Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Technologies for Road User Charging: International Experience and Future Trends

Up to 5 years ago, the mainstream technology for electronic toll collection was Dedicated Short Range Communication (DSRC) and its near relation, Automatic Vehicle Identification (AVI). Schemes based on these formed the backbone of national charging systems in Europe, North and South Americas and South East Asia. Standards were publicly available and these technologies were considered to be a safe solution for tolling. Europe and North America had already deployed regional interoperable schemes, including the Inter Agency Group? s EZ-Pass in the US tri-state area and Telepeage Inter-Societe (TIS) in France. However, the fiscal and economic policies that once supported tolls have also evolved: the decline in revenues from gas tax, increased vehicle miles traveled (VMT) and the unchecked rise in congestion in cities are forcing governments to rethink how we should be charged for road usage. Road User Charging (RUC) is now firmly on the public agenda. National governments and highway operators are now faced with the broadest possible choice of technology options including: satellite positioning (GPS, EGNOS and the future Galileo system), Automatic Number Plate Recognition (ANPR) systems, Electronic Vehicle Identification (EVI), the humble odometer and, the emergence of the electronic tachograph. Enforcement regimes now depend on image-based evidential capture rather than barriers. Toll plazas are also being replaced by Open Road Tolling (ORT) and Multi Lane Free Flow (MLFF) solutions. Classification treadles are being replaced by advanced laser profiling and stereoscopic imaging. Most recently, we have even seen novel Infrared (IR) absorption methods used to count vehicle occupancy to help enforce High Occupancy Toll (HOT) lanes. This paper therefore presents a critical analysis of advanced charging and enforcement technology options and shows the future direction of technology evolution.
SHARED AUTONOMOUS VEHICLES FOR EFFICIENT FIRST-MILE 1 CONNECTION TO EVACUATE VULNERABLE POPULATIONS

20 Shared autonomous vehicle (SAV) fleets can assist in regional evacuations, especially for those without 21 private-car access. A range of evacuation scenarios are microsimulated here, for the coast of Houston, Texas 22 (where hurricanes are increasingly common). The scenarios use different SAV seating capacities, fleet sizes, 23 shared-ride acceptance levels, and vehicle-to-bus coordination principles. While bigger SAVs enable higher 24 seat-count occupancies, overall cost-minimizing results (reflecting evacuee delay and vehicle costs) 25 recommend 5-seater (small) SAVs, and fleet sizes of 1 SAV per 14 evacuees, in this Houston setting. As 26 expected, SAV-to-bus connections are more effectively managed when SAVs are coordinated with bus-27 departure schedules. Moreover, SAV ride-sharing (among strangers) should be actively orchestrated in 28 lower-density/demand settings, to minimize evacuation costs (including traveler delays). While any 29 increase in evacuees’ unwillingness to share rides led to longer wait times, final bus arrival times (at 30 safe/distant shelters) remained largely unaffected.
Crystallographic analysis of the Dome Fuji ice core

A comprehensive study of ice crystal fabrics and textures on the Dome Fuji ice core shows that 1) nucleation-recrystallization did not occur, 2) the distribution of c-axes gradually changes with depth from a random orientation pattern near the surface to one with a single, sharp maximum in the vertical at 2500 m, 3) the mean crystal size steadily increases with depth except for abrupt decreases near 420, 1800,2000, 2200, and 2300 m; and 4) the aspect ratio of individual crystals increases with depth until 1500 m. Analysis of the misorientation angle between adjacent crystals indicates that rotation-recrystallization was activated below 1300 m. Also, the clustering of c-axes is weakened at depths containing layers with high impurity concentrations.
Studying the Velocity Index for various Altmetric.com data sources

