Miracle-dz/UniFAR-FTUs huggingface dataset:
Scattering volume in the collective Thomson scattering measurement using high power gyrotron in the LHD

High-power gyrotrons prepared for the electron cyclotron heating at 77 GHz has been used for a collective Thomson scattering (CTS) study in LHD. Due to the difficulty in removing fundamental and/or second harmonic resonance in the viewing line of sight, the subtraction of the background ECE from measured signal was performed by modulating the probe beam power from a gyrotron. The separation of the scattering component from the background has been performed successfully taking into account the response time difference between both high-energy and bulk components. The other separation was attempted by fast scanning the viewing beam across the probing beam. It is found that the intensity of the scattered spectrum corresponding to the bulk and high energy components were almost proportional to the calculated scattering volume in the relatively low density region, while appreciable background scattered component remains even in the off volume in some high density cases. The ray-trace code TRAVIS is used to estimate the change in the scattering volume due to probing and receiving beam deflection effect.
Simulation of an In-Line Optofluidic Refractive Index Sensor Based on a Seven-Liquid-Core Teflon-Cladding Fiber

We have proposed and theoretically demonstrated a highly sensitive optofluidic refractive index (RI) sensor based on a modal interferometer formed by a section of liquid-filled seven-hole Teflon-cladding fiber sandwiched by two standard single mode fibers (SMFs). When liquid gets into the air hole voids of the seven-hole Teflon-cladding fiber, it forms a seven-liquid-core fiber (SLCF) and the light waves are well guided by the liquid cores because of total inner reflection. When the input SMF is aligned to the central core of the SLCF, only two pairs of supermodes with different effective indices will be excited, and thus a Mach-Zehnder modal interferometer is constructed. The spectral response of the modal interferometer to the variation of RI of the liquid cores is numerically simulated through finite-element method software. The dependence of the RI sensitivity on the size and center distance of air holes of the SLCF are studied, respectively. Finally, a very high sensitivity of 24,577 nm/RIU for RI in the vicinity of 1.333 is achieved. The proposed sensor is potentially useful for label-free biochemical sensing due to its high sensitivity and very little sample consumption.
Occupancy and Thermal Preference-Based HVAC Control Strategy Using Multisensor Network

Human-in-the-loop heating, ventilation, and air conditioning (HVAC) control-based methodologies have gained much attention due to continual discomfort compliance of occupants in residential and commercial buildings; spawning thermal comfort research interest in leveraging emerging advanced technologies to address the prolonged problem of discomfort and energy efficiency. In the past, thermal comfort studies have been conducted to determine the thermal sensation, preference, and comfort based on the American Society of heating, refrigerating, and air-conditioning engineers (ASHRAE) Global Thermal Comfort Database II and customized dataset through machine learning. The ASHRAE Database II is an open-source database that includes sets of objective indoor climatic observations with corresponding subjective evaluations by the building occupants who were used as subjects in experiments. Environmental parameters and occupants’ skin temperature have been used to develop machine learning algorithms to predict thermal comfort indices in both indoor and outdoor settings. However, none of these studies have investigated merging environmental parameters and thermal images to predict thermal comfort indices of occupants. In this study, the holistic understanding of individuals thermal comfort environment was considered by fusing analog environmental sensors and thermal images captured at the time of the subjective measurement. Wavelet-scattering features were obtained from the occupants’ thermal image surroundings and joined to the environmental parameters. This research developed different machine learning models, processing methods and evaluated the results based on the fused dataset. The results show the possibility of real-time prediction of occupancy and thermal preference through classical machine learning, and stacked models with high accuracy. The proposed framework achieved an estimated 45% mean energy savings during a ten-day energy analysis.
State of Health and Life Cycle Prediction of In-Vehicle Lead Acid Battery

