Miracle-dz/UniFAR-FTUs huggingface dataset:
Route overlap metrics to batch orders

In this study different metrics to perform order batching for a unidirectional cyclical picking system are investigated. Batches must be formed to minimise walking distance that is measured as the total number of cycles traversed by pickers.Route overlap metrics are developed to approximate walking distance before picker routing. Thereby, information about route similarity is added to identify orders that are compatible for batching.A span is the length of a section of picking line that a picker walks to collect the items in an order. Three metrics based on the concept of a span are developed, namely the stops non-identical spans, the non-identical stops-spans and the stops-spans ratio metric.Numerical experiments are carried out to determine the best combination between route overlap metric and batching method. A prominent South African retailer's distribution centre provides \(50\)~sample picking waves for an experimental set up with real-life data. The stops non-identical spans metric used together with greedy smallest entry heuristic to perform the batching consistently leads to a low number of cycles traversed to complete a picking wave relative to the other methods.
Steady-state heat conduction in a medium with spatial non-singular fading memory: Derivation of Caputo-Fabrizio space-fractional derivative from Cattaneo concept with Jeffrey`s Kernel and analytical solutions

Starting from the Cattaneo constitutive relation with a Jeffrey's kernel the derivation of a transient heat diffusion equation with relaxation term expressed through the Caputo-Fabrizio time fractional derivative has been developed. This approach allows seeing the physical back ground of the newly defined Caputo-Fabrizio time fractional derivative and demonstrates how other constitutive equations could be modified with non-singular fading memories.
Search for Higgs boson production in association with a top quark pair in two same-sign lepton final states with the ATLAS detector at LHC

The Large Hadron Collider (LHC) at CERN restarted in spring 2015 for three years (Run2) at an unexplored center-of-mass energy of 13 TeV; An ideal place to search for physics beyond the Standard Model such as supersymmetry (SUSY) and extra dimensions. A precise measurement of electron reconstruction efficiency in ATLAS, one of the two general purpose experiments of the LHC, is presented in the first part of this thesis with Z→ee data sample using 3.2 fb−1 of data recorded in 2015. This allows to extract scale factors between data and simulation that are used by all ATLAS physics analyses involving electrons. The results show the high ability of the ATLAS detector to reconstruct electrons from one hand and the good understanding of its performance on the other hand. The second part of the thesis is dedicated to a search for the Higgs boson production in association with a top quark pair (tt¯H), which could allow a first direct measurement of the top quark Yukawa coupling and could reveal new physics. The signature with two same-charge light leptons (electron or muon) without a hadronically decaying tau lepton final state, targeting the decays H→ WW⋆, is examined using the first 10% of the total expected Run2 dataset. Events with fake (non-prompt) leptons represent the main reducible background of this signature. The estimation of this background largely drives the signal sensitivity. An improved method to estimate it has been developped and is discussed in details in this thesis. Driving the total error, fake leptons background is found to be 1.5 to 3.6 times higher than in simulation and represent between 32 and 48% of the total background. The best-fit value of the ratio of observed and Standard Model cross sections of tt¯H production process, combining with other multilepton channels, is 2.5± 0.7 (stat) +1.1−0.9 (syst), and an upper limit on this ratio of 4.9 (2.3 expected) is found at 95% confidence level.
Single-Cell RNA Sequencing: Opportunities and Challenges for Studies on Corneal Biology in Health and Disease

The structure and major cell types of the multi-layer human cornea have been extensively studied. However, various cell states in specific cell types and key genes that define the cell states are not fully understood, hindering our comprehension of corneal homeostasis, related diseases, and therapeutic discovery. Single-cell RNA sequencing is a revolutionary and powerful tool for identifying cell states within tissues such as the cornea. This review provides an overview of current single-cell RNA sequencing studies on the human cornea, highlighting similarities and differences between them, and summarizing the key genes that define corneal cell states reported in these studies. In addition, this review discusses the opportunities and challenges of using single-cell RNA sequencing to study corneal biology in health and disease.
Single Image Super-Resolution Based on Improved WGAN

SRGAN has successfully applied the Generative Adversarial Network to the single image super-resolution reconstruction, which has achieved good results. But the loss function based on feature space in SRGAN objectively sacrifices the pursuit of high peak signal-to-noise-ratio (PSNR), which is the result of a tradeoff. At the same time, Improved Training of Wasserstein GANs makes the training process more stable. We redesign the SRGAN, using VGG16 network for feature extraction, setting discriminator network’s working space as feature space, and adding the loss function based on the mean square error of pixel space, then gain more details and high PSNR in the reconstruction at the same time. We use the design of WGAN-GP for reference to make the training more stable. Keywords-Super-resolution; WGAN-GP; VGG
Sequential fusion estimation for multisensor multirate systems with measurement outliers