In this study the velocity of 12 Altmetric.com data sources in disseminating newly published research outputs is investigated. The Velocity Index is proposed to make a comparison of velocity among Altmetric.com data sources across document types and subject fields. Some altmetric posts accumulated very fast within the first few days after publication, such as Reddit, Twitter, News, and Facebook, while posts of Policy documents, Wikipedia, Q&A, and Peer review with low Velocity Index values accrued relatively slowly. Most data sources’ velocity degree also change by document types and subject fields. The velocity of most data sources confronted with the type of Review is lower than the overall and Article, while Editorial Material and Letter are higher. In general, most altmetric data sources show higher velocity values in the fields of Multidisciplinary Journals and Natural Sciences. Introduction “Speed”, has been highlighted as one of the most important properties of altmetrics (Wouters & Costas, 2012; Bornmann, 2014). Speed in the context of altmetrics is related to the idea that the impact of a given scientific output can be measured and analysed soon after its publication with altmetric data sources. As a result, compared with citations, which has been often criticized for its time delay, altmetrics are assumed to be more immediate, so that online activities on newly published papers can be tracked much earlier (Priem, et al., 2010). The immediacy of some specific altmetric data sources have been discussed elsewhere. Maflahi & Thelwall (2016) found that Mendeley readership counts may be useful in measuring impact for both newer and older articles in the field of Library and Information Sciences. The results based on PeerJ social referrals data of Wang, Fang, & Guo (2016) suggested that the number of “visits” to papers from social media (Twitter and Facebook) accumulates very quickly after publication. Yu, et al. (2017) found that Twitter and Weibo are more immediate than citations, however they also suggested that not all altmetric data sources have the same degree of immediacy. In order to measure the velocity degree of different altmetric data sources in disseminating newly published research outputs, we proposed the Velocity Index. Thus, this paper aims to answer the following research questions: 1 This research is partially funded by the South African DST-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy (SciSTIP), and Zhichao Fang is partially supported by the China Scholarship Council (CSC).
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

We investigate how human choosers adapt their value encoding strategy to the statistics of the choice environment. Specifically, we ask whether the human value encoding mechanism exhibits divisive normalization only in the Pareto-distributed environments in which it is information-maximizing. To test this theory, we conduct a risky choice experiment in which subjects are presented with blocks of choice stimuli drawn from either a Pareto-distributed environment or a uniform-distributed environment. Our results show that subjects exhibit some degree of normalization regardless of whether it is efficient or not, but do adapt the curvature of their encoding function to the environment. These findings suggest that human value coding mechanisms are flexible but biologically constrained to be perfectly efficient only in specific environments. This study provides new insights into the neural mechanism of human decision-making and the role of environmental statistics in shaping it.
The grand challenges of migration ecology that radar aeroecology can help answer

Many migratory species have experienced substantial declines that resulted from rapid and massive expansions of human structures and activities, habitat alterations and climate change. Migrants are also recognized as an integral component of biodiversity and provide a multitude of services and disservices that are relevant to human agriculture, economy and health. The plethora of recently published studies reflects the need for better fundamental knowledge on migrations and for better management of their ecological and human-relevant effects. Yet, where are we in providing answers to fundamental questions and societal challenges? Engaging a broad network of researchers worldwide, we used a horizon-scan approach to identify the most important challenges which need to be overcome in order to gain a fuller understanding of migration ecology, and which could be addressed using radar aeroecological and macroecological approaches. The top challenges include both long-standing and novel topics, ranging from fundamental information on migration routes and phenology, orientation and navigation strategies, and the multitude of effects migrants may have on resident communities, to societal challenges, such as protecting or preventing migrant services and disservices, and the conservation of migrants in the face of environmental changes. We outline these challenges, identify the urgency of addressing them and the primary stakeholders – researchers, policy makers and practitioners, or funders of research. (Less)
Comparative Analysis of Feature Extraction Techniques for Event Detection from News Channels' Facebook Page

