bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced oxidative stress and presence of ventricular aneurysm for risk prediction in cardiac sarcoidosis
Authors: Yoshitomi Ryosuke , Kobayashi Shigeki , Yano Yasutake , Nakashima Yusuke , Fujii Shohei , Nanno Takuma , Ishiguchi Hironori , Fukuda Masakazu , Yoshiga Yasuhiro , Okamura Takayuki , Suga Kazuyoshi , Kawano Reo , Yano Masafumi
Published: January 2022

Objective

Sudden cardiac death (SCD) is the major cause of death in cardiac sarcoidosis (CS). We aimed to identify the prognostic markers for sustained ventricular tachycardia (sVT) and SCD in patients with CS.

Methods

We performed a prospective observational cohort study for patients with CS diagnosed according to the Japanese or Heart Rhythm Society guidelines between June 2008 and March 2020 in our hospital. The primary endpoint was a composite of the first sVT and SCD. The levels of urinary 8-hydroxy-2′-deoxyguanosine (U-8-OHdG), a marker of oxidative DNA damage that reflects the inflammatory activity of CS, other biomarkers, and indices of cardiac function and renal function were measured on admission.

Results

Eighty-nine consecutive patients with CS were enrolled; 28 patients with no abnormal18F-fluorodeoxyglucose (18F-FDG) accumulation in the heart were excluded and 61 patients with abnormal18F-FDG accumulation were followed up for a median of 46 months (IQR: 20–84). During the follow-up period, 15 of 61 patients showed sVT (n=12) or SCD (n=3). A Cox proportional hazard model showed that U-8-OHdG concentration and presence of ventricular aneurysm (VA) were independent predictors of first sVT/SCD. The cut-off U-8-OHdG concentration for predicting first sVT/SCD was 14.9 ng/mg·Cr. Patients with U-8-OHdG concentration ≥14.9 ng/mg·Cr and VA showed a significantly increased risk of sVT/SCD.

Conclusions

U-8-OHdG and presence of VA were powerful predictors of first sVT/SCD in patients with CS, facilitating the stratification of cardiac events and providing relevant information about the substrates of ventricular tachycardia.
Utility of Itraconazole and Terbinafine in Mucormycosis: A Proof-of-Concept Analysis
Authors: Gupta Prashant , Malhotra Hardeep Singh , Saxena Priyamvada , Singh Riddhi , Shukla Deeksha , Hasan Mohd Saqib , Verma Veerendra , Banerjee Gopa , Puri Bipin , Dandu Himanshu
Published: January 2022

An epidemic of mucormycosis followed the second wave of COVID 19 in the state of Uttar Pradesh, India in May 2021. This epidemic, however, had additional challenges to offer in the form of acute shortage of all forms of amphotericin B, posaconazole and isavuconazole. It was, therefore, planned to assess the trends in minimum inhibitory concentration (MIC) of antifungal agents, viz itraconazole and terbinafine, and provide a template for personalized therapy to see whether the results could be translated clinically. This is an observational, single-center study. Samples comprising nasal swab, nasal and paranasal sinus tissue, brain tissue, brain abscess and orbital content, derived from 322 patients from northern India with mucormycosis, of whom 215 were male and 107 were female, were used for analysis. Cultures were identified both by matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) and conventional methods of identification. Antifungal susceptibility was done for amphotericin B, posaconazole, isavuconazole, itraconazole and terbinafine as per Clinical Laboratory Standard Institute M38-A2. The outcome was identification of the species of mucormycosis and susceptibility to itraconazole and terbinafine besides other primary antifungal agents. Patients or the public were not involved in the design, or conduct, or reporting or in the dissemination plans of our research. Of 322 patients, 203 were culture-positive, of whom 173 were positive by both MALDI-TOF and conventional methods of identification. Final antifungal susceptibility testing was available for 150 patients. The most common Mucorales found to cause this epidemic was Rhizopus oryzae, followed by R. microsporus. Amphotericin B, posaconazole and isavuconazole had low MIC values in 98.8% of all Mucorales identified. The MIC of itraconazole was species-dependent. 97.7% of R. oryzae had MIC ≤2 µg/mL. However, only 36.5% of R. microsporus had MIC ≤2 µg/mL. For terbinafine, 85.2% of R. microsporus had MIC ≤2 µg/mL. We conclude that identification at the species level is required as antifungal susceptibilities seem to be species-dependent. Assessment of the efficacy of itraconazole and terbinafine warrants further studies with clinical assessment and therapeutic drug monitoring as they seem to be potential candidates especially when the primary agents are not available.
Differential levels of plasma biomarkers of neurodegeneration in Lewy body dementia, Alzheimer’s disease, frontotemporal dementia and progressive supranuclear palsy
Authors: Chouliaras Leonidas , Thomas Alan , Malpetti Maura , Donaghy Paul , Kane Joseph , Mak Elijah , Savulich George , Prats-Sedano Maria A , Heslegrave Amanda J , Zetterberg Henrik , Su Li , Rowe James Benedict , O'Brien John Tiernan
Published: January 2022

Objectives

This longitudinal study compared emerging plasma biomarkers for neurodegenerative disease between controls, patients with Alzheimer’s disease (AD), Lewy body dementia (LBD), frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP).

