Miracle-dz/UniFAR-FTUs huggingface dataset:
Similarities in the behaviour of dance followers among honey bee species suggest a conserved mechanism of dance communication

Group living organisms rely on intra-group communication to adjust individual and collective behavioural decisions. Complex communication systems are predominantly multimodal and combine modulatory and information bearing signals. The honey bee waggle dance, one of the most elaborate forms of communication in invertebrates, stimulates nestmates to search for food and communicates symbolic information about the location of the food source. Previous studies on the dance behaviour in diverse honey bee species demonstrated distinct differences in the combination of visual, auditory, olfactory, and tactile signals produced by the dancer. We now studied the behaviour of the receivers of the dance signals, the dance followers, to explore the significance of the different signals in the communication process. In particular, we ask whether there are differences in the behaviour of dance followers between the 3 major Asian honey bee species, A. florea, A. dorsata and A. cerana, and whether these might correlate with the differences in the signals produced by the dancing foragers. Our comparison demonstrates that the behaviour of the dance followers is highly conserved across all 3 species despite the differences in the dance signals. The highest number of followers was present lateral to the dancer throughout the waggle run, and the mean body orientation of the dance followers with respect to the waggle dancer was close to 90° throughout the run for all 3 species. These findings suggest that dance communication might be more conserved than implied by the differences in the signals produced by the dancer. Along with studies in A. mellifera, our results indicate that all honey bee species rely on tactile contacts between the dancer and follower to communicate spatial information. The cues and signals that differ between the species may be involved in attracting the followers towards the dancer in the different nest environments.
Fertility Treatment for Women with PCOS

Polycystic ovarian syndrome is the commonest cause of anovulatory infertility. This chapter will explore fertility treatment options for this condition including the risks, benefits and success rates for different treatment methods. The importance of close patient monitoring with hormone levels and pelvic ultrasounds to ensure mono-ovulation and to avoid ovarian hyperstimulation syndrome will be highlighted.
Comparisons of Four Methods for Tropical Cyclone Center Detection in a High-Resolution Simulation

The tropical cyclone (TC) center position is often needed in the study of inner-core processes although there is currently no consensus on the definition of the TC center. While previous studies evaluated center-detecting methods in terms of the center position, vertical tilt and decomposed symmetric, and asymmetric circulations, this study used the 1-km resolution output of the predicted Hurricane Wilma (2005) at 5-minute intervals to evaluate the four TC centers that are frequently used in the diagnostic analysis of the inner-core dynamics processes: the pressure centroid center (PCC), the potential vorticity (PV) centroid center (PVC), the maximum tangential wind center (MTC), and the minimum pressure variance center (MVC) by focusing on the evolution of the small-scale track oscillation and vortex tilt. The differences in the detected center position and vertical tilt are generally small during the course of rapid intensification and eyewall replacement. All four methods lead to similar small-scale track oscillations that rotate cyclonically around the mean track. While the MVC and PVC lead to a relatively smooth rotation, abrupt changes exist in the track oscillation of the MTC; the track oscillation of the PCC contains amplified embedded rotations that are associated with the PV mixing in the eye region. The tracks of the MVC and PVC relative to the lower-level center (vertical tilt) are generally smooth, while the relative tracks of the MTC and PCC contain abrupt changes. The MVC also leads to the strongest symmetric structure in the tangential wind, PV, and radial PV gradient in the eyewall region. This study suggests that the MVC should be selected in the study of inner-core processes.
Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach

Abstract Reynolds-Averaged Navier-Stoke (RANS) models offer an alternative avenue in predicting flow characteristics when the corresponding experiments are difficult to achieve due to geometry complexity, limited budget, or knowledge. RANS models require the knowledge of subgrid scale physics to solve conservation equations for mass, energy, and momentum. Mechanistic turbulence models, such as k-ε, are generally evaluated and calibrated for specific flow conditions with various degrees of uncertainty. These models have limited capability to assimilate a substantial amount of data due to model form constraints. Meanwhile, deep learning (DL) has been proven to be universal approximators with the potential to assimilate available, relevant, and adequately evaluated data. Moreover, deep neural networks (DNNs) can create surrogate models without knowing function forms. Such a data-driven approach can be used in updating fluid models based on observations as opposed to hard-wiring models with precalibrated correlations. The paper presents progress in applying DNNs to model Reynolds stress using two machine learning (ML) frameworks. A novel flow feature coverage mapping is proposed to quantify the physics coverage of DL-based closures. It can be used to examine the sufficiency of training data and input flow features for data-driven turbulence models. The case of a backward-facing step is formulated to demonstrate that not only can DNNs discover underlying correlation behind fluid data but also they can be implemented in RANS to predict flow characteristics without numerical stability issues. The presented research is a crucial stepping-stone toward the data-driven turbulence modeling, which potentially benefits the design of data-driven experiments that can be used to validate fluid models with ML-based fluid closures.
A 24-hour assessment of lifestyle constraint in stroke survivors: A six patient case-based study

