Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Nonprehensile Dynamic Manipulation: Sim2real Vision-Based Policy With a Surgical Robot

Surgical tasks such as tissue retraction, tissue exposure, and needle suturing remain challenging in autonomous surgical robotics. One challenge in these tasks is nonprehensile manipulation such as pushing tissue, pressing cloth, and needle threading. In this work, we isolate the problem of nonprehensile manipulation by implementing a vision-based reinforcement learning agent for rolling a block, a task that has complex dynamics interactions, small scale objects, and a narrow field of view. We train agents in simulation with a reward formulation that encourages efficient and safe learning, domain randomization that allows for robust sim2real transfer, and a recurrent memory layer that enables reasoning about randomized dynamics parameters. We successfully transfer our agents from simulation to real and show robust execution of our vision-based policy with a 96.3% success rate. We analyze and discuss the success rate, trajectories, and recovery behaviours for various models that are either using the recurrent memory layer or are trained with a difficult physics environment.
The 'positive tourism' linkage: a study of motivations, flow and benefits of spa experiences in Southeast Asia

[Extract] Positive psychology is a new field of study that focuses on human thriving. Even though tourism businesses attempt to be profitable through serving happiness, satisfaction and wellness, it is surprising that the relationship between tourism and human thriving remains under-researched (Smith & Kelly, 2006a). One of the facets of wellbeing in the positive psychology field that has also received attention among tourism scholars is wellness. The wellness-tourism interface has long historical roots, but recent studies treat wellness tourism ostensibly as a new form of activity.
Modification of the Electrical Properties of a Silicon Waveguide Avalanche Photodetector Operating at 1550 nm via Defect Engineering

Monolithic silicon sub-bandgap avalanche photodetectors are fabricated using the introduction of lattice defects to silicon waveguides through silicon ion implantation and annealing. The devices are characterized using light at a wavelength of 1550 nm. Through control of implantation dose we create a low-absorption device with 0.4 dB (5.4 dB/cm) excess optical absorption, and a high-absorption device with 3.2 dB (42 dB/cm) excess optical absorption. The low-frequency unity-gain bias point, M = 1, is approximately 12 V for both devices. Increasing reverse bias results in electrical gain such that M = 35 at 32 V for the high-absorption device, with associated responsivity of 13 A/W for coupled optical power ranging from −20 dBm to −5 dBm. The electrical bandwidth of this device peaks at 10 GHz for M = 14 and decreases to 6 GHz for M = 35. The RC limited bandwidth of an unimplanted, control device is measured to be 26 GHz. The gain-bandwidth product of the high-absorption device is 230 GHz. Measurement of the excess noise factor provides an effective k-value of 0.1, consistent with the gain-bandwidth product. Primary carriers in the avalanche process are predominatly electrons, with a carrier ionization threshold of ∼ 0.6eV, indicating for the first time in this device type that both the light detection and avalanche process are mediated by the deep-levels.
Employability competencies of South African Human Resource Development graduates

Higher education institutions are not properly prepared to meet the expectations of employers regarding the work readiness of human resource development (HRD) graduates from universities. Employers seek to recruit graduates who have pertinent employability competencies. The factors that were determined should contribute considerably to meeting this need. The study was commenced to identify the factors relating to employability competencies for South African human resource development graduates. The sample of the survey comprised 134 professionals from the South African Board of People’s Practice (SABPP) in the nine (9) provinces of South Africa. A factor analysis was employed to identify the issues relating to the employability competencies of HRD graduates. Correlation analysis and regression analysis were used to establish the relationships between identified factors. The results confirmed that there were strong relationships between the factors of the current and the expected competencies concerning the employability of South African HRD graduates. The findings served to formulate specific recommendations on employers’ expectations regarding the employability competencies of HRD graduates. The intention of the study was to improve graduates’ competencies in the world of work.
Viscoelastic behaviour and temporal fractal properties of lherzolite and marble: possible extrapolation from experimental results to the geological time‐scale

The viscoelastic relaxation behaviour of lherzolite and marble and their temporal fractal properties are studied. Based on the high‐temperature viscoelastic behaviour for these rock types derived from the time–temperature superposition principle, the relaxation function for the viscoelastic behaviour of lherzolite and marble can be expressed as a flow law from a power‐law function of temperature‐reduced time given by normalizing the various temperature behaviours, with a non‐integer exponent. The relaxation function is thus time‐scale invariant, i.e. there is a temporal fractal property to the temperature‐reduced time. Their rheological behaviour over geological time‐scales at low temperature can be extrapolated from the experimental high‐temperature behaviour based on the temporal fractal property. Our derived flow law satisfies not only transient behaviour but also steady‐state behaviour.
Population-Level Immunity for Transient Suppression of COVID-19 Waves in Japan from April 2021 to September 2022

