Miracle-dz/UniFAR-FTUs huggingface dataset:
Unbalanced 3-Group Split-Ballot Multitrait–Multimethod Design?

A common way of estimating measurement quality is the split-ballot multitrait–multimethod (SB-MTMM) approach. However, this approach leads often to non-convergence or improper solutions when using a 2-group design, whereas the 3-group design performs better. Nevertheless, the 3-group design is rarely implemented because it makes it complicated for applied researchers to use the data. Therefore, we propose to draw groups of unequal sample sizes: two larger groups and one third group as small as possible. Using Monte Carlo simulations and real data analyses, we investigate how well such a design works and which size is needed for the third group. Our results suggest that a 3-group SB-MTMM design with smaller size for the third group (reducing till 5–10%) leads to similar levels of accuracy and no large changes in the model or quality estimates.
Laser ablation comparison by picosecond pulses train and nanosecond pulse

A comparison of laser ablation by a train of picosecond pulses and nanosecond pulses revealed a difference in laser craters, ablation thresholds, plasma sizes and spectral line intensities. Laser ablation with a train of picosecond pulses resulted in improved crater quality while ablated mass decreased up to 30%. A reduction in laser plasma dimensions for picosecond train ablation was observed while the intensity of atomic/ionic lines in the plasma spectra was greater by a factor of 2–4 indicating an improved excitation and atomization in the plasma.
How Does Trait Mindfulness Weaken the Effects of Risk Factors for Adolescent Smartphone Addiction? A Moderated Mediation Model

As a psychological resource of individuals, trait mindfulness is valuable in facilitating individuals to maintain attention intensity, increase efficiency, and alleviate stress and depression. It can also buffer against the risk factors of addictive behaviors. However, applied research combining trait mindfulness and smartphone addiction with the use of psychological resources is relatively scarce and needs further examination. We constructed a moderated mediation model based on compensatory Internet use and conservation of resources theory (OCR) to examine the effects of social anxiety on adolescent smartphone addiction and to describe how trait mindfulness “works” and “in what contexts it works better”. We analyzed 1570 adolescent subjects through a multistage stratified sampling method. Our findings revealed that social anxiety positively predicted smartphone addiction, while trait mindfulness was negatively associated with it. Furthermore, trait mindfulness mitigated smartphone addiction by reducing social anxiety, suggesting a mediating effect of social anxiety on this relationship. Meanwhile, the mediating effect was more pronounced among adolescents with left-behind experience; we found that left-behind experience partially moderated the relationship between social anxiety and smartphone addiction. Adolescents with left-behind experience had more significant compensatory media use with a higher risk of smartphone addiction. This study highlights the potential protective role of trait mindfulness in the development and maintenance of adolescent smartphone addiction. It provides empirical support for applying resource conservation theory and stress buffering theory in this context.
Success Story Sri Lanka's COVID-19 Response Takes a Different Turn: Healthcare Workers' Burnout Impact

The COVID-19 pandemic made many countries respond quickly to suppress the transmission of this virus, strict restrictions on the entry of countries were carried out to successfully suppress transmission, Sri Lanka was successful in handling the initial pandemic. However, it lasted for a while, the policy of relaxing social restrictions made the population uncontrollably infected, and limited personnel made the workload and duration of work shift heavy. length of service plays a role in the burnout of Sri Lankan hospital health workers. This study aims to determine the effect of length of service and duration of work shifts on the incidence of Burnout among health workers during the COVID-19 pandemic in Sri Lanka. The method used an Observational Study based on datasets taken crossectional related to the incidence of burnout in health workers during the COVID-19 pandemic in Sri Lankan hospitals. Using the Copenhagen Burnout Inventory (CBI). The results obtained Length of Service has a significant effect on Personal Burnout, Work related burnout, Client related burnout, while the duration of work shifts has a significant effect on work-related burnout, insignificant on Personal Burnout and Client Related Burnout of Health workers in large hospitals in Sri Lanka during the COVID-19 pandemic
Multi-Agent Reinforcement Learning for Intelligent V2G Integration in Future Transportation Systems

