Miracle-dz/UniFAR-FTUs huggingface dataset:
A computational insight into Rb2ASbX6 (A=Tl, Cu & X=I, Cl) double perovskites for energy storage and optoelectronic applications

Double perovskites are considered to be outstanding materials for encountering the energy crises faced by the world community. This article comprehensively addresses the electronic structure, mechanical, optical and transport attributes of Rb2ASbX6 (A=Tl, Cu & X=I, Cl) double perovskites by using the DFT approach. The structural and thermodynamic stability is confirmed with negative formation energy and tolerance factor. An indirect bandgap of 1.06 eV for Rb2CuSbCl6 and a direct bandgap of 1.12 eV for Rb2TlSbI6 have been found. Mechanical properties are analyzed through elastic parameters. The absorption is found in visible as well as in ultraviolet regions with the minimum energy loss suggesting their possible use in optoelectronic applications. The thermoelectric ability is judged through the computation of transport parameters. The ZT values of 0.79 for Rb2CuSbCl6 and 0.75 for Rb2TlSbI6 endorsed their potential for renewable devices.
Steroid Hormones Effected in Human Body

Disturbances of the steroidogenesis or altered peripheral metabolism of steroids may result in severe clinical manifestations. Therefore, prompt diagnosis and initiation of medical treatment are desirable. The diagnostics of disorders of steroid hormone production, metabolism, and action have been previously based on immunoassay tests. However, in a modern medical laboratory, due to low accuracy of immunoassays, this technique is continuously replaced by chromatographic separation methods coupled to mass spectrometric detection systems. In this review we present current advances in the diagnostics of adrenal gland disorders, focusing on the role of mass spectrometry in prenatal and newborn screening, and in the diagnostics of sexual maturation disorders.
Tunable electronic structures, Rashba splitting, and optical and photocatalytic responses of MSSe-PtO2 (M = Mo, W) van der Waals heterostructures

Binding energies, AIMD simulation and phonon spectra confirm both the thermal and dynamical stabilities of model-I and model-II of MSSe-PtO2 (M = Mo, W) vdWHs. An indirect type-II band alignment in both the models of MSSe-PtO2 vdWHs and a larger Rashba spin splitting in model-II than in model-I provide a platform for experimental design of MSSe-PtO2 vdWHs for optoelectronics and spintronic device applications. Transfer of electrons from the MSSe layer to the PtO2 layer at the interface of MSSe-PtO2 vdWHs makes MSSe (PtO2) p(n)-type. Large absorption in the visible region of MoSSe-PtO2 vdWHs, while blue shifts in WSSe-PtO2 vdWHs are observed. In the case of model-II of MSSe-PtO2 vdWHs, a further blue shift is observed. Furthermore, the photocatalytic response shows that MSSe-PtO2 vdWHs cross the standard water redox potentials confirming their capability to split water into H+/H2 and O2/H2O.
(−)-Epicatechin Inhibits Metastatic-Associated Proliferation, Migration, and Invasion of Murine Breast Cancer Cells In Vitro

Breast cancer, due to its high incidence and mortality, is a public health problem worldwide. Current chemotherapy uses non-specific cytotoxic drugs, which inhibit tumor growth but cause significant adverse effects. (−)-Epicatechin (EC) is part of a large family of biomolecules called flavonoids. It is widely distributed in the plant kingdom; it can be found in green tea, grapes, and cocoa. Several studies in animals and humans have shown that EC induces beneficial effects in the skeletal muscle and the cardiovascular system, reducing risk factors such as arterial hypertension, endothelial dysfunction, damage to skeletal muscle structure, and mitochondrial malfunction by promoting mitochondrial biogenesis, with no adverse effects reported. Recently, we reported that EC had an antitumor effect in a murine triple-negative mammary gland tumor model, decreasing tumoral size and volume and increasing survival by 44%. This work aimed to characterize the effects of flavanol EC on proliferation, migration, and metastasis markers of triple-negative murine breast (4T1) cancer cells in culture. We found proliferation diminished and Bax/Bcl2 ratio increased. When the migration of culture cells was evaluated, we observed a significant reduction in migration. Also, the relative expression of the genes associated with metastasis, Cdh1, Mtss1, Pten, Bmrs, Fat1, and Smad4, was increased. In conclusion, these results contribute to understanding molecular mechanisms activated by EC that can inhibit metastatic-associated proliferation, migration, and invasion of murine breast cancer cells.
Communication in product development in an alliance setting

