Miracle-dz/UniFAR-FTUs huggingface dataset:
Latent Profile Analysis of High School Students’ Fire Safety Awareness

The purpose of this study is to analyze the types of latent profiles of high school students' fire safety awareness and to identify the characteristics of related variables. For this purpose, a survey was conducted from March 22 to May 25, 2021 for 1054 high school students (male; 569, female; 485) in 3 cities, in Jeollabuk-do. The latent profile was analyzed using a scale consisting of 4 sub-factors: 'fire prevention', 'fire preparedness', 'indirect fire response', and 'direct fire response'. It was checked whether there were differences according to the inter-individual differences of the latent group. As a result of the analysis, fire safety awareness of high school students was classified into three latent profiles. The three groups were named ‘High Perception Type’, ‘Moderate Perception Type’, and ‘Low Perception Type’ according to their types. In fire safety awareness, there is a significant difference in the individual differences according to the gender and academic achievement of the latent profile. These results are meaningful as the first study to analyze the latent profile of high school students' fire safety awareness, and it is also meaningful to provide a useful basis for the contents and methods of customized fire safety education by identifying the tendencies of spontaneous groups and their fire safety awareness.
Silibinin and Naringenin against Bisphenol A-Induced Neurotoxicity in Zebrafish Model—Potential Flavonoid Molecules for New Drug Design, Development, and Therapy for Neurological Disorders

Bisphenol A (BPA), a well-known xenoestrogen, is commonly utilised in the production of polycarbonate plastics. Based on the existing evidence, BPA is known to induce neurotoxicity and behavioural issues. Flavonoids such as silibinin and naringenin have been shown to have biological activity against a variety of illnesses. The current research evaluates the neuropharmacological effects of silibinin and naringenin in a zebrafish model against neurotoxicity and oxidative stress caused by Bisphenol A. In this study, a novel tank diving test (NTDT) and light–dark preference test (LDPT) were used in neurobehavioural investigations. The experimental protocol was planned to last 21 days. The neuroprotective effects of silibinin (10 μM) and naringenin (10 μM) in zebrafish (Danio rerio) induced by BPA (17.52 μM) were investigated. In the brine shrimp lethality assay, the 50% fatal concentrations (LC50) were 34.10 μg/mL (silibinin) and 91.33 μg/mL (naringenin) compared to the standard potassium dichromate (13.15 μg/mL). The acute toxicity investigation found no mortality or visible abnormalities in the silibinin- and naringenin-treated groups (LC50 > 100 mg/L). The altered scototaxis behaviour in LDPT caused by BPA was reversed by co-supplementation with silibinin and naringenin, as shown by decreases in the number of transitions to the light zone and the duration spent in the light zone. Our findings point to BPA’s neurotoxic potential in causing altered scototaxis and bottom-dwelling behaviour in zebrafish, as well as the usage of silibinin and naringenin as potential neuroprotectants.
Factors controlling segregation tendency of solute Ti, Ag and Ta into different symmetrical tilt grain boundaries of tungsten: First-principles and experimental study

In previous reports, experimental studies have shown that both thermal stability and strength can be controlled by grain boundary (GB) segregation. In this study, we investigate the segregation behavior of solute (Ti, Ag and Ta) atoms to low/high-angle symmetric tilt grain boundaries (STGBs) of W using density functional theory (DFT) calculations and supported by TEM experiments. We found no segregation preference for Ti or Ta at low-angle STGBs; however, they exhibit a slight segregation tendency to the core of high-angle STGBs. In contrast, Ag is more prone to segregate in and all around the GB plane. We estimated the mechanical and electronic contributions to solution energy and found that the electronic contribution is dominant. Furthermore, the role of d − valence electrons of solute and W atoms, was analyzed using the local density of states (PDOS). We found that substantial d − valence electrons hybridization in the case of Ta plays an important role in stabilizing W-Ta bonds, while the anisotropic nature of W-Ti bond contributes to stabilize stabilizing surrounding W atoms. Charge transfer analysis revealed that Ti and Ta lose electrons to W atoms. Contrary to the electronegativity rule, Ag atoms gain charge from neighboring W atoms and excellent s s − hybridization may explain the increased GB segregation of Ag atoms.
Chapter 3. Determinants of Cross-Border M&A and Shareholder Wealth Effects in a Globalized World