Methods

Plasma phosphorylated tau at threonine-181 (p-tau181), amyloid beta (Αβ)42, Aβ40, neurofilament light (NfL) and glial fibrillar acidic protein (GFAP) were measured using highly sensitive single molecule immunoassays (Simoa) in a multicentre cohort of 300 participants (controls=73, amyloid positive mild cognitive impairment (MCI+) and AD dementia=63, LBD=117, FTD=28, PSP=19). LBD participants had known positron emission tomography (PET)-Aβ status.

Results

P-tau181 was elevated in MCI+AD compared with all other groups. Aβ42/40 was lower in MCI+AD compared with controls and FTD. NfL was elevated in all dementias compared with controls while GFAP was elevated in MCI+AD and LBD. Plasma biomarkers could classify between MCI+AD and controls, FTD and PSP with high accuracy but showed limited ability in differentiating MCI+AD from LBD. No differences were detected in the levels of plasma biomarkers when comparing PET-Aβ positive and negative LBD. P-tau181, NfL and GFAP were associated with baseline and longitudinal cognitive decline in a disease specific pattern.

Conclusion

This large study shows the role of plasma biomarkers in differentiating patients with different dementias, and at monitoring longitudinal change. We confirm that p-tau181 is elevated in MCI+AD, versus controls, FTD and PSP, but is less accurate in the classification between MCI+AD and LBD or detecting amyloid brain pathology in LBD. NfL was elevated in all dementia groups, while GFAP was elevated in MCI+AD and LBD.
Benefit and risk of intravenous alteplase in patients with acute large vessel occlusion stroke and low ASPECTS
Authors: Broocks Gabriel , McDonough Rosalie , Bechstein Matthias , Hanning Uta , Brekenfeld Caspar , Flottmann Fabian , Kniep Helge , Nawka Marie Teresa , Deb-Chatterji Milani , Thomalla Götz , Sporns Peter , Yeo Leonard LL , Tan Benjamin YQ , Gopinathan Anil , Kastrup Andreas , Politi Maria , Papanagiotou Panagiotis , Kemmling Andre , Fiehler Jens , Meyer Lukas
Published: January 2022

Background

The benefit of best medical treatment including intravenous alteplase (IVT) before mechanical thrombectomy (MT) in patients with acute ischemic stroke and extensive early ischemic changes on baseline CT remains uncertain. The purpose of this study was to evaluate the benefit of IVT for patients with low ASPECTS (Alberta Stroke Programme Early CT Score) compared with patients with or without MT.

Methods

This multicenter study pooled consecutive patients with anterior circulation acute stroke and ASPECTS≤5 to analyze the impact of IVT on functional outcome, and to compare bridging IVT with direct MT. Functional endpoints were the rates of good (modified Rankin Scale (mRS) score ≤2) and very poor (mRS ≥5) outcome at day 90. Safety endpoint was the occurrence of symptomatic intracranial hemorrhage (sICH).

Results

429 patients were included. 290 (68%) received IVT and 168 (39%) underwent MT. The rate of good functional outcome was 14.4% (95% CI 7.1% to 21.8%) for patients who received bridging IVT and 24.4% (95% CI 16.5% to 32.2%) for those who underwent direct MT. The rate of sICH was significantly higher in patients with bridging IVT compared with direct MT (17.8% vs 6.4%, p=0.004). In multivariable logistic regression analysis, IVT was significantly associated with very poor outcome (OR 2.22, 95% CI 1.05 to 4.73, p=0.04) and sICH (OR 3.44, 95% CI 1.18 to 10.07, p=0.02). Successful recanalization, age, and ASPECTS were associated with good functional outcome.

Conclusions

Bridging IVT in patients with low ASPECTS was associated with very poor functional outcome and an increased risk of sICH. The benefit of this treatment should therefore be carefully weighed in such scenarios. Further randomized controlled trials are required to validate our findings.
Low-activity cryptochrome 1 plays a role in promoting stem elongation and flower initiation of mature Arabidopsis under blue light associated with low phytochrome activity
Authors: Kong Yun , Zheng Youbin
Published: January 2022

To explore whether cryptochrome contributes to stem elongation and flowering promoted by blue lights associated with low phytochrome activity, wild-type Arabidopsis was compared with its cryptochrome-deficient mutants and cryptochrome-overexpressing transgenic plants. Results indicated that the promotion effects were mainly related to low CRY1 activity, despite partial involvement of high-activity CRY2.
Surface flow–interflow-coupled nitrogen loss in gray fluvo-aquic farmland soils in the Anhui Section of the Huaihe River Basin, China
Authors: Wang Hui , Gao Liangmin , Pang Zhendong , Yang Jie , Chen Xiaoqing , Zhang Jinxin , Wang Shuo , Tong Rongrong , Shi Chuang , Chen Xudong
Published: January 2022

