Miracle-dz/UniFAR-FTUs huggingface dataset:
Manipulating single atoms with optical tweezers

Single atoms are promising candidates for physically implementing quantum bits, the fundamental unit of quantum information. We have built an apparatus for cooling, trapping and imaging single rubidium atoms in microscopic optical tweezers. The traps are formed from a tightly focused off-resonant laser beam, which traps atoms using the optical dipole force. The traps have a diameter of ∼1μm and a depth of ∼1 mK. The novelty of our approach is the use a digital mirror device (DMD) to generate multiple independently movable tweezers from a single laser beam. The DMD consists of an array of micro-mirrors that can be switched on and off, thus acting as a binary amplitude modulator. We use the DMD to imprint a computer-generated hologram on the laser beam, which is converted in to the desired arrangement of traps in the focal plane of a lens. We have developed fast algorithms for calculating binary holograms suitable for the DMD. In addition, we use this method to measure and correct for errors in the phase of the wavefront caused by optical aberrations, which is necessary for producing diffraction-limited focal spots. Using this apparatus, we have trapped arrays of up to 20 atoms with arbitrary geometrical arrangements. We exploit light-assisted collisions between atoms to ensure there is at most one atom per trapping site. We measure the temperature of the atoms in the traps to be 12μK, and their lifetime to be 1.4 s. Finally, we demonstrate the ability to select individual atoms from an array and transport them over a distance of 14μm with laser cooling, and 5μm without.
A Passive Location Algorithm Based on Differential Evolution and Genetic Algorithm Using the Doppler Frequency

In the Doppler frequency location system,the exact and closed-form solution can' t be solved due to the complex nonlinear properties.Based on this condition,a new passive location algorithm is proposed based on the differential evolution(DE) and genetic Algorithm(GA). Because of the combination of DE and GA,the states of target can be estimated accurately by the DE-GA passive location algorithm.The computer simulation results show that the method has powerful performance in the Doppler frequency location system.
EV Charging Behavior Simulation and Analysis Using Real-World Charging Load Data

Accurate short-term load forecasting (STLF) results for charging station charging load are essential to improve the reliability and safety of power distribution systems. In modern electric vehicle (EV) optimization dispatching, in addition to accurate STLF results, it is necessary to know the status of each user's EV, and user charging behavior promptly. In this paper, an EV charging STLF method is proposed and discussed. We first derive accurate STLF results based on a data-driven method. Using the driving distance probability function of vehicle and Monte Carlo method to generate a certain number of different states of EV. Using the two-stage filling method, the number of the EVs charging at each time point in the first step is set as the model's parameters, and then the model is enabled with multiple times to retain the results which are close to results obtained by the data-driven method. The number of the EVs charging at some time point in the second step are set as the model's parameters. Based on the first step, the load is filled, and the accurate model-driven STLF results and charging behavior information are finally obtained. Thus, this information is used to regulate the charging behavior of EVs.
Perspectives of Academics and Graduates into the World of Work: A South African Study

The aim of the study was to ascertain the expectations and perceptions of engineering graduates regarding an engineering career in the world of work, and compare them to perspectives of academic leaders of the engineering disciplines in which these graduates studied. Using a mixed-methods approach, the study firstly entailed a quantitative survey of engineering graduates at a South African University during their graduation using a questionnaire and convenience sampling. Graduates’ needs, aspirations, reason for pursuing engineering and their expectations of the workplace were determined and analysed using the Biggs’ Study Motives and Strategies framework. The study thereafter attempted to ascertain the perspectives of academic leaders at the same university, drawing from their years of experience and liaison with industry partners. Academic leaders at the university were interviewed qualitatively. An interpretivist paradigm was considered using deductive thematic semantic analysis on various themes concerning their views on graduate and employer expectations, trends, training programmes, postgraduate study, outcomes and attributes. The study also drew upon findings by the university Quality Promotions and Assurance survey for further corroboration of findings. Key misalignments between graduates and academic leaders were identified and discussed. The main misalignments included guidance expectations, niche proficiencies and innovation expectations. Key causes included language barriers, lack of engineering drive, high workload and surface study strategies, and assessment changes. Recommendations for the university’s role in mitigating many of the issues and mis-alignments were provided, along with recommendations for any possible future research in this area.
Warfield invariants of normed unit groups in abelian group rings

Abstract. Suppose G$G$ is an abelian group and R$R$ is a commutative ring with identity of prime characteristic p$p$. In some special cases for R$R$ and G$G$ we compute only in their terms and sections the Warfield p$p$-invariants of the normed unit group VR[G]$VR[G]$ in a commutative group ring R[G]$R[G]$. This supplies our recent results in this way.
Modeling Spatial Variation in Area Source Emissions

