Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Purpose Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps. Design/methodology/approach This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing. Findings Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction. Research limitations/implications The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations. Practical implications The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications. Originality/value This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.
A climate resilience research renewal agenda: learning lessons from the COVID-19 pandemic for urban climate resilience

ABSTRACT Learning lessons from the COVID-19 pandemic opens an opportunity for enhanced research and action on inclusive urban resilience to climate change. Lessons and their implications are used to describe a climate resilience research renewal agenda. Three key lessons are identified. The first lesson is generic, that climate change risk coexists and interacts with other risks through overlapping social processes, conditions and decision-making contexts. Two further lessons are urban specific: that networks of connectivity bring risk as well as resilience and that overcrowding is a key indicator of the multiple determinants of vulnerability to both COVID-19 and climate change impacts. From these lessons three research priorities arise: dynamic and compounding vulnerability, systemic risk and risk root cause analysis. These connected agendas identify affordable and healthy housing, social cohesion, minority and local leadership and multiscale governance as entry points for targeted research that can break cycles of multiple risk creation and so build back better for climate change as well as COVID-19 in recovery and renewal.
Realtime autonomous navigation in V-Rep based static and dynamic environment using EKF-SLAM

Localization in an autonomous mobile robot allows it to operate autonomously in an unknown and unpredictable environment with the ability to determine its position and heading. Simultaneous localization and mapping (SLAM) are introduced to solve the problem where no prior information about the environment is available either static or dynamic to achieve standard map-based localization. The primary focus of this research is autonomous mobile robot navigation using the extended Kalman filter (EKF)-SLAM environment modeling technique which provides higher accuracy and reliability in mobile robot localization and mapping results. In this paper, EKF-SLAM performance is verified by simulations performed in a static and dynamic environment designed in V-REP i.e., 3D Robot simulation environment. In this work SLAM problem of two-wheeled differential drive robot i.e., Pioneer 3-DX in indoor static and dynamic environment integrated with Laser range finder i.e., Hokuyo URG-04LX- UG01, LIDAR, and Ultrasonic sensors is solved. EKF-SLAM scripts are developed using MATLAB that is linked to V-REP via remote API feature to evaluate EKF-SLAM performance. The reached results confirm the EKF- SLAM is a reliable approach for real-time autonomous navigation for mobile robots in comparison to other techniques.
Hype News Diffusion and Risk of Misinformation: The Oz Effect in Health Care

Consumers’ choices about health products are heavily influenced by public information, such as news articles, research articles, online customer reviews, online product discussion, and TV shows. Dr. Oz, a celebrity physician, often makes medical recommendations with limited or marginal scientific evidence. Although reputable news agencies have traditionally acted as gatekeepers of reliable information, they face the intense pressure of “the eyeball game. ” Customer reviews, despite their authenticity, may come from deceived consumers. Therefore, it remains unclear whether public information sources can correct the misleading health information. In the context of over-the-counter weight loss products, the authors carefully analyze the cascading of information post endorsement. The analysis of extensive textual content with deep-learning methods reveals that legitimate news outlets respond to Dr. Oz's endorsement by generating more news articles about the ingredient; on average, articles after the endorsement contain higher sentiment, so news agencies seem to amplify rather than rectify the misleading endorsement. The finding highlights a serious concern: the risk of hype news diffusion. Research articles react too slowly to mitigate the problem, and online customer reviews and product discussions provide only marginal corrections. The findings underscore the importance of oversight to mitigate the risk of cascading hype news.
Interactive Effects of Residue and Tillage Methods on Growth, Yield and Yield Components of Melon

