Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Unloading Algorithm for Mobile Edge Computing based on graph neural network

As a cutting-edge and visionary computing mode, edge computing is expected to significantly improve the computing performance of mobile devices (MDs) and improve user experience. This improvement is due to the shift of compute-intensive tasks from MDs with limited computing power to the more well-resourced cloud. However, the task unloading process also faces the limitation of limited communication bandwidth and server resource usage. The task of minimizing the total overhead of the system is especially difficult when the application needs to be completed in a certain amount of time. To overcome these obstacles, our research proposes a novel task offloading and resource allocation strategy based on graph neural networks (GNN). Our strategy can assist in decision-making on computing task offloading, controlling CPU clock frequency, and allocating communication capabilities. To verify the validity of our approach, we conducted a series of simulation experiments in different scale edge computing scenarios. The experimental results show that the task unloading optimization algorithm based on graph neural network can guarantee the quality of service (QoS) and manage the whole system resource cost reasonably. Therefore, our study provides a substantial basis for further exploration of GCN’s potential in the field of edge computing task offloading decision making.
Smart Sensor Network for Environment Parameter Monitoring using IoT

This study presents an Internet of Things (IoT) based approach to monitor sound level and air quality at various locations. The proposed system employs a NodeMCU with an integrated Wi-Fi module and requisite sensors to track the sound level and air quality index (AQI) at specific sites. The system is capable of measuring variables such as humidity and temperature, in addition to the AQI and sound level, at the site. LM393 sound sensor is utilized for sound detection, DHT-11 sensor module for temperature and humidity monitoring, and MQ-135 gas sensor for air quality measurement. ThingSpeak's cloud platform and cloud technology are employed to store the collected data. The user is promptly notified via SMS when the air quality and sound level exceed a predetermined threshold. Furthermore, the end user is kept informed of environmental factors such as temperature and humidity to enable appropriate action.
Research on Reconstruction and Application of Blended Teaching Quality Evaluation System under “New Economics and Management’’ Strategy

Based on the problems and deficiencies of the existing blended teaching quality evaluation index system, this study puts forward the principle of reconstructing the blended teaching quality evaluation system under the strategy of “New Economics and Management’’, and constructs a blended teaching quality evaluation system that serves the strategy of “New Economics and Management’’ in seven aspects, namely, teaching concept, teaching content, teaching means, teaching method, teaching service, teaching management, and teaching guarantee, in order to provide the basis of the school’s blended teaching reform.
Dermatological Changes during Menopause and HRT: What to Expect?

Menopause marks the end of a woman’s reproductive life. It is well-known that skin aging is accelerated during this period, as declining estrogen detrimentally impacts the skin’s extracellular matrix (ECM) which is the provider of strength and elasticity. Menopause also affects scalp hair, reducing hair diameter and leading to shorter hair growth. Objective: To evaluate the main dermatological complaints of Brazilian women during this period, comparing the compatibility of these complaints with similar studies that evaluated skin health at menopause. Methods: This study interviewed 463 postmenopausal women between 42 and 83 years old through a public electronic questionnaire in the SurveyMonkey app about their perception of the changes noticed in their hair and skin after menopause. All statistical analyses were performed in R v. 3.6. 1. Results with p-values < 0.05 were considered significant. Results: Dermatological health in menopausal women, especially the quality of the skin, nails and hair may deteriorate with estrogen deficits. As life expectancy increases and hormone replacement therapy (HRT) becomes more common, a greater understanding of their dermatological effects in menopause is needed. Conclusions: Menopause strongly impacts skin quality and worsens women’s self-esteem and quality of life. There is still a lack of knowledge about HRT and specific treatments for improving skin, hair and nails.
Ship Detection in SAR Image Based on Switchable Cavity Convolution

This paper presents an enhanced SAR image detection algorithm for YOLOv7 to address the issue of low detection accuracy of small-target ships and ships in complex backgrounds in synthetic aperture radar images. The proposed algorithm replaces the traditional convolutional layer in the backbone network of YOLOv7 with a convolutional SAConv, thus enhancing the network's capability to detect small targets and large objects. The introduction of the new metric, Wise IoU, into the loss function aims to improve the convergence speed and robustness while mitigating the adverse gradient effects caused by low-quality examples, thereby enhancing the detection of small targets. In the experimental comparison using the SSDD dataset, the improved method is employed to enhance the precision value of the model by increasing the number of model parameters and computational resources compared to the original YOLOv7. The mean Average Precision increases to 96.59%, showing an improvement of 9.33% over the original YOLOv7. Additionally, the accuracy improves by 3.81%, and the recall rate improves by 16.36%. These results demonstrate the superiority of the proposed method, resulting in enhanced precision compared to the original approach. The experimental results demonstrate that the enhanced algorithm significantly improves the detection accuracy of SAR images. Moreover, it effectively mitigates the issues of false positives and missed detections of ships in complex backgrounds.
Speech-Based Detection of In-Car Escalating Arguments to Prevent Distracted Driving

