Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of Labrador Sea temperature and salinity variability on density and the subpolar AMOC in a decadal prediction system

Labrador Sea density variability is important for Atlantic Meridional Overturning Circulation (AMOC) dynamics and hence decadal variability in the Atlantic. We investigate whether temperature or salinity dominate top 500 m interannual Labrador Sea density variability in gridded observations, an assimilation of the observations, and a set of multiannual hindcasts. We find that salinity dominates in the observations and assimilation. In the hindcasts salinity remains dominant for the first year but from year three these revert to the same temperature dominance seen in the underlying climate model. This is due to damping of the interannual salinity variability, possibly caused by unrealistically large convection that develops. Crucially, the hindcasts have high correlation skill in temperature/salinity throughout, but no skill in density, dynamic sea level, or the subpolar AMOC due to the incorrect drivers. This highlights the importance of correctly simulating both the sign and magnitude of temperature/salinity variability in a prediction system.
Production of natural flavor compounds using Bacillus subtilis-fermented soybean meal extract and their biological potential: a comprehensive in vitro study

This study aims to investigate the production of natural flavor compounds through the utilization of Bacillus subtilis-fermented soybean meal extract and evaluate their biological potential. The experiment involved a comprehensive in vitro investigation to assess the capabilities and effects of the produced flavor compounds. The resulting flavor compounds were subjected to various in vitro tests to assess their properties, including cytotoxicity, antioxidant activity, anticancer potential, antiviral activity, and antimicrobial activity. To enhance the fermentation process, soybean meal extract was fortified with a combination of L-Lysine and L-Threonine. Gas chromatography-mass spectrometry (GC/MS) analysis was conducted on the fermented soybean meal using two strains of Bacillus subtilis, namely NRCH123 and NRCZ144. This analysis revealed the presence of various volatile compounds in all extracts, including Butylated hydroxytoluene. The fermented soybean extract with bacillus subtilis NRCZ144 (B2) fortified with a combination of 2.5% (w/w) L-Lysine and 2.5% w/w L-threonine (SLT2) exhibited a rich profile of flavor compounds, with Eucalyptol being identified as the predominant compound. The antioxidant activity of the SLT2 extract was found to be 72.04% at a concentration of 100 μg/mL, indicating significant antioxidant potential. Furthermore, when tested against the human liver cancer cell line HepG2, the extract demonstrated anticancer activity with an IC50 value of 2.26 μg/mL. The extract exhibited potent cytotoxicity, with an IC50 value of 1.02 μg/mL. Importantly, the SLT2 extract displayed strong antibacterial and antifungal activity, even at very low concentrations. The extract’s antimicrobial properties indicate its potential for inhibiting the growth of bacteria and fungi.
Mindfulness based stress reduction: A randomised trial of a virtual human, teletherapy, and a chatbot

Psychological stress is a significant issue amongst University students. Mindfulness based stress reduction (MBSR) interventions can significantly reduce stress, in both face to face and digital formats. However, adherence to digital interventions is typically low. Virtual humans may improve adherence through their increased humanlike-ness and rapport-building behaviors. This study aimed to investigate whether there were any differences in response to MBSR when delivered by a virtual human, a human teletherapist or a chatbot. A randomized controlled trial (RCT) was conducted with 108 stressed University students randomized to one of the three delivery methods. Participants attended one in-person session of an MBSR intervention using their allocated delivery method and were asked to complete online homework at least twice a week for the following four weeks. The primary outcome was perceived stress, with secondary outcomes of physiological stress indices over the session, mindfulness, homework adherence, and perceived empathy of the agent. Results showed significant reductions in stress and increases in mindfulness across all groups over time. Peripheral skin temperature significantly increased across all three groups, but only the teletherapy group had significantly increased electrodermal activity after the in-person session. There were no significant effects on heart rate or indices of heart rate variability. Homework adherence was significantly greater in the virtual human group compared to the teletherapy group, and homework engagement was lowest in the chatbot group. The teletherapist was rated significantly more empathetic than the other agents. Overall, these results support the use of all three digital interventions for delivering MBSR. Virtual humans may have the advantage of increasing adherence to homework mindfulness sessions in student populations, but more work is needed to increase perceived empathy and to replicate these results in other populations.
Head Pose-Aware Regression for Pupil Localization From a-Pillar Cameras