To reveal the characteristics of nitrogen loss and their coupling relations in the process of surface flow and interflow under various rainfall intensities in gray fluvo-aquic soil areas, the coupling loss characteristics of total nitrogen (TN), nitrate nitrogen (NO3−-N), and ammonium nitrogen (NH4+-N) in the surface flow and interflow under three rainfall intensities (60, 80, and 110 mm·h−1) at a slope of 5° were studied using an artificial rainfall simulation. The results showed that (1) runoff yield and TN concentration were proportional to the rainfall intensity, with higher runoff concentrations of TN in the initial stage, and (2) surface flow yield, which was the main output mode of farmland runoff, was higher than interflow yield under a range of rainfall intensities. The average concentration of NO3−-N in surface flow decreased with increasing rainfall intensity, whereas the opposite was true in interflow. The main loss path of NO3−-N was interflow, whereas the main loss path of NH4+-N was the surface flow; NO3−-N was the main form of TN loss. The surface flow was the main loss path of soil nitrogen loss in farmland, and the runoff yield was an important factor in controlling nitrogen loss.
Alpha-gamma decay studies of $$^{247}$$Md
Authors: Heßberger F. P. , Antalic S. , Giacoppo F. , Andel B. , Ackermann D. , Block M. , Heinz S. , Khuyagbaatar J. , Kojouharov I. , Venhart M.
Published: January 2022

Alpha
decay and spontaneous fission of the ground-state and the isomeric state of $$^{247}$$

247

Md were investigated with specific emphasis to identify the ground-state of the daughter nucleus $$^{243}$$

243

Es. The decay studies were accompanied by measuring $$\alpha $$
α
-$$\gamma $$
γ
coincidences. On the basis of the measured data an improved decay scheme of $$^{247}$$

247

Md is proposed. Spontaneous fission half-lives of $$^{247g}$$

247
g

Md and $$^{247m}$$

247
m

Md were determined and the fission hindrance of the high spin ground-state (7/2$$^{-}$$

-

) relative to the low spin isomeric state (1/2$$^{-}$$

-

) was estimated.
An Efficient Sorting Algorithm for Non-Volatile Memory
Authors: Chu Zhaole , Luo Yongping , Jin Peiquan
Published: January 2022

Non-volatile memory (NVM) has emerged as an alternative of the next-generation memory due to its non-volatility, byte addressability, high storage-density, and low-energy consumption. However, NVM also has some limitations, e.g. asymmetric read and write latency. Therefore, at present, it is not realistic to completely replace DRAM with NVM in computer systems. A more feasible scheme is to adopt the hybrid memory architecture composed of NVM and DRAM. Following the assumption of hybrid memory architecture, in this paper, we propose an NVM-friendly sorting algorithm called NVMSorting. Particularly, we introduce a new concept called Natural Run to improve the existing MONTRES algorithm. Further, we apply the proposed NVMSorting to database join algorithms to improve the performance of the existing sort-merge join. To verify the performance of our proposal, we implement six existing sorting algorithms as baselines, including the MONTRES algorithm, and conduct comparative experiments on real Intel Optane DC persistent memory. The results show that NVMSorting outperforms other sorting algorithms in terms of execution time and NVM writes. In addition, the results of the join experiment show that the NVMSorting algorithm achieves the highest performance among all schemes. Especially, in the partially ordered data, the execution time of NVMSorting is 2.9%, 2.7%, and 4.2% less than MONTRES, external sort, and quick sort, respectively. Also, the amount of NVM writes of the NVMSorting is 26.1%, 43.6%, 96.2% less than MONTRES, external sort, and quick sort, respectively.
Patterns for Reuse in Production Systems Engineering
Authors: Meixner Kristof , Lüder Arndt , Herzog Jan , Winkler Dietmar , Biffl Stefan
Published: January 2022

In Production Systems Engineering (PSE), domain experts aim at reusing partial system designs implemented as Industry 4.0 assets and software. However, the knowledge on assets is often scattered across engineering artifacts from multiple disciplines and domain experts, making it difficult to find reusable assets and map them to requirements. In this paper, we (i) identify challenges and requirements for the representation of reuse knowledge in PSE, based on the results of a domain analysis in automotive manufacturing; (ii) refine the Industry 4.0 Asset Network (I4AN) meta-model that integrates multi-disciplinary dependencies between the assets; (iii) introduce the I4AN reference model that exposes recurring patterns; and (iv) present basic and applied patterns for reuse in PSE that aim at improving reuse efficiency and lowering risks. We evaluate the I4AN reference model and patterns with reuse scenarios in a feasibility study in automotive manufacturing. The study results indicate that the I4AN reference model and patterns satisfy the elicited requirements and enable PSE domain experts to identify patterns for reuse and sufficiently complete sets of reusable assets in their contexts.
An Approach of Automated Anomalous Microservice Ranking in Cloud-Native Environments
Authors: Zhang Zekun , Li Bing , Wang Jian , Liu Yongqiang
Published: January 2022

