Miracle-dz/UniFAR-FTUs huggingface dataset:
Excessive Daytime Sleepiness in Depression and Obstructive Sleep Apnea: More Than Just an Overlapping Symptom

Excessive daytime sleepiness (EDS) is a significant public health concern, with obstructive sleep apnea (OSA) being a common cause, and a particular relationship exists with the severity of depression. A literature search on OSA, depression, and EDS was performed in PubMed. The chosen evidence was limited to human studies. Available evidence was systematically reviewed to ascertain the association of EDS with depression and OSA according to the general population and some specific population subgroups. In addition, effectiveness of continuous positive airway pressure (CPAP) was analyzed as a standard therapy for improving EDS and depression in patients with OSA. In the general population, patients with OSA, and some other subpopulations, the review contributed to: (1) delineating the prevalence of EDS; (2) substantiating the relationship of EDS and depression; (3) presenting the relationship between EDS and OSA; and (4) revealing that the duration of CPAP is crucial for its therapeutic effects in improving EDS and depressive symptoms in patients with OSA.
Seasonal Dynamics of Epiphytic Microbial Communities on Marine Macrophyte Surfaces

Surfaces of marine macrophytes are inhabited by diverse microbial communities. Most studies focusing on epiphytic communities of macrophytes did not take into account temporal changes or applied low sampling frequency approaches. The seasonal dynamics of epiphytic microbial communities was determined in a meadow of Cymodocea nodosa invaded by Caulerpa cylindracea and in a monospecific settlement of C. cylindracea at monthly intervals. For comparison the ambient prokaryotic picoplankton community was also characterized. At the OTU level, the microbial community composition differed between the ambient water and the epiphytic communities exhibiting host-specificity. Also, successional changes were observed connected to the macrophyte growth cycle. Taxonomic analysis, however, showed similar high rank taxa (phyla and classes) in the ambient water and the epiphytic communities, with the exception of Desulfobacterota, which were only found on C. cylindracea. Cyanobacteria showed seasonal changes while other high rank taxa were present throughout the year. In months of high Cyanobacteria presence the majority of cyanobacterial sequences were classified as Pleurocapsa. Phylogenetic groups present throughout the year (e.g., Saprospiraceae, Rhodobacteraceae, members without known relatives within Gammaproteobacteria, Desulfatitalea, and members without known relatives within Desulfocapsaceae) constituted most of the sequences, while less abundant taxa showed seasonal patterns connected to the macrophyte growth cycle. Taken together, epiphytic microbial communities of the seagrass C. nodosa and the macroalga C. cylindracea appear to be host-specific and contain taxa that undergo successional changes.
D-OLIA: A Hybrid MPTCP Congestion Control Algorithm with Network Delay Estimation

With the recent evolution of mobile technology, modern devices equipped with multiple communication interfaces have become popular. The multipath transmission control protocol (MPTCP) has evolved to facilitate multiple communication interfaces through a single TCP connection for faster Internet access. MPTCP congestion control algorithms (MPTCP-CCAs) control data flow by fulfilling three design goals, i.e., ensuring improvement over single-path flows, ensuring fairness, and balancing congestion. Current MPTCP-CCAs cannot fulfill these design goals. For example, the opportunistic-linked increase algorithm (OLIA), a well-known MPTCP-CCA in load balancing, often results in low throughput because it cannot properly utilize the underlying network. In addition, the current Internet has a rapidly changing characteristic due to a large amount of short-lived traffic, making it difficult for MPTCP-CCAs to cope. An awareness of prevailing network delay conditions might help MPTCP-CCAs to utilize the network capacity fully. Therefore, we propose dynamic OLIA (D-OLIA), a hybrid MPTCP-CCA that enhances the performance of OLIA by integrating an awareness of the current network delay condition for deciding the congestion window (CWND) decrease factor. We estimate the current network delay condition, i.e., less-congested or congested, by observing the changes in the round-trip-time (RTT). Based on the estimated network delay condition, we decide the CWND decrease factor in real-time for reducing the CWND during packet loss events. We implemented D-OLIA in the Linux kernel and experimented using the Mininet emulator. The emulation results demonstrate that D-OLIA successfully estimates current network delay conditions and results in approximately a 20% increased throughput compared to the original OLIA. Compared to certain MPTCP-CCAs, it also yields a highly improved performance in terms of throughput, RTT, packet retransmissions, and fairness among the MPTCP sub-flows.
Combination of BMP-2 and relaxin to induce osteogenesis in vivo

