Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexible Multi-aspect Model Integration for Cyber-Physical Production Systems Engineering

Background. Consistent cross-disciplinary engineering data models have become increasingly important for engineers and project managers to validate system designs or implement new features in existing systems. However, discipline-speciﬁc designs (mechanical, electrical, automation engineering etc.) in isolated data models and proprietary software tools often create information silos. Similar to information systems, the challenges in Cyber-Physical Production System (CPPS) are a high amount of heterogeneous data that needs to be analysed and accessible for stakeholders and systems. Aim. The goal of the Flexible Multi-aspect Model Integration project is to support the integration of local engineering views and artefacts using the deﬁnition of common concepts across diﬀerent disciplines. Therefore, the thesis project will provide capabilities to integrate and validate multi-aspect models more eﬃciently to increase the data quality. Method. The project will follow Design Science methodology to design and evaluate i) a method for collecting and deﬁning common concepts across engineering disciplines, ii) a modu-larised software system design that enables ﬂexible model integration processes in a CPPS context, and iii) an exemplary model integration process that supports data integration needs in the planning, operation, and analytics phase. The model integration processes are evaluated with real-world uses cases from industry. Conclusion. The information systems community will gain insight into the requirements in engineering and a method for agreeing on an inter-disciplinary common understanding from this research.
Effect of Soil Water Availability on Physiological Parameters, Yield, and Seed Quality in Four Quinoa Genotypes (Chenopodium quinoa Willd.)

Drought represents significant environmental stress, and improving agriculture water management and yield is a priority goal. The effect of diminishing soil water content in the grain filling (GF) stage, throughout physiological maturity (GM), on the yield and grain quality, leaf water potential (LWP), and maximum quantum yield (Fv/Fm) in four long photoperiod quinoa genotypes was evaluated in the South-Central zone of Chile, during the 2014–2015 and 2015–2016 seasons. Five irrigation treatments (T) were established. Irrigation was carried out when the available water (AW) of the root zone reached values of 100%, 70%, 40%, 20%, and 0%. The lowest LWP values were obtained by T20 and T0 (−1.95 MPa). The ‘Morado’ genotype reached the lowest LWP at both seasons, while the highest average LWP was achieved by the ‘AG 2010’ (2014–2015) and ‘Cahuil’ genotypes (2015–2016). A global trend of Fv/Fm values was observed from GF to GM: 0.74 toward 0.79 (2014/2015), and 0.74 toward 0.82 (2015/2016). Only during the second season, Fv/Fm showed differences among irrigation treatments. Total average grain yields in the second season (2.97 t ha−1) were greater than those in the first season (1.43 t ha−1). In both seasons, the ‘Cahuil’ genotype and T100 reached the highest yields. A significative decrease in yield was observed when AW diminished. A direct relationship between seed yield and leaf water potential (ΔY/ΔLWP) was found in all genotypes, varying between 5.53 (‘Cahuil’) and 2.86 t ha−1 MPa−1 (‘AG 2010’). Total proteins, albumins, and globulins varied between seasons, with almost no differences among irrigation treatments. Only the ‘Morado’ genotype showed a slight trend to obtain a higher content of total protein in both seasons. It is possible to grow quinoa under irrigation deficit conditions between GF throughout GM, maintaining yield parameters and nutritional quality.
Towards emotion recognition in immersive virtual environments

