Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the SAR Distribution in Petri-Dish Cell Cultures

Petri dishes of different types are widely used in bioelectromagnetic experiments for the assessment of the non-thermal biological effects of electromagnetic radiation. Two important qualities required for the experimental setups are to guarantee a sufficiently high level of exposure and to maintain uniformity of the fields affecting the cell culture under study. In this paper, we apply two novel techniques to improve both parameters: the use of dishes with an elliptical shape and the addition of metallic patches underneath the Petri dish. Results for plane-wave illumination at 2.5 GHz are shown. This methodology can also be extended to Petri dishes inside waveguide applicators.
The Delayed Response of the Troposphere‐Stratosphere‐Mesosphere Coupling to the 2019 Southern SSW

A strong Southern Hemisphere (SH) sudden stratospheric warming event occurred in September 2019 and significantly weakened the stratospheric polar vortex. Due to the positive zonal wind anomalies in the troposphere, the barotropic/baroclinic instability, primarily controlled by the horizontal/vertical wind shear, weakened in the upper troposphere at midlatitudes in late September and early October. As a result, planetary waves (PWs) were deflected equatorward near the tropopause rather than upward into the stratosphere, resulting in less perturbation to the stratospheric polar vortex. After October 15, the westward zonal wind anomalies propagate downward and reach the troposphere, increasing the tropospheric barotropic/baroclinic instability. This benefits the propagation of PWs into the stratosphere, leading to the early breaking of the stratospheric polar vortex. In turn, the SH mesosphere becomes anomalously cold due to the stratospheric wind filtering on the gravity waves, leading to the much earlier onset of SH polar mesospheric clouds.
An Improved Hybrid Discrete Particle Swarm Optimization Algorithm to Solve the TSP Problem

Through investigating the issue of solving the TSP problem by discrete particle swarm optimization algorithm, this study finds a new discrete particle swarm optimization algorithm (NDPSO), which is easy to combine with other algorithm and has fast convergence and high accuracy, by introducing the thought of the greedy algorithm and GA algorithm and refining the discrete particle swarm optimization algorithm. And then the study expands NDPSO by Simulated Annealing algorithm and proposes a hybrid discrete particle swarm optimization algorithm (HDPSO). At last, the experiments prove that these two algorithms both have good convergence, but the HDPSO has a better capacity to find the best solution.
The Automated Bias-Corrected and Accelerated Bootstrap Confidence Intervals for Risk Measures

Different approaches to determining two-sided interval estimators for risk measures such as Value-at-Risk (VaR) and conditional tail expectation (CTE) when modeling loss data exist in the actuarial literature. Two contrasting methods can be distinguished: a nonparametric one not relying on distributional assumptions or a fully parametric one relying on standard asymptotic theory to apply. We complement these approaches and take advantage of currently available computer power to propose the bias-corrected and accelerated (BCA) confidence intervals for VaR and CTE. The BCA confidence intervals allow the use of a parametric model but do not require standard asymptotic theory to apply. We outline the details to determine interval estimators for these three different approaches using general computational tools as well as with analytical formulas when assuming the truncated Lognormal distribution as a parametric model for insurance loss data. An extensive simulation study is performed to assess the performance of the proposed BCA method in comparison to the two alternative methods. A real dataset of left-truncated insurance losses is employed to illustrate the implementation of the BCA-VaR and BCA-CTE interval estimators in practice when using the truncated Lognormal distribution for modeling the loss data.
The convergence of q ‐Bernstein polynomials (0 < q < 1) in the complex plane

The paper focuses at the estimates for the rate of convergence of the q ‐Bernstein polynomials (0 < q < 1) in the complex plane. In particular, a generalization of previously known results on the possibility of analytic continuation of the limit function and an elaboration of the theorem by Wang and Meng is presented (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Food Security Review Based on Bibliometrics from 1991 to 2021