Bone grafts are a common procedure for treating craniofacial injuries such as trauma, surgery, and reconstruction. Autografts and allografts have limitations which encourages the use of synthetic bone grafts. While synthetic bone grafts, such as hydroxyapatite (HA) microspheres, are advantageous for their cost and reproducibility, they lack the cells and growth factors needed for proper osteoinduction. morphogenetic protein-2 (BMP-2) has been shown to increase the osteoinduction of grafts but the effective dose is above physiological levels and causes undesired effects on the body. Relaxin has been shown to enhance BMP-2’s ability to induce bone formation in vitro and thus lower the dose of BMP-2 required for effective bone regeneration. In this study, HA microspheres sized 212-250µm were synthesized; the in vitro release of BMP-2 and relaxin into a medium of fetal bovine was measured. The release of both was slow and controlled and no hindrance to release was seen when both proteins were loaded on the microspheres. When implanted into rat calvarial defects for 6 weeks, an enhancement to new bone percent was seen when loading HA microspheres with 0. 5µg BMP-2 and 0.05µg of relaxin compared to microspheres loaded with 0. 5µg BMP-2 alone. A 50% reduction in BMP-2 dose, compared to a previously published dose, was seen when relaxin (at 0.05, 0.1, and 0.25µg per defect) was loaded on the HA microspheres. A 75% reduction in BMP-2 dose was seen when 0.05µg per defect of relaxin was loaded. These results suggest that a combination of relaxin and BMP-2 loaded onto HA microspheres will reduce the dosage of BMP-2 required to provide significant bone regeneration.
Observations by Ground-Based MAX-DOAS of the Vertical Characters of Winter Pollution and the Influencing Factors of HONO Generation in Shanghai, China

Analyzing vertical distribution characters of air pollutants is conducive to study the mechanisms under polluted atmospheric conditions. Nitrous acid (HONO) is a kind of crucial species in photochemical cycles. Exploring the influence and sources of HONO in air pollution at different altitudes offers some insights into the research of tropospheric oxidation chemistry processes. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements were conducted in Shanghai, China, from December 2017 to March 2018 to investigate vertical distributions and diurnal variations of trace gases (NO2, HONO, HCHO, SO2, and water vapor) and aerosol extinction coefficient in the boundary layer. Aerosol and NO2 showed decreasing profile exponentially, SO2 and HCHO concentrations were observed relatively high values in the middle layer. SO2 was caused by industrial emissions, while HCHO was from secondary sources. As for HONO, below 0.82 km, the heterogeneous reactions of NO2 impacted on forming HONO, while in the upper layers, vertical diffusion might be the dominant source. The contribution of OH production from HONO photolysis at different altitudes was mainly controlled by the concentration of HONO. MAX-DOAS measurements characterize the vertical structure of air pollutants in Shanghai and provide further understanding for HONO formation, which can help deploy advanced measurement platforms of regional air pollution over eastern China.
Provisioning, Authentication and Secure Communications for IoT Devices on FIWARE

The increasing pervasiveness of the Internet of Things is resulting in a steady increase of cyberattacks in all of its facets. One of the most predominant attack vectors is related to its identity management, as it grants the ability to impersonate and circumvent current trust mechanisms. Given that identity is paramount to every security mechanism, such as authentication and access control, any vulnerable identity management mechanism undermines any attempt to build secure systems. While digital certificates are one of the most prevalent ways to establish identity and perform authentication, their provision at scale remains open. This provisioning process is usually an arduous task that encompasses device configuration, including identity and key provisioning. Human configuration errors are often the source of many security and privacy issues, so this task should be semi-autonomous to minimize erroneous configurations during this process. In this paper, we propose an identity management (IdM) and authentication method called YubiAuthIoT. The overall provisioning has an average runtime of 1137.8 ms ±65.11+δ. We integrate this method with the FIWARE platform, as a way to provision and authenticate IoT devices.
Laser Cutting Defect Recognition Using Conversion of Processing Light Information into Spectrogram Images - Spectroscopic Measurements in Multiple Work Surface Conditions and Extraction of Spectral Data Features Based on Processing Principle -

At urban production sites, laser cutting is an essential technology for high-speed flexible sheet-metal processing. This study aims to detect defective cuts by sensing laser-cutting-induced light emission and elucidate meaningful features for processing-based detection. The proposed method comprises three steps. In the first step, the sensors installed in the laser head acquire the spectra of light generated during processing, and data analysis software converts the spectral data into spectrograms and stacked-graph images. In the second step, image processing software extracts the edges of both images and emphasizes the periodic features in normal laser cutting. In the final step, a one-class support vector machine recognizes defective cuts from the extracted features. Verification tests using multiple normal and abnormal cut data confirmed that the proposed method accurately detected defective cuts.
A Key Management Protocol Based on the Hash Chain Key Generation for Securing LoRaWAN Networks