Three-wheel battery-operated vehicles are the most popular kind of public transportation in Asian countries. The most apparent and cheaper version of on board energy generation source for this three wheeler is the traction battery pack mainly designed by lead acid batteries. In this study, an effort is made to close the gap by using a real-time battery monitoring system to investigate the battery health and battery aging-related parameter using modified Coulomb counting and a data-driven model. A proposed hardware based methodology is implemented to a series connected multiple lead-acid battery strings of a typical three-wheeler for the selected drive cycle. The method is used to measure the effective depth of discharge for an actual drive cycle, dynamically variation of internal resistance at different depth of discharge level, voltage and current to estimate and compare the battery health of a on board battery pack. The field test and comparison of the test results against three-wheelers provide evidence of the concept that internal resistance varies from 2% to 150% depending on battery ageing and significantly affects the terminal voltage drop at different discharge levels.
Data-Driven Spectrum Allocation and Power Control for NOMA HetNets

Towards a green communication discipline, more energy-efficient wireless networks are needed. In this paper, an energy-efficient interference management technique is proposed for heterogeneous networks (HetNets) with downlink non-orthogonal multiple access (NOMA) operating in millimeter (mm) waves. The proposed technique utilizes data-driven-based power and resource blocks (RBs) allocation scheme to maximize the energy efficiency of the entire network while managing inter-user, co-tier, and cross-tier interference. The studied interference management problem is mathematically formulated as maximizing the energy efficiency of NOMA-HetNets under constraints; quality of service (QoS), successive interference cancelation condition (SIC), maximum power allocation, and the maximum capacity of each RB, which is a non-convex and combinatorial NP-hard problem that needs exhaustive search for an optimum solution. The formulated problem is relaxed into a minimization of a linear $l_{2}$-norm problem. Then, a low-complex model-free data-driven based approach is proposed to find a near-optimum allocation for power and RBs based on a dynamic linearization representation of a time-varying pseudo-gradient parameter estimation procedure. Numerical analyses show that the proposed scheme achieves higher network energy efficiency with lower computational complexity compared to the benchmark schemes.
An inflammatory-related genes signature based model for prognosis prediction in breast cancer

Background: Breast cancer has become the most common malignant tumor in the world. It is vital to discover novel prognostic biomarkers despite the fact that the majority of breast cancer patients have a good prognosis because of the high heterogeneity of breast cancer, which causes the disparity in prognosis. Recently, inflammatory-related genes have been proven to play an important role in the development and progression of breast cancer, so we set out to investigate the predictive usefulness of inflammatory-related genes in breast malignancies. Methods: We assessed the connection between Inflammatory-Related Genes (IRGs) and breast cancer by studying the TCGA database. Following differential and univariate Cox regression analysis, prognosis-related differentially expressed inflammatory genes were estimated. The prognostic model was constructed through the Least Absolute Shrinkage and Selector Operation (LASSO) regression based on the IRGs. The accuracy of the prognostic model was then evaluated using the Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. The nomogram model was established to predict the survival rate of breast cancer patients clinically. Based on the prognostic expression, we also looked at immune cell infiltration and the function of immune-related pathways. The CellMiner database was used to research drug sensitivity. Results: In this study, 7 IRGs were selected to construct a prognostic risk model. Further research revealed a negative relationship between the risk score and the prognosis of breast cancer patients. The ROC curve proved the accuracy of the prognostic model, and the nomogram accurately predicted survival rate. The scores of tumor-infiltrating immune cells and immune-related pathways were utilized to calculate the differences between the low- and high-risk groups, and then explored the relationship between drug susceptibility and the genes that were included in the model. Conclusion: These findings contributed to a better understanding of the function of inflammatory-related genes in breast cancer, and the prognostic risk model provides a potentially promising prognostic strategy for breast cancer.
Beverage Consumption Patterns and Nutrient Intake Are Associated with Cardiovascular Risk Factors among Urban Mexican Young Adults

