Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying model-based Co-Simulation on modular Production Units in Complex Automation Systems

Contemporary manufacturing systems face major challenges driven by the ongoing integration of intelligent manufacturing components, mainly caused by the emergence of the Industrial Internet of Things (IIoT). As the interplay of those Cyber-physical Systems (CPS) itself forms a System of Systems (SoS), engineering such a system becomes a difficult task. This is mainly attributed to the evolutionary development of its independently operating components, which results in some kind of unpredictable and often undesirable behavior. Considering this from a developer’s perspective, it is important to investigate the system’s behaviors before its actual implementation. However, as methods applied in Model Based Systems Engineering (MBSE) have proven to be a major technology driver when it comes to developing the architecture of these industrial systems, their simulation is still not standardized. Thus, this paper introduces an Industry 4.0 specific tool-chain enabling the co-simulation of components within any developed industrial system architecture. This allows the investigation of varying industrial CPS and their interplay during run-time with the goal to detect undesired emergent behaviors. By doing so, the model is developed regarding the specifications of the Reference Architecture Industrie 4.0 (RAMI 4.0), while Mosaik is the tool of choice for creating the Co-Simulation environment. In order to validate the developed tool-chain, a case study utilizing modular production units within an industrial automation system is applied.
Semantic map generation algorithm combined with YOLOv5

Traditional visual SLAM algorithms have problems such as lack of semantic information, low accuracy and slow speed of 3D point cloud segmentation. This paper proposes a semantic map generation algorithm based on YOLOv5 and improved VCCS point cloud segmentation. Firstly, the ORB-SLAM2 algorithm is used to generate the original three-dimensional point cloud. The target is detected by YOLOv5 and the original point cloud is semantically annotated, and the objects in the point cloud are expressed in other colors. Then, the VCCS algorithm was used for over-segmentation to obtain supervoxel clustering. The improved VCCS algorithm was used to merge supervoxel clustering to improve the accuracy of segmentation results. Finally, a three-dimensional point cloud map with semantic information is established. Experiments show that the algorithm can generate semantic maps very well, and the accuracy and speed of 3D point cloud segmentation are greatly improved.
Memory: Neurobiological mechanisms and assessment

Memory is the process of retaining of knowledge over a period for the function of affecting future actions. It can be divided into declarative and procedural types. The process of memory consolidation is done in the hippocampus. The long-term memories are spread among various areas of the cerebrum depending on the different perceptual properties. The process of long-term potentiation and molecular changes occurring during memory formation are discussed in detail below. The steps involved in memory formation include encoding, storage, and recall (retrieval) in that order. Amnesia is a phenomenon in which there is the problem in memory formation which can be due to trauma to the brain, certain diseases, or stressors. While the assessment of memory has greatly improved, we are only beginning to understand the underlying mechanisms.
A correlational study between perceived parenting style, psychological well-being, and emotional intelligence among adolescents

Background: In many reviews, it is found that parental responsiveness, parental emotion-related coaching, and parental positive demandingness are related to children's higher emotional intelligence (EI), while parental negative demandingness is related to children's lower EI. There is a lack of Indian work in this area. Aim: To study the role of EI and parenting style in predicting psychological well-being among adolescents in an Indian scenario. Materials and Methods: This cross-sectional, analytical study was carried out on 75 boys and 75 girls in the age range of 15–18 years and attending school. They were assessed with the sociodemographic data sheet, trait EI questionnaire, psychological well-being inventory, and parental authority questionnaire. After the administration on different scales, the scoring was done and data were analyzed with SPSS. Results: There were no gender differences in the scores of EI, psychological well-being, and self-reported parenting style scores. There was a significant positive co-relation between well being and authoritative, authoritarian and autonomy, positive relationship. and self acceptance. Authoritarian parenting style is highly correlated with EI in comparison to authoritative and permissive parenting styles. Conclusion: Authoritarian parenting style is most suitable for the development of EI and on the other hand, permissive parenting style has the least role in the development of EI. EI and parenting style are found to predict the psychological well-being in adolescents.
Effect of COVID-19 and sociocultural milieu on the psychopathology of mental health disorders: A hospital-based study

Background: Coronavirus disease (COVID-19) has posed a remarkable threat to mental health all around the world. This pandemic has increased the incidence of common as well as severe mental illness (SMI) all around the world. Materials and Methods: We report 10 cases presenting to the psychiatric outpatient department (OPD) of Institute of Medical Sciences, Banaras Hindu University, from August to October 2020. They were either referred by other departments (3 patients) or came primarily to psychiatric OPD (7). Results: Five out of these 10 cases presented with predominant psychotic features; 3 cases had predominant obsessive–compulsive features; and 1 case was of dissociative trance possession. Conclusion: COVID-19 can affect the psychopathology of both types of patients either with preexisting mental illness as well as new-onset SMI.
Design and development of a sensor-based precision crop protection autonomous system for orchard sprayer

