Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Dynamics of Hand Picking Patterns of Fresh Market Apples

Abstract. Patterns and extent of grasping force and moment involved in manual apple picking is a fundamental information needed in designing an effective end-effector for robotic apple harvesting. The investigation of hand picking patterns will be useful to identify efficient way(s) of apple harvesting, which will lead to an effective and efficient end-effector design. In this research, hand grasping force was measured during manual harvesting of apples of different varieties, including â€˜Granny Smithâ€™, â€˜Galaâ€™, and â€˜Fujiâ€™ in 2013 harvest season. The average firmness of these apples ranged from 51.0 to 76.4 N. The grasping force measurement system consisted of two force sensors attached to an ultrathin glove, a data acquisition card, and associated software. Sensorâ€™s electrical output was calibrated to relate to actual force. Normal forces to the apple surface were measured during pulling, cutting, twisting against peduncle orientation, and twisting along peduncle orientation of apples. The magnitude of the grasping force was analyzed in relation to apple varieties, shape, weight, and firmness. Force applied to the apple surface varied substantially from one apple to the other. In general, grasping force ranged between 2.4 and 57.6 N, which was substantially smaller than firmness of the corresponding apple variety. This result indicated that a robotic end-effector can be developed for effective fruit detachment without causing any damage. Grasping force differed substantially between different types of grasping pattern from 9.6 N for cutting to 41.4 N for pulling, but little correlation with apple varieties, shape, weight, and firmness was observed. This knowledge is also useful for engineers to design and develop effective end-effectors to achieve desired grasping patterns.
The Ergodic GN Model for Space-Division Multiplexing With Strong Mode Coupling

The Gaussian noise (GN) model, thanks to its simplicity, is the most widely used model to describe the nonlinear interference (NLI) in fiber-optic systems. This paper gives an extension of the GN model to space-division multiplexed (SDM) systems operating in the regime of strong coupling between modes, where the understanding of the effect of spatial mode dispersion (SMD) on the accumulation of the NLI noise stands as an open problem of utmost importance. We show that SMD may significantly reduce the NLI power and that a reliable estimation can be done with the extended GN model in very fast times, of the order of seconds per channel. We derive a closed-form expression for the NLI power averaged with respect to the frequency-dependence random-mode coupling introduced by mode dispersion, and show that its random fluctuations around the average value are negligible for all practical purposes. The derived expression is proved to be very accurate in the relevant range of SMD values. Scaling properties of the NLI variance with respect to the main system parameters are also discussed to gain some physical insight.
The uncanny valley of a virtual animal

Virtual robots, including virtual animals, are expected to play a major role within affective and aesthetic interfaces, serious games, video instruction, and the personalization of educational instruction. Their actual impact, however, will very much depend on user perception of virtual characters as the uncanny valley hypothesis has shown that the design of virtual characters determines user experiences. In this article, we investigated whether the uncanny valley effect, which has already been found for the human‐like appearance of virtual characters, can also be found for animal‐like appearances. We conducted an online study (N = 163) in which six different animal designs were evaluated in terms of the following properties: familiarity, commonality, naturalness, attractiveness, interestingness, and animateness. The study participants differed in age (under 10–60 years) and origin (Europe, Asia, North America, and South America). For the evaluation of the results, we ranked the animal‐likeness of the character using both expert opinion and participant judgments. Next to that, we investigated the effect of movement and morbidity. The results confirm the existence of the uncanny valley effect for virtual animals, especially with respect to familiarity and commonality, for both still and moving images. The effect was particularly pronounced for morbid images. For naturalness and attractiveness, the effect was only present in the expert‐based ranking, but not in the participant‐based ranking. No uncanny valley effect was detected for interestingness and animateness. This investigation revealed that the appearance of virtual animals directly affects user perception and thus, presumably, impacts user experience when used in applied settings.
A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting

Recent studies have demonstrated the great success of graph convolutional networks in short-term traffic forecasting (e.g., 15–30 min ahead) tasks by capturing dependencies in road network structure. Based on these models, long-term forecasting can be achieved by two approaches: (1) recursively generating a one-step-ahead prediction and (2) adapting the models to sequence-to-sequence (seq2seq) learning. However, in practice, these two approaches often show poor performance in long-term forecasting tasks. The recursive approach suffers from the error accumulation problem, as the model is trained based on one-step-ahead loss. On the other hand, seq2seq shows convergence issues that limit its application. To address the issues for long-term forecasting, in this paper, we propose a universal framework that directly transforms any existing state-of-the-art models for one-step-ahead prediction to achieve more accurate long-term forecasting. The proposed framework consists of two components—a base model and a bias block. The base model is assumed to be a well-trained state-of-the-art one-step-ahead forecasting model, and the bias block is constructed by a spatiotemporal graph neural network composed of gated temporal convolution layers and graph convolution layers. The base model and the bias block are residually-connected so that we can substantially reduce the training complexity. Extensive experiments are conducted on existing benchmark datasets. We experiment with several state-of-the-art models in the literature as base models, and our results demonstrate the ability of the proposed universal framework to greatly improve the long-term prediction accuracy for all models.
Longitudinal vibration control for a suspension bridge subjected to vehicle braking forces and earthquake excitations based on magnetorheological dampers

