Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship between Bone Health Parameters, Vitamin D and Iron Status, and Dietary Calcium Intake in Young Males

Vitamin D, calcium, and iron are micronutrients crucial for bone health. However, their effect has been studied primarily in the cortical bone, with vitamin D status being assessed mainly from the total 25(OH)D serum fraction. The study aimed to investigate the impact of vitamin D (total and free fraction) and iron status (i.e., serum ferritin or soluble transferrin receptor) and calcium intake (ADOS-Ca questionnaire) on lumbar cortical and trabecular bone. In a cohort of 113 male subjects (76 athletes, 37 non-athletes) aged 15–19, the lumbar spine status (Z-score, bone mineral apparent density (BMAD), and trabecular bone score (TBS)) was determined using dual-energy X-ray absorptiometry (DXA). Relationships between the examined micronutrients and bone health parameters were observed only in athletes. Free 25(OH)D was significantly (p < 0.001) correlated with Z-score and BMAD, while total 25(OH)D (p < 0.001) and iron status (ferritin, Fe stores; p < 0.01) correlated solely with BMAD. Free 25(OH)D and ferritin concentrations were the best determinants of bone status (R2 = 0.330; p < 0.001) and explained 25% and 7% of the BMAD variance, respectively. No relationships were found between the micronutrients and TBS. The results confirmed the positive influence of vitamin D and iron on cortical, but not trabecular, bone status solely in physically active subjects. In athletes, free 25(OH)D seems to be a superior indicator of bone health to a total 25(OH)D fraction.
Measuring back: bibliodiversity and the Journal Impact Factor™ brand, a case study of IF-journals included in the 2021 Journal Citations Report™

Little attention has been devoted to whether the Impact Factor (IF) can be considered a responsible metric in light of bibliodiversity. This paper critically engages with this question in measuring the following variables of IF journals included in the 2021 Journal CItation Reports and examining their distribution: publishing models (hybrid, Open Access with or without fees, subscription), world regions, language(s) of publication, subject categories, publishers, and the prices of article processing charges (APC) if any. Our results show that the quest for prestige or perceived quality through the IF brand poses serious threats to bibliodiversity. The IF brand can indeed hardly be considered a responsible metric insofar as it perpetuates publishing concentration, maintains a domination of the Global North and its attendant artificial image of mega producer of scholarly content, does not promote linguistic diversity, and de-incentivizes fair and equitable open access by entrenching fee-based OA delivery options with rather high APCs.
Plane and Spherical Wave Velocities and Energy Flow in Anisotropic Media

Conception of the "wave surface" related to spherical wave and of the "velocity surface" related to plane wave in anisotropic media is mentioned. We measured experimentally the wave surface with Sanbagawa green schist (No. 904). Further, we measured elastic constants of this Sanbagawa green schist, and the wave surfaces obtained from experiments and calculation were consistent. We measured the direction of energy flow with green schist. This direction was closely related to the wave surface.
Advances in Repetitive Transcranial Magnetic Stimulation in the Treatment of Intracranial Atherosclerosis

: Repetitive transcranial magnetic stimulation is widely used in stroke rehabilitation, and there is a large amount of clinical evidence showing its efficacy in improving functional impairments after intracranial atherosclerotic ischemic stroke, however, its molecular mechanism has not been fully elucidated. This article reviews the recent literature on the mechanism of repetitive transcranial magnetic stimulation in intracranial atherosclerotic ischemic stroke, motor dysfunction
A palaeo-glaciological reconstruction of the last Irish Ice Sheet

An inversion of the glacial geomorphological record provides an effective means to reconstruct former ice sheets at ice sheet-scale. The last Irish Ice Sheet has a long history of investigation, but its most basic properties are debated. Much previous research, based on an incremental development of knowledge through field observation of glacigenic landforms and deposits, has locally yielded high levels of detail but this detail is spatially fragmented across the former ice sheet bed. The evidence-base for ice sheet reconstruction is therefore patchy and incomplete, and its internal inconsistencies make an ice sheet reconstruction, via this approach, problematic. This thesis explores new opportunities for palaeo-glaciological reconstruction offered by remotely sensed data. Systematic glacial landform mapping has been conducted throughout Ireland from a variety of satellite imagery and digital elevation models, and yields new Glacial Maps for Ireland comprising >39,000 landforms. These landform maps are the building blocks for a palaeo-glaciological reconstruction of the ice sheet. Adopting a 'flowset' approach, the full population of landform data is summarised as discrete cartographic units - flowsets - and their spatial, temporal and glaciodynamic information is extracted. The flowset record, integrated with the wealth of evidence and dating constraints in the literature, stimulates a reconstruction describing seven broad stages of ice sheet history. These provide a framework for the evolution of the last Irish Ice Sheet. Key elements of the reconstruction confirm and extend an early advance from a British ice source, a maximum period likely dominated by large ice streams, fragmentation of the ice sheet into separate ice bodies during retreat, and final decay in western mountain groups. The pattern of ice sheet evolution is both asymmetric and asynchronous. A range of scales of ice sheet behaviour are observed, from first-order, fundamental changes in ice sheet geometry (centres of mass and ice flow structure) to more local-scale high-frequency fluctuations of ice flow patterns. This new model acts as a framework for continued investigation of the evolution of the Irish Ice Sheet, and the observed ice sheet behaviour demands further exploration of the sensitivities and role of ice sheets in the wider ice - climate system.
The role of deadwood in the carbon cycle: Implications for models, forest management, 1 and future climates

