bluuebunny/crossref_metadata_2025 huggingface dataset:
Lung cancer mortality in the European cohort of titanium dioxide workers: a reanalysis of the exposure–response relationship
Authors: Guseva Canu Irina , Gaillen-Guedy Alan , Antilla Ahti , Charles Sandrine , Fraize-Frontier Sandrine , Luce Danièle , McElvenny Damien Martin , Merletti Franco , Michel Cecile , Pukkala Eero , Schubauer-Berigan Mary K , Straif Kurt , Wild Pascal , Richardson David B
Published: May 2022

Objectives

Animal bioassays have demonstrated convincing evidence of the potential carcinogenicity to humans of titanium dioxide (TiO2), but limitations in cohort studies have been identified, among which is the healthy worker survivor effect (HWSE). We aimed to address this bias in a pooled study of four cohorts of TiO2workers.

Methods

We reanalysed data on respirable TiO2dust exposure and lung cancer mortality among 7341 male workers employed in TiO2production in Finland, France, UK and Italy using the parametric g-formula, considering three hypothetical interventions: setting annual exposures at 2.4 (U.S. occupational exposure limit), 0.3 (German limit) and 0 mg/m3for 25 and 35 years.

Results

The HWSE was evidenced. Taking this into account, we observed a positive association between lagged cumulative exposure to TiO2and lung cancer mortality. The estimated number of lung cancer deaths at each age group decreased across increasingly stringent intervention levels. At age 70 years, the estimated number of lung cancer deaths expected in the cohort after 35-year exposure was 293 for exposure set at 2.4 mg/m3, 235 for exposure set at 0.3 mg/m3, and 211 for exposure set at 0 mg/m3.

Conclusion

This analysis shows that HWSE can hide an exposure–response relationship. It also shows that TiO2epidemiological data could demonstrate an exposure–effects relationship if analysed appropriately. More epidemiological studies and similar reanalyses of existing cohort studies are warranted to corroborate the human carcinogenicity of TiO2. This human evidence, when combined with the animal evidence, strengthens the overall evidence of carcinogenicity of TiO2.
Geochronology, geochemistry, and petrogenesis of Early Cretaceous highly differentiated I-type granites in the central Great Xing’an Range, northeastern China
Authors: Lan Li-xue , Dong Yu , Ge Wen-chun , Gao Yan , Ji Zheng , Jing Yan , Bi Jun-hui , Zhou Hong-ying
Published: May 2022

In this study, we undertook zircon U–Pb dating and Hf isotopic and whole-rock geochemical analysis of Mesozoic granites from the Zhalantun and Arxan areas of the Great Xing’an Range, northeastern China. Zircon U–Pb dating of four samples indicates they formed in the Early Cretaceous (ca. 140.7–130.0 Ma). Geochemically, the samples have high silica (74.00–77.29 wt.%) and total alkali (8.52–8.90 wt.%) and low CaO (0.21–0.71 wt.%), MgO (0.06–0.48 wt.%), and P2O5 (0.01–0.06 wt.%) contents and exhibit the weakly peraluminous (A/CNK = 0.98–1.06) and high-K calc-alkaline characteristics, with highly differentiated I-type granite affinities (differentiation index = 93.19–96.93). The studied granites are enriched in light rare earth and large ion lithophile elements and depleted in heavy rare earth and high field strength elements. Their zircon εHf(t) values are mainly positive (+2.49 to +11.64) and yield two-stage Hf model ages of 1023–445 Ma. The geochemical and isotopic data indicate they might have been derived from the partial melting of medium- to high-K basaltic crust material during the Mesoproterozoic to Phanerozoic. They underwent fractional crystallization in the magma evolution process, and K-feldspar and plagioclase were the major crystallization phases. Combined with the temporal–spatial migration of the Mesozoic magmatic events in the whole northeastern China, we propose that the generation of Early Cretaceous highly differentiated I-type granites in the central Great Xing’an Range was associated with the delamination effect caused by the roll-back of the Paleo-Pacific plate.
Dark matter effect on the weak deflection angle by black holes at the center of Milky Way and M87 galaxies
Authors: Pantig Reggie C. , Övgün Ali
Published: May 2022

