Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Scheduling Based on Priority Ladders for Multiple Performance Requirements in Wireless Body Area Networks

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBANs). However, current solutions still cannot satisfy multiple performance requirements of such networks. In this article, we propose a priority ladders-based resource scheduling (PLRS) scheme to meet these demands. First, we design a multiple-level priority division approach, which is based on health parameters and data types of patients. Unlike only two priorities of nodes in other schemes, there are four priorities in our solution. This is more practical and more fair for different nodes. Moreover, we devise a ladders-based time-slot allocation method, in which time slots of nodes are increased round by round based on the priority ladders of nodes. This reduces the difference of the number of time slots between disparate nodes with close priorities, and accordingly enhances the fairness of nodes. In addition, we design an interval-based task ordering approach, in which nodes use the allocated time slots in dispersed multiple intervals rather than a continuous time span in conventional solutions. This guarantees high throughput of high-priority nodes and short waiting time of emergency data. Extensive simulations exhibit the advantages of PLRS in terms of energy overhead, delay, and utility.
An End-to-End (E2E) Network Slicing Framework for 5G Vehicular Ad-Hoc Networks

Network slicing is emerging as a promising solution for end-to-end resource management and orchestration together with Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. In this paper, a comprehensive network slicing framework is presented to achieve end-to-end (E2E) QoS provisioning among customized services in 5G-driven VANETs. The proposed scheme manages the cooperation of both RAN and Core Network (CN), using SDN, NFV and Edge Computing technologies. Furthermore, a dynamic radio resource slice optimization scheme is formulated mathematically, that handles a mixture of mission-critical and best effort traffic, by delivering the QoS provisioning of Ultra-reliability and low latency. The proposed scheme adjusts the optimal bandwidth slicing and dynamically adapts to instantaneous network load conditions in a way that a targeted performance is guaranteed. The problem is solved using a Genetic Algorithm (GA) and results are compared with the previously proposed 5 G VANET architecture. Simulation reveal that the proposed slicing framework is able to optimize resources and deliver on the key performance metrics for mission critical communication.
A Flyback Converter-Based Hybrid Balancing Method for Series-Connected Battery Pack in Electric Vehicles

An active balancing method based on two flyback converters is proposed for series-connected battery pack. Balanced energy can be transferred between the whole battery and any single cell. The proposed topology reduces the number of energy storage components, the volume and the cost of the balancing system. And it has the characteristics of fast balancing speed and high balancing efficiency. Based on the topology, a dual-objective hybrid control strategy is proposed, which can reduce the highest voltage and boost the lowest voltage in the charging or discharging process of the cells in one sampling period, so as to improve the balancing speed. Simulation and experimental results show that the proposed method has a good balancing effect and can significantly improve the consistency of series battery pack. This work is potentially significant in terms of improved reliability of battery packs and savings of costs and lives in safety-critical applications.
A novel method to regulate negative emotions by odors and evaluate the effect by EEG signals

People's emotional appearance differs due to national, cultural, geographical and educational backgrounds. Few studies have examined the impact of odors to regulate negative emotion for the Asian. In this research, we evaluate EEG signals to demonstrate the impact of odors to regulate negative emotion for Asians. The results show that, during the tension, both FA groups witness a significant increase and beta wave average power spectrum (T8) experienced a slight reduction caused by lavender odor. However, both citrus and peppermint odors cause a significant increase in FA groups and beta wave average power spectrum (T7 and T8). During the depression, the citrus and mint odors cause a more significant increase in positive FA and beta wave average power spectrum (T7) , while the lavender treatment has no significant effect. For the tension-anxiety (T/A) and depression-dejection (D/D), both FA groups get a lower score after odor treatment but not all reach a statistical significance. Both EEG and POMS data suggests that lavender odor produces a higher valance and a lower arousal which indicates that the lavender odor has a better effect on relieving tension. While the citrus and mint odors produce a higher valance and arousal of emotion which indicates that these two odors have a greater effect on mitigating depression.
DKD-S: An Ontology-based Tool for Global Software Development

The Global Software Development has brought an opportunity to software companies to expand the business and market share, also given them the possibility of work with geographically dispersed teams, capitalizing on the advantages of this approach. However, this manner of developing software creates some new challenges, for instance, there is a lack of a formal and normalized model of a software project’s data, and artifacts available in a coherent and easy access way for all members involved. The lack of this model makes it difficult for the project members to communicate, understand each other and what is specified on the project’s artifacts. Then, this paper proposes a knowledge management tool that utilizes a domain-specific ontology for distributed development environments, intending to help distributed teams to overcome the challenges delivered by the distributed software development model, proposing techniques and best practices. The main result of this research is an ontology-based system to support the software development process with distributed teams.
Cost-Effective Surface-Mount Off-Center-Fed Dipole Antenna Element and Its Array for 5G Millimeter Wave New Radio Applications