Conventional tillage is practiced substantialy in melon growing areas. Although conventional tillage has destructive effects on the environment, it also has many negative effects on the health of agroecosystems and crop production. However, very few experiments and data have been conducted to investigate the functional differences (growth, yield and yield components) of melon crop under different tillage methods. Field experiments were accomplished to research the interactive effect of residue and tillage methods on growth analysis, yield and yield components of melon during the 2019 growing season. Tillage treatments in this research included were conventional tillage (CT; moldboard ploughing + two passes of disk harrowing), no-tillage (NT) and minimum tillage (MT; one pass of disk harrowing) and. Residue treatments were included 0%, 30% and 60%. Yield, yield components included fruit weight per plant (FWPP), the number of fruit per plant (NFPP), diameter (D), length (L), sugar (S), radiation use efficiency (RUE) and water use efficiency (WUE) were obtained for all treatments. Results showed that tillage methods significantly (P≤0.05) influenced the yield and yield components. Analysis of variance results showed that yield, FWPP, NFPP, D, L, S and RUE were significantly under the different tillage methods, moreover yield, FWPP, NFPP, D, L and S were significantly under the different residue percentage and interactive effect of tillage and residue. The maximum FWPP (2.678kg), NFPP (6.799), D (18.49cm), L (45.93cm), S (12.51%) and RUE (2.470) as a result yield (16.17 t ha -1 ) were observed with the CT. Also, maximum FWPP (2.192), NFPP (5.353), D (16.66), L (39.52) and yield (12.83 t ha -1 ) were obtained from 30% residue. On the other hand, maximum FWPP (2.850), NFPP (6.790), D (20.71), L (53.53) and yield (17.09 t ha -1 ) from CT30% were obtained. Therefore, moldboard ploughing followed by two passes of disk harrow with 30% residue were found to be more appropriate and profitable tillage method and residue percentage in improving the yield of melon in the temperate cold climatic zone. Almost all of the growth indicators were optimum for conservation tillage treatments, especially with 30% residue.
Is the relationship between socioeconomic status (SES) and student achievement causal? Considering student and parent abilities

ABSTRACT Most studies on the relationship between students’ socioeconomic status (SES) and student achievement assume that its effects are sizable and causal. A large variety of theoretical explanations have been proposed. However, the SES–achievement association may reflect, to some extent, the inter-relationships of parents’ abilities, SES, children’s abilities, and student achievement. The purpose of this study is to quantify the role of SES vis-à-vis child and parents’ abilities, and prior achievement. Analyses of a covariance matrix that includes supplementary correlations for fathers and mothers’ abilities derived from the literature indicate that more than half of the SES–achievement association can be accounted for by parents’ abilities. SES coefficients decline further with the addition of child’s abilities. With the addition of prior achievement, the SES coefficients are trivial implying that SES has little or no contemporaneous effects. These findings are not compatible with standard theoretical explanations for SES inequalities in achievement.
Assessment of the phytochemical profile and antioxidant activities of eight kiwi berry (Actinidia arguta (Siebold & Zuccarini) Miquel) varieties in China

Abstract The kiwi berry (Actinidia arguta) is a new product on the market that expanding worldwide acceptance and consumption. This widespread interest has created an increasing demand to identify the nutritional and health benefits of kiwi berry. Many studies are being actively conducted to investigate the composition and health‐promoting effects of kiwi berry. In this study, the phytochemical content of free and bound fractions of eight kiwi berry varieties were systematically investigated in order to better understand the potential of this superfood crop. Nine phenolic monomers were identified and quantified by ultrahigh‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry and ultrahigh‐performance liquid chromatography‐PAD. Antioxidant activity was further determined via peroxyl radical scavenging capacity and cellular antioxidant activity assays. The free extracts had higher phytochemical contents and antioxidant activities than the corresponding bound extracts among the eight kiwi berry varieties. Bivariate Pearson's and multivariate correlation analyses showed that antioxidant activities were most related to the total phenolic, flavonoid, vitamin C, and phenolic acids contents. The results provide a theoretical basis for the selection of kiwi berry varieties and the utilization of functional foods.
Evaluation of the Erosion Characteristics for a Marine Pump Using 3D RANS Simulations