Multiple COVID-19 waves have been observed worldwide, with varying numbers of positive cases. Population-level immunity can partly explain a transient suppression of epidemic waves, including immunity acquired after vaccination strategies. In this study, we aimed to estimate population-level immunity in 47 Japanese prefectures during the three waves from April 2021 to September 2022. For each wave, characterized by the predominant variants, namely, Delta, Omicron, and BA.5, the estimated rates of population-level immunity in the 10–64-years age group, wherein the most positive cases were observed, were 20%, 35%, and 45%, respectively. The number of infected cases in the BA.5 wave was inversely associated with the vaccination rates for the second and third injections. We employed machine learning to replicate positive cases in three Japanese prefectures to validate the reliability of our model for population-level immunity. Using interpolation based on machine learning, we estimated the impact of behavioral factors and vaccination on the fifth wave of new positive cases that occurred during the Tokyo 2020 Olympic Games. Our computational results highlighted the critical role of population-level immunity, such as vaccination, in infection suppression. These findings underscore the importance of estimating and monitoring population-level immunity to predict the number of infected cases in future waves. Such estimations that combine numerical derivation and machine learning are of utmost significance for effective management of medical resources, including the vaccination strategy.
Outlier Detection Using Three-Way Neighborhood Characteristic Regions and Corresponding Fusion Measurement

—Outliers carry signiﬁcant information to reﬂect an anomaly mechanism, so outlier detection facilitates relevant data mining. In terms of outlier detection, the classical approaches from distances apply to numerical data rather than nominal data, while the recent methods on basic rough sets deal with nominal data rather than numerical data. Aiming at wide outlier detection on numerical, nominal, and hybrid data, this paper investigates three-way neighborhood characteristic regions and corresponding fusion measurement to advance outlier detection. First, neighborhood rough sets are deepened via three-way decision, so they derive three-way neighborhood structures on model boundaries, inner regions, and characteristic regions. Second, the three-way neighborhood characteristic regions motivate the information fusion and weight measurement regarding all features, and thus, a multiple neighborhood outlier factor emerges to establish a new method of outlier detection; furthermore, a relevant outlier detection algorithm (called 3WNCROD) is designed to comprehensively process numerical, nominal, and mixed data. Finally, the 3WNCROD algorithm is experimentally validated, and it generally outperforms 13 contrast algorithms to perform better for outlier detection.
Investigation and Analysis of Pre-service Primary School Mathematics Teachers' Knowledge of History of Mathematics

: This study aims to investigate and analyze the level of knowledge of history of mathematics and the implementation of historical mathematical education among pre-service primary school mathematics teachers, as well as their attitudes and views towards historical mathematical education. Data was collected through a questionnaire survey and analyzed using SPSS statistical software to conclude that the level of knowledge of history of mathematics among pre-service primary teachers is generally low and the implementation of historical mathematical education is not sufficient. Finally, some relevant suggestions are put forward. This study has practical significance for promoting the development of elementary mathematics education in China and improving the literacy of pre-service primary school mathematics teachers.
Extracellular Vesicles in Hepatocellular Carcinoma: Progress and Challenges in the Translation from the Laboratory to Clinic

Extracellular vesicles (EVs) play critical roles in intercellular communication by transporting bioactive cargo to recipient cells. EVs have been implicated in a range of physiological and pathological processes, including tumor progression, metastasis, immune modulation, and drug resistance. The objective of this review is to present a thorough overview of recent studies focusing on EVs in hepatocellular carcinoma (HCC), with an emphasis on their potential utility as diagnostic biomarkers as well as therapeutic agents. Initially, we explore the utility of EVs as diagnostic biomarkers for HCC, followed by a discussion of their potential as carriers of therapeutic payloads. Additionally, we delve into the emerging field of therapeutic EVs for modulating tumor immune responses. Through this review, our ultimate aim is to provide a comprehensive understanding of the opportunities and challenges in the clinical translation of EV research in the domain of HCC.
Coordination in compounding

This chapter deals with the expression of coordination relations in compounding. Two macro-types of compounds are identified, namely hyperonymic coordinating compounds (co-compounds), where the referent of the compound is in a superordinate relationship to the meaning of the parts (as Mandarin dao-qiang ‘sword+spear, weapons’), and hyponymic coordinate compounds, where the referent is in a subordinate relationship to the meaning of the parts (as English actor-director ). The chapter also argues that the distribution of these two macro-types is not random but, rather, areally skewed: whereas co-compounds are common in the Eastern part of Eurasia, New Guinea and Mesoamerica, they seem to be absent in Standard Average European languages, where hyponymic coordinating compounds are formed quite freely. Moreover, while co-compounds may belong to different word classes, hyponymic coordinating compounds are (probably) never verbs.
In-network DDoS detection and mitigation using INT data for IoT ecosystem

