bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners
Authors: Lu Yaoqin , Liu Qi , Yan Huan , Liu Tao
Published: July 2022

Objective

A nomogram for predicting the risk of mental health problems was established in a population of factory workers and miners, in order to quickly calculate the probability of a worker suffering from mental health problems.

Methods

A cross-sectional survey of 7500 factory workers and miners in Urumqi was conducted by means of an electronic questionnaire using cluster sampling method. Participants were randomly assigned to the training group (70%) and the validation group (30%). Questionnaire-based survey was conducted to collect information. A least absolute shrinkage and selection operator (LASSO) regression model was used to screen the predictors related to the risk of mental health problems of the training group. Multivariate logistic regression analysis was applied to construct the prediction model. Calibration plots and receiver operating characteristic-derived area under the curve (AUC) were used for model validation. Decision curve analysis was applied to calculate the net benefit of the screening model.

Results

A total of 7118 participants met the inclusion criteria and the data were randomly divided into a training group (n=4955) and a validation group (n=2163) in a ratio of 3:1. A total of 23 characteristics were included in this study and LASSO regression selected 12 characteristics such as education, professional title, age, Chinese Maslach Burnout Inventory, effort–reward imbalance, asbestos dust, hypertension, diabetes, working hours per day, working years, marital status and work schedule as predictors for the construction of the nomogram. In the validation group, the Brier score was 0.176, the calibration slope was 0.970 and the calibration curve of nomogram showed a good fit. The AUC of training group and verification group were 0.785 and 0.784, respectively.

Conclusion

The nomogram combining these 12 characteristics can be used to predict the risk of suffering mental health problems, providing a useful tool for quickly and accurately screening the risk of mental health problems.
‘It was like being hit with a brick’: a qualitative study on the effect of clinicians’ delivery of a diagnosis of eye disease for patients in primary and secondary care
Authors: Ferrey Anne , Moore Lucy , Jolly Jasleen Kaur
Published: July 2022

Objectives

To explore patients’ experiences of getting a diagnosis of eye disease, the psychological impact of this and how this could be improved.

Design

An exploratory qualitative interview study using a narrative approach and inductive methods.

Setting

This study was conducted with patients who had attended ophthalmic appointments in primary and secondary care and in opticians located in the South of England.

Participants

18 people diagnosed with eye disease in England.

Results

Four themes were identified: the convoluted process of being diagnosed, the impact of clinicians’ words, the search for information and reflections on what could be improved. The prolonged wait for a definitive diagnosis was a source of frustration and anxiety for many patients. Professionals’ words and tone when delivering a diagnosis sometimes affected a patient’s view of their diagnosis and their later ability to come to terms with it. Patients were desperate for information, but many felt they were not provided with sufficient information at the time of diagnosis and did not know whether to trust information found online. Participants felt the provision of a hospital liaison service and/or counselling could mitigate the impact on patients and families.

Conclusions

Interactions with clinicians can have a lasting impact on how a diagnosis is experienced and how well the patient is able to come to terms with their visual impairment. Receiving little or no information left patients feeling lost and unsupported. This led them to search for information from less reliable sources. Clinicians should consider how they communicate a diagnosis to patients, how and when they offer information about diagnosis and prognosis and where possible signpost patients to additional support systems and counselling services as early as possible.
Engaging teaching strategies in neurology: the flipped classroom and simulation training
Authors: Albin Catherine , Berkowitz Aaron L
Published: July 2022

Just as neurology continues to expand its diagnostic and therapeutic modalities, so too does neurology education continue to expand in its pedagogical modalities. In this article, we describe two educational techniques—the flipped classroom and simulation—that we have incorporated in our teaching of neurology to students and doctors in training, with some practical tips for their successful implementation.
Analgesic efficacy of erector spinae plane blocks for lumbar spine surgery: a randomized double-blind controlled clinical trial
Authors: Avis Geoffrey , Gricourt Yann , Vialatte Pierre Baptiste , Meunier Victor , Perin Mikael , Simon Natacha , Claret Pierre-Geraud , El Fertit Hassan , Lefrant Jean-Yves , Bertrand Martin , Cuvillon Philippe
Published: July 2022

Introduction

Erector spinae plane block has been proposed to reduce opioid use and improve pain relief with controversial results. This randomized clinical study aimed to assess the efficacy of erector spinae plane block in major spine surgery including multimodal and ‘Enhance Recovery After Surgery’ programs.

Method

After institutional review board approval, adult patients undergoing elective lumbar spine surgery with standardized general anesthesia, rehabilitation and multimodal analgesia protocols were randomly allocated to receive bilateral ultrasound-guided block with saline versus ropivacaine (3.75 mg/mL). Before surgery, a bilateral erector spinae plane block was performed at lumbar level (third vertebrae) with 20 mL of solution for each side. The primary outcome was morphine consumption after 24 hours. Secondary outcomes included pain scores and side effects, from postanesthesia care unit to discharge, and questionnaires at 3 months on pain and quality of life (EQ-5D).