Food security is related to human wellbeing and sustainable development and an important guarantee for world peace. In the context of global climate change, increased food demand, resource depletion, conflicts, and frequent public health emergencies, food security is widely seen as one of the top challenges. Food security research has obvious interdisciplinary characteristics, involving a wide range of fields. We analyzed the literature on food security in the Web of Science core collection from 1991 to 2021, using bibliometric methods with the aid of the Biblioshiny software package. By collecting, screening, analyzing, and visually expressing the literature data, the following conclusions were drawn: (1) In the past 30 years, the annual number of publications on food security increased. The period can be divided into three stages: 1991–2003 as the budding period, 2004–2012 as the development period, and 2013–2020 as the high-yield and active period. The top three journals discussing food security issues are Food Security, Sustainability, and Food Policy, and these journals focus on the publication of comprehensive views from interdisciplinary perspectives. (2) Studies on food security cover 138 countries or regions. The top three countries in terms of the number of published articles are the United States, the United Kingdom, and China. Among the top 20 countries in terms of the number of published articles, European countries are highlighted. (3) Climate change, food security, agriculture, policy, and management are the other high-frequency keywords in the field of food security; climate change occurred 321 times. The word sub-Saharan Africa also occurred more frequently, indicating that food security in sub-Saharan Africa has attracted wide attention. (4) The food security theme mapping clearly showed the research status and future development trends of various topics in the field. Currently, food production, climate change, and sustainable development are the most popular themes. Research on food sovereignty, ecological agriculture, child obesity, and other aspects is an emerging field. (5) We predict that in the future, the field of food security may focus on the expansion and improvement of the food security evaluation system, the balance between sustainable development and food security goals, the improvement of agricultural production and management efficiency, and the research on government policies and strategies. Our results provide a reference for grasping the current situation, key research direction, and development trend in the field of food security.
A Centralized Energy-Efficient Clustering Protocol for Wireless Sensor Networks

Energy saving is one of the hot research topics in wireless sensor networks (WSNs), and a number of solutions have been proposed. Clustering protocols have become the widely used method to reduce energy consumption. However, the energy efficiency in existing clustering protocols is limited because of the excessive energy consumption in periodic reclustering. In this article, we propose a centralized energy-efficient clustering (CEEC) protocol, which includes two aspects of node energy balancing schemes, to enhance energy efficiency in WSNs. As for intracluster energy balancing, we employ the cluster head (CH)-rotation approach to balance the energy load among nodes in each cluster. With respect to intercluster energy balancing, we suggest four sequential algorithms to balance the energy consumption among clusters, and the main contributions are as follows: 1) a size-balanced cluster formation (SBCF) algorithm aims to partition the whole network into several small clusters of the same size; 2) a low-cost forecast-based maintenance mechanism is raised to help the base station (BS) maintain the total nodes’ energy information of each cluster in the network, and then, BS utilizes our introduced intercluster energy adjustment (ICEA) algorithm to reallocate a few cluster members in related clusters with low overhead; and 3) an energy-aware data forwarding (EDF) scheme is to maintain the nodes’ energy balance among clusters in intercluster communication. Simulation results show that our CEEC protocol outperforms the classical clustering mechanisms in terms of network lifetime, energy consumption, and network throughput.
Disturbances of the Gut Microbiota and Microbiota-Derived Metabolites in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD), comprising Crohn’s disease (CD) and ulcerative colitis (UC), is characterized as a chronic and recurrent inflammatory disease whose pathogenesis is still elusive. The gut microbiota exerts important and diverse effects on host physiology through maintaining immune balance and generating health-benefiting metabolites. Many studies have demonstrated that IBD is associated with disturbances in the composition and function of the gut microbiota. Both the abundance and diversity of gut microbiota are dramatically decreased in IBD patients. Furthermore, some particular classes of microbiota-derived metabolites, principally short-chain fatty acids, tryptophan, and its metabolites, and bile acids have also been implicated in the pathogenesis of IBD. In this review, we aim to define the disturbance of gut microbiota and the key classes of microbiota-derived metabolites in IBD pathogenesis. In addition, we also focus on scientific evidence on probiotics, not only on the molecular mechanisms underlying the beneficial effects of probiotics on IBD but also the challenges it faces in safe and appropriate application.
Parameters Estimation Algorithm of Near-Field Cyclostationary Source Based on Propagator

An improved MUSIC algorithm based on third-order cyclic moment is proposed to estimate the bearing and range parameters of near-field cyclostationary sources. The algorithm adopts the uniform linear array, structures the third-order cyclic moment matrix by the array outputs, and utilizes the propagator method to replace the singular value decomposition, calculate the signal noise subspace directly. Compared with the traditional MUSIC algorithm, the proposed algorithm has high estimation precision and low computational complexity, and effectively solves the two-dimensional parameters estimation problems of near-field cyclostationary sources in the case of interfering signals and non-Gaussian white noise. The performance of the proposed method can be verified by computer simulations.
Essential Oils as Novel Anthelmintic Drug Candidates

Helminths, with an estimated 1.5 billion annual global infections, are one of the major health challenges worldwide. The current strategy of the World Health Organization to prevent helminth infection includes increasing hygienic awareness, providing better sanitation and preventative anthelmintic drug therapy in vulnerable populations. Nowadays, anthelmintic drugs are used heavily in livestock, both in case of infection and as a preventative measure. However, this has led to the development of resistance against several of the most common drugs, such as levamisole, ivermectin and thiabendazole. As many as 70% of the livestock in developed countries now has helminths that are drug resistant, and multiple resistance is common. Because of this, novel anthelmintics are urgently needed to help combat large-scale production losses. Prior to this review, no comprehensive review of the anthelmintic effects of essential oils and their components existed. Multiple review articles have been published on the uses of a single plant and its extracts that only briefly touch upon their anthelmintic activity. This review aims to provide a detailed overview of essential oils and their components as anthelmintic treatment against a wider variety of helminths.
The Green Correspondence for Infinitely Generated Modules