Due to the limited capabilities and diversity of Internet of Things (IoT) devices, it is challenging to implement robust and unified security standards for these devices. Additionally, the fact that vulnerable IoT devices are beyond the network’s control makes them susceptible to being compromised and used as bots or part of botnets, leading to a surge in attacks involving these devices in recent times. We proposed a real-time IoT anomaly detection and mitigation solution at the programmable data plane in a Software-Defined Networking (SDN) environment using Inband Network telemetry (INT) data to address this issue. As far as we know, it is the first experiment in which INT data is used to detect IoT attacks in the programmable data plane. Based on our performance evaluation, the detection delay of our proposed approach is much lower than the results of previous Distributed Denial-of-Service (DDoS) research, and the detection accuracy is similarly high.
Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation

Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.
Effects of Dietary Supplementation of a Resin-Purified Aqueous-Isopropanol Olive Leaf Extract on Meat and Liver Antioxidant Parameters in Broilers

Olive leaves are byproducts οf the agro-industrial sector and are rich in bioactive compounds with antioxidant properties. They could be supplemented in poultry diets powdered or less frequently as extracts to improve performance, health and product quality. The objective of this study was to investigate the possible beneficial effects of an aqueous isopropanol olive leaf extract—purified through filtration (250–25 µm) and a resin (XAD-4)—when supplemented in broiler chickens’ diets, on meat quality parameters, focusing mainly on antioxidant parameters as there is limited published information. For this purpose, four-hundred-and-eighty-day-old broilers were randomly assigned to four dietary treatments: T1 (control: basal diet); T2 (1% olive leaf extract); T3 (2.5% olive leaf extract); T4 (positive control: 0.1% encapsulated oregano oil commercially used as feed additive). At the end of the experimental period (day 42), the birds were slaughtered, and samples from breast, thigh meat and liver were collected for antioxidant parameters evaluation. On day 1, after slaughter, in thigh meat, Malondialdehyde (MDA) was lower in T2 compared to T3, and total phenolic content (TPC) was higher in T2 compared to T3 and T4. Total antioxidant capacity (TAC) was increased in T2 and T4 breast meat compared to the control. In liver, T4 treatment resulted in higher TPC. The lack of dose-dependent effect for olive leaf extract may be attributed to the pro-oxidant effects of some bioactive compounds found in olive leaves, such as oleuropein, when supplemented at higher levels. In summary, it can be inferred that the inclusion of 1% olive leaf extract in the feed of broilers has the potential to mitigate oxidation in broiler meat and maybe enhance its quality.
A 10-Year-Old Girl’s Dysfunctional ‘Self-Help’ in ADHD: Suppression of Hyperkinetic Symptoms via Self-Induced Weight Loss in the Context of Anorexia Nervosa—A Case Report

Anorexia Nervosa (AN) and Attention Deficit Hyperactivity Disorder (ADHD) are frequent mental disorders in child and adolescent psychiatry. Comorbidity of these disorders is, however, rare among minors. Thus, little is known about their mutual impact on illness development as well as diagnostic and therapeutic influencing factors. We report the case of a 10-year old girl with AN and massive underweight. At the age of 5, ADHD had been diagnosed. Application of ADHD-specific medication had been refused by her caregiver. As of 3rd grade, hyperkinetic symptoms were significantly reduced, which was later linked to beginning AN-induced weight loss. At inpatient admission, no clinically relevant ADHD-related symptoms were present. Accompanying weight gain, rather ‘sudden’ appearance of attention difficulties, motoric hyperactivity and impulsivity were reported, widely impairing our patient’s schoolwork and further daily life. Methylphenidate medication showed good clinical response and tolerability. We hypothesize that the former massive underweight had suppressed ADHD-specific behaviour. AN with significant weight loss could possibly mask hyperkinetic symptoms in children. Thus, sufficient clinical diagnostics and intense monitoring during ED treatment are required. Physicians and therapists should be sensitized for interactions in the joint occurrence of these mental disorders among minors.
Reexamining the online environment for self-directed learning