This paper investigates the suboptimal sequential fusion estimation problem for multisensor multirate networked systems with colored measurement noises under the interference of measurement outliers. The saturation function is used to constrain the innovation polluted by measurement outliers. Due to diverse physical restrictions, the sampling period of the sensor is assumed to be different from the update period of the system state, thereby better reflecting the engineering practice. The lifting technique is used to convert the multirate sampling system into a single‐rate form. By solving the matrix difference equation, an upper bound of the filtering error covariance is obtained, and the filter gain is then derived, which can minimize the upper bound of the error covariance. Finally, a simulation example is given to demonstrate the effectiveness of the proposed sequential fusion method for multirate sampling systems under outlier interference.
Measurement error analysis of binocular stereo vision: effective guidelines for bionic eyes

The depth measurement error of bionic eyes consisting of two active cameras is derived by considering the eyes' rotation angle errors and image feature extraction errors. The effect factors of the depth measurement are obtained from the calculation formula of depth error. Based on the analysis of these effect factors, some effective guidelines for bionic eyes are proposed to reduce the measurement errors. The guidelines suggest keeping long baseline, observing the target as nearly as possible, controlling two active cameras with the same angular velocity and keeping the target on Z w axis if possible. The simulation experiments and practical experiments in bionic eyes platform validate the effectiveness of the proposed guidelines.
The Venom Composition of the Snake Tribe Philodryadini: ‘Omic’ Techniques Reveal Intergeneric Variability among South American Racers

Snakes of the Philodryadini tribe are included in the Dipsadidae family, which is a diverse group of rear-fanged snakes widespread in different ecological conditions, including habitats and diet. However, little is known about the composition and effects of their venoms despite their relevance for understanding the evolution of these snakes or even their impact on the occasional cases of human envenoming. In this study, we integrated venom gland transcriptomics, venom proteomics and functional assays to characterize the venoms from eight species of the Philodryadini tribe, which includes the genus Philodryas, Chlorosoma and Xenoxybelis. The most abundant components identified in the venoms were snake venom metalloproteinases (SVMPs), cysteine-rich secretory proteins (CRISPs), C-type lectins (CTLs), snake endogenous matrix metalloproteinases type 9 (seMMP-9) and snake venom serinoproteinases (SVSPs). These protein families showed a variable expression profile in each genus. SVMPs were the most abundant components in Philodryas, while seMMP-9 and CRISPs were the most expressed in Chlorosoma and Xenoxybelis, respectively. Lineage-specific differences in venom composition were also observed among Philodryas species, whereas P. olfersii presented the highest amount of SVSPs and P. agassizii was the only species to express significant amounts of 3FTx. The variability observed in venom composition was confirmed by the venom functional assays. Philodryas species presented the highest SVMP activity, whereas Chlorosoma species showed higher levels of gelatin activity, which may correlate to the seMMP-9 enzymes. The variability observed in the composition of these venoms may be related to the tribe phylogeny and influenced by their diets. In the presented study, we expanded the set of venomics studies of the Philodryadini tribe, which paves new roads for further studies on the evolution and ecology of Dipsadidae snakes.
A Comprehensive Analysis of the Radiation Exposure and the Diagnostic Benefit of PanCT in Pediatric Cases with Multiple Trauma

Background and Objectives: The primary objective of this study was to obtain quantitative data, taking into account the amount of radiation exposure, about the clinical and diagnostic benefit obtained from panCT in pediatric trauma cases. Thus, we aim to create greater awareness in all physicians and primarily emergency medicine physicians regarding correct selection in terms of the patient group where this effective radiological method is to be applied, and to protect children from the adverse effects of radiation. Materials and Methods: The computed tomography (CT) images were retrieved from the hospital radiological archive system (PACS). The effective dose (Ed) was calculated using the standardized method including the tissue weighted parameters. The radiological pathologies determined as a result of CT imaging of the cases were categorized according to clinical significance in accordance with the Modified CT Colonography Reporting and Data System (C-RADS). Results: The data for a total of 268 patients were analyzed, comprising 89 (33.2%) females and 179 (66.8%) males with a mean age of 8.81 ± 5.21 years. The mean Ed was determined to be 18.14 ± 10.44 mSv. The Ed was determined to be statistically significantly higher in the 1–5 years age group than in the 15–18 years age group (p = 0.024). A statistically significant difference was determined between the age groups in terms of the pathologies determined (p = 0.028). Conclusions: In order to prevent performing unnecessary CT imaging, trauma teams in Emergency Departments (ED) should work in harmony and individual decision-making should be based on the severity of the trauma mechanism, the severity of the predicted injury, and the clinical status of the injured child.
Photoluminescence of Two-Dimensional MoS2 Nanosheets Produced by Liquid Exfoliation

