Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-based Automated Fugl-Meyer Assessment System for Upper Limb Motor Function

The Fugl-Meyer Assessment (FMA) is an important method for evaluating upper limb motor functionality in stroke patients. However, traditional assessments by therapists not only increase the cost of labor and money, but also is subjective. In this paper, we propose a novel automated FMA system to overcome these limitations that patients cannot be accurately assessed in time. We use camera to capture the motion of the upper limb and then extract motion features. The MLP classification algorithm is developed to assign FMA scores using the features obtained from camera. Our work achieves average accuracy of 98.9% and 97.7% on a dataset of 15 volunteers and a dataset of S patients. Our result shows high accuracy and offers guidance to an automated assessment system.
Non-Convex Optimization of Energy Efficient Power Control in Interference Networks via Machine Learning

This work presents a machine learning approach to optimize the energy efficiency (EE) in an interference network. This optimization problem is non-convex and it is difficult to find its global optimum. We propose an unsupervised machine learning framework to approach the global optimum. While the training of the neural network (NN) takes moderate time, applying the trained model requires very low computational complexity. In particular, we introduce a novel objective function based on the reparameterization trick, which makes it possible to prune poor local optima to converge to the global optimum. Furthermore, we design a dedicated NN architecture SINRnet for signal-to-interference-noise ratio (SINR)-related optimization problems in interference networks, which is permutation-equivariant and classifies channels according to their positions in the SINR expression. In this way, we encode our domain knowledge into the NN design. Training and testing results show that the proposed method outperforms the successive convex approximation (SCA) algorithm, achieving an EE close to the global optimum found by the branch-and-bound algorithm and with reasonable computational effort. Thus, the proposed approach finds a balance between computational complexity and performance.
Smith ScholarWorks Smith ScholarWorks

A graph G is distinguished if its vertices are labelled by a map φ : V ( G ) −→ { 1 , 2 , . , k } so that no non-trivial graph automorphism preserves φ . The distinguishing number of G is the minimum number k necessary for φ to distinguish the graph. It measures the symmetry of the graph. We extend these deﬁnitions to an arbitrary group action of Γ on a set X . A labelling φ : X −→ { 1 , 2 , . , k } is distinguishing if no element of Γ preserves φ except those which ﬁx each element of X . The distinguishing number of the group action on X is the minimum k needed for φ to distinguish the group action. We show that distinguishing group actions is a more general problem than distinguishing graphs. We completely characterize actions of S n on a set with distinguishing number n , answering an open question of Albertson and Collins.
The Cobalt Supply Chain and Environmental Life Cycle Impacts of Lithium-Ion Battery Energy Storage Systems

Lithium-ion batteries (LIBs) deployed in battery energy storage systems (BESS) can reduce the carbon intensity of the electricity-generating sector and improve environmental sustainability. The aim of this study is to use life cycle assessment (LCA) modeling, using data from peer-reviewed literature and public and private sources, to quantify environmental impacts along the supply chain for cobalt, a crucial component in many types of LIBs. The study seeks to understand where in the life cycle stage the environmental impacts are highest, thus highlighting actions that can be taken to improve sustainability of the LIB supply chain. The system boundary for this LCA is cradle-to-gate. Impact assessment follows ReCiPe Midpoint (H) 2016. We assume a 30-year modeling period, with augmentation occurring at the end of the 3rd, 7th, and 14th years of operations, before a complete replacement in the 21st year. Three refinery locations (China, Canada, and Finland), a range of ore grades, and five battery chemistries (NMC111, NMC532, NMC622, NMC811, and NCA) are used in scenarios to better estimate their effect on the life cycle impacts. Insights from the study are that impacts along nearly all pathways increase according to an inverse power-law relationship with ore grade; refining outside of China can reduce global warming potential (GWP) by over 12%; and GWP impacts for cobalt used in NCA and other NMC battery chemistries are 63% and 45–74% lower than in NMC111, respectively. When analyzed on a single-score basis, marine and freshwater ecotoxicity are prominent. For an ore grade of 0.3%, the GWP values for the Canada route decrease at a rate of 58% to 65%, and those for Finland route decrease by 71% to 76% from the base case. Statistical analysis shows that cobalt content in the battery is the highest predictor (R2 = 0.988), followed by the ore grade (R2 = 0.966) and refining location (R2 = 0.766), when assessed for correlation individually. The results presented here point to areas where environmental burdens of LIBs can be reduced, and thus they are helpful to policy and investment decision makers.
Reduction of Subthreshold Leakage Using Metal Strip in SGO Double-Gate MOSFET