In order to reduce the excessive longitudinal vibration of a suspension bridge induced by vehicle braking forces (as one of the possible dynamic loadings) and earthquake excitations, a mixed control methodology using magnetorheological (MR) dampers is developed in this study. Firstly, the corresponding preferred controls subjected to vehicle braking forces and earthquake excitations, separately, are obtained by simulation analyses for seven control strategies. Then, a mixed control system is established to obtain the best control efficiency and reduce the energy consumption of MR dampers, based on the displacement responses and changing rate of accelerations. Finally, this system is applied to reduce the longitudinal vibration responses of the Pingsheng Bridge. The numerical results show that the passive-on control and the semi-active fuzzy control are the corresponding preferred controls subjected to vehicle braking forces and earthquake excitations, respectively. The mixed control has a good agreement with the corresponding preferred controls under four dynamic loadings including the vehicle braking forces, the Pingsheng Bridge earthquake wave, the El Centro wave, and the Takochi-oki wave, which verifies the reliability and effectiveness of this system. The mixed control has good performance and applicability for different loading combinations composed of vehicle braking forces and earthquake excitations.
Fuzzy model reference learning (FMRL) control applied to a boiler steam drum

The use of fuzzy logic controllers (FLCs) has seen a surge in many applications. Although these are simple to synthesis once the fuzzy rules are known; it takes experience to produce these rules and takes a fair amount of time to tune them. For this reason, a fuzzy system that can learn these rules has significant advantage over one that doesn`t. In this paper, the authors examine one such method for constructing the fuzzy rules. The method is then applied to the problem of boiler steam drum control to demonstrate its feasibility.
Eating out of Home: Influence on Nutrition, Health, and Policies: A Scoping Review

Eating out of home (EOH) is a common practice worldwide but research gaps have been identified. The aims of this review were (a) to find a common definition for EOH, (b) to determine the nutritional contribution of EOH, and (c) to analyze the relationship of EOH with health parameters in adults. Fifty-seven articles were finally selected. The definition of EOH was not harmonized between researchers and the comparison between studies was quite difficult. Restaurant and fast food were the terms most used, followed by chain restaurant, à la carte, sit-down restaurant, eating at table, full service, ready to eat, takeaway, buffet and buffet by weight, bar, cafes, and cafeterias, either alone or attached to at least one of the above. The profile of the main EOH participant was a highly educated, high-income, and unmarried young man. EOH was related to a body mass index (BMI) or being overweight in a different way depending on age, sex, or EOH frequency. A high rate of EOH led to poorer diet quality, characterized by higher intakes of energy, total and saturated fats, sugar, and sodium, as well as lower intakes of fiber, dairy, fruit, vegetables, and micronutrients. Regarding beverages, a higher intake of soft drinks, sugar-sweetened beverages, fruit juices, beer, and other alcohol was observed when EOH. There is a need for a methodological consensus for analyzing the impact of EOH on dietary intake and health to avoid bias. Additionally, measures and policies should be utilized to help consumers to make healthier choices when EOH is compatible with business regarding those running EOH establishments.
Effects of Propolis and Persica Mouthwashes on Three Common Oral Streptococci: A Comparative Study

Background: Dental caries and periodontal diseases are among the most common oral diseases, and research to achieve an effective strategy to overcome these diseases is necessary. One of these strategies is to use anti-septics and disinfectants, including mouthwashes. Although chlorhexidine was the first and most common mouthwash and the gold standard of anti-plaque treatments, it bears many side effects. However, herbal mouthwashes with antimicrobial properties and fewer side effects can effectively treat many of these diseases. Objectives: This study aimed to compare the efficiency of the two herbal types of mouthwash produced in Iran. Methods: The present in vitro study was conducted to investigate the anti-bacterial effects of persica and propolis mouthwashes on three strains of oral streptococci. The Zone of Inhibition (ZOI) was measured by the Disk Diffusion Method (DDM). The Minimum Inhibitory Concentration (MIC) value of each mouthwash was determined for all microorganisms using the macrodilution method. Results: Statistical analysis of data of DDM showed that the anti-bacterial effect of persica was significantly higher than propolis against Streptococcus. salivarius and Streptococcus. mutans (P<0.001), and these two types of mouthwash had similar anti-bacterial effects on Streptococcus. sanguis. Local propolis exhibited better MIC results than persica against S. salivarius and S. mutans, and these two types of mouthwash showed similar results against S. sanguis. Conclusion: Local propolis was more potent than persica in preventing the growth of oral streptococci.
Data-Efﬁcient Exploration with Self Play for Atari

