Miracle-dz/UniFAR-FTUs huggingface dataset:
Functionality of TV Control Applications

The need for remote control of a television is uniform, expressed mainly in the ability to change TV channels, TV settings or use the functions built into the TV set. Remote controls have changed over time to meet users’ expectations. The latest technologies make it possible to customize remote controls and confi gure them according to the user’s needs. For the average person not well familiar with technology and applications, choosing the right one for a particular brand of TV is not the easiest thing to do, as the amount available is overwhelming the Google off er. The aim of the study is to analyze, compare and identify the most functional application for controlling modern TV sets based on classic Smart TV and Android TV software. The analysis covers applications available on the Google platform, i.e. the Google Play Store.
Effect Sizes Reported in Highly Cited Emotion Research Compared With Larger Studies and Meta-Analyses Addressing the Same Questions

We assessed whether the most highly cited studies in emotion research reported larger effect sizes compared with meta-analyses and the largest studies on the same question. We screened all reports with at least 1,000 citations and identified matching meta-analyses for 40 highly cited observational studies and 25 highly cited experimental studies. Highly cited observational studies had effects greater on average by 1.42-fold (95% confidence interval [CI] = [1.09, 1.87]) compared with meta-analyses and 1.99-fold (95% CI = [1.33, 2.99]) compared with largest studies on the same questions. Highly cited experimental studies had increases of 1.29-fold (95% CI = [1.01, 1.63]) compared with meta-analyses and 2.02-fold (95% CI = [1.60, 2.57]) compared with the largest studies. There was substantial between-topics heterogeneity, more prominently for observational studies. Highly cited studies often did not have the largest weight in meta-analyses (12 of 65 topics, 18%) but were frequently the earliest ones published on the topic (31 of 65 topics, 48%). Highly cited studies may offer, on average, exaggerated estimates of effects in both observational and experimental designs.
Overview on Binary Optimization Using Swarm-Inspired Algorithms

Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents - swarm (e.g. fish, bird, and ant) - directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarm-based techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach - binary decision variables and binary search space - is more efficient for binary optimisation in accuracy and computational cost.
N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine—A Review

Simple Summary Clinical trials in pediatric oncology and personalized medicine are challenging due to the rarity of the disease, the low prevalence, and the ever-improving treatment outcomes. Many of the methods designed for small numbers and approaches used in classical population studies are not suitable for personalized pediatric oncology. There has been a change of perspective on the whole issue of rare diseases and personalized medicine. For example, a shift from a population to an individual perspective, generalizing from the individual to the population, using repeated measures and a within-subject design instead of parallel groups, exploring the variability instead of suppressing it, etc. N-of-1 should be understood as a whole range of approaches that fit the new inferential, evidential and analytical paradigms of modern medicine. Abstract Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the “evidence puzzle”.
Short wave limit of the Novikov equation and its integrable semi-discretizations

In this paper, we are concerned with one of the generalized short wave equations proposed by Hone et al (2018 Lett. Math. Phys 108 927). We show that the derivative form of this equation can be viewed as a short wave limit of the Novikov (sw-Novikov) equation. Furthermore, this generalized short wave equation and its derivative form are found to be connected to period 3 reduction of two-dimensional CKP(BKP)-Toda hierarchy, same as the short wave limit of the Depasperis–Procesi (sw-DP) equation. We propose a two-component short wave equation which contain the sw-Novikov equation and sw-DP equation as two special cases. As a main result, we construct two types of integrable semi-discretizations via Hirota’s bilinear method and provide multi-soliton solution to the semi-discrete sw-Novikov equation.
Cognitive Conflicts in Functional Gastrointestinal Disorders

