Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis on Influence of Job-Housing Balance on Commute Travel in Shang-hai

The hypothesis that job-housing spatial balance influencing the journey to work has been studied in the west countries for half a century,while domestic te- search findings in China are few.Using the experience abroad for reference,an in- dicator is built to measure the job-housing spatial balance in Shanghai.The calcula- tions demonstrate that the job-housing spatial imbalance is getting more and more intense.Concretely,core districts are principally characterized by jobs in the tertiary sector;the periphery districts of the central city and the nearby suburban districts are dominated by residence,and in the outer suburban districts jobs in the sec- ondary sector are more important than residence.The job-housing spatial imbalance results further in the growth of commute travel and the rise of average commute distance and commute time.From the findings above the current urban policies in Shanghai are reviewed and several enlightenments including mixed land use are drawn to strengthen the job-housing spatial balance.
Central Lancashire Online Knowledge (CLoK)

—Vehicular networks are one of the main technologies that will be leveraged by the arrival of the future fifth generation (5G) mobile cellular networks. While scalability and latency are the major drawbacks of IEEE 802.11p and 4G LTE enabled vehicular communications, respectively, the 5G technology is a promising solution to empower the real-time services offered by vehicular networks. However, the security and privacy of such services in 5G enabled vehicular networks need to be addressed first. In this paper, we propose a novel system model for a 5G enabled vehicular network that facilitates a reliable, secure and privacy-aware real-time video reporting service. This service is designed for the participating vehicles to instantly report the videos of traffic accidents to guarantee a timely response from official and/or ambulance vehicles toward accidents. While it provides strong security and privacy guarantees for the participating vehicle’s identity and the video contents, the proposed service ensures traceability of misbehaving participants through a cooperation scheme among different authorities. We show the feasibility and the fulfilment of the proposed reporting service in 5G enabled vehicular networks in terms of security, privacy and efficiency.
Critical Thinking Development in Physics Courses by Problem-Based Learning in Virtual Collaboration Environments

This research comprises the design of a strategy to develop critical thinking skills throughout physics courses in engineering degrees through collaborative work, problem-based learning (PBL), and virtual classroom interactions. To know the level of student development, an evaluation instrument is applied. The results allow us to propose improvements in learning strategies, achieving greater learning effectiveness before the end of the course. Various research stages use quantitative, qualitative, and inductive-deductive methods. An open-ended question instrument is adapted to show the students' initial and final critical thinking skills. This didactic instrument focuses on developing critical thinking through the use of collaborative work to solve problems in virtual classrooms. We find that students demonstrate cognitive skills that allow them to find the right solutions based on analysis and inference with various proceedings.
The adequacy of aging techniques in vertebrates for rapid estimation of population mortality rates from age distributions

As a key parameter in population dynamics, mortality rates are frequently estimated using mark–recapture data, which requires extensive, long‐term data sets. As a po‐ tential rapid alternative, we can measure variables correlated to age, allowing the compilation of population age distributions, from which mortality rates can be de‐ rived. However, most studies employing such techniques have ignored
Effects of resistance training on health indicators in breast cancer survivors

The present study aimed to investigate the effects of resistance training on health indicators in breast cancer survivors. Twenty-two women with a history of mastectomy and lymphadenectomy completed 12 weeks of training. Strength, body mass index, body composition, hematological, and biochemical aspects, and erythrocyte membrane stability were performed before and after the training. In all exercises, there were significant gains in maximal strength and strength endurance. Moreover, there was a decrease in body fat percentage, an increase in lean mass percentage, a reduction in erythrocytes, platelets, and hemoglobin, as well as desirable changes for all lipid profile variables. Altogether, these findings highlight the multidimensional impact of resistance training on the health of breast cancer survivors and reveal the need for constant monitoring of this public.
A protocol for systematic review and meta-analysis

Background: At the end of 2019, peoples normal lives were disrupted by a sudden plague (COVID-19), the huge impact of COVID-19 on society has never been appeared. How to effectively prevent and treat COVID-19 is a concern for all health care workers. Exercise as a green and cheap complementary therapy, which has been proven to improve the immune capacity of the body and prevent infection. The main purpose of this study is to provide a reliable methodological guidance and credible evidence for exercise on COVID-19 therapeutic. Methods: This protocol is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols. We will search the following database sources for the Randomized controlled trials: the Cochrane Library, PubMed, EMBASE, Web of Science, Chinese Biomedical Literature Database (CBM), Chinese National Knowledge Infrastructure Database (CNKI), Chinese Science and the Wanfang Database. All randomized controlled trials of exercise therapy for COVID-19 in the above database will be considered for inclusion, and high-quality articles will be screened for data extraction and analysis, to summarize the therapeutic effect of exercise on COVID-19 patients. Results: In this study, we hope to find strong evidence for the treatment of COVID-19 by exercise. Conclusion:The conclusion of our study will provide credible evidence to judge whether exercise is an effective intervention on the COVID-19 patients therapeutic, and guide future researches. PROSPERO registration number: CRD42020200883. Abbreviations: CIs = confidence interval, COVID-19 = Corona Virus Disease 2019, PRISMA-P = Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols, RCT = randomized controlled trial, RR = risk ratio, SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2, WHO = World Health Organization.
Comparison of mean properties of simulated convection in a cloud-resolving model with those produced by cumulus parameterization