If G is a finite group, then the usual version of the Green correspondence applies to finitely generated kG‐modules when k is a field of characteristic p > 0 or a p‐adic ring. The paper presents a categorical version of the Green correspondence and a version of the Burry–Carlson–Puig theorem that remain valid for arbitrary modules and coefficient rings. In this generality, however, it is not clear whether the Green correspondent of a finitely generated module is always finitely generated.
Effectiveness of Smartphone-Based Lifestyle Interventions on Women with Gestational Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background Gestational diabetes mellitus (GDM) is a condition that causes poor glucose tolerance during pregnancy and usually resolves after birth. Having GDM impacts the mother and baby. Smartphone-based lifestyle interventions may offer innovative solutions. Aim To examine the effects of smartphone-based lifestyle interventions on compliance, Hemoglobin A1c (HbA1c), maternal outcomes, infant outcomes, psychological status, satisfaction, and cost effectiveness among women with GDM. Methods Randomized controlled trials (RCTs) of smartphone-based lifestyle interventions for women with GDM aged over 18 years were included. A systematic search of PubMed, Embase, Web of Science, CINAHL, and Cochrane Library for articles published from January 2007 to March 2022 and updated on 12 October 2022. Data were extracted independently by two researchers, and the risk of bias in individual trials was rated using the Cochrane risk-of-bias tool (RoB 2). Meta-analysis was conducted by using RevMan 5.3. Results Ten studies were included involving 1626 participants. The mean ages of the women were 32.42 ±4.68 years. Eight out of 10 studies were conducted in developed countries. Meta-analysis found that smartphone-based lifestyle interventions statistically improved compliance [SMD = 7.36, 95% CI = (4.05 to 10.68), P < 0.0001] and decreased the incidences of neonatal intensive care unit (NICU) admission [RR = 0.64, 95% CI = (0.47,0.86), P = 0.003], compared with controls. However, intervention effects on HbA1c, maternal outcomes, neonatal hypoglycemia, and infant birth weight were non significant. Moreover, the satisfaction of intervention is high in this review. Contrastingly, no significant effects were observed for psychological status and cost-effectiveness. Conclusion Smartphone-based lifestyle interventions may improve patient compliance and reduce NICU admissions. These findings may aid in developing future intervention strategies, help elucidate future research directions, and guide clinical practice for women with GDM. Future high-quality RCTs must be further studied at larger scales to examine smartphone-based lifestyle interventions’ long-term effects and cost-effectiveness.
A Survey on Graph Neural Networks for Microservice-Based Cloud Applications

Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in microservice architecture from prototype design to large-scale service deployment. To appreciate the big picture of this emerging trend, we provide a comprehensive review of recent studies leveraging GNNs for microservice-based applications. To begin, we identify the key areas in which GNNs are applied, and then we review in detail how GNNs can be designed to address the challenges in specific areas found in the literature. Finally, we outline potential research directions where GNN-based solutions can be further applied. Our research shows the popularity of leveraging convolutional graph neural networks (ConGNNs) for microservice-based applications in the current design of cloud systems and the emerging area of adopting spatio-temporal graph neural networks (STGNNs) and dynamic graph neural networks (DGNNs) for more advanced studies.
Accurate segmentation of small targets for LCD defects using deep convolutional neural networks

For the purpose of effectively solving the problems of manual inspection of thin‐film transistor–liquid crystal display (TFT‐LCD) with spot Mura high leakage rate and time consuming, this article proposes a visual detection method for LCD defects based on Mask R‐CNN convolutional neural network. In this article, a new feature extraction network is built by fusing ResNet with efficient channel attention (ECA) channel attention mechanism, and a new feature fusion network is constructed by adding ECA channel attention mechanism to Feature Pyramid Network (FPN). In the dataset of self‐made display defect images collected at the production site, the current more advanced segmentation network was compared qualitatively and quantitatively with our method, and the results showed that the proposed method was more accurate in detecting spot Mura, with a detection accuracy of 91%.
Tearing Instability of Isolated Compressible Vortices