The importance of psychological factors in functional gastrointestinal disorders (FGID) is well-stablished in the literature; however, cognitive factors have hardly been researched and, in particular, cognitive conflicts have not been explored for these disorders. The aim of this study is to compare the cognitive and symptomatic characteristics of a group of 66 FGID patients (33 diagnosed with irritable bowel syndrome and 33 with functional dyspepsia) with a control group of participants without FGID or psychopathological symptoms. Both groups were matched by sex and age. The evaluation of the clinical sample was carried out following the criteria of the DSM-IV-TR. The SCL 90-R, and also the Repertory Grid for the identification of cognitive conflicts (implicative dilemmas) and self-ideal discrepancy, were administered to both the clinical sample and the control group. Results showed that 85% of FGID patients met the criteria for one axis I disorder of the DSM-IV-TR, mainly anxiety and somatization disorders. Regarding axis II, 23% presented at least one personality disorder, the most common ones being those of avoidance and dependence. Regarding axis IV, the patients reported a higher number of problems relative to the primary support group. FGID patients showed greater symptoms compared to the control group on various SCL 90-R scales. On the other hand, FGID patients presented more implicative dilemmas than healthy controls, as well as lower self-esteem. No significant differences were observed depending on the type of FGID (irritable bowel syndrome or functional dyspepsia).
Analisis Performansi Algoritma Informed Rapidly-exploring Random Tree* Dengan Metode Boundary Sampling dan Metode Goal Biasing Sampling

Perencanaan jalur merupakan salah satu program penunjang untuk robotika atau kendaraan. Pada perencanaan jalur dibutuhkan algoritma yang tepat agar mendapatkan hasil yang optimal. Algoritma Informed Rapidly-exploring Random Tree* (Informed-RRT*) merupakan algoritma untuk perencanaan jalur yang berbasis sampling atau pengambilan sampel acak pada ruang pencarian. Pada penelitian ini akan dilakukan analisis performansi algoritma Informed-RRT* dengan metode boundary sampling dan metode goal biasing sampling. Tujuan penelitian ini yaitu membandingkan algoritma Informed-RRT* metode boundary sampling dengan Informed-RRT* metode goal biasing sampling untuk mendapatkan algoritma dengan performansi terbaik dalam melakukan perencanaan jalur. Pengujian dilakukan berbasis simulasi menggunakan software Labview. Dalam pengujian ini digunakan beberapa kasus lingkungan obstacle yang ada, yaitu lingkungan narrow, trap dan clutter. Kriteria untuk menentukan performansi yang optimal yaitu jarak jalur (path cost), waktu komputasi, dan jumlah node. Dari hasil yang didapatkan, nilai rata-rata algoritma Informed-RRT* metode boundary sampling menghasilkan waktu komputasi yang lebih cepat dan juga jumlah node yang lebih sedikit dibandingkan dengan Informed-RRT* metode goal biasing sampling, sedangkan untuk jarak jalur algoritma dengan kedua metode tersebut memiliki hasil yang hampir sama. Dapat disimpulkan bahwa algoritma Informed-RRT* metode boundary sampling memiliki performansi lebih optimal dibandingkan dengan algoritma Informed-RRT* metode goal biasing sampling. Kata kunci : Boundary Sampling, Goal Biasing Sampling, Informed-RRT*, Labview, Perencanaan jalur
Telerehabilitation with Exoskeletons using Adaptive Robust Integral RBF-Neural-Network Impedance Control under Variable Time Delays