Results

From November 2019 to July 2021, 50 patients were enrolled with similar characteristics and surgery for each group. After the first 24 hours, there was no statistical difference regarding cumulative intravenous morphine consumption between ropivacaine and saline groups: 7.3 mg (3.7–19) vs 12.5 mg (3.5–26) (p=0.51). Over the five postoperative days, opioid sparing, pain scores and side effects were similar between groups. At 3 months, pain relief, incidence of chronic pain and EQ-5D were similar between groups.

Discussion

Erector spinae plane block used in conjunction with ‘Enhance Recovery After Surgery’ and multimodal analgesia protocols provides limited reduction in opioid consumption and no long-term benefits.

Trial registration number

EudraCT 2019-001678-26.
Telephone risk-based eligibility assessment for low-dose CT lung cancer screening
Authors: Dickson Jennifer L , Hall Helen , Horst Carolyn , Tisi Sophie , Verghese Priyam , Mullin Anne-Marie , Teague Jonathan , Farrelly Laura , Bowyer Vicky , Gyertson Kylie , Bojang Fanta , Levermore Claire , Anastasiadis Tania , Sennett Karen , McCabe John , Devaraj Anand , Nair Arjun , Navani Neal , Callister Matthew EJ , Hackshaw Allan ,   SUMMIT Consortium, Quaife Samantha L , Janes Sam M
Published: July 2022

Eligibility for lung cancer screening (LCS) requires assessment of lung cancer risk, based on smoking history alongside demographic and medical factors. Reliance on individual face-to-face eligibility assessment risks inefficiency and costliness. The SUMMIT Study introduced a telephone-based lung cancer risk assessment to guide invitation to face-to-face LCS eligibility assessment, which significantly increased the proportion of face-to-face attendees eligible for LCS. However, levels of agreement between phone screener and in-person responses were lower in younger individuals and minority ethnic groups. Telephone-based risk assessment is an efficient way to optimise selection for LCS appointments but requires further iteration to ensure an equitable approach.
A Group-Dependent Due Window Assignment Scheduling Problem with Controllable Learning Effect
Authors: Chen Ke , Han Songqiao , Huang Hailiang , Ji Min
Published: July 2022

In this paper, we study a single machine scheduling problem with group-dependent due window assignment and further incorporate autonomous and induced learning effects. Here, autonomous learning refers to learning by doing, while induced learning denotes that proactive investments can promote the learning effect, i.e., the learning effect is controllable. The proactive spending could include any management efforts like professional training programs, among others. The objective is to find optimal strategies of due window assignments, sequence of groups and jobs, and level of induced learning that optimize the total cost comprising the due window-related penalty costs and the investment cost. We present a polynomial-time algorithm capable of solving this problem and an improved idea to further reduce the time complexity. In addition, a detailed numerical example is conducted. Our study shows that the learning effect can be tuned to fit the demand of the manufacturing system better and lead to a more flexible operating system.
10-Bit 200 kHz/8-Channel Incremental ADC for Biosensor Applications
Authors: Gupta Priya , Earnest Aruj , Mitra Srinjoy
Published: July 2022

In this paper, a second-order discrete-time (DT) modulator for 8-channel Incremental Sigma-Delta analog-to-digital converter (IADC) is presented for biomedical applications ranging from kHz. The proposed DT modulator with input bandwidth of 25[Formula: see text]kHz on each channel is sampled at 51.2[Formula: see text]MHz, with oversampling ratio of 128 implemented at 180[Formula: see text]nm technology. The proposed IADC has high resolution, multichannel A/D conversion, simple architecture, low offset, low gain error and low area over the standard ADCs available. Measured results show that the SNR, DR, ENOB, power consumption and chip area of the proposed IADC are 63.9[Formula: see text]dB, 62.2[Formula: see text]dB, 10.11 bits, 0.832[Formula: see text]mW and 0.032[Formula: see text]mm2, respectively, at 1.8V power supply. For testing purpose, the noncoherent sampling is done to get the FFT plot of the output signal. For further validation, the proposed second-order IADC was also designed and compared in MATLAB/Simulink.
Analysis of charged self-gravitational complex structures evolving quasi-homologously
Authors: Yousaf Z. , Bhatti M. Z. , Khan Suraj
Published: July 2022

This paper studies the complex mechanism of evolving charged self-gravitational (dissipating or non-dissipating) systems using a structure scalar [Formula: see text], resulting from the basic procedure of orthogonal decomposition of the Riemann–Christoffel curvature tensor. The influence of electrical charge on the complexity of the considered system is analyzed in detail. We find several analytical Einstein–Maxwell models fulfilling the quasi-homologous [Formula: see text] evolution plus the vanishing complexity factor ([Formula: see text]) condition. Few of the presented models fulfill the Darmois constraints and exhibit shells, however, others satisfy the Israel constraints on both the boundary surfaces. Finally, some possible applications of the presented solutions are mentioned, which are important from astrophysical stand points. It is expected that some of the provided evolving Einstein–Maxwell fluid configurations may be utilized as a toy model of general frameworks like supernova explosions. It is found that the [Formula: see text] implies the vanishing of the [Formula: see text] and gives rise to a unique and simplest configuration (Friedmann–Lemaître–Robertson–Walker model) fulfilling the condition [Formula: see text] and evolving in the [Formula: see text] regime.
Fuzzy Cluster Analysis for Interval Data Based on the Overlap Distance
Authors: Lethikim Ngoc , Vovan Tai
Published: July 2022