We analyse theoretical insights and empirical regularities related to factors determining the cross-border mergers and acquisitions (M&A) and impact of M&A on shareholder value of acquires and targets. The analysis of cross-border M&A is a relatively new subject and only recently received rigorous attention in academic research. Within this nascent literature, the survey pays particular attention to the emerging markets, which, in line with their growing role of in the global economy, became an increasingly important arena for cross-border M&A. The existing evidence point out to prevailing challenges in studying cross-border M&A by emerging markets firms. The results are often contradictory, and tend to focus on a single country falling short of formally testing existing theories or developing comprehensive theories for emerging economies. We show that the type of factors increasing the value enhancing effects of M&A tend to be similar to the factors affecting the likelihood of M&A transactions. The related methodological challenges for the existing studies are related to strong evidence with respect to non-random selection of acquisition targets, which, among other “selection issues”, has important implications for choosing counterfactual evidence in order to appropriately compare pre- and post-acquisition performance of firms.
Electronic Human Resource Management: A Contemporary Overview

Information technology (IT) has become an integrated element of modern business world. In current years, the usage of IT in functioning human resource management (HRM) activities has amplified significantly. Electronic human resource management (EHRM) has the capability to expand managerial proficiency and elevate the role of employees as the strategic stakeholders. Swift technological advancements have turned EHRM into a considerable theoretical and professional agenda. This theoretical paper has made an investigation into the contemporary level of developments of the theoretical facts linked to EHRM. In this paper, the author has tried to review the various features of EHRM specially the scholastic perceptions around EHRM, the role of EHRM, the categories of EHRM; and the relationship of EHRM with contemporary business practices. The paper will hopefully contribute into HRM literature and regulate the value of EHRM practices, both theoretically and practically. It is also expected that this research will support the academicians and practitioners alike to have a complete representation of the contemporary issues related to EHRM.
TWO STEPS TO HIGH ABSOLUTIVE SYNTAX Two Steps to High Absolutive Syntax

In many ergative languages, the absolutive argument moves to a position above all other arguments in the clause. The mechanisms which underlie this movement remain a matter of dispute: it has been argued to reflect movement to a subject position (Guilfoyle et al. , 1992), or assimilated to a process of definiteness-related object shift (Aldridge, 2004). This paper investigates the syntax which underlies the High Absolutive configuration in Mandar, an Austronesian language of the South Sulawesi subfamily, and finds evidence for the first view. In this language, the process which places the absolutive argument in its high position is visibly connected to its interaction with a set of functional heads that sit relatively high in the clause. This fact is shown through an investigation of the behavior of internal arguments which are definite but not absolutive. In Mandar, as in related languages nearby, these seem not to raise to a position above the external argument. This observation suggests the need to decouple the High Absolutive configuration from the process of definiteness-related object shift in this language and at large.
Response surface-based robust geotechnical design of supported excavation – spreadsheet-based solution

ABSTRACT The robust geotechnical design (RGD) approach which involves optimization to obtain a design that is safe, cost-efficient, and robust in the face of uncertainties, can be computationally challenging for complex geotechnical structures. In this study, the RGD approach has become practical by introducing a response surface as a surrogate to finite element- or finite difference-based computer code that is used for analyzing the system, and developing a fast algorithm for the optimization process. For demonstration purposes, a real-world supported excavation project is designed using this modified RGD approach and it is compared with the one designed by a local expert.
Three-dose mRNA-1273 vaccination schedule: sufficient antibody response in majority of immunocompromised hematology patients

