bluuebunny/crossref_metadata_2025 huggingface dataset:
Approximation Algorithms for Submodular Data Summarization with a Knapsack Constraint
Authors: Han Kai , Cui Shuang , Zhu Tianshuai , Zhang Enpei , Wu Benwei , Yin Zhizhuo , Xu Tong , Tang Shaojie , Huang He
Published: June 2022

Data summarization, a fundamental methodology aimed at selecting a representative subset of data elements from a large pool of ground data, has found numerous applications in big data processing, such as social network analysis [5, 7], crowdsourcing [6], clustering [4], network design [13], and document/corpus summarization [14]. Moreover, it is well acknowledged that the "representativeness" of a dataset in data summarization applications can often be modeled by submodularity - a mathematical concept abstracting the "diminishing returns" property in the real world. Therefore, a lot of studies have cast data summarization as a submodular function maximization problem (e.g., [2]).
Input-Dynamic Distributed Algorithms for Communication Networks
Authors: Foerster Klaus-Tycho , Korhonen Janne H. , Paz Ami , Rybicki Joel , Schmid Stefan
Published: June 2022

Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner?

To address this question, we define the batch dynamic CONGEST model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labelled graph under batch changes. We investigate, when a batch of alpha edge label changes arrive, - how much time as a function of alpha we need to update an existing solution, and - how much information the nodes have to keep in local memory between batches in order to update the solution quickly.

Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. The diverse time complexity of our model spans from constant time, through time polynomial in alpha, and to alpha time, which we show to be enough for any task.
Achievable Stability in Redundancy Systems
Authors: Raaijmakers Youri , Borst Sem
Published: June 2022

We investigate the achievable stability region for redundancy systems and a quite general workload model with different job types and heterogeneous servers, reflecting job-server affinity relations which may arise from data locality issues and soft compatibility constraints. Under the assumption that job types are known beforehand we establish for New-Better-than-Used (NBU) distributed speed variations that no replication gives a strictly larger stability region than replication. Strikingly, this does not depend on the underlying distribution of the intrinsic job sizes, but observing the job types is essential for this statement to hold. In case of non-observable job types we show that for New-Worse-than-Used (NWU) distributed speed variations full replication gives a larger stability region than no replication.
Dynamic Pricing and Matching for Two-Sided Markets with Strategic Servers
Authors: Varma Sushil Mahavir , Castro Francisco , Maguluri Siva Theja
Published: June 2022

Motivated by applications in online marketplaces such as ridesharing, we study dynamic pricing and matching in two-sided queues with strategic servers. We consider a discrete-time process in which, heterogeneous customers and servers arrive. Each customer joins their type's queue, while servers might join a different type's queue depending on the prices posted by the system operator and an inconvenience cost. Then the system operator, constrained by a compatibility graph, decides the matching. The objective is to maximize the profit minus the expected waiting times. We develop a general framework that enables us to analyze a broad range of strategic behaviors. In particular, we encode servers' behavior in a properly defined cost function that can be tailored to various settings. Using this general cost function, we introduce a novel probabilistic fluid problem as an infinite dimensional optimization program. The probabilistic fluid model provides an upper bound on the achievable profit. We then study the system under a large market regime in which the arrival rates are scaled by η and present a probabilistic two-price policy and a max-weight matching policy which results in $O(η^1/3 )$ profit-loss. In addition, under a broad class of customer pricing policies, we show that any matching policy has profit-loss Ω(η1/3). Conditional on a given expected waiting time, we also establish scale-free lower and upper bounds for the profit. Our asymptotic analysis provides insight into near-optimal pricing and matching decisions, and our scale-free bounds provide insights into how different service levels impact the firm's profit.
Stay Connected, Leave no Trace: Enhancing Security and Privacy in WiFi via Obfuscating Radiometric Fingerprints
Authors: Abanto-Leon Luis F. , Bäuml Andreas , Sim Gek Hong (Allyson) , Hollick Matthias , Asadi Arash
Published: June 2022

The intrinsic hardware imperfection of WiFi chipsets manifests itself in the transmitted signal, leading to a unique radiometric (radio frequency) fingerprint. This fingerprint can be used as an additional means of authentication to enhance security. In this paper, we prove analytically and experimentally that these solutions are highly vulnerable to impersonation attacks. We also demonstrate that such a unique device-specific signature can be abused to track devices, thus violating privacy. We propose RF-Veil, a radiometric fingerprinting solution that is not only robust against impersonation attacks but also effective in protecting privacy by obfuscating the radiometric fingerprint of the transmitter for non-legitimate receivers. Specifically, we introduce a randomized pattern of phase errors to the transmitted signal such that only the intended receiver can extract the original fingerprint of the transmitter. In a series of experiments and analyses, we expose the vulnerability of adopting naive randomization to statistical attacks and introduce countermeasures. Finally, we show the efficacy of RF-Veil experimentally in protecting user privacy and enhancing security. More importantly, our proposed solution allows communicating with other devices, which do not employ RF-Veil.
The Gittins Policy is Nearly Optimal in the M/G/k under Extremely General Conditions
Authors: Scully Ziv , Grosof Isaac , Harchol-Balter Mor
Published: June 2022

The Gittins scheduling policy minimizes the mean response in the single-server M/G/1 queue in a wide variety of settings. Most famously, Gittins is optimal when preemption is allowed and service requirements are unknown but drawn from a known distribution. Gittins is also optimal much more generally, adapting to any amount of available information and any preemption restrictions. However, scheduling to minimize mean response time in a multiserver setting, specifically the central-queue M/G/k, is a much more difficult problem.