Background: Stroke often comes with a spectrum of constraints to lifestyle. This study investigated these constraints using a 24-hour lifestyle assessment among 6 stroke survivors. Objectives: The objective of this study was to provide an understanding of the ways in which stroke survivors are experiencing stroke, along with the opportunities and challenges of everyday life. Materials and Methods: Six stroke survivors were recruited to participate in this qualitative study. The study participants were 3 males and 3 females. Participants were engaged in in-depth interviews to generate information regarding the objective of this study. The interview was recorded on audiotape and transcribed. The generated data were analysed by thematic framework analysis. Results: Findings showed that 5 out of the 6 stroke survivors faced challenges in everyday life, in reconstructing their identity and depended on their family members to take care of them. All the stroke survivors reported some form of functional restrictions, with 4 out of the 6 responents indicating that their movement was restricted to their immediate compound and 5 out of the 6 respondents indicating that they were unable to do most of their house-hold chores; and one respondent said that he 'barely could do anything for self'. Conclusion and Recommendations: Among the 6 respondents, stroke has several manifestations on patients' mobility and indepdence. These have significant bearing on their wellbeing. Studies with larger sample sizes incorporating quantitative methods and longitudinal follow-up are recommended to shed more light on these findings.
What do adversarial images tell us about human vision?

Deep convolutional neural networks (DCNNs) are frequently described as promising models of human and primate vision. An obvious challenge to this claim is the existence of adversarial images that fool DCNNs but are uninterpretable to humans. However, recent research has suggested that there may be similarities in how humans and DCNNs interpret these seemingly nonsense images. In this study, we reanalysed data from a high-profile paper and conducted four experiments controlling for different ways in which these images can be generated and selected. We show that agreement between humans and DCNNs is much weaker and more variable than previously reported, and that the weak agreement is contingent on the choice of adversarial images and the design of the experiment. Indeed, it is easy to generate images with no agreement. We conclude that adversarial images still challenge the claim that DCNNs constitute promising models of human and primate vision.
Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters

Gasoline particulate filters (GPFs) are an appropriate means to meet today’s emission standards. As for diesel applications, GPFs can be monitored via differential pressure sensors or using a radio-frequency approach (RF sensor). Due to largely differing soot properties and engine operating modes of gasoline compared to diesel engines (e.g., the possibility of incomplete regenerations), the behavior of both sensor systems must be investigated in detail. For this purpose, extensive measurements on engine test benches are usually required. To simplify the sensor development, a simulation model was developed using COMSOL Multiphysics® that not only allowed for calculating the loading and regeneration process of GPFs under different engine operating conditions but also determined the impact on both sensor systems. To simulate the regeneration behavior of gasoline soot accurately, an oxidation model was developed. To identify the influence of different engine operating points on the sensor behavior, various samples generated at an engine test bench were examined regarding their kinetic parameters using thermogravimetric analysis. Thus, this compared the accuracy of soot mass determination using the RF sensor with the differential pressure method. By simulating a typical driving condition with incomplete regenerations, the effects of the soot kinetics on sensor accuracy was demonstrated exemplarily. Thereby, the RF sensor showed an overall smaller mass determination error, as well as a lower dependence on the soot kinetics.
Anthropometric profile and complications in patients with diabetes mellitus seen at Maluti Adventist Hospital, Lesotho

Background: Patients attending outpatient departments for follow-up of diabetes mellitus (DM) may seem content about the control of their disease. However, complications resulting from DM may present before diagnosis and treatment initiation or continue to develop while on treatment. Aim: This study aimed to report on the prevalence of DM complications in patients with DM at Maluti Adventist Hospital, Lesotho. Methods: A cross-sectional study was conducted in 150 DM patients recruited from Maluti Adventist Hospital’s newly formed clinic between May and June 2009. Demographic information was obtained through a structured interview. Patients underwent physical and eye examinations to obtain an anthropometric profile and identify DM complications. Laboratory and ECG investigations were done to establish level of glycaemic control and identify other complications. Results: Patients were female (80.7%) with a mean age of 58.2 years (standard deviation 13.2). Almost all patients had type 2 DM (94.7%) and 60.1% had been diagnosed during the preceding five years. The most common co-morbidity was hypertension (85.3%) and 49.7% of patients were obese (mean body mass index 30.4 kg/m2). Patients had poor short-term (46.2%) and long-term (57.5%) glycaemic control. Nearly half (43.3%) of the patients had evidence of peripheral neuropathy. Almost 60% of the patients had a blood pressure of > 130/80 mmHg. Of the 128 patients seen by the ophthalmologist, 29.9% had evidence of cataracts in both eyes. Funduscopic abnormalities were found in 32.8% of the patients. Patients with complications or poor glycaemic control had been diagnosed for a significantly longer time. Conclusion: Patients showed evidence of DM complications at the time of study. Coupled with significant co-morbidities and high body mass index, the risk of morbidity and mortality is high. A comprehensive approach is needed in managing these patients, if meaningful reductions in morbidity and mortality are to be achieved.
Assessing environmental health risks.

