Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Analysis for RFID-Enabled Smart Factory: A Case Study

The emergence of Internet of Things (IoT) and new manufacturing paradigms have brought greater complexity of massive datasets. Radio frequency identification (RFID), as one of the key IoT technologies, has been used to collect real-time production data to support the manufacturing decision-making in smart factories. The adoption of these technologies results in a large amount of data collection. To extract useful information from this data, this paper utilizes a big data approach to figure out useful insights from RFID-enabled data regarding possible bottlenecks or inefficiencies on the shop floor so as to improve the quality management. Time and quality are the main metrics measured in this paper, where the longest process times, part accuracy percentage, and failure rate are determined for each of the workers (UserIDs) and process types (ProcCodes). Key findings and observations are significant to make advanced decisions in the smart factory by making full use of the RFID captured data. Note to Practitioners— This paper was motivated by a real-life company which has used RFID technology for over ten years. Great myriad of data has been captured from frontline production shop floors where machines, workers, materials, and manufacturing jobs were logically associated. Processing time and quality data are studied in this paper so as to investigate the production performance in the RFID-enabled smart factory.
Generation of parametric frequency combs with a Brillouin frequency shift spacing based on one CW laser

We numerically and experimentally demonstrate an all fiber parametric frequency combs generator with only one continuous-wave (CW) laser. The light from the laser is launched into a section of fiber to stimulate Brillouin scattering. The scattered Stokes wave and the transmitted wave are used as two seeds to pump parametric process in a highly nonlinear fiber (HNLF). The generated comb lines with a spacing equal to a Brillouin frequency shift of the fiber. To further improve the experimental results, the length of single mode fiber (SMF) used for pulse compression is optimized by numerical simulation. The results show that with a 100-m SMF, a 38-tone frequency comb spaced at 10.67GHz with a 10dB power variation can be obtained.
Environmental Effects on the ISM and Star Formation Properties of Nearby Spiral Galaxies

Abstract We studied molecular gas properties in a sample of 98 Hi - flux selected spiral galaxies within ~ 25 Mpc using the CO J = 3 − 2 line, observed with the JCMT, and subdivided into isolated, group, and Virgo subsamples. We find a larger mean H2 mass in the Virgo galaxies compared to group galaxies, despite their lower mean Hi mass. Combining our data with complementary Hα star formation rate measurements, Virgo galaxies have a longer molecular gas depletion times compared to group galaxies, perhaps due to heating processes in the cluster environment or differences in the turbulent pressure.
A Unified Framework for Data-Driven Optimal Control of Connected Vehicles in Mixed Traffic

This article presents a unified approach to the problem of learning-based optimal control of connected human-driven and autonomous vehicles in mixed-traffic environments including both the freeway and ring road settings. The stabilizability of a string of connected vehicles including multiple autonomous vehicles (AVs) and heterogeneous human-driven vehicles (HDVs) is studied by a model reduction technique and the Popov-Belevitch-Hautus (PBH) test. For this problem setup, a linear quadratic regulator (LQR) problem is formulated and a solution based on adaptive dynamic programming (ADP) techniques is proposed without a priori knowledge on model parameters. To start the learning process, an initial stabilizing control law is obtained using the small-gain theorem for the ring road case. It is shown that the obtained stabilizing control law can achieve general $\mathcal {L}_{p}$ string stability under appropriate conditions. Besides, to minimize the impact of external disturbance, a linear quadratic zero-sum game is introduced and solved by an iterative learning-based algorithm. Finally, the simulation results verify the theoretical analysis and the proposed methods achieve desirable performance for control of a mixed-vehicular network.
Regularized Inverse Problem of Magnetometric Resistivity Response over a Layered Earth

A regularization technique can be applied to inverse problems arising in geoelectrical resistivity sounding. We propose here to use a regularized inversion scheme for interpretation of magnetometric resistivity data gathered from a horizontally stratied layered earth. An iterative scheme using the regularized conjugate gradient method is applied to estimate the conductivity parameters of the ground. The L-curve criterion is used to determine a suitable value of the regularization parameter. The nal inverted model obtained is qualitatively in good agreement with the real model from synthetic data. A comparison of inversion results obtained from our scheme, conventional conjugate gradient and Levenberg-Marquardt methods on a test data set clearly demonstrates an edge over the other two stated schemes as far as the robustness is concerned. The scheme described here has been successfully used for geophysical inversion of magnetometric resistivity data. Mathematics Subject Classication: 86A22, 86A25
Limitations of citation analysis on the measurement of research impact: A summary