Importance In patients with hematologic malignancies, the immunogenicity of the standard 2-dose mRNA-1273 coronavirus disease 19 (COVID-19) vaccination schedule is often insufficient due to underlying disease and current or recent therapy. Objective To determine whether a 3rd mRNA-1273 vaccination raises antibody concentrations in immunocompromised hematology patients to levels obtained in healthy individuals after the standard 2-dose mRNA-1273 vaccination schedule. Design Prospective observational cohort study. Setting Four academic hospitals in the Netherlands. Participants 584 evaluable immunocompromised hematology patients, all grouped in predefined cohorts spanning the spectrum of hematologic malignancies. Exposure One additional vaccination with mRNA-1273 5 months after completion of the standard 2-dose mRNA-1273 vaccination schedule. Main Outcomes and Measures Serum IgG antibodies to spike subunit 1 (S1) antigens prior to and 4 weeks after each vaccination, and pseudovirus neutralization of wildtype, delta and omicron variants in a subgroup of patients. Results In immunocompromised hematology patients, a 3rd mRNA-1273 vaccination led to median S1 IgG concentrations comparable to concentrations obtained by healthy individuals after the 2-dose mRNA-1273 schedule. The rise in S1 IgG concentration after the 3rd vaccination was most pronounced in patients with a recovering immune system, but potent responses were also observed in patients with persistent immunodeficiencies. Specifically, patients with myeloid malignancies or multiple myeloma, and recipients of autologous or allogeneic hematopoietic cell transplantation (HCT) reached median S1 IgG concentrations similar to those obtained by healthy individuals after a 2-dose schedule. Patients on or shortly after rituximab therapy, CD19-directed chimeric antigen receptor T cell therapy recipients, and chronic lymphocytic leukemia patients on ibrutinib were less or unresponsive to the 3rd vaccination. In the 27 patients who received cell therapy between the 2nd and 3rd vaccination, S1 antibodies were preserved, but a 3rd mRNA-1273 vaccination did not significantly enhance S1 IgG concentrations except for multiple myeloma patients receiving autologous HCT. A 3rd vaccination significantly improved neutralization capacity per antibody. Conclusions and Relevance The primary schedule for immunocompromised patients with hematologic malignancies should be supplemented with a delayed 3rd vaccination. B cell lymphoma patients and allogeneic HCT recipients need to be revaccinated after treatment or transplantation. Trial Registration EudraCT 2021-001072-41
Prospective validation of diffusion-weighted MRI as a biomarker of tumor response and oncologic outcomes in head and neck cancer: Results from an observational biomarker pre-qualification study.

Purpose/Objective(s): To determine diffusion-weighted imaging (DWI) MRI parameters associated with tumor response and oncologic outcomes in head and neck (HNC) patients treated with definitive radiation therapy (RT). Materials/Methods: Eighty-six HNC patients enrolled in an active prospective imaging study at The University of Texas MD Anderson Cancer Center were included in the analysis. Patients had MRIs pre-, mid-, and post-RT completion. Inclusion criteria included adults with histologic evidence of malignant head and neck neoplasm indicated for curative-intent treatment with RT with/without chemotherapy, good performance status (ECOG score 0-2), and with no contraindications to MRI. Patients were scanned using a MAGNETOM Aera 1.5T MR scanner (Siemens Healthcare, Germany) with two large four-channel flex phased-array coils. We used fat-suppressed T2-weighted turbo spin echo sequences for tumor segmentation which were co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Treatment response was assessed at mid-RT and at 8-12 weeks post-RT using RECIST 1.1 criteria and was defined as: complete response (CR) vs. non-complete response (non-CR). Pre-RT ADC was correlated with RT response (CR vs. non-CR) at mid- and post-RT. The Mann-Whitney U test was used to compare ADC values between the mid-treatment CR group and the non-CR group. Recursive partitioning analysis (RPA) was performed to identify ADC threshold associated with relapse. Cox proportional hazards models were done for clinical vs. clinical and imaging parameters and internal validation was done using bootstrapping technique. Results: Eighty-one patients were included in this analysis. Median follow-up was 31 months. Pre-treatment ADC was not correlated with tumor response or oncologic outcomes (P>0.05). For patients with post-RT CR, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 {+/-} 0.29) x 10-3 mm2/s vs. (1.37 {+/-} 0.22) x 10-3 mm2/s, p< 0.0001), while patients with non-CR had no statistically significant increase (p >0.05). RPA identified GTV-P delta ({Delta})ADCmean <7% at mid-RT as the most significant parameter associated with worse LC and RFS (p=0.01). Univariable and multivariable analysis of prognostic outcomes showed that GTV-P {Delta}ADCmean at mid-RT [≥]7% was significantly associated with better LC and RFS. The addition of {Delta}ADCmean significantly improved the c-indices of LC and RFS models compared with standard clinical variables (0.85 vs. 0.77 and 0.74 vs. 0.68 for LC and RFS, respectively, p<0.0001 for both). Conclusion: ADC change at mid-RT is a strong predictor of oncologic outcomes in HNC patients. Patients with no significant increase of primary tumor site ADC at mid-RT relative to baseline values are at high risk of disease relapse. Multi-institutional data are needed for validation of our results.
Local treewidth of random and noisy graphs with applications to stopping contagion in networks

