpoints. The research shows that the OPC UA protocol provides superior performance for transmitting sensor data to ETAT Smart Lab.
Preclinical Assessment of Upper Limb Tremor in Parkinson's Disease with Deep Learning and Wearable Technology

Tremors are typically experienced by patients at the beginning of Parkinson's disease (PD). Clinicians evaluate clinical symptoms based on scale and experience, but mild tremors do not have significant characteristics and are difficult to observe with the naked eye. Implementing an intelligent and objective method to identify PD patients with early tremor symptoms and healthy controls (HC) is necessary. This study used wearable sensors to collect 9-axis inertial signals and 2-channel sEMG signals at the wrists of 13 HC and 24 PD patients from Dalian Municipal Central Hospital. Based on a Long Short-Term Memory Network (LSTM), an attention mechanism, and a Fully Convolutional Network (FCN), we develop a model to classify and recognize data from PD patients. Compared the proposed method with several classification methods, the results showed that the proposed method achieved higher classification accuracy (91.78%), precision (100%), recall (87.50%), and F1-score (93.33%) of PD class than Support Vector Machine, FCN, and LSTM. The computing time of the proposed method is approximately 1 second. The proposed method identifies early PD patients by pre-clinical assessment of mild upper limb tremors, which is valuable for early treatment and rehabilitation.
ROLE OF FAT TALK ON THE RELATIONSHIPS IN THE BRUNEIAN ROLE OF FAT TALK ON THE RELATIONSHIPS IN THE BRUNEIAN CHINESE SOCIETY CHINESE SOCIETY

This research paper examines the role of “fat talk” in relationships in Bruneian Chinese society. “Fat talk” refers to negative comments about one's weight or body shape towards oneself or others. Using qualitative research methods, the study explores the perceptions and experiences of Bruneian Chinese individuals regarding “fat talk”, factors leading to the discussion of the topic, and its impact on their relationships with their peers. The findings suggest that while “fat talk” is prevalent in Bruneian Chinese society, it can have both positive and negative effects on relationships. The study aims to highlight the importance of addressing the “fat talk” issue in Bruneian Chinese society to promote body positivity and healthy communication in relationships.
Differential response of corals to regional mass‐warming events as evident from skeletal Sr/Ca and Mg/Ca ratios

During the summer of 2010/2011, a regional marine heat wave resulted in coral bleaching of variable severity along much of the western coastline of Australia. At Ningaloo Reef, a 300 km long fringing reef system and World Heritage site, highly contrasting coral bleaching was observed between two morphologically distinct nearshore reef communities located on either side of the Ningaloo Peninsula: Tantabiddi (∼20% bleaching) and Bundegi (∼90% bleaching). For this study, we collected coral cores (Porites sp.) from Tantabiddi and Bundegi reef sites to assess the response of the Sr/Ca temperature proxy and Mg/Ca ratios to the variable levels of thermal stress imposed at these two sites during the 2010/2011 warming event. We found that there was an anomalous increase in Sr/Ca and decrease in Mg/Ca ratios in the Bundegi record that was coincident with the timing of severe coral bleaching at the site, while no significant changes were observed in the Tantabiddi record. We show that the change in the relationship of Sr/Ca and Mg/Ca ratios with temperature at Bundegi during the 2010/2011 event reflects changes in related coral “vital” processes during periods of environmental stress. These changes were found to be consistent with a reduction in active transport of Ca2+ to the site of calcification leading to a reduction in calcification rates and reduced Rayleigh fractionation of incorporated trace elements.
The Use of SPOD and Spherical Harmonics for the Analysis of EEG Data

The assessment of mental workload from electroencephalogram (EEG) data for brain-computer interfaces (BCI) poses some challenges due to the interaction of spatial and temporal features within the data. Similar challenges are well known in the analysis of turbulent flows, which exhibit complex space-time correlations resulting from large-scale structures. The similarities motivated us to conduct this feasibility work of applying analytic methods of fluid dynamics – i.e., a scheme combining spectral proper orthogonal decomposition (SPOD) and spherical harmonics as basis functions – to EEG data for identifying features representing mental states. Based on EEG data of an existing BCI Hackathon, the scheme yielded some relevant features across subjects and sessions by relying only on a Fourier transform in time and the basis functions in space. The features were then classified by employing a conventional support vector machine algorithm to produce an accuracy comparable to those reported in a previous study on the same EEG data. This performance comparability indicates the scheme's potential for analyzing EEG data in BCI applications. Nevertheless, future work is needed to select specific features as general indicators of mental workload.
Exposing Computer-Generated Images Via Amplified Texture Differences Learning

Many Computer-Generated (CG) images are spreading widely on the Internet, which may deliberately misinform or deceive the public. Therefore, distinguishing CG images from natural photographic (PG) has become a frontier research topic in the field of image forensics. Although many algorithms have been proposed, it is still very challenging to detect CG images generated by the recent cutting-edge generative methods. Besides, most existing algorithms tend to generalize poorly when facing different unseen multimodal generative models. To address this issue, a novel method based on amplified texture differences learning is proposed to tackle this problem. We first design a deep texture enhancement module for discriminative texture amplification. Specifically, a semantic segmentation module is utilized to generate semantic segmentation map for the affine transformation operation guidance, which can be further used to recover the texture in different regions of the input image. Then, the combination of the original image and the high-frequency components of the original and enhanced images are fed into a hybrid neural network equipped with attention mechanisms, which refines intermediate features and facilitates trace exploration in spatial and channel dimensions respectively. By verifying on several commonly used benchmark datasets and a newly constructed dataset11The benchmark is available at https://github.com/191578010/DSGCG. with more realistic and diverse images, the experimental results demonstrate that the proposed approach outperforms some existing methods.