In this work we give the first general analysis of Gittins in the M/G/k. Specifically, we show that under extremely general conditions, Gittins's mean response time in the M/G/k is at most its mean response time in the M/G/1 plus an O(log(1/(1 - ρ))) additive term, where ρ is the system load. A consequence of this result is that Gittins is heavy-traffic optimal in the M/G/k if the service requirement distribution S satisfies E[S2(log S)+] < ∞. This is the most general result on minimizing mean response time in the M/G/k to date.

To prove our results, we combine properties of the Gittins policy and Palm calculus in a novel way. Notably, our technique overcomes the limitations of tagged job methods that were used in prior scheduling analyses.
Tracking Counterfeit Cryptocurrency End-to-end
Authors: Gao Bingyu , Wang Haoyu , Xia Pengcheng , Wu Siwei , Zhou Yajin , Luo Xiapu , Tyson Gareth
Published: June 2022

With the growth of the cryptocurrency ecosystem, there is expanding evidence that counterfeit cryptocurrency has also appeared. In this paper, we empirically explore the presence of counterfeit cryptocurrencies on Ethereum and measure their impact. By analyzing over 190K ERC-20 tokens (or cryptocurrencies) on Ethereum, we have identified 2,117 counterfeit tokens that target 94 of the 100 most popular cryptocurrencies. We perform an end-to-end characterization of the counterfeit token ecosystem, including their popularity, creators and holders, fraudulent behaviors and advertising channels. Through this, we have identified two types of scams related to counterfeit tokens and devised techniques to identify such scams. We observe that over 7,104 victims were deceived in these scams, and the overall financial loss sums to a minimum of $17 million (74,271.7 ETH). Our findings demonstrate the urgency to identify counterfeit cryptocurrencies and mitigate this threat.
Information Aggregation for Constrained Online Control
Authors: Li Tongxin , Chen Yue , Sun Bo , Wierman Adam , Low Steven
Published: June 2022

We consider a two-controller online control problem where a central controller chooses an action from a feasible set that is determined by time-varying and coupling constraints, which depend on all past actions and states. The central controller's goal is to minimize the cumulative cost; however, the controller has access to neither the feasible set nor the dynamics directly, which are determined by a remote local controller. Instead, the central controller receives only an aggregate summary of the feasibility information from the local controller, which does not know the system costs. We show that it is possible for an online algorithm using feasibility information to nearly match the dynamic regret of an online algorithm using perfect information whenever the feasible sets satisfy a causal invariance criterion and there is a sufficiently large prediction window size. To do so, we use a form of feasibility aggregation based on entropic maximization in combination with a novel online algorithm, named Penalized Predictive Control (PPC).
The Importance of Food Processing and Eating Behavior in Promoting Healthy and Sustainable Diets
Authors: Forde Ciarán G. , Decker Eric A.
Published: June 2022

Numerous association studies and findings from a controlled feeding trial have led to the suggestion that “processed” foods are bad for health. Processing technologies and food formulation are essential for food preservation and provide access to safe, nutritious, affordable, appealing and sustainable foods for millions globally. However, food processing at any level can also cause negative health consequences that result from thermal destruction of vitamins; formation of toxins such as acrylamide; or excessive intakes of salt, sugar, and fat. Research on ultraprocessed foods centers on food composition and formulation. In addition, many modern food formulations can have poor nutritional quality and higher energy density. We outline the role of processing in the provision of a safe and secure food supply and explore the characteristics of processed foods that promote greater energy intake. Despite the potential for negative health effects, food processing and formulation represent an opportunity to apply the latest developments in technology and ingredient innovation to improve the food supply by creating foods that decrease the risk of overeating.
Bile acid-induced lung injury: update of reverse translational biology
Authors: De Luca Daniele , Alonso Alejandro , Autilio Chiara
Published: June 2022