In vehicular applications, remote eye pupil tracking is essential, particularly for advanced augmented reality (AR) 3D head-up displays (HUDs), and driver monitoring systems (DMS). However, achieving accurate pupil center localization under varied head poses presents significant challenges, especially when cameras are positioned on a vehicle’s A-pillar. This placement introduces substantial head pose variations, complicating traditional tracking methods. In response, this study presents a remote pupil localization method designed to address the unique challenges posed by a camera situated on a vehicle’s A-pillar, a spot causing significant head pose variations. The proposed technique relies on a head pose-aware pupil localization strategy utilizing A-pillar cameras. Our pupil localization algorithm adopts a Transformer regression approach, into which we integrate head pose estimation data, enhancing its capability across diverse head poses. To further enhance our approach, we used an optimized nine-point eye-nose landmark set, to minimize the pupil center localization loss. To demonstrate the robustness of our method, we conducted evaluations using both the public WIDER Facial Landmarks in-the-wild (WFLW) dataset and a custom in-house dataset focused on A-pillar camera captures. Results indicate a Normalized Mean Error (NME) of 2.79% and a failure rate (FR) of 1.28% on the WFLW dataset. On our in-house dataset, the method achieved an NME of 2.96% and a FR of 0.72%. These impressive results demonstrate the robustness and efficacy of our method, suggesting its potential for implementation in commercial eye tracking systems using A-pillar mounted cameras, especially for AR 3D HUD and DMS applications.
Spatiotemporal Expression Characterization of KRTAP6 Family Genes and Its Effect on Wool Traits

Keratin-related proteins (KAPs) are structural components of wool fibers and are thought to play a key role in regulating the physical and mechanical properties of fibers. Among all KAP genes (KRTAPs), KRTAP6 gene family (KRTAP6-1, KRTAP6-2, KRTAP6-3, KRTAP6-4, and KRTAP6-5) is a very important member with high polymorphism and notable association with some wool traits. In this study, we used real-time fluorescence quantitative PCR (RT-qPCR) and in situ hybridization to investigate spatiotemporal expression of KRTAP6s. The results revealed that KRTAP6 family genes were significantly expressed during anagen compared to other stages (p < 0.05). And it was found the five genes were expressed predominantly in the dermal papillae, inner and outer root sheaths, and showed a distinct spatiotemporal expression pattern. Also, it was found that KRTAP6-1 and KRTAP6-5 mRNA expression was negatively correlated with wool mean fiber diameter (MFD) and mean staple strength (MSS) (p < 0.05). In summary, the KRTAP6 family genes share a similar spatiotemporal expression pattern. And KRTAP6-1 and KRTAP6-5 may regulate the MFD and MSS of Gansu Alpine fine-wool sheep wool by changing the expression.
Expression and Perception of Stress Through the Lens of Multimodal Signals: A Case Study in Interpersonal Communication Settings

Stress is a ubiquitous phenomenon in present days experienced by a considerable portion of the population. Continuous exposure to stress poses negative effects on an individual’s physical and mental well-being. Therefore, understanding stress mechanisms and monitoring stress in a continuous and unobtrusive manner are needed for administering interventions at opportune moments. This requires a proper understanding of the relation between the expression and perception of stress in a continuous manner. Multimodal expression of stress refers to the manifestation of stress expressed by the trajectories of multimodal signals that are often captured via commonly used wearable devices. Meanwhile, perception of stress can be acquired by self-reported measures or third-party annotations. This work investigates the trajectories of multimodal signals (e.g., physiology, speech) and examines their association with self-reported or external observers’ ratings of stress. In order to ensure a naturalistic setting, the data are collected from three different studies involving interpersonal communication settings (e.g., public speaking, job interview). Knowledge from this work can improve our understanding of the causes and antecedents of stress that can help in designing a continuous stress detection module and an associated intervention scheme to reduce the adverse health effect of stress among the general population.
The use of camera traps in Malaysian rainforests

Camera trapping is not a new tool in wildlife science. It was invented in the late 1890s, before being first used in the field in 1913 (Sanderson & Trolle, 2005). In recent decades, it has been widely used throughout the world, with an annual increment of 50%. The results of these studies have been published in internationally recognized journals (Rowcliffe & Carbone, 2008). Due to the increasing number of wildlife studies, over 100 camera traps of various brands and types are now available in the international markets. In Malaysia, the use of camera traps for surveying wildlife has increased drastically. More recently, the camera traps have been used for surveying and estimating tiger densities (Kawanishi & Sunquist, 2004; Mohamad & Darmaraj, 2009), examining activity patterns of large mammals (Laidlaw & Shaharudin, 1999; Wong et al., 2004; Mohd-Azlan & Sharma, 2006), general wildlife surveys (Laidlaw et al., 2000; Numata et al., 2004; Mohd-Azlan, 2006; Giman et al., 2007), cryptic animal surveys (Wells et al., 2005; Mohd-Azlan & Sanderson, 2007), and foraging ecology (Miura, 1997; Matsubayashi et al., 2007). Thus, the main objective of this paper is to discuss the use of camera traps in Malaysian rainforest, promote discussion among local researchers who are currently engaged in this technique and to provide recommendations to meet local needs.
“Becoming a Person Who Does Self-Care”: How Health Care Trainees Naturalistically Develop Successful Self-Care Practices