In this paper, we investigated the effect of dark matter on the weak deflection angle by black holes at the galactic center. We consider three known dark matter density profiles such as the Cold Dark Matter, Scalar Field Dark Matter, and the Universal Rotation Curve from the Burkert profile. To achieve this goal, we used how the positional angles are measured by the Ishihara et al. method based on the Gauss–Bonnet theorem on the optical metric. With the help of the non-asymptotic form of the Gauss-Bonnet theorem, the longitudinal angle difference is also calculated. First, we find the emergence of apparent divergent terms on the said profiles, which indicates that the spacetime describing the black hole-dark matter combination is non-asymptotic. We showed that these apparent divergent terms vanish when the distance of the source and receiver are astronomically distant from the black hole. Using the current observational data in the Milky Way and M87 galaxies, we find interesting behaviors of how the weak deflection angle varies with the impact parameter, which gives us some hint on how dark matter interacts with the null particles for each dark matter density profile. We conclude that since these deviations are evident near the dark matter core radius, the weak deflection angle offers a better alternative for dark matter detection than using the deviation from the black hole shadow. With the dark matter profiles explored in this study, we find that the variation of the values for weak deflection angle strongly depends on the dark matter mass on a particular profile.
Unified Route Planning for Shared Mobility: An Insertion-based Framework
Authors: Tong Yongxin , Zeng Yuxiang , Zhou Zimu , Chen Lei , Xu Ke
Published: May 2022

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery, and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is
route planning
. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called
insertion
. In addition, previous route planning solutions fail to exploit the appearance patterns of future requests hidden in historical data for optimization. In this paper, we present a unified formulation of route planning called URPSM. It has a well-defined parameterized objective function which eliminates the contradicted objectives in previous studies and enables flexible multi-objective route planning for shared mobility. We propose two insertion-based frameworks to solve the URPSM problem. The first is built upon the
plain-insertion
widely used in prior studies, which processes online requests only, whereas the second relies on a new insertion operator called
prophet-insertion
that handles both online and predicted requests. Novel dynamic programming algorithms are designed to accelerate both insertions to only linear time. Theoretical analysis shows that no online algorithm can have a constant competitive ratio for the URPSM problem under the competitive analysis model, yet our prophet-insertion-based framework can achieve a constant optimality ratio under the instance-optimality model. Extensive experimental results on real datasets show that our insertion-based solutions outperform the state-of-the-art algorithms in both effectiveness and efficiency by a large margin (e.g., up to 30

\( \times \)

more effective in the objective and up to 20

\( \times \)

faster).
A New Amharic Speech Emotion Dataset and Classification Benchmark
Authors: Retta Ephrem Afele , Almekhlafi Eiad , Sutcliffe Richard , Mhamed Mustafa , Ali Haider , Feng Jun
Published: May 2022

In this article we present the Amharic Speech Emotion Dataset (ASED), which covers four dialects (Gojjam, Wollo, Shewa, and Gonder) and five different emotions (neutral, fearful, happy, sad, and angry). We believe it is the first Speech Emotion Recognition (SER) dataset for the Amharic language. Sixty-five volunteer participants, all native speakers of Amharic, recorded 2,474 sound samples, 2 to 4 seconds in length. Eight judges (two for each dialect) assigned emotions to the samples with high agreement level (Fleiss kappa = 0.8). The resulting dataset is freely available for download. Next, we developed a four-layer variant of the well-known VGG model, which we call VGGb. Three experiments were then carried out using VGGb for SER, using ASED. First, we investigated which features work best for Amharic, FilterBank, Mel Spectrogram, or Mel-frequency Cepstral Coefficient (MFCC). This was done by training three VGGb SER models on ASED, using FilterBank, Mel Spectrogram, and MFCC features, respectively. Four forms of training were tried, standard cross-validation and three variants based on sentences, dialects, and speaker groups. Thus, a sentence used for training would not be used for testing, and the same for a dialect and speaker group. MFCC features were superior under all four training schemes. MFCC was therefore adopted for Experiment 2, where VGGb and three well-known existing models were compared on ASED: RESNet50, AlexNet, and LSTM. VGGb was found to have very good accuracy (90.73%) as well as the fastest training time. In Experiment 3, the performance of VGGb was compared when trained on two existing SER datasets—RAVDESS (English) and EMO-DB (German)—as well as on ASED (Amharic). Results are comparable across these languages, with ASED being the highest. This suggests that VGGb can be successfully applied to other languages. We hope that ASED will encourage researchers to explore the Amharic language and to experiment with other models for Amharic SER.
Dissecting Optional Micro-Decisions in Online Transactions: Perceptions, Deceptions, and Errors
Authors: Hogan Mairéad , Barry Chris , Lang Michael
Published: May 2022