In recent years, more and more developers have been building applications based on the cloud-native architecture. Container and microservice are two essential components in the cloud-native architecture. Container technologies like Docker and Kubernetes can help developers achieve a consistent and scalable delivery for complex software applications. On the other hand, microservice technologies can facilitate the division of complex applications into multiple functionality-independent and composable components, which further increases the flexibility of applications. With the support of cloud computing platforms, cloud-native applications will be easier to manage and maintain, together with higher scalability. However, it is challenging to identify performance issues on microservices due to the complex runtime environments and the numerous monitoring metrics. Towards this issue, this paper proposes a novel root cause analysis approach. Our approach firstly constructs a service dependency graph based on the metrics collected in real time. Next, the anomaly weight of each microservice is automatically updated by extending the mRank algorithm. Finally, a PageRank-based random walk is adopted to rank root causes further, i.e. to rank potential problematic services. Experiments conducted on Kubernetes clusters show that the proposed approach achieves a good analysis result, which outperforms several baseline methods.
Analyzing the Cloud Performance Using Different User Subscription Times
Authors: Bernal Adrián , Cambronero M. Emilia , Cañizares Pablo C. , Núñez Alberto , Valero Valentín , de la Cruz Hernán-Indibil
Published: January 2022

Cloud providers face the challenge of managing large amounts of heterogeneous resources in real time. It is usually very costly to conduct experiments with real cloud systems. Therefore, tools to analyze and evaluate cloud scenarios and experimental studies are very useful for them. In this paper, we model cloud systems and the user interactions with the cloud provider using the UML2Cloud profile. In general, users request virtual machines according to their needs, but they can also subscribe to the cloud provider and wait to be notified when the requested resources are not available. In this case, users indicate a maximum subscription time, so once this time elapses without being notified, users leave the system unattended. Thus, we present an exhaustive experimental study to measure how the user subscription times affect the overall system responsiveness. To this end, four different cloud configurations are analyzed, and the workloads for these studies are produced by using three distribution functions for the user arrivals, namely, a uniform, a normal, and a cyclic normal distribution. Furthermore, we also analyze the cloud performance with a workload obtained from a real trace. The purpose of this study is to find out the inflection point for the waiting time of the users, from which the cloud responsiveness and its performance do not improve. The obtained information is, therefore, useful for the cloud provider to improve the configuration of the cloud.
A HYBRID APPROACH FOR SYNCHRONIZING BETWEEN TWO REACTION–DIFFUSION SYSTEMS OF INTEGER- AND FRACTIONAL-ORDER APPLIED ON CERTAIN CHEMICAL MODELS
Authors: WANG BO , OUANNAS ADEL , KARACA YELIZ , XIA WEI-FENG , JAHANSHAHI HADI , ALKHATEEB ABDULHAMEED F. , NOUR MAJID
Published: January 2022

In this study, a synchronization problem for spatio-temporal partial differential systems is addressed and researched within a subjectivist framework. In light of Lyapunov direct method and some proposed nonlinear controllers, a new scheme is established to accomplish a full synchronization between two reaction–diffusion systems of integer- and fractional-order. In particular, a novel vector-valued control law is analytically derived to attain the desired synchronization between two chemical models, namely, the Lengyel–Epstein and Gray–Scott models. To validate the obtained theoretical results, further numerical simulations are carried out in 2D and 3D configurations.
SKEWED AUTO-REGRESSIVE PROCESS WITH EXOGENOUS INPUT VARIABLES: AN APPLICATION IN THE ADMINISTERED VACCINE DOSES ON COVID-19 SPREAD
Authors: MALEKI MOHSEN , MAHMOUDI MOHAMMAD REZA , BIDRAM HAMID , MOSAVI AMIR
Published: January 2022

This study focuses on the prevalence of COVID-19 disease along with vaccination in the United States. We have considered the daily total infected cases of COVID-19 with total vaccinated cases as exogenous input and modeled them using light/heavy tailed auto-regressive with exogenous input model based on the innovations that belong to the flexible class of the two-piece scale mixtures of normal (TP–SMN) family. We have shown that the prediction of COVID-19 spread is affected by the rate of vaccine injection. In fact, the presence of exogenous input variables in time series models not only increases the accuracy of modeling, but also causes better and closer approximations in some issues including predictions. An Expectation-Maximization (EM) type algorithm has been considered for finding the maximum likelihood (ML) estimations of the model parameters, and modeling as well as predicting the infected numbers of COVID-19 in the presence of the vaccinated cases in the US.
FRACTAL EVOLUTION IN INTERNET INFORMATION PROPAGATION
Authors: YANG YANG , NIAN FUZHONG
Published: January 2022

In view of the fractal evolution phenomenon in Internet information dissemination, this paper constructs a generalized fractal network by analyzing the characteristics of user dissemination behavior and according to the dissemination trend of actual information. On this basis, it reveals the fractal evolution mechanism in Internet information dissemination and its dissemination dynamic characteristics and rules. First, based on node similarity, a fractal propagation model of Internet information is constructed. Second, based on the scale-free network and according to the user characteristics, the information dissemination process is simulated and a generalized fractal network is evolved. Then, the key factors influencing the formation of fractal networks, the characteristics, and the rules of propagation dynamics are analyzed. Finally, the rationality and validity of the model are verified by crawling the real user forwarded data in the micro-blog.
ON THE GENERALIZED WEIGHTED CAPUTO-TYPE DIFFERENTIAL OPERATOR
Authors: LIU JIAN-GEN , YANG XIAO-JUN , FENG YI-YING , GENG LU-LU
Published: January 2022

