Miracle-dz/UniFAR-FTUs huggingface dataset:
Subjective Dry Eye Symptoms and Objective Ocular Surface Signs in a Civil Air Crew Population: A Cross-Sectional Study

Background Aviation professionals are often exposed to conditions such as low cabin air pressure, reduced humidity, and prolonged artificial lighting, which may predispose them to dry eye disease (DED). We therefore designed a cross-sectional study with three primary objectives. Our first aim was to determine the prevalence of subjective dry eye symptoms among civil flight personnel. To achieve this, we administered the Standard Patient Evaluation of Eye Dryness (SPEED) questionnaire. Second, we performed ocular examinations to assess objective DED indicators, such as the Schirmer’s-1 (SCH-1) test and tear film break-up time (TBUT). We then correlated the results of these objective tests with the subjective symptoms reported by the participants. Last, we aimed to identify the independent risk factors for positive SCH-1 and TBUT results among aircrew personnel who did not report subjective dry eye symptoms. Methods The study sample consisted of 189 aircrew personnel (94 men and 95 women; mean age: 35.8 ± 10.4 years). They completed the SPEED questionnaire, a tool for assessing ocular surface symptoms. Participants' symptoms were classified as normal (SPEED scores 0-6), moderate (SPEED scores 7-15), or severe (SPEED scores 16-28). The objective assessment included the SCH-1 test for tear production and the TBUT test for tear film quality. Results A significant majority of participants (n = 165; 87.3%) did not report any subjective symptoms of DED. However, 12.2% (n = 23) and 0.5% (n = 1) of the study subjects experienced moderate and severe symptoms, respectively. The SCH-1 test and TBUT test were positive in 25.4% (n = 48) and 24.9% (n = 47) of the participants, respectively. Interestingly, among the aircrew personnel who did not report any subjective dry eye symptoms (SPEED scores 0-6), 18.8% (n = 31) and 17.6% (n = 29) showed abnormal results on the SCH-1 test and TBUT, respectively. Age was identified as the only independent predictor of a positive TBUT (odds ratio = 1.05, 95% confidence interval (CI) = 1.01-1.08, p = 0.01), with a borderline significant association with a positive SCH-1 test (odds ratio = 1.03, 95% CI = 0.99-1.07, p = 0.06). Conclusions The disparity between subjective symptoms and objective tests emphasizes the significance of incorporating objective measures for screening and diagnosing DED in civil flight personnel. If independently confirmed by future research, our findings could potentially lead to the routine implementation of surveillance protocols that incorporate objective DED indicators. Moreover, as age emerged as an independent predictor of positive results on objective tests, it is crucial to consider age-specific screening strategies.
Predictive Analytics for Stress Management in the Digital Workplace Using IoT

It is a well-known fact that Work from Home (WFH) had put people under more stress in the recent times. In general, the human body's natural response to both internal and external stimuli is stress. Despite the fact that stress is a usual part of human life, working online for an extended period of time without taking any steps to reduce, it can have serious detrimental impact on one's health. The most important factors that are associated with the level of stress felt by Work from Home employees have been carefully examined. The main objective of this research work is to give online workers a precise measure of their stress level in real-time and the best solutions for enjoying healthy lives. Human stress signals are collected by the Galvanic Skin Response (GSR) sensor and analyzed digitally. The obtained information is then utilized by the proposed Internet of Things (IoT) architecture to calculate the stress level. Recommendations for stress management include meditation, yoga, music, or exercise.
Residual Attention Module on EEGN et for Brain-Computer Interface

