Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychometric Properties of the Body–Mind–Spirit Wellness Behavior and Characteristic Inventory for the Greek Population

The Body–Mind–Spirit Wellness Behavior and Characteristic Inventory (BMS-WBCI) is a free-of-charge wellness tool with good psychometric properties, widely used mainly in studies assessing quality of life and healthy lifestyle habits. This certain tool is based on the Hettler’s (1980) model and has been validated for use with students aged 18–36. The purpose of this study was to adapt the BMS-WBCI in the Greek language and at the same time to validate it for use in the general population. This study included 520 participants aged 16–75 (M = 39.86, SD = 10.5), who were recruited from the Greek population using the snowball procedure. The BMS-WBCI was adapted into Greek language, following a multiple forward-and-backward translation protocol. Confirmatory Factor Analysis (CFA) was used to validate the overall construct of the Greek BMS-WBCI. The final solution was a three-factor model with 38 items, after removing the items B1, B8, B9, M11, M24, and S43. This final model demonstrated an acceptable to good fit, presenting higher goodness-of-fit indices (CFI = 0.91, TLI = 0.90) and lower badness-of-fit indices (χ2/653 = 2.29, p < 0.001, RMSEA = 0.05, SRMR = 0.06). All items in the hypothesized model exhibited statistically significant standardized factor loadings (p < 0.001), with loadings consistently above 0.40. A very good internal consistency was found using the composite reliability measures (Body 0.86, Mind 0.95, Spirit 0.94). Further analysis indicated a good convergent validity (average variance extracted values: Body 50.5%, Mind 50.7%, Spirit 54.9%). The results indicated adequate discriminant validity, as all square roots of average variance extracted were higher than the correlation between construct items. In conclusion, this psychometric evaluation of the BMS-WBCI adds to the evidence supporting its use in the Greek language, not only in students, but also in the general population.
SIMULASI PERGERAKAN PENGUNJUNG MALL MENGGUNAKAN POTENTIAL FIELD

Simulasi kerumunan manusia yang real-time termasuk sesuatu yang sulit karena perilaku yang dipamerkan pada kelompok besar ini sangat kompleksitas dan pelik. Beberapa hal yang perlu dicermati antara lain penanganan terhadap permasalahan path planning (perencanaan jalur), collision avoidance (penghindaran tabrakan), serta separation (pemisahan jarak). Salah satu metode untuk perencanaan jalur adalah potential field yang berbasis grid. Metode ini memiliki prinsip seperti medan magnet yang menarik atau menolak partikel besi yang ada di sekitarnya. Berdasarkan karakteristik metode ini, maka metode ini dapat digunakan untuk mensimulasikan pergerakan simulasi kerumunan manusia. Perilaku menolak partikel besi diaplikasikan untuk menhindari halangan dinding dan perilaku menarik diaplikasikan untuk keluar menuju pintu utama. Selain itu, juga diatur agar pergerakan antar orang tidak menimbulkan tabrakan. Setelah diadakan simulasi dan penelitian, maka pergerakan kerumunan manusia yang tersebar dalam ruang-ruang dan lorong-lorong di mall dengan menggunakan metode potential field dapat menghindari halangan dinding dan dapat bergerak keluar menuju pintu utama tanpa terjebak local minima. Namun masih ada kendala, yakni bertabrakan dengan orang lain jika daerah yang dituju sama. Tingkat frekuensi tabrakan semakin meningkat seiring meningkatnya jumlah populasi, untuk jumlah populasi 10 terjadi 1 kali tabrakan dan bisa mencapai tabrakan 9558 untuk jumlah populasi 500.
Does polycentricism influence residential values? an analysis for the Metropolitan Region of Barcelona

This paper explores the relationship between centrality and land values. While in literature centrality is simplified as the proximity to centres, which normally are identified using qualitative approaches, in this paper a novelty vision is proposed. It differs from state of the art approaches since centrality is derived from an integrated indicator coming from data regarding the time-space behavior of citizens. In order to test whether centrality correlates with urban values, a regression model is used departing from data of Metropolitan Barcelona, such a method allows for controlling architectural variables that influence residential housings, as well as other locational factors beyond centrality that may affect land values. The results suggest that urban form does influence the spatial distribution of urban values; nevertheless such an impact is not as high as other locative factors such as socioresidential segregation. So urban policies intended to democratize urban quality should be focused not only in urban structural factors but also those regarding social differentiation of space.
The Influence of Augmented Reality on Purchase Intentions through Consumers of Madame Gie Products