This paper investigates and compares the reduction of subthreshold leakage current ISUB in the stack-gate-oxide double-gate metal-oxide-semiconductor field-effect transistor (SGO-DG-MOSFET) and stack-gate-oxide low work function live-strip double gate MOSFET (SGO-LWLS-DG-MOSFET). A comparative discussion is carried out in the simulation results using the technology computer-aided design (TCAD) tool. Reduction in ISUB of $10^{-3}\mathrm{A}/\mu \mathrm{m}$ is observed in SGO-LWLS-DG-MOSFET compared to SGO-DG-MOSFET. However, 11.23% enhancement is noticed in the transconductance $\mathrm{g}_{\mathrm{m}}$ of SGO-LWLS-DG-MOSFET. Due to the low ISUB, SGO-LWLS-DG-MOSFET exhibits a considerable improvement in the switching ratio as well.
Ensemble machine learning reveals key features for diabetes duration from electronic health records

Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R2. Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort.
Comparison of Various Techniques Implemented for Educational Data Mining and Learning Analytics

Learning Analytics is closely related to EDM and encompasses activities such as measuring, collecting, analyzing, and reporting data about learners and their learning environments. The goal is to enhance our understanding of learning processes and optimize both learning and educational settings. Therefore, when ‘LA and EDM’ are mentioned together, it generally implies the convergence or collaboration between Learning Analytics and Educational Data Mining. This collaboration aims to leverage data-driven methodologies to enhance education and improve learning outcomes. These two domains often collaborate to examine educational data, extracting valuable insights regarding student performance and the efficacy of teaching methods. The author explores a range of techniques aimed at enhancing the learning environment and its outcomes. The paper's main objective is to compare and contrast various techniques implemented for Educational Data Mining and Learning Analytic approaches, with an aim to find out probable pros and cons if any; related to the proposed techniques.
Analysis of Factors Contributing to Adverse Events and Evaluation of Their Impact on Prognosis in Metastatic Renal Cell Carcinoma Patients—Real-World Experience in a Single-Center Retrospective Study and Narrative Review

Background and Objectives: More than 430,000 new cases of renal cell carcinoma (RCC) were reported in 2020. Clear cell RCC, which occurs in 80% of cases, is often associated with mutations in the VHL gene, leading to dysregulation of hypoxia-induced transcription factors pathways and carcinogenesis. The purpose of this study is to examine the adverse events (AEs) of cabozantinib treatment and the relationship between individual patient factors and the frequency of their occurrence in detail. Materials and Methods: Seventy-one patients with metastatic RCC were treated with second or further lines of cabozantinib at the Department of Genitourinary Oncology, Maria Sklodowska-Curie National Research Institute of Oncology. Comprehensive data, including demographics, clinicopathological factors, and AEs, were collected from January 2017 to June 2021. This study evaluated the impact of various patient-related factors on the rate of adverse events and treatment tolerance using a Cox proportional hazards model. Results: Cabozantinib-induced AEs were significantly associated with body mass index (BMI), body surface area (BSA), IMDC prognostic score, and treatment line. Notably, patients receiving cabozantinib post-tyrosine kinase inhibitors reported fewer AEs. Dose reduction was unrelated to adverse event frequency, but patients requiring dose reduction were characterized with lower body mass and BSA but not BMI. Conclusions: The factors described make it possible to predict the incidence of AEs, which allows for faster detection and easier management, especially in the high-risk group. AEs should be reported in detail in real-world studies, as their occurrence has a significant impact on prognosis.
Investigation on carbon nanotube electronics structure

In this paper, Graphenes’ electronic structure of carbon nanotubes is analyzed. The analysis is done to calculate energy dispersion relation, effective mass and intrinsic carrier concentration of graphene to establish different carbon nanotubes. Diameters with different chiral vector (n, m) of carbon nanotubes vary the electronics properties of graphene. Different chiral vector of a graphene allows designing carbon nanotube (CNT) for different types of appliance, which can be achieved from the analyzed carrier concentration calculation. This investigation will helpful for further designing of CNT-based nano device.
Vertical directivity modelling with shallow receiver of deep-sea ambient noise

The result of the deep-sea trial experiment suggests that when the depth of the receiver is located in 1000m, the sea surface effect can significantly affect the platform for the detection of the target. Under the effect of the high frequency component from the sea surface, the ambient ocean noise received by the receiver turns out to be anisotropic in the vertical direction, instead of becoming isotropic. Given the above-described result, the vertical directivity of deep-sea ambient noise is modelled in this study. First, four hypotheses are proposed in accordance with the deep-sea ambient noise theory. Subsequently, the correlation between vertical energy of the ocean ambient noise field and the pitch angle is developed from the perspective of acoustic intensity. Thus, the integral representation of vertical directivity function is developed. The vertical directivity at different frequencies and a range of receiving depths is numerically calculated. As revealed by the comparison with the measured ocean ambient noise data in the South China Sea, the high frequency component of the ocean ambient noise is primarily concentrated in the vertical upward sea surface. It is noteworthy that the measured data comply with the results achieved using the theoretical model.
Effects of Liraglutide, Empagliflozin and Their Combination on Left Atrial Strain and Arterial Function