Brain-computer interfaces (BCI) allow for the brain to communicate with electronic devices. Concerning the BCI paradigms, motor imagery uses brain signals to decode an imagined movement. However, this is a hard task given the low signal-to-noise ratio. Usually, the main steps in BCI models are pre-processing, feature extraction, and classification. In recent years, Convolutional Neural Networks (CNNs) have been gaining relevance in several areas of science due to their feature extraction, translation invariance, and parameter sharing capa-bilities. Another, more recent way of feature extraction is using attention mechanisms, which are layers of neural networks based on human attention and have the ability to highlight important features. A variation of the attention mechanism is the Convolutional Block Attention Module, which combines the CNN structure with the attention mechanism. In this work, we propose a new model that joins the core architecture of EEGNet, a compact CNN widely used in the literature, with the Convolutional Block Attention Module and residual connections. The residual connections were introduced to lower data degradation throughout the model. The results highlight the residual connection's importance for the performance of the model. The proposed model obtained a kappa result 5.2% better than the EEGNet with a p-value less than 0.01 on BCI Competition IV dataset 2a, which is a well-known dataset for Motor Imagery. Furthermore, the proposal was better than EEGNet for most subjects and had the best-worst case.
Multi-Modal SLAM for Accurate Localisation in Self-similar Environments

Regular inspection and maintenance (I&M) of road tunnels is critical for ensuring safe operation and maximising the infrastructure's longevity. Today's I&M operations are time-consuming and disruptive to normal operations, but advances within robotics, automation, and digitalisation promise significant productivity gains. Accurate and reliable localisation is key to achieving this, but poses significant challenges in tunnels due to the absence of GNSS signals and the self-similar nature of the environment. This paper presents a novel approach for achieving real-time high-accuracy localisation in tunnels such that it can be used for autonomous navigation. The proposed system implements a simultaneous localisation and mapping (SLAM) solution that integrates data from scanning LiDAR, camera and inertial measurement unit (IMU). We have developed a novel approach that fuses the information from these sensors at the feature level and jointly optimises over all constraints. This enables our system to overcome the degeneracy of typical SLAM solutions in self-similar environments such as tunnels. To evaluate the performance of the proposed system, experiments and autonomous missions were conducted in real tunnels, and comparisons were made against existing localisation methods. The results demonstrate that the proposed system achieves high accuracy and exhibits good robustness in challenging tunnel conditions.
Soccer Artificial Intelligence Commentary Service on the Base of Video Analytic and Large Language Models

Our article describes an approach to creating automatic commentaries for soccer games. The soccer match is logged by an artificial intelligence video processing component. This log is used to generate a football games commentary on the base of Large Language Models. The time limits of the generated comment are controlled by a separate neural network that ensures the ranking of episodes. The generated commentary text is spoken using the text-to-speak solution.
Deep Feature Contrasting for Industrial Image Anomaly Segmentation

Industrial image anomaly segmentation is pivotal in ensuring the quality inspection of products within intelligent manufacturing systems. Recent research efforts have predominantly focused on deep learning-based approaches to address this challenge. However, unsupervised methods are often susceptible to distribution shifting, while supervised methods face significant obstacles due to imbalanced samples, resulting in suboptimal accuracy for anomaly segmentation. This article introduces a novel end-to-end deep feature contrasting (DFC) method aimed at enhancing segmentation accuracy in scenarios with limited supervision and pixel-level labeled anomalous images. DFC includes the introduction of a novel backbone feature contrasting pyramid (BFCP) featuring dual-model channels, which effectively captures the distribution of normality. In addition, a novel model-independent semantic feature contrasting (SFC) technique, implemented through self-contrasting, is proposed to train a discriminative segmenting head module (SHM) that addresses the challenge of sample unbalance. Furthermore, this article presents two types of SFC, namely, global and local, which are suggested to improve the overall performance. The proposed DFC method achieves F1 of 0.787 and 0.665 on two real-world industrial datasets (Kolektor Surface Defect Dataset (KSDD) and KSDD2) and is applied to a practical application of anomaly segmentation of printed circuit boards, demonstrating the superior anomaly segmenting performance.
A Logical Cyber Security Enabled Methodology Design for Identifying Distributed Denial of Service Attacks Using Enhanced Learning Principles