Recent years have shown enthusiastic research interest to reduce pollution of non-target such as sensitive environmental areas, human and non-targeted crops. In this way, the most important issue is that intermittent spraying on the trees simply needs a detection system to apply the spray only to the target trees, allowing the sprayer to turn off between trees or in areas where trees are missing. So, in this study, we present a low-cost sensing system that can apply a variable amount of liquid according to detection of the tree canopy for plant protection products applications in orchards. Target detecting system was implemented on a 2000 liter conventional towed air-blast orchard sprayer. In this study, the target detecting system was designed on the basis of “sensor-equipped spraying’. Generally, the optical sensors were used to detect the tree canopy, then send signal to PLC until which solenoid ON/OFF valves is opened. Laboratory and field experiments were conducted to evaluate its performance. During laboratory experiments, delay time to start and stop spraying, spatial lag were investigated. During field experiments, evaluation of water sensitive paper and evaluation of pesticide consumption were studied. In the laboratory test, forward speed did not show any significant effect on delay times of starting or stopping the spraying. Variance analysis of data of sprayed area on water sensitive paper (WSP) indicated that the effects of forward speed, vertical and horizontal place of water sensitive papers on the area of WSPs covered by sprayed liquid were significant at the 1% of probability level. The results of measuring the amount of sprayed liquid at studied forward speed levels showed about 50% saving in pesticide consumption implementing variable rate in comparison with the conventional spraying.
TEASER: Towards Efficient Aspect-based SEntiment Analysis and Recognition

Sentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims to extract the aspects of the given target entities and their respective sentiments. Prior works formulate this as a sequence tagging problem or solve this task using a span-based extract-then-classify framework where first all the opinion targets are extracted from the sentence, and then with the help of span representations, the targets are classified as positive, negative, or neutral. The sequence tagging problem suffers from issues like sentiment inconsistency and colossal search space. Whereas, Span-based extract-then-classify framework suffers from issues such as half-word coverage and overlapping spans. To overcome this, we propose a similar span-based extract-then-classify framework with a novel and improved heuristic. Experiments on the three benchmark datasets (Restaurant14, Laptop14, Restaurant15) show our model consistently outperforms the current state-of-the-art. Moreover, we also present a novel supervised movie reviews dataset (Movie20) and a pseudo-labeled movie reviews dataset (moviesLarge) made explicitly for this task and report the results on the novel Movie20 dataset as well.
Experimental sexual selection affects the evolution of physiological and life history traits

Sexual selection and sexual conflict are expected to affect all aspects of the phenotype, not only traits that are directly involved in reproduction. Here, we show coordinated evolution of multiple physiological and life history traits in response to long-term experimental manipulation of the mating system in populations of Drosophila pseudoobscura. Development time was extended under polyandry relative to monogamy in both sexes, potentially due to higher investment in traits linked to sexual selection and sexual conflict. Individuals (especially males) evolving under polyandry had higher metabolic rates and locomotor activity than those evolving under monogamy. Polyandry individuals also invested more in metabolites associated with increased endurance capacity and efficient energy metabolism and regulation, namely lipid and glycogen. Finally, polyandry males were less desiccation- and starvation-resistant than monogamy males, suggesting trade-offs between resistance and sexually selected traits. Our results provide experimental evidence that mating systems can impose selection that influences the evolution of non-sexual phenotypes such as development, activity, metabolism, and nutrient homeostasis.
On the Relation between Willingness to Accept and Willingness to Pay∗

A vast literature documents that willingness to pay (WTP) is less than willingness to accept (WTA) a monetary amount for an object, a phenomenon called the endowment effect. Using data from three incentivized studies with a total representative sample of 4,000 U.S. adults, we add one additional finding: WTA and WTP for a lottery are (essentially) uncorrelated. In contrast, independent measures of WTA (or WTP) are highly correlated, and relatively stable across time. Leading models of referencedependent preferences are compatible with a zero correlation between WTA and WTP, but only for specific parameterizations and ruling out popular special cases. These models also predict a relationship between the endowment effect and loss aversion, which we do not find. JEL Classifications: C90, D81, D91
On the revealed preference analysis of stable aggregate matchings

