Miracle-dz/UniFAR-FTUs huggingface dataset:
Untargeted Metabolomics Reveals Alterations of Rhythmic Pulmonary Metabolism in IPF

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive condition characterized by the impairment of alveolar epithelial cells. Despite continued research efforts, the effective therapeutic medication is still absent due to an incomplete understanding of the underlying etiology. It has been shown that rhythmic alterations are of significant importance in the pathophysiology of IPF. However, a comprehensive understanding of how metabolite level changes with circadian rhythms in individuals with IPF is lacking. Here, we constructed an extensive metabolite database by utilizing an unbiased reference system culturing with 13C or 15N labeled nutrients. Using LC-MS analysis via ESI and APCI ion sources, 1300 potential water-soluble metabolites were characterized and applied to evaluate the metabolic changes with rhythm in the lung from both wild-type mice and mice with IPF. The metabolites, such as glycerophospholipids and amino acids, in WT mice exhibited notable rhythmic oscillations. The concentrations of phospholipids reached the highest during the fast state, while those of amino acids reached their peak during fed state. Similar diurnal variations in the metabolite rhythm of amino acids and phospholipids were also observed in IPF mice. Although the rhythmic oscillation of metabolites in the urea cycle remained unchanged, there was a significant up-regulation in their levels in the lungs of IPF mice. 15N-ammonia in vivo isotope tracing further showed an increase in urea cycle activity in the lungs of mice with IPF, which may compensate for the reduced efficiency of the hepatic urea cycle. In sum, our metabolomics database and method provide evidence of the periodic changes in lung metabolites, thereby offering valuable insights to advance our understanding of metabolic reprogramming in the context of IPF.
IMPULSE BUYING OF GENERATION Z MUSLIMS: A STUDY ON HEDONIC SHOPPING MOTIVATION, SHOPPING LIFESTYLE, AND HEDONIC SHOPPING VALUE

The aim of this study was to identify the factors influencing impulse buying among Generation Z Muslims in Kudus Regency. The research was conducted on the Generation Z Muslim community in Kudus Regency, with a total sample size of 100, utilizing the purposive sampling technique. Data processing involved the use of statistical analysis methods through the SPSS program. The findings of this study revealed that the hedonic shopping motivation variable did not exhibit a positive and significant impact on impulse buying among Generation Z Muslims in Kudus Regency. Conversely, the Shopping Lifestyle variable demonstrated a positive, albeit insignificant, effect on impulse buying behavior of Generation Z Muslims in Kudus Regency. Moreover, the Hedonic Shopping Value variable exhibited a positive and significant influence on impulse buying among Generation Z Muslims in Kudus Regency. The managerial implication of this research underscores the importance of considering shopping lifestyle behavior and hedonic shopping value, as these factors can have an impact on the impulse buying tendencies of Generation Z Muslims.
Temperature effects on water absorption by three different porous materials

Abstract Absorption of water by relatively dry soil and by chromatography paper, and desorption of water by clay, were measured at constant temperatures close to 278K, 293K, and 306K. Within experimental error, temperature affected absorption of water by soil and chromatography paper simply according to the temperature dependence of the kinematic viscosity and of the air–liquid surface tension of pure water. Desorption of water by saturated bentonite slurry, where there were no air–water interfaces, scaled simply according to the temperature effects on the kinematic viscosity of pure water.
Risk Factors for Salmonella Detection in Commercial Layer Flocks in Spain

Simple Summary Foodborne salmonellosis remains one of the top zoonotic diseases affecting public health worldwide, and its incidence has remained stable in the last years in the European Union (EU) triggering questions on the usefulness of currently available measures to prevent its occurrence. A main focus of Salmonella national control programs is monitoring the presence of the bacteria in animal reservoirs, especially in poultry, and for this reason, thousands of samples are collected every year in poultry farms in EU countries, but the importance of certain factors in the probability of detecting Salmonella remains poorly understood. A thorough analysis conducted on data collected in all laying hen flocks sampled in Spain in 2015–2020 revealed that even though the presence of Salmonella was rare (<3.5% of positive sampling events), when samples were collected in certain months (fall–winter) and housing systems (caged flocks) and by competent authorities (as opposed to food business operators), the probability of detecting Salmonella increased significantly. These results demonstrate that the sensitivity of the sampling strategy may be influenced by how and when samples are collected and that certain flocks may be at an increased risk of infection. Abstract Trends in Salmonella human infections are assumed to be related to the distribution of the pathogen in the animal reservoir/food products, and cases in humans are most often linked to poultry and poultry products (eggs, meat). Therefore, ongoing Salmonella national control programs (NCPs) in European Union Member States have the objective of monitoring and reducing its prevalence in commercial poultry flocks. Results from NCPs have shown certain factors (housing systems, season of sampling and if sampling is conducted by food business operators (FBOps) or competent authorities (CAs), among others) can influence detection rates, but associations are often not consistent. Here, we analyzed data from the Spanish NCP on 7216 laying hen flocks subjected to 36,193 sampling events over a six-year period to characterize its performance and identify variables influencing detection rates. Overall, 1205 sampling events were positive for Salmonella spp. (any serovar) and 132 for S. Enteritidis-S. Typhimurium/monophasic. Bayesian multivariable models adjusting for multiple covariates concluded that sampling events later in the year, in caged flocks with older animals and conducted by CAs had increased odds of positivity for Salmonella spp. , revealing aspects linked with a differential estimation of Salmonella levels in laying hen flocks.
New estimates of secular sea level rise from tide gauge data and GIA modelling