This paper deals with communication across functional and cultural borders in product development in an alliance context. It shows how the multiple levels of co-ordination in an alliance increase complexity. Product development takes place in meetings in a nexus of meetings. Communication is studied by the use of video. Sequences from such recordings are played back to participants who assist in the interpretation of communicative situations. It was found that participants tend to become myopic in their attributions of implications of behaviour in meetings, which hampers communication efficiency. It is suggested that Complexity Theory provides an explanation and that a solution can be sought in feedback of video sequences and reflection on communication problems.
Particle-in-cell Simulations of Firehose Instability Driven by Bi-Kappa Electrons

We report the first results from particle-in-cell simulations of the fast-growing aperiodic electron firehose instability driven by the anisotropic bi-Kappa distributed electrons. Such electrons characterize space plasmas, e.g., solar wind and planetary magnetospheres. Predictions made by the linear theory for full wave-frequency and wave-vector spectra of instabilities are confirmed by the simulations showing that only the aperiodic branch develops at oblique angles with respect to the magnetic field direction. Angles corresponding to the peak magnetic field fluctuating power spectrum increase with the increase in the anisotropy and with the decrease in the inverse power-law index κ. The instability saturation and later nonlinear evolutions are also dominated by the oblique fluctuations, which are enhanced by the suprathermals and trigger a faster relaxation of the anisotropic electrons. Diffusion in velocity space is stimulated by the growing fluctuations, which scatter the electrons, starting with the more energetic suprathermal populations, as appears already before the saturation. After saturation the fluctuating magnetic field power shows decay patterns in the wave-vector space and a shift toward lower angles of propagation.
MLA-TCN: Multioutput Prediction of Dam Displacement Based on Temporal Convolutional Network with Attention Mechanism

The displacement of concrete dams effectively reflects their structural integrity and operational status. Therefore, establishing a model for predicting the displacement of concrete dams and studying the evolution mechanism of dam displacement is essential for monitoring the structural safety of dams. Current data-driven models utilize artificial data that cannot reflect the actual status of dams for network training. They also have difficulty extracting the temporal patterns from long-term dependencies and obtaining the interactions between the targets and variables. To address such problems, we propose a novel model for predicting the displacement of dams based on the temporal convolutional network (TCN) with the attention mechanism and multioutput regression branches, named MLA-TCN (where MLA is multioutput model with attention mechanism). The attention mechanism implements information screening and weight distribution based on the importance of the input variables. The TCN extracts long-term temporal information using the dilated causal convolutional network and residual connection, and the multioutput regression branch achieves simultaneous multitarget prediction by establishing multiple regression tasks. Finally, the applicability of the proposed model is demonstrated using data on a concrete gravity dam within 14 years, and its accuracy is validated by comparing it with seven state-of-the-art benchmarks. The results show that the MLA-TCN model, with a mean absolute error (MAE) of 0.05 mm, a root-mean-square error (RMSE) of 0.07 mm, and a coefficient of determination (R2) of 0.99, has a comparably high predictive capability and outperforms the benchmarks, providing an accurate and effective method to estimate the displacement of dams.
The Use of Medical Grade Honey on Infected Chronic Diabetic Foot Ulcers—A Prospective Case-Control Study