Extraordinary properties of two-dimensional materials make them attractive for applications in different fields. One of the prospective niches is optical applications, where such types of materials demonstrate extremely sensitive performance and can be used for labeling. However, the optical properties of liquid-exfoliated 2D materials need to be analyzed. The purpose of this work is to study the absorption and luminescent properties of MoS2 exfoliated in the presence of sodium cholate, which is the most often used surfactant. Ultrasound bath and mixer-assisted exfoliation in water and dimethyl sulfoxide were used. The best quality of MoS2 nanosheets was achieved using shear-assisted liquid-phase exfoliation as a production method and sodium cholate (SC) as a surfactant. The photoluminescent properties of MoS2 nanosheets varied slightly when changing the surfactant concentrations in the range C(SC) = 0.5–2.5 mg/mL. This work is of high practical importance for further enhancement of MoS2 photoluminescent properties via chemical functionalization.
Algorithms for 3D isometric shape correspondence (3B izometrik şekil eşleme için algoritmalar)

There are many pairs of objects in the digital world that need to be related before performing any comparison, transfer, or analysis in between. The shape correspondence algorithms essentially address this problem by taking two shapes as input with the aim of finding a mapping that couples similar or semantically equivalent surface points of the given shapes. We focus on computing correspondences between some featured or all present points of two semantically similar 3D shapes whose surfaces overlap completely or partially up to isometric, i.e., distance-preserving, deformations and scaling. Differently put, our isometric shape correspondence algorithms handle several different cases for the shape correspondence problem that can be differentiated based on how similar the shape pairs are, whether they are partially overlapped, the resolution of the desired mapping, etc. Although there exist methods that can, in most cases, satisfactorily establish 3D correspondences between two given shapes, these methods commonly suffer from certain drawbacks such as high computational load, incapability of establishing a correspondence which is partial and dense at the same time, approximation and embedding errors, and confusion of symmetrical parts of the shapes. While the existing methods constitute a solid foundation and a good starting point for the shape correspondence problem, our novel solutions designed for a given scenario achieve significant improvements as well as contributions. We specifically explore the 3D shape correspondence problem under two categories as complete and partial correspondences where the former is categorized further according to the output resolution as coarse and dense correspondences. For complete correspondence at coarse resolution, after jointly sampling evenly-spaced feature vertices on shapes, we formulate the problem as combinatorial optimization over the domain of all possible mappings between source and target features, which then reduces within a probabilistic framework to a log-likelihood maximization problem that we solve via EM (Expectation Maximization) algorithm. Due to computational limitations of this approach, we design a fast coarse-
Predicting Perceived Music Emotions with Respect to Instrument Combinations

Music Emotion Recognition has attracted a lot of academic research work in recent years because it has a wide range of applications, including song recommendation and music visualization. As music is a way for humans to express emotion, there is a need for a machine to automatically infer the perceived emotion of pieces of music. In this paper, we compare the accuracy difference between music emotion recognition models given music pieces as a whole versus music pieces separated by instruments. To compare the models' emotion predictions, which are distributions over valence and arousal values, we provide a metric that compares two distribution curves. Using this metric, we provide empirical evidence that training Random Forest and Convolution Recurrent Neural Network with mixed instrumental music data conveys a better understanding of emotion than training the same models with music that are separated into each instrumental source.
The Effect of Emotional Intelligence on Public Sector Employee’s Affective Commitment

Commitment is vital in keeping and maintaining integrity within the organization, especially in public organizations. In addition to maintaining integrity, commitment is much needed in organizational change. People with affective commitment who join the organization of their own volition will support the changes made if they feel they are good for the organization. This quantitative study used primary data collected with an online survey facility. The data collection used the non-probability sampling technique of convenience sampling. The sample involved 255 active Directorate General of Customs and Excise employees with work locations across various parts of Indonesia. The data obtained were processed with JASP v 0.16 application with simple regression techniques and Structural Equation Modeling (SEM). The study’s results support the initial hypothesis that emotional intelligence positively affects employees’ affective commitment. In addition, the study’s results support the second hypothesis that public service motivation mediates the effect of emotional intelligence on affective commitment.
Exploring causality from observational data: An example assessing whether religiosity promotes cooperation

Abstract Abstract Causal inference from observational data is notoriously difficult, and relies upon many unverifiable assumptions, including no confounding or selection bias. Here, we demonstrate how to apply a range of sensitivity analyses to examine whether a causal interpretation from observational data may be justified. These methods include: testing different confounding structures (as the assumed confounding model may be incorrect), exploring potential residual confounding and assessing the impact of selection bias due to missing data. We aim to answer the causal question ‘Does religiosity promote cooperative behaviour? ’ as a motivating example of how these methods can be applied. We use data from the parental generation of a large-scale (n = approximately 14,000) prospective UK birth cohort (the Avon Longitudinal Study of Parents and Children), which has detailed information on religiosity and potential confounding variables, while cooperation was measured via self-reported history of blood donation. In this study, there was no association between religious belief or affiliation and blood donation. Religious attendance was positively associated with blood donation, but could plausibly be explained by unmeasured confounding. In this population, evidence that religiosity causes blood donation is suggestive, but rather weak. These analyses illustrate how sensitivity analyses can aid causal inference from observational research.
Transient Stability Enhancement of DC–DC Boost Converters Feeding Constant Power Loads in DC Microgrid Applications via Composite Nonlinear Control Techniques

