Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction and Analysis of Heart Failure Decompensation Events Based on Telemonitored Data and Artificial Intelligence Methods

Cardiovascular diseases are the leading cause of death globally, taking an estimated 17.9 million lives each year. Heart failure (HF) occurs when the heart is not able to pump enough blood to satisfy metabolic needs. People diagnosed with chronic HF may suffer from cardiac decompensation events (CDEs), which cause patients’ worsening. Being able to intervene before decompensation occurs is the major challenge addressed in this study. The aim of this study is to exploit available patient data to develop an artificial intelligence (AI) model capable of predicting the risk of CDEs timely and accurately. Materials and Methods: The vital variables of patients (n = 488) diagnosed with chronic heart failure were monitored between 2014 and 2022. Several supervised classification models were trained with these monitoring data to predict CDEs, using clinicians’ annotations as the gold standard. Feature extraction methods were applied to identify significant variables. Results: The XGBoost classifier achieved an AUC of 0.72 in the cross-validation process and 0.69 in the testing set. The most predictive physiological variables for CAE decompensations are weight gain, oxygen saturation in the final days, and heart rate. Additionally, the answers to questionnaires on wellbeing, orthopnoea, and ankles are strongly significant predictors.
Thin layers of microwave absorbing metamaterials with carbon fibers and FeSi alloy ribbons to enhance the absorption properties

In order to break through the bottleneck of narrow effective absorption bandwidth (reflection loss RL ≤ −10 dB) of microwave absorbing materials, herein, we fabricate the metamaterials with carbon fiber (CF) and FeSi alloy (FSA) ribbon metastructure which is distributed in the carbonyl iron powders (CIP)/polyurethane (PU) matrix. The experimental results show that the microwave absorption capacity of the matrix can be significantly enhanced by CF. Compared with the pure matrix, the effective absorption bandwidth increases from 9.4–13.44 GHz to 11–16.8 GHz when the CF is parallel to the electric field vector and the spacing between adjacent CF is 20 mm. Furthermore, the CF and FSA ribbons are arranged in the matrix as an orthogonal arrangement, and the best absorption bandwidth cover 9.76–14.46 GHz when the electric field is parallel and 9.96–14.1GHz when the electric field is vertical when the spacing is 30 mm. The electromagnetic simulation of the metamaterials is calculated, it is proved that the increase of effective absorption bandwidth is due to the strengthening of carbon fiber and its coupling with FSA ribbon. This paper provides a new research path for improving the absorption properties of thin layer microwave absorbing materials.
Role of Annexin A1 Secreted by Neutrophils in Melanoma Metastasis

Annexin A1 (AnxA1) is highly secreted by neutrophils and binds to formyl peptide receptors (FPRs) to trigger anti-inflammatory effects and efferocytosis. AnxA1 is also expressed in the tumor microenvironment, being mainly attributed to cancer cells. As recruited neutrophils are player cells at the tumor sites, the role of neutrophil-derived AnxA1 in lung melanoma metastasis was investigated here. Melanoma cells and neutrophils expressing AnxA1 were detected in biopsies from primary melanoma patients, which also presented higher levels of serum AnxA1 and augmented neutrophil–lymphocyte ratio (NLR) in the blood. Lung melanoma metastatic mice (C57BL/6; i.v. injected B16F10 cells) showed neutrophilia, elevated AnxA1 serum levels, and higher labeling for AnxA1 in neutrophils than in tumor cells at the lungs with metastasis. Peritoneal neutrophils collected from naïve mice were co-cultured with B16F10 cells or employed to obtain neutrophil-conditioned medium (NCM; 18 h incubation). B16F10 cells co-cultured with neutrophils or with NCM presented higher invasion, which was abolished if B16F10 cells were previously incubated with FPR antagonists or co-cultured with AnxA1 knockout (AnxA1-/-) neutrophils. The depletion of peripheral neutrophils during lung melanoma metastasis development (anti-Gr1; i.p. every 48 h for 21 days) reduced the number of metastases and AnxA1 serum levels in mice. Our findings show that AnxA1 secreted by neutrophils favors melanoma metastasis evolution via FPR pathways, addressing AnxA1 as a potential biomarker for the detection or progression of melanoma.
Leishmania major-Infected Phlebotomus duboscqi Sand Fly Bites Enhance Mast Cell Degranulation