This article builds the fuzzy clustering algorithm for interval data (FCAI). In the proposed algorithm, we use the overlap distance as a criterion to cluster for interval data. The FCAI can determine not only the suitable number of clusters, the elements in each cluster but also the probability of assigning the elements to the established clusters at the same time. In addition, we also consider the convergence of the proposed algorithm by the theory and illustrated it by the numerical examples. The FCAI is applied well in image recognition, a problem with many challenges nowadays. Using the Grey Level Co-occurrence matrices (GLCMs), we propose a novel texture extraction approach to generate featured intervals. The complex computations of the FCAI can be performed conveniently and efficiently by the established Matlab program. We utilize the corrected rand indexes (CR) to find the suitable number of clusters while a partition entropy (PE) and partition coefficients (PC) are applied to argue the quality of fuzzy clusters. As a result, the experiments on the data sets having different characteristics and elements show the reasonableness of the proposed algorithm and its advantages in comparison to the existing ones. Regarding our best knowledge, it has also shown potential in the real application of this study.
INFLUENCE OF WEDM-TEXTURED TOOLS ON THE MACHINABILITY OF TITANIUM GRADE-23 ALLOY
Authors: ROY RIMA SINGHA , MAITY KALIPADA
Published: July 2022

This study has been carried out to understand and reveal the efficacy of using surface-textured tools in the dry turning of Ti grade-23 alloys. The textures have been fabricated nearer to the primary cutting edge (in the rake face) and the secondary cutting edge (in the flank face) of the tool. The method used for texture fabrication is a nonconventional machining technique, Wire Electro-discharge Machining (WEDM). Micro-grooves/channels have been fabricated in the tools (rake face and flank face). The study also aimed to reveal the influence of texture pattern as well as its position on the cutting edges. The rake face-textured tools had four varieties: vertically textured (VT-R), diagonally textured (DT-R), horizontally textured (HT-R) and cross-textured (CT-R). On the other hand, the flank face had three varieties: cross-textured (CT-F), horizontally textured (HT-F) and vertically textured (VT-F). The cutting performance was measured and compared among the aforementioned different textured tools and nontextured (NT) ones in terms of the forces ([Formula: see text], [Formula: see text] and [Formula: see text]), temperature, tool wear and chip morphology. The study revealed that the textured tools performed better and the VT-R tool performed the best followed by the DT-R and VT-F tools as compared to NT ones for the dry turning of Ti grade-23 alloys.
A TWO-LAYER CLASSIFICATION MODEL ON HUMAN ACTIVITY RECOGNITION BASED ON CLUSTERING ALGORITHM AND FEATURE SELECTION
Authors: LIU LIJUE , WANG KEWEI , LI YI , LIU ZHUO
Published: July 2022

Human activity recognition (HAR) has a wide application in daily life. With wearable sensors, people’s activity can be monitored, recorded and analysed. However, most existing methods did not make full use of human activity data and their features. In this paper, a new method based on feature selection and clustering algorithm is proposed. We established two-layer classification models, respectively, in the ankle, the chest, the wrist and the mixed accelerometer data. K-means clustering algorithm was first used to obtain a broad classification of the activities and then we conducted two rounds of classification, among which feature selection was performed in each layer. A significance analysis was also carried out in the final experiment and we compared the performance of the final model from the mixed accelerometer data with other algorithms, the results showed that the recognition performance of our model was significantly better, and the average [Formula: see text]1 score was as high as 0.969 in publicly available PAMAP2 dataset. Compared with other methods, our model achieved the highest recognition rate. The method proposed in this paper can greatly improve the recognition rate of human activity and effectively evaluate daily activity.
The kernel regularized learning algorithm for solving Laplace equation with Dirichlet boundary
Authors: Sheng Baohuai , Zhou Dapao , Wang Shuhua
Published: July 2022

In this paper, we give an investigation on the problem of solving Laplace equation with the kernel regularized regression. We provide a Sobolev type space corresponding to the Dirichlet boundary value problem on a compact domain, and with which define a reproducing kernel space (RKS), which is used as the hypothesis space for constructing kernel regularized learning algorithm. We give theory analysis for the convergence of the learning algorithm, bound an upper bound for the error. The discussions show that the learning rate is controlled by a [Formula: see text]-functional corresponding to the RKS. As an application we give the learning rate in case that the domain is the unit ball. The simulations show that the algorithm has better fitting performance. The investigations show that the problem of solving an elliptic boundary problem can be attributed to constructing an orthonormal basis with respect to a bilinear form corresponding to the boundary value condition.
Unified Complex Variable Element-Free Methods: A Comprehensive Framework of Complex Variable Meshless Approaches
Authors: Li D. M. , Pan Jin-Hu , Sun Zhen-Hu , Xue Bing-Zhang
Published: July 2022

