Miracle-dz/UniFAR-FTUs huggingface dataset:
Multicenter Study to Validate a Hospitalization Risk Assessment Tool in Hemodialysis Patients

Introduction Protein-energy wasting is a prevalent condition in patients with chronic kidney disease. Our goal was to validate the risk assessment tool (Hashmi’s tool) in multiple centers, developed in 2018, as it was easily applicable and cost-effective. Methods The following variables were scored as 0, 1, 2, or 3 as per severity: body mass index, HD vintage in years, functional capacity, serum albumin, serum ferritin, and the number of co-morbid conditions (diabetes mellitus, hypertension, ischemic heart disease, and cerebrovascular disease). This scoring system was applied to maintenance hemodialysis patients in six different centers. The patient's record was evaluated for two years. Patients were divided into low-risk (score <6) and high-risk (score ≥6). We compared the two groups using the chi-square test for the difference in hospitalization and mortality. Results A total of 868 patients' records were analyzed, and the maximum score was 13 with the application of Hashmi’s tool. Four hundred twenty-nine patients were in the low-risk group, and 439 patients fell into the high-risk group. Four hundred sixty-seven patients were male, and 401 were females; 84% had hypertension, and 54% had diabetes mellitus. In the high-risk group, we identified more females. Patients' likelihood of being in the high-risk group was higher if they had diabetes mellitus, hypertension, or ischemic heart disease. Hospitalization due to vascular or non-vascular etiologies was more common in the high-risk group (p=0.036 and p<0.001, respectively). A total of 123 patients died during the study period, 92 from the high-risk group as compared to 31 from the low-risk group. This was three times higher and statistically significant (p<0.001). Conclusion Using a simple and cost-effective tool, we have identified malnourished patients who are at risk of hospitalization and mortality. This study has validated the previous work at a single center, which has now been reflected in six dialysis units across Saudi Arabia.
Increasing Vehicular Visible Light Communications Range Based on LED Current Overdriving: Experimental Demonstration in Outdoor Conditions

Wireless communication sensors are on the way of becoming the new normal on future intelligent and semiautonomous vehicles. In this context, this paper provides the results concerning the experimental evaluation of a new Visible Light Communications (VLC) technique that provides a significant improvement of the communication range. This concept is based on LED current overdriving compensated by a proportional reduction of the pulse width. Thus, by increasing the pulse current through LEDs, the optical power is proportionally increased, whereas in order to maintain the same average optical irradiance and to avoid LEDs destruction, the duty cycle is proportionally decreased, maintaining the average value of the current. The proposed concept has been tested in an outdoor Vehicle-to-Vehicle (V2V) communication setup. Experimental results demonstrated that the proposed technique can more than double the communication range. Thus, by gradually decreasing the duty cycle from 50% to 10% and by proportionally increasing the pulse current through the LEDs, the basic communication distance has been increased from 12 to 27 meters, while maintaining the BER lower than 10−6.
Shift of the principal mode of Pan-Asian monsoon summer precipitation in terms of spatial pattern

Abstract This paper documents that the principal mode of Pan-Asian monsoon summer precipitation experienced a prominent interdecadal shift around 1992/1993 in terms of spatial pattern and major driving factors. During 1979‒1992 (Period 1, P1), Pan-Asian monsoon summer precipitation anomalies mainly display a meridional dipole pattern from north to south, whereas in the period 1993‒2016 (Period 2, P2), it shows a meridional tripole pattern instead. The summer precipitation in P1 is primarily associated with a combination of the developing phase (central-eastern Pacific type) and decaying phase (eastern Pacific type) of El Niño–Southern Oscillation (ENSO); while in P2, it is mainly associated with the decaying phase of central-eastern-Pacific-type ENSO.
SWDAE: A New Degradation State Evaluation Method for Metro Wheels With Interpretable Health Indicator Construction Based on Unsupervised Deep Learning

Machine learning has shown advantages in assessing wheel degradation in metro vehicles for fault prognostic and health management (PHM). However, practical implementation faces challenges due to disturbances in wheel vibration signals caused by factors like load, road conditions, and temperature, introducing significant noise that masks evaluation tendencies. This article develops a novel unsupervised approach to evaluate wheel degradation under strong noise disturbance. It utilizes a second-generation wavelet transform for time–frequency analysis of noisy signals and introduces a second-generation wavelet deep autoencoder (SWDAE) network to extract adaptive feature representations in different frequency bands. The training algorithm alternately optimizes the wavelet transform and DAE. With frequency-saliency interpretability, health indicators (HIs) for the degradation process are constructed using principal component analysis (PCA) on the obtained features in each frequency band, selecting the most representative frequency component based on the monotonicity of the HIs. State changes are automatically determined using a second-order derivative-based assessment method aligned with the first/second warning strategy. Comparative experiments using Beijing Subway wheel data demonstrate the monotonicity and physical significance of the constructed HIs, with warning locations accurately matching changes in wheel diameter recorded during repairs.
Load Measurement Based on Forward Stimulated Brillouin Scattering in Photonic Crystal Fiber