The National Center for Atmospheric Research (NCAR)/Penn State Mesoscale Model (MM5) has been used to simulate this period on a 60-km domain with 20and 6.67-km nests centered on Lamont, Oklahoma. Simulations are being run with data assimilation by the nudging technique (Kuo and Guo 1989; Stauffer and Seaman 1990) to incorporate upper-air and surface data from a variety of platforms. The model maintains dynamical consistency between the fields, while the data correct for model biases that may occur during long-term simulations and provide boundary conditions. For the The model features and options used in this study are as work reported here the Mesoscale Atmospheric Prediction follows. Equations are for nonhydrostatic, compressible System (MAPS) of the National Ocean and Atmospheric motion, in terrain-following coordinates with a Administration (NOAA) 3-hourly analyses were used to polar-stereographic map projection. Prognostic equations drive the 60-km domain while the inner domains were exist for wind components, vertical velocity, pressure unforced. A continuous 10-day period was simulated. perturbation, temperature, water vapor, ground
The Investigation on Adversarial Attacks of Adversarial Samples Generated by Filter Effects

In contemporary times, there has been a growing inclination among individuals to engage in photography and employ uncomplicated filters to enhance their visual outputs. Although these seemingly straightforward and aesthetically enhanced images are favored by many, they can inadvertently lead to erroneous interpretations by computer vision systems. Such misinterpretations often arise due to the presence of imperceptible image noise, which remains undetectable to the human eye. In this paper, we aim to add some filter effects to the image to verify the effectiveness of the classification results of the interference model, conduct black-box disturbance attacks on the model, and generate adversarial attack samples. For specific anti-attack implementation, we will use the following algorithms to filter the image, among which the contrast and brightness of the image are improved using the histogram equalization procedure; the blur filter algorithm is used to reduce the noise, texture or details in the image to make it more blurred; Utilize the sharpening algorithm to improve the image's edges and features for a crisper, sharper appearance; through the smoothing algorithm to make the image look smoother; through the edge enhancement algorithm to make it clearer. We will use the classic CNNs model to conduct experiments on two datasets of similar size and number but with large differences in image content. The final experimental findings demonstrate that filter interference does affect the model's categorization outcomes.
Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network

In this paper, we focus on the template inversion attack against face recognition systems and propose a new method to reconstruct face images from facial templates. Within a generative adversarial network (GAN)-based framework, we learn a mapping from facial templates to the intermediate latent space of a pre-trained face generation network, from which we can generate high-resolution realistic reconstructed face images. We show that our proposed method can be applied in whitebox and blackbox attacks against face recognition systems. Furthermore, we evaluate the transferability of our attack when the adversary uses the reconstructed face image to impersonate the underlying subject in an attack against another face recognition system. Considering the adversary’s knowledge and the target face recognition system, we deﬁne ﬁve different attacks and evaluate the vulnerability of state-of-the-art face recognition systems. Our experiments show that our proposed method achieves high success attack rates in whitebox and blackbox scenarios. Furthermore, the reconstructed face images are transferable and can be used to enter target face recognition systems with a different feature extractor model. We also explore important areas in the reconstructed face images that can fool the target face recognition system.
Research on Image Classification Based on Convolutional Neural Network

The convolutional neural networks (CNNs) are widely used for image classification tasks because CNNs can successfully capture spatial hierarchies and patterns in images. A dataset can be utilized to evaluate the performance of various types of CNNs. To compare the effectiveness of four CNN models for image classification on specific datasets, this study utilizes the MNIST dataset to train four classic CNNs and subsequently compares and evaluates the classification outcomes. The four models are LeNet (LeNet), AlexNet, Visual Geometry Group Network (VGGNet) and Visual Geometry Group Network (ResNet). In order to address the performance of four neural network models in image classification, a controlled experiment is conducted. The results of this study indicate LeNet is the most suitable model on the MNIST dataset. While the other three models also exhibit commendable classification results, they fall short of the overall performance achieved by the LeNet model. The other three models can be used with challenging datasets.
Sentiment Analysis Using Support Vector Machines, Neural Networks, and Random Forests