The Distributed Denial of Service (DDoS) attack is a common type of cyber-attack that targets computer networks. When a hostile user seeks to prohibit access to a service by overwhelming the system with data that is not intended, this is an example of the distributed denial of service attack, also known as a DDoS attack. Because of this, the provision of services to legitimate users is slowed down. DDoS attacks are launched when a large number of sources, such as a Botnet that controls the devices remotely for malicious reasons, simultaneously launch attacks on a target. DDoS refers to “distributed denial of service” attacks. In order to detect and prevent Distributed Denial of Service attacks, classification algorithms have been widely employed in research. Attacks using a Distributed Denial of Service may be easily carried out by using the weaknesses of networks as well as making application service demands. The relevance of this strategy cannot be overstated due to the fact that DDoS attacks are difficult to detect and prevent in real time and can have devastating effects. This study led to the development of a deep learning model called the Learning Model to Detect Cyber Attacks (LMDCA). Its purpose is to anticipate application layer DDoS attacks in real time. It is the responsibility of the availability component to guarantee that authorized users will always have access to the system. During the course of this study, we modeled both a denial-of-service attack as well as normal network traffic. Following that, we utilized packet-level analysis in an effort to differentiate between the aforementioned two usage patterns. Then, in order to detect and classify the various DOS attacks, we make use of the LMDCA-based classifier logic that was recommended.
Investigation of a Subwavelength Grating Bimodal Interferometric Sensor Built on Silicon Nitride Platform

We design a bimodal interference sensor on a 400-nm-thick silicon nitride (SiN) platform, using a subwavelength grating (SWG) waveguide structure. By optimizing the structure parameters, the sensor exhibits a bulk sensitivity of ~2100 nm/RIU and the surface sensitivity of ~0.72 nm/nm. Compared with its counterparts in silicon, our device has similar performance, but can already be fabricated with accessed commercial foundry.
Oxidative Stress-Induced Adverse Effects of Three Statins Following Single or Repetitive Treatments in Mice

Background and objective The hypolipidemic statins have been associated with various side effects, and in some cases, adverse reactions in humans and experimental animals, such as myotoxicity, neurobehavioral toxicity, as well as liver and kidney injuries. The purpose of the present study was to examine the possibility of the induction of oxidative stress in the brain and plasma of mice dosed with single or repetitive doses of three statins (atorvastatin, simvastatin, and rosuvastatin). Methods Male Swiss-origin mice were dosed orally with single doses of each of the three statins at 500 or 1000 mg/kg of body weight. Other groups of mice were dosed orally with repeated daily doses of each of the statins at 200 mg/kg of body weight/day for 14 or 28 consecutive days. These doses of statins were chosen to not produce overt toxicity in mice within the time frame allocated for each experiment. Brain and plasma glutathione (GSH) and malondialdehyde (MDA) levels, as well as liver enzymes activities alanine transaminase (ALT) and aspartate transaminase (AST), were determined using commercial kits. Results Single-dose treatments of the mice with the statins at either 500 or 1000 mg/kg significantly and dose-dependently (p < 0.05) reduced the GSH level in the plasma and the whole brain when compared with respective control values. Atorvastatin was the least effective statin, as only the high dose achieved a significant reduction in brain GSH level in comparison with the respective control value. Repetitive administration of the three statins at 200 mg/kg of body weight/day for 14 or 28 consecutive days significantly and time-dependently reduced plasma and brain GSH levels in comparison with respective control values. The oxidative stress biomarker MDA level significantly increased in the plasma and brain of mice following single or repetitive treatments with the three statins, and the most effective one was rosuvastatin. In association with these changes, activities of the liver enzymes ALT and AST were also increased in the plasma with single and repetitive statin treatments, and the most effective one was rosuvastatin. Conclusion The data suggest an association of high doses of three statins (atorvastatin, simvastatin, and rosuvastatin) with the induction of oxidative stress manifested as GSH reduction and MDA elevation as adverse effects in the brain and plasma of mice, which suffered from the additional burden of liver injury. These effects could be the basis of an in-depth exploration of statin adverse effects in experimental animals and to find an animal model, probably the mice, for the induction of adverse effects of statins that target the brain, as well as to shed light on potential statin intolerance outcomes following single-dose treatments in this species.
The Computer Vision Simulation of Athlete’s Wrong Actions Recognition Model Based on Artificial Intelligence

