Miracle-dz/UniFAR-FTUs huggingface dataset:
The Psychomotor Cognition Test for Measurement of Sleepiness/Fatigue on a Touch Screen*

The Psychomotor Vigilance Test (PVT) is a simple and reliable performance test that measures sustained attention, alertness level, and fatigue level. The PVT is a convenient tool that can be used in real time in situ through a mobile device without the assistance of experts and therefore can be used to improve safety and prevent accidents. However, the original PVT is vulnerable to the subject’s intentional concentration on the test, and the variance range among tests is narrow; these factors limit its usefulness in classifying the level of fatigue. This study overcome these limitations and develop the Psychomotor Cognition Test (PCT) by transforming the PVT into a tool that stably classifies fatigue levels, still requiring a short period of time. In the PCT, compared to the PVT, reaction time is significantly longer, and success rate is significantly lower (both p<0.0001). Whereas reaction time and success rate of the PVT do not show a significant correlation with fatigue level, those of the PCT show significant correlations with fatigue level, respectively (p<0.001). This study suggests that the PCT can be used in real time in situ as a risk management tool for workers performing dangerous tasks and can become an even more powerful tool when combined with other physiological indicators.
matrix composition for transdermal therapeutic system and system based on such a composition

Transdermal therapeutic system comprising a drug reservoir layer 13 and an adhesive layer 16, the medicament 14 is moderately soluble in mineral oil, and a diaphragm 15 for adjusting the rate of release of the drug, the reservoir layers and adhesive comprising a mixture of mineral oil and polyisobutylene and at least about 6% colloidal silicon dioxide, and having a viscosity of at least 1.5 x 1,000,000 Pa. and a ratio of mineral oil polyisobutylene and at least 1.2.
Identifying a Upper-Limb Phase-Dependent Variable under Perturbations for Powered Prosthesis Arm Control

This paper investigates upper-limb kinematic reaching responses during a mechanical perturbation to understand interjoint arm coordination used towards powered prosthesis control development. Common prosthesis arm controllers use electromyography sensors with data-driven models to decode muscle activation signals in controlling prosthesis joint movements. However, these control approaches produce non-natural, discrete movements with no guarantee the controller can react to unexpected disturbances during continuous task motion. Determining a continuous phase-dependent variable for measuring a human’s progression during reaching can derive a time-invariant kinematic function to control the prosthesis joint in a natural, continuous manner. A perturbation experimental study was conducted across three participants in evaluating the shoulder and elbow joint kinematics to examine the existence of a phase shift during reaching. Experimental results demonstrated the effects of arm proximal-distal interjoint coordination that validated the proposed mechanical phase variable of the shoulder used in parameterizing elbow joint kinematic for reaching. This could allow for a continuous phase-based control strategy that can handle disturbances to achieve arm reaching in prosthesis control.
A Parametric Model for Characterizing Time-Variant Single Trials of Block-Design fNIRS Experiments

Block-design is a popular experimental paradigm for functional near-infrared spectroscopy (fNIRS). Traditional block-design analysis techniques such as generalized linear modeling (GLM) and waveform averaging (WA) assume that the brain is a time-invariant system. This is a flawed assumption. In this paper, we propose a parametric Gaussian model to quantify the time-variant behavior found across consecutive trials of block-design fNIRS experiments. Using simulated data at different signal-to-noise ratios (SNRs), we demonstrate that our proposed technique is capable of characterizing Gaussian-like fNIRS signal features with ≥3dB SNR. When used to fit recorded data from an auditory block-design experiment, model parameter values quantitatively revealed statistically significant changes in fNIRS responses across trials, consistent with visual inspection of data from individual trials. Our results suggest that our model effectively captures trial-to-trial differences in response, which enables researchers to study time-variant brain responses using block-design fNIRS experiments.
A Dual Target Neural Network Method for Speech Enhancement