Although� citation� analysis� is� broadly� used� to� design� and� develop� bibliometric� indicators� or� methods�measuring�the�research�impact,�some�limitations�of�citation�analysis� regarding� citing� motivation� and� behavior,� database� coverage� and� bias,� as� well� as� the� analytical� methods� may� threaten�the�validity�and�reliability�of�the�measurement. �This�paper�reviews�the�literature�on�the� limitations�of�citation�analysis�in�the�measurement�of�research�impact,�which�is�not�addressed�by� many� bibliometric� studies� introducing� new� indicators� or� methods� for� research� evaluation,� to� remindresearchersofthepossibleinappropriateuseofcitation-basedindicatorsormethodson research�evaluation.
Line width variation with absorber thickness in extreme ultraviolet lithography

Selectivity of extreme ultra-violet lithography mask's material and thickness significantly influences on pattern formation. Since the reflectance changes periodically depending on absorber thickness, we investigated the absorber thickness effect on to near field and aerial image for 32 nm line/space and isolated pattern. We chose germanium and chromium as absorber materials. We also investigated the line width variation by absorber thickness change with different duty ratios. SOLID-EUV of sigma-C was used for this study.
Configurational Theory and Methods in Organizational Research Chapter 3 The Two QCAs : From a Small-N to a Large-N Set Theoretic Approach

Although QCA was originally developed specifically for small-N settings, recent studies have shown its potential for large-N organization studies. In this chapter, we provide guidance to prospective researchers with the goal of opening up QCA’s potential for widespread use in organization studies involving large-N settings, both as an alternative and as a complement to conventional regression analyses. We compare small-N and large-N QCA with respect to theoretical assumptions and objectives, processes and decisions involved in building the causal model, selecting the sample, as well as analyzing the data and interpreting the results. Finally, we discuss the prospects for large-N configurational analysis in organization studies and related fields going forward.
Ethnomedicinal and biological activities of tropical mahua (Madhuca species) – A comprehensive review

The genus Madhuca, of the family Sapotaceae is a multi-functional tropical tree with the species, Madhuca indica (latifolia), M. longifolia, and M. butyracea being the most prevalent. The nutritional and health benefits of mahua are due to the richness of its anti-oxidant, vitamin, mineral, and biochemical composition. Mahua plant (flowers, fruits, roots, bark, seeds, and leaves) extracts have a variety of bioactivities and ethnomedicinal applications (antidiabetic, anti-carcinogenic, anti-inflammatory, antimicrobial, etc.). They are traditionally well-known for their ability to prevent/treat various ailments. The phytochemical constituents have a defense mechanism and therapeutic role in the treatment of disease. Madhuca spp. has built the linkage between ethnic people, traditional knowledge, and livelihood support of the resources. This study/literature review has been undertaken to investigate the mahua as a forest resource that can be used for building livelihood resilience among ethnic communities. This review gives an extensive overview of the scientifically evaluated mahua plant extract bioactive molecules and a possible mechanism for the therapeutic potential for treating various diseases. Further, analyses of the opportunities and scope with future openings for the development of health-giving properties/abilities are extensively summarized.
Protection of Line Faults in HVDC Grids Through Convexity Detection in Backward Traveling Wave Voltages

Short-circuit faults in high-voltage DC (HVDC) grids must be timely isolated. Conventional fault isolation schemes for HVDC grids typically rely on numerical simulations or analytical calculations for threshold setting, which complicate engineering implementation. To solve this issue, this article first introduces the convex point of backward traveling wave (BTW) voltage as the reference for the faulty line isolation in HVDC grids, which is interpreted as the singular feature in the second derivative (SD) of BTW voltage. The singularity in the SD of BTW voltage is independent of system or fault parameters, having good generality in HVDC grids. Through recognizing the unique pattern in the wavelet transform modulus maxima of the singular feature, the proposed protection method can identify faulty lines in HVDC grids in the initial phase of faults. Numerical studies carried out with PSCAD/EMTDC and MATLAB/Simulink demonstrate the effectiveness, robustness and applicability of the proposed method in detecting and isolating various single- and double-pole faults in HVDC grids.
Brain-inspired nanophotonic spike computing: challenges and prospects

Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III–V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.
A Comprehensive Examination of the Protocols, Technologies, and Safety Requirements for Electric Vehicle Charging Infrastructure

Electric vehicles (EVs) have various advantages over traditional internal combustion engines (ICEs), including reduced carbon emissions, greater energy efficiency, and a lessened reliance on petroleum supplies. The use of EV charging infrastructure and power levels are reviewed in this article. Battery performance is affected by the design of the battery as well as the charger parameters and infrastructure. In this paper, the off-board and on-board charging methods with bidirectional and unidirectional power flow are compared. Hardware restrictions and connectivity concerns are eased with a unidirectional charger. The bidirectional charger enables both battery energy injection back into the grid and the vehicle. Power is constrained by the onboard charger due to its size, weight, and price. Both conductive and inductive onboard chargers are viable. For high current rates, which are not supported by EVs, it is feasible to develop an off-board charger. The time required for charging, amount of power, cost, equipment, location, infrastructure configurations, and other parameters are provided, compared, and reviewed for different power level chargers, such as level-1 (slow), level-2 (semi-fast), and level-3 (fast).
Tensors for Improving Compressed Sensing Reconstruction