The presence of bile acids in lung tissue is associated with some clinical features observed in various medical specialties, but it took time to understand that these are due to a “bile acid-induced lung injury” since specific translational studies and cross-disciplinary awareness were lacking. We used a reverse translational approach to update and summarize the current knowledge about the mechanisms of bile acid-induced lung injury. This has been done in a cross-disciplinary fashion since these conditions may occur in patients of various ages and in different medical fields. We here define these clinical conditions, then we review the physiopathology of these conditions and the animal models used to mimic them, and, finally, their pathobiology. Mechanisms of bile acid-induced lung injury have been partially clarified over time and are represented by 1) the interaction with secretory phospholipase A2 pathway, 2) the effect on surfactant function and structure, 3) the biological effects on inflammation and local immunity, and 4) the direct cellular toxicity. These mechanisms are schematically illustrated and histological comparisons between acute respiratory distress syndrome (ARDS) induced by bile acids and other triggers are also provided. Based on these mechanisms, we propose possible direct therapeutic applications and, finally, we discuss further research steps to improve the understanding of processes that generate pathological clinical conditions.
Direct advice for directing an academic biomedical research laboratory
Authors: Seals Douglas R.
Published: June 2022

This Perspective shares “insider” thoughts and recommendations for success with early-career stage scientists on directing an independent biomedical research laboratory.
Metabolic consequences of PGC-1α dysregulation in adult zebrafish muscle
Authors: Kurchaba Nicholas , Charette J. Michael , LeMoine Christophe M. R.
Published: June 2022

The peroxisome proliferator-activated receptor γ coactivator 1 α (PGC-1α) is central to the regulation of cellular and mitochondrial energy homeostasis in mammals, but its role in other vertebrates remains unclear. Indeed, previous work suggests extensive structural and functional divergence of PGC-1α in teleosts but this remains to be directly tested. Here, we describe the initial characterization of heterozygous PGC-1α mutant zebrafish lines created by CRISPR-Cas9 disruptions of an evolutionarily conserved regulatory region of the PGC-1α proximal promoter. Using qPCR, we confirmed the disruption of PGC-1α gene expression in striated muscle, leading to a simultaneous fourfold increase in mixed skeletal muscle PGC-1α mRNA levels and an opposite fourfold downregulation in cardiac muscle. In mixed skeletal muscle, most downstream effector genes were largely unaffected yet two mitochondrial lipid transporters, carnitine palmitoyltransferase-1 and -2, were strongly induced. Conversely, PGC-1α depression in cardiac muscle reduced the expression of several transcriptional regulators (estrogen-related receptor α, nuclear respiratory factor 1, and PGC-1β) without altering metabolic gene expression. Using high-resolution respirometry, we determined that white muscle exhibited increased lipid oxidative capacity with little difference in markers of mitochondrial abundance. Finally, using whole animal intermittent respirometry, we show that mutant fish exhibit a twofold higher basal metabolism than their wild-type counterparts. Altogether, this new model confirms a central but complex role for PGC-1α in mediating energy utilization in zebrafish, and we propose its use as a valuable tool to explore the intricate regulatory pathways of energy homeostasis in a popular biomedical model.
Evaluation of Magnetic Resonance Imaging for Microsurgical Efficacy and Relapse of Rolandic Meningioma
Authors: Cao Peng , Wang Nianhua
Published: June 2022

In this study, magnetic resonance imaging (MRI) was used to evaluate the relapse features of patients with Rolandic meningioma after the microsurgery. 53 patients with Rolandic meningioma were selected as the research objects, and they were divided into the relapse group (n = 16) and nonrelapse group (n = 37) according to whether patients had a relapse during the follow-up period. Differences in quality of life, 1H-MRS index, vascular density, and cell proliferation between the two groups were assessed as well as imaging differences between the two groups were analyzed using MRI. The results showed that the patients’ quality-of-life scores in the two groups increased notably after the surgical treatment (P < 0.05). Compared with the nonrelapse group, the proportion of irregular boundary and uneven enhancement of focal tissue in the relapse group was signally increased (P < 0.05). Compared with the nonrelapse group, cell proliferation index, vascular density and imaging index, mean tumor diameter, mean transit time (MTT), time to peak (TTP), fractional anisotropy (FA), choline (Cho)/N-acetylaspartic acid (NAA), Cho/creatine (Cr), lactic acid (Lac)/Cr, and the maximum value of relative cerebral blood volume (rCBVmax) in the relapse group were obviously increased (P < 0.05). However, the apparent dispersion coefficient, NAA/Cr, and Lac/NAA values decreased greatly (P < 0.05). To sum up, the microsurgical treatment helped improve the quality of life of patients with Rolandic meningioma, and MR imaging could be used to determine the relapse of Rolandic meningioma after microsurgical treatment.
Neuroinflammation in Low-Level PM2.5-Exposed Rats Illustrated by PET via an Improved Automated Produced [18F]FEPPA: A Feasibility Study
Authors: Cheng Mei-Fang , Cheng Tsun-Jen , Guo Yue Leon , Chiu Ching-Hung , Wu Hung-Ming , Yen Ruoh-Fang , Huang Ya-Yao , Huang Wen-Sheng , Shiue Chyng-Yann
Published: June 2022