Electric vehicles (EVs) are the backbone of the future intelligent transportation system (ITS). They are environmentally friendly and can also be integrated as distributed energy resources (DERs) into the smart grid using vehicle-to-grid (V2G) scheme. Specifically, utility companies can push back EV batteries into the electric grid to reduce the peak load. However, integrating EVs into the power grid efficiently requires accurate artificial intelligence (AI) mechanisms to forecast, coordinate, and dispatch the EVs into the grid. This paper proposes a Multi-agent Reinforcement Learning (MARL) mechanism that schedules the day-ahead discharging process of EV batteries to optimize the peak shaving performance of the electric grid. The proposed MARL overcomes the inaccuracy of energy prediction by allowing the agents, i.e. EVs, to make autonomous decisions. These agents are trained in a centralized fashion but make decisions locally to maintain autonomy and privacy. In particular, the model does not require that the EVs communicate with a centralized entity during the execution stage, which assures the model’s integrity and protects the EVs’ private information. To evaluate the model, a comprehensive series of experiments were carried out to prove the effectiveness of the MARL coordination and scheduling mechanism and to show that the model can indeed flatten the peak load.
Maxentropic and Quantitative Methods in Operational Risk Modeling

In risk management the estimation of the distribution of random sums or collective models from historical data is not a trivial problem. This is due to problems related with scarcity of the data, asymmetries and heavy tails that makes difficult a good fit of the data to the most frequent distributions and existing methods. In this work we prove that the maximum entropy approach has important applications in risk management and Insurance Mathematics for the calculation of the density of aggregated risk events, and even for the calculation of the individual losses that come from the aggregated data, when the available information consists of an observed sample, which we usually do not have any information about the underlying process. From the knowledge of a few fractional moments, the Maxentropic methodologies provide an efficient methodology to determine densities when the data is scarce, or when the data presents correlation, large tails or multimodal characteristics. For this procedure, the input would be the sample moments E[e −αS ] = μ(α) or some interval that encloses the difference between the true value of μ(α) and the sample moments (for eight values of the Laplace transform), this interval would be related to the uncertainty (error) in the data, where the width of the interval may be adjusted by convenience. Through a simulation study we analyze the quality of the results, considering the differences with respect to the true density and in some cases the study of the size of the gradient and the time of convergence. We compare four different extensions of Maxentropic methodologies, the Standard Method of Maximum Entropy (SME), an extension of this methodology allows to incorporate additional information through a reference measure, called Method of Entropy in the Mean (MEM) and two extensions of the SME that allow introduce errors, called SME with errors or SMEE. Although our motivating example come from the field of Operational Risk analysis, the developed methodology may be applied to any branch of applied sciences.
The Influence Of Leadership, Organizational Culture, And Work Competence On Employee Performance Through Work Motivation

This research was conducted to explore the influence of leadership, organizational culture, and work competence on employee performance which is mediated by work motivation. This research is a development of previous research on employees in one of the local government agencies. The contribution of this research is expected to be able to provide new information at the theoretical/scientific level of human resource management and add new references and managerial implications that can be positively applied to non-profit organizations. Referring to the results of previous research and empirical theory related to the learning process of human resource management, this research was conducted using a survey data collection method by distributing questionnaires online, respondents were selected based on a purposive method sampling of ministry employees in charge of coordination in the fields of politics, law, and security. The analysis in this study uses factor analysis and Lisrel structural equation modeling (SEM). The findings from this study are that leadership and organizational culture are not directly related to employee performance, but the role of leadership and organizational culture can be related to employee performance through the mediation of work motivation. The managerial implication of this research for the organization is being able to encourage the role of a leader to be able to improve the performance of its employees in the organization and the various cultures within the organization can be uniformed following the culture which can provide positive changes to the work motivation of employees to achieve organizational goals.
Antifungal Substances Produced by B. subtilis Strain W3.15 Inhibit the Fusarium oxysporum and Trigger Cellular Damage