Leishmania parasites infect mammalian hosts through the bites of sand fly vectors. The response by mast cells (MC) to the parasite and vector-derived factors, delivered by sand fly bites, has not been characterized. We analyzed MC numbers and their mediators in BALB/c mice naturally infected in the ear with Leishmania major through the bite of the sand fly vector Phlebotomus duboscqi and compared them to non-infected sand fly bites. MC were found at the bite sites of infective and non-infected sand flies throughout 48 h, showing the release of granules with intense TNF-α, histamine, and tryptase staining. At 30 min and 48 h, the MC numbers were significantly higher (p < 0.001) in infected as compared to non-infected bites or controls. Neutrophil recruitment was intense during the first 6 h in the skin of infected and non-infected sand fly bites and decreased thereafter. An influx of neutrophils also occurred in lymph nodes, where a strong TNF-α stain was observed in mononuclear cells. Our data show that MC orchestrate an early inflammatory response after infected and non-infected sand fly bites, leading to neutrophilic recruitment, which potentially provides a safe passage for the parasite within the mammalian host.
Turmeric and Cumin Instead of Stock Cubes: An Internet Survey of Spices and Culinary Herbs Used in Poland Compared with Historical Cookbooks and Herbals

(Background) Culinary cultures undergo dramatic changes due to globalization; however, the shift in the use of culinary spices and herbs is little documented. We aimed to list seasoning products used in contemporary Polish cuisine and assess whether they have roots in historic recipes. (Methods) Via an online questionnaire distributed via social media, we gathered data from 916 respondents from all over Poland. (Results) Altogether 132 single-ingredient taxa of plant or fungi species were mentioned in the questionnaire. Capsicums, black pepper, turmeric, oregano, cinnamon, marjoram, ginger, basil, thyme, and bay leaf were the 10 most quoted seasonings throughout the year. While local herbs are likely to have been long used in local cooking, the most commonly used spices have been known in Poland since at least 17–18th c. However, a few Asian, mainly Indian spices have become mainstream in the last few years. In particular, these are turmeric, cumin, and fenugreek. The seasonings which decreased the most are various seasoning mixes and stock cubes, unpopular due to the large amount of monosodium glutamate and salt. (Conclusions) Polish cuisine is undergoing a strong process of globalization, and curry spices have become standard cooking ingredients.
An H-theorem for the general relativistic Ornstein-Uhlenbeck process

We construct conditional entropy four-currents for the general relativistic Ornstein-Uhlenbeck process and we prove that the four-divergences of these currents are always non-negative. This H-theorem is then discussed in detail. In particular, the theorem is valid in any Lorentzian space-time, even those presenting well-known chronological violations.
A Study on the Relationship between Internet Overdependence and Anger Response among Young Adults during COVID-19 Pandemic: Moderating Effect on Negative Emotions

The aim of this study is to examine how Internet dependence affects anger responses during the COVID-19 pandemic. Owing to social distancing policies, Internet dependence has intensified, and the prevalence of anger has significantly increased. To understand this phenomenon and draw some implications, the “frustration–aggression hypothesis” was utilized for the theoretical framework and anger response was categorized into functional and dysfunctional anger responses. An analysis shows that overdependence on the Internet has a positive effect on the dysfunctional anger response. At the same time, other negative emotions replace anger, reducing the possibility of a dysfunctional anger response. Accordingly, a need for a constant effort to understand the circumstances of the young generation living in the “new normal” is emphasized; moreover, this paper suggests some theoretical and practical implications.
A novel framework for detecting non‐recurrent road traffic anomalies by combining a temporal graph convolutional network and hierarchical time memory detector

