Miracle-dz/UniFAR-FTUs huggingface dataset:
Japan's south foehn on the Toyama Plain: Dynamical or thermodynamical mechanisms?

Japanese society has recently taken a greater interest in foehn warming because it has caused record‐breaking high temperatures and sudden damage to rice crops. This is the first comprehensive climatological study focused on Japan's south foehn, which blows across the Toyama Plain in the Hokuriku region. Climatological analyses, including an objective self‐organizing map and subjective analysis of 198 south foehn cases, revealed that ~68.2% of the foehns occurred while an extratropical cyclone was passing through the Sea of Japan. Approximately 19.7% of the remaining foehns blew while an anticyclone covered Japan. Only 5.1% of all foehns occurred during a typhoon, but very high temperatures occurred when typhoons were approaching. Foehns were observed in all seasons but tended to blow more often in spring, when there are many migratory anticyclones and cyclones. Most of the foehns begin at night and end or pause during the day. This is due to the removal of the nocturnal stable layer and the development of a local daytime pressure gradient on the lee side. The dynamical mechanism provides the primary explanation for Japan's south foehn. Surprisingly, the foehns with precipitation on windward mountains slopes accounted for only ~19.2% of all cases, with ~40.0% being typhoon cases. Most of these are likely of multiple type; a pure thermodynamical type of foehn is a rare occurrence. Numerical simulations and back trajectory analyses for 76 selected foehn cases revealed that the majority of the air parcels originated from the south and passed straight over the Hida Highlands, between two mountain ranges, as a hybrid type of gap and foehn winds.
An adaptive algorithm for multipath-assisted simultaneous localization and mapping using belief propagation

In multipath-assisted simultaneous localization and mapping (SLAM), position-related information (delays and angles) in multipath components of radio signals is used to simultaneously localize user equipment (UE) and map the environment. Multipath-assisted SLAM often involves unknown parameters that are potentially time varying, such as measurement noise. Knowledge of the measurement noise is of critical importance to multipath-assisted SLAM, and uncertainty in such knowledge will seriously affect estimation accuracy. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing an adaptive multipath-assisted SLAM algorithm in a Bayesian tracking framework, which enables accommodation of a model mismatch of the measurement noise online. Specifically, we describe the evolution of the measurement noise standard deviation via a Markov chain and integrate it into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the UE, environmental features and the measurement noise standard deviation to achieve SLAM and the adaptive adjustment of the measurement noise. Finally, the experimental results verify the robustness of the proposed adaptive multipath-assisted SLAM algorithm against the uncertainty of the measurement noise.
Automated retrieval of information on threatened species from online sources using machine learning

As resources for conservation are limited, gathering and analysing information from digital platforms can help investigate the global biodiversity crisis in a cost‐efficient manner. Development and application of methods for automated content analysis of digital data sources are especially important in the context of investigating human–nature interactions. In this study, we introduce novel application methods to automatically collect and analyse textual data on species of conservation concern from digital platforms. An end‐to‐end pipeline is constructed that begins from searching and downloading news articles about species listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) along with news articles from specific Twitter handles and proceeds with implementing natural language processing and machine learning methods to filter and retain only relevant articles. A crucial aspect here is the automatic annotation of training data, which can be challenging in many machine learning applications. A Named Entity Recognition model is then used to extract additional relevant information for each article. The data collected over a 1‐month period included 15,088 articles focusing on 585 species listed in Appendix I of CITES. The accuracy of the neural network to detect relevant articles was 95.91% while the Named Entity recognition model helped extract information on prices, location and quantities of traded animals and plants. A regularly updated database, which can be queried and analysed for various research purposes and to inform conservation decision making, is generated by the system. The results demonstrate that natural language processing can be used successfully to extract information from digital text content. The proposed methods can be applied to multiple digital data platforms at the same time and used to investigate human–nature interactions in conservation science and practice.
Carotid Stenting without Embolic Protection Increases Major Adverse Events: Analysis of the National Surgical Quality Improvement Program