In this paper, we introduce a neural network based speech enhancement method for heavy noisy scenarios. Comprising bidirectional long-short term memory (BLSTM), convolutional neural network (CNN) and deep feedforward neural network (DNN), a neural network architecture with a novel dual target scheme is proposed. The dual target scheme leverages the speech features from both linear and Mel spectral domains in order to retrieve very weak speech spectrum hidden in heavy noise. Experiments show that our proposed neural network architecture outperforms many traditional enhancement methods in terms of objective distortion measure and quality measure.
An in Silico Study on Nanocomposite Magnetic Implants for Microwave Cancer Theranostics

Microwave (MW) hyperthermia (HT) is a novel cancer therapy to treat deep-seated tumors, such as bone cancers. The heat administration can be troublesome if external antennas are used. So, magnetic nanocomposite biomaterials, termed magnetic scaffolds, have been proposed as local thermo-seeds for controlling local recurrence rate of bone tumors. However, the possibility of using magneto-dielectric biocompatible implant as MW-responsive theranostic agents has poorly been assessed. In this work, an in silico study based on a 1D MW propagation model in a multilayer structure has been proposed to study if magnetic scaffolds can be used to perform and, contemporary, monitor the HT. The numerical study identified that a matching medium of relative dielectric permittivity of 40 would enables sufficient signal transmission at the frequencies of 0.434, 1.25, 2.45 and 5.8 GHz, thus enabling the HT and the MW monitoring. Indeed, by performing a differential analysis of the variation of the transmission coefficient during the HT, it has been assessed the possibility of using magnetic scaffolds as MW theranostic agents.
Improving Accuracy of ADHD Diagnosis with the Combination of Brain Imaging and Behavioral Measures

Neuropsychological measures may improve Attention-deficit/hyperactivity disorder (ADHD) diagnostic accuracy and enhance treatment response detection. Highquality evaluation indicators are necessary for accurate diagnosis of ADHD. Due to the high complexity of the pathogenesis of ADHD, it may not be possible to accurately diagnose ADHD only by relying on behavioral assessment or brain imaging examination. Therefore, the authors propose a comprehensive index that combines brain imaging behavioral and measures. The results showed that the classification performance of the composite index was better than that of the single behavior or brain image index.Clinical Relevance— The results of this study help to remind practicing clinicians to consider the results of multiple clinical examinations when clinically diagnosing ADHD patients.
A Minimal Collision Strategy of Synergy Between Pushing and Grasping for Large Clusters of Objects

Grasping and moving objects in a large cluster is a common real scenario. In such scenarios, tens of objects are adjacent to each other, even stacked layer by layer, so that simple grasp would not work due to obstruction. In this paper, we propose a well-designed strategy to use synergy of pushing and grasping to automatically push and grasp objects in a large tightly packed cluster of objects. Our strategy is to detect and grasp isolated graspable objects first before other actions. We then use a smart strategy that pushes objects at the narrowest edge of the clusters. For push action, the robot pushes the edge at the perpendicular direction relative to the cluster, thus improving the performance of isolation and minimizing collisions. We have conducted experiments in both simulation and real-world environments with more than 20 cluttered objects and demonstrated that our solution outperforms existing deep learning based methods, especially in challenging cases, and achieves significantly higher completion rate, grasp success rate, picked rate and efficiency.
Understanding Design Rules of Fan-in WLCSP Concerning Thermo-Mechanical Challenges