SUMMARY During the last three decades, at least 30 independent estimates of the secular global mean sea level rise (GMSLR) have been published, based on sufficiently long tide gauge (TG) records. Despite its apparent simplicity, the problem of GMSLR is fraught with a number of difficulties, which make it one of the most challenging questions of climate change science. Not surprisingly, published estimates show considerable scatter, with rates ranging between 1 and 2 mm yr−1 for observations on the century timescale. In previous work, the importance of Glacial Isostatic Adjustment (GIA) upon the assessment of the GMSLR has been clearly demonstrated. In particular, starting from the 1980s, GIA models have been routinely employed to decontaminate TG observations from the effects of melting of the late-Pleistocene ice sheets, to fully highlight the sea level variations driven by climate change. However, uncertainties associated with the Earth’s rheological profile and the time history of the past continental ice sheets can propagate into the GIA corrections. After revisiting previous work and estimates, we suggest a significant modification of the criteria for the selection of the TGs which are most suitable for the robust assessment of the secular GMSLR. In particular, we seek a set of TGs for which GIA corrections are essentially independent of the parametrization of the rheological profile of the Earth’s mantle and of the detailed time chronology of surface loading. This insensitivity is established by considering predictions based upon three GIA models widely employed in the recent literature (namely, ICE–3G, ICE–5G and the one developed at the Research School of Earth Sciences of the National Australian University). Applying this approach and selection criteria previously proposed in the literature, we identify a set of 22 sufficiently evenly distributed TGs. By simple statistical methods, these records yield a ‘preferred’, GIA-independent GMSLR estimate since 1880, namely 1.5 ± 0.1 mm yr−1 (rms = 0.4 mm yr−1, wrms = 0.3 mm yr−1). This value is consistent with various previous estimates based on secular TG observations and with that proposed, for the 20th century, by the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (1.7 ± 0.5 mm yr−1).
Occurrence of Clinoptilolite and Mordenite in Tertiary Calc-Alkaline Pyroclastites from Sardinia (Italy)

Clinoptilolite and mordenite occur as diagenetic products of medium-grained, moderately welded and poorly sorted pyroclastic flows belonging to the Tertiary calc-alkaline volcanism of Sardinia. Both clinoptilolite and mordenite occur within pyroclastic flows of the same stratigraphic unit. Mordenite frequently occurs in the late volcanic sequences from Anglona area (northern Sardinia). Textural features indicate that zeolites are products of glass alteration. Thin sections show either complete alteration of glassy shards by clinoptilolite and mordenite or unaltered shards with clinoptilolite or mordenite confined to the cineritic matrix. During the zeolitization process, interacting fluids were important in the mobilization and distribution of alkali elements. The compositional variations of clinoptilolite and mordenite within a single sample showed trends that suggest steps in a continuous process probably evolving towards equilibrium conditions.
Lip-Reading with Visual Form Classification using Residual Networks and Bidirectional Gated Recurrent Units

Lip-reading is a method that focuses on the observation and interpretation of lip movements to understand spoken language. Previous studies have exclusively concentrated on a single variation of residual networks (ResNets). This study primarily aimed to conduct a comparative analysis of several types of ResNets. This study additionally calculates metrics for several word structures included in the GRID dataset, encompassing verbs, colors, prepositions, letters, and numerals. This component has not been previously investigated in other studies. The proposed approach encompasses several stages, namely pre-processing, which involves face detection and mouth location, feature extraction, and classification. The architecture for feature extraction comprises a 3-dimensional convolutional neural network (3D-CNN) integrated with ResNets. The management of temporal sequences during the classification phase is accomplished through the utilization of the bidirectional gated recurrent units (Bi-GRU) model. The experimental results demonstrated a character error rate (CER) of 14.09% and a word error rate (WER) of 28.51%. The combination of 3D-CNN ResNet-34 and Bi-GRU yielded superior outcomes in comparison to ResNet-18 and ResNet-50. The correlation between increased network depth and enhanced performance in lip-reading models was not consistently observed. Nevertheless, the incorporation of additional trained parameters offers certain benefits. Moreover, it has demonstrated superior levels of precision in comparison to human professionals in the task of distinguishing diverse word structures. Doi: 10.28991/HIJ-2023-04-02-010 Full Text: PDF
On-road observational survey of restraint and child restraint use