In the present study, an erosion analysis of an industrial pump’s casing and impeller blades has been performed computationally. Effects of various critical parameters, i.e., the concentration and size of solid particles, exit pressure head, and cavitation on the erosion rate density of the casing and blade have been investigated. Commercial codes CFX, ICEM-CFD, and ANSYS Turbogrid are employed to solve the model, mesh generation for the casing, and mesh generation of the impeller, respectively. The Eulerian-Eulerian method is employed to model the pump domain’s flow to solve the two phases (water and solid particles) and the interaction between the phases. Published experimental data was utilized to validate the employed computational model. Later, a parametric study was conducted to evaluate the effects of the parameters mentioned above on the erosion characteristics of the pump’s casing and impeller’s blade. The results show that the concentration of the solid particles significantly affects the pump’s erosion characteristics, followed by the particle size and distribution of the particle size. On the other hand, the exit pressure head and cavitation do not affect the erosion rates considerably but significantly influence the regions of high erosion rate densities.
The relationship between debt aversion and college enrollment by gender, race, and ethnicity: a propensity scoring approach

ABSTRACT Borrowing has become increasingly central to U.S. students’ college financing choices. However, concerns about indebtedness may serve as a barrier to attaining a college degree for many students, particularly women and Black and Hispanic students. This study explores the determinants and heterogeneous impacts of debt aversion, or aversion to borrowing for college. I use a propensity score matching strategy to estimate the relationship between debt aversion and the college enrolment and financing choices of high school students while controlling for confounding variables such as students’ pecuniary valuation of college, college preparedness, and family background. More debt-averse students borrow less than other students if they enroll in college and generally attend lower cost colleges. Women and Black students are more likely on average to report moderate debt aversion than other students. Strong debt aversion is associated with lower college enrollment only for men and white, non-Hispanic students, and highly debt-averse women tend to report lower borrowing relative to the control group than similarly situated men. The results point to the need for a combination of robust college counseling in high schools and readily accessible income-based repayment programs.
Evaluation of the effects of water quality on drinking preferences of heifer calves

Graphical Abstract Summary: The purpose of this study was to assess the intake and preference of heifer calves for 3 types of drinking water: clean (tap water), well water, and water treated with a reverse osmosis (RO) purification system. The RO water purification system uses semipermeable membranes to remove larger mineral particles and some bacteria from drinking water. To determine water drinking preference, the consumption of each type of water by heifer calves was measured. Results demonstrated that heifers preferred to drink RO water over tap water or well water.
Obsessive Compulsive Symptoms Are Associated With Heightened Avoidance of Low-Probability, High-Aversion Threats: A Preliminary Test of the Improbable-Catastrophe Hypothesis

Although symptoms of obsessive compulsive disorder (OCD) may vary markedly, they often involve a fear of consequences that are both catastrophic and highly improbable (e.g., contracting HIV from a doorknob). Accordingly, a heightened sensitivity to what we refer to as improbable catastrophes may represent an underlying feature of OCD, yet this possibility awaits experimental validation. To fill this gap, 78 undergraduates with wide-ranging levels of OCD symptom severity completed a fear-conditioning paradigm designed to elicit varying degrees of perceived threat probability/aversiveness to test whether OCD symptoms predict heightened reactivity to unlikely, high-aversion threats. Consistent with predictions, participants with higher OCD symptoms were more avoidant of low-probability, high-aversion threats and also exhibited greater threat expectancy and physiological reactivity to more improbable threats in general. These findings implicate excessive avoidance of improbable catastrophes and heightened reactivity to unlikely threats more generally as underlying features of OCD.
An overview of information extraction techniques for legal document analysis and processing

In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.
‘Access necessitates being seen’: Queer visibility and intersectional embodiment within the health information practices of queer community leaders

Navigating healthcare infrastructures is particularly challenging for queer-identifying individuals, with significant barriers emerging around stigma and practitioner ignorance. Further intersecting, historically marginalised identities such as one’s race, age or ability exacerbate such engagement with healthcare, particularly the access to and use of reliable and appropriate health information. We explore the salience of one’s queer identity relative to other embodied identities when navigating health information and care for themselves and their communities. Thirty semi-structured interviews with queer community leaders from South Carolina inform our discussion of the role one’s queer visibility plays relational to the visibility of other identities. We find that leaders and their communities navigate these intersectional visibilities through unique and iterative approaches to health information seeking, sharing and use predicated upon anti-queer, racist, ableist and misogynistic sentiments. Findings can inform queer-inclusive, intersectionally informed interventions by health and information professionals such as non-profit advocacy organisations and medical librarians.
Adaptive sliding mode observers for sector‐bounded nonlinear systems