Compressed Sensing (CS) proposes a framework that any signal can be efficiently reconstructed by observing fewer measurements than required by the famous Shannon-Nyquist theorem, the observing signal must be sparse in some transform domain. Many methods have been proposed to further improve the quality of reconstructed images such as Group Sparsity, Structural Group Sparsity, Total-Variation etc. In this paper we unify tensor based approach with compressed sensing to efficiently reconstruct the original signal. Tensor based approach helps in preserving the intrinsic structure of the signal. Two methods of tensor decomposition Tucker and CANDE/PARAFAC (CP) are unified to give the best low-rank representation of the sparse signal.
The Gaussian Predictability of Wind Speeds

4 The linear predictability of wind speed using Gaussian predictors, relative to the 5 predictability of orthogonal vector wind components, is considered. Analytic expres6 sions for the correlation-based wind speed prediction skill are obtained in terms of the 7 prediction skills of the vector wind components and their statistical moments. This 8 analysis is facilitated by the assumption that the vector winds are Gaussian, and by the 9 fact that fluctuations of the wind speed are highly correlated with those of its square. 10 It is shown that: 11 • at least one of the vector wind components is generally better predicted than the 12 wind speed (often much more so) 13 • wind speed predictions constructed from the predictions of vector wind compo14 nents are more skillful than direct wind speed predictions 15 • the linear predictability of wind speed (relative to that of the vector wind compo16 nents) is a decreasing function of the ratio of the standard deviation to the mean 17 of the vector winds 18 These idealised model results are shown to be broadly consistent with linear predictive 19 skills assessed using sea surface wind observations from the SeaWinds scatterometer. 20 Biases in the model predictions are shown to be related to the degree to which vector 21 wind fluctuations are non-Gaussian. 22
Mott variable range hopping and bad-metal in lightly doped spin-orbit Mott insulator 
BaIrO3

The Mott transition and the vicinity of a Mott insulating phase have constantly been a fertile ground for exploring exotic quantum states, most notably the high-${T}_{c}$ cuprates. The layered iridate represents another intriguing example. The nature of the Mott insulator phase and transition mechanism to a metallic state is still under debate. Much of the challenge originates from a series of energy scales involved in the electronics phases. Here, we report synthetization, characterization, and transport measurements on doped and undoped $\mathrm{B}{\mathrm{a}}_{1\ensuremath{-}x}\mathrm{L}{\mathrm{a}}_{x}\mathrm{Ir}{\mathrm{O}}_{3}$ films grown on $\mathrm{SrTi}{\mathrm{O}}_{3}$. The films are fully strained up to 70 nm thick and have a tetragonal lattice structure. For a doping level of $x=0.1$, a bad-metal state with linear temperature dependence of the resistivity beyond the Mott-Ioffe-Regel limit emerges in a wide temperature range down to a critical temperature ${T}_{c}\ensuremath{\sim}30\phantom{\rule{0.16em}{0ex}}\mathrm{K}$, below which the system shows Mott variable range hopping (Mott VRH) conduction behavior. The ground state is confirmed to be insulating by the Mobius criterion. A strong correlation between the bad-metal state and Mott VRH localized state is found as the slope of the linear resistivity is inversely proportional to film thickness and the size of the Mott VRH activation energy $\mathrm{\ensuremath{\Delta}}$ is linearly proportional to ${T}_{c}$. We further show that upon doping the spin-orbit Mott insulator, itinerant metallic regions coexist with localized regions within a nanoscale phase-separated ground state with a small activation energy. Our results shed light on the nature of the metallic state and a crossover to a bad-metal phase for doping the spin-orbit Mott insulator.
Cubically Convergent Zero-Finders for The Trust Region Problem

iv Acknowledgments . v
Powerful surface-wave oscillators with one-dimensional and two-dimensional periodic planar structures

Within the framework of a quasi-optical approach, we develop self-consistent theory of relativistic surface-wave oscillators [1]. Presenting the radiation field as a sum of two counter-propagating wavebeams coupled on a shallow corrugated surface, we describe formation of an evanescent slow wave. Dispersion characteristics of the evanescent wave following from this method are in good compliance with those found from the direct CST simulations. Considering excitation of the slow wave by a sheet electron beam, we simulate linear and nonlinear stages of interaction, which allows us to determine oscillation threshold conditions, electron efficiency and output coupling. The transition from the model of surfacewave oscillator operating in the π mode regime to the canonical model of relativistic BWO is considered. We also described a modified scheme of planar relativistic surface-wave oscillators exploiting twodimensional periodic gratings [2]. Additional transverse propagating waves emerging on these gratings synchronize the emission from a wide sheet rectilinear electron beam allowing realization of a Cherenkov millimeter-wave oscillators with subgigawatt output power level.
Network analytics: an introduction and illustrative applications in health data science