Virtual Reality (VR) is, thus, proposed as a powerful tool to simulate complex, real situations and environments, offering researchers unprecedented opportunities to investigate human behaviour in closely controlled designs in controlled laboratory conditions. Facial emotion recognition has attracted a great deal of interest for interaction in virtual reality, healthcare system: therapeutic applications, surveillance video application etc. In this paper, we propose a method for facial emotion recognition for immersive virtual environment based on 2D and 3D geometrical features. We used our collected dataset of 17 subjects’ performance of six basic facial emotions (anger, fear, happiness, surprise, sadness, and neutral) using three devices: Kinect (v1), Kinect (v2), and RGB HD camera. In addition, we present the performance results of the RGB data for facial emotion recognition using Bagged Trees algorithm. To assess the performance of the proposed system, we used leave-one-out-subject cross-validation. We compared the 2D and 3D data performance for facial expression recognition. The obtained results show the superior performance of the RGB-D features provided by Kinect (v2). Our findings highlight that the 2D images are not robust enough for facial emotion recognition. The built facial emotion models will animate virtual characters that can express emotions via facial expressions. This could be deployed for Chatting, Learning and Therapeutic Intervention.
Optimal Hardy inequalities associated with multipolar Schr\"odinger operators

We proved some optimal Hardy inequalities in RNwhich is closely related to multipolar Schr\"odinger operators with mean-value type potentials, these sharp inequalities imply some multipolar type Heisenberg inequalities. We also obtained someimproved multipolar Hardy inequalities on bounded domains, moreover, we got the range of the best Hardy constant for a specific Hardy inequality.
Otitis Media İçin Evrişimsel Sinir Ağlarına Dayalı Bütünleşik Bir Tanı Sistemi

Otitis media (OM) is a medical concept representing a range of inflammatory middle ear disorders. OM is one of the most common diseases worldwide, especially in childhood. In clinical practice, the diagnosis of OM is carried out by examining the images of the middle ear obtained via the otoscope device by specialists. The subjective examination leads to arise the variabilities among observers. At the same time, the use of computer-aided systems in this area is not common enough. Failure to diagnose OM disorders in a timely manner leads to the progression of the diseases, the emergence of hearing, speech, and cognitive disorders. To overcome all these disadvantages, an integrated diagnostic system based on the pretrained deep convolutional neural networks is proposed for the diagnosis of OM in this study. Experimental studies were carried out on 898 otoscope images, representing five different classes, collected from volunteer patients admitted to Özel Van Akdamar Hospital. As a result, the proposed model achieved 82.16% classification success. With the end-to-end learning and high sensitivity provided by the proposed model based on convolutional neural networks, OM diagnosis can be realized objectively and physicians' decision-making process can be supported using this system. The proposed method has produced promising results in these respects.
Forecasting model of Covid-19 cases using fuzzy time series using persentage change

The pandemic caused by the novel corona virus (covid-19) has affected various aspects of life throughout the world. Indonesia is one of the country with a daily high number of Covid-19-19 spread cases. This study aims to obtain a forecasting model of Covid-19 cases that can be used to predict Covid-19 cases daily and it can increase the readiness of Covid-19 health protocols system. In this study, we get a very good model for Covid-19 forecasting in Indonesia obtained by the fuzzy time series method using frequency density-based partitioning. The universe of this method is the percentage of case changes from day to day. The percentage change as a universe in fuzzy time series forecasting method makes the results of comparison of actual data and predictions increasingly similar. We use data of the Covid 19 cases taken from the Nasional Kompas website during June 2020. Forecast results show very good with MSE value of 457,83 and small AFER value of 0,0425138%.
New physicochemical interpretations for Adsorption of Trichloroethylene on Activated Carbon: Steric and Energetic Interpretation using Statistical Physics Approach

Experimental adsorption isotherms of trichloroethylene (TCE) on activated carbon at four temperatures were analyzed theoretically by a double layer with one energy established through statistical physics approach. This model contains three parameters affecting the adsorption process i.e. the number of molecules per site n , the monolayer adsorbed quantity Q 0 and the concentration at half- saturation c 1/2 . In this work, we propose a steric and energetic interpretation for the different adsorption isotherms of TCE on activated carbon. We calculate also the thermodynamic functions to characterize the adsorption process such as, entropy, free energy, and internal energy as function concentration. The different parameters and the thermodynamic functions were discussed with temperature.
Genetics and epigenetics of class II and class III malocclusions

