Miracle-dz/UniFAR-FTUs huggingface dataset:
Collision Avoidance with Transitional Drift Control

Enhancing driving safety is a continuing effort towards safer transportation in the automotive industry. It is generally accepted that one of the benefits brought about by increasing levels of automation in vehicles is increased safety. In this paper we propose an automated emergency maneuver controller for safely and stably operating the vehicle at its handling limit in emergency situations. This paper explores the possible scenarios where such an ability is critical to keep an autonomous vehicle safe in an emergency scenario, by implementing a mode switching control design that successfully enhances safety in a driving scenario that would have caused an accident had the vehicle been operated by traditional vehicle control algorithms. The proposed mode switching control design uses geometric and kinematic criteria to determine the mode of operation for a vehicle, and applies tailored optimal control in each mode to minimize the safety risk in a finite planning horizon. The proposed method outperforms two control baselines including a drift sustaining controller in simulation by having the additional flexibility in following a challenging direction-changing path at considerable speed.
Detecting the Complex Relationships and Driving Mechanisms of Key Ecosystem Services in the Central Urban Area Chongqing Municipality, China

Ecosystem services (ESs) are highly vulnerable to human activities. Understanding the relationships among multiple ESs and driving mechanisms are crucial for multi-objective management in complex social-ecological systems. The goals of this study are to quantitatively evaluate and identify ESs hotspots, explore the relationships among ESs and elucidate the driving mechanisms. Taking central urban area Chongqing municipality as the study area, biodiversity (BI), carbon fixation (CF), soil conservation (SC) and water conservation (WC) were evaluated based on the InVEST model and ESs hotspots were identified. The complex interactions among multiple ESs were determined by utilizing multiple methods: spearman correlation analysis, bivariate local spatial autocorrelation and K-means clustering. The linear or nonlinear relationships between ESs and drivers were discussed by generalized additive models (GAMs). The results showed that during 2000–2018, except for CF that exhibited no obvious change, all other ESs showed a decrease tendency. High ESs were clustered in mountains, while ESs in urban areas were lowest. At administrative districts scale, ESs were relatively higher in Beibei, Banan and Yubei, and drastically decreased in Jiangbei. Multiple ES hotspots demonstrated clear spatial heterogeneity, which were mainly composed of forestland and distributed in mountainous areas with high altitude and steep slope. The relationships between ES pairs were synergistic at the entire scale. However, at grid scale, the synergies were mainly concentrated in the high-high and low-low clusters, i.e., mountainous areas and urban central areas. Five ESs bundles presented the interactions among multiple ESs, which showed well correspondence with social-ecological conditions. GAMs indicated that forestland and grassland had positive impact on BI and CF. Additionally, SC was mainly determined by geomorphological factors, while WC were mainly influenced by precipitation. Furthermore, policy factors were confirmed to have a certain positive effect on ESs. This study provides credible references for ecosystem management and urban planning.
Generating Name-Like Vectors for Testing Large-Scale Entity Resolution

Entity resolution (ER), the problem of identifying and linking records that belong to the same real-world entities in structured and unstructured data, is a primary task in data integration. Accurate and efficient ER has a major practical impact on various applications across commercial, security and scientific domains. Recently, scalable ER techniques have received enormous attention with the increasing need to combine large-scale datasets. The shortage of training and ground truth data impedes the development and testing of ER algorithms. Good public datasets, especially those containing personal information, are restricted in this area and usually small in size. Due to privacy and confidential issues, testing algorithms or techniques with real datasets is challenging in ER research. Simulation is one technique for generating synthetic datasets that have characteristics similar to those of real data for testing algorithms. Many existing simulation tools in ER lack support for generating large-scale data and have problems in complexity, scalability, and limitations of resampling. In our work, we propose a simple, inexpensive, and fast synthetic data generation tool. Our tool only generates entity names in the first stage, but these are commonly used as identification keys in ER algorithms. We avoid the detail-level simulation of entity names using a simple vector representation that delivers simplicity and efficiency. In this paper, we discuss how to simulate simple vectors that approximate the properties of entity names. We describe the overall construction of the tool based on data analysis of a namespace that contains entity names collected from the actual environment.
Prediction of Genes That Function in Methanogenesis and CO2 Pathways in Extremophiles