A cost-effective ball grid array (BGA) packaged surface-mount off-center-fed dipole antenna element and its array are proposed for 5G millimeter-wave new radio (NR) applications. The proposed antenna element exhibits a compact size of <inline-formula> <tex-math notation="LaTeX">$0.61 \,\,\lambda _{0} \times 0.61\,\,\lambda _{0} \times 0.16\,\,\lambda _{0}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the wavelength in free space at the center operating frequency) and is realized by only one FR4 layer to minimize cost. Plated through holes (PTHs) connect the dipole patch on the top layer to the bottom layer. Furthermore, tin-lead solder balls are mounted under the bottom layer to form the BGA packaging. The surface-mountable feature enables the antenna element to be integrated with other surface mount devices without any waveguide or coaxial connectors. The evaluation board uses a 50-<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> grounded coplanar waveguide (GCPW) line to test the performance of the antenna element and array. The antenna prototype and evaluation board have been manufactured and measured. The measurement results show that the −10-dB impedance bandwidth of the proposed antenna element achieves 35.7% in the range of 25.1–36 GHz. The measured peak gains of the antenna element and the <inline-formula> <tex-math notation="LaTeX">$1 \times 8$ </tex-math></inline-formula> arrays are 6.72 and 13.53 dBi, respectively.
Mapping Microscale PM2.5 Distribution on Walkable Roads in a High-Density City

Monitoring pollution of PM2.5 on walkable roads is important for resident health in high-density cities. Due to the spatiotemporal resolution limitations of aerosol optical depth observation, fixed-point monitoring, or traditional mobile measurement instruments, the microscale PM2.5 distribution in the walking environment cannot be fully estimated at the fine scale. In this article, by the integration of mobile measurement data, OpenStreetMap (OSM) data, Landsat images, and other multisource data in land-use regression (LUR) models, a novel framework is proposed to estimate and map PM2.5 distribution in a typical microscale walkable environment of the high-density city Hong Kong. First, the PM2.5 data on the typical walking paths were collected by the handheld mobile measuring instruments, to be selected as the dependent variables. Second, geographic prediction factors calculated by Google Street View, OSM data, Landsat images, and other multisource data were further selected as independent variables. Then, these dependent and independent variables were put into the LUR models to estimate the PM2.5 concentration on sidewalks, footbridges, and footpaths in the microscale walkable environment. The proposed models showed high performance relative to those in similar studies (adj R2, 0.593 to 0.615 [sidewalks]; 0.641 to 0.682 [footpaths]; 0.783 to 0.797 [footbridges]). This article is beneficial for mapping PM2.5 concentration in the microscale walking environment and the identification of hot spots of air pollution, thereby helping people avoid the PM2.5 hotspots and indicating a healthier walking path.
A Novel Grading System for Autism Severity Level Using Task-Based Functional MRI: A Response to Speech Study

Autism spectrum disorder (ASD) is a neuro-developmental disorder associated with impairments in social and lingual abilities. Failure in language development is variable in the ASD population and follows a wide spectrum. The autism diagnostic observation schedule (ADOS) is the current gold standard for diagnosing plus expert clinical judgment. Currently, studies aim to develop objective computer-aided technologies to diagnose autism with brain imaging modalities and machine learning. Task-based fMRI is a discriminating image modality that measures the functional activation of the brain. Computer-aided diagnosis systems aim to classify autistic subjects against typically developed peers despite the fact that autism is defined over a wide spectrum. Here, we propose a novel computer-aided grading framework in infants and toddlers (between 12 and 40 months) dependent on the analysis of brain activation in response to a speech experiment. First, brain activation responses are analyzed for 157 autistic subjects divided into three groups of: 92 mild, 32 moderate,and 33 severe as defined by ADOS calibrated severity score. Increased hypoactivation of the superior temporal cortex, angular gyrus, primary auditory cortex and cingulate gyri is exhibited with increasing autism spectrum severity. Less lateralization is also present when activation of the left hemisphere regions is recorded. Second, only these region of interest (ROI) areas are included for further local and global feature extraction in our ASD grading system. A comprehensive, two-stage system is developed using different classifiers. Four-fold cross-validation is adopted for testing. The first stage discriminates between moderate and the other two groups with an accuracy of 0.83 (sensitivity = 0.73, specificity = 0.83). Subsequently, a second stage classifies subjects as mild or severe autism with an accuracy of 0.81 (sensitivity = 0.81, specificity = 0.76). Finally, two validation techniques of synthesizing for oversampling and e of multiple random training and testing sets were adopted. The validation results proved the robustness of the proposed framework for an early computer-aided grading system to place subjects on the autism spectrum.
Data Aided Quasistatic and Doubly-Selective CSI Estimation Using Affine-Precoded Superimposed Pilots in Millimeter Wave MIMO-OFDM Systems