The present paper addresses the instability of a compressible vortex flow. We consider a family of so-called isolated vortices – the circular planar vortices that have zero circulation (net vorticity). The term “isolation” implies the presence of a shield in the vorticity field – a region of vorticity of common sign surrounding the central domain where the vorticity is oppositely signed. To model such a vorticity field, a generalized Taylor-type profile for the swirl velocity in the radial direction is employed, which involves two parameters: intensity μ (proportional to the maximal velocity) and steepness β (characterizing the scale of the shield zone). Vortices are assumed to be compressible and homentropic. The linear-stability analysis is carried out, which shows that isolated vortices can exhibit both stable and unstable behavior depending on the model parameters μ and β. By numerical simulations of the non-linear stage, the unstable normal modes are shown to evolve towards tearing of the basic vortex with formation of smaller secondary vortical structures.
Effects of Virtual Reality-Based Graded Exposure Therapy on PTSD Symptoms: A Systematic Review and Meta-Analysis

Previous studies reported that virtual reality (VR)-based exposure therapy (VRET) was a clinically beneficial intervention for specific phobias. However, among VRET, VR-based graded exposure therapy (VR-GET) is little known about its efficacy on posttraumatic stress disorder (PTSD) symptoms. Therefore, this meta-analysis investigated the effects of VR-GET for PTSD symptoms. A literature search yielded seven randomized controlled trials. The differences between conditions regarding the primary outcome of PTSD symptoms in the effect size of the individual study were calculated using Hedges’ g. The findings showed VR-GET showed a significantly larger effect size for PTSD symptoms (g = 1.100, p = 0.001), compared to controls. However, no significant difference between conventional VRET and controls was found for PTSD symptoms (g = −0.279, p = 0.970). These findings indicated the superiority of VR-GET for PTSD symptoms compared to controls, supporting the importance of immersive PTSD treatments. Nevertheless, the results need to be interpreted with caution due to the substantial number of military service personnel studies. Future trials, considering individually tailored scenarios in virtual environments to cover a wider range of trauma types, are required to investigate its evidence on treating PTSD.
AllInfoLog: Robust Diverse Anomalies Detection Based on All Log Features

Large-scale services are generating massive logs, which trace the runtime states and critical events. Anomaly detection via logs is critical for service maintenance and reliability assurance. Existing log-based anomaly detection methods make use of the limited information in log data, resulting in their incapability of detecting diverse anomalies related to unused log features. In this paper, we propose AllInfoLog, a robust log-based anomaly detection method taking advantage of all log information, to detect diverse types of anomalies. To capture all log features, AllInfoLog utilizes four encoders to extract semantic, parameter, time, and other feature embeddings, respectively. The embeddings of all log features are then combined to train an attention-based Bi-LSTM model to detect diverse anomalies. The experimental evaluations on real-world log datasets, synthetic datasets, and unstable log datasets demonstrate AllInfoLog outperforms the state-of-the-art log-based anomaly detection methods from aspects of performance and robustness, and has effectiveness to detect diverse types of anomalies.
Development and Analysis of a HMSIW-Fed Broadband Superstrate Patch Array Using Dual-Resonance Mechanism for Ka-Band Applications

A novel broadband half-mode substrate integrated waveguide (HMSIW) fed, slot coupled superstrate patch array is developed and analyzed for Ka-band applications. The proposed antenna achieved large bandwidth due to the presence of additional resonance of the slots of HMSIW feed apart from the standard resonance of uniform patch array. It also exhibits improved power gain due to the lensing mechanism of the patch array. The achieved gain of the proposed antenna is 15.66 dBi, and exhibits impedance bandwidth $|S_{11}|< -10 \,\text{dB}$ of 20.45% from 28.45 to 34.96 GHz. In the E-plane and H-plane, the maximum sidelobe levels are less than −23 and −31 dB, and the minimum cross-pol discrimination is 32.08 dB. The achieved radiation efficiency is 93% in the desired operating band. Measured results are in good agreement with the simulated ones. The proposed antenna exhibits improved performance and compactness when compared to other state-of-the-art published designs and is thus a useful candidate for Ka-band applications.
Ulysses observations of a discrete wavepacket upstream of an interplanetary shock

We present a single example of a previously unidentified type of upstream wave structure, found several minutes ahead of a forward propagating, oblique interplanetary shock observed on day 6, 1992, using Ulysses magnetometer data. The wave appears as an isolated high frequency wavepacket associated with a larger scale compressive structure. In the region between the wavepacket and the shock the magnetic field is moderately, but significantly, compressed and deflected suggesting the existence of a foreshock. The wavepacket is dispersive and wavelet analysis shows a near linear decrease in frequency from 0.3 to 0.1 Hz, continuing across the large scale structure which is assumed to be the source of these waves. We suggest that the feature is similar in nature to the shocklet and associated discrete wavepacket commonly found in planetary foreshocks, but further examples are required before a definite conclusion can be reached.