Most reinforcement learning (RL) algorithms rely on hand-crafted extrinsic rewards to learn skills. However, crafting a reward function for each skill is not scalable and results in narrow agents that learn reward-speciﬁc skills. To alleviate the reliance on reward engineering it is important to develop RL algorithms capable of efﬁciently acquiring skills with no rewards extrinsic to the agent. While much progress has been made on reward-free exploration in RL, current methods struggle to explore efﬁciently. Self-play has long been a promising approach for acquiring skills but most successful applications have been in multi-agent zero-sum games with extrinsic reward. In this work, we present SelfPlayer, a data-efﬁcient single-agent self-play exploration algorithm. Self-Player samples hard but achievable goals from the agent’s past by maximizing a symmetric KL divergence between the visitation distributions of two copies of the agent, Alice and Bob. We show that SelfPlayer outperforms prior leading self-supervised exploration algorithms such as Go-Explore and Curiosity on the data-efﬁcient Atari benchmark.
Corporate Social Responsibility in International Channels of Distribution

This study attempts to provide an understanding of the effects of corporate social responsibility (CSR) engagement on outcomes in international channels of distribution. Despite the importance of CSR in international business model and strategy, few studies have addressed the effects of CSR activities on the outcome in these channels. Building on the stakeholder theory and relationship marketing theory, the authors develop an empirically testable model that explains the effectiveness of firms’ CSR engagement in their international channels of distribution. Using dyadic data collected from two surveys, the authors empirically test the model. Findings from an examination of 126 Korean multinational corporation-foreign distributor relationships reveal the positive effects of firms’ CSR efforts on the international channel outcome through distinct mechanisms where ethical CSR improves channel partners’ trust whereas philanthropic CSR enhances relationship commitment. In addition, the authors find that foreign partners’ CSR orientation positively moderates the relationship between each CSR perception and each of relational mediators. This paper offers important implications for theory and practice and concludes with the limitations of the current study and the directions for future research.
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales

Abstract. Large-scale (i.e., continental and global) hydrologic simulation is an appealing yet challenging topic for the hydrologic community. First and foremost, model efficiency and scalability (flexibility in resolution and discretization) have to be prioritized. Then, sufficient model accuracy and precision are required to provide useful information for water resource applications. Towards this goal, we craft two objectives for improving US current operational hydrological models: (1) vectorized routing and (2) improved hydrological processes. This study presents a hydrologic modeling framework, CREST-VEC, that combines a gridded water balance model and a newly developed vector-based routing scheme. First, in contrast to a conventional fully gridded model, this framework can significantly reduce the computational cost of river routing by at least 10 times, based on experiments at regional (0.07 vs. 0.002 s per step) and continental scales (0.35 vs. 7.2 s per step). This provides adequate time efficiency for generating operational ensemble streamflow forecasts and even probabilistic estimates across scales. Second, the performance using the new vector-based routing is improved, with the median-aggregated NSE (Nash–Sutcliffe efficiency) score increasing from −0.06 to 0.18 over the CONUS (contiguous US). Third, with the lake module incorporated, the NSE score is further improved by 56.2 % and the systematic bias is reduced by 17 %. Lastly, over 20 % of the false alarms on 2-year floods in the US can be mitigated with the lake module enabled, at the expense of only missing 2.3 % more events. This study demonstrated the advantages of the proposed hydrological modeling framework, which could provide a solid basis for continental- and global-scale water modeling at fine resolution. Furthermore, the use of ensemble forecasts can be incorporated into this framework; and thus, optimized streamflow prediction with quantified uncertainty information can be achieved in an operational fashion for stakeholders and decision-makers.
In Silico Screening of Molecular Interactions of Umbelliferone, A Type of Coumarin Compound with Multiple Therapeutic Targets of Epilepsy

