Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical and Multiple-Perspective Interaction Network for Long Text Matching

Long text matching is widely used in various sub-tasks of natural language processing. However, conducting research in this field can be challenging due to excessive redundant and distracting information, the complex semantic context, and the limited availability of high quality public datasets. Existing long text matching methods generally do not fully use the rich local features embedded in text information, and focus more on encoding long text as fixed length vectors to calculate the semantic distance, disregarding the importance of feature interaction in the text matching process. Therefore, the performance of the relevant models needs to be improved. To address these problems, a hierarchical and multiple-perspective interaction network (HMIN) is proposed in this paper. First, the long text is encoded at the word and sentence levels to extract global features, while one-dimensional convolutional neural networks and attention mechanisms are used to focus on important local features in long texts. Second, the different types of features are compared separately using the comparison function, and then, the comparison results are aggregated. Finally, whether long texts are matched is determined in the prediction layer. We have conducted comparative experiments on two datasets, the results show that HMIN has an improvement in accuracy and F1 values compared with the same type of existing algorithms, and the related experimental analysis demonstrates the effectiveness of the proposed method.
Chemical and Isotopic Tracer Evaluation of Water Mixing and Evaporation in a Dammed Texas River During Drought

The interaction between drought and river regulation is monitored to better understand river flow mixing, evaporation and surface‐groundwater exchange in changing regional climates and in increasingly regulated waterways. This study compared Brazos River stable isotope (δ18O and δD) and electrical conductivity values with reservoir, creek and aquifer samples in the Brazos watershed, the largest watershed in Texas. The combination of tributaries, rainfall and the Brazos River Alluvium Aquifer, on the one hand, and the Lake Whitney reservoir, on the other hand, represent endmembers of dilute run‐off water and evaporated saline water, respectively. A simple isotope mixing model that uses monthly river discharge, Lake Whitney discharge, historical monthly precipitation δ18O and pan evaporation accurately reconstructs river δ18O (±0.5‰ on average). Data and isotope balance modelling support continued evaporation of 18O‐enriched Lake Whitney water as it flows downstream, although the most evaporation took place in Lake Whitney. The difference between river and precipitation δ18O, or Δ18ORIV‐PPT, here a measurement of degree of evaporation, ranged from −0.1‰ for a small creek, to 1.7‰ for the Brazos River, to at least 2.7‰ in Lake Whitney. This study indicates that drought in regulated rivers may enhance reservoir discharge dominance in river flows during peak drought conditions when combined run‐off and baseflow dominance would be expected in a similar undammed river. Copyright © 2016 John Wiley & Sons, Ltd.
Umbilical Cord Mesenchymal Stromal/Stem Cells and Their Interplay with Th-17 Cell Response Pathway

As a form of immunomodulatory therapeutics, mesenchymal stromal/stem cells (MSCs) from umbilical cord (UC) tissue were assessed for their dynamic interplay with the Th-17 immune response pathway. UC-MSCs were able to modulate lymphocyte response by promoting a Th-17-like profile. Such modulation depended on the cell ratio of the cocultures as well as the presence of an inflammatory setting underlying their plasticity. UC-MSCs significantly increased the expression of IL-17A and RORγt but differentially modulated T cell expression of IL-23R. In parallel, the secretion profile of the fifteen factors (IL1β, IL-4, IL-6, IL-10, IL-17A, IL-17F, IL-22, IL-21, IL-23, IL-25, IL-31, IL-33, INF-γ, sCD40, and TNF-α) involved in the Th-17 immune response pathway was substantially altered during these cocultures. The modulation of these factors demonstrates the capacity of UC-MSCs to sense and actively respond to tissue challenges. Protein network and functional enrichment analysis indicated that several biological processes, molecular functions, and cellular components linked to distinct Th-17 signaling interactions are involved in several trophic, inflammatory, and immune network responses. These immunological changes and interactions with the Th-17 pathway are likely critical to tissue healing and may help to identify molecular targets that will improve therapeutic strategies involving UC-MSCs.
Does social isolation mediate the association between hearing loss and cognition in adults? A systematic review and meta-analysis of longitudinal studies