There are long-term traditions and efforts to apply the complex variable methods to the theoretical and numerical solutions of applied mechanics. Based on the aim of establishing a unified format of the complex variable meshless approximation, this paper proposes a unified complex variable moving least-squares (UCVMLS) approximation for any pair of independent real field variables. In the UCVMLS approximation, based on the complex variable form of real variable pair and the adopted complex variable basis, four independent approximation functions can be constructed, in which two different improved complex variable moving least-squares (ICVMLS) approximations developed before are included. The four trial functions in UCVMLS are proved to be able to construct two different approximations to the same real variable pair. The unified complex variable element-free Galerkin (UCVEFG) method for elasticity is established. Numerical examples are provided by applying UCVEFG method in three elastic problems to show its accuracy.
Uncertainty Propagation Analysis of Fuzzy Uncertain Structures Involving Imprecise Membership Functions
Authors: Lü Hui , Zheng Zebin , Huang Xiaoting , Shangguan Wen-Bin , Zhao Kegang
Published: July 2022

Uncertain structures may exhibit fuzzy uncertainty involving imprecise membership function (FuIMF). In this study, the uncertain parameters in FuIMF case are characterized as fuzzy variables, whereas the key parameters of their membership functions are treated as interval variables rather than exact values. Two ideas are put forward to handle FuIMF variables. First, the interval-boundary interval method (IBIM) is derived to conduct uncertainty propagation analysis, in which the [Formula: see text]-cut of FuIMF variables are considered as interval-boundary intervals. Second, the [Formula: see text]-cut of FuIMF variables are presented by the conservative and radical approximations, and the conservative and radical approximations method I (CRAM I) is proposed to conduct uncertainty propagation analysis. To further promote the computational efficiency, the conservative and radical approximations method II (CRAM II) is developed. Afterwards, a reference method based on Monte Carlo simulation is presented to verify the proposed methods. Finally, the effectiveness of proposed methods is demonstrated by numerical examples.
On self-duality and anti-self-duality of four-dimensional neutral Lie groups
Authors: Zaeim Amirhesam , Ghadimi Fatemeh , Chaichi Mohammad
Published: July 2022

Half conformally flat pseudo-Riemannian neutral Lie groups of dimension four are studied and examples of cyclic Lie groups as well as naturally reductive cases are completely classified.
Spectral analysis of Jacobi operators and asymptotic behavior of orthogonal polynomials
Authors: Yafaev D. R.
Published: July 2022

We find and discuss asymptotic formulas for orthonormal polynomials [Formula: see text] with recurrence coefficients [Formula: see text]. Our main goal is to consider the case where off-diagonal elements [Formula: see text] as [Formula: see text]. Formulas obtained are essentially different for relatively small and large diagonal elements [Formula: see text]. Our analysis is intimately linked with spectral theory of Jacobi operators [Formula: see text] with coefficients [Formula: see text] and a study of the corresponding second order difference equations. We introduce the Jost solutions [Formula: see text], [Formula: see text], of such equations by a condition for [Formula: see text] and suggest an Ansatz for them playing the role of the semiclassical Liouville–Green Ansatz for solutions of the Schrödinger equation. This allows us to study the spectral structure of Jacobi operators and their eigenfunctions [Formula: see text] by traditional methods of spectral theory developed for differential equations. In particular, we express all coefficients in asymptotic formulas for [Formula: see text] as [Formula: see text] in terms of the Wronskian of the solutions [Formula: see text] and [Formula: see text]. The formulas obtained for [Formula: see text] generalize the asymptotic formulas for the classical Hermite polynomials where [Formula: see text] and [Formula: see text]. The spectral structure of Jacobi operators [Formula: see text] depends crucially on a rate of growth of the off-diagonal elements [Formula: see text] as [Formula: see text]. If the Carleman condition is satisfied, which, roughly speaking, means that [Formula: see text], and the diagonal elements [Formula: see text] are small compared to [Formula: see text], then [Formula: see text] has the absolutely continuous spectrum covering the whole real axis. We obtain an expression for the corresponding spectral measure in terms of the boundary values [Formula: see text] of the Jost solutions. On the contrary, if the Carleman condition is violated, then the spectrum of [Formula: see text] is discrete. We also review the case of stabilizing recurrence coefficients when [Formula: see text] tend to a positive constant and [Formula: see text] as [Formula: see text]. It turns out that the cases of stabilizing and increasing recurrence coefficients can be treated in an essentially same way.
A New Theoretical Framework for Couple Stress Analysis of Reddy–Levinson Micro-Beams
Authors: Golbakhshi H. , Dashtbayazi M. R. , Saidi A. R.
Published: July 2022