Regular consumption of sugar-sweetened beverages has been related to metabolic diseases. Our objective was to identify beverage consumption patterns, nutrient intake, and their possible association with the prevalence of cardiovascular risk factors among Mexican young adults. A cross-sectional survey was conducted. Beverage consumption patterns were obtained by principal components analysis. Logistic regression models were applied to assess the association between the beverage patterns and cardiovascular risk factors. Four beverage patterns were identified. Higher consumption of alcoholic beverages was associated with lower odds for high body fat percent (OR: 0.371; 95% CI: 0.173–0.798), high blood pressure (OR: 0.318; 95% CI: 0.116–0.871), and high glucose (OR: 0.232; 95% CI: 0.061–0.875). Higher consumption of yogurt was associated with lower odds for high glucose (OR: 0.110; 95% CI: 0.22–0.559). In contrast, highest consumption of juice had greater odds for high triglycerides (OR: 1.084; 95% CI: 1.011–4.656). Higher consumption of milk was associated with greater odds for high glucose (OR: 5.304; 95% CI: 1.292–21.773). Beverage consumption habits in Mexican young adults are associated with increased risk factors for cardiovascular disease. Therefore, intervening during young adulthood should be considered in order to improve current health and prevent cardiovascular mortality in later decades.
A DDoS attack detection algorithm based on improved grid search to optimize SVM in SDN environment

Software defined network has proposed a new architecture idea, but the controller is vulnerable to distributed denial of service attacks, resulting in a single point of failure. This paper proposes a DDoS attack detection algorithm based on improved grid search optimization SVM in SDN environment. Firstly, according to the characteristics of DDoS attacks and information of flow table items, the most relevant six-dimensional features are selected, then use the improved grid search algorithm to calculate the optimal penalty coefficient and kernel function parameters of SVM model, finally use SVM algorithm to detect DDoS attacks and improve the performance of intrusion detection algorithm. The experimental results in the same scene show that the improved SVM has higher detection rate and recall rate than the SVM algorithm.
Knowledge and Practices of Menstrual Hygiene Management: A Descriptive Cross-Sectional Study among Adolescent Girls in North Gonja District in the Savannah Region of Ghana

Purpose: The study aims to assess the knowledge and practices of menstrual hygiene management among adolescent girls in the North Gonja District of the Savannah Region of Ghana. Methodology: The study used a descriptive cross-sectional study with a quantitative approach. The study population was adolescent girls who have reached menarche. A total of 217 respondents were recruited using consecutive sampling. A validated questionnaire was used to gather data and analyzed using the SPSS version 25 software. The significance level was estimated as a p-value less the . 05. Findings: The study showed that the majority of the respondents (59.0%) were within 15 to 19 years, 90.8% were in school, 77.9% were Muslims and nearly half (49.8%) of the girls were living with both parents. Almost all of the respondents (97.7%) have heard about menstruation through their mothers (30.4%) followed by the teachers/school (18.9%) with media (4.1%) being the least source of information. Most respondents (78.8%) had good knowledge about menstruation and MHM. The majority of the girls (64.5%) used sanitary pads, 30.9% used clothes and 4.6% used other absorbent materials during menstrual flow. Nearly all girls (98.2%) clean their genitals during menses with the majority (65.4%) using soap and water to clean their genitals whereas (32.7%) used only water. Overall, 71.0% of the respondents had adequate MHM practices. The study revealed a significant statistical association between MHM practices and age (X2 = 34.1, p < .001), earning a living (X2 = 11.9, p = .021), and knowledge about menstruation (X2 = 11.8, p = .002). Unique Contribution to Theory and Practice: In all, appreciable knowledge and sufficient MHM practices were observed among respondents. Age, ability to earn a living, and knowledge about menstruation and MHM were associated with the practice of good MHM practices. We recommend that parents should be sensitized and encouraged to provide for the sanitary needs of their girl child as well as introduce the topic of menstruation to the girls in readiness for menarche.
Critical Path Awareness Techniques for Large-Scale Graph Partitioning