Background. [18F]FEPPA is a potent TSPO imaging agent that has been found to be a potential tracer for imaging neuroinflammation. In order to fulfill the demand of this tracer for preclinical and clinical studies, we have developed a one-pot automated synthesis with simplified HPLC purification of this tracer, which was then used for PET imaging of neuroinflammation in fine particulate matter- (PM2.5-) exposed rats. Results. Using this automated synthesis method, the RCY of the [18F]FEPPA was [Formula: see text] ([Formula: see text], EOB) in a synthesis time of [Formula: see text] min from EOB. The radiochemical purity and molar activities were greater than 99% and [Formula: see text] GBq/ μmol (EOS, [Formula: see text]), respectively. The quality of the [18F]FEPPA synthesized by this method met the U.S. Pharmacopoeia (USP) criteria. The stability test showed that the [18F]FEPPA was stable at [Formula: see text]°C for up to 4 hr after the end of synthesis (EOS). Moreover, microPET imaging showed that increased tracer activity of [18F]FEPPA in the brain of PM2.5-exposed rats ([Formula: see text]) were higher than that of normal controls ([Formula: see text]) and regional-specific. Conclusions. Using the improved semipreparative HPLC purification, [18F]FEPPA has been produced in high quantity, high quality, and high reproducibility and, for the first time, used for PET imaging the effects of PM2.5 in the rat brain. It is ready to be used for imaging inflammation in various clinical or preclinical studies, especially for nearby PET centers without cyclotrons.
Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm
Authors: Ajay P. , Nagaraj B. , Kumar R. Arun , Huang Ruihang , Ananthi P.
Published: June 2022

Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using

k

-means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms’ capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Supervised learning needs a lot of data. Deep learning is vital in modern AI. Supervised learning requires a large labeled dataset. The selection of parameters prevents over- or underfitting. Unsupervised learning is used to overcome the challenges outlined above (performed by the clustering algorithm). To accomplish this, two processing processes were used: (1) utilizing nonlinear deep learning networks to turn data into a latent feature space (

Z

). The Kullback–Leibler divergence is used to test the objective function convergence. This article explores a novel research on hyperspectral microscopic picture using deep learning and effective unsupervised learning.
Evaluation and Comparison of Mucormycosis Patients’ Features Undergoing Functional Endoscopic Sinus Surgery Prior to and during the COVID-19 Pandemic: A Case-Control Study
Authors: Dehghanpisheh Laleh , Eghbal Mohammadhossein , Salari Mehrdad , Shahriarirad Reza , Borzou Niloofar , Vatankhah Pooya
Published: June 2022

Background. Rhinocerebral mucormycosis is a serious invasive fungal infection that is one of the most aggressive and lethal of invasive mycoses. The coronavirus disease (COVID-19) has been linked to immune dysregulation, and patients with COVID-19 have been reported to be at risk for developing invasive fungal infections. This study is conducted to evaluate the concurrence of mucormycosis among COVID-19 patients. Methods. In this retrospective, cross-sectional study, hospital records of patients with mucormycosis, as well as COVID-19 admitted to Khalili Hospital, as the major referral center for functional endoscopic sinus surgery (FESS) in southern Iran, were collected. Demographic and clinical information was extracted and subsequently analyzed. Results. Among 59 mucormycosis patients undergoing FESS, 41 (69.5%) were during the COVID-19 pandemic, while 18 (30.5%) were during one year before the COVID-19 pandemic. The average age was 49.33 ± 20.52, and 64.4% had diabetes mellitus, while 62.7% had COVID-19. The most common presentation was periorbital edema (56.9%), followed by necrotic tissue (48.3%). Although the total number of cases increased during the COVID-19 period compared to the case before the pandemic, the overall pattern and features of the patients had no significant difference, except regarding a significant increase in the presentation of necrotic tissue and also the use of corticosteroids. Most cases developed mucormycosis two weeks after COVID-19. The overall mortality was 36.8%, which is not statistically associated with COVID-19. Conclusion. Even in the absence of comorbidities, physicians should be aware of the risk of secondary fungal infections in patients with COVID-19 who were treated with corticosteroids.
Development and Application of an Integrated Monitoring and Early Warning Platform for Land Transportation Infrastructure under Special Engineering Conditions
Authors: Zhang Liujun , Qiu Youqiang , Yu Hui , Yin Lihua , Zhang Faru , Wu Tong
Published: June 2022

To ensure the safety service performance of land transportation infrastructure under special engineering conditions, centering on the characteristics of structures such as the road high-steep slope, high embankment, special subgrade, railway track, and airport pavement, an integrated monitoring and early warning platform based on health monitoring and safety evaluation was built through the combined design of the safety indicator module, monitoring data module, information processing module, numerical simulation module, inversion prediction analysis module, and safety early warning module. The data interaction between the integrated platform and five monitoring subplatforms was realized by the B/S mode, and the related function interfaces were established. The research results show that as the monitoring data storage analysis and early warning carrier, the integrated monitoring and early warning platform not only presents key data such as comprehensive early warning information, evaluation indicators, and monitoring elements but also realizes the intelligent, automatic, and visual presentation of monitoring data. In addition, relying on the high-steep slope of the Hohhot-Bikeqi Highway (section: K528+215~K528+525), through the application of the high-steep slope monitoring and early warning subplatform system in the practical project, it is verified that the platform has a good monitoring and early warning effect, which provides a reliable platform guarantee for the health monitoring and safety operation of the project.
Advanced FMECA Method Based on Intuitionistic 2-Tuple Linguistic Variables and the Triangular Fuzzy Analytic Hierarchy Process
Authors: Pan Guangze , Li Dan , Li Qian , Li Yaqiu , Wang Yuanhang
Published: June 2022

