Miracle-dz/UniFAR-FTUs huggingface dataset:
An assessment of crisis management techniques in hotels in London and Stockholm as response to COVID-19’s economic impact

Purpose The purpose of this study is to gain a deeper understanding on if and how hotel senior managers (HSMs) in four-star chain hotels in London and Stockholm implemented crisis management techniques (CMTs) as a response to the economic impacts of the COVID-19 pandemic in 2020. Design/methodology/approach Qualitative questionnaires were sent to 30 HSMs in London and Stockholm. From the feedback, this paper assesses, using thematic analysis, differences in CMTs used, past and present, alluding to COVID-19’s economic impacts on hotels. Findings Results determined that crises broadly economically impacted destinations similarly through loss of travellers and thus revenue. However, with a more intricate and specific assessment, destinations are impacted differently; thus, CMTs must alter. Findings show many CMTs can be implemented to reduce crises’ economic impacts. The literature review and empirical results allude to many previous and current CMTs, although these must be relevant and specific to the crisis, hotel and/or destination. Practical implications This paper has theoretical implications for academics on, among other things, theory building. Practically, it supports hotels in developing and determining CMTs to reduce the economic impacts of crises, to be better prepared when another pandemic emerges and contributes to the tourism and hospitality industry’s knowledge of management strategy within crises. Originality/value To the best of the authors’ knowledge, this is the first Swedish study focusing on post-COVID-19 crisis management in hotels in London and Stockholm.
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information

Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent research hotspot due to its ability of enabling fast and flexible information extraction on remote sensing (RS) images. However, current RSCTIR methods mainly focus on global features of RS images, which leads to the neglect of local features that reflect target relationships and saliency. In this article, we first propose a novel RSCTIR framework based on global and local information (GaLR), and design a multi-level information dynamic fusion (MIDF) module to efficaciously integrate features of different levels. MIDF leverages local information to correct global information, utilizes global information to supplement local information, and uses the dynamic addition of the two to generate prominent visual representation. To alleviate the pressure of the redundant targets on the graph convolution network (GCN) and to improve the model’s attention on salient instances during modeling local features, the denoised representation matrix and the enhanced adjacency matrix (DREA) are devised to assist GCN in producing superior local representations. DREA not only filters out redundant features with high similarity, but also obtains more powerful local features by enhancing the features of prominent objects. Finally, to make full use of the information in the similarity matrix during inference, we come up with a plug-and-play multivariate rerank (MR) algorithm. The algorithm utilizes the $k$ nearest neighbors of the retrieval results to perform a reverse search, and improves the performance by combining multiple components of bidirectional retrieval. Extensive experiments on public datasets strongly demonstrate the state-of-the-art performance of GaLR methods on the RSCTIR task. The code of GaLR method, MR algorithm, and corresponding files have been made available at: https://github.com/xiaoyuan1996/GaLR.
Localization of Thioredoxin-Interacting Protein in Aging and Alzheimer’s Disease Brains

Thioredoxin-Interacting Protein (TXNIP) has been shown to have significant pathogenic roles in many human diseases, particularly those associated with diabetes and hyperglycemia. Its main mode of action is to sequester thioredoxins, resulting in enhanced oxidative stress. The aim of this study was to identify if cellular expression of TXNIP in human aged and Alzheimer’s disease (AD) brains correlated with pathological structures. This study employed fixed tissue sections and protein extracts of temporal cortex from AD and aged control brains. Studies employed light and fluorescent immunohistochemical techniques using the monoclonal antibody JY2 to TXNIP to identify cellular structures. Immunoblots were used to quantify relative amounts of TXNIP in brain protein extracts. The major finding was the identification of TXNIP immunoreactivity in selective neuronal populations and structures, particularly in non-AD brains. In AD brains, less neuronal TXNIP but increased numbers of TXNIP-positive plaque-associated microglia were observed. Immunoblot analyses showed no significant increase in levels of TXNIP protein in the AD samples tested. In conclusion, this study identified altered patterns of expression of TXNIP in human brains with progression of AD pathology.
An Improved Force Characteristic Curve Fitting Algorithm of Urban Rail Vehicles

In this paper, an improved force characteristic curve fitting memetic algorithm of urban rail vehicles is proposed for establishing precise train operation models. In order to improve the memetic algorithm global convergence, three strategies are adopted. In the improved memetic algorithm framework, an improved moth-flame optimization is used in global search; an improved simulated annealing is applied in local search; a new learning mechanism incorporated into reverse learning is adopted. Experimental simulation results under real-time data monitoring system show that the improved memetic algorithm proposed in this paper can increase the optimization performance effectively so more perfect force characteristic curve fitting effort can be obtained, and the calculated average force error and max running distance error can be reduced effectively. Moreover, the above relative results indicate that the train energy consumption model using the improved force characteristic curve fitting algorithm can obtain more precise energy consumption. Obviously, the improved force characteristic curve fitting algorithm can effectively improve the curve fitting precision.
Three Major Failed Rifts in Central North America: Similarities and Differences