Recently, many Low Power Wide Area Network (LPWAN) protocols have been proposed for securing resource-constrained Internet of Things (IoT) devices with negligible power consumption. The Long Range Wide Area Network (LoRaWAN) is a low power communication protocol that supports message authentication, integrity, and encryption using two-session preshared secret keys. However, although the LoRaWAN supports some security functions, it suffers from session key generation and key update problems. This motivates us to introduce a new key management protocol that resolves the LoRaWAN problems and supports key updates. The proposed protocol is based on hash chain generation using a one-way hash function. Network entities share a common hash chain of n key elements to allow using a unique signing key per message. We also propose a salt hashing algorithm that encrypts the original keys into a different form to avoid the physical attacks at the end device side. We analyzed the proposed key generation performance in terms of the computation time, the required storage, and the communication overhead. We implemented and tested the proposed key generation protocol using the NS-3 network simulator. The proposed lightweight key generation protocol significantly enhances the security of the original LoRaWAN at a negligible overhead. The proposed protocol reduces the power consumption and transmission time by two times compared with some previous protocols. In addition, the proposed key generation protocol can resist attacks, such as key compromising attacks and replay attacks, and it supports the Perfect Forward Secrecy, which was not supported by LoRaWAN.
An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

With the growing adoption of the Internet of Things (IoT) technology in the agricultural sector, smart devices are becoming more prevalent. The availability of new, timely, and precise data offers a great opportunity to develop advanced analytical models. Therefore, the platform used to deliver new developments to the final user is a key enabler for adopting IoT technology. This work presents a generic design of a software platform based on the cloud and implemented using microservices to facilitate the use of predictive or prescriptive analytics under different IoT scenarios. Several technologies are combined to comply with the essential features—scalability, portability, interoperability, and usability—that the platform must consider to assist decision-making in agricultural 4.0 contexts. The platform is prepared to integrate new sensor devices, perform data operations, integrate several data sources, transfer complex statistical model developments seamlessly, and provide a user-friendly graphical interface. The proposed software architecture is implemented with open-source technologies and validated in a smart farming scenario. The growth of a batch of pigs at the fattening stage is estimated from the data provided by a level sensor installed in the silo that stores the feed from which the animals are fed. With this application, we demonstrate how farmers can monitor the weight distribution and receive alarms when high deviations happen.
Classification of Tennis Shots with a Neural Network Approach

Data analysis plays an increasingly valuable role in sports. The better the data that is analysed, the more concise training methods that can be chosen. Several solutions already exist for this purpose in the tennis industry; however, none of them combine data generation with a wristband and classification with a deep convolutional neural network (CNN). In this article, we demonstrate the development of a reliable shot detection trigger and a deep neural network that classifies tennis shots into three and five shot types. We generate a dataset for the training of neural networks with the help of a sensor wristband, which recorded 11 signals, including an inertial measurement unit (IMU). The final dataset included 5682 labelled shots of 16 players of age 13–70 years, predominantly at an amateur level. Two state-of-the-art architectures for time series classification (TSC) are compared, namely a fully convolutional network (FCN) and a residual network (ResNet). Recent advances in the field of machine learning, like the Mish activation function and the Ranger optimizer, are utilized. Training with the rather inhomogeneous dataset led to an F1 score of 96% in classification of the main shots and 94% for the expansion. Consequently, the study yielded a solid base for more complex tennis analysis tools, such as the indication of success rates per shot type.
A Bidirectional Versatile Buck–Boost Converter Driver for Electric Vehicle Applications

This work presents a novel dc-dc bidirectional buck–boost converter between a battery pack and the inverter to regulate the dc-bus in an electric vehicle (EV) powertrain. The converter is based on the versatile buck–boost converter, which has shown an excellent performance in different fuel cell systems operating in low-voltage and hard-switching applications. Therefore, extending this converter to higher voltage applications such as the EV is a challenging task reported in this work. A high-efficiency step-up/step-down versatile converter can improve the EV powertrain efficiency for an extended range of electric motor (EM) speeds, comprising urban and highway driving cycles while allowing the operation under motoring and regeneration (regenerative brake) conditions. DC-bus voltage regulation is implemented using a digital two-loop control strategy. The inner feedback loop is based on the discrete-time sliding-mode current control (DSMCC) strategy, and for the outer feedback loop, a proportional-integral (PI) control is employed. Both digital control loops and the necessary transition mode strategy are implemented using a digital signal controller TMS320F28377S. The theoretical analysis has been validated on a 400 V 1.6 kW prototype and tested through simulation and an EV powertrain system testing.
3D Wavelet Finite-Element Modeling of Frequency-Domain Airborne EM Data Based on B-Spline Wavelet on the Interval Using Potentials