Malocclusion is the major diagnosis in orthodontic field. According to Angle’s classification, malocclusion is divided into Class I, Class II, and Class III malocclusion. All of these classifications have etiologic factors that establish every specific characteristic of malocclusion. Genetic, is one of the most important etiology in malocclusion since it can be inherited congenitally, for instance many variations in transcription and translation of multifarious genes occurred in masseter muscles, which is the main mastication muscle in human. MYH/MyH-C, MMPs and IGF genes are frequently used to determine the expression of genes in masseter muscle using RT-PCR or cRT-PCR. On the other hand, polymorphism of ACTN3, which can influence fibre type proportions and also muscle performance is found in muscle and skeletal type of Class II malocclusion. MATN1, HSPG2, ALPL, and EPB41 genes are found linked to lp36 related to Class III malocclusion. However, genetic factor does not usually stand alone. It can be influenced by environment which called epigenetic factors. Increasing acetylation activity will initiate a chromatin domain formation that consists of genes for MyH-C fast type gene expression. In contrast, increased of deacetylation activity resulting in closed chromatin confirmation on the chromatin area to limit the access into transcription complexes for MyHC type I gene expression in malocclusion cases. Recently, the most common way to study about heritability in malocclusion is using masseter muscles by analyzing their types of fibers related to every malocclusion’s phenotype. In this review, writer will explain more about genetic study in masseter, histone modification, and also genetic and epigenetic factors of Class II and Class III malocclusions, which involve gene mutation and polymorphism for genetic factors and histone acetylation and deacetylation for epigenetic factors.
Modification frequency density with euclid distance classify for weather forecasting

Method of weather forecasting fuzzy time series has some drawbacks, weather forecasting needs many weather components, big data size and forecaster experience, one of which is very difficult if it is worn on large data whereas weather Forecasting using time series forecast has large data characteristics, high dimensions, and continuous also require many large components and amounts of data, this affects speed and accuracy in weather forecasts, but the science develops in tandem with the development of the fuzzy Forecasting method, the fuzzy forecasting method that can be worn on large data will facilitate the weather forecasting, in this paper will be explained methods of forecasting for large data that is using the Euclid distance to classify data, then using the frequency density partitioning modification method applied to the database that has been grouped. The data used was KW Hipel Al McLeod 1994 using year variables and temperature, data from 1782 to 1988, data covers 206 global average temperature data annually. The final solution of the weather forecasting fuzzy time series problem is to analyze the value of AFER and MSE, the value of this paper is AFER 0.0021 and MSE 0.0025, the smaller the AFER and MSE values indicate that the method is good enough to forecast the weather.
Effect of Cu Nanoparticles on the Properties of PP-g-PHMG/PE Monofilament and Its Antifouling Application

The modified polyethylene monofilament was prepared by melt blending and spinning using polyethylene (PE), polypropylene-grafted poly(hexamethylene guanidine) (PP-g-PHMG), and Cu nanoparticles (CuNPs). The effect of CuNP content on the structure, mechanical properties, and antimicrobial properties of the monofilaments was studied. In addition, the antifouling property of fishing nets using the modified polyethylene monofilaments was evaluated. The results showed that CuNPs were dispersed homogeneously in the monofilament matrix as microaggregates. The initial increase in the tensile strength of monofilament was attributed to the mechanical restraint, whereas a decrease in the tensile properties at 1.0 wt% CuNP content was due to the predominant effect of a decrease in the total crystallinity. A bacteriostatic test showed that the monofilament had an obvious inhibitory effect on Staphylococcus aureus. Furthermore, coupon test results showed that PP-g-PHMG/PE/CuNP nanocomposite netting had lower weight gain than PP-g-PHMG/PE netting (23.6% reduction) because of the combined antibacterial effect of both PHMG and CuNP. Therefore, PP-g-PHMG/PE/CuNP monofilament has the potential to produce green and efficient antifouling fishing nets.
Data-Driven Online Decision Making with Costly Information Acquisition