Sentiment analysis, often referred to as opinion mining, plays a crucial role in the field of natural language processing by computationally analyzing text to extract sentiments or opinions. It has become essential in understanding public sentiment, evaluating customer feedback, and identifying market trends, particularly in the age of online platforms and social media. The background of sentiment analysis stems from the need to process vast amounts of textual data and extract meaningful sentiment information. Manual analysis is impractical due to the exponential growth of online content, prompting the development of automated sentiment analysis techniques. Sentiment analysis holds significant value in uncovering insights related to customer satisfaction, brand perception, and sentiment trends. It empowers businesses to improve strategies, enhance customer experiences, and researchers to understand public opinion on various issues. Government agencies can also benefit from assessing public sentiment for evidence-based decision-making. This paper provides an overview of three prominent sub-topics in sentiment analysis: Support Vector Machines (SVMs), Neural Networks, and Random Forests. These approaches have gained significant attention in both research and industry applications, contributing to sentiment classification advancements.
Do Investors Care About ESG Ratings?

ESG ratings and sustainability are becoming a more important topics by the day. However, do investors care about ESG ratings and thus sustainability? Following the methodology of Bolton and Kacperczyk (2021), we investigate the relationship between ESG scores and returns for US public-listed firms from 2006 to 2022. We find that there is a positive relationship between ESG ratings and returns. This effect is larger after the Paris Agreement compared to before and does not depend on the state of the economy. We construct self-financing portfolios using ESG scores and Carbon Emissions. Traditional Risk factors are not able to explain the returns of these portfolios. The findings regarding the alphas of the ESG portfolios are inconclusive.
Atomic reconstructions and dynamic-evolutionary pathways on a model polar surface of ionic crystal

Preparation of atomic reconstructions and tracking its dynamic-evolutionary pathways for some polar surfaces of ionic crystals are essential in surface science but remain a formidable challenge. Mainly by scanning tunneling microscopy, we investigate various reconstructions as well as their dynamic-evolutionary pathways on ZnO(0001) surface as a prototypical ionic crystal, which have been successfully prepared by introducing Ga surface segregation. We observe three complex pathways from an initial disorder to �nally well-ordered surface with (2×8) reconstruction undergoing complicated atom desorption, missing and moving dynamic processes, one of which is “disorder → [disorder + ( √ 3 × √ 3)R30°] → [(2 × 2)] → zigzag arrangement → [(2 × 8)]”. The atomic reconstructions are proposed to originate from the modi�cation of empty states of dangling bonds of the surface by surface segregation. Our �ndings provide a general model for preparing plentifully atomic reconstructions and revealing dynamic-evolutionary pathways on complex polar surfaces of ionic crystals.
Patient experiences of diabetes and hypertension care during an evolving humanitarian crisis in Lebanon: A qualitative study

Humanitarian health care models increasingly incorporate care for non-communicable diseases (NCDs). Current research evidence focuses on burden of disease, service provision and access to care, and less is known about patient’s experience of the continuum of care in humanitarian settings. To address this gap, this study explored experiences of displaced Syrian and vulnerable Lebanese patients receiving care for hypertension and/or diabetes at four health facilities supported by humanitarian organisations in Lebanon. We conducted in-depth, semi-structured qualitative interviews with a purposive sample of patients (n = 18) and their informal caregivers (n = 10). Data were analysed thematically using both deductive and inductive approaches. Both Syrian and Lebanese patients reported interrupted pathways of care. We identified three typologies of patient experience at the time of interview; (1) managing adequately from the patient’s perspective; (2) fragile management and (3) unable to manage their condition(s) adequately, with the majority falling into typologies 2 and 3. Patients and their families recognised the importance of maintaining continuity of care and self-management, but experienced substantial challenges due to changing availability and cost of medications and services, and decreasing economic resources during a period of national crises. Family support underpinned patient’s response to challenges. Navigating the changing care landscape was a significant burden for patients and their families. Interactions were identified between mental health and NCD management. This study suggests that patients experienced disrupted, non-linear pathways in maintaining care for hypertension and diabetes in a humanitarian setting, and family support networks were key in absorbing treatment burden and sustaining NCD management. Recommendations are made to reduce treatment burden for patients and their families and to support sustainable condition management.
Seeking help for mental health during the COVID-19 pandemic: A longitudinal analysis of adults’ experiences with digital technologies and services