With an unprecedented increase in the global aging population and with it, the age-related neuromuscular dysfunction diseases, there is an exorbitant and escalating need for physical rehabilitation. Delivering these services - especially to those that are most vulnerable - under the current COVID-19 pandemic restriction for physical-distancing, is an even greater challenge. Interest in telerehabilitation is spiking, and robotic tel-erehabilitation could drastically improve patients' access to care. Some of the major challenges in developing the control methods for these robots are identifying, estimating, and overcoming the effects of dynamic modeling uncertainties, nonlinearities, and disturbances. Having humans in the loop creates the additional need for safety and compliance. Telerehabilitation control methods have the added requirement of delivering telepresence and addressing communication delays which, if not managed, could result in ineffective therapy, destabilize the system, and even cause injury. In this paper, we present a novel adaptive robust integral Radial Basis Function Neural Network Impedance model (RBFNN-I) control method for telerehabilitation with robotic exoskeletons which compensates for dynamic modeling uncertainties in the presence of external human torques and time delays. One of the salient features of the proposed control system is the implementation of a new human torque regulator which improves telepresence. Stability proof using Lyapunov stability theory is shown for the proposed control method. An exoskeleton was designed and used for unilateral and bilateral telerehabilitation simulations. Excellent tracking performance, telepresence and stability was achieved in the presence of large, variable and asymmetric time delays and human torques.
Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning

Many real-world tasks require multiple agents to work together. When talking about multiple agents in robotics, it is usually referenced to multiple manipulators in collaboration to solve a given task, where each one is controlled by a single agent. However, due to the increasing development of modular and re-configurable robots, it is also important to investigate the possibility of implementing multi-agent controllers that learn how to manage the manipulator’s degrees of freedom (DoF) in separated clusters for the execution of a given application (e.g., being able to face faults or, partially, new kinematics configurations). Within this context, this paper focuses on the decentralization of the robot control action learning and (re)execution considering a generic multi-DoF manipulator. Indeed, the proposed framework employs a multi-agent paradigm and investigates how such a framework impacts the control action learning process. Multiple variations of the multi-agent framework have been proposed and tested in this research, comparing the achieved performance w.r. t. a centralized (i.e., single-agent) control action learning framework, previously proposed by some of the authors. As a case study, a manipulation task (i.e., grasping and lifting) of an unknown object (to the robot controller) has been considered for validation, employing a Franka EMIKA panda robot. The MuJoCo environment has been employed to implement and test the proposed multi-agent framework. The achieved results show that the proposed decentralized approach is capable of accelerating the learning process at the beginning with respect to the single-agent framework while also reducing the computational effort. In fact, when decentralizing the controller, it is shown that the number of variables involved in the action space can be efficiently separated into several groups and several agents. This simplifies the original complex problem into multiple ones, efficiently improving the task learning process.
Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses

Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep learning algorithms are increasing in the development of agricultural robots since deep learning algorithms that use convolutional neural networks have been proven to show outstanding performance in image classification, segmentation, and object detection. However, most of these applications are focused on the development of harvesting robots, and thus, there are only a few studies that improve and develop monitoring robots through the use of deep learning. For this reason, we aimed to develop a real-time robot monitoring system for the generative growth of tomatoes. The presented method detects tomato fruits grown in hydroponic greenhouses using the Faster R-CNN (region-based convolutional neural network). In addition, we sought to select a color model that was robust to external light, and we used hue values to develop an image-based maturity standard for tomato fruits; furthermore, the developed maturity standard was verified through comparison with expert classification. Finally, the number of tomatoes was counted using a centroid-based tracking algorithm. We trained the detection model using an open dataset and tested the whole system in real-time in a hydroponic greenhouse. A total of 53 tomato fruits were used to verify the developed system, and the developed system achieved 88.6% detection accuracy when completely obscured fruits not captured by the camera were included. When excluding obscured fruits, the system’s accuracy was 90.2%. For the maturity classification, we conducted qualitative evaluations with the assistance of experts.
A Web-Based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: A Population-Based Study

Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT. Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients. Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging. Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.
I’m not Alone: A Phenomenological Study of Psychological Help Sources in Suicide Crisis

This research was conducted to explore the experiences of suicidal survivors in seeking psychological help for their crisis. A descriptive phenomenological study was chosen to get the dynamics of mental health help-seeking. Semi-structured interviews were conducted with youth participants who had previously experienced a suicide crisis. Seven participants (5 women; mean age 26 years-old) were involved voluntarily through the snowball technique recruitment. Descriptive phenomenological analysis was applied to get the synthesis and meaning of experiences in psychological help-seeking. This study found psychological help sources: self-help, close relationships, socio-cultural, and institutional. This article highlighted that social relations played a crucial role in the success of seeking help. Suicide prevention should be carried out by campaigning for self-disclosure to seeking help by utilizing various available psychological help sources.
Implementation and Evaluation of Latency Visualization Method for Teleoperated Vehicle