OBJECTIVES Self-care is an ethical imperative for health professionals as it can mitigate the adverse effects of stress on professional functioning and health. Yet, there tends to be a lack of self-care among healthcare trainees and an insufficient focus on self-care in medical education. The objective of this study was to develop a grounded theory of how health trainees become successful self-care users. METHODS Semi-structured interviews were conducted with 17 students in a variety of healthcare disciplines. Data were analyzed using grounded theory methodology. RESULTS Health trainees underwent 4 iterative phases to become successful at self-care: Having a Wake-Up Call, Building Skills, Gaining Confidence, and Building an Identity. Our model also explained why some trainees were unsuccessful at developing self-care practices. CONCLUSION We offer the first theory to explain how health trainees develop effective self-care habits. Understanding how self-care practices naturalistically develop has critical implications for developing interventions and curricula: By basing curricula about self-care on knowledge of what works, we have an opportunity to be more successful as educators. Indeed, other researchers have noted a lack of success in self-care and anti-burnout interventions for healthcare professionals. We conclude by discussing implications and recommendations for medical training and curriculum for health professions, including augmenting naturally occurring processes, linking self-care to personalized values, providing opportunities for deliberate practice, focusing on persistence with self-care, and faculty promotion and acceptance of trainee self-care.
Overhead power line inspection using a prediction model based on system analysis Surveillance of Temperature Using a Modeled Optical System

Modulation of an optical fiber’s index of refraction at regular intervals is known as “Fiber Bragg Grating. ”. It operates on the basis that a rise in temperature above the reference point causes a change in the wavelength of the light source, and vice versa. In this study, we discuss the design and analysis of a Fiber Bragg Grating Sensor for tracking and measuring powerline temperatures across a certain temperature range. The highest reflectance of the Bragg wavelength was calculated by Optisystem simulation. The FBG interrogator claims that temperature changes are associated with shifts in the reflected light spectrum.
A Deep Artificial Neural Network-Based Daphne Leaf Classification Study

Today, machine learning methods make life easier in almost every field, from production to consumption, and increase the speed of technology in a dizzying way. By integrating smart systems into industrial production, less costly but more effective products are offered to humanity. Undoubtedly, machine learning techniques contribute to the industry mostly in the field of image processing. In this study, a model is proposed to classify the leaves of the daphne plant, which is used in many different fields from cosmetics to gastronomy, by using YOLOv8, the most current version of the deep artificial neural network-based YOLO algorithm, which is one of the technologies in the field of image processing. Thanks to the model trained using the originally prepared dataset in the study, real-time daphne leaf classification can be performed successfully.
Centralized Attention Modules for Hyperspectral Remote Sensing Image Classification

Hyperspectral image classification based upon the spectral-spatial information is a research hotspot in the field of remote sensing. The popular deep learning technique, attention mechanism, is generally used to search the influential bands and pixels for hyperspectral image classification. However, the spectral and spatial attention inferred by the existing attention modules may be weakly related to the center pixel, which is harmful to realize better classification. Inspired by the recent centralization ideology designed exclusively for hyperspectral image, the core components of three popular attention modules, including the dot-product part of self-attention module, the global semantic information of squeeze-and-excitation module, and the discovery of the relation between the global representation and crucial elements of convolutional block attention module, are improved. Therefore, the center pixel will be stimulated to make more contribution to the relevancy of attention. Experimental results on two classic data sets demonstrate the effectiveness of the centralized attention modules.
Implementing Deep Learning and CNNs for Vehicle Detection to Alleviate Highway Traffic Congestion

Traffic jams are a common problem in urban areas, and detecting them in real time can help alleviate congestion and improve traffic flow. This project, propose CNN-based method for detecting traffic jams from live video feeds. Our system involves capturing video streams from cameras placed in strategic locations and processing them using a deep-learning model. The study aims to develop a system for detecting traffic jams using (CNN) Convolutional Neural Networks. The system analyzes real-time traffic images to identify congested areas and provides insights to traffic management authorities for timely intervention. The study examines the accuracy of the system by testing it on a dataset of traffic videos. We evaluated our system on a dataset of traffic videos and obtained an average accuracy 89.05. 11%, the error average 0.10%. , and average F-score 89.61% indicating that our approach can effectively detect traffic jams. Our experiments show that our CNN-based approach outperforms traditional methods of traffic jam detection and can provide accurate and real-time information to drivers and traffic management systems.
A Voice-Assisted Approach for Vehicular Data Querying from Automotive IoT-Based Databases