Online firms frequently increase profit by selling optional extras. However, opt-in rates tend to be low. In response, questionable design practices have emerged to nudge consumers into inadvertent choices. Many of these design constructs are presented using an opt-out design. Using eye tracking and think-aloud data techniques, this research investigates the impact of the framing and optionality of micro-decisions on user perceptions and error rates. Focusing on opt-out decisions, the study found: up to one in three users make errors in decision-making; there is a higher error rate for rejection-framed opt-out decisions; users widely misinterpret decision framing; and failure to read decision text results in rushed and unsighted decisions, even leading users to automatically construe un-ticked checkboxes as opt-in decisions. In talking afterwards about their experiences, users expressed strong negative emotions, feeling confused, manipulated and resentful. Many suggested they would, in practice, steer away from similar encounters toward more unambiguous and honest sites. These findings might alert managers and developers, tempted to use dark patterns, that such a strategy might backfire over time.
Semi-Supervised Graph Pattern Matching and Rematching for Expert Community Location
Authors: Li Lei , Yan Mengjiao , Tao Zhenchao , Chen Huanhuan , Wu Xindong
Published: May 2022

Graph pattern matching (GPM) is widely used in social network analysis, such as expert finding, social group query, and social position detection. Technically, GPM is to find matched subgraphs that meet the requirements of pattern graphs in big social networks. In the application of expert community location, the nodes in the pattern graph and data graph represent expert entities, and the edges represent previous cooperations between them. However, the existing GPM methods focus on shortening the matching time and without considering the preference of the decision maker (DM), which makes it difficult for the DM to find ideal teams from numerous matches to complete the assigned task. In this article, as for the process of graph pattern matching and rematching, with a preferred expert set, i.e., the DM hopes that one or more experts in this set will appear in matched subgraphs, we propose a Dual Simulation-based Edge Sequencing-oriented Semi-Supervised GPM method (DsEs-ssGPM). In addition, considering a preferred expert set and a dispreferred expert set together, the DM hopes that experts in the dispreferred expert set will not appear in final matches, so we have the DsEs-ssGPM+ method. Technically, these DsEs-ssGPM methods conduct the matching process from the preferred expert set during dual simulation-based edge sequencing, and based on the edge sequence, these edges are searched recursively. Especially, as for the rematching process, when the preferred and/or the dispreferred expert sets change continuously, to process the GPM again is unnecessary and it is possible to revise the previous matched results partially with DsEs-ssGPM methods. Experiments on four large datasets demonstrate the effectiveness, efficiency and stability of our proposed DsEs-ssGPM methods, and the necessity of introducing an edge sequencing mechanism.
Dynamic Binary Translation for SGX Enclaves
Authors: Cui Jinhua , Shinde Shweta , Sen Satyaki , Saxena Prateek , Yuan Pinghai
Published: May 2022

Enclaves, such as those enabled by Intel SGX, offer a hardware primitive for shielding user-level applications from the OS. While enclaves are a useful starting point, code running in the enclave requires additional checks whenever control or data is transferred to/from the untrusted OS. The enclave-OS interface on SGX, however, can be extremely large if we wish to run existing unmodified binaries inside enclaves. This article presents
Ratel
, a dynamic binary translation engine running inside SGX enclaves on Linux.
Ratel
offers
complete interposition
, the ability to interpose on all executed instructions in the enclave and monitor all interactions with the OS. Instruction-level interposition offers a general foundation for implementing a large variety of inline security monitors in thefuture.

We take a principled approach in explaining why complete interposition on SGX is challenging. We draw attention to five design decisions in SGX that create fundamental trade-offs between performance and ensuring complete interposition, and we explain how to resolve them in the favor of complete interposition. To illustrate the utility of the
Ratel
framework, we present the first attempt to offer
binary compatibility
with existing software on SGX. We report that
Ratel
offers binary compatibility with over 200 programs we tested, including micro-benchmarks and real applications, such as Linux shell utilities. Runtimes for two programming languages, namely, Python and R, tested with standard benchmarks work out-of-the-box on
Ratel
without any specialized handling.
What is Beautiful is Secure
Authors: Stojmenović Milica , Spero Eric , Stojmenović Miloš , Biddle Robert
Published: May 2022

Visual appeal has been shown to influence perceptions of usability and credibility, and we hypothesize that something similar is happening with user judgments of website security:
What is beautiful is secure
. Web certificates provide reliable information about a website’s level of security, presented in browser interfaces. Users should use this to inform their trust decisions online, but evidence from laboratory studies and real-world usage suggests that they do not. We conducted two studies—one in lab, and one online—in which participants view and interact with websites with high and low visual appeal, and various security levels, and then make security-related judgments. In both studies, participants consistently rated visually appealing websites as more secure, and indicated they would be more likely to enter sensitive information into visually appealing websites—even when they were less secure. Our results provide evidence that users rely on visual appeal when making security and trust decisions on websites. We discuss how these results may be used to help users.
Stance-level Sarcasm Detection with BERT and Stance-centered Graph Attention Networks
Authors: Zhang Yazhou , Ma Dan , Tiwari Prayag , Zhang Chen , Masud Mehedi , Shorfuzzaman Mohammad , Song Dawei
Published: May 2022

Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT)-enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, home automation, and so on. Due to the emergence of machine translation, CL solutions have increased tremendously for different natural language processing (NLP) applications. Nowadays, NLP-enabled IoMT is essential for its success. Sarcasm detection, a recently emerging artificial intelligence (AI) and NLP task, aims at discovering sarcastic, ironic, and metaphoric information implied in texts that are generated in the IoMT. It has drawn much attention from the AI and IoMT research community. The advance of sarcasm detection and NLP techniques will provide a cost-effective, intelligent way to work together with machine devices and high-level human-to-device interactions. However, existing sarcasm detection approaches neglect the hidden stance behind texts, thus insufficient to exploit the full potential of the task. Indeed, the stance, i.e., whether the author of a text is in favor of, against, or neutral toward the proposition or target talked in the text, largely determines the text’s actual sarcasm orientation. To fill the gap, in this research, we propose a new task: stance-level sarcasm detection (SLSD), where the goal is to uncover the author’s latent stance and based on it to identify the sarcasm polarity expressed in the text. We then propose an integral framework, which consists of Bidirectional Encoder Representations from Transformers (BERT) and a novel stance-centered graph attention networks (SCGAT). Specifically, BERT is used to capture the sentence representation, and SCGAT is designed to capture the stance information on specific target. Extensive experiments are conducted on a Chinese sarcasm sentiment dataset we created and the SemEval-2018 Task 3 English sarcasm dataset. The experimental results prove the effectiveness of the SCGAT framework over state-of-the-art baselines by a large margin.
Restricted Delaunay Triangulation for Explicit Surface Reconstruction
Authors: Wang Pengfei , Wang Zixiong , Xin Shiqing , Gao Xifeng , Wang Wenping , Tu Changhe
Published: May 2022

The task of explicit surface reconstruction is to generate a surface mesh by interpolating a given point cloud. Explicit surface reconstruction is necessary when the point cloud is required to appear exactly on the surface. However, for a non-perfect input, such as lack of normals, low density, irregular distribution, thin and tiny parts, and high genus, a robust explicit reconstruction method that can generate a high-quality manifold triangulation is missing.

We propose a robust explicit surface reconstruction method that starts from an initial simple surface mesh, alternately performs a Filmsticking step and a Sculpting step of the initial mesh, and converges when the surface mesh interpolates all input points (except outliers) and remains stable. The Filmsticking is to minimize the geometric distance between the surface mesh and the point cloud through iteratively performing a restricted Voronoi diagram technique on the surface mesh, whereas the Sculpting is to bootstrap the Filmsticking iteration from local minima by applying appropriate geometric and topological changes of the surface mesh.

Our algorithm is fully automatic and produces high-quality surface meshes for non-perfect inputs that are typically considered to be challenging for prior state of the art. We conducted extensive experiments on simulated scans and real scans to validate the effectiveness of our approach.
Parameter Estimation for Electrode Degradation: Learning in the Face of Model-Experiment Discrepancies
Authors: Mayilvahanan Karthik S. , Soni Jwal R. , Takeuchi Kenneth J. , Takeuchi Esther S. , Marschilok Amy C. , West Alan C.
Published: May 2022

Use of physics-based models to interpret battery degradation data over the course of cycling can provide deeper physical insight into the internal states of the system and how they evolve. We present a neural network trained on simulations generated by a previously published physics-based model for a lithium trivanadate (LVO) cathode to estimate parameters that evolve over the course of cycling. We focus on the robustness of the neural network through two case studies that probe different kinds of discrepancies between model and experiment: nonideal data and imperfect model. In the former, the experimental protocols do not meet the assumption made in the training data generated by the physics-based model, while in the latter, the physics-based model fails to describe all of the measured cathode behavior even under ideal conditions. When there is total model-experiment agreement, a neural network estimates parameters with improved accuracy compared to a maximum likelihood analysis using the same set of simulations. However, in both types of model-experiment discrepancy, the neural network returned biased parameter estimates. We introduce a data augmentation procedure into the neural network training to mitigate these effects and improve robustness, and employ it to estimate parameters for a cycling LVO cathode.
Research for Pedestrian Steel Bridge Design of Neural Network in Structural Model Updating
Authors: Zhao Rui , Wu Yuhang , Feng Zehua
Published: May 2022