On-road observational surveys of restraint use provide a valuable means of obtaining information on the current level of restraint use and on the characteristics (vehicle, occupant, location) of non-use of restraints. Several observational surveys of restraint use in passenger vehicles have been conducted in South Australia between 1998 and 2002. This report presents the results of a new on-road observational survey undertaken in March 2009 to monitor the levels of restraint use by occupants of passenger vehicles in areas previously surveyed in South Australia: metropolitan Adelaide and five rural regions. Trained field observers recorded the vehicle type, plates displayed on the vehicle, and the seating position, gender and restraint use of vehicle occupants including the uses and types of any child restraints. For the first time in this series of surveys, restraint use was observed on weekends in addition to weekdays. Observed rates of restraint wearing for all vehicle occupants were relatively high, ranging from 98.9 per cent in Murray Bridge (on a weekend) to 96.8 per cent in Clare (on a weekday). Rear seat passengers had the lowest observed rates of restraint wearing. Adult males wore seat belts less often than adult females. Child restraint use varied by region but was relatively high. Findings from this survey might assist in the development of restraint use publicity campaigns and the monitoring of their effectiveness. (a)
SparseACC: A Generalized Linear Model Accelerator for Sparse Datasets

Stochastic gradient descent (SGD) is widely used for training generalized linear models (GLMs), such as support vector machine and logistic regression, on large industry datasets. Such a training consumes plenty of computing power and therefore plenty of accelerators are proposed to accelerate the GLM training. However, real-world datasets are always highly sparse. For example, YouTube’s social network connectivity contains only 2.31% nonzero elements (NZs). It is not trivial to design an accelerator that is able to efficiently train on a sparse dataset that is stored in a compressed sparse format (e.g., compressed sparse row (CSR) format). The design of such an accelerator faces three challenges: 1) bank conflicts, which may happen when multiple processing engines in the accelerator access multiple memory banks; 2) complex interconnections, which are necessary to allow all processing engines to access any memory bank; and 3) high-synchronization overhead, since each sample in sparse dataset has a different number of NZs and these elements have different distributions, thus it is hard to overlap gradient computation and model update of neighboring batches. To this end, we propose SparseACC, a sparsity-aware accelerator for training generalized linear models (GLMs). SparseACC is based on two key mechanisms. First, a software/hardware co-design approach solves the first two design challenges by proposing a novel bank-conflict-free (BCF) and bank-balanced CSR format. Second, a weight-aware ping-pong model solves the third challenge, thus maximizing the utilization of the processing engines. SparseACC leverages these two mechanisms to orchestrate training over sparse datasets, such that the training time decreases linearly with the sparsity of the dataset. We prototype SparseACC on a Xilinx Alveo U280 FPGA (Xilinx, 2020). The experimental evaluation shows that SparseACC converges up to <inline-formula> <tex-math notation="LaTeX">$3.5\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$18\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$38\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$110\times $ </tex-math></inline-formula> faster than the state-of-the-art counterparts on a sparse accelerator, a Tesla V100 GPU, an Intel i9-10900k CPU, and a dense accelerator, respectively.
A review of metal–organic framework (MOF) materials as an effective photocatalyst for degradation of organic pollutants

Water plays a vital role in all aspects of life. Recently, water pollution has increased exponentially due to various organic and inorganic pollutants. Organic pollutants are hard to degrade; therefore, cost-effective and sustainable approaches are needed to degrade these pollutants. Organic dyes are the major source of organic pollutants from coloring industries. The photoactive metal–organic frameworks (MOFs) offer an ultimate strategy for constructing photocatalysts to degrade pollutants present in wastewater. Therefore, tuning the metal ions/clusters and organic ligands for the better photocatalytic activity of MOFs is a tremendous approach for wastewater treatment. This review comprehensively reports various MOFs and their composites, especially POM-based MOF composites, for the enhanced photocatalytic degradation of organic pollutants in the aqueous phase. A brief discussion on various theoretical aspects such as density functional theory (DFT) and machine learning (ML) related to MOF and MOF composite-based photocatalysts has been presented. Thus, this article may eventually pave the way for applying different structural features to modulate novel porous materials for enhanced photodegradation properties toward organic pollutants.
Impact of a Statistical Bias Correction on the Projected Hydrological Changes Obtained from Three GCMs and Two Hydrology Models