Urban and regional air quality needs to be analyzed at various geographical scales. Area source emission inventories usually estimate total emissions for various industrial and commercial activities at the county or larger scales. Consequently, information on spatial variation of emissions within a county, a critical requirement for urban airshed modeling, is largely unavailable. This paper proposes a Poisson regression approach that enables us to model small-area variation in source activities and to allocate county or regionwide emission estimates to subcounty units. The new approach is used to model the spatial distribution of automobile refinishing activities in the Sacramento modeling region in California. The paper addresses the problem of overdispersion of variance in Poisson regression and evaluates the effect of grid location on modeling results. The usefulness of geographical information systems in spatial statistical analysis is demonstrated.
Wikipedia Vandalism Predictor Using Sentiment Analysis

The largest encyclopedia in the world, Wikipedia, is edited online by millions of volunteers. Each article is editable by any user. The fact that the content on this website is crowdsourced, however, has led to “Digital Vandalism” or the malicious modification of website pages to provide false information. An illustration would be the definition of an economic recession being changed just when the United States was experiencing one. In more recent times, it could have been the inclusion of Khalistani information on Arshdeep Singh’s Wikipedia page, which occurred at the same time as widespread internet propaganda that he was a supporter of Khalistan. Version control and a system of volunteer reviewers are the sole defences against vandalism and poor-quality contributions to Wikipedia. To ensure the protection and correctness of information on Wikipedia there needs to be a system which can predict possible attempts of vandalism on articles.
Application of Six Sigma and KAIZEN Techniques to Non-Conformities:A Case Study of Pharmaceutical Companies

Every company certainly wants a good product and lots of it in order to get a profit, where at this time the competition is very tight. Therefore, this research is motivated by the many reject problems in the primary packaging product process (filling). This research was carried out at a pharmaceutical company located in Bogor, Wanaherang by taking problems with infusion bottles in January -March 2022, researchers also used 3 comparisons of infusion bottle products in companies, includes: levofloxacin, metronidaol, paracetamol infusion where the number of rejects in January and the most rejects in paracetamol infusion products are 2,303 pcs for 3 months, therefore this study aims to reduce the existing rejects by using the DMAIC, KAIZEN, Six sigma, FMEA and 7tools methodswith the results of the study finding 3 critical to quality that covers: fiber, dirt, packaging. The researcher proposes to improve human resources using the KAIZEN 5Smethodand also scheduling operator training and machine maintenance checks every month so that during the production process minimize all obstacles.
Quantitative determination of trace elements in frozen and chilled chicken using ICP OES and related health risk assessment

A graphitic digestion method linked with inductive coupled plasma optical emission spectroscopy (ICP OES) instrument was used to determine the levels of trace elements in frozen and chilled chicken samples collected from markets in Jouf city, Saudi Arabia. The current method is innovative because samples are digested using a graphitic digestion system, which is safer, facile sample handling, short time for assessment, and more environmentally friendly than existing procedures. The results revealed that the chilled chicken samples contained higher concentrations of Na, Fe, Cr, and Mn than the frozen samples, which were below detection limit. Furthermore, the Cu, Al, Zn, and Cr contents in some of the analyzed chicken samples were over the maximum permissible limit of international standards, indicating a serious risk to consumers. The estimation of health-risk indicated that trace metal contamination causes health risks to the consumers, but the effect decreases with decreasing the target hazard quotient value in the rank of Cr > Al > Zn > Cu > Mn > Fe, with all values less than 1 except for Cr in chilled chicken. Moreover, the tolerable limit (1 × 10−6) for the carcinogenic risk for Cr in chilled chicken was observed to be exceeded.
RF Energy Harvesting and Wireless Power Transfer for Energy Autonomous Wireless Devices and RFIDs

Radio frequency (RF) energy harvesting and wireless power transmission (WPT) technologies —both near-field and far-field—have attracted significant interest for wireless applications and RFID systems. We already utilize near-field WPT products in our life and it is expected that RF EH and far-field WPT systems can drive the future low-power wireless systems. In this article, we initially present a brief historical overview of these technologies. The main technical challenges of rectennas and WPT transmitters are discussed. Furthermore, this paper presents the recent advances on the development of these technologies, including the possibility of powering RFID systems through the millimeter wave power from 5G networks, the trends in flexible rectennas design and the technological developments on the simultaneous wireless information and power transfer (SWIPT).
Fast Reliability Assessment of Power Systems Based on Interpretable Autoencoder