Every year, 2.5 million car crashes involve distracted drivers globally. It takes a few seconds for a car crash to happen after the driver has been distracted. Distracted driving thus poses a critical threat to road safety, needing innovative approaches for its detection and mitigation. This paper introduces a novel system to monitor in-car conversations and identify potential distractions from escalating arguments. The system analyzes Mel spectrograms generated from real-time audio signals containing in-car discussions by combining continuous voice recording and deep learning techniques. First, a denoiser employs a convolutional autoencoder to reduce car engine noise within the spectrograms. Then, a classifier uses convolutional and recurrent neural networks to determine whether the audio corresponds to a calm conversation or a quarrel based on the denoised spectrogram. The experimental results showed that the system achieved a 91.8% classification accuracy. This system addresses a previously unexplored dimension of cognitive distraction, offering valuable insights into strategies for reducing the risk of road accidents. Ongoing research is focused on accounting for other environmental noises, such as radio speakers, music, wind from open windows, and engine sounds from surrounding vehicles, which may influence classification accuracy. The system is also being extended to consider more than two occupants in the car.
VERTICAL-HORIZONTAL WATER QUALITY PROFILES OF BATUR LAKE, BANGLI DISTRICT, BALI SUPPORTING SUSTAINABLE LAKE MANAGEMENT

Batur Lake located in Bangli District is one of fisheries-basedregions in Bali with niletilapia as the main cultured species. Monitoring on environmental conditions of the lake was carried out to get the vertical and horizontal water quality profiles of the lake. Climatology condition of the area was also presented in the study. The study area were between 8o13’-8o18’ S and 115o22’-115o26’ E. Field survey was done with random and transect sampling points distribution. The water quality monitored in the lake essential ly covers important parameters such as depth, transparency, temperature, pH, dissolved oxygen, salinity, nutrients (ammonia, nitrate, nitrite, and phosphate), sulfide, chlorophyll, and planktonic composition. Results of spatial analysis (vertically and horizontal ly) of water quality indicated that oxygen concentration up to 10 m deep is still appropriate for nile tilapia culture. Nutrient (phosphate, ammonia, nitrate, and nitrite) contents of the lake meet the standard levels set by Ministry of Environment of Indonesia, and were still suitable for aquaculture operation at depth 710 m. From climatology point of view, it is known that the extreme climate occurred in June-August resulting in water mass changes, thus, affecting aquaculture sustainability in the lake.
Artificial Intelligence-Based Plant Breeding using Genotype and Phenotype Data: Methods and Future Work

Food shortages, driven by population growth and climate change, pose a significant global challenge. Addressing this issue requires a dual focus on increasing food production and optimizing land use efficiency. Rather than expanding farmland, current efforts emphasize enhancing crop productivity through plant breeding. Especially, plant breeding research has emerged as a critical solution to the escalating challenges posed by a rapidly growing population and unpredictable climate changes. While plant breeding has a history of contributing to improved crop productivity, addressing current food problems requires the application of innovative technologies. Therefore, digital breeding, which incorporates new technologies, has gained prominence recently. Digital breeding uses high-throughput sequencing technology known as next-generation sequencing (NGS) to decode genome sequences and collect mutation information from diverse individuals. This method selects individuals with specific traits by analyzing the relationship between genotype and phenotype. By leveraging genotype information, digital breeding accurately pinpoints individuals with desired characteristics, significantly expediting the breeding process compared with traditional or molecular breeding methods. The distinctive advantages of digital breeding, such as precise genome selection, make it a promising approach for addressing food security challenges arising from population growth and climate change. Consequently, ongoing research in this domain, particularly combining big data and artificial intelligence technology, further underscores the significance of these advancements. This study explores the significance of artificial intelligence-based genome selection techniques, and associated technologies and outlines future research.
Long-Term Water Level Prediction of Hongze Lake Based on ICEEMDAN-AdaBoost-GRU-LSTM

To overcome the limitations of traditional time series models in dealing with monthly average water level time series, such as high noise, strong nonlinearity, limited data, and low prediction accuracy, this study introduces a novel water level prediction model called ICEEMDAN-AdaBoost-GRU-LSTM. The proposed model combines the Adaptive Boosting (AdaBoost) ensemble strategy with Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and two neural network architectures: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The modeling process involves the following steps: Firstly, a stacked GRU-LSTM model is established as the base model. Then, an AdaBoost ensemble technique is applied to enhance the predictive power of the base model. The input data is preprocessed and decomposed into several Intrinsic Mode Functions (IMFs) and a residue (RES) using the ICEEMDAN method. The high-frequency noise component IMF1 is eliminated, and the remaining mid-to-low-frequency components are predicted simultaneously using the strong predictor. Finally, the predicted values of the middle and low frequency components are superimposed to get the final predicted value. Experimental results demonstrate that the proposed ICEEMDAN-AdaBoost-GRU-LSTM model outperforms other models such as LSTM, GRU, Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and others in terms of prediction accuracy.
Pruning Federated Learning Models for Anomaly Detection in Resource-Constrained Environments