Graph partitioning is one of the fundamental problems in many graph-based applications and systems. It enables the division of a graph into smaller sub-graphs for subsequent parallel processing, reducing the processing latency of the graph. The critical path of a graph is the logical path with the longest delay from input to output. The processing time of the graph mainly depends on the delay incurred by the critical path, independent of other paths with small delays. Therefore, it can reduce the processing time of the graph by protecting the critical path of the graph from partition. However, existing approaches to graph partitioning only focus on metrics such as minimum cut and partition balance. As a result, the critical paths of graphs may be destroyed in the partitioning procedure. To address this problem, we present a critical path awareness approach, namely path-metis, to protect the critical paths and alleviate the processing latency after graph partitioning. In path-metis, two efficient strategies, including Slack and critical path fix strategies, are introduced. The Slack strategy, which incorporates critical path information into the weights of DAG, is used as pre-processing before traditional multi-level partitioning methods, like Metis. Then, for the generated partitioning scheme, the critical path fix strategy is proposed to further protect critical paths from being cut. We demonstrate the effectiveness of our approach on both real and synthetic datasets. From the experimental results, compared to Metis, our method improves critical path performance by 17.70%.
Accurate Gate Charge Modeling of HV LDMOS Transistors for Power Circuit Applications

Accurate modeling of the gate-drain capacitance Cgd for HV LDMOS transistors is important but is challenging because of its strong bias dependence. We present an improved Cgd model, based on the physics that the drift region under the poly-gate is fully depleted at high Vdg, and validate our model against gate charge measurements for both n-and p-type 90V LDMOS transistors.
Detecting True Change in Keratoconus after Intracorneal Ring Segment Implantation

Confirming the progression of keratoconus is of paramount relevance to providing the appropriate treatment. Real change should be considered consistent over time. It must be greater than the variability of the measurement of the device used to monitor the cornea. The present study aimed to assess the intraobserver repeatability and intersession reproducibility of a Scheimpflug camera in measuring corneal parameters in virgin keratoconus and intrastromal corneal ring segments (ICRS) implantation eyes to discriminate real change from measurement noise. Sixty keratoconus and 30 ICRS eyes were included. Corneal parameters were determined in three consecutive measurements and were repeated 2 weeks later. The precision within the same session for all parameters was better in the keratoconic eyes, with mean repeatability limits 33% narrower (range 13% to 55%) compared with ICRS eyes. Mean reproducibility limits were 16% narrower (range +48% to −45%) compared with ICRS eyes. The cutoff values to consider a real corneal shape change were lower for virgin keratoconic than for ICRS, except for the thinnest corneal thickness and Stage C (ABCD system), which were the opposite. Corneal tomography measurements in ICRS eyes showed worse accuracy than in virgin keratoconus, which should be taken into account by practitioners in patients’ follow up.
Optimal Meter Configuration Based on Harmonic State Estimation Model and Binary Particle Swarm Optimization

The monitoring and estimation of harmonic is of great significance to maintain the stable operation of distribution networks. In order to determine the harmonic state level in network and reduce the investment and difficulty of installation, it is necessary to carry out research on the optimization of meter configuration. Therefore, this paper proposes a method of optimal meter configuration combining harmonic state estimation model and binary particle swarm optimization, which aims at estimating all harmonic state variables in the network while using fewer meters. Harmonic state estimation is the process of estimating the harmonic voltage and current state with limited data collected by meters according to the harmonic state estimation model. The rules of state estimation model are established based on the topology connection in network and Kirchhoff's law. IEEE-18 bus network is employed to apply the state estimation model and the result of optimization algorithm shows that only six meters are required to measure and estimate all the harmonic state variables. Finally, the real harmonic values of this network are obtained from Simulink/MATLAB to compare with the estimation values, which verifies the effectiveness of the proposed method.
Recent Advances on Electrospun Nanofibers for Periodontal Regeneration