Soybean Fusarium wilt and root rot disease caused by a necrotrophic ascomycete pathogen, F. oxysporum, triggered severe damage to the plant tissues and organs and impacted heavy losses. Biocontrol agents, Bacillus subtilis, were commonly used to produce a broad spectrum of antifungal substances and were gradually used in biocontrol studies for plant disease management. Investigation and determination of the inhibiting mechanism of antifungal substance produced by B. subtilis on F. oxysporum should be done to protect the soybean plant. This study revealed that basal nutrient broth (NB) gives the best antifungal activity. The stationary phase of the bacterial growth curve was obtained on two days of cultivation and showed the maximum antifungal activity against F. oxysporum. Ethyl acetate (EA) extraction of bacterial supernatant generated crude EA extract, which showed half inhibition (IC50) at 306.42 µg/ml obtained from the dose-response regression curve. Post-treatment mycelia of F. oxysporum with bacterial extract were demonstrated as hyphal deformation followed by malondialdehyde (MDA) accumulation. Furthermore, cellular leakage on fungal cells that may be triggered by antifungal compounds from strain W3.15 occurred. Last, the related antifungal compounds were predicted to be epicatechin and benzophenone from the LC-MS/MS analysis of crude EA extract. Accordingly, the biocontrol agent B. subtilis strain W3.15 promises a strong potency for biofungicide development.
Théorie de Fontaine en égales caractéristiques

Les chtoucas locaux sont des analogues en egales caracteristiques des groupes p-divisibles - par exemple on leur associe un module de Tate, qui est un module libre sur l'anneau d'entiers d'un corps local K de caracteristique positive. Nous associons a un chtouca local une structure de Hodge (ou, plus precisement, une structure de Hodge-Pink), ce qui induit un morphisme de periodes analogue a celui construit par Rapoport et Zink. Pour les structures de Hodge-Pink definies sur une extension finie de K nous demontrons un analogue du theoreme « faiblement admissible implique admissible » de Colmez et Fontaine. Nous developpons aussi une theorie entiere. Les demonstrations sont elementaires et ne font pas intervenir de cloture algebrique de K. Les arguments utilises dans la theorie entiere sont tres proches de ceux qui interviennent dans la theorie rationnelle.
Disease duration and herpes zoster infection related to neutropenia in patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disease involving several organs. Neutropenia in patients with SLE may be a factor associated with infection leading to higher morbidity and mortality. There are several inconsistent predictors of neutropenia in patients with SLE. The present study is a retrospective, analytical study, which aimed to identify other predictors of neutropenia in patients with SLE. Patients with SLE who had been regularly followed up for ≥1 year were included in this study. Clinical factors, including history of disease, comorbidities, previous infection, laboratory results and treatment, were collected. The primary analyzed indicator was the occurrence of neutropenia. Factors associated with neutropenia were calculated by multivariate logistic regression analysis. A total of 84 patients met the study criteria. Of those 84 patients, 36 (42.86%) developed neutropenia. There were seven factors placed in the predictive model for neutropenia. Two factors were independently associated with the presence of neutropenia: Disease duration and herpes zoster infection. The first factor was negatively related with neutropenia with an adjusted odds ratio of 0.70 (95% confidence interval, 0.54, 0.92), whereas herpes zoster infection was an independent risk factor for neutropenia with an adjusted odds ratio of 8.46 (95% confidence interval, 1.30, 54.80). In conclusion, the present study revealed that short duration of disease and herpes zoster infection are predictors of neutropenia in patients with SLE.
Underdetermined Blind Identification Via $k$-Sparse Component Analysis: RANSAC-driven Orthogonal Subspace Search