The evolving complexity of modern IT infrastructures has paved the way for malicious actors to exploit a wide array of vulnerabilities that can compromise the integrity of these systems. Monitoring complex IT systems is expensive and often requires dedicated infrastructure for deploying Intrusion and/or Anomaly Detection Systems. Moreover, ML-based solutions need large training sets, which add to the overall cost. To tackle these challenges we present INTELLECT, a novel approach to Intrusion and/or Anomaly Detection System, which leverages Federated Learning and model pruning techniques to cooperatively train high-accuracy models using distributed datasets and derive a fleet of lightweight models, which can be deployed without incurring additional costs for dedicated infrastructure. INTELLECT expands on the state-of-the-art techniques for feature selection, model pruning, and model distillation to create an interconnected pipeline. We empirically demonstrate the effectiveness of the methodology on benchmark datasets, and we present guidelines for the deployment in production systems.
Metropolitan Expansion and Migrant Population: Correlation Patterns and Influencing Factors in Chengdu, China

The ongoing urban spatial transformation contributes to a more intricate and varied spatiotemporal correlation pattern between metropolitan expansion and the migrant population. In this study, the coupled coordination model (CCD) is applied to enable the quantification of the spatiotemporal correlation index of metropolitan expansion and migrant population from 2010 to 2020. Moreover, various correlation patterns are identified in this research, and the multiscale geographical weighted regression model (MGWR) is employed to examine the spatiotemporal heterogeneity of the influencing factors that contribute to this correlation. The research findings reveal the following insights: (1) The built-up areas increased twofold between 2010 and 2020, with an evident tendency toward southward expansion. In addition, population migration indicates slow migration in the inner metropolitan area and accelerated migration in the outer metropolitan area. The correlation between metropolitan expansion and migrant population follows a central–peripheral layer pattern that is characterized by a low-high-low progression. (2) While the balanced development of public services has somewhat decreased the differentiation patterns observed, the adjustments made to economic growth, employment, and residential markets have intensified the differentiation of spatiotemporal correlation characteristics between the metropolitan expansion and migrant population. (3) This study demonstrates that the Chengdu metropolitan area is transitioning from a rapid development stage driven by structuralism to a human-oriented new urbanization stage. This shift is evident through the clear stage pattern and central–peripheral layer features observed. Through the scientific planning of industry and public service layouts, the promotion of integration employment, residential markets, and the facilitation of urban–rural transformation can be achieved. Implementing these strategies can elevate the standard of human-oriented urban spatial governance, achieve coordinated and balanced development between built-up and residential spaces, and advance the high-quality, sustainable, and inclusive development of metropolitan areas.
Frequency of Smoking, Alcohol Consumption, and Substance Use in Relation to General Health Indicators in Guilan University of Medical Sciences

Background: The risk of substance use is increasing among university students, especially medical students. This study aimed to investigate the frequency of smoking, alcohol consumption, and substance use and its relationship with general health indicators among the students of Guilan University of Medical Sciences (GUMS) in 2020. The differences in the frequency of substance use were also investigated from 2005. Methods: This was a cross-sectional analytical study conducted on 406 students of GUMS, Iran, in 2020. The data were collected through three online questionnaires including a demographic questionnaire, the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST), and the General Health Questionnaire (GHQ). The data were entered into SPSS software version 22 and analyzed using Chi-square, Fisher’s Exact tests, and logistic regression at a significance level of 0.05. Findings: In this study, 53.9% of the participants were female, 46.1% were male, and 54.4% of them experienced using substances in their lifetime. The highest prevalence of use was in consumers of tobacco (46.8%), alcoholic beverages (34.7%), and cannabinoid compounds (19%). Besides, 48.5% of the participants were suspected of having some degree of psychiatric disorders, among which depression (11.6%) and anxiety (8.4%) were the most common. Moreover, substance use during students’ lifetime had a statistically significant association with anxiety, depression, and mental health. Conclusion: The results of this study showed the prevalence of smoking, alcohol consumption, and substance use among the students of GUMS is worrying. The relationship between consumption and consumer’s health indicators highlights the necessity of intervention and purposeful planning by policymakers in this field.
Linked cross-sectional study for determining norms and growth rates- a pilot survey on Indian school-going boys