Background and Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RA) and sodium-glucose cotransporter-2 inhibitors (SGLT-2i) are cardioprotective drugs. We investigated their effects on left atrial function, a major determinant of cardiac diastolic dysfunction in type 2 diabetes mellitus. We also explored the association of changes in arterial stiffness with those of the LA strain after treatment. Materials and Methods: A total of 200 patients (59.5 ± 9.1 year old, 151 male) with type 2 diabetes mellitus treated with metformin were randomized to insulin (n = 50 served as controls), liraglutide (n = 50), empagliflozin (n = 50) or their combination (liraglutide + empagliflozin) (n = 50). We measured at baseline and 6 months post-treatment: (a) left atrial and global left ventricular longitudinal strain by speckle tracking echocardiography; (b) pulse wave velocity (PWV) and central systolic blood pressure. Results: At baseline, there was a correlation of the LA reservoir strain with PWV (r = −0.209, p = 0.008), central SBP (r = −0.151, p = 0.030), EF (r = 0.214, p = 0.004) and GLS (r = −0.279, p = 0.009). The LA reservoir change 6 months post-treatment was correlated with the PWV change in all groups (r = −0.242, p = 0.028). The LA reservoir change 6 months post-treatment was correlated with the GLS change in all groups (r = −0.322, p = 0.004). Six months after intervention, patients treated with liraglutide, empagliflozin and their combination improved the left atrial reservoir strain (GLP1RA 30.7 ± 9.3 vs. 33.9 ± 9.7%, p = 0.011, SGLT2i 30 ± 8.3 vs. 32.3 ± 7.3%, p = 0.04, GLP1&SGLT2i 29.1 ± 8.7 vs. 31.3 ± 8.2, p = 0.007) compared to those treated with insulin (33 ± 8.3% vs. 32.8 ± 7.4, p = 0.829). Also, patients treated with liraglutide and the combination liraglutide and empagliflozin had improved left atrial conduction strain (p < 0.05). Empagliflozin or the combination liraglutide and empagliflozin showed a greater decrease of PWV and central and brachial systolic blood pressure than insulin or GLP-1RA. (p < 0.05). Conclusions: Impaired aortic elastic properties are associated with a decreased LA strain in type 2 diabetics. Treatment with liraglutide, empagliflozin and their combination for 6 months showed a greater improvement of left atrial function compared to insulin treatment in parallel with the improvement of arterial and myocardial functions.
Enhancing employee’s job satisfaction in Vietnam: Exploring the impact of organizational commitment, supervisor emotional support and training

Job satisfaction is an important factor in promoting an employee’s well-being, productivity, and organizational success. Generally, when job satisfaction is high, employees experience greater emotions of fulfillment, motivation, and engagement. This can result in increased productivity, improved performance, and a decrease in employee turnover rates. Low job satisfaction, on the other hand, can result in tension, exhaustion, and a diminished sense of accomplishment and job security. It can also have negative effects on a person's physical and mental health and lead to decreased productivity, increased absenteeism, and increased turnover rates. Therefore, it is interesting to study some factors behind job satisfaction, especially in the context of a developing country. In particular, this study examines the roles of organizational commitment, supervisory emotional support, and training in job satisfaction. Furthermore, this research also investigates the moderating impact of training on the organizational commitment-job satisfaction relationship. The research collected data on office employees in Vietnam from December 2022 to January 2023. The quantitative analysis was conducted using SPSS and AMOS software. The findings reveal that organizational commitment, supervisory emotional support, and training have positive influences on job satisfaction. Additionally, training has a positive moderating role in the organizational commitment -job satisfaction nexus. The practical implications emphasize that organizations should create a supportive work environment and encourage emotional support from managers. Moreover, companies should prioritize training opportunities for staff members to improve their work satisfaction.
Pedogenesis of vesicular horizons in disturbed and undisturbed soils

Pedogenesis of Vesicular Horizons in Disturbed and Undisturbed Soils by Maureen L. Yonovitz Dr. Patrick Drohan, Examination Committee Chair Assistant Professor of Pedology Pennsylvania State University Increasing desertification and anthropogenic soil disturbance is of growing concern to restoration ecologists in the Southwestern United States. This study examines how a common soil horizon of arid lands, the vesicular horizon, which is characterized by its discontinuous pores, responds to disturbance. The vesicular horizon is a near surface soil horizon typically composed of fine grained windblown material found beneath a desert pavement. Disturbance to the vesicular horizon may result in a change in pore morphology that will alter water movement through the horizon, affecting the ecology of the region. This research examines differences in pore morphology (area, perimeter, length and width), particle size, pH, calcium carbonate content (CUE) and conductivity (EC) between disturbed and undisturbed soils. Results indicate no significant difference in vesicle pore size with disturbance. Analyses of pore shape do indicate differences in types of pores formed; the percentage of pores with vesicular shape decrease while interstitial (irregular, between grains) shaped pores increase with disturbance. An increase in interstitial pores may result in more facilitated water movement to lower
Multiple Redundant K-Means Clustered Scheme Based on Weighted Cluster Head Selection for Damaged S-UAV