We study the notion of local treewidth in sparse random graphs: the maximum treewidth over all $k$-vertex subgraphs of an $n$-vertex graph. When $k$ is not too large, we give nearly tight bounds for this local treewidth parameter; we also derive tight bounds for the local treewidth of noisy trees, trees where every non-edge is added independently with small probability. We apply our upper bounds on the local treewidth to obtain fixed parameter tractable algorithms (on random graphs and noisy trees) for edge-removal problems centered around containing a contagious process evolving over a network. In these problems, our main parameter of study is $k$, the number of initially ``infected'' vertices in the network. For the random graph models we consider and a certain range of parameters the running time of our algorithms on $n$-vertex graphs is $2^{o(k)}\textrm{poly}(n)$, improving upon the $2^{\Omega(k)}\textrm{poly}(n)$ performance of the best-known algorithms designed for worst-case instances of these edge deletion problems.
Event-aided Direct Sparse Odometry

We introduce EDS, a direct monocular visual odometry using events and frames. Our algorithm leverages the event generation model to track the camera motion in the blind time between frames. The method formulates a direct probabilistic approach of observed brightness increments. Per-pixel brightness increments are predicted using a sparse number of selected 3D points and are compared to the events via the brightness increment error to estimate camera motion. The method recovers a semi-dense 3D map using photometric bundle adjustment. EDS is the first method to perform 6-DOF VO using events and frames with a direct approach. By design it overcomes the problem of changing appearance in indirect methods. Our results outperform all previous event-based odometry solutions. We also show that, for a target error performance, EDS can work at lower frame rates than state-of-the-art frame-based VO solutions. This opens the door to low-power motion-tracking applications where frames are sparingly triggered “on demand” and our method tracks the motion in between. We release code and datasets to the public.
Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction

Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines.
Bad Weather, Social Network, and Internal Migration; Case of Japanese Sumo Wrestlers 1946-1985

Post-World War II , there was massive internal migration from rural to urban areas in Japan. The location of Sumo stables was concentrated in Tokyo. Hence, supply of Sumo wrestlers from rural areas to Tokyo was considered as migration. Using a panel dataset covering forty years, specifically 1946-1985, this study investigates how weather conditions and social networks influenced the labor supply of Sumo wrestlers. Major findings are; (1) inclemency of the weather in local areas increased supply of Sumo wrestlers in the period 1946-1965, (2) the effect of the bad weather conditions is greater in the locality where large number of Sumo wrestlers were supplied in the pre-war period, (3) neither the occurrence of bad weather conditions nor their interactions with sumo-wrestlers influenced the supply of Sumo wrestlers in the period 1966-1985. These findings imply that the negative shock of bad weather conditions on agriculture in the rural areas incentivized young individuals to be apprenticed in Sumo stables in Tokyo. Additionally, in such situations, the social networks within Sumo wrestler communities from the same locality are important. However, once the share of workers in agricultural sectors became very low, this mechanism did not work. on a basis.
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation

Controlled text generation tasks such as unsupervised text style transfer have increasingly adopted the use of Reinforcement Learning (RL). A major challenge in applying RL to such tasks is the sparse reward, which is available only after the full text is generated. Sparse rewards, combined with a large action space make RL training sample-inefficient and difficult to converge. Recently proposed reward-shaping strategies to address this issue have shown only negligible gains. In contrast, this work proposes a novel approach that provides dense rewards to each generated token. We evaluate our approach by its usage in unsupervised text style transfer. Averaged across datasets, our style transfer system improves upon current state-of-art systems by 21\% on human evaluation and 12\% on automatic evaluation. Upon ablated comparison with the current reward shaping approach (the `roll-out strategy'), using dense rewards improves the overall style transfer quality by 22\% based on human evaluation. Further the RL training is 2.5 times as sample efficient, and 7 times faster.
Neural Space-filling Curves

We present Neural S pace- f illing C urves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many applications such as video scrambling, compression, and auto-regressive models that are used in generative modeling for images. Existing algorithms resort to a ﬁxed scanning algorithm such as Raster scan or Hilbert scan. Instead, our work learns a spatially coherent linear ordering of pixels from the dataset of images using a graph-based neural network. The resulting Neural SFC is optimized for an objective suitable for the downstream task when the image is traversed along with the scan line order. We show the advantage of using Neural SFCs in downstream applications such as image compression. Code and additional results will be made available at https://hywang66.github. io/ publication/neuralsfc .
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.