We present a wavelet finite-element method (WFEM) based on B-spline wavelets on the interval (BSWI) for three-dimensional (3D) frequency-domain airborne EM modeling using a secondary coupled-potential formulation. The BSWI, which is constructed on the interval (0, 1) by joining piecewise B-spline polynomials between nodes together, has proved to have excellent numerical properties of multiresolution and sparsity and thus is utilized as the basis function in our WFEM. Compared to conventional basis functions, the BSWI is able to provide higher interpolating accuracy and boundary stability. Furthermore, due to the sparsity of the wavelet, the coefficient matrix obtained by BSWI-based WFEM is sparser than that formed by general FEM, which can lead to shorter solution time for the linear equations system. To verify the accuracy and efficiency of our proposed method, we ran numerical experiments on a half-space model and a layered model and compared the results with one-dimensional (1D) semi-analytic solutions and those obtained from conventional FEM. We then studied a synthetic 3D model using different meshes and BSWI basis at different scales. The results show that our method depends less on the mesh, and the accuracy can be improved by both mesh refinement and scale enhancement.
Proof-of-Concept of a Sensor-Based Evaluation Method for Better Sensitivity of Upper-Extremity Motor Function Assessment

In rehabilitation, the Fugl–Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment.
A Tutorial on Hardware-Implemented Fault Injection and Online Fault Diagnosis for High-Speed Trains

Electrical drive systems are the core of high-speed trains, providing energy transmission from electric power to traction force. Therefore, their safety and reliability topics are always active in practice. Among the current research, fault injection (FI) and fault diagnosis (FD) are representative techniques, where FI is an important way to recur faults, and FD ensures the recurring faults can be successfully detected as soon as possible. In this paper, a tutorial on a hardware-implemented (HIL) platform that blends FI and FD techniques is given for electrical drive systems of high-speed trains. The main contributions of this work are fourfold: (1) An HIL platform is elaborated for realistic simulation of faults, which provides the test and verification environment for FD tasks. (2) Basics of both the static and dynamic FD methods are reviewed, whose purpose is to guide the engineers and researchers. (3) Multiple performance indexes are defined for comprehensively evaluating the FD approaches from the application viewpoints. (4) It is an integrated platform making the FI and FD work together. Finally, a summary of FD research based on the HIL platform is made.
Evaluating the Quality of Experience Performance Metric for UAV-Based Networks

In this work, we consider a UAV-assisted cell in a single user scenario. We consider the Quality of Experience (QoE) performance metric calculating it as a function of the packet loss ratio. In order to acquire this metric, a radio-channel emulation system was developed and tested under different conditions. The system consists of two independent blocks, separately emulating connections between the User Equipment (UE) and unmanned aerial vehicle (UAV) and between the UAV and Base station (BS). In order to estimate scenario usage constraints, an analytical model was developed. The results show that, in the described scenario, cell coverage can be enhanced with minimal impact on QoE.
Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing

Grain moisture content (GMC) is a key indicator of the appropriate harvest period of rice. Conventional testing is time-consuming and laborious, thus not to be implemented over vast areas and to enable the estimation of future changes for revealing optimal harvesting. Images of single panicles were shot with smartphones and corrected using a spectral–geometric correction board. In total, 86 panicle samples were obtained each time and then dried at 80 °C for 7 days to acquire the wet-basis GMC. In total, 517 valid samples were obtained, in which 80% was randomly used for training and 20% was used for testing to construct the image-based GMC assessment model. In total, 17 GMC surveys from a total of 201 samples were also performed from an area of 1 m2 representing on-site GMC, which enabled a multi-day GMC prediction. Eight color indices were selected using principal component analysis for building four machine learning models, including random forest, multilayer perceptron, support vector regression (SVR), and multivariate linear regression. The SVR model with a MAE of 1.23% was the most suitable for GMC of less than 40%. This study provides a real-time and cost-effective non-destructive GMC measurement using smartphones that enables on-farm prediction of harvest dates and facilitates the harvesting scheduling of agricultural machinery.