Miracle-dz/UniFAR-FTUs huggingface dataset:
Topological order and Fermi surface reconstruction

This review describes how topological order can reconstruct Fermi surfaces of metals, even in the absence of translational symmetry breaking. We begin with an introduction to topological order using Wegner's quantum $\mathbb{Z}_2$ gauge theory on the square lattice: the topological state is characterized by the expulsion of defects, carrying $\mathbb{Z}_2$ magnetic flux. The interplay between topological order and the breaking of global symmetry is described by the non-zero temperature statistical mechanics of classical XY models in dimension $D=3$; such models also describe the zero temperature quantum phases of bosons with short-range interactions on the square lattice at integer filling. The topological state is again characterized by the expulsion of certain defects, in a state with fluctuating symmetry-breaking order. The phase diagrams of the $\mathbb{Z}_2$ gauge theory and the XY models are obtained by embedding them in U(1) gauge theories, and by studying their Higgs and confining phases. These ideas are then applied to the single-band Hubbard model on the square lattice. A SU(2) gauge theory describes the fluctuations of spin-density-wave order, and its phase diagram is presented by analogy to the XY models. We obtain a class of zero temperature metallic states with fluctuating spin-density wave order, topological order associated with defect expulsion, reconstructed Fermi surfaces (with `chargon' or electron-like quasiparticles), but no broken symmetry. We conclude with the application of such metallic states to the pseudogap phase of the cuprates, and note the recent comparison with numerical studies of the Hubbard model. In a detour, we also discuss the influence of Berry phases, and how they can lead to deconfined quantum critical points: this applies to bosons on the square lattice at half-integer filling, and to quantum dimer models.
Research on indoor wireless network coverage layout based on hybrid particle swarm optimization algorithm

In this paper, the number and location of the deployment of indoor small base stations are used as optimization variables to ensure coverage and reduce the interference between base stations as conditions. Based on the corrected model, a differential evolution hybrid particle swarm optimization algorithm is used to solve the optimization objectives. In the process of each iteration, the solution solution given by the optimization algorithm is covered by the prediction model. The signal coverage degree in the target area is calculated and fed back to the particle swarm optimization algorithm as the historical experience data for learning, and the final planning scheme is output, which has certain engineering practice basis and guiding significance for operation and maintenance work.
154 GHz collective Thomson scattering in LHD

Collective Thomson scattering (CTS) was developed by using a 154 GHz gyrotron, and the first data has been obtained. Already, 77 GHz CTS has worked successfully. However, in order to access higher density region, 154 GHz option enhances the usability that reduces the refraction effect, which deteriorates in the local measurements. The system in the down converted frequency was almost identical to the system for 77 GHz. Probing beam, a notch filter, a mixer, and a local oscillator in the receiver system for 77 GHz option were replaced to those for the 154 GHz option. 154 GHz gyrotron was originally prepared for the second harmonic electron cyclotron heating (ECRH) at 2.75 T. However, scattering signal was masked by the second harmonic electron cyclotron emission (ECE) at 2.75 T. Therefore, 154 GHz CTS was operated at 1.375 T with fourth harmonic ECE, and an acceptable signal to noise ratio was obtained. There is a signature of fast ion components with neutral beam (NB) injection. In addition, the CTS spectrum became broader in hydrogen discharge than in deuterium discharge, as the theoretical CTS spectrum expects. This observation indicates a possibility to identify ion species ratio by the 154 GHz CTS diagnostic.
An energy‐efficient power allocation algorithm under reliability constraint for industrial Internet of Thing systems

With the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat‐reQuest (ARQ) retransmission mechanism in the 802.15. 4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms.
Underwater Image Processing Method of Boric Acid Pool in Nuclear Power Plant Based on Machine Vision

Due to the particularity of nuclear energy, safe operation has always been the basis of nuclear energy development. The state of thermocouples is an important factor affecting the safe operation of nuclear power systems. Defects on the surface of thermocouples will cause dangerous accidents. Therefore, how to realize thermocouple surface defect detection is an important link related to the safe operation of nuclear power equipment, and it is also one of the research hotspots in the field of nondestructive testing. In the operation stage of nuclear power plant, some equipment is only accessible on one side, making the vision based detection method the only optional surface defect detection method. However, due to the particularity of the nuclear power plant testing environment, most of the testing needs to be carried out underwater, resulting in difficulties in the application of related technologies. Aiming at the technical difficulties and application problems of machine vision technology in the surface defect detection of underwater nuclear power equipment, this paper studies the image quality enhancement method of underwater detection to solve the problems that the detection method based on two-dimensional image is affected by image degradation and limited by the camera field of view.
Molecular Data Needs for Modelling AGB Stellar Winds and Other Molecular Environments

