Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Service Digitalization in Moderating the Influence of Social Capital on Supply Chain Resilience and the Competitiveness of Sea Transportation Service Companies in Indonesia

Social capital in the form of strong internal and external networks is useful in maintaining the company's supply chain resilience and company competitiveness. During the COVID-19 pandemic, social capital in the form of relationship strength that is the mainstay of transportation service companies is not utilized optimally due to restrictions on direct interaction between companies and stakeholders. Effort to digitize company services is one option in empowering social capital through relationship strength to maintain supply chain resilience and company competitiveness. This research explores the role of service digitalization in the empowerment of social capital to generate supply chain resilience and company competitiveness. Data and information are collected from 446 Indonesia’s marine transportation services companies through online surveys and analyzed statistically using SEM WarPls. The results showed that social capital has a significant effect on the company’s supply chain resilience and also on the company competitiveness. Furthermore, service digitalization strengthens the influence of social capital on the supply chain resilience. Likewise, service digitalization strengthens the influence of social capital on company competitiveness. On the contrary, service digitalization actually weakens the influence of supply chain resilience on the company competitiveness.
A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets

Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies
Efficient probabilistic Poisson solver for complex geometries based on sparse markov matrices

Abstract This article introduces an efficient stochastic method to approximate the solution to Poisson’s Equation (PE). The method starts by computing sparse Transition Probability Matrices (TPMs), assembled by averaging short continuous random walks based on the transient diffusion equation with a source. The TPMs can be used within a false-transient approach to approximate the solution to PE in the steady-state limit. The method’s efficiency and memory usage are comparable to the Finite-Volume method. However, the ease of implementation, stability, and versatility makes the method attractive in complex scenarios. Moreover, given that the code can be easily implemented in parallel, it displays superior efficiency and practical importance in scenarios where a large number of nodes is mandatory.
A reinterpreted-texture strategy for rendering point symbols based on graphics processing unit

ABSTRACT The increasing demands of presenting large numbers of points in maps have promoted the progress of rendering point symbols in GPUs. Although the drawing efficiency issue can be handled with texture mapping methods, the rendering quality problem due to the fixed resolution that affects map renders’ visual experiences remains. The method of directly drawing vector paths of a point symbol can be used to satisfy the sharper effect of point symbols. However, it requires high memory cost and affects the drawing efficiency. This paper proposes a point symbol rendering method using the idea of reinterpreted textures. The rendering data used in this method are based on vectors to achieve refined results. Vector properties of symbols are encoded and organized into the texture structure with specific layout schemes. In the rendering phase, an instanced pipeline is launched to accept the texture and decode the required attributes. The proposed method takes advantage of fast access and continuity of textures while retaining geometric transformations. These features allow all symbols to be drawn in one single draw call and rotated or scaled arbitrarily. Experiments on drawing quality and efficiency demonstrate that the proposed method achieves fast and stable performance while maintaining the rendering quality.
Commonalities in frequency-dependent viscoelastic damping in glasses in the MHz to THz regime

We use non-equilibrium molecular dynamics oscillatory shear simulations to study frequency-dependent viscoelastic damping spanning nearly six decades in frequency range (MHz to THz), in a wide range of model glasses including binary glasses such as Cu-Zr metallic glass (MG), Wahnstrom glass and amorphous silica, and unary glasses, namely, Dzugutov glass and amorphous silicon. First, for the Cu-Zr MG, we elucidate the role of quench rate, number of shear cycles, shear amplitude, and shear temperature on the damping characteristics. We observe striking commonalities in damping characteristics for all glasses studied—(i) a peak in the loss modulus in the high-frequency regime (∼THz) and (ii) persistent damping in the low-frequency regime (extending down to 10 s of MHz). The high-frequency peak is seen to overlap with the range of natural vibrational frequencies for each glass, and arises from coupling between the excited harmonic vibrational modes. On the other hand, persistent damping at intermediate and lo.
Mixture of Faults Identification on Power Transformers Using Multi-Method Dissolved Gas Analysis