Two primary families of methods exist for under-determined blind identification (UBI) based on the sparsity of the source matrix: sparse component analysis (SCA) and $k$-SCA. SCA assumes one active source at each time instant, while $k$-SCA allows for varying numbers of active sources represented by $k$. However, existing $k$-SCA methods, which claim to solve UBI problems by accommodating $k$-sparse sources, predominantly rely on 1-sparse sources, limiting their effectiveness in real-world scenarios with high noise levels. In this paper, we propose an effective and computationally less complex approach for UBI, specifically focusing on the challenging case when the number of active sources is equal to the number of sensors minus one $(k\ =\ m-1)$. Our approach overcomes limitations by using a two-step scenario: (1) estimating the orthogonal complement subspaces of the overall space and (2) identifying the mixing vectors. We present an integrated algorithm based on the Gram-Schmidt process and random sample consensus (RANSAC) method to solve both steps. Experimental results using simulated data demonstrate the superior effectiveness of our proposed method compared to existing algorithms.
Reasons and Associated Factors for Emergency Room Visits of Cancer Patients in a Tertiary Hospital, Thailand

Objective: To investigate the reasons leading cancer patients to visit an emergency room (ER) and the associated factors, which will suggest policies to reduce unnecessary ER visits.Material and Methods: A cross-sectional study was done in Songklanagarind Hospital, the major referral center in Southern Thailand. The data of cancer patients aged 18 years or older diagnosed during 2011 to 2018 were retrieved from our Hospital Information System. The reasons for the ER visits were defined and classified using the “chief complaints” from doctors’ notes. A negative binomial regression model was used to assess factors associated with the ER visits.Results: A total of 42,574 cancer patients were identified during the study period. 8,307 (19.5%) had visited the ER at least once. The most common cancer type was of the digestive organs (28.1%). The top 5 reasons for an ER visit were pain (25.4%), dyspnea (11.5%), abdominal discomfort (6.0%), fever (5.9%), and stoma and catheter device-related problems (5.2%). The most common factors associated with ER visits were age, metastasis, palliative chemotherapy or radiotherapy, previous hospitalization, years with cancer, type of cancer and healthcare scheme.Conclusion: Pain and dyspnea were the two most common chief complaints leading cancer patients to visit the ER. These two symptoms should be given special attention in cancer patients, especially those receiving chemotherapy or radiation, to ensure they are well controlled and do not lead to unnecessary ER visits.
Domatinostat Targets the FOXM1–Survivin Axis to Reduce the Viability of Ovarian Cancer Cells Alone and in Combination with Chemotherapeutic Agents

The deregulation of the FOXM1 transcription factor is a key molecular alteration in ovarian cancer, contributing to the development and progression of ovarian cancer via activation of the target genes. As such, FOXM1 is a highly attractive therapeutic target in the treatment of ovarian cancer, but there has been no clinically tested FOXM1 inhibitor to date. We investigated in this study the effects of domatinostat, a class I-selective HDAC inhibitor currently in the clinical stage of development as a cancer therapeutic, on the expression of FOXM1 and viability of ovarian cancer cells. Cell viability, as well as protein and mRNA expression of FOXM1 and its transcriptional target survivin, was examined after domatinostat treatment of TOV21G and SKOV3 ovarian cancer cell lines in the absence or presence of cisplatin and paclitaxel. The effect of FOXM1 knockdown on survivin expression and those of genetic and pharmacological inhibition of survivin alone or in combination with the chemotherapeutic agents on cell viability were also examined. Domatinostat reduced the protein and mRNA expression of FOXM1 and survivin and also the viability of ovarian cancer cells alone and in combination with cisplatin or paclitaxel at clinically relevant concentrations. Knockdown experiments showed survivin expression was dependent on FOXM1 in ovarian cancer cells. Survivin inhibition was sufficient to reduce the viability of ovarian cancer cells alone and in combination with the chemotherapeutic agents. Our findings suggest that domatinostat, which effectively targets the FOXM1–survivin axis required for the viability of ovarian cancer cells, is a promising option for the treatment of ovarian cancer.
A Note on Lawton's Theorem

Abstract We prove Lawton's conjecture about the upper bound on themeasure of the set on the unit circle on which a complex polynomial with a bounded number of coefficients takes small values. Namely, we prove that Lawton's bound holds for polynomials that are not necessarily monic. We also provide an analogous bound for polynomials in several variables. Finally, we investigate the dependence of the bound on the multiplicity of zeros for polynomials in one variable.
Acceleration of Large Transformer Model Training by Sensitivity-Based Layer Dropping