The modern era of highly sensitive telescopes is enabling the detection of more and more molecular species in various astronomical environments. Many of these are now being carefully examined for the first time. However, to move beyond detection to more detailed analysis such as radiative transfer modelling, certain molecular properties need to be properly measured and calculated. The importance of contributions from vibrationally excited states or collisional (de-)excitations can vary greatly, depending on the specific molecule and the environment being studied. Here, we discuss the present molecular data needs for detailed radiative transfer modelling of observations of molecular rotational transitions, primarily in the (sub-)millimetre and adjacent regimes, and with a focus on the stellar winds of AGB stars.
Mechanical model of the left ventricle of the heart approximated by axisymmetric geometry

Abstract An axisymmetric model is suggested to simulate mechanical performance of the left ventricle of the heart. Cardiac muscle is treated as incompressible anisotropic material with active tension directed along muscle fibres. This tension depends on kinetic variables that characterize interaction of contractile proteins and regulation of muscle contraction by calcium ions. For numerical simulation of heartbeats the finite element method was implemented. The model reproduces well changes in ventricle geometry between systole and diastole, ejection fraction, pulse wave of ventricular and arterial pressure typical for normal human heart. The model also reproduces well the dependence of the stroke volume on end-diastolic and arterial pressures (the Frank–Starling law of the heart and Anrep effect). The results demonstrate that our model of cardiac muscle can be successfully applied to multiscale 3D simulation of the heart.
Integration of real-world evidence from different data sources in health technology assessment

Real-world evidence (RWE) is being increasingly used by a wide range of stakeholders involved in the therapeutic product lifecycle but remains underutilized in the health technology assessment (HTA) process. RWE aims to fill the current evidence gaps, reduce the uncertainty around the benefits of medical technologies, and better understand the long-term impact of health technologies in real-world conditions. Despite the minimal use of RWE in some elements of HTA, there has been a larger push to further utilize RWE in the HTA processes. HTA bodies, as other stakeholders, work towards developing more robust means to leverage RWE from various data sources in the HTA processes. However, these agencies need to overcome important challenges before the broader incorporation of RWE into their routine practice. This paper aims to explore the extensive integration of RWE utilizing diverse sources of RWD. We discuss the utilization of RWE in HTA processes, considering aspects such as when, where, and how RWE can be effectively applied. Additionally, we seek the potential challenges and barriers associated with the utilization of different data sources.
Chemical Profiling and Antimicrobial Activity of Essential Oils from Hypericum adenotrichum Spach. An Endemic Species

: The purpose of this study was to determine the content of essential oils isolated from the herba and roots of Hypericum adenotrichum Spach. , an endemic, wild-growing species of Turkish flora. Gas Chromatography-Mass Spectroscopy (GC-MS) and Gas Chromatography-Flame Ionization Detector (GC-FID) were used to examine the essential oils (EO). In the essential oils of the root of H. adenotrichum , the compounds were found, with undecane (64.4%), hexadecanoic acid (5.3%), and α -copaene (3.9%) as the primary components. Three significant herba compounds, germacrene D (13.3%), hexadecanoic acid (11.3%), and cadinene (6%) were found. Moreover, in this study antimicrobial effects of EO of endemic H. adenotrichum against Escherichia coli NRRL B-3008, Staphylococcus aureus ATCC 6538, Salmonella tymphirium ATCC 14028, Staphylococcus aureus ATCC 700699, Candida albicans ATCC 90028 and Candida krusei ATTC 6258. H. adenotrichum root and herba extracts were made, and LC-MS/MS analysis was used to determine the primary constituents of the extracts. The chemicals found in the herb and root, respectively, include 3-caffeoylquinic acid, 5-caffeoylquinic acid, quercetin hexoside, quinic acid, quercetin, and catechine.
Decoding the Chronic Obstructive Pulmonary Disease (COPD) Puzzle: Investigating the Significance of Exacerbation Scores in Triage Decision-Making

Chronic obstructive pulmonary disease (COPD) is a complex disease pathology of the lungs that has a significant impact on global health. It has been a major contributor to global mortality and morbidity, with COPD exacerbations posing a substantial economic burden on the healthcare systems. Appropriate triaging of patients with COPD exacerbation is crucial to reduce the burden of hospitalization, especially in the intensive care unit (ICU). Understanding the significance of exacerbation scores in triage decision-making is essential for improving outcomes and optimizing patient care. To aid this triage decision-making, several scoring systems have been developed. This review article aims to discuss the different scores, including assessment of Confusion, Urea, Respiratory rate, Blood pressure, and Age (≥65 years) (CURB-65); Dyspnoea, Eosinopenia, Consolidation, Acidaemia and atrial Fibrillation (DECAF), Neutrophil to lymphocyte ratio (NLR); Platelet-lymphocyte ratio (PLR); Pneumonia severity index/Pneumonia Patient Outcomes Research Team (PSI/PORT); and elevated BUN, Altered mental status, Pulse, Age (>65 years) (BAP-65), and their role in triaging COPD exacerbations. Proper triaging allows for the appropriate allocation of resources and timely interventions based on severity. Further research and validation are needed to establish the optimal use and integration of these scores in clinical practice, particularly in ICU settings.
Human Modeling in Physical Human-Robot Interaction: A Brief Survey