The North American craton preserves nearly two billion years of geologic history, including three major rifts that failed rather than evolving to continental breakup and seafloor spreading. The Midcontinent Rift (MCR) and Southern Oklahoma Aulacogen (SOA) show prominent gravity anomalies due to large volumes of igneous rift-filling rock. The Reelfoot Rift (RR), though obscure in gravity data, is of interest due to its seismicity. The ca. 1.1 Ga MCR records aspects of the assembly of Rodinia, whereas the ca. 560 Ma SOA and RR initiated during the later breakup of Rodinia and were inverted during the assembly of Pangea. Comparative study of these rifts using geophysical and geological data shows intriguing similarities and differences. The rifts formed in similar tectonic set-tings and followed similar evolutionary paths of extension, magmatism, subsidence, and inversion by later compression, leading to similar width and architecture. Differences between the rifts reflect the extent to which these processes occurred. Further study of failed rifts would give additional insight into the final stages of continental rifting and early stages of seafloor spreading.
Battery charge equalization circuit with a multi-winding transformer

Abstract This study proposes a battery charge equalization circuit with a multi-winding transformer. A full-bridge converter with soft switching is applied at the primary side of a multi-winding transformer for reducing the switching loss and increasing the system efficiency. A voltage doubler type rectifier is used at the secondary side of multi-winding transformer to decrease the number of transformer windings and the conduction loss. The important advantages of the proposed circuit include the following: The charge current is limited by the transformer leakage inductance without any choke inductor, and charge equalization is realized by adjusting the transformer’s turn ratio. A measurement index, charge equalization performance (C.E.P.), is introduced for the performance measurement of the charge equalization method. In experiments, four LiFePO4 battery modules are connected in series and discharged at different C-rates. The experimental results show that the discharging time of the series-connected battery modules can be greatly increased using the proposed battery charge equalization circuit.
Critical nodes identification in complex networks via similarity coefficient

Critical nodes of complex networks are pivotal for effective information propagation. Several centrality methods have been presented to identify critical nodes relied on the relationships between node and its neighbors, which indicates that the interaction between them will account for determining the importance of nodes. In this paper, a novel ranking method based on the defined similarity coefficient is proposed. The main thought of the proposed method is that the importance of nodes being linked to the local structural similarity between node and its neighbors. By quantifying the distinction between the node’s local structure information, a similarity matrix of the entire network is constructed first. Further, the similarity coefficient of node is defined by taking into account the interaction relationships between nodes and the local structural attribute of complex networks. Finally, a novel method, named Extended Similarity Coefficient Ranking Method (ESCRM), is proposed to determine the influentiality of node based on its neighbors’ information. We perform extensive experiments on ten real-world datasets to comprehensively estimate the performance of the ESCRM and other ten representative centralities. The experimental results demonstrate that under the Susceptible-Infected-Recovered (SIR) model, the ESCRM outperforms the other centralities on identifying critical nodes, and as a relatively efficient ranking method can provide better performance.
Evaluation of Computer Model for Operation Efficiency of Initial Rainwater Detention Tank

Hydraulic model for drainage system was used to hydraulically simulate the planning rainwater system in Shanghai EXPO area,and evaluate the long-term efficiency of initial rainwater detention tanks for controlling urban non-point pollution.The result shows that according to the design standard of 30 m3/hm2,the detention tanks can annually intercept about 50% storm runoff under average annual rainfalls in Shanghai.When the separate rainwater system has a certain initial effect,the pollutants removal rates of the detention tanks are between 42.9% and 67.4%. For the semi-separate system,if interception is allowed during rainfall,the detention percentage is higher.
Sensitivity investigation for solar still of double slope type included with N similar CPC integrated ETC by incorporating yield and efficiency

This work investigates solar still of double slope type included with N similar CPC integrated ETC having a series connection for sensitivity analysis using one at a time method by incorporating yield and efficiency. The energy-based analysis has been done by feeding all expressions of differ- ent parameters to computer code written in MATLAB-2015a for May month of New Delhi climatic situation. Fresh water yielding and energy efficiency values have been estimated for dissimilari- ties of mass flow rate maintaining all other parameters at the input of system constant and this procedure has been repeated till estimation is completed for all parameters at the input of sys- tem. All results so obtained have been 2D plotted. The fresh water yielding and energy efficiency for the considered system has been estimated to be most sensitive with respect to concentration ratio having mean sensitivity figure values as 0.91 and 0.78 in that order. Sensible conclusions have been presented based on the current analysis.
MODEL FOR NETWORK SECURITY SERVICE PROVIDER SELECTION WITH PROBABILISTIC UNCERTAIN LINGUISTIC TODIM METHOD BASED ON PROSPECT THEORY