Transformer models are widely used in AI applications such as Natural Language Processing (NLP), Computer Vision (CV), etc. However, enormous computation workload be-comes an obstacle to train large transformer models efficiently. Recently, some methods focus on reducing the computation workload during the training by skipping some layers. How-ever, these methods use simple probability distribution and coarse-grained probability calculation, which significantly affect the model accuracy. To address the issue, in this paper we propose a novel method to accelerate training—Sensitivity-Based Layer Dropping (SBLD). SBLD uses lay-er-wise sensitivity data to switch on/off transformer layers in proper order to keep high accuracy. Besides, we adjust the probability of skipping transformer layers with a scheduler to accelerate training speed and get faster convergence. Our results show that SBLD solves the accuracy drop issue com-pared with prior layer dropping methods. Our SBLD method can decrease end-to-end training time by 19.67% during training of GPT-3 Medium model, the same time increasing the accuracy by 1.65% w.r. t. baseline. Furthermore, for SwinV2-L model the obtained Top-1 and Top-5 accuracies are also higher vs. the baseline. Thus, the proposed method is efficient and practical to improve the large transformer model training.
Orthotopic Ferret Tracheal Transplantation Using a Recellularized Bioengineered Graft Produces Functional Epithelia

Tracheal grafts may be necessary to bridge long-segment defects after curative resection for airway obstructions. Bioengineered grafts have emerged as an appealing option, given the possibilities of altering the histologic and cellular profile of the conduit. We previously designed a bioreactor capable of luminally decellularizing and recellularizing a ferret trachea with surface airway epithelia (SAE) basal cells (BCs), and we sought to assess the fate of these grafts when transplanted in an orthotopic fashion. As adjuncts to the procedure, we investigated the use of a vascular endothelial growth factor (VEGF)-laden hydrogel and of immunosuppression (IS) in graft revascularization and viability. IS was shown to limit early graft revascularization, but this effect could be counteracted with VEGF supplementation. Submucosal gland (SMG) loss was shown to be inevitable regardless of the revascularization strategy. Lastly, the bioengineered tracheas survived one month after transplant with differentiation of our implanted BCs that then transitioned into a recipient-derived functional epithelium. The work presented in this manuscript has important implications for future cellular and regenerative therapies.
A Novel High Isolation 4-Port Compact MIMO Antenna with DGS for 5G Applications

This paper presents the design and realization of a simple and low-profile, four-port multiple-input-multiple-output (MIMO) antenna operating in a mm-wave band supporting 5G communication technologies. As part of the design methodology, the initial stage involved the development of a conventional monopole patch antenna optimized for operation at 26 GHz, which was matched to a 50 Ω stepped feed line. Afterward, a square-shaped defected ground structure (DGS) with semi-circle slots on the edges was placed on the ground to improve the isolation, and the circular and rectangular slots were incorporated as DGSs to optimize the antenna impedance bandwidth. Etching semi-circular-shaped slots on the ground plane achieved more than 34.2 dB isolation in the 26 GHz operating band. In addition, an arrangement of four symmetrical radiating elements was positioned orthogonally to minimize the antenna’s physical size and improve the isolation. The proposed MIMO antenna’s overall dimension was 25 × 25 mm2, which was printed on a Rogers 5880 substrate at a width of 0.787 mm and εr = 2.2. The proposed antenna covered the 5G mm-wave band with a 10 dB bandwidth ranging from 25.28–28.02 GHz, whereas the maximum gain attained for the proposed structure was 8.72 dBi. Additionally, the implementation of these slots effectively mitigated mutual coupling, resulting in reduced envelope correlation coefficient (ECC) values. Furthermore, other MIMO performance metrics, including channel capacity loss (CCL), mean effective gain (MEG), and diversity gain (DG), were analyzed for the proposed structure. The obtained results indicate its suitability for various usage areas, such as smart devices, mobile phones, and sensors in 5G applications.