Background There has been extensive research on the relationship between hearing and cognitive impairment in older adults but little examination of the role of mediating factors. Social isolation is a potential mediator, occurring because of hearing loss, and contributing to accelerated cognitive decline. Previous systematic reviews on this topic area have not considered the temporal nature of hearing loss and cognitive impairment exclusively or examined potential mediators within a longitudinal study design. Methods A systematic review was conducted. Electronic searches were performed in Web of Science, PubMed (Medline), Scopus, EMBASE, PsychInfo, and ProQuest (PsychArticles and ProQuest Dissertation and Theses) based on a search string of keywords relating to hearing loss, social isolation, and cognitive impairment/dementia in June 2023. Papers were critically appraised using the CASP checklists for cohort studies. Risk of bias in the selected studies was assessed using the Item Bank for Assessment of Risk of Bias and Precision for Observational Studies of Interventions or Exposures. Results Eleven of the 15 included studies provide evidence of a dose-dependent association between hearing threshold (40 dB HL or greater) and later cognitive impairment or incident dementia. Only one study included social isolation as a mediator, which was found to not be a significant contributing factor. The meta-analysis of 5 studies pooled hazard ratio for cognitive impairment due to hearing loss is 1.11 (95% CI: 1.06 to 1.15, p < 0.001). The pooled hazard ratio for incident dementia due to hearing loss was HR 1.21 (95% CI: 1.11 to 1.31, p = 0.002). Conclusion The analysis of included studies indicate that hearing threshold level affects later cognitive status or dementia diagnosis. There is not enough evidence to determine the role of social isolation as a mediator. Future epidemiology studies need to measure different elements of social isolation and ensure that hearing and cognition are measured at multiple time points.
Capturing Interactions between Arousal and Cortical Dynamics with Simultaneous Pupillometry and EEG-fMRI

One of the two major axes for operationalizing emotion is arousal. While valence accounts for the positive or negative degree of a certain emotional state, arousal refers to the intensity of such a state. The locus coeruleus (LC), a small nucleus situated in the brainstem, is one of the key subcortical structures which plays a central role in regulating global arousal. In recent years, pupil diameter under constant luminance has also emerged as a widely used marker to index arousal. This study demonstrates how interactions between cortical processes and pupil/LC-linked arousal can be characterized with high temporal and spatial resolutions using a unique multi-modal approach. Specifically, we simultaneously recorded the participant’s pupillometry, EEG, and fMRI while they performed a simple auditory discrimination task. Our findings revealed that both pupil-and LC-linked arousal relate to a cascade of task-driven cognitive processes throughout the duration of the task. These neuroscientific findings can potentially be used to develop new human computer interfaces which utilize the dynamics of arousal to optimally deliver information, engage artificial intelligence and optimize human machine interaction.
Ensemble Learning in QSAR: Unveiling Insights into PTP1B Inhibitors for Diabetes Mellitus Treatment

Diabetes Mellitus (DM) stands as a metabolic disorder characterized by abnormal elevations in blood glucose levels. It encompasses various forms, including type 1, type 2, maturity-onset diabetes of the young (MODY), gestational diabetes, neonatal diabetes, and secondary instances arising from endocrinopathies, steroid usage, and other contributing factors. In accordance with data from the International Diabetes Federation (IDF), the prevalence of diabetes in Indonesia reached 4.8 percent in 2012. To address this public health challenge, a Quantitative Structure-Activity Relationship (QSAR) model employing the Ensemble Method was developed for diabetes drug discovery. QSAR represents a computational or mathematical modeling approach employing chemometrics, aimed at establishing statistically significant correlations between functionality and molecular structure. Within this research, the Ensemble Method was deployed, specifically examining the comparative performance of Random Forest, AdaBoost, and XGBoost algorithms. This ensemble approach effectively predicted the activity of the PTP1B inhibitor. Notably, it involved comprehensive feature selection and parameter tuning, ultimately identifying XGBoost as the superior algorithm, employing 90% of the selected features. The performance metrics of XGBoost were notable, with an accuracy rate of 94.84%, a recall rate of 94.74%, a precision rate of 94.86%, and an F1-score of 95.27%.
Machine Learning Based Feature Optimization and Early Detection System in Heart Diseases

In today’s world, humanity faces a myriad of challenges, many of which pose significant threats to our well-being. Chief among these challenges are health-related issues. Among these health problems, heart diseases stand out as the leading cause of mortality. Consequently, the early diagnosis of heart diseases plays a pivotal role in mitigating mortality rates and enhancing people’s overall quality of life. This study aims to employ machine learning algorithms to enhance the early detection capabilities of heart disease. A dataset comprising the health records of 253,680 patients with heart disease is analyzed using five distinct machine learning algorithms: Logistic Regression, K-Nearest Neighbors Classifier, Decision Tree Classifier, Naïve Bayes, and Linear Support Vector Machine (Linear SVM). The dataset is partitioned, with 80% allocated for training the algorithms and the remaining 20% for testing. Furthermore, the study’s evaluation employs four different metrics: accuracy, precision, recall, and the F1measure. Initially, early diagnosis of heart disease is attempted using the complete set of features in the dataset. However, this approach results in excessive costs and time consumption. Subsequently, a feature reduction process is implemented to optimize resource utilization, yielding an improved early detection rate. The research findings indicate that Logistic Regression outperforms the other algorithms, achieving the highest success rate with an accuracy score of 90.67%. These research results underscore the substantial contribution of machine learning algorithms to the early detection of heart disease, ultimately enhancing the quality of life for individuals.
Relay Station Attacks on Different RFID Access Systems