In this paper, we defined a generalized weighted Caputo-type differential operator. Then, it can express as a convergent series through the Riemann–Liouville integral. At the same time, by solving related linear differential equation, we can construct the generalized weighted Caputo-type integral operator. Lastly, some theorems and properties of these considered operators were also studied.
Heterogeneous Fault Prediction Using Feature Selection and Supervised Learning Algorithms
Authors: Arora Rashmi , Kaur Arvinder
Published: January 2022

Software Fault Prediction (SFP) is the most persuasive research area of software engineering. Software Fault Prediction which is carried out within the same software project is known as With-In Fault Prediction. However, local data repositories are not enough to build the model of With-in software Fault prediction. The idea of cross-project fault prediction (CPFP) has been suggested in recent years, which aims to construct a prediction model on one project, and use that model to predict the other project. However, CPFP requires that both the training and testing datasets use the same set of metrics. As a consequence, traditional CPFP approaches are challenging to implement through projects with diverse metric sets. The specific case of CPFP is Heterogeneous Fault Prediction (HFP), which allows the program to predict faults among projects with diverse metrics. The proposed framework aims to achieve an HFP model by implementing Feature Selection on both the source and target datasets to build an efficient prediction model using supervised machine learning techniques. Our approach is applied on two open-source projects, Linux and MySQL, and prediction is evaluated based on Area Under Curve (AUC) performance measure. The key results of the proposed approach are as follows: It significantly gives better results of prediction performance for heterogeneous projects as compared with cross projects. Also, it demonstrates that feature selection with feature mapping has a significant effect on HFP models. Non-parametric statistical analyses, such as the Friedman and Nemenyi Post-hoc Tests, are applied, demonstrating that Logistic Regression performed significantly better than other supervised learning algorithms in HFP models.
The Impact of Subway Network Expansion on Housing Rents: An Empirical Study in Beijing, China
Authors: MA Xin , HUANG Tao
Published: January 2022

Most previous studies have focused on the impact of subways on housing prices instead of rents, while the latter could better measure residential values. Based on a dataset collected from a real estate agency in Beijing, which contains more than 900,000 housing rental transaction records from 2011 to 2020, this paper empirically evaluates the causal effect of subway network expansion on housing rents. It employs a series of progressive difference-in-difference (DID) approaches, to estimate the impact and determine the impact scope. The findings demonstrate that a reduction of the distance to subway stations by 1 km increases the rents by 2.32%; the impact scope is about 1.5 km and the average rent appreciation within the range is 5%. The addition of a line for non-transfer stations raises the rents by 10% for houses 1.5–2 km away from the stations, extending the impact scope. Houses with large areas in upscale and old neighborhoods near the city center are affected less by subways. It also confirms the siphon effect in the rental market, i.e.: rents of houses far away from the new stations fall after the opening of the stations.
Design and Control of an Autonomous Robot for Mobility-Impaired Patients
Authors: Deb Aarya , Wypych Zachery , Lonner Jess , Ashrafiuon Hashem
Published: January 2022

In this paper, a novel autonomous mobile robotic arm is presented that can autonomously follow mobility impaired patients and provide protection against backward and sideway falls. The arm is equipped with a Lidar sensor to estimate the subject distance and orientation for autonomous tracking. An end-effector with is designed to prevent backward and sideway falls when the subject becomes unbalanced. The robot is tested experimentally to demonstrate that it can effectively follow a subject and limit backward and sideway falls.
Fully Distributed Adaptive Fault-Tolerant Attitude Synchronization for a Team of Heterogenous Minisatellites
Authors: Liu Hao , Liu Deyuan , Cheng Ming
Published: January 2022

In this paper, the fault-tolerant attitude synchronization control problem for a team of heterogeneous minisatellites is addressed, subject to multiple actuator faults and uncertainties in the satellite dynamics. A fully distributed adaptive fault-tolerant controller, completely independently of the knowledge of the global information, is proposed to achieve the desired attitude synchronization. The attitude tracking error of the global closed-loop control system is proven to converge into a given neighborhood of the origin ultimately. Simulation results are given to validate the effectiveness and advantages of the proposed adaptive controller for the heterogeneous satellite formation flying team, against the influence of actuator faults and uncertainties.
Adaptive Modeling for Downwash Effects in Multi-UAV Path Planning
Authors: Chen Chih-Chun , Liu Hugh H.-T.
Published: January 2022

This paper develops a novel method to model the air flow downwash force generated by the quadrotor unmanned aerial vehicle (UAV) and its effect on the neighboring UAVs. Each UAV is shaped by a virtual structure for collision-free path planning. The shape is modified from a standard spherical body to a proposed adaptive cylinder to optimize the path planning while minimizing the downwash impact. The cylinder height varies based on the UAV circumstance and the predicted downwash impact. Furthermore, the downwash model can aid in the cylinder height extreme value appointment. A flock-based path planning algorithm is investigated in this study to compare the spherical UAV shape model with the proposed cylindrical UAV shape model. The UAV with the adaptive cylindrical model is simulated and verified via Gazebo and Robot Operating System (ROS) simulation platform.
Exploring Relations in Untrimmed Videos for Self-Supervised Learning
Authors: Luo Dezhao , Zhou Yu , Fang Bo , Zhou Yucan , Wu Dayan , Wang Weiping
Published: January 2022