Beauty products are needed by every individual, with various types of products offered, including quality, price, brand, and packaging. Therefore, the Purchase intentions of beauty products are very diverse. Madame Gie is one of the beauty product brands that has plunged into the use of Augmented Reality to make it easier for consumers to choose product variances. This study aims to determine and analyze the effect of Augmented Reality experiences on the purchase intention of Madame Gie products in Bandung, with Consumers’ Control as an intervening variable. This study uses a quantitative approach with a questionnaire through Google Form of 385 samples and analyzed by the Smart-PLS statistical program based on this research, the AR experience positively influences consumer Purchase Intention toward Madame Gie products. Consumer’s Control also plays a crucial role in the relationship between AR experience and Purchase Intention. Therefore, companies need to consider the importance of giving control to consumers in AR technology usage to maximize its impact on consumer purchase intention and enhance the performance of their beauty product marketing.
Modified structure of deep neural network for training multi-fidelity data with non-common input variables

Multi-fidelity surrogate (MFS) modeling technology, which efficiently constructs surrogate models using low-fidelity (LF) and high-fidelity (HF) data, has been studied to enhance the predictive capability of engineering performances. In addition, several neural network (NN) structures for MFS modeling have been introduced, benefiting from recent developments in deep learning research. However, existing multi-fidelity (MF) NNs have been developed assuming identical sets of input variables for LF and HF data, a condition that is often not met in practical engineering systems. Therefore, this study proposes a new structure of composite NN designed for MF data with different input variables. The proposed network structure includes an input mapping network that connects the LF and HF data's input variables. Even when the physical relationship between these variables is unknown, the input mapping network can be concurrently trained during the process of training the whole network model. Customized loss functions and activation variables are suggested in this study to facilitate forward and backward propagation for the proposed NN structures when training MF data with different inputs. The effectiveness of the proposed method, in terms of prediction accuracy, is demonstrated through mathematical examples and practical engineering problems related to tire performances. The results confirm that the proposed method offers better accuracy than existing surrogate models in most problems. Moreover, the proposed method proves advantageous for surrogate modeling of nonlinear or discrete functions, a characteristic feature of NN-based methods.
Geofluid migration process inferred from a 3-D electrical conductivity model beneath Tohoku district.

We carried out long-period MT observation using the state-of-the-art equipments. The MT impeadance responses were in-verted into 3-D electrical conductivity model using WSINV3D (Siripunvaraporn et al, 2005). The 3-D model delineates vertical continuous conductive zone from subducting Pacifc plate surface to lower crust below Ou backbone range. The conductive body indicates saline ﬂuids and/or melt pathway from the subducting slab surface The resistivity of the lower crust Ω m or more conductive and saline ﬂuids and/or melt volume fraction is estimated to be 7 at minimum. resistivity proﬁle in the across-arc direction indicates that conductive body separated from Paciﬁc plate surface at 80-100 km depth and assumes an overturned form towards backarc direction. The head of the conducting body attains to the lower crust just below Mt. Gassan. This suggests the backarc volcanisms are caused by saline ﬂuids and/or melt overturn rising towards backarc direction.
Finding Connected Components on Map-reduce in Logarithmic Rounds

Given a large graph G = (V,E) with millions of nodes and edges, how do we compute its connected components efficiently? Recent work addresses this problem in map-reduce, where a fundamental trade-off exists between the number of map-reduce rounds and the communication of each round. Denoting d the diameter of the graph, and n the number of nodes in the largest component, all prior map-reduce techniques either require d rounds, or require about n|V| + |E| communication per round. We propose two randomized map-reduce algorithms -- (i) Hash-Greater-To-Min, which provably requires at most 3log(n) rounds with high probability, and at most 2(|V| + |E|) communication per round, and (ii) Hash-to-Min, which has a worse theoretical complexity, but in practice completes in at most 2log(d) rounds and 3(|V| + |E|) communication per rounds. Our techniques for connected components can be applied to clustering as well. We propose a novel algorithm for agglomerative single linkage clustering in map-reduce. This is the first algorithm that can provably compute a clustering in at most O(log(n)) rounds, where n is the size of the largest cluster. We show the effectiveness of all our algorithms through detailed experiments on large synthetic as well as real-world datasets.
Proton Induced Reactions on Molybdenum: Implications for Medical 99m Tc Production

by Edward A. Lamere Recent shortages in the supply of Tc, a critical medical isotope, have prompted research into an alternative production method,Mo(p,2n), which could exploit an existing network of medical cyclotrons. One complication of this method, however, is the unavoidable co-production of other Tc isotopes which will affect both diagnostic imaging quality and radiation dosimetry. Currently, the production of these isotopes is being evaluated with statistical nuclear physics models, whose accuracy is highly dependent on input model parameters. The objective of this study is to provide experimental cross section measurements that can be used to predict Tc production in thick yield targets, as well as guide the choice of these model parameters. In this thesis, production cross sections for (p,x) reactions from all stable Mo species were measured using the charged particle activation method. We report the majority of the cross sections for production of Tc, Mo, and Nb isotopes with half-lives between four minutes and ninety-one days. While not directly relevant to radiopharmaceuticals, Nb and Mo production will have an impact on target composition after repeated recycling and dosimetry in the case of incomplete chemical purification. From these measurements, a set of input parameters for a TALYS calculated Hauser Feshbach model were identified as best reproducing the experimental measurements. Using this experimentally guided model, we have estimated yields for
Investigation of New Accelerometer Based on Capacitive Micromachined Ultrasonic Transducer (CMUT) with Ring-Perforation Membrane