Failure mode effects and criticality analysis (FMECA) is a commonly adopted approach to defining, assessing, and reducing possible failures in designs, systems, processes, products, and services. Traditional FMECA ranks the failure modes of products based on a risk priority number (RPN), which is obtained by multiplying the risk elements. Conventional FMECA has the shortcomings of badly handling unknown information and unreasonably assessing RPNs. To deal with these issues, an advanced FMECA method based on intuitionistic 2-tuple linguistic variables (I2LVs) and the triangular fuzzy analytic hierarchy process (TFAHP) is proposed. In this method, the fuzzy evaluation of risk elements given by different FMECA members is represented by I2LVs, which can efficiently handle unknown information. The TFAHP method is adopted to assess the weights of risky elements and rank the risk priorities of different failure modes. Finally, an application case of an insulated-gate bipolar transistor is used to verify the effectiveness and robustness of the proposed method.
Uncertainty Evaluation of Stochastic Structural Response with Correlated Random Variables
Authors: Fu Qiang , Liu Jianjun , Shi Jiarui , Li Xiao , Cai Xueji , Meng Zilong
Published: June 2022

It has been realized that the influence of system parameter uncertainties may be very significant, even dominant, in stochastic response evaluation. Nevertheless, in reality, this evaluation process may be difficult to conduct due to these parameter variables (viz. structural property parameters, such as stiffness, damping, and strength, and excitation characteristics parameters, such as frequency content and duration) that are usually correlated with each other. Therefore, this study devotes to develop a method for evaluating stochastic response uncertainty involving correlated system parameter variables. In this method, the evaluation expression for the mean and standard deviation of the maximum response including uncertainty parameter variables are provided first; subsequently, a third-moment pseudo-correlation normal transformation is able to be performed for converting the correlated and non-normal system parameter variables with unknown joint probability density function (PDF) or marginal PDF into the mutually independent standard normal ones; ultimately, a point estimate procedure (PEP) based on univariate dimension reduction integration can be carried out for evaluating the structural stochastic response including uncertainty system parameters. Several numerical examples with an engineering background involving correlated system parameter variables are analyzed and discussed under stochastic excitation, and their results are compared with those yielded by Monte Carlo simulation (MCS) so as to demonstrate the effectiveness of the approach proposed. It indicated that the method proposed, in this study, provides an effective path to deal with uncertainty evaluation of stochastic structural response involving correlated random variables.
Quantitative and Fiber-Selective Evaluation for Central Poststroke Pain
Authors: Chen Jian-Min , Chen Qing-Fa , Wang Zhi-Yong , Ni Guo-Xin
Published: June 2022

The electrophysiological recording can be used to quantify the clinical features of central poststroke pain (CPSP) caused by different lesion locations. We aimed to explore the relationship between clinical features and lesion location in patients with CPSP using the current perception threshold (CPT) approach. Here, patients underwent the standardized CPT measure at five detection sites on both the contralesional and ipsilesional sides, using a constant alternating-current sinusoid waveform stimulus at three frequencies: 2000 Hz, 250 Hz, and 5 Hz. 57 CPSP patients were recruited in this cross-sectional study, including 13 patients with thalamic lesions and 44 patients with internal capsule lesions. Patients with a thalamic lesion had more frequent abnormal Aδ and C fibers than those with an internal capsule lesion (69.2% versus 36.4%,

p

value = 0.038; 53.8% versus 63.6%,

p

value = 0.038). The patients with internal capsule lesions had more frequent abnormal Aβ fibers than those with thalamic lesions (53.8% versus 63.6%,

p

value < 0.001). The sensory dysfunction in the patients with thalamic lesions was more likely to occur in the upper limbs (i.e., the shoulder (

p

value = 0.027) and the finger (

p

value = 0.040)). The lower limbs (i.e., the knee (

p

value = 0.040) and the toe (

p

value = 0.005)) were more likely to experience sensory dysfunction in the patients with internal capsule lesions. Hyperesthesia was more likely to occur in the thalamic patients, and hypoesthesia was more likely to occur in the patients with internal capsule lesions (

p

value < 0.001). In patients with thalamic lesions, Visual Analogue Scale (VAS) had a positive correlation with 5 Hz CPT on the shoulder (