In this paper a new scheme, other than the modified couple stress theory (M-CST) and consistent couple stress theory (C-CST), is proposed for the analysis of micro-beams and based on the main kinematical assumptions in the beam theories, the couple-stress tensor is constrained to be skew-symmetric. This new consideration also makes it possible to revise the governing equations and boundary conditions, derived from Hamilton’s principle into a more convenient form. Then according to the third-order Reddy–Levinson beam theory, a revised couple stress model similar to the classical Timoshenko beam theory is presented in the current study. The validity of the model is demonstrated by static bending and free vibration investigations of isotropic and FG porous micro-beams subjected to a concentrated load at the mid-span.
Study on the Differences of Transition and Separation Characteristics Between Water and Air
Authors: Peng Dewei , Xiao Jingping , Han Xiangdong , Li Yuhui
Published: July 2022

The change of flow state in the flow field significantly impacts the operational performance of fluid machinery equipment such as aircraft or underwater vehicles, among which transition and separation are essential links in a flow state change. Because the physical properties of water and air are very different, the transition and separation characteristics in water and air will change even for the same experimental object. Based on the general law of flow state change in the flow field, from the perspective of kinematics and dynamics and the difference in medium, the basic theoretical model of transition and separation is established via the employment of applied mathematics methods, the accuracy and applicability of the method are verified. The influence of water and air on the critical parameters of the flow state change in the flow field is analyzed, and the differences in transition and separation characteristics in different media are summarized. The research results could provide theoretical support to solve the water–air correlation study.
Dielectric spectroscopy of solid solutions based on sodium–potassium–cadmium in the temperature range (10 ÷ 900) K
Authors: Moysa M. O. , Andryushin K. P. , Pavlenko A. V. , Kubrin S. P. , Reznichenko L. A.
Published: July 2022

This paper studies the dielectric spectra of solid solutions (SS) of the system (1 − x − y)NaNbO3–[Formula: see text]KNbO3 –[Formula: see text]CdNb2O6 [Formula: see text]= 0.075, [Formula: see text]= 0.05 ÷ 0.30 in the temperature range (10 ÷ 900) K. The formation of a local maximum was established in the interval (260 ÷ 300) K at small [Formula: see text] values, which, as KNbO3 increases, is gradually blurred and becomes an inflection point. Detected in SS with [Formula: see text] = 0.05 ÷ 0.10, the shift of the maxima of dependences [Formula: see text]′/ [Formula: see text]0([Formula: see text]) and [Formula: see text]′′/[Formula: see text]0 ([Formula: see text]), depending on the frequency of the electric field at the temperature ranges (300 ÷ 304) K and (258 ÷ 271) K, is not related to relaxation. This anomaly may indicate a crystallographic disorder to A and B positions. The conclusion is made about the expediency of using the obtained results for the development of functional ferroactive materials.
New evidence of Neolithic industries from the West Garo Hills, northeastern India
Authors: Padhan Tosabanta
Published: July 2022

This paper is based on the field investigations conducted in the West Garo Hills of northeastern India. The work presents a brief report on the Neolithic manufacturing sites of the West Garo Hills. It addresses the various issues of lithic sequence, typological classifications, lithic technology, settlement pattern and site distribution pattern of the known sites and newly discovered sites in the West Garo Hills. Large clusters of Neolithic manufacturing sites have been located on the hilltops and hillslopes close to present-day rivers, streams, natural springs, water channels and modern Garo community habitats. Dolerite raw materials were mostly preferred for fashioning the artefacts, and manufacturing units are found close to the dyke basalt rock sources. Findings of habitation deposits or pottery are very sparse; however, evidence of plant and animal domestication, burial, bone tools, and metal use pose new hypotheses on their use owing to poor preservation. The Palaeolithic evidence and the region's transition to early farming cultures remain vague.
Transforming science through cyberinfrastructure
Authors: Parashar Manish , Friedlander Amy , Gianchandani Erwin , Martonosi Margaret
Published: July 2022

NSF's vision for the U.S. cyberinfrastructure ecosystem for science and engineering in the 21
st
century.
Defending against Adversarial Attacks in Deep Learning with Robust Auxiliary Classifiers Utilizing Bit-plane Slicing
Authors: Liu Yuan , Dong Jinxin , Zhou Pingqiang
Published: July 2022

Deep Neural Networks (DNNs) have been widely used in variety of fields with great success. However, recent research indicates that DNNs are susceptible to adversarial attacks, which can easily fool the well-trained DNN-based classifiers without being detected by human eyes. In this article, we propose to integrate the target DNN model with our robust bit-plane classifiers to defend against adversarial attacks. The bit-plane classifiers take bit-planes of input images for convolution, which is motivated by our observation that successful attacks aim to generate imperceptible perturbations, and they mainly affect the low-order bits of pixels in clean images when adding the perturbations. We also propose two metrics, bit-plane perturbation rate and channel modification rate, to further explain the robustness of bit-plane classifiers. We discuss potential adaptive attack and find that our defense can be effective as long as the adversarial examples are qualified. We conduct experiments on dataset CIFAR-10 and GTSRB under white-box attack and black-box attack. The results show that our defense method can effectively increase the average model accuracy from 16.23% to 83.53% under white-box attack and from 40.65% to 88.14% under black-box attack on CIFAR-10 without sacrificing the accuracy of clean images.
A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation and Comparison Study
Authors: Zhang Pengyi , Dou Huanzhang , Zhang Wenhu , Zhao Yuhan , Qin Zequn , Hu Dongping , Fang Yi , Li Xi
Published: July 2022