Teleoperation is a crucial technology for unmanned autonomous vehicles. When an autonomous vehicle does not work as desired, an operator teleoperates the vehicle and drives it to a safe place. Therefore, driving accuracy is essential in teleoperation. In teleoperation, a latency, consisting of transmission time and signal processing time, exists and impairs driving accuracy. A latency visualization method, which shows the gap due to latency between the vehicle's actual position and the position that the driver can see on the on-board camera videos, is considered a promising solution. Several studies have shown the latency visualization method's effectiveness through simulation-based evaluations, but it has not been evaluated in a real environment. In this paper, we proposed a latency visualization method that can be implemented in a real environment, where the latency can change frequently, and evaluated the method through a user test. The results show that the proposed latency visualization method improves driving accuracy in a real environment, and the improvement is more significant with longer latency.
Application of the Proposed Fiber Optic Time Differential BOCDA Sensor System for Impact Damage Detection of a Composite Cylinder

An optical-fiber-embedded composite cylinder was fabricated using the filament winding process with an interval of 12 mm in the longitudinal direction of the cylinder. The optical fiber was wound 160 turns around the cylinder, and the straight length was about 125 m. After a total of twelve impact events of 5, 10, 15, and 20 J, the residual strain in the cylinder was measured using the proposed time differential BOCDA sensor system. This method makes the traditionally used optical delay unnecessary while increasing the degrees of freedom of using the modulation rate, which determines the spatial resolution of this measurement system. The modulation rates of optical light in the system were applied up to 16 Gbps, which is an eight-fold increase compared to our previous experiments. Damage maps were obtained by mapping the measured residual strain onto the structure of the cylinder, and compared using three spatial resolutions of 20, 10, and 6.25 mm. In the measured damage map, expansion deformation due to impact was measured at all impact points, and the impact location on the map and the actual location on the cylinder were exactly the same. The map measured from the composite showed a clear point-symmetrical shape with an increase in sharpness as the measurement resolution increased. At the highest resolution, material expansion and compression were observed to alternate with respect to the center of impact, like the surface deformation of a liquid caused by a thrown object. Furthermore, considered together with our previous experiments, we confirmed that this phenomenon propagated from the surface of the composite material to the interior, where the optical fiber was embedded. The total amount of residual strain formed around each impact point was linearly proportional to the applied external impact energy.
Sampling-Rate Based Co-design Method of Control Systems Integrated with Time Sensitive Networking

Traditional methods for designing cyber-physical systems (CPS) are mostly based on the best-effort communication to design control and transmission mechanisms independently, which limits the overall system performance. Moreover, these methods focus on the utilization of transmission resources without sufficient consideration of deterministic low latency communication. Hence, the co-design of control and transmission in CPS integrated with Time Sensitive Networking (TSN), that provides deterministic transmission, is a promising and challenging problem. In this paper, we propose a co-design method of control and transmission mechanisms by optimizing the sampling rates of control systems with a shared multi-hop TSN. This co-design method is formulated as a mixed-integer nonlinear programming (MINLP) problem to minimize the overall control cost while ensuring all data flows to meet their end-to-end deadlines. To solve this MINLP problem with the greatest common divisor (GCD) and bilinear constraints efficiently, we adopt the scaling method and McCormick envelopes to eliminate the non-differentiable and nonlinear terms. Moreover, we design an optimized linearization method to solve the trade-off problem between the algorithm runtime and accuracy while reducing computational complexity. Simulations demonstrate that the proposed approach significantly improves the overall system performance.