Event detection from the Social Network sites (SNs) has attracted significant attention of many researchers to understand user perceptions and opinions on certain incidents that have occurred. Facebook is the most famous SNs among internet users to express their opinions, emotions and thoughts. Due to its popularity, many news channels such as BBC have created a Facebook page to allow reader to comment on news reported, which has led to an explosion of user-generated data posted on the Internet. Monitoring and analyzing this rich and continuous user-generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. Previously, in the context of text mining research, various feature extraction techniques have been proposed to extract relevant key features that could be used to detect the news posts into corresponding event. However, these techniques are separately tested on different data. Moreover, analyzing large number of news posts over a period of time is a challenging task due to its complex properties and unstructured data. Thus, this paper has proposed a comparative analysis on various types of feature extraction techniques on three different classifiers, namely Support Vector Machine (SVM), Naive Bayes (NB) and KNearest Neighbor (kNN). The aim of this research is to discover the appropriate feature extraction technique and classifier that could correct detect event and offer optimal accuracy result. This analysis has been tested on three news channels datasets, namely, BBC, Aljazeera, and Al-Arabiya news channels. The experimental results have shown that Chisquare and SVM has proven to be a better extraction and classifier technique compared to other techniques with optimal accuracy of 92.29%, 87.12%, 87.00% have been observed in BBC, Aljazeera, and Al-Arabiya news channels respectively
Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture

Assessing gaze behaviour during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make finding gaze fixations challenging. Current approaches involve laborious coding of pupil positions overlaid on video. One solution is to combine eye tracking with motion tracking to generate 3D gaze vectors. When combined with tracked or known object locations, fixation detection can be automated. Here we use combined eye and motion tracking and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three data types: the performance of calibration routines were assessed using calibration data, a validation task that demands short fixations on task-relevant locations, and an object interaction task we used to bridge the gap between laboratory and "in the wild" studies. Further, we generated and compared models using spherical and cartesian coordinate systems and monocular (Left or Right) or binocular data. Our results suggest that all calibration routines perform similarly, with the best performance (i.e., sub-centimeter errors) coming from the task (i.e., the most "natural") trials when the participant is looking at an object in front of them. Further, we found that spherical coordinate systems generate more accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend recording one-minute calibration datasets, using a binocular eye tracking headset (for redundancy), a spherical coordinate system when depth is not considered, and ensuring data quality (i.e., tracker positioning) is high when recording datasets.
Clustering Review Pengguna Aplikasi Zenius pada Layanan Google Play Store Menggunakan Metode DBSCAN dan HDBSCAN

Advances in technology have given birth to an innovation, namely the creation of E-learning or Edutech applications which are currently in great demand to assist students in independent learning at home. On the Google Play Store several application ratings and reviews make it easier for users to find applications with the best reviews. The existence of an analysis of a rating and review allows developers to assess which parts of the application need to focus on improvement. Analysis of reviews on review topics can be done in various ways, one of which can use the clustering method. This study is head to find out the reviews of an application that application users most often write. To obtain these results, a clustering review of e-learning application users was carried out on the Google Play Store using the DBSCAN and HDBSCAN methods. Furthermore, to assess which method is the best for clustering, it is done by comparing the average SC value (silhouette coefficient) of the two ways. The results of this study are that the average SC value of the HDBSCAN method is better than the DBSCAN method. HDBSCAN has an average SC value of 0.2941, while the DBSCAN method only has an average SC value of 0.1310.
Structured, Reduced-Order $\mathcal{H}_{2}$-Conic Control

Practical controllers for large-scale systems must be low-order and sparsely communicating, as well as high-performing and robust to uncertainty. <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_{2}$</tex-math></inline-formula>-conic design addresses the latter requirements where passivity and <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_\infty$</tex-math></inline-formula> methods are inapplicable, but it has yet to address the former. Here, the fixed-order <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_{2}$</tex-math></inline-formula>-conic design problem is posed as a series of convergent approximations. The resulting synthesis algorithm uses all controller parameters as explicit design variables, and is guaranteed to find a feasible controller under mild assumptions. This improves performance over previous <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_{2}$</tex-math></inline-formula>-conic designs while permitting controller dimension and communication structure to be arbitrarily chosen, which allows reduced-order and sparse distributed designs. The relationship between variable structure and computational complexity is explored, and the main algorithm is applied to numerical examples.
Psychometric analysis of the obesity risk control scale in mexican young people