Choosing the optimal network security service provider (NSSP) is a very important part of enterprise management decision. And the choice of NSSP is a typical multiple attribute group decision making (MAGDM) issue. In order to provide a better decision method for MAGDM problems, this paper integrates the TODIM method and the probabilistic uncertainty linguistic term set (PULTS), so as to propose the probabilistic uncertain linguistic TODIM model based on the prospect theory (PT-PUL-TODIM). The model combines the advantages of the prospect theory and TODIM method. In the end, a case study concerning NSSP selection problem is given to demonstrate the merits of the developed methods.
Stretch-Contract Operator in the Ellipsoidal Approximation of the Minkowski Sum of Convex Sets

The space expansion-contraction operator was originally developed to solve mathematical programming problems. However, it can be successfully applied to solve the problem of ellipsoidal approximation of the information set in the state space analytically specified. In this case, a main property of the operator - space compression is used to minimize the approximating ellipsoid by a multidimensional volume. The paper shows the use of the specified expansion-contraction operator to approximate a set of attainability of the linear control system as an example. The main goal of the paper is to give analytical and geometric representations of the specified operator in order to show its action in the approximation problem. For this purpose, the paper shows an analytical derivation of the operator and a geometric illustration of each parameter of the operator. The results of minimum approximation modeling by this operator compared with other known solutions have been also presented. The simulation results are given both numerically and graphically. Based on the results of comparison, conclusions are made and recommendations are given in the use of ellipsoidal approximation of information sets according to different criteria for minimizing the approximating ellipsoid. Typical examples of ellipsoidal approximation, which show when it is expedient to use the proposed of expansion-contraction operator, have been given.
Chemerin Effect on the Endometrial Proteome of the Domestic Pig during Implantation Obtained by LC-MS/MS Analysis

Chemerin (CHEM) is a hormone mainly expressed in adipocytes involved in the regulation of energy homeostasis and inflammatory response. CHEM expression has been demonstrated in the structures of the porcine hypothalamic-pituitary-gonadal axis, as well as in the uterus, trophoblasts and conceptuses of pigs. In this study, we performed high-throughput proteomic analyses (liquid chromatography with tandem mass spectrometry, LC-MS/MS) to examine the influence of CHEM (400 ng/mL) on differentially regulated proteins (DRPs) in the porcine endometrial tissue explants during implantation (15 to 16 days of gestation). Among all 352 DRPs, 164 were up-regulated and 188 were down-regulated in CHEM-treated group. DRPs were assigned to 47 gene ontology (GO) terms (p-adjusted < 0.05). Validation of four DRPs (IFIT5, TGFβ1, ACO1 and PGRMC1) by Western blot analysis confirmed the veracity and accuracy of the LC-MS/MS method used in the present study. We suggest that CHEM, by modulating various protein expressions, takes part in the endometrial cell proliferation, migration and invasion at the time of implantation. It also regulates the endometrial immune response, sensitivity to P4 and the formation of new blood vessels. Additionally, CHEM appears to be an important factor involved in endothelial cell dysfunction during the pathogenesis of preeclampsia. The identification of a large number of DRPs under the influence of CHEM provides a valuable resource for understanding the molecular mechanisms of this hormone action during implantation, which is a prerequisite for better control of pig reproduction.
NAISS - MODEL FOR TRAFFIC NOISE PREDICTION UDC:534.836

The traffic noise of motor vehicles, as main source in urban areas, makes up part of general environment problem which inflicts serious damage to the health of human beings and lowers their labor productivity. Therefore, the control of traffic noise has become a matter of major concern for communities trying to maintain a satisfactory environment in which to live and work. To ensure a high quality environment, methods for prediction the noise imission of motor vehicles are necessary tools. In order to modeling traffic noise and selecting corresponding noise control measures it is necessary to know functional relationships between noise emission and certain numbers of traffic parameters. In this paper, the results of traffic noise prediction based on NAISS-model obtained by trending of the experimental data collected by systematic noise measurement in urban areas of Nis as well as comparative analysis with other models will be shown.
Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach

Investors nowadays post heterogeneous sentiments on social media about financial assets based on their trading preferences. However, existing works typically analyze the sentiment by its content only and do not account for investor profiles and trading preferences in different types of assets. This paper explicitly considers how investor sentiment about financial market events is shaped by the relative discussions of different types of investors. We leverage a large-scale financial social media dataset and employ a structural topic modeling approach to extract topical contents of investor sentiment across multiple finance-specific factors. The identified topics reveal important events related to the financial market and show strong heterogeneity in the social media content in terms of compositions of investor profiles, asset categories, and bullish/bearish sentiment. Results show that investors with different profiles and trading preferences tend to discuss financial markets with heterogeneous beliefs, leading to divergent opinions about those events regarding the topic prevalence and proportion. Moreover, our findings may shed light on the mechanism that underlies the efficient investor sentiment extraction and aggregation while considering the heterogeneity of investor sentiment across different dimensions.
Implementação Paralela de uma Metaheurística GRASP com Path-Relinking para o Problema da Árvore Geradora de Custo Mínimo com Grupamentos

A metaheurística GRASP é um processo iterativo, cujas iterações consistem de duas fases: uma fase de construção e outra de busca local. O algoritmo retorna a melhor solução encontrada depois de um determinado número de iterações. Neste trabalho, a metaheurística GRASP é aplicada conjuntamente à técnica path-relinking a um problema variante da Árvore Geradora Mínima (AGM), denominado Árvore Geradora de custo Mínimo com Grupamentos (AGMG). O objetivo é reduzir o tempo computacional e melhorar a qualidade das soluções GRASP através de uma implementação paralela desta metaheurística. Os resultados obtidos mostraram speedup linear para esta implementação.
The Associations of Intimate Partner Violence and Non-Partner Sexual Violence with Hypertension in South African Women

This study describes associations of intimate partner violence (IPV), non-partner sexual violence (NPSV) and sexual harassment (SH) exposures with hypertension in South African women aged 18–40 years. Baseline data (n = 1742) from the Rape Impact Cohort Evaluation study, including a history of sexual, physical, emotional and economic IPV, NPSV and SH were examined. Hypertension was based on blood pressure ≥140/90 mmHg or a previous diagnosis. Logistic regressions were adjusted for traditional hypertension risk factors and previous trauma (e.g., recent rape). Hypertension was more prevalent in women with a history of all forms of IPV, NPSV, and SH, all p ≤ 0.001, compared to women without. Frequent NPSV (adjusted odds ratio: 1.63; 95% CI: 1.27–2.67) any SH (2.56; 1.60–4.03), frequent physical (1.44; 1.06–1.95) and emotional IPV (1.45; 1.06–1.98), and greater severity of emotional IPV (1.05; 1.02–1.08) were associated with hypertension. Current depression, post-traumatic stress symptoms and/or alcohol binge-drinking completely or partially mediated these associations. This study shows that exposure to gender-based violence is associated with hypertension in young women. Understanding the role of psychological stress arising from abuse may enable the development of prevention and management strategies for hypertension among women with histories of abuse.
Virtual ultrasound transducers in the human brain

We aim to construct the signal between two points inside a model of the human skull as if there were virtual transducers located inside the skull. We show how this can be achieved through the use of time-reversed Green's functions that are measured on opposite sides of the skull. We then demonstrate how to achieve similar results using special wavefields named focusing functions, which are designed to work specifically when injected from a single-side of the medium of interest. We show two ways to obtain these focusing functions, through the use of the iterative Marchenko method and by inversion of a measured Green's function. The inversion of the Green's function shows potential benefits over the use of the Marchenko method, however, this approach requires further studies. We demonstrate how these wavefields function on 2D acoustic in silico data by injecting the time-reversed Green's functions and focusing functions into the model. We also demonstrate how the response between virtual transducers can be obtained directly through use of the homogeneous Green's function representation.
Stroke lesion localization in 3D MRI datasets with deep reinforcement learning

The efficacy of stroke treatments is highly time-sensitive, and any computer-aided diagnosis support method that can accelerate diagnosis and treatment initiation may improve patient outcomes. Within this context, lesion identification in MRI datasets can be time consuming and challenging, even for trained clinicians. Automatic lesion localization can expedite diagnosis by flagging datasets and corresponding regions of interest for further assessment. In this work, we propose a deep reinforcement learning agent to localize acute ischemic stroke lesions in MRI images. Therefore, we adapt novel techniques from the computer vision domain to medical image analysis, allowing the agent to sequentially localize multiple lesions in a single dataset. The proposed method was developed and evaluated using a database consisting of fluid attenuated inversion recovery (FLAIR) MRI datasets from 466 ischemic stroke patients acquired at multiple centers. 372 patients were used for training while 94 patients (20% of available data) were employed for testing. Furthermore, the model was tested using 58 datasets from an out-of-distribution test set to investigate the generalization error in more detail. The model achieved a Dice score of 0.45 on the hold-out test set and 0.43 on images from the out-of-distribution test set. In conclusion, we apply deep reinforcement learning to the clinically well-motivated task of localizing multiple ischemic stroke lesions in MRI images, and achieve promising results validated on a large and heterogeneous collection of datasets.