The chemical industry has piled many chemicals on humans which cause many serious health consequences. Research on chemical effects shows that a definite link exists between exposure to some industrial chemicals and specific serious diseases especially cancer. For example benzene is linked to leukemia. Recent studies indicate that increases in the incidence and mortality rate of cancers with no known links to smoking are occurring. These increases effect the young and old alike. Genetic factors do not account for all of the increase. Evidence that many environmental pollutants at low levels of exposure undermine biological function is growing. This impaired functioning occurs without any obvious signs of disease. US regulatory agencies view of chemical risks (i.e. cancer prevention) is thus incomplete. Toxic effects at lower levels of exposure are particularly worrisome since almost everyone and every plant and animal are exposed to low levels of some chemicals. Neurotoxins (e.g. lead and solvents) cause short-term memory problems dizziness fatigue irritability inability to concentrate and structural changes to the nervous system. The best evidence for a link between environmental pollution and immune dysfunction is asthma and respiratory illnesses. The cause of about 60% of reproductive and development disorders is not known. People who work with glycol ether have a high rate of adverse reproductive outcomes. These links and identified effects call for a more conservative approach to chemical regulation. Regulatory agencies e.g. US Environmental Protection Agency need to include the growing evidence of toxic effects at lower levels into their chemical risk assessments. Specifically their specialists must consider potential effects on the nervous system the endocrine and reproductive systems and the immune system.
Follicular Lymphoma with Widespread Skin Lesions and Chromosome 14;18 Translocation in the Skin

Background: We report a case of systemic follicular B-cell lymphoma presenting with widespread skin lesions. Objective: The clinical features, histology, and treatment of primary and secondary cutaneous follicular lymphomas are discussed. Conclusion: The demonstration of chromosome 14; 18 translocation in the skin that is commonly found in primary, but rare in secondary, cutaneous follicular lymphoma can be helpful in confirming the diagnosis.
A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systems

This research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the same workstation multiple times and dynamic dispatching oftentimes aims to dynamically assign different priorities to various job groups to minimise weighted cycle time or maximise throughput. MDP is an effective tool for dynamic production control, but it suffers from two major challenges in dynamic control problems. First, the curse of dimensionality limits the computational performance of solving large MDP problems. Second, a different model should be built and solved after system configuration is changed. DNN is used to overcome both challenges by learning directly from optimal dispatching policies generated by MDP. Results suggest that a properly trained DNN model can instantly generate near-optimal dynamic control policies for large problems. The quality of the DNN solution is compared with the optimal dynamic control policies through the standard K-fold cross-validation test and discrete event simulation. On average, the performance of the DNN policy is within 2% of optimal in both tests. The proposed artificial intelligence algorithm illustrates the potential of machine learning methods in manufacturing applications.
Beyond Medications: Understanding Executive Function Deficits of Children with ADHD Through Jungian Sandplay Therapy

This study aims to search for effective therapeutic approach for children with ADHD by understanding their symptoms as well as their difficulties. This researcher will review the relationships between executive function deficits of Children with ADHD and their symptoms of inattention and hyperactivity-impulsivity. Then, the effectiveness and limitations of psycho-stimulants, the major intervention for children with ADHD, will be reviewed. To understand executive function deficits of Children with ADHD in analytical perspective, this research will review Jung’s archetypal psychology, the theories of Erich Neumann as well as Erick Edinger, and Jungian sandplay therapy. Finally this researcher will analysis a clinical case of child with ADHD based on Jungian sandplay therapy. The result is that the sandplay therapy provided for child with ADHD a safe place to explore his difficulty and assisted him to meet the Self who would revitalize his fragile self so that he could reduce symptoms of ADHD and increase social skills.
Individualized inference through fusion learning