Introduction: Obesity is a public health problem that has been increasing in the young population. The health-promoting behavior that prevents it is obesity risk control, which is part of the nursing taxonomy outcomes; however, it does not have any reliability or validity precedents. Objective: To evaluate the psychometric properties of reliability, exploratory and confirmatory factor analysis of the Obesity Risk Control scale in young Mexicans. Methodology: Correlational cross-sectional descriptive study. It included 167 young people with normal weight and overweight enrolled in a public university in Mexico, using non-probabilistic sampling. Young people who were not available due to illness, disability or temporary academic leave were excluded. Sociodemographic data card, Family Affluence Scale questionnaire and Obesity Risk Control scale were used. The research adhered to Mexican ethical and legal principles. Reliability was estimated with Cronbach's α coefficient and construct validity with exploratory and confirmatory factor analysis. Results: The average age of the participants was 19 years, overweight was predominant and young people sometimes showed control of the risk of obesity. Reliability was α = . 927. The Obesity Risk Control scale consisted of two factors and 16 items, with explained variance of 56.2%. Conclusions: The Obesity Risk Control scale is reliable and valid to be employed in young Mexicans; however, it is suggested to review the content validity and criterion validity in the described population.
Ancaman Resesi: Peran UMKM Dalam Akselerasi Perekonomian Jawa Barat Pasca Pandemi

Pandemi COVID-19 yang melanda Indonesia, termasuk Jawa Barat berdampak besar pada peningkatan jumlah pengangguran yang tentunya berhubungan dengan keterpurukan kondisi perekonomian. Selain itu timbul isu resesi tahun 2023 pada masa pemulihan pasca pandemi COVID-19 saat ini yang sangat dikhawatirkan oleh khalayak. Usaha Mikro, Kecil, dan Menengah (UMKM) dapat menjadi sumber pendapatan masyarakat dan menyerap ketersediaan angkatan kerja karena menciptakan lapangan kerja baru sehingga dapat mengurangi pengangguran. Pada penelitian ini, ingin diketahui pengaruh jumlah Usaha Mikro, Kecil, dan Menengah (UMKM) dan pengeluaran per kapita terhadap Tingkat Pengangguran Terbuka (TPT) serta memprediksi Tingkat Pengangguran Terbuka (TPT) berdasarkan kedua faktor tersebut serta dilakukan pula analisis klaster hierarki untuk mengelompokkan kabupaten dan kota di Jawa Barat berdasarkan Tingkat Pengangguran Terbuka (TPT), pengeluaran per kapita, dan jumlah Usaha Mikro, Kecil, dan Menengah (UMKM). Hasil penelitian menunjukkan bahwa potensi resesi yang diperkirakan terjadi pada tahun 2023 dapat dicegah melalui pemberdayaan Usaha Mikro, Kecil, dan Menengah (UMKM). Usaha Mikro, Kecil, dan Menengah (UMKM) dapat dengan cepat mencari pasar ekspor ketika dampak resesi mulai terjadi, seperti naiknya harga akibat pemutusan kerja dan adanya inflasi.
Vascular Dysfunctions Contribute to the Long-Term Cognitive Deficits Following COVID-19