r
=
0.010

,

p

value = 0.005), 250 Hz CPT on the finger (

r
=
0.690

,

p

value = 0.009) from the contralesional side, and 2000 Hz CPT on the knee (

r
=
0.690

,

p

value = 0.009). In patients with internal capsule lesions, VAS had a positive correlation with 2000 Hz CPT on the knee (

r
=
0.312

,

p

value = 0.039) and foot (

r
=
0.538

,

p

value < 0.001). In conclusion, the abnormal fiber types, sensory dysfunction territory, and clinical signs of CPSP in thalamic stroke differ from those in internal capsule stroke. Implementation of the portable and convenient CPT protocol may help clarify the locations of different stroke lesions in various clinical settings.
Monitoring of Nitrogen Transport Data in Pear Leaves Based on Infrared Spectroscopy
Authors: Fan Zehua , Wang Desheng , Zhang Nannan
Published: June 2022

In order to better monitor the data of nitrogen transport in pear leaves, a method based on infrared spectroscopy was proposed. The near-infrared reflection spectrum imaging technology is used to collect the leaf scale spectral image of the target crop. Computer image analysis software is used to process the spectral digital image and extract the spectral data. After statistical analysis, the data are selected as variables. Combined with the chemical analysis test results, the crop nutrition detection model is established, and the conclusion is drawn. The experimental results show that the band gray data involved in the model are scaled and reorganized according to the coefficient proportion by using ENVI through the band calculation command. The final gray image, the original image, and the gray image in the process default to the three-channel analog image of the band (the wavelengths of the bands are 1446, 1373, and 1304 nm, respectively); 944 nm gray image; 1043 nm gray scale image; 1662 nm gray image; (0.102R944 +0.103R1 043 +0.206R1662)/(0.102 + 0.103 + 0.206) grayscale image with signal scaling according to the scale of model coefficient. It is proved that infrared spectroscopy can effectively monitor the data of nitrogen transport in pear leaves.
A Structural Design of a Child Seat Based on Morphological Elements and Ergonomics
Authors: Jiang Xiaoshuo , Meng Xiangjia
Published: June 2022

In order to improve the safety and comfort of child seat structural design, this paper combines ergonomics and morphological elements to analyze the structural design of child seat and establish a crash model. Moreover, the obtained kinematic response and injury curves are compared with the corresponding actual tested kinematic response and injury parameters to analyze the biomechanics of child occupant injury, the injury characteristics of child occupants in frontal and side collisions, and the evaluation criteria for head, neck, and chest injuries. In addition, this paper combines the intelligent design method to design a safety seat that meets the needs of children. The results show that the structural design method of child seat based on morphological elements and ergonomics proposed in this paper can play an important role in the design of child seat.
The Relevance of Microblog Sentiment Analysis on College Students' Growth Development and Management
Authors: Wang Lan
Published: June 2022

Most of the existing studies focus on the analysis of college student users of blogs, virtual communities, and other online application platforms, while the research on the motivation of micro-blogging college student users focuses on the research of usage motivation. In this paper, we established a multilevel psychological early warning model based on personality, mood, and emotion space and mapped personality, mood, and emotion to accurately simulate the law of human emotion change for the relevance of micro-blog sentiment analysis to college students' growth development and management. The experiment shows that the method effectively realizes the analysis and description of micro-blog emotion; finally, the designed micro-blog emotion early warning system realizes the visual analysis and timely warning of the psychological condition of the observed subjects, which verifies the effectiveness of the research method.
Analysis on the Adjustment of Marine Economic Industrial Structure Based on the Least Square Regression Model
Authors: Ma Zhen
Published: June 2022

In order to intuitively analyze and clearly understand the effect of industrial structure adjustment of the marine economy in recent years in China, modeling and empirical research on industrial structure adjustment of the marine economy are carried out. Three comparisons of marine economic industrial structure and per capita output value coefficient of the marine industry are selected as scientific measurement indexes, the ratio of regional entropy to the output value of marine scientific research management service industry is selected as the height analysis index, and the econometric model is constructed according to the influence of the scientificity and advancement of marine economic industrial structure on marine economic GDP. Based on the empirical analysis of the relevant economic data of China’s marine industry from 2008 to 2018, the model variable stationarity test and least squares regression analysis showed that the fitting degree of the model was good and significant; with every 1% increase in the scientificity and height of marine economic industrial structure adjustment, the marine economy increased by 1.88% and 2.80%, respectively. The development of marine science and technology can promote the rise of the marine economy. According to the model, the adjustment strategy of the marine economy industrial structure is made to further promote the development of the marine economy.
Correlation Analysis Model of Social Capital and Innovation Performance Based on Knowledge Mapping
Authors: Ding Yuanyi
Published: June 2022