Gait recognition has a rapid development in recent years. However, current gait recognition focuses primarily on ideal laboratory scenes, leaving the gait in the wild unexplored. One of the main reasons is the difficulty of collecting in-the-wild gait datasets, which must ensure diversity of both intrinsic and extrinsic human gait factors. To remedy this problem, we propose to construct a large-scale gait dataset with the help of controllable computer simulation. In detail, to diversify the intrinsic factors of gait, we generate numerous characters with diverse attributes and associate them with various types of walking styles. To diversify the extrinsic factors of gait, we build a complicated scene with a dense camera layout. Then we design an automatic generation toolkit under Unity3D for simulating the walking scenarios and capturing the gait data. As a result, we obtain a dataset simulating towards the in-the-wild scenario, called VersatileGait, which has more than one million silhouette sequences of 10,000 subjects with diverse scenarios. VersatileGait possesses several nice properties, including huge dataset size, diverse pedestrian attributes, complicated camera layout, high-quality annotations, small domain gap with the real one, good scalability for new demands, and no privacy issues. By conducting a series of experiments, we first explore the effects of different factors on gait recognition. We further illustrate the effectiveness of using our dataset to pre-train models, which obtain considerable performance gain on CASIA-B, OU-MVLP, and CASIA-E. Besides, we show the great potential of the fine-grained labels other than the ID label in improving the efficiency and effectiveness of models. Our dataset and its corresponding generation toolkit are available at https://github.com/peterzpy/VersatileGait.
Formalizing fairness
Authors: Krakovsky Marina
Published: July 2022

Algorithmic fairness aims to remedy issues stemming from algorithmic bias.
hXDP
Authors: Brunella Marco Spaziani , Belocchi Giacomo , Bonola Marco , Pontarelli Salvatore , Siracusano Giuseppe , Bianchi Giuseppe , Cammarano Aniello , Palumbo Alessandro , Petrucci Luca , Bifulco Roberto
Published: July 2022

The network interface cards (NICs) of modern computers are changing to adapt to faster data rates and to help with the scaling issues of general-purpose CPU technologies. Among the ongoing innovations, the inclusion of programmable accelerators on the NIC's data path is particularly interesting, since it provides the opportunity to offload some of the CPU's network packet processing tasks to the accelerator. Given the strict latency constraints of packet processing tasks, accelerators are often implemented leveraging platforms such as Field-Programmable Gate Arrays (FPGAs). FPGAs can be re-programmed after deployment, to adapt to changing application requirements, and can achieve both high throughput and low latency when implementing packet processing tasks. However, they have limited resources that may need to be shared among diverse applications, and programming them is difficult and requires hardware design expertise.

We present hXDP, a solution to run on FPGAs software packet processing tasks described with the eBPF technology and targeting the Linux's eXpress Data Path. hXDP uses only a fraction of the available FPGA resources, while matching the performance of high-end CPUs. The iterative execution model of eBPF is not a good fit for FPGA accelerators. Nonetheless, we show that many of the instructions of an eBPF program can be compressed, parallelized, or completely removed, when targeting a purpose-built FPGA design, thereby significantly improving performance.

We implement hXDP on an FPGA NIC and evaluate it running real-world unmodified eBPF programs. Our implementation runs at 156.25MHz and uses about 15% of the FPGA resources. Despite these modest requirements, it can run dynamically loaded programs, achieves the packet processing throughput of a high-end CPU core, and provides a 10X lower packet forwarding latency.
Compositional Verification of Railway Interlocking Systems
Authors: Haxthausen Anne Elisabeth , Fantechi Alessandro
Published: July 2022

Model checking techniques have often been applied to the verification of railway interlocking systems, responsible for guiding trains safely through a given railway network. However, these techniques fail to scale to the interlocking systems controlling large stations, composed of hundreds and even thousands of controlled entities, due to the state space explosion problem. Indeed, interlocking systems exhibit a certain degree of locality that allows some reasoning only on the mere set of entities that regard the train movements, but safe routing through a complex station layout requires a global reservation policy, which can require global state conditions to be taken into account.

In this article, we present a compositional approach aimed at chopping the verification of a large interlocking system into that of smaller fragments, exploiting in each fragment a proper abstraction of the global information on routing state. A proof is given of the thesis that verifying the safety of the smaller fragments is sufficient to verify the safety of the whole network. Experiments using this compositional approach have shown important gains in performance of the verification, as well as in the size of affordable station layouts.
Aligning Image Semantics and Label Concepts for Image Multi-Label Classification
Authors: Zhou Wei , Xia Zhiwu , Dou Peng , Su Tao , Hu Haifeng
Published: July 2022