Fusion learning methods, developed for the purpose of analyzing datasets from many different sources, have become a popular research topic in recent years. Individualized inference approaches through fusion learning extend fusion learning approaches to individualized inference problems over a heterogeneous population, where similar individuals are fused together to enhance the inference over the target individual. Both classical fusion learning and individualized inference approaches through fusion learning are established based on weighted aggregation of individual information, but the weight used in the latter is localized to the target individual. This article provides a review on two individualized inference methods through fusion learning, iFusion and iGroup, that are developed under different asymptotic settings. Both procedures guarantee optimal asymptotic theoretical performance and computational scalability.
Indicators for secondary carcinoma in head and neck cancer patients following curative therapy: A retrospective clinical study

Patients with head and neck squamous cell carcinoma (HNSCC) are at risk for local and regional relapse, as well as for occurrence of synchronous or metachronous secondary carcinoma. The aim of this retrospective study was to evaluate the frequency of secondary malignancies amongst HNSCC patients and their association with age, sex and TNM classification, as well as smoking and alcohol abuse. Data from 394 patients with HNSCC who were treated at the Department of Otorhinolaryngology-Head and Neck Surgery of the University Hospital Mannheim between 2011 and 2015 were retrieved and analyzed using t-tests and P≤0.05 was considered statistically significant. Of the 394 patients, 50 (12.7%) developed a secondary carcinoma. In this cohort, >70% of secondary malignancies were diagnosed using clinical inspection or microlaryngoscopy. The majority of secondary malignancies were identified in the oropharynx, with men being more often affected overall. Continued abuse of carcinogenic substances appeared to increase the incidence of secondary carcinoma, whereas the localization of the primary tumor, age, sex or TNM classification were not identified as significant indicators of the occurrence of a secondary carcinoma. The purpose of the present study was to emphasize the importance of intensive follow-up to ensure early detection of secondary malignancies. The aim was to help predict numbers and occurrence within smaller cohorts, and to evaluate the quality of data collected during the establishment of a certified tumor center. To fully evaluate the role of continued exposure to noxious substances and other possible contributing factors, and in order to improve the rate of early diagnosis and establish preventive strategies, multicentered studies with larger cohorts are required.
An Affected Child with Arrhythmogenic Right Ventricular Dysplasia and Severe Left Ventricle Dysfunction: A Rare Case Report

ArrhythmogenicRightVentricularDysplasia(ARVD)wasdiagnosedinanine-year-oldboyfollowingthepresentationof generalized edema, ascites, and palpitation. Typical signs of ARVD on electrocardiography (ECG) and echocardiography were detected in initial assessments. On echocardiography, severe right ventricle dysfunction, free tricuspid regurgitation, and left ventricle ejection frac-tion(EF)of 45% -50% werereported. Cardiacmagneticresonanceimagesshowedseverelyenlargedrightventriclesizewithseverely reducedsystolicfunction. Prolongedepisodesof sinusbradycardiaandatrialﬁbrillationwerenotedateachhospitalization. Severe LV dysfunction and free Tricuspid regurgitation have not heretofore been reported in the medical literature.
The Impact of the Extreme 2015–2016 El Niño on the Mass Balance of the Antarctic Ice Sheet

Interannual variations associated with El Niño‐Southern Oscillation can alter the surface‐pressure distribution and moisture transport over Antarctica, potentially affecting the contribution of the Antarctic ice sheet to sea level. Here, we combine satellite gravimetry with auxiliary atmospheric data sets to investigate interannual ice‐mass changes during the extreme 2015–2016 El Niño. Enhanced precipitation during this event contributed positively to the mass of the Antarctic Peninsula and West Antarctic ice sheets, with the mass gain on the peninsula being unprecedented within GRACE's observational record. Over the coastal basins of East Antarctica, the precipitation‐driven mass loss observed in recent years was arrested, with pronounced accumulation over Terre Adélie dominating this response. Little change was observed over Central Antarctica where, after a brief pause, enhanced mass‐loss due to weakened precipitation continued. Overall, precipitation changes over this period were sufficient to temporarily offset Antarctica's usual (approximately 0.4 mm yr−1) contribution to global mean sea level rise.
Corporate meets startup : exploring factors affecting inter-organizational knowledge transfer

Startups are a potential source of new knowledge for corporates. Corporates are increasingly setting up startup programs to institutionalize cooperation with startups and to realize joint projects. However, promising collaborative projects often fail because knowledge cannot be transferred between startup and corporate. This paper examines factors that increase the probability of successful knowledge transfer. An abductive research approach with case studies and expert interviews was chosen. Within the framework of the elaboration, factors for knowledge transfer that have hardly been studied so far are discussed: in particular, the employees involved on the corporate side are decisive for successful knowledge transfer. Liaison or transfer managers of the startup program act as bridge builders between startup and corporate. Within the corporate, employees must be involved in projects at an early stage in order to create ownership, while prioritization and commitment must be exemplified by the management. The theoretical findings are supplemented by a checklist for users such as innovation managers.