The advancement and development of human modeling have greatly benefited from principles used in robotics, for instance, multibody dynamics laid the foundations for physics engines of human movement simulation, and the robotics and control theory were used to contextualize human sensorimotor control. There are many common interests and interconnections between the fields of human modeling and robotics. In recent years, as robots have become safer and smarter, they actively participate in our lives and help us in various scenarios. Roboticists need tools and data from human modeling to build next-generation robots that better assist humans. In this survey, we focus on the connections between physical human-robot interaction and human modeling. On one hand, human neuromusculoskeletal and sensorimotor control models provide novel insights into the human response that robots can utilize to improve human performance. On the other hand, robots are becoming instrumental in quantifying the performance of the (neuro)musculoskeletal system. Thus, the combined use of human modeling and robotic methods in physical human-robot interaction can lead to both improved human understanding and functional assistance.
Investigation of vibration characteristics for bladed-disks with dry friction nonlinearity

This paper focuses on the vibration characteristics of the bladed-disk subjected to the dry friction damping under periodic excitation. Firstly, the multi-harmonic equation basing on the frequency-domain analysis is established to predicate the steady response of the bladed-disk. Then, the algorithm to solve the nonlinear multi-harmonic balance equation is given step by step. In the numerical simulation, a simplified lumped parameter model of a turbine bladeddisk as well as the elastic Coulomb friction model between the root and disk contacting surfaces are applied. The normal load level of the friction interfaces, which are the focal points of the bladed-disk design, is analysed for the nonlinear vibration characteristics of the blades. The results show that vibration response of bladed-disk is affected significantly by normal load, and there exists an optimal value of the normal load under the operating condition of the turbomachinery, which can provide a qualitative assessment for the design practice of friction dampers.
Stimming Behavior Dataset - Unifying Stereotype Behavior Dataset in the Wild

Non-intrusive vision-assisted methods that can recognize complex human behaviors can help the diagnosis and treatment of neurological disorders where stimming behaviors are prominent, such as Autism Spectrum Disorder (ASD). Machine learning methods, especially those related to computer vision are a promising direction for this kind of application, however, the effectiveness of these methods depends on the existence of good-quality datasets. Building a dataset in this domain is challenging due to privacy matters and also to the need to conduct experiments mostly with children. Therefore, we propose a consolidated, annotated, and standardized Stimming Behavior Dataset. This unified dataset can enable researchers to have access to a larger pool of data, classified and annotated for the stimming behaviors, enabling a faster and more streamlined approach when working with this kind of video collection. The Autism Stimming Behavior Dataset - ASBD11https://github.com/OckerGui/Stimming-Behavior-Dataset can be used for the development of machine learning algorithms that can detect behavioral patterns associated with autism spectrum disorder and other applications where this kind of analysis is important. The dataset is comprised of 165 annotated short duration clips, based on excerpts from 155 distinct publicly accessible Youtube videos. , with 154 unique subjects of ages spanning from toddlers to young adults and divided into 48 female and 106 male individuals. The average duration of the clips is 10.6 seconds. The annotated clips show not only the URL and class of the video but the moment where the relevant behavior starts and its duration. In addition to providing a unified dataset, this paper also includes predictions of each category based on a classic I3D model trained in the Kinetics 400 dataset, which serves as a baseline for the action-recognition methods and analysis on the previous source datasets and models, providing insights for future action-recognition-based systems that can be used to assist in decisions regarding ASD diagnosis and treatment.
Efficient ASIC Implementation for Satellite-IoT Security Co-processor

ASIC implementation of a security co-processor for Satellite-IoT devices is introduced. It provides a hardware acceleration for AES-GCM, the CCSDS-compliant Authenticated Encryption algorithm. The Key size is programmable, supporting both 128 and 256-bit modes, to comply with baseline implementation of CCSDS SDLS protocol, and Extended Procedures. Implementation targets 65nm CMOS technology using low-power standard cell library, resulting in a synthesis cell area of 52.2kGE. Achieving max. clock frequency of 617.3MHz, data throughput is 7.18Gbps for 128-bit mode, and 5.27Gbps for 256-bit mode. Output Tag throughput is 418 and 333Mbps, for 128-bit and 256bit modes, respectively. Implementation is primarily optimized for power to cater for battery-operated IoT devices. Average post-synthesis energy consumption is 1.25pJ/bit at 128-bit mode, which is almost the same as the most efficient design in literature with >91% area saving. Energy consumption is 1.54pJ/bit for the 256-bit mode. Post-PnR’s GDSII area is 125. 2µm2, and energy consumption is 2.82 and 3.55pJ/bit, for 128 and 256-bit modes, respectively.
Utilization of multiple genetic methods for prenatal diagnosis of rare thalassemia variants