Gaet’ale (GAL) and Mud’ara (MUP) are two hypersaline ponds located in the Danakil Depression recharged by underground water from the surrounding highlands. These two ponds have different pH, salinity, and show variation in the concentration of many ionic components. Metagenomic analysis concludes that GAL is dominated by bacteria as in the case of the other hypersaline and acidic ponds in the Danakil Depression. However, Archaea dominated the ponds of MUP. In the current study, the application of SEED and KEGG helped to map the ordered steps of specific enzyme catalyzed reaction in converting CO2 into cell products. We predict that highly efficient and light-independent carbon fixation involving phosphoenolpyruvate carboxylase takes place in MUP. On the contrary, genes encoding enzymes involved in hydrogenotrophic and acetoclastic methanogenesis appeared solely in ponds of GAL, implying the biological source of the hazardous methane gas in that environment. Based on the investigation of the sources of the genes of interest, it is clear that cooperative interactions between members of the two communities and syntrophic metabolism is the main strategy adapted to utilize inorganic carbon as a carbon source in both MUP and GAL. This insight can be used to design biotechnological applications of microbial communities in production of methane biogas or to minimize CO2 emissions.
Spanish adaptation of the abbreviated form for the Self-Esteem Rating Scale

Introduction: self-esteem could be a mediating variable in the success of the rehabilitation in schizophrenia. Some authors recommend the use of scales that differentiate between the positive and negative dimensions of self-esteem, such as the Self-Esteem Rating Scale (SERS). This study aims to validate the abbreviated form of this scale into Spanish (SERS-SF). Method: the study was carried out with 370 participants, 328 control subjects and 42 belonging to a clinical group (diagnosis of schizophrenia). The existence of two dimensions of self-esteem was analyzed by means of a confirmatory factorial analysis, and the test-retest reliability, the criterion validity and the internal consistency for each dimension were calculated. The relationships between self-esteem and socio-demographic variables were also analyzed in both samples, as well as the symptomatology in the patients’ case. Lastly, the level of self-esteem between the controls and patients was compared. Results: the Spanish version of the SERSSF showed good psychometric properties in both samples. No significant relationships were obtained with any socio-demographic variable. In the case of the patients, the positive dimension of self-esteem was related to symptoms of grandiosity, lack of judgment and insight, and depression. The comparison between the control subjects and the patients showed that the latter had worse self-esteem, although it was within normalized values in a high percentage. Discussion: The results obtained enable us to conclude that the Spanish version of the SERS-SF is a good test for evaluating the positive and negative dimensions of self-esteem in a differentiated way.
The level of serum Tumor Marker CA15-3 in women with Breast Cancer

Background: Breast cancer is the most frequently diagnosed type of cancer in women in all world regions. About 1.7 million cases of breast cancer are diagnosed every year. Breast cancer is the most common cause of death in women worldwide. There were approximately 0.5 million deaths worldwide from breast cancer in 2012, several serum tumor markers have been evaluated in breast cancer. CA15-3 is the most important serum biomarkers used for diagnosis and monitoring of breast cancer. Objective: To evaluate the level of CA15-3 of studied groups according to age categories, residence nearby industries, and history of breast feeding. Patients and Methods: This study was carried out at the Breast Surgery Outpatients and the Oncology Clinic at Oncology Teaching Hospital in Baghdad. It included 50 women who were newly diagnosed by oncology group to have had breast cancer and didn’t receive chemotherapy yet and 50 healthy women as control. Results: There was significant increase at (P> 0.05) in serum CA15-3 level of patients with age category (71-80) years (198.33 ± 136.60 U/ml) and significant elevation in serum CA15-3 level of patients who lived near industries or incinerators (120 ± 94.52 U/ml) also there was significant increase in serum CA15-3 level in healthy women with breast feeding history (25.8 ± 15.26 U/ml). Conclusion: CA15-3 level significantly elevated in elderly patients with age category (7180) years and in patients with residence nearby industries or incinerators. Therefore, those patients at high risk of developing metastatic breast cancer or advanced stages of breast cancer.
Solar Signage Business Model Design Using the EPSS Framework

The purpose of this study is to design a business model that generates profits by developing eco-friendly convergent products. This study proposes a new concept of solar signage in the digital signage system that helps carbon emission reduction. This study developed a solar signage business model using the eco-science methodology specialized in manufacturing servitization. Following the ecosystem platform service strategy (EPSS) framework of eco-science optimized for convergent industry service design, this study implemented service derivation, convergent ecosystem definition, and business model development. The developed business model was evaluated by 10 experts in the field. The business model obtained 43 points, which exceeded the standard commercialization decision cutoff of 35 points. This study’s results imply that the business model is developed from an integrative perspective and defines the convergent industry ecosystem, a convergent knowledge service methodology, in digital signage. Moreover, through the business model, the perspectives on technological development can be expanded and the model can play an important role in carrying out new industry commercialization based on convergent technologies.
Rule-Based Decision-Making System for Autonomous Vehicles at Intersections with Mixed Traffic Environment