BACKGROUND AND PURPOSE: Published data regarding embolic protection device efficacy is mixed, and its use during carotid artery stent placement remains variable. We, therefore, examined the frequency of embolic protection device use and its association with outcomes after carotid artery stent placement using a national quality improvement data base. MATERIALS AND METHODS: Patients undergoing carotid artery stent placement with or without embolic protection devices were identified in the American College of Surgeons National Surgical Quality Improvement Program data base. The primary outcome was the incidence of major adverse cardiovascular events (defined as death, stroke, or myocardial infarction/arrhythmia) within 30 days. Propensity scoring was used to create 2 matching cohorts of patients using demographic and baseline variables. RESULTS: Between 2011 and 2018, among 1200 adult patients undergoing carotid artery stent placement, 23.8% did not have embolic protection devices. There was no trend toward increased embolic protection device use with time. Patients without embolic protection device use received preoperative antiplatelets less frequently (90.6% versus 94.6%, P = .02), underwent more emergent carotid artery stent placement (7.2% versus 3.6%, P = .01), and had a higher incidence of major adverse cardiovascular events (OR = 1.81; 95% CI, 1.11–2.94) and stroke (OR = 3.31; 95% CI, 1.71–6.39). After compensating for baseline imbalances using propensity-matched cohorts (n = 261 for both), carotid artery stent placement without an embolic protection device remained associated with increased major adverse cardiovascular events (9.2% versus 4.2%; OR = 2.30; 95% CI, 1.10–4.80) and stroke (6.5% versus 1.5%; OR = 4.48; 95% CI, 1.49–13.49). CONCLUSIONS: Lack of embolic protection device use during carotid artery stent placement is associated with a 4-fold increase in the likelihood of perioperative stroke. Nevertheless, nearly one-quarter of patients in the American College of Surgeons National Surgical Quality Improvement Program underwent unprotected carotid artery stent placement. Efforts targeting improved embolic protection device use during carotid artery stent placement are warranted.
A Numerical Method to Improve the Representativeness of Real Microstructure Cut-Outs Applied in Finite Element Simulations

To improve the representativeness of a real microstructural cut-out for modeling purposes, a numerical method named as “boundary pixel color alteration (BPCA)” is presented to modify measured 2D microstructure cut-outs. Its physical background is related to the phase growth. For the application, the precondition is that the representativeness of the microstructure is already satisfied to a certain extent. This method resolves the problem that the phase composition of a small cut-out can have a large discrepancy to the real one. The main idea is to change the pixel color among neighboring pixels belonging to different phases. Our process simultaneously maintains most of the characteristics of the original morphology and is applicable for nearly all kinds of multi-phase or polycrystalline metallic alloys, as well. From our axisymmetric finite element (FE) simulations (ABAQUS ) applied with 2D real microstructures, it shows that the volume ratios of microstructural phases, as a function of the structure position to the symmetric axis, converge to phase area ratios in the 2D cut-out, even though the axisymmetric element volume is position dependent. A mathematical proof provides the reason for the aforementioned convergence. As examples to achieve real compositions and to numerically prove the aforementioned convergence, four different materials including multiphase polycrystals are implemented. An improvement of the predicted FE result is presented for the application of a modified microstructure (with a higher representativeness) compared to the original one.
“To go, or not to go, that is the question”: perceived inaccessibility among individuals with disabilities in Shanghai

Abstract This study explored how individuals with disabilities in Shanghai perceive the accessibility of public spaces and their experiences of various barriers in transit to public spaces. We conducted semi-structured, in-depth interviews with community-dwelling individuals with mild to severe physical, sensory, and multiple disabilities (N = 16). Our findings suggest that despite government efforts to improve accessibility, participants still perceived public spaces to be inaccessible. They encountered physical barriers, social barriers, and unusable accessible facilities in the process of accessing public spaces. Their sense of being a burden to family caregivers also compelled participants to limit their mobility radius. Despite some participants’ attempts to advocate for their mobility and accessibility rights, they ultimately chose to compromise when their efforts were not acknowledged. This study seeks to inform policy and practice by increasing understanding of public space accessibility from the perspectives of people with disabilities in urban China. Points of interest This study is among the first to explore what people with disabilities experience when going to public spaces in urban China, and how they understand these experiences. People with disabilities in Shanghai face various difficulties when going to public spaces. Although there are facilities designed to assist people with disabilities in moving around public spaces in Shanghai, these facilities are often occupied, blocked, or broken, and thus cannot be properly used. Most study participants chose to stay at home and in familiar places to avoid difficulties in going to public spaces and burdening their family members. Participants who tried to advocate for their rights gave up after their efforts were not responded to. Policymakers and practitioners should understand the difficulties of people with disabilities and their families when they go to public spaces and include them when developing and implementing relevant policies and services.
Ionic Liquids in Drug Delivery