In most real-world settings such as recommender systems, finance, and healthcare, collecting useful information is costly and requires an active choice on the part of the decision maker. The decision-maker needs to learn simultaneously what observations to make and what actions to take. This paper incorporates the information acquisition decision into an online learning framework. We propose two different algorithms for this dual learning problem: Sim-OOS and Seq-OOS where observations are made simultaneously and sequentially, respectively. We prove that both algorithms achieve a regret that is sublinear in time. The developed framework and algorithms can be used in many applications including medical informatics, recommender systems and actionable intelligence in transportation, finance, cyber-security etc., in which collecting information prior to making decisions is costly. We validate our algorithms in a breast cancer example setting in which we show substantial performance gains for our proposed algorithms.
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication

This paper addresses the problem of decentralized spectrum sharing in vehicle-to-everything (V2X) communication networks. The aim is to provide resource-efficient coexistence of vehicle-to-infrastructure(V2I) and vehicle-to-vehicle(V2V) links. A recent work on the topic proposes a multi-agent reinforcement learning (MARL) approach based on deep Q-learning, which leverages a fingerprint-based deep Q-network (DQN) architecture. This work considers an extension of this framework by combining Double Q-learning (via Double DQN) and transfer learning. The motivation behind is that Double Q-learning can alleviate the problem of overestimation of the action values present in conventional Q-learning, while transfer learning can leverage knowledge acquired by an expert model to accelerate learning in the MARL setting. The proposed algorithm is evaluated in a realistic V2X setting, with synthetic data generated based on a geometry-based propagation model that incorporates location-specific geographical descriptors of the simulated environment(outlines of buildings, foliage, and vehicles). The advantages of the proposed approach are demonstrated via numerical simulations.
Environmental Air Pollutants Inhaled during Pregnancy Are Associated with Altered Cord Blood Immune Cell Profiles

Air pollution exposure during pregnancy may be a risk factor for altered immune maturation in the offspring. We investigated the association between ambient air pollutants during pregnancy and cell populations in cord blood from babies born to mothers with asthma enrolled in the Breathing for Life Trial. For each patient (n = 91), daily mean ambient air pollutant levels were extracted during their entire pregnancy for sulfur dioxide (SO2), nitric oxide, nitrogen dioxide, carbon monoxide, ozone, particulate matter <10 μm (PM10) or <2.5 μm (PM2.5), humidity, and temperature. Ninety-one cord blood samples were collected, stained, and assessed using fluorescence-activated cell sorting (FACS). Principal Component (PC) analyses of both air pollutants and cell types with linear regression were employed to define associations. Considering risk factors and correlations between PCs, only one PC from air pollutants and two from cell types were statistically significant. PCs from air pollutants were characterized by higher PM2.5 and lower SO2 levels. PCs from cell types were characterized by high numbers of CD8 T cells, low numbers of CD4 T cells, and by high numbers of plasmacytoid dendritic cells (pDC) and low numbers of myeloid DCs (mDCs). PM2.5 levels during pregnancy were significantly associated with high numbers of pDCs (p = 0.006), and SO2 with high numbers of CD8 T cells (p = 0.002) and low numbers of CD4 T cells (p = 0.011) and mDCs (p = 4.43 × 10−6) in cord blood. These data suggest that ambient SO2 and PM2.5 exposure are associated with shifts in cord blood cell types that are known to play significant roles in inflammatory respiratory disease in childhood.
Online Monitoring and Quality Prediction of Multiphase Batch Processes with Uneven Length Problem