Epilepsy is one of the most common neurological disorders in all age groups of individuals. Still, the currently existing antiepileptic drugs are showing side effects and are resistant to various epileptic patients. Hence, there is a need to find out effective novel drug molecules without side effects. We have selected umbelliferone, a group of natural coumarin compounds, to study its binding efficacy and inhibitory activity against selected therapeutic targets of epilepsy using molecular docking studies. Further, we have evaluated its drug screening through Lipinski’s rule and toxicity properties. The results demonstrate that umbelliferone is potentially and competitively inhibiting all the different therapeutic targets of epilepsy compared to the diazepam reference drug. Furthermore, the umbelliferone falls in with Lipinski’s rule, which shows all standard drug parameters and is nontoxic to human beings. The docking results reveal that the umbelliferone could be a therapeutic drug to study its anticonvulsant activity using in vitro cellular and in vivo animal models.
Current Update on Management of Infantile Colic

Infant colic is a troubling condition for both parents and pediatrician, but it is benign and self-limiting. About 40% infants experience colic and it resolves between 12-16 week of life. ROME III and IV criteria are used for diagnosing infantile colic, but ROME IV is more specific for research purposes. The cause of colic is unknown but some hypotheses were proposed such as bile acid immaturity, microbial alterations and immaturity of gastrointestinal motility. Parental reassurance is the key of management of this condition, other therapies such as dietary modification, pharmacological and other approaches were not proven to be effective. Probiotics (Lactobacillus reuteri DSM 17938) are proven to reduce the symptoms and can prevent the colic. Other strains are still need further studies to be recommended.
The Effect of Training Lecture, e-Learning on the Improvement of Professional Competency and its Impact on the Medical Representative Performance of PT XYZ

Professional competence is important in supporting employee performance, especially marketing staff. In this study, professional competence is the focus of this research because it is a very important basis for marketing staff to be able to market their products well to customers so that it will impact on performance. To increase the professional competence of marketing staff, it is necessary to provide ongoing training such as face-to-face training and independent study. Face-to-face training / training lectures (variable X1) and e-Learning (Variable X2) are associated with professional competence (Variable Y1) and professional competence is associated with performance (Variable Y2). This research was conducted on 126 injection line medical representatives and 224 oral lines from 11 major cities in Indonesia by filling out a questionnaire. The data obtained were analyzed using appropriate software to analyze the characteristics of Medical representatives who were used as respondents and for the analysis of the validity test and reliability test of the questionnaire items, then the data were also analyzed using SEM to see the relationship between the variables. From the results of the analysis, the results of the Training lecture can significantly increase professional competence, e-Learning can significantly increase professional competence, Professional competence has a significant impact on performance, training lectures do not have a significant impact on performance and e-Learning has a significant impact on performance.
Answers to referees’ comments for “Elemental composition and clustering of α- pinene oxidation products for different oxidation conditions” (acp-2014-883)

A. P. Praplan, S. Schobesberger, F. Bianchi, M. P. Rissanen, M. Ehn, T. Jokinen, H. Junninen, A. Adamov, A. Amorim, J. Dommen, J. Duplissy, J. Hakala, A. Hansel, M. Heinritzi, J. Kangasluoma, J. Kirkby, M. Krapf, A. Kürten, K. Lehtipalo, F. Riccobono, L. Rondo, N. Sarnela, M. Simon, A. Tomé, J. Tröstl, P. M. Winkler, C. Williamson, P. Ye, J. Curtius, U. Baltensperger, N. M. Donahue, M. Kulmala, and D. R. Worsnop.
An Investigation Of the Role Of Feature Selection On the Classification Performance Of Machine Learning Algorithms

The recent and rapid increase in the size of data and the large variety of data types that are required to be processed within different scientific fields has made it extremely important to find an optimal set of features that reduces the complexity of the data and extract valuable and beneficial information from within these large sums of unorganized data. This is especially true in the cases where datasets suffer from the problem of high dimensionality. These challenges require an in depth understanding of the effect of feature selection algorithms and how to utilize them to achieve higher rates of accuracy within high dimensional data sets. In this paper we investigate the role of proper feature selection in the context of classification of anomaly network intrusion detection systems. We show that the appropriate selection of important features can have a huge effect on the accuracy level of the classifiers. We also compare the performance of different classifiers, and show that the Restricted Boltzmann Machine classifier outperforms other type of classifiers in some cases.
V Modeling of Capacitive Coupled Interconnects for Crosstalk Analysis in VLSI Circuits

—In this paper, we have estimated the crosstalk noise for CMOS driver with coupled capacitive interconnect model in digital High-speed VLSI circuits. Crosstalk noise is one of the important factor for designing accurate interconnect models. Crosstalk by which a net/wire creates undesired effect on the neighbouring circuit due to the capacitive coupling which as a result increases the clock frequency , density of the circuit and reduces the performance The design of L - Model Interconnect with capacitively coupled at high frequencies, estimating the crosstalk noise on both ends of victim lines with respect to aggressor line for global interconnects at 180nm Technology Node. It is also analyzed analytically and simulated results using SPICE tool.