Echenique, Lee, Shum, and Yenmez (2013) established the testable revealed preference restrictions for stable aggregate matching with transferable and nontransferable utility and for extremal stable matchings. In this paper, we rephrase their restrictions in terms of properties on a corresponding bipartite graph. From this, we obtain a simple condition that verifies whether a given aggregate matching is rationalizable. For matchings that are not rationalizable, we provide a simple greedy algorithm that computes the minimum number of matches that need to be removed to obtain a rationalizable matching. We also show that the related problem of finding the minimum number of types that we need to remove in order to obtain a rationalizable matching is NP‐complete.
Anomaly Detection in Crowded Scenes With Machine Learning Anomaly Detection in Crowded Scenes With Machine Learning Algorithms Algorithms

Camera systems have a very important place as they are widely used in providing security in crowded environments. The video images recorded by cameras are examined to check whether there is dangerous or unusual behavior. It is tried to develop appropriate measures according to the result of this control. Modeling human behaviors for the definition and detection of abnormal behavior has become a popular research area in recent years. This study was carried out by applying supervised learning algorithms, one of the machine learning methods, on five different scenes that in two open data sets. Normal and abnormal motion scenes were detected on the videos in the data sets. In these two data sets, abnormal motion was detected in a total of five different locations. Random Forest, Support Vector Machines and k Nearest Neighbor algorithms, which are among the supervised learning algorithms, were used in this process. The algorithms used were compared with performance criteria such as accuracy, sensitivity, precision and F1 score.
The LogiLegoLab: A Problem-based Learning Approach for Higher Education Institutions

Industry 4.0 concepts and technologies offer a multitude of possibilities for the systematic improvement of material and information flow processes in logistics systems. However, the usage of laboratories for the experimental testing and subsequent further development of Industry 4.0 approaches in industrial logistics still can be considered as an underdeveloped subject area in higher education institutions. Based on a problem-based learning approach, this paper investigates the recent literature on teaching and learning laboratories in the context of Industry 4.0 to identify the potential areas of action for an industrial logistics laboratory in the fields of research, academic teaching, and industry-related applications. Moreover, the authors develop a new concept for a module-based laboratory and derive implications for further research and industrial projects.
Input-Output Efficiency Evaluation of Financial Block Chain Concept Stocks: Based on Dea-Bcc and Malmquist Index Model

This paper selects relevant data of financial listed companies in blockchain concept stocks from 2016 to 2019, uses DEA-BCC model and Malmquist index model to construct static analysis and dynamic analysis. The static analysis shows that, on the whole, from 2016 to 2019, the overall inputting and output efficiency of listed companies performs well, with technical efficiency above 0.7 on average, pure technical efficiency above 0.79 on average, and scale efficiency above 0.89 on average. However, there is still a certain distance from DEA effectiveness. From the perspective of time change, the technical efficiency of the 15 listed companies showed a trend of fluctuation with time change, but the overall fluctuation was not obvious. The difference between the maximum and minimum value of technical efficiency of Rendong Holding, the listed company with the largest fluctuation, was 0.456, while the difference between the maximum and minimum value of ICBC with the least fluctuation was 0.009. Dynamic analysis shows that from 2016 to 2019, the average value of Malmquist index is 0.935, representing a decline in total factor production efficiency, mainly because the average value of technology progress index is less than 1. The development of blockchain technology is slow, and the average value of technical efficiency change index is 1.010, mainly because of the rise of pure technical efficiency. Therefore, financial enterprises should increase the research and development of blockchain technology, promote the progress of blockchain technology, make the perfect integration of blockchain technology and financial business, reasonably adjust the size of the company and related investment, so as to improve their input-output efficiency.
TREMA-UNH at TREC 2017: Complex Answer Retrieval

Our long-term goals are to develop new IR algorithms that utilize knowledge graphs and semantic annotations for composing comprehensive essays on complex questions. We participated both in the passage and entity retrieval task. Our passage retrieval runs are based on a class experiment from the course on data science for knowledge graphs and text, taught in Spring 2017. The best performing methods, BM25 and a special form of query expansion, were submitted. Our entity retrieval runs are based on a novel method to develop graph walk algorithms for knowledge graphs that can reason in every step which edges to follow to stay on topic.
NON-LINEAR DYNAMIC RELATIONSHIP BETWEEN EXCHANGE RATE AND STOCK PRICE OF PAKISTAN FROM 1965 TO 2015

This study aims to determine the dynamic association between stock prices and exchange rates of Pakistani currency (PKR) from 1965 to 2015, including the war era. Long and short-run associations are explored between these variables. Vector Autoregressive Model has been used to examine the association between the stock prices and exchange rate of PKR. Daily time series data of Pakistan from 1 Jan 1965 to 31 May 2015, from Thomson Reuter’s software, is used. The Johansson co-integration approach reveals that two co-integration factors are existing between the two series, which shows that these two-time series have long-run movement. Nevertheless, the Granger causality test reveals that bi-direction causality exists among the variables. While the Vector Error Correction Model reveals that there is a short-run association among the series.
The modified approximation of SH-SH wave reflection coefficient for the VTI media Considering a discrete subsurface model, the linear approximation of the SH-SH wave reflection coefficient for an interface separating two adjacent VTI layers can be expressed as (Rüger, 2002)