Image multi-label classification task is mainly to correctly predict multiple object categories in the images. To capture the correlation between labels, graph convolution network based methods have to manually count the label co-occurrence probability from training data to construct a pre-defined graph as the input of graph network, which is inflexible and may degrade model generalizability. Moreover, most of the current methods cannot effectively align the learned salient object features with the label concepts, so that the predicted results of model may not be consistent with the image content. Therefore, how to learn the salient semantic features of images and capture the correlation between labels, and then effectively align them is one of the key to improve the performance of image multi-label classification task. To this end, we propose a novel image multi-label classification framework which aims to align
I
mage
S
emantics with
L
abel
C
oncepts (
ISLC
). Specifically, we propose a residual encoder to learn salient object features in the images, and exploit the self-attention layer in aligned decoder to automatically capture the correlation between labels. Then, we leverage the cross-attention layers in aligned decoder to align image semantic features with label concepts, so as to make the labels predicted by model more consistent with image content. Finally, the output features of the last layer of residual encoder and aligned decoder are fused to obtain the final output feature for classification. The proposed ISLC model achieves good performance on various prevalent multi-label image datasets such as MS-COCO 2014, PASCAL VOC 2007, VG-500, and NUS-WIDE with 87.2%, 96.9%, 39.4%, and 64.2%, respectively.
Thermodynamic Analysis of the Hydrolysis of Borate-Based Lithium Salts by Density Functional Theory
Authors: Di Muzio Simone , Palumbo Oriele , Brutti Sergio , Paolone Annalisa
Published: July 2022

Here we discuss the thermodynamics of the hydrolysis of three borate-based lithium salts commonly used in aprotic electrolytes for lithium-ion batteries: lithium tetrafluoroborate (LiBF4), lithium difluoro(oxalate)borate (LiODBF) and lithium bis(oxalate)borate (LiBOB). We performed density functional theory calculations at the ωB97M-V/6–31++G** level to compute the thermodynamic stability of reagents, intermediate and products in solution phase. The variations of Gibbs free energy in heterogeneous reactions were evaluated by coupling DFT values with thermochemical cycles. LiBF4 and LiODBF can be easily hydrolyzed by the direct reaction with water at room temperature: the salts degradation is driven by the precipitation of LiF(s) and by the release of HF. On the contrary, LiBOB is much more stable and only weakly reacts with water: it is therefore more resilient to hydrolyzation and therefore suitable for application in Li-ion battery electrolytes.
Perspective—Recent Advances and Thoughts on Ceria Particle Applications in Chemical Mechanical Planarization
Authors: Seo Jihoon , Kim Kijung , Kang Hyungoo , Babu S.V.
Published: July 2022

Along with the remarkable growth in the complexity of semiconductor fabrication technology, chemical mechanical planarization (CMP) has evolved and become progressively more sophisticated over the years, enabling the implementation of novel integration schemes. This paper discusses current research and development trends in one specific aspect of the CMP technology, namely, ceria particle usage for advanced technology nodes and provides some perspectives on how to improve CMP performance metrics of the current ceria abrasives and ceria-based CMP slurries and move forward to the next phase.
Small airways dysfunction is associated with increased exacerbations in patients with asthma
Authors: O’Sullivan Claire F. , Nilsen Kris , Borg Brigitte , Ellis Matthew , Matsas Pam , Thien Frank , Douglass Jo A. , Stuart-Andrews Chris , King Gregory G. , Prisk G. Kim , Thompson Bruce R.
Published: July 2022

This study assessed the relationship between peripheral airway function, measured by multiple breath nitrogen washout and oscillometry impedance, and exacerbation history. We found that those with a history of exacerbation in the last year had worse peripheral airway function, whereas those recently treated for an asthma exacerbation had peripheral airway function that was comparable to the stable group. These findings implicate active peripheral airway dysfunction in the pathophysiology of an asthma exacerbation.
Dose-dependent phosphorylation of endogenous Tau by intermittent hypoxia in rat brain
Authors: Marciante Alexandria B. , Howard John , Kelly Mia N. , Santiago Moreno Juan , Allen Latoya L. , Gonzalez-Rothi Elisa J. , Mitchell Gordon S.
Published: July 2022

Sleep apnea is highly prevalent in people with Alzheimer’s disease, suggesting the potential to accelerate disease onset and/or progression. These studies demonstrate that intermittent hypoxia (IH) induces dose-dependent, region-specific Tau phosphorylation, and are the first to indicate that higher IH “doses” elicit both endogenous, (rat) Tau hyperphosphorylation and accumulation in the hippocampus. These findings are essential for development and implementation of new treatment strategies that minimize sleep apnea and its adverse impact on neurodegenerative diseases.
Case Studies in Physiology: Training adaptation in an elite athlete after breast cancer diagnosis
Authors: Pérez-Bilbao Txomin , Alonso-Dueñas María , San Juan Alejandro F. , Bezares Susana , Gil Herrero Lucía , Pollán Marina , González-Frutos Pablo , Peinado Ana B. , Casla-Barrio Soraya
Published: July 2022