Existing video self-supervised learning methods mainly rely on trimmed videos for model training. They apply their methods and verify the effectiveness on trimmed video datasets including UCF101 and Kinetics-400, among others. However, trimmed datasets are manually annotated from untrimmed videos. In this sense, these methods are not truly unsupervised. In this article, we propose a novel self-supervised method, referred to as Exploring Relations in Untrimmed Videos (ERUV), which can be straightforwardly applied to untrimmed videos (real unlabeled) to learn spatio-temporal features. ERUV first generates single-shot videos by shot change detection. After that, some designed sampling strategies are used to model relations for video clips. The strategies are saved as our self-supervision signals. Finally, the network learns representations by predicting the category of relations between the video clips. ERUV is able to compare the differences and similarities of video clips, which is also an essential procedure for video-related tasks. We validate our learned models with action recognition, video retrieval, and action similarity labeling tasks with four kinds of 3D convolutional neural networks. Experimental results show that ERUV is able to learn richer representations with untrimmed videos, and it outperforms state-of-the-art self-supervised methods with significant margins.
Seeing Crucial Parts: Vehicle Model Verification via a Discriminative Representation Model
Authors: Liang Liqian , Lang Congyan , Li Zun , Zhao Jian , Wang Tao , Feng Songhe
Published: January 2022

Widely used surveillance cameras have promoted large amounts of street scene data, which contains one important but long-neglected object: the vehicle. Here we focus on the challenging problem of vehicle model verification. Most previous works usually employ global features (e.g., fully connected features) to further perform vehicle-level deep metric learning (e.g., triplet-based network). However, we argue that it is noteworthy to investigate the distinctiveness of local features and consider vehicle-part-level metric learning by reducing the intra-class variance as much as possible. In this article, we introduce a simple yet powerful deep model—the enforced intra-class alignment network (EIA-Net)—which can learn a more discriminative image representation by localizing key vehicle parts and jointly incorporating two distance metrics: vehicle-level embedding and vehicle-part-sensitive embedding. For learning features, we propose an effective feature extraction module that is composed of two components: the regional proposal network (RPN)-based network and part-based CNN. The RPN is used to define key vehicle regions and aggregate local features on these regions, whereas part-based CNN offers supplementary global features for the RPN-based network. The fusion features learned by feature extraction module are cast into the deep metric learning module. Especially, we derived an enforced intra-class alignment loss by re-utilizing key vehicle part information to enhance reducing intra-class variance. Furthermore, we modify the coupled cluster loss to model the vehicle-level embedding by enlarging the inter-class variance while shortening intra-class variance. Extensive experiments over benchmark datasets VehicleID and CompCars have shown that the proposed EIA-Net significantly outperforms the state-of-the-art approaches for vehicle model verification. Furthermore, we also conduct comprehensive experiments on vehicle re-identification datasets (i.e., VehicleID and VeRi776) to validate the generalization ability effectiveness of our proposed method.
Fine-Grained Adversarial Semi-Supervised Learning
Authors: Mugnai Daniele , Pernici Federico , Turchini Francesco , Del Bimbo Alberto
Published: January 2022

In this article, we exploit
Semi-Supervised Learning
(
SSL
) to increase the amount of training data to improve the performance of
Fine-Grained Visual Categorization
(
FGVC
). This problem has not been investigated in the past in spite of prohibitive annotation costs that FGVC requires. Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model. This combination allows one to back-propagate the information of the parts, represented by second-order pooling, onto unlabeled data in an adversarial training setting. We demonstrate the effectiveness of the combined use by conducting experiments on six state-of-the-art fine-grained datasets, which include Aircrafts, Stanford Cars, CUB-200-2011, Oxford Flowers, Stanford Dogs, and the recent Semi-Supervised iNaturalist-Aves. Experimental results clearly show that our proposed method has better performance than the only previous approach that examined this problem; it also obtained higher classification accuracy with respect to the supervised learning methods with which we compared.
Knowledge Accumulation in Design Science Research
Authors: Reining Stefan , Ahlemann Frederik , Mueller Benjamin , Thakurta Rahul
Published: January 2022

Design science research (DSR) is increasingly prominent in the information systems discipline. However, despite a lively discourse, and methodical and conceptual advances notwithstanding, DSR contributions are rarely cumulative and mostly remain isolated. This lack of cumulative research impedes scientific progress and practical impact. Our goal in this paper is to help establish a cumulative tradition in DSR by proposing (1) a general, consensus-oriented, evolutionary process model of scientific progress based on a synthesis of different streams in the philosophy of science, (2) quality criteria for DSR contributions corresponding to the individual steps in the process model, (3) actions that the IS community as a whole can take to facilitate adherence to these quality criteria. We expect that our work can pave the way towards developing a more cumulative research tradition in DSR and thereby foster genuine scientific progress in the field.
Common Misunderstandings of Deterrence Theory in Information Systems Research and Future Research Directions
Authors: Siponen Mikko , Soliman Wael , Vance Anthony
Published: January 2022