A non‐recurrent road traffic anomaly refers to a sudden change in the capacity of a road segment, which deviates from the general traffic patterns, and is usually caused by abnormal traffic events such as traffic accidents and unexpected road maintenance. Timely and accurate detection of non‐recurrent road traffic anomalies facilitates immediate handling to reduce the wastage of resources and the risk of secondary accidents. Compared with other types of traffic anomaly detection methods, prediction algorithms are suitable for detecting non‐recurrent anomalies for their potential ability to distinguish non‐recurrent anomalies from recurrent congestion (e.g., rush hours). A typical prediction algorithm detects an anomaly when the difference between the predicted traffic parameter (i.e., speed) and the actual one is greater than a threshold. However, the subjective setting of thresholds in many prediction algorithms greatly affects the detection performance. This study proposes a novel framework for non‐recurrent road traffic anomaly detection (NRRTAD). The temporal graph convolutional network (T‐GCN) model acts as the predictor to learn the general traffic patterns of road segments by capturing both the topological effects and temporal patterns of traffic flows, and to predict the “normal” traffic speeds. The hierarchical time memory detector (HTM‐detector) algorithm acts as the detector to evaluate the differences between the predicted speeds and the actual speeds to detect non‐recurrent anomalies without setting a threshold. In the experiments with traffic datasets of Beijing, NRRTAD outperformed other methods, not only achieving the highest detection rates but also exhibiting higher resilience to noise. The main advantages of NRRTAD are as follows: (1) adopting the T‐GCN with a weighted graph to integrate differentiated connection strengths of multiple types of topological relations between road segments as well as temporal traffic patterns improves the prediction performance; and (2) utilizing a flexible mechanism in the HTM‐detector to adapt to changing stream data not only avoids subjective setting of a threshold, but also improves the accuracy and robustness of anomaly detection.
Clustering Method for Reduction of Cluster Center Distortion

Clustering is a method to classify the given data set with same property into several classes. To cluster data, many methods such as K-Means, Fuzzy C-Means(FCM), Mountain Method(MM), and etc, have been proposed and used. But the clustering results of conventional methods are sensitively influenced by initial values given for clustering in each method. Especially, FCM is very sensitive to noisy data, and cluster center distortion phenomenon is occurred because the method dose clustering through minimization of within-clusters variance. In this paper, we propose a clustering method which reduces cluster center distortion through merging the nearest data based on the data weight, and not being influenced by initial values. We show the effectiveness of the proposed through experimental results applied it to various types of data sets, and comparison of cluster centers with those of FCM.
A New Virtual Tracking Sub-Algorithm Based Hybrid Active Control System for Narrowband Noise With Impulsive Interference

Mechanical noise is usually a mixture of narrowband and impulsive noise which needs complex active noise control (ANC) algorithms to improve the de-noising performance. But the ANC algorithm with a high computation load will reduce the real-time performance of an ANC system, thus decreasing the attenuation performance and even leading to divergence. To alleviate this contradiction in narrowband ANC systems, a new virtual filtered-x L0 norm discrete Fourier cancellation (FxL0DFC) based hybrid FxNLMS(filtered-x normalized least mean square)-FxDFC framework is proposed to decrease the total computing load and keep good attenuation performance. For a fast-changing noise, the FxNLMS algorithm is employed. The new virtual FxL0DFC algorithm serves to prepare parameters for steady-state, and when this happens, the FxDFC algorithm with the parameters provided by FxL0DFC is applied. Compared to using the FxNLMS algorithm to attenuate narrowband periodical noise, the FxDFC algorithm has nearly the same tracking performance while having a low computational load. As a result, the FxL0DFC-based FxNLMS-FxDFC algorithm leads to a reduction of the total computational load. Moreover, the proposed method performs excellently in terms of tracking in simulations and experiments on actual data, particularly in environments with rapid power changes and impulsive noise.
Data-Based Adaptive Estimation in an Investment Model