Utilities rely on the various Dissolved Gas Analysis (DGA) interpretation methods to identify incipient fault within power transformers. However, confusion often arises due to the use of different DGA methods which can have different results or fail to detect mixture of faults (multiple faults). This paper implements the combination of Duval Triangle and Duval Pentagon as one of the most consistent methods to do fault identification based on DGA. Recent historical DGA data of generator step up high voltage power transformers and other several previously published historical data were collected and analyzed. This article aims to assist transformer asset manager in detecting mixture of faults using multi method. The use of combined Duval triangle and Pentagon method is done in accordance to the guideline in IEEE C57.104-2019.
Institutional Practice for Engineering Students Employability: Automate Offline Employability Tracking Instrument with Data Mining

The analysis of graduate employability is substantial to interpret Higher Education Institutions (HEIs) capability to produce career-ready graduates. Therefore, data gathering related to employment and employability to assess the current trends is crucial. However, developing a fast and accurate programming language for analysing enormous data is a challenging task. This paper presents an Automate Offline Employability (AOE) data analysis using a spreadsheet program; it reduces the time consumption in analysing lots of data and increases data analysis accuracy. The system utilises various Excel formulas and functions to execute multitasking algorithms simultaneously. The instrument can sort the data according to various study fields, matching the initial and current data, analysing them based on employability attributes, and generating multiple interpretable graphs. It can also extract the Uniform Resource Locator (URL) of email and WhatsApp of graduate. Additionally, it has a desktop layout to help the user retrieve any information with a single click. All functions provided by the instrument has successfully executed and verified to achieve the design objectives. Using the instrument, the user can only gather 211 data within 12 days. The instrument provides various information such as the percentage of employment status, field of work, salary, and entrepreneurship.
Shiga-Toxin-Producing Strains of Escherichia coli O104:H4 and a Strain of O157:H7, Which Can Cause Human Hemolytic Uremic Syndrome, Differ in Biofilm Formation in the Presence of CO2 and in Their Ability to Grow in a Novel Cell Culture Medium

One pathogen that commonly causes gastrointestinal illnesses from the consumption of contaminated food is Escherichia coli O157:H7. In 2011 in Germany, however, there was a prominent outbreak of bloody diarrhea with a high incidence of hemolytic uremic syndrome (HUS) caused by an atypical, more virulent E. coli O104:H4 strain. To facilitate the identification of this lesser-known, atypical E. coli O104:H4 strain, we wanted to identify phenotypic differences between it and a strain of O157:H7 in different media and culture conditions. We found that E. coli O104:H4 strains produced considerably more biofilm than the strain of O157:H7 at 37 °C (p = 0.0470–0.0182) Biofilm production was significantly enhanced by the presence of 5% CO2 (p = 0.0348–0.0320). In our study on the innate immune response to the E. coli strains, we used HEK293 cells that express Toll-like receptors (TLRs) 2 or 4. We found that E. coli O104:H4 strains had the ability to grow in a novel HEK293 cell culture medium, while the E. coli O157:H7 strain could not. Thus, we uncovered previously unknown phenotypic properties of E. coli O104:H4 to further differentiate this pathogen from E. coli O157:H7.
Molecular Characterization and Marker Development of the HMW-GS Gene from Thinopyrum elongatum for Improving Wheat Quality

The quality of wheat primarily depends on its storage protein quality, especially in regards to gluten content and high-molecular-weight glutenin subunits (HMW-GS). The number of HMW-GS alleles is limited in bread wheat (Triticum aestivum L.), whereas it is abundant in wheat relatives. Therefore, HMW-GS alleles from wheat relatives could provide a potential for improving quality in wheat breeding. Thinopyrum elongatum (EE) is one of the relatives of wheat. The E genome is closely related to the ABD genome in wheat; therefore, Th. elongatum is often used as an excellent exogenous gene donor for wheat genetic improvement. In this study, the high-molecular glutenin subunit gene was cloned and sequenced from Th. elongatum. A specific molecular marker for identifying the Glu-1Ey subunit gene was developed and applied to detected wheat-Th. elongatum alien introgression lines. Quality analysis indicated that the substitution and addition lines containing Th. elongatum alleles significantly (p < 0.05) increased grain protein content by 3.76% to 5.11%, wet-gluten content by 6.55% to 8.73%, flour 8-MW by 0.25% to 6.35%, and bread volume value by 33.77 mL to 246.50 mL, in comparing it with Chinese Spring. The GMP content and lactic acid SRC showed significant positive correlations with flour processing quality and might be used as indicators for wheat quality. The results were expected to provide a novel route for improving processing quality in wheat quality breeding.
Classification of micro-Doppler signatures of human aquatic activity through simulation and measurement using transferred learning