Capacitive micromachined ultrasonic transducer (CMUT) has been widely studied due to its excellent resonance characteristics and array integration. This paper presents the first study of the CMUT electrostatic stiffness resonant accelerometer. To improve the sensitivity of the CMUT accelerometer, this paper innovatively proposes the CMUT ring-perforation membrane structure, which effectively improves the acceleration sensitivity by reducing the mechanical stiffness of the elastic membrane. The acceleration sensitivity is 10.9 (Hz/g) in the acceleration range of 0–20 g, which is 100% higher than that of the conventional CMUT structure. This research contributes to the acceleration measurement field of CMUT and can effectively contribute to the breakthrough of vibration acceleration monitoring technology in aerospace, medical equipment, and automotive electronics.
Effect of prothymosin α on neuroplasticity following cerebral ischemia‑reperfusion injury

Prothymosin α (ProT), a highly acidic nuclear protein with multiple cellular functions, has shown potential neuroprotective properties attributed to its anti-necrotic and anti-apoptotic activities. The present study aimed to investigate the beneficial effect of ProT on neuroplasticity after ischemia-reperfusion injury and elucidate its underlying mechanism of action. Primary cortical neurons were either treated with ProT or overexpressing ProT by gene transfection and exposed to oxygen-glucose deprivation for 2 h in vitro. Immunofluorescence staining for ProT and MAP-2 was performed to quantify ProT protein expression and assess neuronal arborization. Mice treated with vehicle or ProT (100 µg/kg) and ProT overexpression in transgenic mice received middle cerebral artery occlusion for 50 min to evaluate the effect of ProT on neuroplasticity-associated protein following ischemia-reperfusion injury. The results demonstrated that in cultured neurons ProT significantly increased neurite lengths and the number of branches, accompanied by an upregulation mRNA level of brain-derived neurotrophic factor. Furthermore, ProT administration improved the protein expressions of synaptosomal-associated protein, 25 kDa and postsynaptic density protein 95 after ischemic-reperfusion injury in vivo. These findings suggested that ProT can potentially induce neuroplasticity effects following ischemia-reperfusion injury.
Analysis and design on acceptance of blockchain based e-voting system

Elections are a critical aspect of democratic governance, providing citizens with the power and right to express their views. A secure voting system with innovative features can improve this process. Blockchain technology is considered a disruptive innovation, and its potential for enhancing the e-voting system is significant. The modern voting system is focusing more on blockchain technology to strengthen and secure the process. Blockchain is a reliable, decentralized database that can offer increased security compared to electronic voting machines (EVMs). This research paper presents a detailed study of the design, smart contracts, evaluation of action, and survey on the acceptance of blockchain-based e-voting systems. It examines the requirements for such a system and provides an understanding of the model. As the acceptance of information technology-based services and products increases, future innovation in the e-voting system may depend on blockchain technology. The survey conducted in this paper explores the differences in opinion based on gender, age, and profession among eligible voters from India regarding the acceptance of blockchain technology-based secure e-voting systems. The analysis of these differences sheds light on the potential for blockchain-based e-voting systems to enhance trust and security in the voting process.
Health literacy, eHealth literacy and their association with burden, distress, and self-efficacy among cancer caregivers

Purpose Health literacy skills are vital for cancer caregivers in helping cancer survivors to navigate their diagnosis, treatment, and recovery but little is known. This study explored health literacy and eHealth literacy among cancer caregivers and the relationship between health literacy/eHealth literacy and potential associated factors. Methods Informal caregivers who had cared for an individual with cancer completed a survey which collected demographic data and measured caregiver health literacy, eHealth literacy, self-efficacy, burden, and distress. Results Seven percent of caregivers had inadequate health literacy. Caregivers scored lowest on health literacy domains related to caregiver social support, information seeking and understanding care recipient preferences. eHealth literacy was associated with self-efficacy and burden while, different health literacy domains were associated with burden (‘Understanding care recipient needs and preferences’), self-efficacy (‘Cancer-related communication with the care recipient’ and ‘Understanding care recipients needs and preferences’) and distress (‘Proactivity and determination to seek information’, ‘Understanding care recipient needs and preferences’, ‘Understanding the healthcare system’). Conclusion Findings highlight key areas of need regarding cancer caregiver health literacy which future research can target. Given the observed relationship between aspects of health literacy and burden, distress and self-efficacy future work could be carried out on how to alleviate high levels of burden and distress and how to enhance self-efficacy among cancer caregivers by addressing health literacy skills. Implications for cancer survivors Findings from this study will inform the development of health literacy interventions to support caregivers to build their health literacy skills and enable this group to better support cancer survivors as a result.
Key role of subsurface doping in optimizing active sites of IrO2 for the oxygen evolution reaction.