AbstractFuture climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Me.
Evaluating Walkability in Budapest through Space Syntax Analysis

Walking is a fundamental and intuitive means of transportation both on an individual and urban scale, offering social and personal benefits like improved health, equity, and reduced greenhouse gas emissions. Despite these benefits, urban sprawl and increased car dependency remain significant challenges. To combat this trend, urban planners have emphasized walking as a way to reduce vehicle travel and emissions, while public health researchers recognize its potential to improve general health.This study explores the concept of walkability and its relationship to urban design, with a specific focus on two recently renovated areas in Budapest, namely Egyetem Square and Corvin Promenade. Utilizing space syntax analysis, a promising approach to developing a walkability index, this research investigates the functional aspects of these urban spaces and their impact on walkability.The study aims to demonstrate the potential of space syntax analysis in enhancing urban planning efforts aimed at promoting walkability. By analyzing the spatial configuration, visibility, and accessibility of the case studies, this research examines how the design and layout of these areas influence pedestrian movement patterns and the overall walkability experience.By demonstrating the potential of space syntax analysis in informing urban planning decisions, this study provides insights into the relationship between spatial configuration and walkability. The findings highlight the importance of well-connected street networks, clear pathways, and visually appealing urban elements in creating walkable environments. The results of the analysis contribute to the understanding of how urban design factors can be leveraged to enhance walkability and create sustainable, livable cities.
Further Light on the Lives of Christopher and Robert Saxton

In the first chapter of their book Christopher Saxton, Elizabethan Map-maker Ifor Evans and Heather Lawrence described what they had discovered about Christopher Saxton and his son Robert, their families and their life in Dunningley. It is the purpose of this note to draw attention to some further facts relating to their lives and properties that have come to light since the publication of the book in 1979. Information about land surveys made by Christopher and Robert but not described in the book, most of which also emerged after the book was written, has been published already.
Serengeti–Masai Mara ecosystem dynamics inferred from rainfall extremes

The Serengeti–Masai Mara Ecosystem (SMME) is an iconic ecological and biodiversity resource in East Africa with a spectacular great animal migration feature. Environmental shocks like droughts, floods, and land degradation threaten the SMME’s ecological functioning. However, the spatiotemporal ecosystem dynamics during climate extremes are inadequately examined. Here we quantified water availability and vegetation changes during extreme wet (EWE) and dry (EDE) events over the SMME for wet and dry seasons in 1982–2020. We derived extreme events from seasonal mean precipitation anomaly exceeding standard deviation and applied composite and correlation techniques to assess their dynamics with animal populations and migration prospects. Wet season EWE increases vegetative and moist conditions over southern SMME, suggesting elongating migrants’ occupancy compared to normal periods and delayed northward migration. Wet season EDE significantly suppresses these features, heightening ecosystem competition and survival threats, proposing an earlier northward migration. Dry season EWE increases vegetation and water availability over northern SMME, prompting the late southward migration. Dry season EDE significantly reduces vegetation and water availability over northern SMME, suggesting an early southward migration towards regions with more vegetation and increased water availability. The SMME also experiences multiple EDE occurring in consecutive seasons, prolonging dry conditions that aggregate wildlife survival threats. Notably, two EDE prevailed consecutively from the 1993 dry season to the 1994 wet season, coupled with a population decline of wildebeest (1.2–0.9 million), buffalo (40–20 thousand), and lion (1.3–0.9 thousand). We also note a reversal from more EDE to EWE during the study period. Prevalent EWE can lessen wet and vegetative conditions distribution gradient, which is imperative for the functioning of the SMME migratory ecosystem. Our study unveiled hotspot areas of extremes-driven ecosystem changes for the sustainable SMME migratory functioning essential for framing meaningful conservation management policies under climate change.
Cognitive Conflict Based on Thinking Errors in Constructing Mathematical Concept

This study aims to evaluate cognitive conflict in constructing mathematical concepts, based on thinking errors. The data were collected through observations of words or sentences, leading to the derivation of qualitative outputs. Furthermore, the results showed that the two selected subjects experienced cognitive conflicts regarding thinking errors in mathematical concept construction. This selection process was conducted through the provision of the questions obtained in the first semester of mathematics, at Universitas Negeri Makassar, Indonesia. These questions consisted of several indicators, with each of them having three components, namely the main, tracking, and supporting items. Based on the results, two subjects experienced thinking errors in constructing the concept of algebraic root form addition. This emphasized the errors in placing concepts (misplaced), pseudo-thinking, and misanalogy. A conflict was also observed between the initial concept knowledge of using variables in algebraic addition and the new principles understood through the root summation in algebra. Furthermore, there was also a conflict between the concept understanding he had in terms of the addition operation of the root form and a new understanding of the addition operation of the root form in algebra.