This case study follows an elite athlete and measures her performance during cancer treatment. It improves the knowledge on applied physiology showing the details of her training program and demonstrating the strong ability of the athlete to continue training and competing at a high level during antineoplastic treatment. Exercise training improved body composition but failed to restore previous cardiorespiratory, muscle strength and power, and running performance levels.
Noninvasive indicators of intracranial pressure before, during, and after long-duration spaceflight
Authors: Jasien Jessica V. , Laurie Steven S. , Lee Stuart M. C. , Martin David S. , Kemp David T. , Ebert Douglas J. , Ploutz-Snyder Robert , Marshall-Goebel Karina , Alferova Irina V. , Sargsyan Ashot , Danielson Richard W. , Hargens Alan R. , Dulchavsky Scott A. , Stenger Michael B. , Macias Brandon R.
Published: July 2022

The current study provides new evidence that intracranial pressure (ICP), as assessed with noninvasive measures, may not be elevated during long-duration spaceflight. In addition, the acute use of lower body negative pressure did not significantly reduce indicators of ICP during weightlessness.
Privacy-Preserving Outsourced Logistic Regression on Encrypted Data from Homomorphic Encryption
Authors: Yu Xiaopeng , Zhao Wei , Huang Yunfan , Ren Juan , Tang Dianhua
Published: July 2022

Logistic regression is a data statistical technique, which is used to predict the probability that an event occurs. For some scenarios where the storage capabilities and computing resources of the data owner are limited, the data owner wants to train the logistic regression model on the cloud service provider, while the high sensitivity of training data requires effective privacy protection methods that enable efficient model training without exposing information about the training data to untrusted cloud service providers. Recently, several works have used cryptographic techniques to implement privacy-preserving logistic regression in such application scenarios. However, on large-scale training datasets, the existing works still have the problems of long model training time and poor model performance. To solve these problems, based on the homomorphic encryption (HE), we propose an efficient privacy-preserving outsourced logistic regression (P2OLR) on encrypted training data, which enables data owners to utilize the powerful storage and computing resources of cloud service providers for logistic regression analysis without exposing data privacy. Furthermore, the proposed scheme can pack multiple messages into one ciphertext and perform the same arithmetic evaluations on multiple plaintext slots by using the batching technique and single instruction multiple data (SIMD) mechanism in HE. On three public training datasets, the experimental results show that, compared with the existing schemes, the proposed scheme has better performance in terms of the encryption and decryption time of the data owner, the storage of encrypted training data, and the training time and accuracy of the model.
Prediction and Risk Assessment of Extreme Weather Events Based on Gumbel Copula Function
Authors: Yang Peng-Hui , Yu Yao , Gu Feng , Qu Meng-Jie , Zhu Jia-Ming
Published: July 2022

Damage caused by climate catastrophes is severe, especially for the 1-in-100-year events. This study is aimed at assessing the frequency and spatiotemporal regularity of extreme weather events. Based on the selected Gumbel copula function, a joint trivariate distribution of weather events is established. In this study, different univariate return periods and return periods of the joint trivariate distribution are calculated separately. Second, the Moran index is used to determine whether there is a spatial correlation between weather events. In this paper, the spatial and temporal patterns of weather events are determined based on a geographically weighted regression model. The suggestion of adding Bayesian information to the model measurements to improve the model accuracy is presented. Finally, a wavelet neural network model is constructed to predict the probability of extreme weather events throughout the Americas.
Rural Tourism Resource Research Based on Multisensor and Geographic Information Big Data
Authors: Wang Lixia
Published: July 2022

With the continuous improvement of living standards, the rapid development of tourism, and increasing maturity of traveler behavior, tourism has become an essential part of people’s daily life. Also, in recent years, with the continued development of economic and global integration, coordinated development has become a global trend. After more than 30 years of development and exploration, rural tourism in China is increasingly diversified. The accelerated development of a new socialist countryside and the integrated development of urban and rural areas provide important options for urban residents to escape from their habitual environment and seek leisure, comfort, and relaxation. Meanwhile, rural tourism development is based on certain resources and has a great coupling relationship with China’s urban and rural development. As a result, rural tourism has long become a hot issue for academic research. However, with the continuous promotion of rural revitalization, rural tourism in China has presented some serious problems such as resource overdraft. These issues have caused serious harm to China’s rural tourism resources and cultural heritage. On this basis, it is necessary to view rural tourism development from the perspective of coordinated regional development. Specifically, rural tourism resources should be characterized through a method of character selection to sort out rural development paths. At the same time, the development of regional tourism linkages for villages with shared source markets, dense location distribution, and similar landscape resources can effectively avoid regional homogeneous competition, form complementary advantages, and achieve sustainable development of regional economy, culture, and society. In addition, with the continuous development of information technology and computer technology in recent years, the construction of digital countryside has been completed. In other words, the conditions are in place to establish a rural tourism resource information platform that combines tourism information database and geospatial framework. The establishment of rural tourism resources information system will meet the information requirements of tourists and tourism management departments and lay the foundation of information technology for the development of rural tourism. Therefore, this study combines multisensor and geographic information big data technology to explore rural tourism resources. After that, based on the analysis of the evaluation results, specific regional overall linkage development strategies are proposed in four aspects: coordinating regional resources, optimizing resource allocation, integrating spatial types, and establishing regional brands. This can provide an effective strategic support for the diversified development direction of rural tourism.