At present, in basketball teaching in China, the traditional basketball training method is for coaches to communicate face-to-face with athletes, observe their basketball movements, and judge the correctness of the movements based on the coach’s personal experience. However, this method mainly relies on the subjective judgment of the coach and lacks objective evaluation of athletes, making it impossible to objectively evaluate their performance. This article mainly studies an athlete’s incorrect action recognition model based on artificial intelligence algorithms and computer vision, and constructs an athlete’s incorrect action recognition model based on a dual channel 3D convolutional neural network (CNN). In this project, spatial attention mechanism (SA) was introduced into 3D CNN. By using inter frame difference information that can represent significant changes in athletes’ motion status, and combining it with grayscale video data, accurate recognition of athletes’ incorrect actions was achieved. The simulation results show that as the number of basketball technical errors increases, the recognition accuracy of this method decreases slowly. When the number of basketball technical errors reaches 400, the accuracy of action recognition is still as high as 87.552%. This indicates that this method can control the error rate within a reasonable range, improve the ability to identify basketball technical errors, and provide strong support for basketball teaching. In addition, the experimental results of this method also include various other achievements in performance calculation, further verifying its superiority in identifying basketball technical errors.
Trophic State Index estimation from remote sensing of lake Chapala, México

El Lago de Chapala (Mexico), muestra un deterioro en su calidad de agua, asociado con el enriquecimiento de nutrientes derivados de drenajes de areas urbanas, agricultura y ganadera. Mediante tecnicas de percepcion remota se estimo el Indice de Estado Trofico (IET) del lago, como un indice de calidad de agua. Los parametros de clorofila_a y Profundidad de disco de Secchi (que estan relacionados con el estado trofico), fueron monitoreados mediante el uso de imagenes satelitales multiespectrales, con base en el aumento en los valores de turbidez y concentracion de clorofila_a, los cuales estan relacionados con las propiedades opticas del agua. Se estimo la concentracion de clorofila_a y turbidez (parametro inverso a la profundidad de Disco de Secchi) para el Lago de Chapala a partir de relaciones empiricas entre reflectancia en la banda SPOT B2 y la profundidad de disco de Secchi asi como la relacion de las bandas SPOT S3/S2 y la concentracion de clorofila_a. Los resultados de la determinacion del Indice de Estado Trofico basada en la reflectancia de las imagenes multiespectrales muestran un lago eutrofico. El uso de datos multiespectrales permite el monitoreo del Indice de Estado Trofico de forma continua, de bajo costo y eficiente para el apoyo en la implementacion de regulaciones que permitan el mejoramiento de la calidad del agua en el Lago de Chapala
Learning Experience of Students Using the Learning Management System: User’s Perspective on the Use of Moodle in the University of Jordan

This study aimed to evaluate the effectiveness of Moodle LMS as an alternative delivery mode for education, particularly in the higher education system of Jordan, which has not been evaluated before. The research is based on Moore’s transactional distance learning theory, which considers interactions among students, instructors, and course material, as well as personal characteristics of students. The study included 50 participants who have taken courses on Moodle LMS at the University of Jordan. Both interviews and research surveys were conducted to gather in-depth perceptions and analysis of the participants’ experiences with Moodle LMS. The findings suggest that Moodle LMS was an effective alternative study tool during the COVID-19 pandemic and the user-friendly course design enhanced the user experience of using Moodle LMS. Participants also provided suggestions to optimize the system for better integration in the University of Jordan. Fixing Moodle LMS based on the recommendations of the study participants will significantly improve the learning experience of students. This study contributes to the advancement of the state of the art by evaluating the effectiveness of Moodle LMS in the higher education system of Jordan and providing recommendations for improvement.
A Perspective Review of Cancer Therapy (Part I): Immunotherapy, Gene Therapy, and Nanotechnology