The COVID-19 pandemic brought about dramatic changes in how patients access healthcare from its outset. Lockdown restrictions and remote working led to a proliferation of digital technologies and services, which also impacted mental health provisions. Against the backdrop of new and changing support services, along with an unprecedented emphasis on mental health, relatively little is known about how adults sought out and received support for their mental health during this period. With a sample of 27,574 adults assessed longitudinally online over 12 months of the pandemic in the UK, we analysed reports of help-seeking for mental health, as well as sources of treatment or support and the perceived helpfulness of treatments received. We observed that the proportions of participants who reported seeking help remained relatively consistent throughout the 12-month period (ranging from 12.6% to 17.0%). Online talking therapies were among the most frequently sought sources (15.3%), whereas online self-guided treatments were among the least frequently sought sources (5%). Telephone lines, both NHS and non–governmental, had marked treatment ‘gaps’. These treatment gaps, where individuals sought treatment but did not receive it, were especially evident for men and older adults. Our findings underscore online talking therapies as being a widely-sought and helpful source of mental health support. This is important given the current global need for accessible treatment options.
Promoting jet fuel production by utilizing a Ru-doped Co-based catalyst of Ru-Co@C(Z-d)@Void@CeO2 in Fischer Tropsch synthesis

In this study, the MOF-derived hollow void catalyst Co@C(Z-d)@Void@CeO2 is promoted using ruthenium (Ru) for application as an efficient catalyst for the Fischer Tropsch synthesis (FTS). The reducibility of Co active sites is significantly improved in the presence of the Ru nanoparticles (NPs), leading to a higher degree of reduction (DOR) and dispersion. Hence, the catalyst performance, i.e., CO conversion, was enhanced by 56% at 12 bar in comparison with the catalyst without Ru. Moreover, the Ru-doped catalyst promotes the jet fuel production yields more than the other FTS products. Remarkably, both the experimental results and the molecular dynamics (MD) simulation confirmed the desired effects, where the calculated Gibbs free energy (ΔG) of paraffinic hydrocarbon formation, particularly in the jet fuel range, was lower in the presence of Ru. The thermal stability of the Ru-doped catalyst was characterized by thermogravimetric analysis (TGA) and confirmed by a dramatic low-performance loss of 4.2% at 17.5 bar during TOS of 192 h.
Leadership effectiveness through coaching: Authentic and change-oriented leadership

There has been an increasing shift towards individually owned leader development programs within organizations. Whilst leadership coaching is one of these and is gaining in popularity, the mechanisms of its effect remain poorly understood. We develop and investigate a model in which leadership coaching enhances leader effectiveness through coaching’s positive effect on authentic and change-oriented leadership behaviours as well as self-efficacy. To assess the model, multi-source data were collected for organizational leaders (N = 70) pre- and post-coaching. To investigate mechanisms of coaching’s effect, relations between latent change scores were assessed in structural equation modelling using partial least squares indicating that after accounting for base-line scores, coaching-related increases in authentic leadership behaviour has the largest total effect on leadership effectiveness.
RELATIONSHIP BETWEEN SPATIAL DISTRIBUTION OF RESIDENT TRIPS AND MIXED DEGREE OF LAND USE: A CASE STUDY OF GUANGZHOU

By analyzing the population, employment, land use and residents trips of Guangzhou, this paper discusses how to calculate the mixed degree of land use and sets up a relation-ship model between the residents trips and mixed degree of land use. It is concludes that more mixed land use and improvement of internal function of dis-tricts especially newly developed districts would balance the spatial distribution of resident trips there-fore ease the pressure on urban transportation. Simultaneously, attracting residents to live or work in the new districts by providing with convenient public transportation could improve the old districts’ living environment and traffic conditions, which could balance the development of new districts and rede-velopment of old districts.
Journal of the Association for Information Systems

Digital infrastructures enable delivery of information services in functional areas such as health, payment, and transportation by providing a sociotechnical foundation for partnership governance, resource reuse, and system integration. To effectively serve emerging possibilities and changing purposes, however, a key question concerns how an infrastructure can be extended to cater for future services in its functional area. In this paper, we approach such digital infrastructure growth as a challenge of aligning new partners whose digital capabilities spur innovative services that attract more users. We advance an initial typology that covers four growth tactics (i.e., adding services, inventing processes, opening identifiers, and providing interfaces) with the potential to set extension of infrastructures in motion. We then explore the proposed typology by investigating the ways in which its particular tactics successfully extended the scope of a digital infrastructure for public transportation in Stockholm, Sweden. Our insights invite IS scholars to engage more deeply in the development of growth tactics that achieve infrastructure extensions necessary for improving the durability of service delivery.