We propose and demonstrate a novel optical fiber transverse load sensor based on forward stimulated Brillouin scattering (FSBS) in photonic crystal fiber (PCF), where a dual-wavelength Sagnac loop is employed for the activation and observation of FSBS. Due to the unique geometry of PCF that contains many air holes, transverse load that is applied to the PCF gives rise to the linewidth variations of the corresponding radial acoustic modes $(\boldsymbol{R}_{0,m})$ induced FSBS spectrum. Highly linear dependence between the linewidth of specific $\boldsymbol{R}_{0,m}$ induced FSBS spectrum and the applied transverse load has been experimentally verified. For proof of concept, $\boldsymbol{R}_{0,5}$ acoustic mode induced FSBS spectrum is selected for measurement, and the calibrated linewidth linearity with respect to load is 1.7455 KHz/g. The proposed scheme shows immense potential for application in engineering measurements.
Data-Driven Based Optimal Operation of Distribution Systems Integrated with Electric Vehicles Using Deep Neural Network

This paper proposes a data-driven based optimal operation of distribution systems integrated with electric vehicles (EV) using Deep Neural Networks (DNNs). Historical data on EV charging patterns and loads are employed to train the DNN, which is then tested on distribution systems. It is observed that the used DNN architecture accurately predicts the voltage magnitudes and angles, which can be used for calculating the remaining optimal power flow (OPF) parameters. This process reduces the computational burden substantially and demonstrates adaptability for varying EV penetration levels and load patterns, making it a scalable and flexible solution for smart grid implementations. The proposed model is implemented and validated on the IEEE-33 bus distribution system.
Evolving Behavior Allocations in Robot Swarms

Behavioral diversity is known to benefit problem-solving in biological social systems such as insect colonies and human societies, as well as in artificial distributed systems including large-scale software and swarm-robotics systems. We investigate methods of evolving robot swarms in which individuals have heterogeneous behaviours. Two approaches are investigated to create swarm of size $n$. The first encodes a repertoire of $n$ behaviours on a single individual, and hence evolves the swarm directly. The second approach uses two phases. First, a large repertoire of diverse behaviours is evolved and then another evolutionary algorithm is used to search for an optimal allocation of behaviours to the swarm. Results indicate that the two phase approach of generate then allocate produces significantly more effective collective behaviors (in terms of task accomplishment) than the direct evolution of behaviorally heterogeneous swarms.
Online Open-Circuit Fault Diagnosis for Neutral Point Clamped Inverter Based on an Improved Convolutional Neural Network and Sample Amplification Method Under Varying Operating Conditions

The accuracy of data-driven open-circuit fault (OCF) diagnosis methods is affected by varying operating conditions. This issue is often ignored. In this article, an improved convolutional neural network (CNN) and a sample amplification method are proposed to eliminate the influence of varying operating conditions on the online OCF diagnosis for neutral point clamped (NPC) inverter. First, 73 types of OCF sample collection can be greatly reduced to 14 by following the sample amplification method. The signals of any phase can be generated by a single fundamental period signal. This provides a significant savings in sample collection time. Second, the spatial attention mechanism (SAM) is added after the first convolutional layer of the CNN model. The feature extraction capability of the model is enhanced for time-domain waveform scaling under variable operating conditions. Simultaneously, the last full connection (FC) layer of the CNN model is retained and the other FC layers are substituted with a global maximum pooling (GMP) layer. This has the advantage of reducing the number of network parameters and further conserving the effective feature information. In conclusion, the experimental results show that the sample amplification method and the improved CNN model for online fault diagnosis under varying operating conditions exceed 99% accuracy. The CNN with spatial attention mechanism and global maximum pooling (SAMCNN-GMP) is more effective and stable than the CNN model.
A Task-Aware Lightweight Link Design Framework for Robots Under Dynamic Loading

The demand for robotic applications involving dynamic motions in environments with frequent contact is steadily rising. Since these applications also require human-friendly, safe, and robust collaborative operations, the need for more dynamically capable lightweight robots is rising. Although the current trend is focused on elastic joint-rigid link robot models, the contact task performance can be improved with better modelling and identification of link structures. In this paper, we explore the key design parameters of links that affect the robot at the system level, by creating a relationship between the high-level task requirements and the component level ones, i.e., the links. By considering the system's life and application needs, we proposed a three-layer design routine for robot links under dynamic loadings. Moreover, we validated the proposed link structure with Finite Element Analysis (FEA) and Experimental Modal Analysis (EMA). The precision of the analytical overall model is validated, and additional acting effects are identified by comparing the EMA with the FEA results. The results show that the flanges and boundary conditions of the link have direct effects on the overall system performance, and these effects can be either controlled or identified to achieve a requirement-matching system for the link design.
Height Optimization and Channel Gain based User Pairing for UAV-Assisted Emergency Communications