The oxygen evolution reaction (OER) over a family of metal-doped rutile IrO2 catalysts is theoretically investigated by controlling the species and position of doped elements. The subsurface substitution doping is demonstrated to efficiently regulate the eg-filling of surface iridium sites and lower the adsorption strength of oxygen intermediates, improving the catalytic activity for the OER. Finally, based on screening, subsurface Cu- and Li-doped IrO2 models stand near the top of the volcano plot and display high levels of structural stability toward acidic OER.
Assessment of Dust Size Retrievals Based on AERONET: A Case Study of Radiative Closure From Visible‐Near‐Infrared to Thermal Infrared

Super‐coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super‐coarse dust accounts for less dust extinction in the visible‐to‐near‐infrared (VIS‐NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS‐NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET‐retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super‐coarse dust in the AERONET retrievals due to the limited VIS‐NIR sensitivity. An extra super‐coarse mode included in the AERONET‐retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS‐NIR.
Simulation of Village-Level Population Distribution Based on Land Use:A Case Study of Hefeng County in Hubei Province

The problem that population data is usually missing in small scale areas such as administrative villages which are always mentioned in population distribution studies and related researches.In this context,we took the Hefeng County in Hubei Province as the study area and analyzed the correlation between land use type index and population density.The simulation of the village-level population distribution is performed using Geographically Weighted Regression(GWR) method,grid method and BP neural network method respectively.Then,from the perspective of global-local and linear-nonlinear,the comparative precision validation was taken to verify the simulation accuracy of the population in villages with missing population data,which utilizes cross-validation method between the simulated population and the actual population.Results show that:(1) in all kinds of land use types,the main factors affecting population distribution are farmland,woodland,urban industrial land,and transportation land;(2) with regard to the three simulation methods we concerned,the errors of the simulated total population using these methods are all less than 3% for the 30 invested villages.By comparing the ratios of estimated values to the actual values of population in each village,and taking 10% as the tolerance,the reliability of GWR method is 43.33%,while grid method is 76.67% and BP neural network is 86.67 %. It shows that the BP neural network method is the optimal method among the three methods for the study area,and grid method is better than GWR method.In addition,the prediction accuracy of nonlinear regression is higher than that of linear regression;(3) population spatial distribution in the study area shows a high spatial positive correlation and a"high–high"agglomeration type which is also the main type in the study area; moreover,it shows that the population densities of the county are not spatially independent but intensively agglomerated.
Distribution of antimicrobial resistance genes of methicillin-resistant Staphylococcus aureus isolated from animals and humans in Yogyakarta Indonesia

Background and Aim: Methicillin-resistant Staphylococcus aureus (MRSA) has been known as a highly pathogenic bacteria in animals and humans, which is still becoming a global health issue. The prevalence of MRSA infection continues to increase worldwide and has become a global concern as a dangerous zoonotic disease. The World Health Organization estimates that by 2050 MRSA will be the leading cause of death. This study aimed to estimate the prevalence of MRSA in S. aureus isolates of veterinary and human origin in Yogyakarta, Indonesia. Materials and Methods: A total of 42 cases of S. aureus infection were examined in this study, consisting of nine isolates from cattle, five from goat, and 28 from human. All isolates were confirmed as S. aureus based on bacterial culture and detection of 23S rRNA and thermonuclease nuc gene by polymerase chain reaction (PCR). Results: Among 42 isolates, 35 isolates (83.3%) were identified as MRSA by PCR positive of mecA gene encoding methicillin resistance. Most MRSA strains were found in human isolates (100%), followed by cattle isolates (55.5%) and goats (40%). All MRSA strains were also multi-resistant to penicillin (blaZ gene) and tetracycline (tetK, and tetM genes) with a prevalence of about 98%. Conclusion: MRSA prevalence in humans and animals has increased significantly in Yogyakarta, Indonesia, compared to the previous study. The antimicrobial resistance pattern of MRSA animal isolates tends to be similar to humans and, thus, raises public health concerns about MRSA zoonotic spread. Keywords: animal, antimicrobial resistance, human, methicillin-resistant Staphylococcus aureus, Staphylococcus aureus.