Periodontitis is an inflammatory infection caused by bacterial plaque accumulation that affects the periodontal tissues. Current treatments lack bioactive signals to induce tissue repair and coordinated regeneration of the periodontium, thus alternative strategies are needed to improve clinical outcomes. Electrospun nanofibers present high porosity and surface area and are able to mimic the natural extracellular matrix, which modulates cell attachment, migration, proliferation, and differentiation. Recently, several electrospun nanofibrous membranes have been fabricated with antibacterial, anti-inflammatory, and osteogenic properties, showing promising results for periodontal regeneration. Thus, this review aims to provide an overview of the current state of the art of these nanofibrous scaffolds in periodontal regeneration strategies. First, we describe the periodontal tissues and periodontitis, as well as the currently available treatments. Next, periodontal tissue engineering (TE) strategies, as promising alternatives to the current treatments, are addressed. Electrospinning is briefly explained, the characteristics of electrospun nanofibrous scaffolds are highlighted, and a detailed overview of electrospun nanofibers applied to periodontal TE is provided. Finally, current limitations and possible future developments of electrospun nanofibrous scaffolds for periodontitis treatment are also discussed.
Semantic Images Segmentation for Autonomous Driving Using Self-Attention Knowledge Distillation

In this paper, we exploit the principle of Knowledge distillation to reduce the computational complexity of neural networks for a suitable embedding in self-driving cars. A new method is proposed for training small-size neural network (student) with the supervision of a large one (teacher) for semantic scene segmentation. The main novelty consists of transferring the knowledge from the teacher to the student through a self-attention module extracted from the feature maps. This module captures the semantic association between the different feature map channels which is an effective knowledge for the student training. Experiments on Cityscapes database demonstrate the effectiveness of the proposed method.
A Deep Learning-Based Unbalanced Force Identification of the Hypergravity Centrifuge

Accurate and quantitative identification of unbalanced force during operation is of utmost importance to reduce the impact of unbalanced force on a hypergravity centrifuge, guarantee the safe operation of a unit, and improve the accuracy of a hypergravity model test. Therefore, this paper proposes a deep learning-based unbalanced force identification model, then establishes a feature fusion framework incorporating the Residual Network (ResNet) with meaningful handcrafted features in this model, followed by loss function optimization for the imbalanced dataset. Finally, after an artificially added, unbalanced mass was used to build a shaft oscillation dataset based on the ZJU-400 hypergravity centrifuge, we used this dataset to train the unbalanced force identification model. The analysis showed that the proposed identification model performed considerably better than other benchmark models based on accuracy and stability, reducing the mean absolute error (MAE) by 15% to 51% and the root mean square error (RMSE) by 22% to 55% in the test dataset. Simultaneously, the proposed method showed high accuracy and strong stability in continuous identification during the speed-up process, surpassing the current traditional method by 75% in the MAE and by 85% in the median error, which provided guidance for counterweight and guaranteed the unit’s stability.
A Fast Matrix Completion Method Based on Matrix Bifactorization and QR Decomposition

The problem of recovering the missing values in an incomplete matrix, i.e., matrix completion, has attracted a great deal of interests in the fields of machine learning and signal processing. A matrix bifactorization method, which is abbreviated as MBF, is a fast method of matrix completion that has a better speed than the traditional nuclear norm minimization methods. However, it may become inaccurate and slow when solving matrices of not low rank. In this paper, an improved fast and accurate MBF method based on Qatar Riyal (QR) decomposition is proposed, which can be called FMBF-QR. On one side, the optimization problem of MBF is improved to be an iteratively reweighted L 2 , 1 norm minimization problem to enhance the accuracy of MBF. On the other side, the minimization problem of FMBF-QR is optimized very efficiently by using QR decomposition for improving the speed of MBF. Sufficient experimental results verify that FMBF-QR can converge with a higher accuracy and a faster speed than the traditional matrix completion methods.