This paper proposes affine-precoded superimposed pilot (SIP) design, followed by channel state information (CSI) estimation techniques for millimeter wave (mmWave) MIMO-OFDM systems. Toward this end, a multiple measurement vector (MMV) sparse Bayesian learning (SBL)-based SIP (M-SIP) technique is initially derived to exploit the simultaneous-sparsity of the beamspace channel vector across the subcarriers, thereby leading to improved performance in comparison to the existing single measurement vector (SMV)-based SBL and other techniques. This is subsequently extended to the data-aided J-SIP technique that performs joint channel estimation and data detection, and is shown to ultimately yield a better mean squared error (MSE) of the channel estimate and bit error-rate (BER). The SIP-based simultaneous-sparse channel estimation framework is extended to time-selective wideband, i.e. doubly-selective mmWave MIMO channels, via the MMV sparse Kalman filtering-based SIP (MK-SIP) technique for tracking the CSI, and subsequently also to the joint Kalman filtering-based SIP (JK-SIP) for data-aided CSI acquisition. Bayesian Cramér-Rao bounds are determined both for quasistatic and doubly-selective mmWave MIMO-OFDM channel estimation. Simulation results, which also incorporate practical channel realizations, are provided to demonstrate performance improvement considering various metrics, such as MSE and BER.
An Omnibus Test for Differential Distribution Analysis of Continuous Microbiome Data

The human microbiome is comprised of thousands of microbial species. These species will substantially influence the normal physiology of humans and cause numerous diseases. Microbiome data can be measured by sequencing, microarray, or other technologies. With the fast development of these technologies, downstream analysis methods should also be designed to effectively and accurately discover the valuable information that is hidden in the data. Many methods have been designed for the count data of microbiome. However, to our knowledge, there are only a few methods developed for the continuous microbiome data. Many microbiome data have an over-dispersed and zero-inflated data structure. Traditional methods rarely characterize this data structure and only focus on the differences in the abundance between different samples. In this study, we introduce a novel method, the zero-inflated gamma (ZIG) omnibus test, to specifically test the continuous and zero-inflated microbiome data. In this test, abundance will be tested along with zero prevalence and dispersion. We compared this method with five other popular methods. We found that ZIG omnibus test has significantly higher power and a similar or lower false-positive rate than the competing methods in the tests of simulated data. It also found more proved microbiomes in the real data with tonsil cancer. So, we conclude that ZIG omnibus test is a robust method across various biological conditions in the differential expression test of microbiome data.
Chemical Constituents from Ampelopsis grossedentata

Aim To study chemical constituents from Ampelopsis grossedentata. Methods Separation and purification were performed by using silica gel, polyamide, reverse-phase silica gel, Sephadex LH-20 column chromatographic techniques and silica gel PTLC. Structures were determined by means of physicochemical properties and spectral analysis. Results Four flavonoids were separated and identified from Ampelopsis grossedentata including dihydromyricetin (1), myricetin (2), myricitrin (3), and myricetin-3-O-β-D-galactopyranoside (4). Conclusion Compound 4 was isolated from this genus for the first time, and 2D NMR techniques permitted the correct assignment of ~ 1 H and ~ 13 C NMR signals of 1.
A Hybrid Data Fusion Approach to AI-Assisted Indirect Centralized Integrated SINS/GNSS Navigation System During GNSS Outage

The main challenges for integration between low-cost strap-down inertial navigation system (SINS) and global navigation satellite system (GNSS) are to manage the non-Gaussian measurement noises and to access the reliable measurements during GNSS outages. Therefore, in this paper, some approaches are taken in the proposed integrated navigation system: (1) using an iterative empirical mode decomposition (EMD) interval thresholding de-noising method for low-cost inertial measurements to provide smoother and more accurate SINS measurements with less complexity, (2) designing an enhanced indirect centralized correntropy Kalman filter using a novel fuzzy parameter adjustment approach, which adaptively changes the estimation algorithm parameters to further improve the robustness of the proposed algorithm in the presence of dynamical maneuvers and non-Gaussian measurement noise, and (3) utilizing a pre-filtered nonlinear autoregressive network with exogenous inputs (PFNARX) designed to provide reliable interrupted positioning information during GNSS outages. The proposed SINS/GNSS system is experimentally evaluated through real-world flight tests. The obtained results reveal that the designed integration algorithm remarkably improves the estimation accuracy by adaptively tuning the parameters in an improved correntropy Kalman filter and an efficient and well-trained artificial neural network-based model in the absence of GNSS signal.
Random Forest for Data Aggregation to Monitor and Predict COVID-19 Using Edge Networks