Wafer Level Chip Scale Packaging (WLCSP) is a type of chip packaging technology that allows for the integration of multiple semiconductor devices on a single wafer. This is achieved by using a wafer-level process to package the chips directly onto the wafer surface, eliminating the need for a separate package. It offers several advantages over traditional packaging methods, such as smaller size; improved thermal performance; lower cost and higher density. As a result, it is becoming increasingly popular in the semiconductor industry. While WLCSP solution offers numerous benefits, it also presents some challenges in terms of mechanical reliability. The absence of a separate package in WLCSP makes the chips more vulnerable to physical damage, such as cracking or chipping, during handling or assembly. The solder joints used to connect the chip to the external components can create stress concentration points on the chip, which can lead to die cracking or other types of damage. Package level qualification tests for the assessment of component reliability may cause delamination and cracking of the die active layer as well as re-passivation, which is essential of the WLCSP process for re-routing. These cracks could expose the die to the environment, which can lead to corrosion and other long term reliability issues in its field service. This paper addresses such problems as re-passivation and die active layer cracking in a systematical way and comes up with a general guideline of design rules aiming at enhancing its integrity during the package level qualification. The impact of geometry and process related variables is evaluated using the general-purpose finite element code ANSYSY Mechanical in terms of relevant stress components. It is found that simulation results are in good agreement with failure analysis observations regarding the cracking initiation and propagation. Detailed numerical studies and discussions are presented about the re-passivation termination structures, die corner configurations, space between the openings of the first and second re-passivation layer as well as re-passivation thickness and its curing process.
A Guided Mutation Strategy for Smart Contract Fuzzing

Smart contracts manage a large number of digital assets which is attractive to attackers. There have been many attacks that have caused huge financial losses. Therefore, it is of great importance to detect vulnerabilities in smart contracts. Fuzzing is considered a promising approach to test smart contracts. However, the complexity of changing state variables and the handling of external parameters during mutation pose critical technical challenges for current smart contract fuzzers, hindering their ability to cover branches under complex constraints and leaving potential vulnerabilities for attackers to exploit. To tackle these problems, we design a guided mutation strategy combined with two novel techniques: Dynamic Dependency Learning (DDL) and Dynamic Variables Analysis (DVA). DDL learns the dependencies of sequences to provide guided transaction sequence generation for handling state variables in complex constraints, while DVA leverages variable-level dynamic taint analysis to process the external parameters and guide the mutation. We implement the proposed strategy on a fuzzer, called SeqFuzz. The experimental results show that SeqFuzz could cover more branches and detect more bugs in real-world smart contracts compared with state-of-the-art tools.
Unravelling the mechanisms of intracellular antioxidant activity of platinum nanozymes

Platinum nanoparticles (PtNPs) are considered promising nanomaterials for biomedical applications thanks to their formidable catalytic and antioxidant properties. To improve their therapeutic potential and to boost their applicability in nanomedicine, it is fundamental to understand the biological mechanisms that control PtNP interaction with cells and tissues. In this context, an important issue, still open and shared among other nanomaterials, is how internalized PtNPs, that are confined within endo-lysosomal compartments, can exert their activities within the cells. In this framework, here we overviewed the antioxidant and catalytic properties of PtNPs, that make them useful tools for theragnostic applications, and their interactions with cells. In particular, we focused the attention on the endocytic pathways of NPs and their intracellular localization and discussed the formation of inter-organelle contact sites as new and possible mechanisms of communications between cells and NPs, based on recent findings and never reported before. This brief review sheds light on possible new players in the process of nano-biointeractions at the intracellular level (i.e., the interorganelle contact sites), offering new insights about the mechanisms of action of PtNPs compared to previous works. In this context, we contemplated the opportunity to exploit this new mechanism of nano-biointeraction, e.g., by modulating the formation of inter-organelle contact sites and by targeting them to increase thepotential applications of PtNPs in biomedicine and theranostics.
Comparative Study of Knowledge Management Framework

Knowledge Management (KM) is an integral part of an organization's operations. This study conducted a comparative analysis through comprehensive data collection, including cross-searching related literature sources, such as scientific journals, books, and documents, with a focus on SECI, Wiig KM Cycle, Zack's KM Framework, and Williams KM Cycle. The assessment centers on evaluating each criterion and dimension of the framework. Findings reveal the SECI Model's emphasis on transforming individual knowledge into shared knowledge through social processes, Wiig KM Cycle's continuous knowledge management cycle, Zack's KM Framework assisting in recognizing organizational knowledge gaps, and the Bukowitz & Williams KM Cycle's provision of a comprehensive view of knowledge management processes. This study highlights the criticality of selecting a framework that aligns with the needs of different organizations. The optimal choice offers comprehensive guidance, enhances knowledge management efficacy, and supports overall goals. This research contributes to advancing knowledge in the field, particularly regarding the application of safe management frameworks in organizations.
Recombination Time in Drift-Diffusion Models of Graphene Field-Effect Transistors