Ionic liquids (ILs) are molten salts composed of a large organic cation and an organic/inorganic anion. Due to their ionic character, most ILs present advantageous properties over conventional solvents, such as negligible volatility at atmospheric conditions and high thermal and chemical stabilities. The wide variety of IL anion–cation combinations allows these solvents to be designed to display a strong solvation ability for a myriad of active pharmaceutical ingredients (APIs) and (bio)polymers. Given these properties, ILs have been used as solvents and as formulation components in different areas of drug delivery, as well as novel liquid forms of APIs (API-ILs) applied in different stages of development of novel drug delivery systems. Furthermore, their combination with polymers and biopolymers has enabled the design of drug delivery systems for new therapeutic routes of administration.
Combined Citric Acid and Glutathione Augments Lead (Pb) Stress Tolerance and Phytoremediation of Castorbean through Antioxidant Machinery and Pb Uptake

Lead (Pb) is one of the most toxic elements on earth. The main origins of Pb pollution are automobiles, paint and electroplating industries. Pb-induced stress has very toxic effects on plant growth and biomass. The concentration of reactive oxygen species (ROS) in plant cells significantly increases under Pb stress, which interrupts the biochemical cycles in cells and leads to cell death. Therefore, it is essential to clean up the Pb-polluted soils. Among all techniques that are used to clean soil that is metal-contaminated, the best technique is phytoremediation. The present study intends to determine the role of citric acid (CA) and glutathione (GSH) in the phytoremediation of Pb by using castor bean plants. Plant biomass was significantly reduced due to Pb stress. Lead toxicity was also harmful to the photosynthetic pigments and antioxidant enzymes activities. In reverse, the content of malondialdehyde (MDA), H2O2 concentration and electrolyte leakage (EL) were increased under Pb stress. The combined application of GSH and CA enhanced photosynthetic pigments, antioxidant enzyme activities and plant biomass and minimized MDA, H2O2 and EL under Pb stress. The amount of Pb in roots and leaves remarkably increased by the joint application of CA and GSH. The combined application of CA and GSH (5 mM + 25 mM, respectively) was proven to be beneficial compared to the control. From the present results, we can conclude that the combined application of CA and GSH promoted the phytoremediation of Pb and helped the host plant to combat Pb toxicity.
Monte Carlo Node Localization Based on Improved QUARTE Optimization

Wireless sensor network (WSN) is a research hot spot of scholars in recent years, in which node localization technology is one of the key technologies in the field of wireless sensor network. At present, there are more researches on static node localization, but relatively few on mobile node localization. The Monte Carlo mobile node localization algorithm utilizes the mobility of nodes to overcome the impact of node velocity on positioning accuracy. However, there are still several problems: first, the demand for anchor nodes is large, which makes the positioning cost too high; second, the sampling efficiency is low, and it is easy to fall into the infinite loop of sampling and filtering; and third, the positioning accuracy and positioning coverage are not high. In order to solve the above three problems, this paper proposes a Monte Carlo node location algorithm based on improved QUasi-Affine TRansformation Evolutionary (QUATRE) optimization. The algorithm firstly selects the high-quality common nodes in the range of one hop of unknown nodes as temporary anchor nodes, and takes the temporary anchor nodes and anchor nodes as the reference nodes for positioning, so as to construct a more accurate sampling area; then, the improved QUATRE optimization algorithm is used to obtain the estimated location of unknown nodes in the sampling area. Simulation experiments show that the Monte Carlo node positioning algorithm based on the improved QUATRE optimization has higher positioning accuracy and positioning coverage, especially when the number of anchor nodes is relatively small.
Influenced by Media Brands? A Conjoint Experiment on the Effect of Media Brands on Online Media Planners’ Decision-Making

ABSTRACT Advertising revenues are a major source of income for media companies such as publishing houses. In the age of digitalisation, generating revenue in the online advertising market is an essential ingredient for future economic viability and competitiveness. However, programmatic advertising as a new marketing technique, as well as the proliferated number of websites and increased competition, has challenged publishing houses to generate revenue within the online advertising market. Therefore, media branding has emerged as an important issue. Since brands in such a B2B market share the same purpose as they do in consumer markets, they too offer the potential for media companies to gain a substantial competitive advantage. This article investigates the importance of media brands in relation to other purchase criteria within online media planning. The results of an Adaptive Choice-Based Conjoint analysis (ACBC) with online media planners suggest that the role of media brands within online media planning is limited and secondary to price and its influence varies over different career levels. Furthermore, a price premium can be obtained for a media brand with a higher perceived utility. This article concludes with implications for media companies.
A hybrid robust‐stochastic approach for optimal scheduling of interconnected hydrogen‐based energy hubs