Remote detection of human aquatic activity can be applied not only to ocean surveillance but also to rescue operations. When a human is illuminated by electromagnetic waves, a Doppler signal is generated from his or her moving parts. Indeed, bodily movements are what make humans’ micro-Doppler signatures unique, offering a chance to classify human motions. Certain studies have analyzed and attempted to recognize human aquatic activity, but the topic has yet to be extensively studied. In the present research, we simulate the micro-Doppler signatures of a swimming person in an attempt to investigate those signatures’ characteristics. We model human arms as point scatterers while assuming a simple arm motion. By means of such a simulation, we can obtain spectrograms from a swimming person, then extend our measurement to multiple participants. Measurements are taken from five aquatic activities featuring five participants, comprising freestyle, backstroke, and breaststroke, pulling a boat, and rowing. As suggested by the simulation study, the spectrograms for the five activities show different micro-Doppler signatures; hence, we propose to classify them using a deep convolutional neural network (DCNN). In particular, we suggest the use of a transfer-learned DCNN, which is based on a DCNN pretrained by a large-scale RGB image dataset that is, ImageNet. The classification accuracy is calculated using fivefold cross-validation on our dataset. We find that a DCNN trained through transfer learning achieves the highest accuracy while also providing a significant performance boost over the conventional classification method.
Accurate Detection of Weld Seams for Laser Welding in Real-World Manufacturing

Welding is a fabrication process used to join or fuse two mechanical parts. Modern welding machines have automated lasers that follow a pre-defined weld seam path between the two parts to create a bond. Previous efforts have used simple computer vision edge detectors to automatically detect the weld seam edge on an image at the junction of two metals to be welded. However, these systems lack reliability and accuracy resulting in manual human verification of the detected edges. This paper presents a neural network architecture that automatically detects the weld seam edge between two metals with high accuracy. We augment this system with a pre-classifier that filters out anomalous workpieces (e.g., incorrect placement). Finally, we justify our design choices by evaluating against several existing deep network pipelines as well as proof through real-world use. We also describe in detail the process of deploying this system in a real-world shop floor including evaluation and monitoring. We make public a large, well-labeled laser seam dataset to perform deep learning-based edge detection in industrial settings.
Kinematics, Structural Mechanics, and Design of Origami Structures with Smooth Folds

Origami provides novel approaches to the fabrication, assembly, and functionality of engineering structures in various fields such as aerospace, robotics, etc. With the increase in complexity of the geometry and materials for origami structures that provide engineering utility, models and design methods for such structures have become essential. Currently available models and design methods for origami structures are generally limited to the idealization of the folds as creases of zeroth-order geometric continuity. Such an idealization is not proper for origami structures having non-negligible thickness or maximum curvature at the folds restricted by material limitations. Thus, for general structures, creased folds of merely zeroth-order geometric continuity are not appropriate representations of structural response and a new approach is needed. The first contribution of this dissertation is a model for the kinematics of origami structures having realistic folds of non-zero surface area and exhibiting higher-order geometric continuity, here termed smooth folds. The geometry of the smooth folds and constraints on their associated kinematic variables are presented. A numerical implementation of the model allowing for kinematic simulation of structures having arbitrary fold patterns is also described. Examples illustrating the capability of the model to capture realistic structural folding response are provided. Subsequently, a method for the design of a single planar sheet and its pattern of smooth folds that morphs into a given threedimensional goal shape, discretized as a polygonal mesh, is presented. The design parameterization of the planar sheet and the constraints that allow for a valid pattern of smooth folds and approximation of the goal shape in a known folded configuration are presented. Various testing examples considering goal shapes of diverse geometries are provided. Afterwards, a model for the structural mechanics of continuum origami bodies with smooth folds is presented. Such a model entails the integration of the presented kinematic model and existing plate theories in order to obtain a structural representation for folds having non-zero thickness and comprised of arbitrary materials. The model is validated against finite element analysis. The last contribution addresses the design and analysis of active material-based self-folding structures that morph via simultaneous folding towards arbitrary three-dimensional goal shapes starting from a planar configuration. Implementation examples including shape memory alloy (SMA)-based self-folding structures are provided. Edwin Peraza Hernandez is a PHD candidate in the Aerospace Engineering Department working under the supervision of Professors Dimitris Lagoudas and Darren Hartl. His research interests are in the areas of active material-based morphing structures and origami. Edwin has co-authored 10 peer-reviewed journal publications and 13 conference proceedings papers. He is continuing his research as a postdoc in the Aerospace Engineering Department.
Towards the first B(B0 (s)→ μ+μ−) measurements with the LHCb detector