Non-healing wounds are usually colonised and contaminated by different types of bacteria. An alternative to antibiotic treatment in patients with infected wounds with local signs of inflammation may be medical grade honey (MGH). MGH has antioxidant, antimicrobial, anti-inflammatory, and immunomodulatory features. This study aims to evaluate the effect of MGH therapy on infected non-healing wounds, especially for diabetic foot syndrome. Prospective, observational case series (n = 5) of patients with wounds of diabetic foot syndrome are presented. There were five males with an average age of 61.6 years. All wounds were treated with MGH, and the healing trajectory was rigorously and objectively monitored. In all cases, there was a gradual disappearance of odour, pain, and exudation. Moreover, the wound areas significantly reduced within 40 days and there was a decrease in glycated haemoglobin and glycaemia values. All these outcomes resulted in improved quality of life of the patients. Despite bacterial colonisation, antibiotic treatment was not necessary. All wounds were completely healed. MGH has antimicrobial, anti-inflammatory, and antioxidant effects in diabetic foot syndrome wounds, does not increase glycated haemoglobin or glycaemia levels, and thus constitutes an effective alternative to the use of antibiotics in the treatment of locally infected wounds.
A Self-Clocked and Variation-Tolerant Unified Voltage-and-Frequency Regulator for In-Order Executed Digital Loads

A self-clocked unified voltage-and-frequency regulator (UVFR) is proposed to provide a highly correlated voltage-and-clock pair and to guarantee error-free operation of an arbitrary in-order executed digital load. Built on a digital low-dropout regulator (D-LDR), the unified control loop is highly synthesizable and adaptively clocked to achieve both fast transient response and low quiescent current. Replica frequency-locked loops (FLLs) and hysteresis switching logic (HSL) are employed to compensate for the built-in offset of conventional beat-frequency (BF) quantization and provide calibration-free regulation to tolerate global and random variations. Fabricated in a 65-nm CMOS process, the proposed UVFR reduced the voltage undershoot by 25%, reduced the steady-state offset by 84%, and achieved a 5.9X wider adaptive clocking range compared to the baseline with the help of our proposed replica FLLs and HSL.
How to Naturally Support the Immune System in Inflammation—Essential Oils as Immune Boosters

Efficient functionality of the immune system is needed to fight against the development of infectious diseases, including, among others, serious recurrent chronic infections. Research has shown that many modern common diseases, such as inflammatory bowel diseases and cardiovascular diseases, e.g., thromboembolism, cancer, obesity, or depression, are connected with inflammatory processes. Therefore, new, good stimulators of the immune system’s response are sought. They include synthetic compounds as well as biological preparations such as lipopolysaccharides, enzymes, bacterial metabolites, and secondary metabolites of plants, demonstrating a multidirectional effect. Essential oils are characterized by many invaluable activities, including antimicrobial, antioxidant, anti-inflammatory, and immunostimulating. Essential oils may stimulate the immune system via the utilization of their constituents, such as antibodies, cytokines, and dendritic cells. Some essential oils may stimulate the proliferation of immune-competent cells, including polymorphonuclear leukocytes, macrophages, dendritic cells, natural killer cells, and B and T lymphocytes. This review is focused on the ability of essential oils to affect the immune system. It is also possible that essential oil components positively interact with recommended anti-inflammatory and antimicrobial drugs. Thus, there is a need to explore possible synergies between essential oils and their active ingredients for medical use.
Enhancing SPARQL Query Performance With Recurrent Neural Networks

DBpedia is one of the most resourceful link databases today, and users need to use query syntax (e.g., SPARQL) to access information in DBpedia databases. However, not all users know SPARQL, so a natural language query system can be used to translate the user’s query into the corresponding syntax. Generating query syntax through the query system is both time-consuming and expensive. To improve the efficiency of query syntax generation from user questions, the multi-label template approach, specifically Light-QAwizard, is utilized. Light-QAwizard transforms the problem into one or more single-label classifications using multi-label learning template approaches. By implementing Light-QAwizard, query costs can be reduced by 50%, but it introduces a new label during the transformation process leading to sample imbalance, compromised accuracy, and limited scalability. To overcome these limitations, this paper employs two multi-label learning methods, Binary Relevance (BR) and Classifier Chains (CC), for question transformation. By employing Recurrent Neural Networks (RNNs) as a multi-label classifier for generating RDF (Resource Description Framework) triples, all the labels are predicted to align with the query intentions. To better account for the relationship between RDF triples, BR is integrated into an ensemble learning approach to result in the Ensemble BR. Experimental results demonstrate that our proposed method outperforms previous research in terms of improving query accuracy. The favorable experiments substantiate that the Ensemble BR or CC model demonstrates competitiveness by integrating label relationships into the trained model.
Feeding of fish oil and medium-chain triglycerides to canines impacts circulating structural and energetic lipids, endocannabinoids, and non-lipid metabolite profiles