Danida Fellowship Centre, Grant/Award Number: 18‐M06‐AAU Abstract The energy hub (EH) concept is an efficient way to integrate various energy carriers. In addition, demand response programmes (DRPs) are complementary to improving an EH's efficiency and increase energy system flexibility. The hydrogen storage system, as a green energy carrier, has an essential role in balancing supply and demand precisely, similar to other storage systems. A hybrid robust‐stochastic approach is applied herein to address fluctuations in wind power generation, multiple demands, and electricity market price in a hydrogen‐based smart micro‐energy hub (SMEH) with multi‐energy storage systems. The proposed hybrid approach enables the operator to manage the existing uncertainties with more flexibility. Also, flexible electrical and thermal demands under an integrated demand response programme (IDRP) are implemented in the proposed SMEH. The optimal scheduling of the hydrogen‐based SMEH problem considering wind power generation and electricity market price fluctuations, as well as IDRP, is modelled via a mixed‐integer linear programming problem. Finally, the validity and applicability of the proposed model are verified through simulation and numerical results.
The gut microbiome composition and degradation enzymes activity of black Amur bream (Megalobrama terminalis) in response to breeding migratory behavior

Abstract Black Amur bream (Megalobrama terminalis), a dominant species, resides in the Pearl River basin, known for its high plasticity in digestive ability. During spawning season, M. terminalis individuals with large body size and high fertility undergo a spawn migratory phase, while other smaller individuals prefer to settlement over migration. It is well known that gut microbial community often underpins the metabolic capability and regulates a wide variety of important functions in fish. However, little was known about how the gut microbiomes affect fish breeding migration. To investigate the variations in the gut microbiome of M. terminalis during the migration, we used high‐throughput 16S rRNA gene sequencing to reveal the distinct composition and diversity of the whole gut microbiome of migrated and nonmigrated population during period of peak reproduction, respectively. Our results indicated that nonmigrated population in estuary had a higher alpha diversity than that of migrated population in main stem. Additionally, an obvious abundant taxa shift between the gut microbiota community of nonmigrated and migrated M. terminalis was also observed. Change of dominant gut taxa from nonmigrated to migrated population was thought to be closely related to their degradation enzymes. Our results suggested that amino acid metabolism and lipid metabolism in migrated population were higher than that in nonmigrated population, providing a line of evidence for that M. terminalis change from partial herbivorous to partial carnivorous diet during breeding migration. We further concluded that, in order to digest foods of higher nutrition to supply energy to spawning migration, M. terminalis regulate activities of the gut microbiome and degradation enzymes, considered to be a key physiological strategy for reproduction.
DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients

In this study, we proposed a novel machine-learning-based functional electrical stimulation (FES) control algorithm to enhance gait rehabilitation in post-stroke hemiplegic patients. The electrical stimulation of the muscles on the paretic side was controlled via deep neural networks, which were trained using muscle activity data from healthy people during gait. The performance of the developed system in comparison with that of a conventional FES control method was tested with healthy human subjects.
Pole‐zero assignment in model predictive control, using analytical tuning approach

This paper proposes a new analytical tuning strategy for Model Predictive Control (MPC). In the proposed approach, MPC tuning problem is restated as a pole‐zero placement problem. New analytically tuning equations are given and a deep study on the places of poles and zeros of the closed‐loop system is performed. It is known that, appropriate zero placement can improve the robust stability of the closed‐loop system, so the proposed tuning strategy can be applicable. In MPC tuning strategies, analytical equations are very interesting and useful. To achieve analytical equations, First Order plus Dead Time models are used in this work. These models with adequate accuracy can describe many industrial processes. With simulation studies, the effectiveness of the proposed pole‐zero assignment strategy is indicated.
A novel T-shaped sensor cluster for acoustic source localization

This study proposes a new sensor cluster configuration for localizing an acoustic source in a plate using uniform linear array beamforming and T-shaped sensor clusters. This technique requires neither the properties of the plate material nor a dense array of sensors to find the direction of arrival of the acoustic source. It functions by placing four sensors in a cluster in the shape of the letter “‘T” over a small region of the plate. Uniform linear array beamforming-based source localization is carried out by the constructive interference of different sensor signals. However, this approach has the disadvantage of a very low resolution when the direction of arrival approaches certain values. The L-shaped sensor clusters use the information from the time difference of arrival between the sensors to estimate the direction of arrival, which has a high resolution in all directions except for the direction that is very close to vertical to the cluster. In this study, we numerically and experimentally demonstrate that the proposed T-shaped sensor cluster can accurately localize the acoustic source with no blind area. We also detail its superior performance compared to both uniform linear array beamforming and L-shaped clusters. In the experimental investigation, the maximum deviation of impact source localization was reduced significantly, from 54° to 4° for an aluminum plate, and from 42° to 3° for a composite plate. Furthermore, this novel combined sensor array layout requires only a few sensors, which can significantly reduce the cost of structural health monitoring practice.