This article introduces a novel sliding mode observer design for sector bounded nonlinear systems. The proposed observer can simultaneously estimate both states and unknown parameters in the presence of disturbances and measurement noise. The observer is developed by using a time‐averaged Lyapunov (TAL) functional to analyze the effect of noise (Gaussian) and to adequately reduce its effect on the system. The TAL averages the Lyapunov analysis over a small finite time interval, allowing for intuitive analysis of noises and disturbances acting on the system. The TAL is shown to satisfy all the requirements of a Lyapunov candidate function. The article focuses on the observer design for sector bounded nonlinear systems since several nonlinearities can be modeled using a sector bound. An optimization approach is also developed to provide a tight bound on the effect of the uncertainty on the estimated parameters. The conditions for the existence of the observer are presented in the form of linear matrix inequality (LMI), which can be explicitly solved offline using commercial LMI solvers. Furthermore, the LMI design is also extended to a specific case of a dissipative nonlinear system. The observer design has also been extended to the case where the input disturbance is correlated with the sensor noise.
Role of tourist motivation as mediating variable on visitor decisions at Indonesian tourism village

The tourism sector has become a truly global force for promoting economic growth and development. Therefore, the study of tourism has become an interesting topic for researchers lately. On the other hand, local tourism, generally in developing countries, is often neglected by academics and policymakers. For this reason, this study aims to examine and analyze the role of tourist motivation in mediating accessibility, amenities, and attractions on visiting decisions. This study is a survey research with an explanatory method. The population is tourists who visit the tourism village of Bumiaji, Indonesia, in the low and busy seasons. The population is infinite and the number of respondents who were interviewed is 100 respondents; data were collected by distributing questionnaires to domestic tourists who came from outside the tourist village of Bumiaji, then the data were processed and analyzed using Warp Partial Least Squares. The findings indicate that the effect of accessibility on visiting decisions is not mediated by tourist motivation. This shows that the decision of tourists to visit can be directly influenced by the time and means of transportation available. Meanwhile, the influence of amenities and attractions on the decision to visit is mediated by the motivation of tourists. This means that amenities and attractions can influence a tourist’s decision to visit if there is an urge from tourist to relax or make friends or enjoy the culture at tourist attractions, etc.
A comparison of different approaches for the definition of seasons in the Mediterranean region

Α new method, based on the definition of weather types (WTs), for the determination of seasons in the Mediterranean region is adopted. This method combines principal component analysis and k‐means Cluster Analysis on daily grid point meteorological data for the 70‐year period 1949–2018 obtained from the NCEP/NCAR database. At first, the method is applied to the classification of WTs and subsequently to the definition of seasons based on the mean intra‐annual variations of the WTs' frequency. The resulting seasons are compared to a previous method of the seasons' definition, based on the long‐term mean intra‐annual variations of climatological parameters. Furthermore, a synthesis of the two methods is proposed. The analysis is applied not only to the 70‐year period 1949–2018, but also to the five overlapping 30‐year sub‐periods 1949–1978, 1959–1988, 1969–1998, 1979–2008, 1989–2018 in order to investigate possible long‐term changes of the seasons' characteristics. Four seasons are identified for all periods and for both methodological approaches. These seasons broadly correspond to the four conventional ones, but they differ in their onset and cessation dates as well as their duration. For the composite method, it is found that winter lasts about 4 months, summer is a little longer than 3 months and spring and autumn last around 2.5 months. The most remarkable findings regarding the seasons' characteristics are: (a) the start/end dates and duration of winter are almost identical in all three approaches, while summers' characteristics differ; (b) shortening of winter and spring in recent periods, associated with the delayed start and expedited end dates, respectively; and (c) the prolongation of autumn and summer, due to the delay of its end date, and the earlier onset and delayed end dates, respectively.