In the 1980s, information systems (IS) borrowed deterrence theory (DT) from the field of criminology to explain information security behaviors (or intention). Today, DT is among the most commonly used theories in IS security research. Our review of IS research applying DT highlights that many fundamental assumptions of DT are unrecognized and therefore unexamined. This may have resulted in misunderstandings and conceptual confusions regarding some of the basic concepts of DT. For example, some IS studies confuse general deterrence with specific deterrence or do not recognize the difference between the two. Moreover, these fundamental assumptions, when directly examined, may provide important information about the applicability of DT in certain IS security contexts. This research commentary aims to identify and discuss some of the fundamental assumptions of DT and their implications for IS research. By examining these assumptions, IS researchers can study the previously unexplored aspects of DT in different IS contexts. Further, by recognizing these assumptions, IS scholars can revise them and build new variants of DT to better account for specific characteristics of IS behaviors and contexts.
Effects of Organizational Controls on Employees' Cyber-loafing
Authors: Luo Xin (Robert) , Xu Feng , Zhang Junmei , Xiao Shan , Xue Botong
Published: January 2022

Cyber-loafing is a workplace deviant behavior that may impose perennial losses and security threats to organizations. Due to the destructive impact, organizations take controls to manage employees' cyber-loafing behavior. While previous research concerning these cyber-loafing controls has yielded inconsistent results, this research further advances this line of research through two primarily adopted organizational controls: computer monitoring and Internet usage policy. We contend that the inconsistent results of the effects of computer monitoring and Internet usage policy on cyber-loafing behavior are due to ignoring the influence of individual differences. Motivated thus, we draw on pertaining literature of trait mindfulness to propose that trait mindfulness moderates the effect of perceived computer monitoring and perceived Internet usage policy on behavioral automaticity towards cyber-loafing, which leads to employees' cyber-loafing behavior. In essence, this paper sheds new light on the relationships amid organizational controls, cyber-loafing behavior, and the moderating effects of trait mindfulness. 450 responses were collected to test these hypotheses. Our results show that trait mindfulness enhances the negative effect of perceived Internet usage policy on behavioral automaticity towards cyber-loafing. In addition, we find out that trait mindfulness has an indirect effect on employees' cyber-loafing behavior by reducing behavioral automaticity.
The Construction of Accurate Recommendation Model of Learning Resources of Knowledge Graph under Deep Learning
Authors: Yang Xia , Tan Leping
Published: January 2022

With the rapid development of science and technology and the continuous progress of teaching, it is now flooded with rich learning resources. Massive learning resources provide learners with a good learning foundation. At the same time, learners want to be precise from many learning resources. Second, it becomes more and more difficult to quickly obtain the learning resources you want. Therefore, it is very important to accurately and quickly recommend learning resources to learners. During the last two decades, a large number of different types of recommendation systems were adopted that present the users with contents of their choice, such as videos, products, and educational content recommendation systems. The knowledge graph has been fully applied in this process. The application of deep learning in the recommendation systems has further enhanced their performance. This article proposes a learning resource accurate recommendation model based on the knowledge graph under deep learning. We build a recommendation system based on deep learning that is comprised of a learner knowledge representation (KR) model and a learning resource KR model. Information such as learner’s basic information, learning resource information, and other data is used by the recommendation engine to calculate the target learner’s score based on the learner KR and the learning resource KR and generate a recommendation list for the target learner. We use mean absolute error (MAE) as the evaluation indicator. The experimental results show that the proposed recommendation system achieves better results as compared to the traditional systems.
The Effect of Season and Meteorological Conditions on Parasite Infection in Farm-Maintained Mouflons (Ovis aries Musimon)
Authors: Pilarczyk B. , Tomza-Marciniak A. , Pilarczyk R. , Sadowska N. , Udała J. , Kuba J.
Published: January 2022

Due to their limited environment, farm animals are at greater risk of parasitic infection than free-living animals, which also have greater natural resistance to parasitic diseases. The aim of the present study was to determine the influence of season and meteorological conditions (temperature and humidity) on the species composition and dynamics of parasitic infections in farmed mouflons. The study was conducted in a herd of mouflon (

n
=
40

) in an extensive system: the animals remained on pasture all year round. The herd was dewormed twice per year with albendazole. Fecal samples were collected at monthly intervals over three years and tested. The prevalence of infection was defined based on coproscopic methods. For most of the studied protozoans (except for E. parva), a greater prevalence was recorded in spring and summer (i.w. from May to September). Regarding nematodes, Capillaria spp., Nematodirus sp., and the Trichostrongylidae demonstrated a much greater prevalence in winter (i.e., in January and December). Temperature and precipitation were found to be positively correlated with intensity of infection by protozoans. However, maximum air temperature was negatively correlated with infection intensity by some nematodes. The deworming practice used in the herd (selection of substance, date, and method of dosing) did not effectively protect the mouflons against parasitoses. Changes in the microclimate resulted in high extent and intensity of mouflon infection with gastrointestinal parasites. Understanding the dynamics of parasitic infections in mouflons during the year allows the development of an appropriate preventive programme.
Service-Oriented Architecture-Guided Information Service System for Design and Implementation of Rural Tourism
Authors: You Yukun
Published: January 2022