Medical sensors and distributed edge networks hold promise in advanced control and prediction of infectious diseases, such as COVID-19. Their integration can lower communication latency, bandwidth utilization, and energy consumption while achieving high application scalability and reliability. Existing edge-devices-enabled e-healthcare frameworks face two serious challenges that affect their success. First, real-time medical sensors generate similar or redundant readings used for disease prediction, mostly for non-COVID-19 patients, which increases data transmission latency and energy consumption. Second, predicting the risk level of COVID-19 patients using lightweight machine learning requires minimal training time to be useful. To address these challenges, we develop an edge-centric e-healthcare framework for online health data monitoring and analysis to predict the risk level of COVID-19 patients. In the proposed framework, a statistical data aggregation method (MEAN function) is deployed at local gateway devices to remove redundant data to minimize communication latency and energy usage. Geo-distributed edge servers are used to predict with 97 percent accuracy the risk level to each patient with Random Forest (RF) on the aggregated data. Finally, the efficiency of the RF algorithm is demonstrated with standard classification techniques.
Petri Net-Based Robust Supervisory Control of Automated Manufacturing Systems With Multiple Unreliable Resources

In recent years, there has been a growing concern for robust supervisory control policies that can handle both deadlock and blockage propagation in automated manufacturing systems in the event of resource failures. This work proposes a novel robust supervisory control policy for automated manufacturing systems with multiple unreliable resources without using central buffers. The policy permits legal states as many as possible and ensures that parts not requiring unreliable resources can be automatically processed without human intervention if one or multiple unreliable resources fail. It is based on the modified neighborhood policy, namely single route neighborhood, which handles the allocation of failure-prone resources in a system. To guarantee deadlock-free operations in the remaining parts of the system, monitors are designed for emptiable strictly minimal siphons. Through examples, the applicability of the proposed policy is demonstrated.
Ultrafine particles over Germany – an aerial survey

Ultrafine particles in the atmosphere may have important climate and health effects. As they are below visible size and not visible for remote sensing techniques, the majority of observations thus come from ground-based measurements. Some of those observations indicate elevated sources for ultrafine particles. Here we present for the first time airborne measurements of number concentration and size distributions of ultrafine particles along defined flight paths across Germany, allowing to derive background concentrations and to identify major single sources. A significant impact of fossil fuel–related emissions on background and maximum concentrations was found. Maxima reaching up to 90 000 particles cm−3 were encountered in plumes of single large sources extending over more than 200 km. Modelling shows that about 10–40 % of Germany were continuously affected by such plumes. Regional-scale transport and boundary layer dynamics were identified as major factors controlling spatial and temporal patterns of size and number distributions.
In the Trenches of the Solar-stellar Connection. IV. Solar Full-disk Scans of C ii, Si iv, and Mg ii by the Interface Region Imaging Spectrograph

About once a month, the Interface Region Imaging Spectrograph conducts day-long raster scans of the full Sun in three ultraviolet spectral channels. These full-disk mosaics are valuable in the solar context, but provide a unique connection to the distant, unresolved stars. Here, 10 deep-exposure scans (4–8 s per slit step), collected during the peak and decline of sunspot Cycle 24, were analyzed. Spatial spectra (2″ pixels) of resonance lines of C ii (T ∼ 104 K), Si iv (8 × 104 K), and Mg ii (8 × 103 K) were fitted with a pseudo-Gaussian model to track the emission strengths, widths, and shifts in the various surface features that comprise the quiet Sun and active regions. The full-disk mosaic spectra compare well to tracings of solar-twin α Centauri A (HD 128620; G2 V). The contrast between disk-average spectra from cycles MIN and MAX is relatively modest (∼50% in Si iv), but, remarkably, the brightest solar pixels in active regions, at 2″ resolution, exceed the global-average intensities of the most active Sun-like stars, suggesting a deeper solar-stellar connection. Si iv shows a conspicuous bright ring at the limb, whereas optically thicker C ii and Mg ii are suppressed (more so for the latter). The Si iv emission favors the bright knots of the large-scale supergranulation network, while the cooler Mg ii emission is more ubiquitous and C ii intermediate. The non-Gaussian profile of full-disk C iv, similar in formation temperature to Si iv, was previously interpreted as a combination of narrow and broad dynamical components, but the prevalence of the characteristic line shape in the finest resolution spatial pixels of Si iv here provides support for alternative formation scenarios, for example, invoking κ-distributions.
A New Design and Evaluation for Sharing Analog Electronic Labs

In universities with a large number of students, the cost of these RLs becomes a problem, then they will be hard to respond to high demand, however, sharing and pooling resources involves setting up platforms that use the same language, offer the same services and exchange the same data formats. The proposed system allows sharing experiences for different students over a web interface. In addition, it allows developing remote laboratory at low cost, which will allow serving more users at the same time. A practical case will be detailed to show the feasibility and validation of the approach.