SUMMARY. The paper deals with the statistical analysis of measurements obtained on school-going boys of various ages for estimating norms and growth rates, in what is termed as an LCS (Linked Cross-Sectional) study. This is a cross between a longitudinal study where some boys are chosen at a fixed young age and are measured periodically over a certain number of years covering the range of study, and a cross-sectioml study where boys of different ages are chosen and each boy is measured only at one age and at one point of time. In an LCS study, boys of different ages are chosen and each boy is measured periodically over a comparatively smaller number of years. Thus, if one is interested in studying growth of boys between the ages of 5 and 16, a certain number of boys at each age (between 5 and 16) may be chosen and measured on three consecutive birth days. The study is thus restricted to a period of about 2 years, whereas in a longitudinal study the period will be about 11 years. The advantages of such a short range study compared to a purely longitudinal study extended over a larger number of years are explained.
A Qualitative Analysis of the Barriers to Healthcare and Education for Adolescent Girls in Tanzania

Introduction: Tanzania has a high adolescent birth rate and many sexually active adolescents do not have access to effective contraception. Teenage pregnancy is considered a high-risk pregnancy. Furthermore, it leads to social inequalities for both mother and offspring. Methods: We conducted semistructured interviews with 12 adolescent mothers during their stay in the postnatal ward of the maternity department of a tertiary hospital in Zanzibar. The study took place between November and December 2022. Data were then analyzed qualitatively. Results: The main theme that emerged from the interview data was that pregnancy seemed to affect the lives of young girls in a negative way. The majority of pregnancies were unplanned, and the girls reported low family planning uptake. Another recurring theme was that girls had many οbstacles in their education prior to pregnancy, which left them uncertain about their future. Finally, despite the advice of local doctors, the majority of the girls received minimal prenatal care, mostly because they did not regard obstetric care to be a priority. Conclusions: Adolescent pregnancy remains an important public health issue in Tanzania, despite significant measures by authorities to reduce it. Educational changes and professional opportunities as well as family planning services may enable young girls to achieve professional and personal goals while delaying motherhood into adulthood.
Using CATS near‐real‐time lidar observations to monitor and constrain volcanic sulfur dioxide (SO2) forecasts

An eruption of Italian volcano Mount Etna on 3 December 2015 produced fast‐moving sulfur dioxide (SO2) and sulfate aerosol clouds that traveled across Asia and the Pacific Ocean, reaching North America in just 5 days. The Ozone Profiler and Mapping Suite's Nadir Mapping UV spectrometer aboard the U.S. National Polar‐orbiting Partnership satellite observed the horizontal transport of the SO2 cloud. Vertical profiles of the colocated volcanic sulfate aerosols were observed between 11.5 and 13.5 km by the new Cloud Aerosol Transport System (CATS) space‐based lidar aboard the International Space Station. Backward trajectory analysis estimates the SO2 cloud altitude at 7–12 km. Eulerian model simulations of the SO2 cloud constrained by CATS measurements produced more accurate dispersion patterns compared to those initialized with the back trajectory height estimate. The near‐real‐time data processing capabilities of CATS are unique, and this work demonstrates the use of these observations to monitor and model volcanic clouds.
Comparative Study on Image Classification Models of Ancient Ceramic Types

One of the main problems faced by image big data is how to efficiently automate image classification. The current mainstream approach is to use image recognition models for training. Although there are many different image recognition models applicable to the field of image classification at present, there is a lack of comparative research on multiple neural network models for specific usage scenarios. This article focuses on the recognition and classification of ancient pottery and porcelain types, and compares and studies three mainstream image recognition models in the industry. Multiple batches of dual parameter optimization training schemes are used to obtain multiple sets of comparable training results. The test data shows that the accuracy of the three image recognition models (Inception, ResNet, MobileNet) is up to 90%, and they can effectively recognize ceramic types. Under the same hardware conditions, the MobileNet model has the fastest recognition speed, with an average of 21.35ms per image, and its computational power requirement (FLOPS) is only 0.56G, far lower than the other two recognition models, making it suitable for mobile internet application scenarios such as mobile phone image recognition.
OptiGait: Gait Monitoring Using an Ankle-Worn Stereo Camera System

We developed an ankle-worn gait monitoring system for tracking gait parameters, including length, width, and height. The system utilizes ankle bracelets equipped with wide-angle infrared (IR) stereo cameras tasked with monitoring a marker on the opposing ankle. A computer vision algorithm we have also developed processes the imaged marker positions to estimate the length, width, and height of the person’s gait. Through testing on multiple participants, the prototype of the proposed gait monitoring system exhibited notable performance, achieving an average accuracy of 96.52%, 94.46%, and 95.29% for gait length, width, and height measurements, respectively, despite distorted wide-angle images. The OptiGait system offers a cost-effective and user-friendly alternative compared to existing gait parameter sensing systems, delivering comparable accuracy in measuring gait length and width. Notably, the system demonstrates a novel capability in measuring gait height, a feature not previously reported in the literature.