Introduction The effect of medium-chain fatty acid-containing triglycerides (MCT), long-chain polyunsaturated fatty acid-containing triglycerides from fish oil (FO), and their combination (FO+MCT) on the serum metabolome of dogs (Canis familiaris) was evaluated. Methods Dogs (N = 64) were randomized to either a control food, one with 7% MCT, one with FO (0.18% eicosapentaenoate and 1.3% docosahexaenoate), or one with FO+MCT for 28 days following a 14-day washout period on the control food. Serum metabolites were analyzed via chromatography followed by mass spectrometry. Results Additive effects of serum metabolites were observed for a number of metabolite classes, including fatty acids, phospholipids, acylated amines including endocannabinoids, alpha-oxidized fatty acids, and methyl donors. Some effects of the addition of FO+MCT were different when the oils were combined compared with when each oil was fed separately, namely for acylcarnitines, omega-oxidized dicarboxylic acids, and amino acids. Several potentially beneficial effects on health were observed, including decreased circulating triglycerides and total cholesterol with the addition of FO (with or without MCT) and decreases in N-acyl taurines with the addition of MCT, FO, or FO+MCT. Discussion Overall, the results of this study provide a phenotypic characterization of the serum lipidomic response to dietary supplementation of long-chain n3-polyunsaturated and medium-chain saturated fats in canines.
Improvements on a simple model of the global carbon cycle

This report describes improvements and changes made to a simple model of the global carbon cycle originally developed by Bice (2007). The model simulation period is from 1850 to 2000, and the steady state for atmospheric CO2 concentration changed from 280ppm in the original model to 285ppm in the improved model. The original model has 5 reservoirs: atmosphere, surface ocean, deep ocean, land biota and soil. An additional ‘earth’ reservoir is added to the improved model. These reservoirs have various fluxes between them, and all are assumed to have equally distributed carbon, analogous to ‘well-mixed tanks’. Changes are made to the half-saturation value (effecting the Michaelis-Menten kinetics describing photosynthesis); and it is found that the temperature sensitivity of photosynthesis is best parameterised to reflect average measured land surface temperature of around 8oC. An improved method for accounting for anthropogenic changes due to burning of land biota is developed. A global energy balance is incorporated into the model. This energy balance uses a ‘grey atmosphere’ approximation, as developed by Lenton (2000). Results of this energy model are in line with historical measured average global surface temperature (GST) anomalies from the Climate Research Unit (CRU) CRUTEM3 temperature dataset. However, it is shown that this energy model is somewhat unreliable, as the same ‘global warming’ effect can be reproduced by making small changes to planetary albedo, and possible changes to solar irradiation and relative humidity are not accounted for. Regardless of this, the energy model output for future predictions is in the same range as those made by the Intergovernmental Panel on Climate Change (IPCC) with more complex General Circulation Models (GCMs).
Vitamin D insufficiency and disease risk in the elderly