In this paper, a composite nonlinear controller is developed for stabilizing a constant power load (CPL) feeding DC-DC boost converter (DBC) for DC microgrid applications. If the boost converter is not properly controlled, the negative incremental impedance (NII) of CPLs can cause DC-bus voltage instability. As the original model is not adequate for designing the proposed controller, the nonlinear dynamical model of the DBC is transformed into Brunovsky’s canonical forms. Large load and input voltage changes hinder the controller’s performance. Therefore, the dynamical model has been updated to include constrained external uncertainty. Then, the proposed hybrid controller, which is a combination of the nonlinear backstepping controller and the sliding mode controller, is designed. The sliding mode controller has a double-integral-based sliding surface and a fast power-based reaching law to improve hybrid controller performance. The global stability of the proposed system is analyzed using the control Lyapunov function theory. Lastly, simulation studies in MATLAB/Simulink and experiments on an in-house hardware platform show that the proposed composite controller works well even when there are big changes.
Construction and performance of the sTGC and MicroMegas chambers for ATLAS NSW upgrade

The innermost stations of the current ATLAS muon end-cap system, the Small Wheels, must be upgraded in 2019 to retain their good precision tracking and trigger capabilities in the high background environment expected with the upcoming luminosity increase of the LHC. The New Small Wheels (NSW) will employ two chamber technologies: eight layers of MicroMegas (MM) arranged in two quadruplets, sandwiched between two quadruplets of small-strip Thin Gap Chambers (sTGC) for a total of about 2400 m2 of detection planes. All quadruplets have trapezoidal shapes with surface areas between 1 and 3 m2. Both MM and sTGC systems will independently provide trigger and tracking capabilities. The readout boards are industrially produced for both technologies and an accurate quality control is needed. In order to achieve a 15% transverse momentum resolution for 1 TeV muons, in addition to an excellent intrinsic resolution (010 μm), the mechanical precision of each plane of the assembled modules must be as good as 30 μm along the precision coordinate and 80 μm perpendicular to the chamber. In 2016 the milestone to build the first module-0 prototypes for both technologies has been reached. The construction procedure of the module-0 detectors will be reviewed, along with the results of the quality control checks performed during construction. The module-0 have been measured and subjected to a thorough validation. Results obtained with high-energy particle beams, with cosmic rays and with X-rays will be presented.
Global convergence analysis of the aggregate constraint homotopy method for nonlinear programming problems with both inequality and equality constraints

Abstract In this paper, we construct appropriate aggregate mappings and a new aggregate constraint homotopy (ACH) equation by converting equality constraints to inequality constraints and introducing two variable parameters. Then, we propose an ACH method for nonlinear programming problems with inequality and equality constraints. Under suitable conditions, we obtain the global convergence of this ACH method, which makes us prove the existence of a bounded smooth path that connects a given point to a Karush–Kuhn–Tucker point of nonlinear programming problems. The numerical tracking of this path can lead to an implementable globally convergent algorithm. A numerical procedure is given to implement the proposed ACH method, and the computational results are reported.
Distributed coordinated tracking control of multiple Euler–Lagrange systems by state and output feedback

This study addresses the distributed state and output feedback coordinated tracking problem for multiple Euler–Lagrange systems with a moving leader in the presence of non-linear uncertainties under a directed topology graph. The authors first design a distributed coordinated tracking control scheme by full-state feedback, where each follower can only share information with its neighbours. Next, the adaptive neural networks are applied to deal with the model uncertainties due to their superior approximation capability. To deal with the absence of velocity sensors, then they propose a distributed output feedback coordinated tracking control law combined with a high-gain observer. The uniform ultimate boundedness of all the state errors can be guaranteed in the sense of Lyapunov stability theory. Furthermore, the leaderless synchronisation problem is solved using the proposed control schemes. Finally, simulation examples are presented to illustrate the feasibility of the theoretical results.
On modeling of “plasmoid” created by electric discharge

Gas dynamic modeling approach was applied to “plasmoid” formed in experimental electrical discharge breakdown in a cathode ceramic tube filled by a conductive liquid. Measured parameters were applied to the model in order to improve it. Qualitative agreement observed between behavior of the experimental “plasmoid” and model results. Temperature distribution results also agree with experiment in which temperature of 2000 K was registered.