A swarm of unmanned aerial vehicles (S-UAVs) consists of UAVs flying together with the target of accomplishing a certain task in a faster and more reliable way as compared to a single UAV. In a crisis scenario, UAVs have been widely used in rescue missions. Clustering is one of the most reliable routing schemes for S-UAVs. The UAVs are grouped into clusters with a cluster-head (CH) and cluster-members (CM). The CH plays a major role in clustering schemes as it handles all inter-cluster communication. In a crisis case, any UAV is at risk of getting non-functional, thus resulting in a disconnected cluster. This paper proposes a new clustering scheme based on K-means and weighted formulas. The K-means protocol is applied to generate pilot phase clusters. Afterward, whenever the metrics of the networks are established, the weighted formula is applied for cluster formation and CH selection. The weighted formula is based on the performance index, the relative movement, and the remaining energy. To ensure end-to-end communication despite CH non-functionality, our proposed protocol selects a redundant CH for every CH. This protocol had been simulated using MATLAB. The results obtained and analyzed towards the end of this paper demonstrate that the proposed scheme is very promising.
Identification of Viruses Infecting Phalaenopsis Orchids Using Nanopore Sequencing and Development of an RT-RPA-CRISPR/Cas12a for Rapid Visual Detection of Nerine Latent Virus

Phalaenopsis orchids are one of the most popular ornamental plants. More than thirty orchid viruses have been reported, and virus-infected Phalaenopsis orchids significantly lose their commercial value. Therefore, the development of improved viral disease detection methods could be useful for quality control in orchid cultivation. In this study, we first utilized the MinION, a portable sequencing device based on Oxford Nanopore Technologies (ONT) to rapidly detect plant viruses in Phalaenopsis orchids. Nanopore sequencing revealed the presence of three plant viruses in Phalaenopsis orchids: odontoglossum ringspot virus, cymbidium mosaic virus, and nerine latent virus (NeLV). Furthermore, for the first time, we detected NeLV infection in Phalaenopsis orchids using nanopore sequencing and developed the reverse transcription–recombinase polymerase amplification (RT-RPA)-CRISPR/Cas12a method for rapid, instrument-flexible, and accurate diagnosis. The developed RT-RPA-CRISPR/Cas12a technique can confirm NeLV infection in less than 20 min and exhibits no cross-reactivity with other viruses. To determine the sensitivity of RT-RPA-CRISPR/Cas12a for NeLV, we compared it with RT-PCR using serially diluted transcripts and found a detection limit of 10 zg/μL, which is approximately 1000-fold more sensitive. Taken together, the ONT platform offers an efficient strategy for monitoring plant viral pathogens, and the RT-RPA-CRISPR/Cas12a method has great potential as a useful tool for the rapid and sensitive diagnosis of NeLV.
Normal faulting and mass movement during ridge subduction inferred from porosity transition and zeolitization in the Costa Rica subduction zone

Subduction of the buoyant Cocos Ridge offshore the Osa Peninsula, Costa Rica substantially affects the upper plate structure through a variety of processes, including outer forearc uplift, erosion, and focused fluid flow. To investigate the nature of a major seismic reflector (MSR) developed between slope sediments (late Pliocene‐late Pleistocene silty clay) and underlying higher velocity upper plate materials (late Pliocene‐early Pleistocene clayey siltstone), we infer possible mechanisms of sediment removal by examining the consolidation state, microstructure, and zeolite assemblages of sediments recovered from Integrated Ocean Drilling Program Expedition 344 Site U1380. Formation of Ca‐type zeolites, laumontite and heulandite, inferred to form in the presence of Ca‐rich fluids, has caused porosity reduction. We adjust measured porosity values for these pore‐filling zeolites and evaluated the new porosity profile to estimate how much material was removed at the MSR. Based on the composite porosity‐depth curve, we infer the past burial depth of the sediments directly below the MSR. The corrected and uncorrected porosity‐depth curves yield values of 800 ± 70 m and 900 ± 70 m, respectively. We argue that deposition and removal of this entire estimated thickness in 0.49 Ma would require unrealistically large sedimentation rates and suggest that normal faulting at the MSR must contribute. The porosity offset could be explained with maximum 250 ± 70 m of normal fault throw, or 350 ± 70 m if the porosity were not corrected. The porosity correction significantly reduces the amount of sediment removal needed for the combination of mass movement and normal faulting that characterize the slope in this margin.