ABSTRACT Analytics researchers are widely using network analysis as a part of their methodology. In this review paper, we discuss different network concepts while summarizing some studies conducted using descriptive, predictive, and prescriptive analytics approaches. These applications illustrate the value of incorporating network properties of a phenomenon in better understanding the problem, prediction, and optimization of an outcome of interest, especially in the health domain.
Numerical simulation of heat transfer in a micro-cavity

The behaviour of rarefied monatomic gas of Maxwell particles within a rectangular enclosure is investigated, with the Navier–Stokes and Fourier field of equations with first- (NSF) and second-order boundary conditions (NSF2) of the velocity slip and temperature jump, and the regularized 13 moments approach (R13). The enclosure considered has a heated bottom with lateral walls that have specular reflection. The effect of the three dimensionless parameters characterizing the simulated problem, the cavity aspect ratio, the Knudsen number, and the temperature ratio of the hot over the cold walls, on the flow and bulk quantities is examined. For the small Knudsen numbers the flow presents one type of streamlines from the cold to hot plate in both NSF and R13 theories, while by increasing the Knudsen number the flow becomes more complex and presents hot to cold flow streamlines in the extended approach of R13. These rarefaction effects cannot be predicted by the classical continuum approach of NSF. The increase.
AN INVESTIGATION INTO THE POLYMORPHISM OF TPO GENE AND ITS ASSOCIATION WITH HYPOTHYROIDISM IN KASHMIRI POPULATION.

Gousia Qayoom Mir 1 , Bashir Ahmad Ganai 2 , Shariq Rashid Masoodi 3 , Shagufta Sheikh 1 and Akbar Masood 1 . 1. Department of Biochemistry, University of Kashmir, Hazratbal, Srinagar, India. 2. Center of Research for Development (CORD), University of Kashmir, Hazratbal, Srinagar, India. 3. Sheri Kashmir Institute of Medical Sciences (SKIMS), Srinagar. Manuscript Info Abstract . Manuscript History
Three-Dimensional Monte Carlo Simulation of Silicon Field Emitters

Using a particle-based Monte Carlo modeling approach we study the characteristics of silicon field emitter devices. We use an unstructured mesh based on tetrahedral elements to describe the device geometry in 3-D, and we treat silicon and vacuum on equal footing when tracking electrons in both regions. We compare several tunnel models by evaluating the current-voltage characteristics of a single field-emitting silicon pillar. Furthermore, we perform an initial model validation, comparing the simulated current–voltage characteristic to the measured values for gated silicon filed emitter.
Trigonella kargilensis (Trifolieae, Fabaceae), a New Species from India Based on Morphological and Molecular Evidence

Trigonella kargilensis K. Mittal, R. Dangi & P. Ingle sp. nova (Trifolieae, Fabaceae) is described and illustrated from Kargil District of Ladakh, India. It belongs in Trigonella sect. Ellipticae and shares several morphological similarities with T. cachemiriana, such as habit, floral features and phenology. However, it differs from it in leaf margin, inflorescence, ovary and legume characters. Phylogenetic analysis based on nuclear ribosomal DNA internal transcribed spacers (ITS) and plastid trnL-trnF + psbE-petL regions supports the inclusion of T. kargilensis in sect. Ellipticae and its status as a new species.
On application of the regularized lattice Boltzmann method for isothermal flows with non-vanishing Knudsen numbers

Abstract Despite the advances in microscale modelling capabilities, physically observed non-zero Knudsen number (Kn) effects still elude the lattice Boltzmann method (LBM). This article details various Regularized LBM (RLBM) strategies used to model microscale flows and presents a thorough analysis on their applicability for the force-driven Poiseuille flow problem. In particular, we compare the formulations of eight schemes: Projected RLBM (P-RLBM), standard and Galilean Invariant Filtered Collision RLBM, Kn-dependant P-RLBM, Recursively Regularized LBM (R-RLBM), Hybrid R-RLBM, Central-Moment RLBM, and Onsager-Regularized LBM. We demonstrate, at different Kn with discrete Maxwell’s diffuse kinetic wall boundary conditions, the numerical equivalence of the considered RLBM schemes for hard sphere molecules on the D2Q9 lattice. Thereafter, we include a Kn-dependant wall-modified mean-free-path and extend the characterization onto higher-order D2Q17, D2Q21 and Q2Q25-Zero-One-Three, Q2Q49-Zero-One-Two-Three lattices. The study indicates that RLBM schemes are Navier-Stokes solvers which fail to entirely capture all the non-equilibrium phenomenon of slip/transition regime flows.