The Internet of Things (IOT) has been featured for its ability to connect and control numerous real and virtual objects with wireless communication systems. Recently, IOT has received wide popularity in beyond fifth generation communication systems also (B5G). To exploit both the energy efficiency and spectrum efficiency in a heterogeneous IOT networks, a promising technique named Non -Orthogonal Multiple Access (NOMA) is proved to be an effective one. Meanwhile, IOT is susceptible to an unexpected disaster which results in power cut and ultimately damage the system. To assist this emergency situation, Unmanned Aerial Vehicle (UAV) can be utilized for delivering messages from base stations (BSs) to the users present in the disaster region. In a heterogeneous IOT networks, all users and devices will be operated with batteries and once the disaster occurs, all base stations and users will be broken down. To handle this emergency situation, it is necessary to employ multiple UAVs. The signal is first sent to the UAVs stationed in the disaster region by UAVs deployed outside the disaster area. UAVs then transmit the signals to any devices that are within their coverage range. This article considers the problem of user clustering and three dimensional UAV location planning for NOMA-assisted multi-UAV emergency communication. Users are paired to perform NOMA scheme using uniform channel gain difference (UCGD) method. Moreover, this study utilize particle swarm optimization (PSO) algorithm to find the optimal location for multiple UAVs which maximizes the system sum rate. Extensive computer simulations have been carried out to validate the superiority of the proposed algorithms.
Imitating Tool-Based Garment Folding From a Single Visual Observation Using Hand-Object Graph Dynamics

—Garment folding is a ubiquitous domestic task that is difficult to automate due to the highly deformable nature of fabrics. In this paper, we propose a novel method of learning from demonstrations that enables robots to autonomously manipulate an assistive tool to fold garments. In contrast to traditional methods (that rely on low-level pixel features), our proposed solution uses a dense visual descriptor to encode the demonstration into a high-level hand-object graph (HoG) that allows to efficiently represent the interactions between the manipulated tool and robots. With that, we leverage Graph Neural Network (GNN) to autonomously learn the forward dynamics model from HoGs; then, given only a single demonstration, the imitation policy is optimized with a Model Predictive Controller (MPC) to accomplish the folding task. To validate the proposed approach, we conducted a detailed experimental study on a robotic platform instrumented with vision sensors and a custom-made end-effector that interacts with the folding board.
Formal Verification of Aircraft Departure Procedure Using Spin Model Checker

The volume of air traffic increase dramatically and safety of air traffic systems are becoming critical issue day by day. Failure of system can cause loss of human life, cost and hard work of many peoples. Therefore, safety of such system is necessary to prevent these loses. Safety can be ensured by checking correctness of the system. We address problem related to ensuring safety and correctness of the aircraft departure procedure using SPIN. Research exists related to aircraft departure procedure analysis but according to our knowledge we do not find its formal verification in SPIN model checker. In this paper, we verify aircraft departure procedure by checking specific safety properties on the system. We generate program graph for system and give LTL formula for safety properties we define on the system. Linear temprol logic (LTL) is use to specify properties in the form of formula. Model checker take program graph and LTL formula as input and check whether our system satisfied with LTL formula or not. Verification and result analysis is done using SPIN model checker. This research is beneficial to ensure correctness of safety critical systems.
THE CONSUMPTION OF FOOD PRODUCTS IN SHORT CHAINS: the case of social and reputational labels

There have been an increasing demand for products of short food supply chains, which enable consumers to identity of the product and its origin, which is established through information transmitted by labels and certifications. This article analyzed consumers’ motivations for demanding products with labels of local or social reputation. A survey was conducted with consumers in São Paulo. Descriptive statistics was used to raise the consumer's profile and their motivations for consuming these products. The consumer's motivations are related to aspects of the place of origin, as well as the benefits of acquiring it, without considering price aspects.
References to sex and gender differences in the social sciences: analysis of journal publication records (1971–2021)