Background: Thalassemia is the most prevalent monogenic disorder caused by an imbalance between the α- and β-globin chains as a result of pathogenic variants in the α- or β-globin genes. Novel or complex structural changes in globin genes are major hurdles for genetic consulting and prenatal diagnosis. Methods: From 2020 to 2022, genetic analysis was performed on 1,316 families suspected of having children with thalassemia major, including 42 pregnant couples suspected of being thalassemia carriers with rare variants. Multiple techniques including multiplex ligation-dependent probe amplification (MLPA), Sanger sequencing, targeted next-generation sequencing, and single-molecule real-time (SMRT) sequencing were used to diagnose rare thalassemia. Results: The rate of prenatal diagnosis for rare thalassemia variants was 3.19% (42/1,316). The most prevalent alleles of α- and β-thalassemia are Chinese Gγ(Aγδβ)0and -- THAI deletion. In addition, ten rare complex genotypes include one Chinese Gγ(Aγδβ)0 deletion combined with HBG1-HBG2 fusion, two rare deletions at HBB gene (hg38, Chr11: 5224211-5232470, hg38, Chr11: 5224303-5227790), one complete 7,412 bp fusion gene for anti-Lepore Hong Kong, two complex rearrangements of the α-globin gene cluster, two novel duplications, and two rare large deletions in the α-globin gene cluster. Conclusion: Accurate gene diagnosis for probands with combined molecular biology techniques is the key to prenatal diagnosis of rare thalassemia.
Stability analysis for a passive/active human model in physical human–robot interaction with multiple users

ABSTRACT Human–robot–human interaction (HRH), understood as a physical human–robot interaction (pHRI) with two humans, can be applied when lifting heavy, bulky and large-sized objects with a robot. In combination with a virtual environment, this system can become nonlinear. In this article, we prove sufficient stability conditions for a stationary point of such a particular type of nonlinear multiple time-delay systems. In addition, a new human model consisting of a passive and an active part will be introduced and validated on experimental data. The derived stability conditions are applied to a single-user pHRI system including this human model. The results indicate that these conditions are very conservative. Then four approaches for the analysis of a multi-user pHRI are introduced and compared with each other. Finally, a potential HRH application with a nonlinear environment in the form of a potential force field is presented.
Multivariate rational regression and its application in semiconductor device modeling

Physics equation-based semiconductor device modeling is accurate but time and money consuming. The need for studying new material and devices is increasing so that there has to be an efficient and accurate device modeling method. In this paper, two methods based on multivariate rational regression (MRR) for device modeling are proposed. They are single-pole MRR and double-pole MRR. The two MRR methods are proved to be powerful in nonlinear curve fitting and have good numerical stability. Two methods are compared with OLS and LASSO by fitting the SMIC 40 nm MOS-FET I–V characteristic curve and the normalized mean square error of Single-pole MRR is which is 4 magnitudes less than an ordinary least square. The I–V characteristics of CNT-FET and performance indicators (noise factor, gain, power) of a low noise amplifier are also modeled by using MRR methods. The results show MRR methods are very powerful methods for semiconductor device modeling and have a strong nonlinear curve fitting ability.
Mediating broadband light into graphene–silicon Schottky photodiodes by asymmetric silver nanospheroids: effect of shape anisotropy

Designing thinner, more efficient and cost-effective 2D materials/silicon Schottky photodiodes using the plasmonic concept is one of the most recent quests for the photovoltaic research community. This work demonstrates the enhanced performance of graphene–Si Schottky junction solar cells by introducing asymmetric spheroidal shaped Ag nanoparticles (NPs) embedded in a graphene monolayer (GML). The optical signatures of these Ag NPs (oblate, ortho-oblate, prolate and ortho-prolate) have been analyzed by discrete dipole approximation in terms of extinction efficiency and surface plasmon resonance tunability, against the quasi-static approximation. The spatial field distribution is enhanced by optimizing the size (aeff = 100 nm) and aspect ratio (0.4) for all of the utilized Ag NPs with an optimized graphene environment (t = 0.1 nm). An improvement of photon absorption in the thin Si wafer for the polychromatic spectral region (λ ~ 300–1100 nm) under an AM 1.5 G solar spectrum has been observed. This resulted in a photocurrent enhancement from 7.98 mA cm−2 to 10.0 mA cm−2 for oblate-shaped NPs integrated into GML/Si Schottky junction solar cells as compared to the bare cell. The structure used in this study to improve the graphene–Si Schottky junction’s performance is also advantageous for other graphene-like 2D material-based Schottky devices.