To shift the reliability computational burden from real-time to offline, recent works use a deep learning method for rapid reliability assessment. Most existing data-driven methods are based on training the neural network which is considered a black-box model. In lack of interpretability, it fails to provide convincing information for operating staff. In this regard, this paper proposes an interpretable autoencoder method for reliability assessment. First, a deep network is constructed for rapid calculation of the system reliability, and a weight initialization method is proposed based on feature reconstruction. Thereafter, the model is interpreted based on partial dependence (PD) functions to map relationships between reliability and power injections. Additionally, the Gaussian noise strategy is designed for input features. The proposed method is tested in the RTS-79 system. The partial dependence of reliability can be analyzed with the proposed approach.
The Segmentation of Multi-target MRI Image Based on Principal Component Analysis and Support Vector Machine

In MRI image,the boundary of each encephalic tissue is very complicated and irregular.It is a big challenge to the traditional segmentation algorithms.Principal Component Analysis(PCA) can realize dimension reduction and eliminate redundant information,make the sample space for Support Vector Machine(SVM) more compact and reasonable.In this paper,PCA was used to process 57 dimensional feature vectors of image and the segmentation of multi-target MRI image based on multi-classification SVM was investigated.The result showed that the irregular boundary of each encephalic tissue was extracted successfully.The theory analysis and experimental results indicated that the segmentation based on PCA and SVM displayed better performance than the segmentation based on SVM only.
6.—Oscillation Theory for Semilinear Schrödinger Equations and Inequalities

Synopsis Sufficient conditions are derived for every solution of a nonlinear Schrödinger equation (or inequality) to be oscillatory in an exterior domain of En. Such results apply in particular to the n-dimensional Emden-Fowler equation. The method involves oscillatory behaviour of solutions of a nonlinear ordinary differential inequality satisfied by the spherical mean of a positive solution of the Schrödinger equation.
Potentials of semantic internet of things in smart cities: an overview and roadmap

In the last period, realizing the smart cities vision has required utilizing many technologies, such as internet of things and others. Our investigation in this study reveals that semantic internet of things technologies have a vital role in addressing essential issues in smart cities, primarily interoperability issues. Hence, in this paper we extrapolate and highlight the potentials of semantic internet of things in smart cities based on examining many sample cases. Additionally, this paper provides a general roadmap and an abstract architecture to facilitate exploiting semantic internet of things in smart cities. Common frameworks, ontologies, standards, and tools that are used in many sample cases are extracted and accentuated. The study also pinpoints the most frequent quality attributes that are considered in semantic internet of things in smart cities’ field. Finally, the paper identifies the ongoing issues that still require improvement regarding the semantic internet of things in smart cities.
Explicit versions of the local duality theorem in ${\mathbb{C}}^{n}$

We consider versions of the local duality theorem in $\mathbb{C}^n$. We show that there exist canonical pairings in these versions of the duality theorem which can be expressed explicitly in terms of residues of Grothendieck, or in terms of residue currents of Coleff-Herrera and Andersson-Wulcan, and we give several different proofs of non-degeneracy of the pairings. One of the proofs of non-degeneracy uses the theory of linkage, and conversely, we can use the non-degeneracy to obtain results about linkage for modules. We also discuss a variant of such pairings based on residues considered by Passare, Lejeune-Jalabert and Lundqvist.
Autonomous Spacecraft Rendezvous Using Tube-Based Model Predictive Control: Design and Application

As the concentration of large space debris increases, how rendezvous maneuvers involving these typically non-cooperative, freely tumbling bodies are planned and executed is evolving. The rendezvous must be carefully planned, employing up-to-date in situ data to identify the inertial and motion parameters of the target body, and executed in a manner that accounts for the remaining uncertainty in these parameters. This paper presents an extension of the Tumbling Rendezvous via Autonomous Characterization and Execution (TRACE) pipeline used in the ROAM/TumbleDock Astrobee experiment campaign, which sequences the target state estimation, motion planning, controller design, and maneuver execution tasks while additionally providing logical loop-back avenues to previous tasks, increasing the chances of a successful maneuver. The pipeline’s performance is analyzed in simulation, utilizing target state estimates generated in a previous activity on a dedicated on-ground test bed; online motion planning, based on nonlinear programming and warm-started using a trajectory library generated offline with a novel graphics-processing-unit-based method; and tube-based model predictive control to robustly track the planned trajectory. Tube-based model predictive control is an actively evolving subject, distributed over multiple publications and various research interests. The necessary theory and considerations for practical implementation of the method are consolidated; its use, features, and limitations in the proposed task are demonstrated.
Detection of false information in environment-based complaint reports based on graph convolution