In this paper, the correlation between social capital and innovation performance is analyzed using a knowledge graph approach, and a correlation analysis model is designed for practical use. Given the advantages of knowledge graph technology and the fact that most recommendation models fail to make full use of the correlation between knowledge graph and recommended items, this paper proposes a cross-attention fusion-based knowledge graph recommendation algorithm (CAKR). The CAKR model contains a cross-fusion module, a recommendation task module, and a knowledge graph embedding task module, and the inputs of the two task modules are alternately learned in a low-dimensional space by the cross-fusion module. The input of the two task modules is alternately learned in the low-dimensional space by the cross-fusion module to interact with the two embedding vectors of items and entities, and then, the obtained feature vectors are fed into the relevant task modules, respectively, and then, the results are calculated by the respective prediction functions. There is a mediating role of knowledge management capabilities in the innovation ecology perspective between social capital and innovation performance of SMEs. Among them, it fully mediates the relationship dimension of technical social capital, the cognitive dimension of institutional social capital, the relationship and cognitive dimension of market-based social capital, and innovation performance, and partially mediates the structural and cognitive dimension of technical social capital, the structural and relationship dimension of institutional social capital, and innovation performance. The relationship between knowledge management capability and innovation performance is positively mediated by environmental dynamics in the innovation ecology perspective, and the positive effect of knowledge management capability on innovation performance is more significant when the environmental dynamics are stronger, and vice versa. Finally, based on the results of the empirical study, some management suggestions are provided on how SMEs can reasonably utilize social capital to enhance their innovation performance.
Innovation of Human Body Positioning System and Basketball Training System
Authors: Yue Qiang , Wei Chao
Published: June 2022

With the development of information management of sports events, basketball teams have higher requirements for the management of training data. The development of data visualization technology has provided convenience for information management which evolves from a digital management model to an efficient graphical management model. In this article, we will mainly design a human body positioning system based on wireless sensor networks. The weak signal from the sensor is tuned by the circuit, and the cylindrical Fresnel lens array is selected to modulate the field of view to ensure an effective response to the infrared signal of the moving human body. The wireless sensor network is used to integrate the human body detected by each pyroelectric sensor node, and the infrared signal is sent to the upper computer for analysis and processing. Through the host computer interface, observe and analyze the relationship between the detection signal of a single pyroelectric sensor and the position, speed, and movement of the moving human body, and deeply explore the operating process of the wireless pyroelectric infrared sensor network system. With the development of the new era, Chinese basketball training and teaching methods generally have some drawbacks, which have seriously affected the quality and effect of college basketball training and restricted the development of college basketball. Therefore, the focus of research is to solve these problems in order to achieve more effective college basketball training and education effects. This article mainly through the research of 5G technology wireless sensing human body positioning shows that wireless sensing technology has made great progress in its aspects, systematically analyzes basketball training, and proposes better training methods.
Abbreviated MDS-UPDRS for Remote Monitoring in PD Identified Using Exhaustive Computational Search
Authors: Morinan Gareth , Hauser Robert A. , Schrag Anette , Tang Jingxuan , O’Keeffe Jonathan , MDS-NMS Scale Development Study Group
Published: June 2022

Background. The Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) comprises 50 items, consisting of historical questions and motor ratings, typically taking around 30 minutes to complete. We sought to identify an abbreviated version that could facilitate use in clinical practice or used remotely via telemedicine. Methods. To create an 8-item version we conducted an “exhaustive search” of all possible subsets. We measured explained variance in comparison to the 50-item version using linear regression, with the “optimal” subset maximising this while also meeting remote assessment practicality constraints. The subset was identified using a dataset collected by the Parkinson’s Progression Markers Initiative and validated using an MDS Non-Motor Symptoms Scale validation study dataset. Results. The optimal remote version comprised items from all parts of the MDS-UPDRS and was found to act as an unbiased estimator of the total 50-item score. This version had an explained variance score of 0.844 and was highly correlated with the total MDS-UPDRS score (Pearson’s r = 0.919,

p

-value <0.0001). Another subset that maximised explained variance score without adhering to remote assessment practicality constraints provided similar results. Conclusion. This result demonstrates that the total scores of an abbreviated form identified by computational statistics had high agreement with the MDS-UPDRS total score. Whilst it cannot capture the richness of information of the full MDS-UPDRS, it can be used to create a total score where practicality limits the application of the full MDS-UPDRS, such as remote monitoring. Further validation will be required, including in specific subgroups and advanced disease stages, and full validation of clinimetric properties.
High Expression of PDLIM2 Predicts a Poor Prognosis in Prostate Cancer and Is Correlated with Epithelial-Mesenchymal Transition and Immune Cell Infiltration
Authors: Piao Songzhe , Zheng Lan , Zheng Haihong , Zhou Mengya , Feng Qin , Zhou Suna , Ke Mang , Yang Haihua , Wang Xuequan
Published: June 2022

Purpose. To elucidate the clinical and prognostic role of PDZ and LIM domain protein (PDLIM) genes and the association to epithelial-mesenchymal transition (EMT) and immune cell infiltration in patients with prostate cancer (PRAD). Methods. The data of RNA-seq, DNA methylation, and clinical features of PRAD patients were collected from The Cancer Genome Atlas (TCGA) database to define the prognostic value of PDLIM gene expression and the association with EMT and immune cell infiltration. A tissue microarray including 134 radical prostatectomy specimens was served as validation by immunohistochemistry (IHC) staining analysis. Results. The mRNA levels of PDLIM1/2/3/4/6/7 were significantly downregulated, while PDLIM5 was upregulated in PRAD (