We consider improved estimation strategies for the parameters in a capital asset pricing model under a general linear constraint; we suggest candidate subspace, a preliminary test, and shrinkage estimators. We develop a large sample theory for the estimators that include derivation of asymptotic bias and asymptotic distributional risk of the suggested estimators. The asymptotic results demonstrate the superiority of the suggested estimation technique. A simulation study shows that the method suggested here has sound finite sample properties and strongly corroborates with the theoretical result of the article. A data example is also presented to illustrate the suggested estimation strategies.
Luteolin ameliorates loperamide-induced functional constipation in mice

Functional constipation (FC) is one of the most common gastrointestinal disorders characterized by hard stools and infrequent bowel movements, which is associated with dysfunction of the enteric nervous system and intestinal motility. Luteolin, a naturally occurring flavone, was reported to possess potential pharmacological activities on intestinal inflammation and nerve injury. This study aimed to explore the role of luteolin and its functional mechanism in loperamide-induced FC mice. Our results showed that luteolin treatment reversed the reduction in defecation frequency, fecal water content, and intestinal transit ratio, and the elevation in transit time of FC models. Consistently, luteolin increased the thickness of the muscular layer and lessened colonic histopathological injury induced by loperamide. Furthermore, we revealed that luteolin treatment increased the expression of neuronal protein HuC/D and the levels of intestinal motility-related biomarkers, including substance P (SP), vasoactive intestinal polypeptide (VIP), and acetylcholine (ACh), as well as interstitial cells of Cajal (ICC) biomarker KIT proto-oncogene, receptor tyrosine kinase (C-Kit), and anoctamin-1 (ANO1), implying that luteolin mediated enhancement of colonic function and contributed to the anti-intestinal dysmotility against loperamide-induced FC. Additionally, luteolin decreased the upregulation of aquaporin (AQP)-3, AQP-4, and AQP-8 in the colon of FC mice. In summary, our data showed that luteolin might be an attractive option for developing FC-relieving medications.
Towards Understanding the Effects of Evolving the MCTS UCT Selection Policy

Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the MCTS statistical tree is built and the selection policy plays a fundamental role in this. A selection policy that works particularly well, widely adopted in MCTS, is the Upper Confidence Bounds for Trees, referred to as UCT. Other more sophisticated bounds have been proposed by the community with the goal to improve MCTS's performance on particular problems. Thus, it is evident that while the MCTS UCT behaves generally well, some variants might behave better. As a result of this, multiple works have been proposed to evolve a selection policy to be used in MCTS. Although all these works are inspiring, none of them have carried out an in-depth analysis shedding light under what circumstances an evolved alternative of MCTS UCT might be beneficial in MCTS due to focusing on a single type of problem. In sharp contrast to this, in this work we use five functions of different nature, going from a unimodal function, covering multimodal functions to deceptive functions. We demonstrate how the evolution of the MCTS UCT might be beneficial in multimodal and deceptive scenarios, whereas the MCTS UCT is robust in unimodal scenarios and competitive in the rest of the scenarios used in this study.
Strong Conservative Form of the Incompressible Navier—Stokes Equations in a Rotating Frame with a Solution Procedure

The Navier? Stokes equations in a rotating frame of reference have been formulated in the so-called strong conservative form, i.e., without the traditional source terms, viz., the Coriolis and centrifugal forces. These equations have been coupled with the continuity equation by using the modified artificial compressibility method in order to develop an implicit numerical scheme that has third order accuracy in space and second order accuracy in time. This scheme uses the Roe fluxes and the MUSCL extrapolation techniques to obtain the desired accuracies in space and the backward Euler formula to obtain the desired time accuracy. The flux Jacobians and their eigensystem are presented which are required in the development of the numerical scheme. The resulting scheme was used to solve the Ekman boundary layer problems with (a) no slip and (b) applied surface stress boundary conditions and excellent agreement between computed and exact solutions has been obtained, supporting the new formulation of the governing equations as well as the solution procedure.
Establishment of possibility of transferring part of machine irrigation land to gravity irrigation