Anisotropy has significant influence on seismic wave propagation (Thomsen, 1986). PP-wave inversion for anisotropic media has been studied extensively and applied in practical exploration. Li. (1999) proposed a method for determining the fracture orientation based on the azimuthal variations in the PPwave reflection moveout. Zhang et al. (2019) presented a simultaneous inversion and a stepwise method to recover model parameters for the VTI media. SS-waves are more capable to invert shear-wave impedance, shear-wave velocity, density and anisotropy parameters than the PP-wave, because SSwaves are more sensitive to these parameters (Luo et al., 2018). Zhang and Li (2020) and Zhang (2021) showed that density and S-wave velocity can be well inverted from the reflected SV-SV wave. The inversion of SS-waves are still under research.
< INCOMPLETE > Variational Inference: A Review for Optimizers

This document is the evil twin of a recent paper by Blei et al. entitled Variational Inference: A Review for Statisticians [1]. In that paper, the authors present the foundational concepts of Variational Inference (VI) and list some open problems in VI research. It provides a useful starting point for anyone interested in the ﬁeld. Variational Inference is at the intersection of statistics and optimization. While the original Review paper is explicitly aimed at statisticians, this document attempts to ﬁll a similar role for optimization researchers interested in VI. Requisite statistical background can be found in appendix A
Investigation of the wake energy recovery of cross-flow turbines in paired configuration by means of 3d-CFD and analysis of the streamwise momentum budget

Literature suggests that cross-flow turbines (CFTs) could be suitable for off-shore wind farms because of the high power-density achievable by shortening the distance between arrays as allowed by a fast wake energy recovery experimentally detectable. By means of 3d-CFD, we analysed the effect of the rotation verse of CFTs in counter-rotating paired configuration on the wake behaviour. Then, we applied the momentum-budget approach to identify the fluid dynamic mechanisms which are more effective in supporting the streamwise momentum recovery. The following results are found. (A) The counter-rotating vortices occurring in the near-wake as a consequence of the vortex shedding at the blade tip are responsible for the vertical advection that enters high momentum flow inside the wake. (B) The turbulent transport contribution is less important, yet it becomes significant starting from the medium wake. (C) For the inner-downwind layout the wake shape appears similar to that of a single turbine, whereas for the inner-upwind layout it is greatly contracted in horizontal direction and enlarged in vertical direction (D). The momentum recovery appears slightly more delayed than in case of a single turbine, yet the velocity deficit appears less extensive for the inner upwind layout that, especially thanks to the wake lateral contraction, could be the preferable in farms consisting in staggered arrays, allowing to shorten the lateral distance between adjacent pairs.
Automatic Utility Pole Inclination Angle Measurement Using Unmanned Aerial Vehicle and Deep Learning

Measuring the inclination angles of utility poles of the electric power distribution lines is critical to maintain power distribution systems and minimize power outages, because the poles are very vulnerable to natural disasters. However, traditional human-based pole inspection methods are very costly and require heavy workloads. In this paper, we propose a novel pole monitoring system to measure the inclination angle of utility poles from images captured by unmanned aerial vehicle (UAV) automatically. A state-of-the-art deep learning neural network is used to detect and segment utility poles from UAV street view images, and computer vision techniques are used to calculate the inclination angles based on the segmented poles. The proposed method was evaluated using 64 images with 84 utility poles taken in different weather conditions. The pole segmentation accurcy is 93.74% and the average inclination angle error is 0.59 degrees, which demonstate the efficiency of the proposed utility pole monitoring system.
Mixed Logit and Pure Characteristics Models ∗

Mixed logit or random coefficients logit models are used extensively in empirical work while pure characteristic models feature in much of theoretical work. We provide a theoretical analysis of the relationship between the two classes of models. First, we show an approximation theorem that precisely characterizes the extent and limitations of mixed logit approximations of pure characteristic models. Second, we present two conditions that highlight novel behavioral differences. The first is a substitutability condition that is satisfied by many pure characteristic models (including models of horizontal differentiation such as Hotelling) but is violated by almost all mixed logit models. The second is a continuity condition that is satisfied by all pure characteristic models but is violated by all mixed logit models. Both conditions pertain to choice patterns when product characteristics change or new products are introduced and illustrate the limitations of using mixed logit models for counterfactual analysis.