Deadwood represents a significant carbon pool in forests and savannas. Although previous 44 research has focused mainly on forests, we synthesise deadwood studies across all 45 ecosystems with woody vegetation. Storage and release of carbon from deadwood is 46 controlled by interacting decomposition drivers including biotic consumers (animals, 47 microbes) and abiotic factors (water, fire, sunlight, freeze-thaw). As changing climates and 48 land use practices alter the landscape, we expect accelerating but variable rates of inputs 49 and outputs from deadwood pools. Currently, Earth system models implicitly represent only 50 microbial consumers as drivers of wood decomposition; we show that many other factors 51 influence deadwood pools. Forest management practices increasingly recognise the 52 importance of deadwood for controlling forest dynamics, biodiversity and carbon emissions. 53 Together, emerging knowledge from modelling and management suggests a dire need for 54 additional knowledge on deadwood contributions to carbon storage and greenhouse gas 55 emissions. 56 57
The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee

This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R2 values > 0.75 and low error) over 20 volatile compounds, including furans, ketones, phenols, and pyridines. Overall, this study demonstrates the potential of NIR spectroscopy as a practical and rapid method to complement current techniques for monitoring and predicting volatile compounds during the coffee roasting process.
Benzofuran Derivatives from Cortex Mori Radicis and Their Cholinesterase-Inhibitory Activity

The phytochemical investigation of Cortex Mori Radicis led to the isolation and identification of a new prenylated benzofuranone (1) and four ring-opening derivatives (2–5) named albaphenol A–E, as well as nigranol A (6), together with ten 2-arylbenzofuran derivatives (7–16). The characterization of the structures of the new compounds and the structural revision of nigranol A (6) were conducted using the comprehensive analysis of spectroscopic data (1D/2D NMR, HRESIMS, CD, and XRD). Compounds 1–16 were tested for their inhibitory effects on acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). Compounds 1 and 4 showed weak BChE-inhibitory activity (IC50 45.5 and 61.0 μM); six 2-arylbenzofuran derivatives showed more-potent BChE-inhibitory activity (IC50 2.5–32.8 μM) than the positive control galantamine (IC50 35.3 μM), while being inactive or weakly inhibitory toward AChE. Cathafuran C (14) exhibited the most potent and selective inhibitory activity against BChE in a competitive manner, with a Ki value of 1.7 μM. The structure–activity relationships of the benzofuran-type stilbenes were discussed. Furthermore, molecular docking and dynamic simulations were performed to clarify the interactions of the inhibitor–enzyme complex.
A Highly Digital 143.2-dB DR Sub-1° Phase Error Impedance Monitoring IC With Pulsewidth Modulation Frontend

This article presents a highly digital impedance monitoring IC that achieves a wide dynamic range (DR) (>140 dB), small area (~0.2 <inline-formula> <tex-math notation="LaTeX">$\text {mm}^{{2}}$ </tex-math></inline-formula>), and small phase error (< 1°). It consists of a square-wave current stimulator, a pulsewidth modulation (PWM) frontend, and two continuous-time (CT) delta-sigma (<inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula>) modulators. The stimulated signal is encoded using the PWM frontend and the complex impedance information can be extracted by in (I) and quadrature (Q) demodulation. Each output can then be digitized using a second-order digitally controlled oscillator (DCO)-based CT <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> modulator. This highly digital architecture enhances energy and area efficiencies. The prototype IC occupies only 0.21 <inline-formula> <tex-math notation="LaTeX">$\text {mm}^{{2}}$ </tex-math></inline-formula> in a 65-nm CMOS process. It achieves a DR of 143.2 dB and a resolution of 17.7 <inline-formula> <tex-math notation="LaTeX">${\text {fF}}_{\text {rms}}$ </tex-math></inline-formula> for capacitance detection within a conversion time of 16 ms. Despite the use of a square-wave stimulator, the PWM frontend allows achieving a small phase error of 0.675°, while consuming only 310.9 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> from a 1.2-V supply.
A Fault Diagnosis Method for 5G Cellular Networks Based on Knowledge and Data Fusion