The application of genomic profiling approaches has propelled the advancement of precision medicine, enabling the development of personalized treatment programs that are specifically customized for each particular patient. This method considers the distinct genetic composition of each tumor, allowing for the detection of crucial driver mutations and possible weaknesses that might be targeted for therapeutic purposes. Furthermore, immunotherapy has emerged as a promising approach in cancer treatment, utilizing the potential of the immune system to identify and eradicate cancerous cells. Medical breakthroughs like CAR-T cell therapy have already demonstrated exceptional efficacy in treating hematologic malignancies. Moreover, state-of-the-art technologies such as CRISPR-Cas9 gene editing offer potential for directly altering tumor cells or augmenting the efficacy of current treatments. The future of cancer therapy is focused on developing more precise and targeted techniques to enhance patient outcomes and ultimately overcome this destructive illness. This involves continual research and cooperation among scientists, physicians, and pharmaceutical corporations on a worldwide level.
Humorous Linguistic Memes as Students' Coping Relief Against COVID-19 Anxiety

This quasi-experimental study investigated the effects of humorous linguistic memes as a coping strategy against anxiety. The respondents consisted of 41 students who were purposefully selected based on their manifestation of COVID-19 anxiety. These participants received interventions using humorous linguistic memes. The study used a researcher-made instrument on COVID-19 anxiety composed of 30 items on common anxiety about the coronavirus pandemic. The study utilized the mean for descriptive data analysis, the Mann-Whitney U, and the t-test for inferential data analysis. The researcher also analyzed the respondents' impressions of humorous linguistic memes. Data analysis deduced that funny linguistic memes can potentially reduce a person's anxiety about COVID-19. Frequent exposure to funny memes may contribute to good mental and emotional health. The study suggests that individuals can be exposed to humorous linguistic memes when confronted with a stressful situation.
Design and Development of the MIT Humanoid: A Dynamic and Robust Research Platform

Enabling humanoid robots to achieve human-level athletic feats, such as running and jumping, is at the frontier of robotics research. To execute these behaviors, a robot platform must have high power density and robust mechanical and electrical systems. In this paper, we present the MIT Humanoid, a robust research platform that is designed to meet these requirements and be able to perform highly dynamic, parkour-style motions. The robot is just over 1 m tall and weighs approximately 24 kg, with 18 actuated degrees of freedom, each of which has a custom high-torque proprioceptive motor module. We also present initial hardware results from our new platform, demonstrating model-based controllers for pose control, walking, and jumping. The robot's high control bandwidth allows us to achieve stable pose control and walking, and the high power density allows the robot to achieve vertical jumps of roughly 30 cm, as measured by torso displacement.
BLB-GAFS: An Efficient, Multi-Objective Genetic Algorithm Based Feature Selection Method for Intrusion Detection Systems

Protecting Internet of Things (IoT) networks from threats is becoming increasingly important as these devices continue to grow in adoption. Modern and unseen attacks that require the analysis of more complex network traffic data for effective identification and mitigation are becoming more prevalent. Traditional machine learning approaches in current intrusion detection systems (IDS) struggle with these volumes of data, prompting exploration into the feature selection space. One class of such feature selection methods is evolutionary algorithms, in which systems mimicking real-life evolution optimize solutions for some problem. In this paper, we propose bag-of-little-bootstraps genetic algorithm feature selection (BLB-GAFS), a novel variant of the genetic algorithm feature selection method that maintains a global search of the solution space while reducing computational cost. This is accomplished with the bag-of-little-bootstraps method for approximating classifier performance. We test the BLB-GAFS technique on three modern intrusion datasets-CCD-INID-V1, detection_of_IoT_botnet_attacks_N_BaIoT, and CIRA-CIC-DoHBrw-2020-that represent updated network patterns and are highly dimensional. We found that the BLB-GAFS method matches or outperforms embedded feature selection methods on the same datasets. Furthermore, the feature sets selected by BLB-GAFS result in significantly improved multiclass precision, recall, and F1-score. Traditionally expensive wrapper feature selection methods like the genetic algorithm can be used on larger datasets through BLB-GAFS, opening the door to other applications with highly dimensional data.