Graphene field-effect transistors (GFETs) have been extensively studied for electronic applications such as sensors and radiofrequency. Several physics-based models have been developed to explain carrier transport in these transistors, but the approach for introducing carrier generation-recombination is still a topic of debate. This work presents a method for calculating the net recombination rate, which is then applied to a self-consistent Poisson-drift-diffusion GFET model. The method is adapted from semiconductor physics to suit the two-dimensional nature of graphene. The study also explores how recombination time affects carrier transport when both electrons and holes are present in the graphene channel, which mainly occurs when the transistor is biased near the Dirac point. The results confirm that a single quasi-Fermi level for electrons and holes is a good approximation when recombination times are sufficiently small. Additionally, the study finds that the single level approximation requires lower recombination times in short channel GFETs.
Bilateral cuff-induced lower limb post-ischemia hyperemia as a method for acute reduction in blood pressure for cuffless blood pressure device testing

Objective. Development and testing of cuffless blood pressure (BP) devices requires methods to increase and decrease BP. This is also required by cuffless BP validation standards. Pharmacological interventions, whilst successful, are not always feasible for all subpopulations or research settings. Non-pharmacological approaches for increasing BP are available, however, methods for decreasing BP are not well described. This study investigates the hyperemic response following bilateral leg-cuff ischemia as a method for acute BP lowering.Design and Method. Participants (n=8, 24±8 years, 6 female) had their BP measured by continuous (finger, Peňáz technique) and intermittent (brachial cuff, oscillometric) methods before, during and following 3-minute leg-ischemia with the participant in an upright position. Total peripheral resistance (TPR) and cardiac output (CO) were calculated from finger BP waveforms. Maxima and minima responses in the variables were extracted and compared to resting conditions by repeated measures analysis of covariance.Results. During the hyperemic period, systolic BP decreased by -22±3 mmHg (finger) and -6±1 mmHg (brachial). Diastolic BP decreased by -14±5 mmHg (finger) and -4 ±1 mmHg (brachial). Calculated TPR and CO varied, with both decreasing by half and almost doubling during the hyperemic response period.Conclusions. Leg-cuff ischemia provides a controlled, non-pharmacological intervention for decreasing systemic arterial BP. This removes some of the limitations in testing, development and validation of cuffless BP techniques and devices.
Research on the Detection Method of Driving Fatigue Based on Anchor-Free And Attention Mechanism

Driver fatigue is one of the significant causes of road safety hazards, making real-time monitoring of driver fatigue crucial. The changing network coverage in the vehicular environment, as vehicles move, can result in degraded response times and affect detection efficiency. To incorporate a lightweight detection model in the vehicular environment, this paper proposes a fatigue detection method based on anchor-free detection and attention mechanism network model. The improved single-stage face detector, CenterFace, is used to localize facial landmarks and divide the driver's eye and mouth regions proportionally. A fatigue recognition model, built using the attention mechanism from Ghost modules and Sknet, detects the states of the eyes and mouth. Fatigue assessment is achieved by calculating the PERCLOS (Percentage of Eye Closure) values of the eyes and mouth. Experimental results on the NHTU-DDD dataset and a self-built dataset demonstrate that the proposed network reduces the single-frame eye-mouth state recognition time to 6.4 ms while achieving an accuracy of 98.87%. The model size is reduced to 2.96 MB, achieving a balance between accuracy and real-time performance.
Utilizing Deep Learning to Identify an Ultrasound-guided Nerve Block Target Zone