As 5G networks become more complex and heterogeneous, the difficulty of network operation and maintenance forces mobile operators to find new strategies to stay competitive. However, most existing network fault diagnosis methods rely on manual testing and time stacking, which suffer from long optimization cycles and high resource consumption. Therefore, we herein propose a knowledge- and data-fusion-based fault diagnosis algorithm for 5G cellular networks from the perspective of big data and artificial intelligence. The algorithm uses a generative adversarial network (GAN) to expand the data set collected from real network scenarios to balance the number of samples under different network fault categories. In the process of fault diagnosis, a naive Bayesian model (NBM) combined with domain expert knowledge is firstly used to pre-diagnose the expanded data set and generate a topological association graph between the data with solid engineering significance and interpretability. Then, as the pre-diagnostic prior knowledge, the topological association graph is fed into the graph convolutional neural network (GCN) model simultaneously with the training data set for model training. We use a data set collected by Minimization of Drive Tests under real network scenarios in Lu’an City, Anhui Province, in August 2019. The simulation results demonstrate that the algorithm outperforms other traditional models in fault detection and diagnosis tasks, achieving an accuracy of 90.56% and a macro F1 score of 88.41%.
A block Arnoldi method for the SPN equations

ABSTRACT The simplified spherical harmonics equations are a useful approximation to the stationary neutron transport equation. The eigenvalue problem associated with them is a challenging problem from the computational point of view. In this work, we take advantage of the block structure of the involved matrices to propose the block inverse-free preconditioned Arnoldi method as an efficient method to solve this eigenvalue problem. For the spatial discretization, a continuous Galerkin finite element method implemented with a matrix-free technique is used to keep reasonable memory demands. A multilevel initialization using linear shape functions in the finite element method is proposed to improve the method convergence. This initialization only takes a small percentage of the total computational time. The proposed eigenvalue solver is compared to the standard power iteration method, the Krylov-Schur method and the generalized Davidson method. The numerical results show that it reduces the computational time to solve the eigenvalue problem.
PREDICTORS OF PURCHASE DECISION ON HALAL LIPSTICK PRODUCT IN BANDUNG

The halal cosmetics industry presents potential trends and contributes significantly to the economy. Wardah, an Indonesian halal cosmetic brand, was the first to obtain halal certification. While lipstick remains a popular product, the sales index of Wardah lipstick has experienced a decline. This study focuses on how brand image, product quality, and word-of-mouth (WOM) influence purchase decisions. The method employed is descriptive verification, utilizing multiple linear regression analysis processed with SPSS 26. The unit of analysis is individuals, specifically working women, with several hundred respondents. The results indicate that both brand image and product quality have a significant impact on purchase decisions, while WOM does not significantly influence these decisions. Increased purchase decisions are essential for a business to grow and maintain sustainability.
A new twist on mud deposition; mud ripples in experiment and rock record

4 | June 2009 ABSTRACT Recent flume experiments with muddy suspensions have demonstrated that low-energy settings are not a prerequisite for the accumulation of muds and shales. Flocculation produces deposition-prone aggregates even in freshwater, and these floccules form bedload ripples at flow velocities that would also produce ripples in sandy sediments. Floccule ripples are crosslaminated but have a much higher water content (80-90 % by volume) than sand ripples and are thus subject to substantial compaction. Because of this, we must expect that original crosslaminae become severely flattened and are difficult to recognize in the rock record. Nonetheless, a survey of ancient shales has shown that certain intrinsic features of cross-lamination, such as basal downlap and top truncation of laminae, are still recognizable and allow identification of current-deposited muds. The example of the Cambrian Eau Claire Formation is used to illustrate how this approach can change our perception of the depositional setting of certain mudstone successions.
Response of Weihe River to climatic change in Longxi zone

The seven terraces preverved along Weihe River in Longxi basin since middle Pleistence were estimated to form about 860ka、787ka、621ka、412ka、128ka、60ka、11ka.Analysis on sedimentation characteristics and dating of these terraces showed that seven terraces may be jointly generated by tectonic uplifts and climatic changes.Tectonic uplift provided incision space for the river,and the terraces were controlled by cycles of a period of about 0.1 Ma.The loess information indicated that ten climatic transition events were found in Longxi basin since 860Ka,and the three Absent terraces was considered to be the response to the instability and moist degree of the climate.
Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations

Machine learning can predict neurosurgical diagnosis and outcomes, power imaging analysis, and perform robotic navigation and tumor labeling. State-of-the-art models can reconstruct and generate images, predict surgical events from video, and assist in intraoperative decision-making. In this review, we will detail the neurosurgical applications of machine learning, ranging from simple to advanced models, and their potential to transform patient care. As machine learning techniques, outputs, and methods become increasingly complex, their performance is often more impactful yet increasingly difficult to evaluate. We aim to introduce these advancements to the neurosurgical audience while suggesting major potential roadblocks to their safe and effective translation. Unlike the previous generation of machine learning in neurosurgery, the safe translation of recent advancements will be contingent on neurosurgeons’ involvement in model development and validation.
Social Support During Emergency Work and Subsequent Serious Psychological Distress: A Cross-Sectional Study Among Emergency Workers who Responded to the Fukushima Daiichi Nuclear Power Plant Accident.

OBJECTIVE To examine the association between social support for emergency workers and levels of subsequent psychological distress, focusing on workers who responded to the Fukushima Nuclear Power Plant accident. METHODS Male emergency workers (N = 1405) completed self-administered questionnaires, measuring social support during emergency work, subsequent serious psychological stress, occupational and demographic characteristics, and some confounders. Multiple logistic regression analyses were performed. RESULTS Supervisor support was significantly associated with lower psychological distress, particularly for workers present at the site for over 31 days. For workers present for 10 days or less, coworker support was marginally significant. CONCLUSIONS Social support is an important resource for reducing the risk of subsequent serious psychological distress among emergency workers. The associations of social support with psychological distress can differ depending on work duration and the providers of the support.
A music generation model based on Bi-LSTM

The unidirectional LSTM based music generation model does not take into account the influence of future information when generating music. It solely focuses on learning the dependencies of the current moment on past information, resulting in music with poor stability and subpar quality. To address this issue, we have developed a music generation model based on bidirectional LSTM. During the training phase, this model effectively captures musical information from both past and future time steps, resulting in a probability distribution of musical elements that closely approximates real-world music. This, in turn, leads to enhanced structural stability and improved music quality in the generated compositions. Finally, we conducted validation experiments on our proposed approach, and the results unequivocally demonstrate its effectiveness.
Do you trust ChatGPTs? Effects of the ethical and quality issues of generative AI on travel decisions

ABSTRACT This study investigated the impact of ChatGPT’s recommendation quality and ethical concerns on travelers’ acceptance, satisfaction, and perceived trustworthiness. Results showed that when quality and ethical concerns were prominent, acceptance of and satisfaction with ChatGPT’s recommendations decreased significantly, and the negative effects were mediated by perceived trustworthiness. This study also identified that message framing containing ChatGPT’s errors, and the information types delivered by ChatGPT, acted as moderators of the positive effect of its recommendations. These findings underscore the significance of addressing ethical and quality concerns in using AI (Artificial intelligence)-powered chatbots, with implications for AI acceptance and satisfaction.
Site characterization of Piedmont residuum of North America

The residual soils of the Piedmont geology of eastern North America consist of variable clayey to sandy silts and silty sands that have formed by the in-place weathering of schist, gneiss, and granites. Their behavior can vary from undrained to drained, as well as partially drained, depending upon the applied rate of loading, groundwater level, and capillarity. Results from in-situ and laboratory testing are presented to show the characteristics of these materials, in some cases reflecting a dichotomy with aspects of both stiff fissured clays and loose granular soils.
The effect of general health and locus of control on happiness among nursing students: The mediating role of psychological well-being

Introduction: Happiness is one of the basic components of people’s lives, especially in the educational environment, and it is important to study the factors affecting it. Therefore, the present study aimed to measure the mediating role of psychological well-being in the relationship between locus of control and general health with happiness among nursing students. Material and methods: This descriptive and cross-sectional study was carried out on 219 nursing students of Fars University of Medical Sciences in southern Iran from October to December 2018 through the convenience sampling method. The Oxford Happiness Questionnaire, Locus of Control Questionnaire, General Health Questionnaire, and Psychological Well-Being Questionnaire were the tools used to collect the data. Structural equation modeling was applied using AMOD and SPSS software version 25 for data analysis. Results: The results showed that the psychological well-being partially mediated the relationship between the locus of control ( β = 0.08, p < 0.05) and general health ( β = –0.24, p < 0.001) with happiness. Conclusions: The results of this research can be used to develop programs aiming at improving students’ happiness. Also, designing programs based on the present study model, while strengthening the general health and locus of control of the students, can lead to their psychological improvement and happiness for them.