Rural tourism has become a way for people to pursue a simple life and relax, thanks to the variety of tourism forms available. Rural tourism, on the other hand, lacks a unified information platform for providing services to users and is unable to provide humanized services based on tourism experience. Rural tourism around the city has become the main force of tourism recovery because outbound tourism has yet to begin and domestic interprovincial tourism has only recently recovered. The development of rural tourism can be aided by an information service system that can effectively improve the utilization efficiency of tourism resources and explore new and reasonable paths for the development of rural tourism. This paper examines the opportunities and challenges that rural tourism faces in the face of the COVID-19 epidemic, as well as actionable strategies for the rapid growth of rural tourism. This paper introduces the Service-Oriented Architecture (SOA) technology required in the construction of the tourism service information sharing platform as the research object. In addition, the rural tourism information service system was established. So that rural tourism can be at the forefront of tourism’s rapid recovery and revitalization, it should be promoted to become more vibrant.
CCL18 Knockdown Suppresses Cell Growth and Migration in Thyroid Cancer
Authors: Sun Caixia , Wang Ping , Gao Tiantian , Chi Jinfeng
Published: January 2022

Background. Chemokine (C-C motif) ligand 18 (CCL18) is a chemokine that plays a key role in immune and inflammatory responses. In recent years, CCL18 participates in the development and progression of various cancers, but its expression and role in thyroid cancer (TC) remain unclear. Methods. RT-qPCR assay and Western blot assay were used to explore the expression level of CCL18 in TC tissues and cells. Cell proliferation was measured by MTT assay. Transwell assay was adopted to detect cell migration in TC cells. Dual luciferase reporter assay was performed to assess the relationship between CCL18 and miR-149-5p. Results. There was an uptrend of CCL18 in TC tissues and cells. Our findings indicated that CCL18 overexpression facilitated lymph node metastasis in patients with TC. CCL18 silencing was found to inhibit cell migration, proliferation, and EMT progression in TC cells. CCL18 was proved to be a target gene of miR-149-5p. Additionally, miR-149-5p weakened the effect of CCL18 in the progression of TC. Conclusion. Therefore, our results indicated that CCL18 knockdown restrained TC progression and suggested that CCL18 might be a potential therapeutic target for TC.
Diagnostic Value of Magnetic Resonance Image Feature Analysis under Reconstruction Algorithm for Knee Epiphyseal Injury
Authors: He Ning , Tang Changyou , Zhou Xianchao , Feng Shuolin
Published: January 2022

To analyze the diagnostic value of magnetic resonance imaging (MRI) in epiphyseal injury of adolescent children, MRI images of 26 adolescent knee joint injuries were selected. The image display before and after reconstruction based on the compressed sensing theory (CS) algorithm was compared and analyzed, so did the signal-to-noise ratio of knee MRI reconstructed images under 2D random sampling and radial sampling at different sampling rates. The results showed that the sharpness, specificity, and accuracy of images reconstructed by the CS algorithm were significantly higher than those before reconstructionP<0.05. When the sampling rates were almost the same (20%, 40%, and 60%), the signal-to-noise ratio (SNR) of the reconstructed knee MRI images by random sampling was higher than that by radial sampling (about 9%, 6%, and 3% higher, respectively), that is, the information contained by random sampling was larger than that by radial sampling. It overcame the defects of undisplaced epiphyseal injuries in children and adolescents that cannot be found by plain X-ray films and can clearly and accurately diagnose epiphyseal injuries and fracture types that cannot be seen by plain X-ray films. To sum up, the MRI image reconstruction algorithm based on compressed sensing theory can effectively improve the diagnosis effect of knee epiphyseal injury.
Resource Scheduling Based on Unequal Clustering in Internet of Things
Authors: Bai Hongying , Zhang Xiaotong , Xie Yingdong , Gong Haiyan , Li Zhuang , Liu Shilong
Published: January 2022

Resource scheduling in a fair and efficient manner is a significant challenge in the Internet of Things. Although unequal clustering is an effective technique for alleviating the “energy holes” problem in multihop communication, resource scheduling based on unequal clustering is scarcely conducted. In the present study, a new resource scheduling based on unequal clustering in the Internet of Things (RSUC) is proposed. In RSUC, unequal clustering and multihop routing are considered, and the “energy holes” problem is alleviated effectively. RSUC includes resource scheduling of intracluster communication and intercluster communication. In resource scheduling of intracluster communication, according to the threshold of the number of cluster members, the cluster heads (CHs) recycle resources of failed nodes. In resource scheduling of intercluster communication, CHs in the different layers based on transmission chain obtain different sending and receiving time slots. In RSUC, CHs that are near the base station (BS) have fewer intracluster communication time slots and more intercluster communication time slots. Clusters that are further away from the BS end intercluster communication earlier and enter into intracluster communication instead of waiting for all CHs to complete intercluster communication. The simulation results reveal that RSUC significantly increases the network throughput and reduces the energy consumption of the Internet of Things.