P
<
0.05

). High expression of PDLIM2 mRNA suggests poor progression free interval in PRAD patients. DNA methylation of PDLIM2 was correlated with its mRNA expression level, and that the cg22973076 methylation site in PDLIM2 was associated with shorter PFI (

P
<
0.05

) in PRAD. Single-sample gene-set enrichment and gene functional enrichment results showed that PDLIM2 was correlated with EMT and immune processes. Spearman’s test showed a significant correlation with six reported EMT signatures and several EMT signature-related genes. Tumor microenvironment analysis revealed that the PDLIM2 mRNA expression was positively correlated with the immune score, stromal score, and various tumor infiltrating immune cells. Additionally, the results showed that patients in the high-PDLIM2 mRNA expression group may be more sensitive to immune checkpoint blockade therapy. Finally, IHC analysis further implicated the protein level of PDLIM2 was upregulated in PRAD and acts as a novel potential biomarker in predicting tumor progression. Conclusion. Our study suggests that PDLIM family genes might be significantly correlated with oncogenesis and the progression of PRAD. PDLIM2 correlated with EMT and immune cell infiltration by acting as an oncogene in PRAD, which may serve as a potential prognostic biomarker for PRAD patients.
Critical Nodes Identification of Scientific Achievement Commercialization Network under k‐Core
Authors: Weng Wuyan , Li Zi , Qiu Qirong , Cheng Junheng
Published: June 2022

Aiming to improve the commercialization efficiency of scientific innovative achievements, this paper utilizes the time series visualization method to construct the time series network of each subsystem. After that, the network similarity is calculated by the cosine similarity theorem. On this basis, a new multilayer network adjacency matrix is obtained. With the adoption of k‐core technology, the critical nodes can be identified to study the transformation efficiency of the innovation value in the network. Finally, according to the provincial innovation value transformation data of China from 1998 to 2016, an empirical study was carried out to calculate and analyze the transformation efficiency of innovation achievements in 30 provinces. The results indicate that (1) the transformation efficiency of innovation value can be expressed by the structure of the time series network constructed by the input‐output vectors; (2) the mapping relationship of the value transformation vectors could be reflected by the cosine similarity of the time series network, while the transformation efficiency of innovation value could be identified using the k‐core; and (3) the transformation efficiency of innovation value in three coastal provinces is relatively higher, while that of the rest of the provinces is roughly the same among the 30 provinces.
Ultrasound Multiparametric Assessment of the Impact of Hypertensive Disorders of Pregnancy on Fetal Cardiac Function and Growth and Development
Authors: Lv Maoting , Yu Shanshan , Li Yongzhen , Zhang Xiaoting , Zhao Dan
Published: June 2022

Objective.To evaluate the ultrasound multiparametric assessment of the impact of hypertensive disorders of pregnancy (HDP) on fetal cardiac function and growth and development. Methods. In this prospective study, 98 cases of HDP treated in our institution were recruited into a study group, and 100 pregnant women with healthy singleton pregnancies were included in a control group. All eligible patients were also assigned to either study group A (HDP fetuses with growth restriction) or study group B (HDP fetuses with normal growth). Fetal echocardiography was performed on all eligible participants to obtain hemodynamic and cardiac function parameters for the evaluation of fetal growth and development, and the impact of HDP on fetal heart function and growth and development was analyzed. Results. HDP fetuses were associated with smaller head circumference, biparietal diameter, femoral length, and abdominal circumference versus healthy fetuses. The study group had a higher resistance index (RI) and pulsatility index (PI) of umbilical artery (UA), ductus venous (DV), pulmonary vein (PV), and lower RI and PI of aortic isthmus (AoI) than the control group. The study group showed higher left and right ventricular isovolumic contraction time (IVCT), isovolumic relaxation time (IVRT), and myocardial performance index (MPI) values and lower mitral and tricuspid E wave and E/A values than the control group. The systolic blood pressure was positively correlated with PI, RI of UA, DV, and PV, and left and right ventricular IVCT, IVRT, and MPI and negatively correlated with PI and RI of AoI and mitral and tricuspid E wave and E/A values of HDP fetuses. The peak systolic/diastolic flow rate (S/D), PI, and RI of umbilical blood flow in study group A were higher than those in study group B. Umbilical blood flow S/D showed the highest AUC and specificity for predicting fetal growth restriction, and PI had the highest sensitivity for predicting fetal growth restriction. Conclusion. HDP compromises fetal cardiac function and growth, and ultrasound multiparametric assessment provides accurate detection of fetal cardiac function and hemodynamics changes. The patient’s condition can be monitored through the assessment of ultrasound parameters of fetal growth and development.