Abstract By the beginning of 2020, there were widespread school closures caused by the spread of coronavirus for an unknown duration. Consequently, many schools turned to online learning using a software application, or a video conferencing system. As high levels of self-directed learning (SDL) are important for the quality of online learning, the present study aimed to understand learner SDL in an online environment and identify its challenges and barriers at one of the higher education institutes in Oman. It addressed a case study that documented how learning went in three online courses and reported perceptions of learners pertaining to effective SDL. Data were collected through interviews with seven selected students from each course, virtual observations of online teaching, and course documents. Results revealed four issues related to students’ SDL in online classes: 1) student cognitive engagement with online learning, 2) student-centered approach, 3) time-management and 4) student emotional engagement with online learning. The findings suggest a need to design an effective online learning environment through reinforcing students’ satisfaction and motivation in online classes, their time management skill, a student-centered approach and teacher presence.
The migration of lesser spotted eagle Aquila pomarina, European honey buzzard Pernis apivorus, levant sparrowhawk Accipiter brevipes and white stork Ciconia ciconia over northern Israel - a balance over 30 years of counts

Israel is located at a bird migration bottleneck of global importance. On its entire north-south extension the country is part of the Eurasian-East African flyway and the Great Rift Valley, the second most important migration route on a global scale. Thanks to its geographical situation as a land bridge between Europe, Asia and Africa, an estimated 500 million birds pass over Israel during spring and autumn migration. Nearly the entire world populations of some species, such as the Lesser Spotted Eagle Aquila pomarina and the Levant Sparrowhawk Accipiter brevipes, pass over Israel twice a year. A large part of the Western Palaearctic population of the White Stork Ciconia ciconia migrates here, as well as the biggest proportion of the East European and Asian population of European Honey Buzzard Pernis apivorus. Since the 1980s bird migration over Northern Israel has been systematically recorded. Data gathered during the annual survey of soaring birds between 2000 and 2012 are analysed in this paper. Additionally a list with details of all seven raptors that were ringed in Poland and refound in Israel is provided. Abstrakt. Migracja orlika krzykliwego Aquila pomarina, trzmielojada Pernis apivorus, krogulca krotkonogiego Accipiter brevipes i bociana bialego Ciconia ciconia przez polnocny Izrael – podsumowanie wynikow 30 lat badan. Izrael znajduje sie w wąskim gardle migracji ptakow o światowym znaczeniu. Na calej swojej dlugości na linii polnoc –poludnie, kraj jest cześcią szlaku Eurazja – Wschodnia Afryka i Great Rift Valley, drugiego najwazniejszego szlaku migracji w skali globalnej. Dzieki swemu polozeniu geograficznemu jako pomostu lądowego pomiedzy Europą, Azją i Afryką, nad Izraelem przelatuje okolo 500 milionow ptakow podczas wiosennej i jesiennej migracji. Prawie cale światowe populacje niektorych gatunkow, takich jak orlik krzykliwy Aquila pomarina i krogulec Accipiter brevipes, przelatuje nad Izraelem dwa razy w roku. Migruje tu duza cześc populacji zachodniopalearktycznego gatunku bociana bialego Ciconia ciconia, jak rowniez najwiekszą cześc populacji wschdonioeuropejskiej i azjatyckiej trzmielojada Pernis apivorus. Od lat osiemdziesiątych XX wieku migracja ptakow nad polnocnym Izraelem jest systematycznie rejestrowane. Dane zgromadzone w czasie rocznego badania ptakow szybujących miedzy 2000 i 2012 są przedmiotem analizy w niniejszej pracy. Dodatkowo podano liste ze szczegolami wszystkich siedmiu ptakow drapieznych zaobrączkowanych w Polsce i odnalezionych w Izraelu. AR TY KU ŁY / AR TI CL ES
Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients

The current contribution aimed to evaluate the capacity of the naive Bayes classifier to predict the progression of dengue fever to severe infection in children based on a defined set of clinical conditions and laboratory parameters. This case-control study was conducted by reviewing patient files in two public hospitals in an endemic area in Mexico. All 99 qualifying files showed a confirmed diagnosis of dengue. The 32 cases consisted of patients who entered the intensive care unit, while the 67 control patients did not require intensive care. The naive Bayes classifier could identify factors predictive of severe dengue, evidenced by 78% sensitivity, 91% specificity, a positive predictive value of 8.7, a negative predictive value of 0.24, and a global yield of 0.69. The factors that exhibited the greatest predictive capacity in the model were seven clinical conditions (tachycardia, respiratory failure, cold hands and feet, capillary leak leading to the escape of blood plasma, dyspnea, and alterations in consciousness) and three laboratory parameters (hypoalbuminemia, hypoproteinemia, and leukocytosis). Thus, the present model showed a predictive and adaptive capacity in a small pediatric population. It also identified attributes (i.e., hypoalbuminemia and hypoproteinemia) that may strengthen the WHO criteria for predicting progression to severe dengue.