To solve the problem of strong subjectivity and low credibility of complaints, a new detection model of false information in environment complaints based on graph convolution was proposed. Firstly, the text of environment complaint is preprocessed: then the word in the complaint text and lexicon are used as nodes, the edges between words are constructed by point mutual information, the edges between text and words are constructed by term frequency-inverse document frequency, and the edges between text and text are constructed by text similarity. The edges between words, text-word relationship and text-text relationship are used to construct the text graph of environmental complaint based on word co-occurrence, text-word relationship and text-text relationship; finally, the node information is passed between nodes through graph convolution, and the coupling between complaint information is used to detect complaint false information. Through designing experiments, the model is compared with the methods of common classification models, and the experimental results show that the model has good performance. It is suitable to be applied in the task of environment-based false complaint detection.
Intelligent technology management based on patent topic modeling : *Note: Sub-titles are not captured in Xplore and should not be used

Competitive intelligence and technology insights are the results of using the materials and methods of big data analytics to arrange existing knowledge, integrating big data analytics in the field of patent analysis is an urgent need for any company in the marketplace to gain strong knowledge about the computational environment, this paper is a new paradigm in patent analysis by proposing a full methodology from feature selection to clustering phase based on singular value decomposition (SVD) as a central tool to determine the correlation between the patent’s data, semantic patent topics could emerge more relevant topics helping the senior managers in the companies in claiming new management strategy to fill the gaps with the markets with decreasing the cost of the production and time-consuming of the decision-making in each level of the organization.
The Effect of Recipient Back-Table Duration on Graft Outcome of Deceased Donor Kidneys: A Single-Center Prospective Cohort Study

Background: Little is known about the influence of the duration of the kidney back-table preparation period and kidney temperature on graft outcomes after transplantation. The aim of this study is to investigate the back-table duration and its relation to graft outcome and the relation between kidney temperature and graft outcome. Methods: In this prospective cohort study, deceased donor kidney temperature is measured at fixed time points using an infrared thermometer during back-table preparation and transplantation. Additionally, the back-table duration is measured using a timer. Results: Between September 2020 and July 2021, 49 kidneys were prospectively included in this study. Median back-table duration was 33.7 (standard deviation ± 14.1) min and donor kidney temperature increased up to 14.9 °C (±2.8) after 60 min of back-table preparation. Mean implantation time was 24.9 (±7.6) min and kidney temperature increased up to 25.9 °C (±2.4) after 30 min of implantation time. Longer back-table duration was significantly associated with higher rates of delayed graft function (p = 0.037). However, this observation did not sustain at 3 and 6-months post-transplantation. No association was found between kidney temperature and graft outcomes. Conclusion: Longer back-table duration is significantly associated with DGF after deceased donor kidney transplantation. No association was observed between kidney temperature and graft outcomes of deceased donor kidneys.
Spontaneous Symmetry Breakdown without Massless Bosons

We examine a simple relativistic theory of two scalar fields, first discussed by Goldstone, in which as a result of spontaneous breakdown of U(1) symmetry one of the scalar bosons is massless, in conformity with the Goldstone theorem. When the symmetry group of the Lagrangian is extended from global to local U(1) transformations by the introduction of coupling with a vector gauge field, the Goldstone boson becomes the longitudinal state of a massive vector boson whose transverse states are the quanta of the transverse gauge field. A perturbative treatment of the model is developed in which the major features of these phenomena are present in zero order. Transition amplitudes for decay and scattering processes are evaluated in lowest order, and it is shown that they may be obtained more directly from an equivalent Lagrangian in which the original symmetry is no longer manifest. When the system is coupled to other systems in a U(1) invariant Lagrangian, the other systems display an induced symmetry breakdown, associated with a partially conserved current which interacts with itself via the massive vector boson.
Using supervised kernel entropy component analysis for fault diagnosis of rolling bearings

Rolling bearings as vital components are present ubiquitously in manufacturing machines and systems, timely fault diagnosis is of great importance in avoiding serious catastrophe, and a variety of methods have been developed to detect faults of rolling bearings. In this study, we introduce labels to adjust the distance of classes and propose a method called supervised kernel entropy component analysis for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis which attempts to preserve the Renyi entropy of the data set after dimension reduction and presents a good performance for nonlinear feature extraction. Simulation and experiments are carried out to verify the method. The intrinsic geometric features derived from original vibration signals are extracted, and then classified by support vector machine. The results prove the feasibility and effectivity of the supervised kernel entropy component analysis in comparison with other similar methods, demonstrating its potentiality for fault diagnosis of rolling bearings.