Based on the publication records of journal articles indexed in the Web of Science Social Sciences Citation Index, our analysis examines the underlying factors influencing the usage of ‘sex differences’ over ‘gender differences’ in Titles and Author Keywords. Our search query identified 16,362 articles published in 1971–2021 that use either of the phrases and have at least one of their Research Areas belonging to the Social Sciences. In concurrence with earlier research, we find a substantial shift towards using ‘gender’ in the 1980s. However, for records published after 1992, the Publication Year has a negligible aggregate impact on the likelihood of ‘gender’ over ‘sex’, although meaningful trend differences occur across subsets defined by article-level disciplinary associations. Using the available publication meta-data (Publication Year, Research Area, Publication Journal) as well as the results of topic modelling (LDA) on Titles and Abstracts, we implement multi-level regression modelling to demonstrate that the likelihood of referring to ‘gender’ rather than ‘sex’ is strongly influenced by article-level disciplinary associations and their topical classification. We find that Psychology articles, by far the most numerous, exhibit a lower propensity to use ‘gender’ than all the other Social Sciences, especially when collaborating with Life Sciences & Biomedicine.
Flexible and High-Fidelity Concurrent Dual-Band Delta-Sigma Modulator for Diverse Applications Convergence

We propose and experimentally demonstrate a concurrent dual-band delta-sigma modulation for fronthaul. The proposed modulator can flexibly adjust the center frequency of each passband with ~3.0-dB SNR improvement, supporting 2.67GHz 1024-QAM transmission over 10-km SSMF.
A Novel Method for Along-Track Motion Compensation of the Airborne Strip-Map SAR

A method for along-track motion compensation of the airborne strip-map SAR(Syn thetic Aperture Radar) is presented.No complex interpolation is involved in this algorithm,hence the high computational efficiency and the improved image quality are achieved.Theor etical analys is,simulation results and the real data images show that it is of great value in real-time imaging.
EEG-Based Emotion Recognition Using DWT and Artificial Neural Network: A Case Study on Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) impacts brain development, leading to social communication challenges and interaction. Researchers are increasingly exploring using Artificial Intelligence (AI) to diagnose ASD, interpret their emotions, and search for effective change interventions. This study investigates computer-aided ASD emotion recognition using electroencephalography (EEG) signals. The proposed method implements a four-level Discrete Wavelet Transform (DWT) for feature extraction and an Artificial Neural Network (ANN) to classify three dimensions of emotions: valence, arousal, and dominance. The model achieved 83% accuracy for valence and 96% for arousal and dominance. These findings hold potential for developing an adaptable closed-loop ASD intervention system. In conclusion, EEG-based emotion recognition using DWT and ANN appears promising for identifying emotional challenges in autism. However, further research is needed, considering limitations like sample size and static stimuli.
Optimizing the Zn, Mn, and Fe mineral dose as tank mix foliar application for improvement of fruit yield, quality, and uptake of nutrients in the kinnow mandarin

Kinnow production is hampered due to the lack of micronutrient applications such as zinc (Zn), iron (Fe), and manganese (Mn), which play a significant role in the metabolic activities of the plant, affecting yield and quality. The farmers of the region use mineral micronutrient fertilizers, but it leads to phytotoxicity due to unoptimized fertilizer application dose. In the present investigation, an attempt has been made to optimize the Zn, Mn, and Fe minerals dose as tank mix foliar application for improvement of fruit yield, quality, and uptake of nutrients. The twelve combinations of different doses of zinc sulphate, manganese sulphate, and ferrous sulphate fertilizers replicated three times were tested at kinnow orchards established at Krishi Vigyan Kendra, Bathinda, Punjab, India. The data revealed that the fruit drop was significantly low in the treatment F12 (43.4%) (tank mix spray of 0.3% ZnSO4 + 0.2% MnSO4 + 0.1% FeSO4 ) compared to control treatment. The fruit yield per tree was significantly higher in the treatment F12 compared to untreated control. The juice percentage was also recorded higher in treatment F12 as compared to control, and the juice percentage improved by 2.6%. The leaf nutrient analysis also revealed translocation of higher amount of nutrient from leaf to fruit under optimized supply of micronutrient. Thus, the application of tank mix spray of 0.3% ZnSO4 + 0.2% MnSO4 + 0.1% FeSO4 may be used for better fruit yield and quality.
Tuberculosis and the Effectiveness of the Revised National Tuberculosis Control Program (RNTCP) to Control Tuberculosis: A Narrative Review

The revised National Tuberculosis (TB) Control Program is an initiative undertaken by the government of India and was active from 1997 to 2020. Later it was renamed as National TB Elimination Program, which eyes the complete eradication of TB by 2025. The revised National Tuberculosis Control Programme (RNTCP) is preceded by the National TB Control Program which was activated when the cases of TB were on the rise in the early 1960s and police intervention was needed. National Tobacco Control Cell (NTCP) guided the efforts until 1997 when various shortcomings, which were registered over the course of time, were addressed and the revised program was launched. It has been a mixed success as beneficiaries belonging to the reachable, urban areas were benefitted, and tribal, and backward areas were lagging behind. Although the RNTCP proved to be effective in containing TB and curing it to a certain extent, the successor of the program, which is NTEP, has set an ambitious goal of eradicating TB by 2025 which needs concerted efforts on behalf of all stakeholders.