Vitamin D insufficiency, milder than deficiency, is common, and a risk of various diseases. Since vitamin D exert diverse actions, both skeletal and non-skeletal, its insufficiency is a risk of various diseases including osteoporosis, sarcopenia, cardiovascular disease, cancer, and even mortality. Regarding the association of vitamin D status and disease risk, a marked discrepancy exists between the results from the observational studies and intervention studies, mostly yielding the positive and negative results in the former and latter, respectively. Such inconsistency probably arises from methodological problems, of which the baseline vitamin D status would be the most important. Vitamin D intervention would be effective in the deficient/insufficient subjects, but not in sufficient subjects. Since the elderly subjects, especially the institutionalized people, are mostly vitamin D deficient/insufficient, they are likely to benefit from improvement of vitamin D status. Vitamin insufficiency is a risk of various diseases, and correcting the vitamin status alone would reduce the risk of many diseases, and favorable to avoid the undesirable consequences of polypharmacy in the elderly. Additionally, disease prevention by nutritional improvement is cheap and free from side effects, and suited for the primary prevention of diseases.
An Effective Multi-Scale Framework for Driver Behavior Recognition With Incomplete Skeletons

One essential issue in skeleton-based driver action recognition is that incomplete skeletons collected from real scenes would degrade model performance. However, existing models often ignore the missing joint preprocessing and tend to be over-parameterized. In this work, we propose 1) a padding strategy SmoothNode and 2) a skeleton-based Multi-Scale Excitation Graph Convolution Network (MSE-GCN). Firstly, SmoothNode, as a part of preprocessing, fills both missing frames and nodes in a smooth style and repairs the incomplete skeletons to a relatively complete state. Secondly, inspired by the efficient modeling ability of EfficientGCN in dynamic skeletons, the MSE-GCN model is designed to reason multi-scale spatial-temporal features through two improvements, i.e., Spatial Graph Convolution layer based on the Independent Self-connecting formulation mode (SGC-IS) and Multi-Scale Wrapper Fused Spatial-Temporal Excitation layer (MSW-FSTE). SGC-IS optimizes the normalized adjacency matrix formulation mode and strengthens connections between each pair of nodes, while MSW-FSTE excites temporal patterns with the global spatiotemporal features in a hierarchical residual-like style and learns multi-scale features in the temporal domain. By coupling these proposals, we develop SMOMS, a driver behavior recognition framework. Extensive experiments on three released datasets, i.e., Drive&Act, 3MDAD, and EBDD, demonstrate that the proposed SMOMS framework outperforms other methods.
RSA based consensus algorithm for resource‐constrained distributed devices

With the proliferation of resource‐constrained distributed devices, such as Internet of Things devices, there is a growing need for efficient consensus algorithms to enable secure and reliable communication among these devices. This paper proposes an RSA‐based consensus algorithm explicitly designed for resource‐constrained distributed devices. The algorithm leverages the RSA (Rivest‐Shamir‐Adleman) cryptography scheme to achieve secure communication and consensus among the devices. To evaluate the performance of the RSA‐based consensus algorithm, a simulation is conducted in the Python programming language. The simulation considers a network with a fixed number of participants and miner nodes. The average execution time of the algorithm is measured over multiple iterations to assess its efficiency. The simulation results demonstrate that the proposed algorithm reduces latency in block validation, and the algorithm's low computational requirement reduces energy consumption for each transaction.
A Small Steerable Tip Based on Dielectric Elastomer Actuators

Soft robotics has sought to emulate natural behaviors. One impressive ability of living systems is operating in confined and narrow pathways, from caterpillars to tapeworms. Among the soft actuators, dielectric elastomer actuators (DEAs) are suitable candidates for this ability with fully soft systems, energy, and power densities on par with natural muscles and simple electro-mechanical drive. In this letter, we present the systematic guideline for designing and fabricating a steerable tip based on bimorph DEAs capable of guiding a small flexible insertion tube. We developed the tip by assembling identical DEAs into a multilayer structure, which induces stable bending at the same bias voltage. We investigated various structural parameters such as length, passive zone, and multilayer/scaling effect for active and smooth navigation. Then, we suggested the methodology to provide the tip with not only flexibility to move along with pathways but also sufficient stiffness to lead the flexible tube attached to the tip in the desired direction. The tip's steerable performance was analyzed in various conditions, such as deflected by gravity or suspended in liquid media of various viscosities and conductivities. We demonstrated fast, reliable actuation with active tip control and the ability of shaking or stirring functions based on dynamic actuation.