Before autonomous vehicles are widely spread, they will share the roads with non-autonomous cars. Furthermore, to ensure functional safety of the self-driving cars, the cause of decision-making and control algorithms' failure must be precisely identified. Rule-based methods are promising solutions due to their transparency and comprehensibility. In this paper, a rule-based decision-making system for autonomous vehicles solving a challenge of complex intersection with mixed driving environment is proposed. The system is designed to prioritize road safety and avoid collision with other road users at any cost. The proposed algorithm relies only on available on-board perception and localization sensors, allowing the automated car to operate among human-driven vehicle and without vehicle-to-vehicle communication technology. The system is validated in a simulation study on cross-intersection, where the ego vehicle deals with multiple cars arriving from different sides of the road. The results demonstrate algorithm's robustness and effectiveness under multiple scenarios, when neither intention nor trajectory of other traffic participants is known. Thus, the proposed solution is also potentially applicable to other types of intersections with different traffic rules.
Football Players' Shooting Posture Norm Based on Deep Learning in Sports Event Video

Football is one of the favorite sports of people nowadays. Shooting is the ultimate goal of all offensive tactics in football matches. This is the most basic way to score a goal and the only way to score a goal. The choice and use of shooting technical indicators can have a great impact on the final result of the game. Therefore, how to improve the shooting technique of football players and how to adjust the shooting posture of football players are important issues faced by coaches and athletes. In recent years, deep learning has been widely used in various fields such as image classification and recognition and language processing. How to apply deep learning optimization to shooting gesture recognition is a very promising research direction. This article aims to study the football player’s shooting posture specification based on deep learning in sports event videos. Based on the analysis of target motion detection algorithm, target motion tracking algorithm, target motion recognition algorithm, and football shooting posture classification, KTH and Weizmann data sets are used. As the experimental verification data set of this article, the shooting posture of football players in the sports event video is recognized, and the accuracy of the action recognition is finally calculated to standardize the football shooting posture. The experimental results show that the Weizmann data set has a higher accuracy rate than the KTH data set and is more suitable for shooting attitude specifications.
Devastated Bladder Outlet in Pelvic Cancer Survivors: Issues on Surgical Reconstruction and Quality of Life

Bladder outlet obstruction following treatment of pelvic cancer, predominantly prostate cancer, occurs in 1–8% of patients. The high incidence of prostate cancer combined with the long-life expectancy after treatment has increased concerns with cancer survivorship care. However, despite increased oncological cure rates, these adverse events do occur, compromising patients’ quality of life. Non-traumatic obstruction of the posterior urethra and bladder neck include membranous and prostatic urethral stenosis and bladder neck stenosis (also known as contracture). The devastated bladder outlet can result from benign conditions, such as neurogenic dysfunction, trauma, iatrogenic causes, or more frequently from complications of oncologic treatment, such as prostate, bladder and rectum. Most posterior urethral stenoses may respond to endoluminal treatments such as dilatation, direct vision internal urethrotomy, and occasionally urethral stents. Although surgical reconstruction offers the best chance of durable success, these reconstructive options are fraught with severe complications and, therefore, are far from being ideal. In patients with prior RT, failed reconstruction, densely fibrotic and/or necrotic and calcified posterior urethra, refractory incontinence or severe comorbidities, reconstruction may not be either feasible or recommended. In these cases, urinary diversion with or without cystectomy is usually required. This review aims to discuss the diagnostic evaluation and treatment options for patients with bladder outlet obstruction with a special emphasis on patients unsuitable for reconstruction of the posterior urethra and requiring urinary diversion.
Brain Image Classification Using Time Frequency Extraction with Histogram Intensity Similarity

Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done.
Post-exercise Cold Water Immersion Does Not Improve Subsequent 4-km Cycling Time-Trial Compared With Passive and Active Recovery in Normothermia

Background: We investigated whether a brief cold water immersion between two cycling time trials (TT) improves the performance of the latter compared with passive and active recovery in normothermic conditions (~20°C). Methods: In Experiment 1 10 active participants (4 women) completed two 4-km TT (Ex1 and Ex2, each preceded by a 12 min moderate-intensity warm-up) separated by a 15 min recovery period consisting of: (a) passive rest (PAS) or (b) 5 min cold water immersion at 8°C (CWI-5). In Experiment 2, 13 different active males completed the same Ex1 and Ex2 bouts separated by a 15 min recovery consisting of: (a) PAS, (b) 10 min cold water immersion at 8°C (CWI-10) or (c) 15 min of moderate-intensity active recovery (ACT). Results: In both experiments, the time to complete the 4-km TT-s was not different (P > 0.05, ES = 0.1) among the trials neither in Ex1 (Experiment 1: PAS: 414 ± 39 s; CWI-5: 410 ± 39 s; Experiment 2: PAS: 402 ± 41 s; CWI-10: 404 ± 43 s; ACT: 407 ± 41 s) nor Ex2 (Experiment 1: PAS: 432 ± 43 s; CWI-5: 428 ± 47 s; Experiment 2: PAS: 418 ± 52 s; CWI-10: 416 ± 57 s; ACT: 421 ± 50 s). In addition, in all conditions, the time to complete the time trials was longer (P < 0.05, ES = 0.4) in Ex2 than Ex1. Core temperature was lower (P < 0.05) during the majority of Ex2 after CW-5 compared with passive rest (Experiment 1) and after CWI-10 compared with PAS and ACT (Experiment 2). Perceived exertion was also lower (P < 0.05) at mid-point of Ex2 after CWI-5 compared with PAS (Experiment 1) as well as overall lower during the CWI-10 compared with PAS and ACT conditions (Experiment 2). Conclusion: A post-exercise 5–10 min cold water immersion does not influence subsequent 4-km TT performance in normothermia, despite evoking reductions in thermal strain.
Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor

Distribution of tickets to the destination unit is a very important function in the helpdesk application, but the process of distributing tickets manually by admin officers has drawbacks, namely ticket distribution errors can occur and increase ticket completion time if the number of tickets is large. Helpdesk text classification becomes important to automatically distribute tickets to the appropriate destination units in a short time. This study was conducted to compare the performance of helpdesk text classification at the Directorate General of State Assets of the Ministry of Finance using the K-Nearest Neighbor (KNN) method with the TF-ABS and TF-IDF weighting methods. The research was conducted by collecting complaint documents, preprocessing, word weighting, feature reduction, classification, and testing. Classification using KNN with parameters n_neighbor (k) namely k=1, k=3, k=5, k=7, k=9, k=11, k=13, k=15, k=17, and k=19 to classify 10,537 helpdesk texts into 8 categories. The test uses a confusion matrix based on the accuracy value and score-f1. The test results show that the TF-ABS weighting method is better than TF-IDF with the highest accuracy value of 90.04% at 15% and k=3.
Effect of Al and Mg Doping on Reducing Gases Detection of ZnO Nanoparticles

In this work, the main objective is to enhance the gas sensing capability through investigating the effect of Al and Mg doping on ZnO based sensors. ZnO, Mg1% doped ZnO, Al5% doped ZnO and (Al5%, Mg1%) co-doped ZnO nanoparticles (NPs) were synthesized by a modified sol-gel method. The structural characterization showed the hexagonal crystalline structure of the prepared samples. Morphological characterizations confirmed the nanometric sizes of the NPs (27–57 nm) and elemental composition investigation proved the existence of Al and Mg with low concentrations. The optical characterization showed the high absorbance of the synthesized samples in the UV range. The gas sensing performances of the synthesized samples, prepared in the form of thick films, were investigated. Sensing tests demonstrated the high influence of the Al and Mg on the sensing performances towards H2 and CO gas, respectively. The 5A1MZO-based sensor exhibits high sensitivity and low detection limits to H2 (<2 ppm) and CO (<1 ppm). It showed a response around 70 (at 250 °C) towards 2000 ppm H2 and 2 (at 250 °C) towards CO.
Situational Understanding

— This article examines cognitive processes engaged in situational understanding and outlines a framework for simulating such processes in computational systems (‘machine situational understanding’). Discussion focuses on elucidating key distinctions between ‘machine learning’ and ‘machine situational understanding’, and apprehending limitations of conventional AI methods (learning, reasoning) in approximating the capacity for situational understanding and/or facilitating situational understanding in the human operators.
Zegar molekularny w teorii i praktyce: przewodnik dla początkujących..

Koncepcja zegara molekularnego powstała ponad półwieku temu i od tamtej pory odegrała ważną rolę w badaniachbiologicznych, zajmujących się zarówno opisemprocesów ewolucyjnych, jak i szacowaniem wieku dywergencji.Zegar molekularny odmierza czas pomiędzy poszczególnymiwydarzeniami ewolucyjnymi, opierając sięna założonym modelu ewolucji (czyli tempie substytucji). Dziesiątki lat badań pokazały, że tempo ewolucji nie jestwartością uniwersalną dla wszystkich organizmów i zależyod szeregu czynników, takich jak: wielkość ciała,długość trwania cyklu życiowego czy tempo metabolizmu. Spowodowało to opracowanie wielu modeli zegaramolekularnego biorących pod uwagę zmienność tempawzdłuż gałęzi drzewa filogenetycznego. Ogromną rolę wodtwarzaniu filogenezy przy użyciu zegara molekularnegojako narzędzia, stanowi jego kalibracja. Istnieje kilkametod kalibracyjnych, których zastosowanie zależy oddostępności punktów kalibracyjnych dla badanej grupyorganizmów. Głównym celem artykułu jest zapoznanieczytelnika zarówno z teoretycznymi, jak i z praktycznymizagadnieniami dotyczącymi zegara molekularnego.