Simple Summary Although SARS-CoV-2 primarily affects the upper respiratory system and the lungs, it can also affect vasculature, leading to the impairment of endothelial and multi-organ function. SARS-CoV-2 can trigger the release of pro-inflammatory cytokines, leading to disruption of tight junction proteins between endothelial cells and impairment of the blood-brain barrier (BBB). This process further allows the infiltration of immune cells and other particles into the brain, worsening brain injury. Prolonged neuro-inflammation and disruption of the BBB have been postulated as the potential primary causes of both acute and chronic cognitive dysfunction in COVID-19 patients. This review explores the effects of COVID-19 on vascular dysfunction and consequent cognitive impairment in patients. Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus and a member of the corona virus family, primarily affecting the upper respiratory system and the lungs. Like many other respiratory viruses, SARS-CoV-2 can spread to other organ systems. Apart from causing diarrhea, another very common but debilitating complication caused by SARS-CoV-2 is neurological symptoms and cognitive difficulties, which occur in up to two thirds of hospitalized COVID-19 patients and range from shortness of concentration and overall declined cognitive speed to executive or memory function impairment. Neuro-cognitive dysfunction and “brain fog” are frequently present in COVID-19 cases, which can last several months after the infection, leading to disruption of daily life. Cumulative evidence suggests that SARS-CoV-2 affects vasculature in the extra-pulmonary systems directly or indirectly, leading to impairment of endothelial function and even multi-organ damage. The post COVID-19 long-lasting neurocognitive impairments have not been studied fully and their underlying mechanism remains elusive. In this review, we summarize the current understanding of the effects of COVID-19 on vascular dysfunction and how vascular dysfunction leads to cognitive impairment in patients.
Different Methods in Assessing El Niño Flavors Lead to Opposite Results

El Niño‐Southern Oscillation (ENSO) flavors have been defined to characterize ENSO events and their teleconnections. Studying El Niño flavor evolution during the Holocene period can provide valuable insights into changes over long time scales. We investigated ENSO flavor evolution using simulations spanning the last 6,000 years and present‐day observations. Two approaches to computing ENSO flavors, in agreement in the present, lead to opposite trends in the last 6,000 years. The methods also differ significantly in their representation of ENSO flavor patterns. However, incorporating the sensitivity of the methods to calibration periods and mean state changes yields similar interpretations of ENSO variability changes. Both methods suggest an increase in El Niño variability spreading to the west and east tropical Pacific over the past 6,000 years. Standardizing El Niño flavor definitions is necessary for meaningful comparisons between studies and robust climate variability analysis.
Frequentist-Bayesian Monte Carlo testing

Abstract Conventional methods for statistical hypothesis testing has historically been categorized as frequentist or Bayesian. But, a third option based on a reconciling hybrid frequentist-Bayesian framework is quickly emerging. Although prominent, there are applications where the exact hybrid test is not computable. For such cases, the present paper introduces a straightforward Monte Carlo procedure for performing frequentist-Bayesian testing.
Indian Red Jungle fowl reveals a genetic relationship with South East Asian Red Jungle fowl and Indian native chicken breeds as evidenced through whole mitochondrial genome sequences

Background: Native chickens are dispersed in a wide geographical range and have hereditary assets that are kept by farmers for various purposes. Mitochondrial DNA (mtDNA) is a widely utilized marker in molecular studies because of its quick advancement, matrilineal legacy, and simple molecular structure. Method and Results: We performed NGS sequencing to investigate mitochondrial genomes and to evaluate the hereditary connections, diversity, and measure of gene stream estimation in Indian native chicken breeds and Red Jungle fowl. The chicken breeds were genotyped using the D-loop region and 23 haplotypes were identified. When compared to Indian native breeds, more haplotypes were identified in the NADH dehydrogenase subunits, Cytochrome c oxidase, Cytochrome b, ATP synthase subunit 6, and Ribosomal RNA genes. The phylogenetic examination indicated that the analyzed chicken breeds were divided into six significant clades, namely A, B, C, D, E, and F, of which the F clade indicated the domestication of chicken breeds in India. Additionally, our work affirmed that the Indian Red Jungle Fowl is the origin for both reference Red Jungle Fowl as well as all Indian breeds, which is reflected in the dendrogram as well as network analysis based on the whole mtDNA and D-loop region. Indian Red Jungle Fowl is distributed as an outgroup, suggesting that this ancestry was reciprocally monophyletic. Conclusion: The mtDNA sequences of Indian native chickens provided novel insights into adaptation mechanisms and the significance of important mtDNA variations in understanding the maternal lineages of native birds.