In a practical multiphase batch process, the durations of batches are probably different, and at the same time, the length of each phase may also vary from batch to batch. This paper proposes an effective method to handle the uneven length problem in multiphase batch processes, which is also useful when the number of training batches is limited. To address the data nonlinearity, two nonlinear modeling techniques are introduced. For online monitoring and quality prediction, a key step is how to locate the current data sample to the specific phase, on which basis an appropriate model can be employed. In this paper, a phase discrimination model for data localization in different phases that is based on the support vector data description model is developed. For performance evaluation of the proposed method, detailed illustrations of a typical multiphase batch process are provided.
Barriers and Dynamical Paths in Alternating Gibbs Sampling of Restricted Boltzmann Machines

Restricted Boltzmann machines (RBM) are bilayer neural networks used for the unsupervised learning of model distributions from data. The bipartite architecture of RBM naturally defines an elegant sampling procedure, called alternating Gibbs sampling (AGS), where the configurations of the latent-variable layer are sampled conditional to the data-variable layer and vice versa. We study here the performance of AGS on several analytically tractable models borrowed from statistical mechanics. We show that standard AGS is not more efficient than classical Metropolis-Hastings (MH) sampling of the effective energy landscape defined on the data layer. However, RBM can identify meaningful representations of training data in their latent space. Furthermore, using these representations and combining Gibbs sampling with the MH algorithm in the latent space can enhance the sampling performance of the RBM when the hidden units encode weakly dependent features of the data. We illustrate our findings on three datasets: Bars and Stripes and MNIST, well known in machine learning, and the so-called lattice proteins dataset, introduced in theoretical biology to study the sequence-to-structure mapping in proteins.
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities

In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly. Kitting is a critical step as it can decrease downstream processing and handling times and enable lower storage and shipping costs. We present Kit-Net, a framework for kitting previously unseen 3D objects into cavities given depth images of both the target cavity and an object held by a gripper in an unknown initial orientation. Kit-Net uses self-supervised deep learning and data-augmentation to train a convolutional neural network (CNN) to robustly estimate 3D rotations between objects and matching concave or convex cavities using a large training dataset of simulated depth images pairs. Kit-Net then uses the trained CNN to implement a controller to orient and position novel objects for insertion into novel prismatic and conformal 3D cavities. Experiments in simulation suggest that Kit-Net can orient objects to have a 98.9% average intersection volume between the object mesh and that of the target cavity. Physical experiments with industrial objects succeed in 18 % of trials using a baseline method and in 63% of trials with Kit-Net. Video, code, and data are available at https://github.com/BerkeleyAutomation/Kit-Net.
Solving discrete constrained problems on de Rham complex

The main difficulty in solving the discrete constrained problem is its poor and even ill condition. In this paper, we transform the discrete constrained problems on de Rham complex to Laplace-like problems. This transformation not only make the constrained problems solvable, but also make it easy to use the existing iterative methods and preconditioning techniques to solving large-scale discrete constrained problems.
Inflationary universe from anomaly-free $F(R)$-gravity

By adding a three dimensional manifold to an eleven dimensional manifold in supergravity, we obtain the action of F (R)-gravity and find that it is anomaly free. We calculate the scale factor of the inflationaty universe in this model, and observe that it is related to the slow-roll parameters. The scalar-tensor ratio R (scalar-tensor) is in good agreement with experimental data. PACS numbers: 98.80. -k, 04.50. Gh, 11.25. Yb, 98.80. Qc
Generalisation in Neural Networks Does not Require Feature Overlap

That shared features between train and test data are required for generalisation in artificial neural networks has been a common assumption of both proponents and critics of these models. Here, we show that convolutional architectures avoid this limitation by applying them to two well known challenges, based on learning the identity function and learning rules governing sequences of words. In each case, successful performance on the test set requires generalising to features that were not present in the training data, which is typically not feasible for standard connectionist models. However, our experiments demonstrate that neural networks can succeed on such problems when they incorporate the weight sharing employed by convolutional architectures. In the image processing domain, such architectures are intended to reflect the symmetry under spatial translations of the natural world that such images depict. We discuss the role of symmetry in the two tasks and its connection to generalisation.