The automotive industry's transformative conver-gence of cutting-edge technologies, such as the Internet of Things (loT), electronic voice assistants, and custom APls, have paved the way for a remarkable array of opportunities to enhance vehicle security while revolutionizing user-car interactions. Actually, when leveraging loT sensors of multiple types and configurations, their produced, collected, and stored real-time data may offer in-depth and comprehensive insights into vehicle information, opening many possibilities when loT-generated databases are created. In this context, the primary goal of this paper is to create a new approach based on the popular Amazon Alexa voice assistant, which would allow facilitated queries of vehicular data from cloud-based databases. For that, a new Alexa service (skill) is created, as well as a custom API, allowing easy access to different types of data previously retrieved from vehicular sensors and properly stored. Doing so, the implemented skill indirectly processes data from loT sensors through the custom API, enabling users to access vital vehicular information using intuitive voice commands, remotely. A case study in a real scenario is conducted to validate and confirm the feasibility of real-time vehicle information access, showcasing the benefits of the proposed approach when combined with other solutions in a macro vehicular-centric loT ecosystem.
A Novel Modified Ant Colony Optimization for Solving Multi-Skill Resource-Constrained Project Scheduling Problem

multi-skills resource-constrained project scheduling problem (MS-RCPSP) is assigned to employees under the predecessor tasks constraint and the multi-skills constraint in order to create the feasible schedule with both the cheapest cost and the shortest completion duration. MS-RCPSP has multi-goals and complexity so Ant Colony Optimization (ACO) is applied to solve MS-RCPSP. Many researchers can find either the cheapest cost plan or the shortest completion duration plan. However, the practical applications want them both together. Hence, this paper proposes modifying ACO solving MS-RCPSP and obtaining good results both the cheapest cost and the shortest completion duration. The proposed algorithm was tested on thirty-six test cases from an MS-RCPSP iMOPSE datasets and provided more satisfactory results in comparison with other ACO algorithms.
Admission Prediction in Undergraduate Applications: an Interpretable Deep Learning Approach

This article addresses the challenge of validating the admission committee's decisions for undergraduate admissions. In recent years, the traditional review process has struggled to handle the overwhelmingly large amount of applicants' data. Moreover, this traditional assessment often leads to human bias, which might result in discrimination among applicants. Although classical machine learning-based approaches exist that aim to verify the quantitative assessment made by the application reviewers, these methods lack scalability and suffer from performance issues when a large volume of data is in place. In this context, we propose deep learning-based classifiers, namely Feed-Forward and Input Convex neural networks, which overcome the challenges faced by the existing methods. Furthermore, we give additional insights into our model by incorporating an interpretability module, namely LIME. Our training and test datasets comprise applicants' data with a wide range of variables and information. Our models achieve higher accuracy compared to the best-performing traditional machine learning-based approach by a considerable margin of 3.03 %. Additionally, we show the sensitivity of different features and their relative impacts on the overall admission decision using the LIME technique.
Thin Film Transistor Array Based Active Matrix Flat Panel Displays—Past and Next Application in Immersive Displays

The availability of high-quality flat panel displays today can be contributed to the advancement of the thin film transistor technology that enables the tight-control of the transmission, emission, or reflection of light from individual pixels in a large array. The development history of amorphous silicon, polycrystalline silicon, and metal oxide thin film transistors for commercial liquid crystal, organic light emitting device, micro-light emitting device, and electrophoretic displays are reviewed. Characteristics of these transistors are compared and limitations on applications are discussed. The trend of market growth of these displays is examined. Examples of the thin film transistor array in non-display products are shown. The advancement in two thin film transistor active matrix areas − flexible displays/electronics and smart display systems − have been identified and discussed as critical to the emerging high performance immersive displays.
MAGI: Multi-Annotated Explanation-Guided Learning

Explanation supervision is a technique in which the model is guided by human-generated explanations during training. This technique aims to improve the predictability of the model by incorporating human understanding of the prediction process into the training phase. This is a challenging task since it relies on the accuracy of human annotation labels. To obtain high-quality explanation annotations, using multiple annotations to do explanation supervision is a reasonable method. However, how to use multiple annotations to improve accuracy is particularly challenging due to the following: 1) The noisiness of annotations from different annotators; 2) The lack of pre-given information about the corresponding relationship between annotations and annotators; 3) Missing annotations since some images are not labeled by all annotators. To solve these challenges, we propose a Multi-annotated explanation-guided learning (MAGI) framework to do explanation supervision with comprehensive and high-quality generated annotations. We first propose a novel generative model to generate annotations from all annotators and infer them using a newly proposed variational inference-based technique by learning the characteristics of each annotator. We also incorporate an alignment mechanism into the generative model to infer the correspondence between annotations and annotators in the training process. Extensive experiments on two datasets from the medical imaging domain demonstrate the effectiveness of our proposed framework in handling noisy annotations while obtaining superior prediction performance compared with previous SOTA.