Ultrasound guided nerve blocks are increasingly being used in perioperative care as a means of safely delivering analgesia. Unfortunately, identifying nerves in ultrasound images presents a challenging task for novice anesthesiologists. Drawing from online resources, here we attempted to address this issue by developing a deep learning algorithm capable of automatically identifying the transversus abdominis plane region in ultrasound images. Training of our dataset was done using the U-Net architecture and artificial augmentation was done to optimize our training dataset. The Dice score coefficient was used to evaluate our model, with further evaluation against a test set composed of manually drawn labels from a pool of (n=10) expert anesthesiologists.Across all labelers the model achieved a global Dice score of 73.31% over the entire test set. These preliminary results highlight the potential effectiveness of this model as a future ultrasound decision support system in the field of anesthesia.
Quantifying Strain Dependence of Multi-Frequency Shearwave Elasticity Imaging

Nonlinear shear modulus (NLSM) can potentially differentiate benign and malignant pathologies. Previous studies demonstrated the discriminatory value of mapping quantitative estimates of material non-linearity, yet estimates remain biased due to unaccounted shear-wave dispersion within the tissue. We demonstrate a finite element simulation of an inversion technique that combines traditional quasi-static and dynamic elasticity imaging to generate a multi-frequency estimate of the third-order shear modulus A(x,z,ω) over a broad bandwidth (200-600 Hz). We show that our reconstruction results are superior to those obtained by traditional group speed by a factor of two in terms of contrast enhancement measures. Experimental ultrasound elastography on a custom oil-in-gelatin emulsion confirms the presence of dispersion in the NLSM parameter. Constitutive hyper-viscoelastic model responses of spectral NLSM are simulated based on matched linear shear modulus data of experimental phantoms. Hyper-elastic Ogden with a Voigt loss is shown to describe NLSM behavior at frequencies greater than 400 Hz. Reconstruction accuracy in the presence of simulated tracking and correlation noise demonstrates the robustness of our estimates.
Adaptive Graph Convolutional Networks for Medical Image Segmentation

Medical image segmentation is very essential for computer-aided diagnosis in the field of medical imaging. In the last decade, Deep Learning-based frameworks (e.g., UNet) have been widely used in medical applications such as image segmentation tasks. Recently, numerous Transformer-based frameworks are presented for the image segmentation tasks as their design can utilize long-range dependencies. Transformer’s design has a weak inductive bias since it does not take advantage of local relationships between pixels and lacks scale invariance. Consequently, Transformers require large datasets for convergence whereas the availability of massive medical datasets is challenging. In this paper, we present a graph-based approach replacing Transformer design to capture long-range dependencies and reduce computational cost. Our proposed framework achieves competitive performance using publicly available dataset Synapse.
Molecular Dynamics Study of Stress Relaxation During Ge Deposition on Si(100) $2\times 1$ Substrates

We studied epitaxial growth of Ge films on Si(001) $2\times 1$ at different temperatures using classical molecular dynamics simulations. Ge-Si intermixing contributes to strain accommodation mostly in the original Si substrate surface and first grown Ge layer. Stress accumulation is further released by the generation of dislocations whose amount and type depend on temperature. At high temperatures, a larger amount and more variety of dislocations are formed, thus affecting the surface morphology and consequently the size of 3D islands.
A general assessment of environmental and biological features of Windermere and their susceptibility to change

Bassenthwaite (Lake) is one of the larger Cumbrian lakes, certainly one of the most distinctive, and of considerable conservation and amenity value. Although its shores lack sizeable settlements, its main inflow receives sewage effluent from a major tourist centre (Keswick) and is subject to episodic floods. These influences, the growing development of leisure activities at the lake (e.g. sailing, time-share units), and recent road-construction, have led to past appraisals of ecological impacts and lake management. The lake has not been the subject of intense and long-term ecological study, but much scattered information exists that is relevant to future management decisions. In the present Report, commissioned by North West Water, such information - published and unpublished - is surveyed. Especial attention is given to evidence bearing on susceptibility to change, affecting the lake environment and its biota or species of conservation interest. Extensive use has been made of the results of a recent (1986-7) seasonal survey by the FBA.