The rare decays B0s→μ+μ− and B0→μ+μ− are benchmark channels to constrain models beyond the Standard Model (BSM) with a larger Higgs sector. In the SM, the branching fraction of these decays is predicted with a good accuracy: B(B0(s)→μ+μ−)=(3.2±0.2)×10−9 and B(B0→μ+μ−)=(0.10±0.01)×10−10. Any deviation from these values can lead to indications of physics BSM. The core of this thesis comprises two main topics: the background rejection and the signal yields extraction. We have optimized a multivariate classifier based on the boosted decision trees technique allowing for a drastic reduction of the B→h+h′− (h≡π,K) background. After the selection process, about 76% of the combinatorial background for B0s→μ+μ− is removed, while keeping a signal efficiency of about 92%. A further discrimination between signal and background is accomplished with another multivariate classifier optimized to have a large background rejection in the low signal efficiency region. The work presented in this thesis describes the optimization of a boosted decision trees classifier that suppresses 99.9% of the background, after the aforementioned selection process, for a signal efficiency of 50%. We have proposed a method to estimate the signal yields present in our data sample using an extended maximum likelihood fit. The validation of the fit using simulation reflects the proper estimation of the statistical uncertainties, and systematic uncertainties have been carefully studied and taken into account in the final results for the 2011 1 fb−1 data sample: B(B0s→μ+μ−)=(1.4(+1.6−1.1)(stat)(+0.5−0.8)(syst))×10−9 and B(B0→μ+μ−)=(0.3(+0.5−0.4)(stat)(+0.5−0.3)(syst))×10−9. Given the lack of signal evidence, upper limits on the branching fractions are computed: B(B0(s)→μ+μ−)<4.5×10−9 and B(B0→μ+μ−)<1.0×10−10, which are the most restrictive limits up to date.
Research on Variational Bayesian-based Data Classification Algorithm

With the rapid development of Internet technology, the size and complexity of the database are continually growing, the traditional classification method can no longer meet the demand of the classification of complex data. As for such problems, a data classification algorithm based on variational bayesian is proposed. The algorithm introduce variational approximation theory on the basis of traditional bayesian inference, combined with the thought of maximum expected algorithm, utilizing the mean field theory in the statistical physics, and take gaussian mixture model as an experiment simulation. The experimental results show that randomly generated data can be seen clearly mixtured by three groups of gaussian model after 382 iterations, lower bound of likelihood function rises with the increase of iteration number, curve becomes flat as expected after 350 iterations, and getting mean value and the inverse of covariance matrix close to the real data in the range of allowable error, realizing its classification processing. Under the requirement of high precision, its calculation speed is faster, calculation efficiency is higher, which can in accordance with actual engineering application background.
Dynamic texture segmentation using spectral clustering based on CHMMs

In this paper, we introduce the spectral clustering method based on Continuous Hidden Markov Model (CHMM) into dynamic texture (DT) segmentation. In order to characterize the DT, CHMMs are used to model all spatial subblocks of the DT. The initial segmentation is realized by utilizing the spectral clustering based on CHMMs. The similarity between two different CHMMs is measured with approximated Kullback-Leibler divergence (KLD). To improve the DT segmentation performance, the mathematical morphology method is also applied into further processing which is operated on the pixel level. Experimental results on artificially synthesized DT samples of DynTex dataset demonstrate the effectiveness of the proposed method.