The advance of digitalization in today's society also has an impact on the field of security technology. Whereas in the past simple constructions were used as locks and keys, nowadays electronic access systems are increasingly used. These can be utilized either as a replacement or as an extension to traditional access systems. One technology used for this purpose is Radio Frequency Identification (RFID), which is often employed in access systems in office buildings, corporate buildings or for Passive Keyless Entry (PKE) systems in vehicles. Due to the increasing use of RFID systems, they are increasingly being targeted by attackers. One method used is the Relay Station attack. The general task of the paper is the investigation of Relay Station attacks on different RFID access systems. The aim is a successful realization of this attack method. Different approaches are considered, from which the applied methods are derived in order to be able to understand the functional principles. Due to the wide range of applications and the differences between the individual systems, two examples of RFID access systems are examined and implemented.
Determinant of Successful Tuberculosis Treatment in Puskesmas Singosari in 2020-2022

Indonesia is one of the countries that contribute the most mortality of TB in the world. The tuberculosis treatment success rate in Indonesia is 86% meanwhile is still far from the target coverage of the TB treatment success rate (90%). This study conducted to determine the determinant factors of successful TB treatment in Puskesmas Singosari. A cross-sectional design study was constructed for this study and used SITB and form TB as a research instrument. The population of this study were all drug-susceptible TB patients who had completed treatment at the Puskesmas Singosari in 2020, 2021, and QI-QII in 2022. The sample of this study was 71 people from 99 total population. The dependent variable is the success of drug-susceptible TB treatment. Independent variables are sex, age, weight, regular use of anti-tuberculosis drugs, and comorbidities. The data processing and analysis are being conducted using SPSS software. Chi-square analysis showed that the variable weight changes have a significant assosiation to successful tuberculosis treatment (p = 0,014; OR = 9,818). Puskesmas Singosari's TB Team can collaborate with the nutritionist team to optimize TB treatment by monitoring nutritional status and weight gain of drug-susceptible TB patients.
Non-Coaxial Event-guided Motion Deblurring with Spatial Alignment

Motion deblurring from a blurred image is a challenging computer vision problem because frame-based cameras lose information during the blurring process. Several attempts have compensated for the loss of motion information by using event cameras, which are bio-inspired sensors with a high temporal resolution. Even though most studies have assumed that image and event data are pixel-wise aligned, this is only possible with low-quality active-pixel sensor (APS) images and synthetic datasets. In real scenarios, obtaining per-pixel aligned event-RGB data is technically challenging since event and frame cameras have different optical axes. For the application of the event camera, we propose the first Non-coaxial Event-guided Image Deblurring (NEID) approach that utilizes the camera setup composed of a standard frame-based camera with a non-coaxial single event camera. To consider the per-pixel alignment between the image and event without additional devices, we propose the first NEID network that spatially aligns events to images while refining the image features from temporally dense event features. For training and evaluation of our network, we also present the first large-scale dataset, consisting of RGB frames with non-aligned events aimed at a breakthrough in motion deblurring with an event camera. Extensive experiments on various datasets demonstrate that the proposed method achieves significantly better results than the prior works in terms of performance and speed, and it can be applied for practical uses of event cameras.
ICT development and total factor productivity growth

Total factor productivity (TFP) is essential for disentangling the determinants of economic growth, productivity, and the standard of living. Understanding the variations in TFP, however, is greatly challenging because of the many assumptions that comprise the theoretical growth framework. In this paper, we aim to explore the determinants of TFP growth for countries at different stages of information and communication technology (ICT) development. To address the endogenous nature of the associated growth variables, we implement a three-stage-least (3SLS) square panel regression to improve the efficiency and asymptomatic accuracy of the estimators. We find that transmission channels, such as financial openness and trade globalization, have contributed substantially to growth in both advanced and developing countries. However, we also discover that greater financial openness can undermine a country’s TFP growth if the financial system is not sufficiently developed. When time horizons are decomposed into pre-ICT development and post-ICT development periods, a significant crowding-out effect is observed between ICT investment and financial openness in the pre-period, implying that the allocation of resources is critical for countries in the developing stage. Trade and finance policies that are adopted by advanced and developed countries might not be ideal for underdeveloped countries. Discretion in choosing adequate policies regarding financial integration and trade liberalization is advised for these emerging countries.
A 28-GHz Low-Power Variable-Gain Low-Noise Amplifier Using Twice Current Reuse Technique