The increasing shortage of energy resources and difficulties associated with repairing or replacing pumping equipment requires the search for alternative solutions to machine irrigation of land. This article substantiates the possibility of a phased transition of lands in the Bandykhan district from machine irrigation to gravity irrigation. To save money, the transition to gravity irrigation is planned to be carried out without reconstruction of the canal, obtaining the maximum benefit from water accumulation.
Likelihood‐based and marginal inference methods for recurrent event data with covariate measurement error

Recurrent event data arise commonly in medical and public health studies. The analysis of such data has received extensive research attention and various methods have been developed in the literature. Depending on the focus of scientific interest, the methods may be broadly classified as intensity‐based counting process methods, mean function‐based estimating equation methods, and the analysis of times to events or times between events. These methods and models cover a wide variety of practical applications. However, there is a critical assumption underlying those methods–variables need to be correctly measured. Unfortunately, this assumption is frequently violated in practice. It is quite common that some covariates are subject to measurement error. It is well known that covariate measurement error can substantially distort inference results if it is not properly taken into account. In the literature, there has been extensive research concerning measurement error problems in various settings. However, with recurrent events, there is little discussion on this topic. It is the objective of this paper to address this important issue. In this paper, we develop inferential methods which account for measurement error in covariates for models with multiplicative intensity functions or rate functions. Both likelihood‐based inference and robust inference based on estimating equations are discussed. The Canadian Journal of Statistics 40: 530–549; 2012 © 2012 Statistical Society of Canada
Is a diet of Planococcus citri nymphs and adults suitable for Chrysoperla externa for use in biological control?

Several studies have shown that the survival and reproduction of lacewings varies depending on the type of prey they consumed, especially during the first instar. In this study, we evaluated the effect of a diet consisting of Ephestia kuehniella (EK) and/or Planococcus-citri (PC) eggs offered to first instar Chrysoperla externa on subsequent development and survival. Larvae supplied only with P. citri (PC) nymphs had extended immature development, low survival, adults with lower weight and longevity, low fecundity and egg viability, and lower net reproduction rate (R 0 ). Larvae submitted to the treatment EK+PC responded positively to the diet, showing results similar to those obtained in the control-EK treatment. Therefore, this mealybug supports suitable development and reproduction of C. externa when it is provided to second instar larvae after they fed on E. kuehniella eggs as first instars. As far as we know, this is the first report that demonstrates the effect of natural prey and alternatives on different larval stages of lacewings. This information is valuable in developing protocols for use of C. externaas a pest control agent.
Novel semisupervised high-dimensional correspondences learning method

Correspondence is one of the big challenges in machine learning and image processing. To match two high-dimensional data sets with a certain number of aligned training examples, a novel semisuper- vised method is proposed. It is mainly based on two manifold learning approaches: maximum variance unfolding MVU and locally linear em- bedding LLE. We have modified MVU to a semi-supervised version to solve the correspondence problem. Additionally, the nonuniform warps and folds caused by employing LLE alone and the computational burden of MVU disappear when they are combined. The proposed algorithm outperforms traditional methods in accuracy and efficiency. Three ex- amples are performed to demonstrate the potential of this method. © 2008
Local Scheduling in KubeEdge-Based Edge Computing Environment

KubeEdge is an open-source platform that orchestrates containerized Internet of Things (IoT) application services in IoT edge computing environments. Based on Kubernetes, it supports heterogeneous IoT device protocols on edge nodes and provides various functions necessary to build edge computing infrastructure, such as network management between cloud and edge nodes. However, the resulting cloud-based systems are subject to several limitations. In this study, we evaluated the performance of KubeEdge in terms of the computational resource distribution and delay between edge nodes. We found that forwarding traffic between edge nodes degrades the throughput of clusters and causes service delay in edge computing environments. Based on these results, we proposed a local scheduling scheme that handles user traffic locally at each edge node. The performance evaluation results revealed that local scheduling outperforms the existing load-balancing algorithm in the edge computing environment.