In this letter, a 28-GHz low-power variable-gain low-noise amplifier (VGLNA) is designed for fifth-generation millimeter-wave applications and implemented using the twice current reuse (CR) technique and a tunable load. This amplifier employing the twice CR technique exhibits low dc power while delivering enhanced gain. The gain control (GC) range of the amplifier is extended using a tunable load, which is composed of a pMOS device and an inductance, and the phase variation is improved by resonating the inductance with the parasitic capacitance of the intrastage CR amplifier. Because the tunable load selectively attenuates only ac signals, <inline-formula> <tex-math notation="LaTeX">${\mathrm{ IP}}_{1 \rm dB}$ </tex-math></inline-formula> can be proportionally increased by reducing the gain. At 28 GHz, the measured gain and GC range are 21.2 and 13.8 dB, respectively. In the entire GC range, the measured <inline-formula> <tex-math notation="LaTeX">${\mathrm{ IP}}_{1 \rm dB}$ </tex-math></inline-formula> ranges from −20 to −7 dBm, the noise figure (NF) ranges from 3.7 to 6.8 dB, and the RMS phase error is 1.05° at 28 GHz. At a supply voltage of 1.2 V, the dc power of the proposed VGLNA is only 5.0 mW. These results highlight that the proposed VGLNA has the lowest dc power, higher gain, and better figure of merit compared to other works.
The pools of soil organic carbon accumulated in the surface layers of forest soils in the Karkonosze Mountains, SW Poland

Abstract Differentiation of soil organic carbon (SOC) concentrations and pools in topsoil horizons of forest soils in the Karkonosze Mountains was examined in relation to environmental and human-induced factors, with special focus on altitudinal gradient, related climatic conditions, and a zonality of vegetation. The samples were collected from the forest litter and soil layers 0–10 cm and 10–20 cm, in 621 plots arranged in a regular network of monitoring established in the Karkonosze National Park. The concentrations of SOC were determined in laboratory and used for calculation of SOC pools. Four elevation zones were distinguished for analysis: 500–750 m, 750–1000 m, 1000–1250 m, and >1250 m. The concentrations of SOC in forest litter (38.3–44.1%) showed an insignificant increasing trend with altitude. The concentrations of SOC in the layers 0–10 cm and 10–20 cm, were in a very broad range 0.27–47.6%, thus indicating a high differentiation, and also tended to insignificantly increase along with altitude. The largest share of accumulated SOC pools was proved to be present in the layer 0–10 cm, except for the highest zone >1250 m in which forest litter contains slightly larger amounts of SOC. The pools of SOC accumulated in the 20 cm thick topsoil and forest litter turned out to vary considerably (3.6–58.2 kg·m−2), but the mean values and medians in particular elevation zones fall in a narrow range 10.5–11.9 kg·m−2, close to the values reported from the Alps. The lack of statistical significance of reported tendencies was explained by a monitoring sites-oriented random soil sampling, i.e. in forest stands of various age, species-composition and degradation degree.
Effect of an Escherichia coli–derived phytase and a carbohydrase–protease cocktail derived from Bacillus spp. on performance, digestibility, bone mineralization and gut morphology in broilers fed different nutrient density diets

Abstract Background Enzyme combinations, particularly phytase (PHY) with various carbohydrases and proteases, are utilized in commercial broiler production to enhance nutrient and energy bioavailability. Objective A feeding study was undertaken to determine whether the efficiency of an Escherichia coli–derived PHY and a feed enzyme complex (FEC) derived from Bacillus spp. containing carbohydrase and protease as main activities in broiler chickens is dependent on diet quality. A total of 900 male one‐day‐old broiler chickens (Ross 308) were assigned to a 2 × 3 factorial arrangement of the treatments with 2 different nutrient density diets, standard nutrient diet (SN diet) and a low‐nutrient diet (LN diet; −100 kcal/kg for AMEn and −5% for crude protein [CP] and limiting amino acids), and 3 enzyme treatments (control [no enzymes], PHY and PHY + FEC). Each treatment group was composed of 6 replicates of 25 birds each. Results The LN diet caused a decrease in performance index, tibia length and diameter, tibia calcium content and jejunal villus surface area (VSA). The interaction effects between diet and enzyme supplementation were observed (p < 0.05) on overall average daily gain (ADG), performance index, tibia ash content and jejunal villus height (VH) and VSA, with the favourable benefits of PHY + FEC treatment being more pronounced in the LN diets. Regardless of dietary nutrient density, supplementation with PHY alone or combined with FEC enhanced (p < 0.05) final body weight, overall ADG and jejunal villus height (VH)/crypt depth, with the highest values observed in the PHY + FEC group. The PHY + FEC treatment also improved (p < 0.05) overall feed conversion ratio, apparent ileal digestibility of dry matter, organic matter, CP, and energy, and tibia phosphorus content compared to the control treatment. Conclusions The results indicate that the simultaneous addition of PHY and FEC to the LN diets improved the growth rate, bone mineralization and gut morphology.