The application of the neural network method in health monitoring and structural system identification has received extensive attention. A reasonable neural network structure is very important for its performance. This paper takes the pedestrian bridge of the Xingfu intersection in Urumqi, China, as the research object and uses MIDAS/Civil to establish a finite element analysis model. Taking the natural vibration frequency obtained from the dynamic test of the actual bridge as the target, two kinds of neural networks are used to predict the structural material parameters. An appropriate bridge model correction method is selected by comparing the prediction results of the BP neural network and the GRNN. The test results show that the pedestrian bridge model based on MIDAS/Civil has a high accuracy, but it still does not meet the actual needs. The modified model based on the BP neural network is close to the actual measured results, and a more accurate finite element analysis model can be established by this method, which makes the modified model closer to the real stress state of the structure.
Analysis of Therapeutic Targets of A Novel Peptide Athycaltide-1 in the Treatment of Isoproterenol-Induced Pathological Myocardial Hypertrophy
Authors: Zheng Xi , Su Fuxiang , Kang Ze , Li Jingyuan , Zhang Chenyang , Zhang Yujia , Hao Liying
Published: May 2022

Myocardial hypertrophy is a pathological feature of many heart diseases. This is a complex process involving all types of cells in the heart and interactions with circulating cells. This study is aimed at identifying the differentially expressed proteins (DEPs) in myocardial hypertrophy rats induced by isoprenaline (ISO) and treated with novel peptide Athycaltide-1 (ATH-1) and exploring the mechanism of its improvement. ITRAQ was performed to compare the three different heart states in control group, ISO group, and ATH-1 group. Pairwise comparison showed that there were 121 DEPs in ISO/control (96 upregulated and 25 downregulated), 47 DEPs in ATH-1/ISO (27 upregulated and 20 downregulated), and 116 DEPs in ATH-1/control (77 upregulated and 39 downregulated). Protein network analysis was then performed using the STRING software. Functional analysis revealed that Hspa1 protein, oxidative stress, and MAPK signaling pathway were significantly involved in the occurrence and development of myocardial hypertrophy, which was further validated by vivo model. It is proved that ATH-1 can reduce the expression of Hspa1 protein and the level of oxidative stress in hypertrophic myocardium and further inhibit the phosphorylation of p38 MAPK, JNK, and ERK1/2.
Exploration of Water-Saving and High-Yield Irrigation Model for Tomato under Microsprinkler Irrigation with Plastic Film in a Greenhouse Based on Spatial Analysis
Authors: Zhang Mingzhi , Wang Lu , Wang Hui , Xiao Na , Liu Jianfei
Published: May 2022

The purpose of this study was to analyse the suitability of the frequency and amount of microsprinkler irrigation under plastic film technology (MSPF) in a greenhouse. This study was aimed at determining the effects of different irrigation frequency and irrigation amount of the MSPF on the quality, yield (Y), and water use efficiency (WUE) of tomato, and the optimal irrigation period and irrigation amount of tomato with MSPF were determined by spatial analysis method. Irrigation frequency was set 3 levels, 3 d (F1), 5 d (F3), and 7 d (F3), respectively. Irrigation amount was set 3 levels, 0.7

E

pan

(I1), 1.0

E

pan

(I2), and 1.2

E

pan

(I3) (

E

pan

is the diameter of 20 cm standard pan evaporation, mm), respectively. The results show that increase in the single fruit weight (SFW), transverse diameter (TD), vertical diameter (VD), total soluble solids (TSS), total soluble sugar (TSU), soluble solid content (SSC), sugar-acid ratio (SAR), soluble protein (SP), vitamin C (VC), lycopene (L), Y, and WUE of spring and autumn tomatoes was observed initially with decreasing irrigation frequency; however, decrease in all these variables was observed thereafter. Similarly, SFW, TD, VD, and SAR of spring and autumn tomatoes first added and then reduced with increasing irrigation amount, whereas TSS, SP, TSU, VC, L, and WUE exhibited an overall decreasing trend. On the basis of spatial analysis, the comprehensive score, Y, and WUE of F2I2 tomato were predicted to simultaneously reach more than 95% of the maximum, which is consistent with the earlier studies on comprehensive evaluation of yield and WUE.
A Right Transfer Access Control Model of Internet of Things Based on Smart Contract
Authors: Wang Jiuru , Gong Ping , Wang Haifeng , Zhang Wenyin , Sun Chongran , Zhao Bin
Published: May 2022

Sensor nodes play a crucial role in the promotion of development of Internet of Things (IoT). Through this transaction, RO defines access control policies in script form based on ABAC's access control model to grant access right. The identity of all users in the model is identified by address. This paper builds a more flexible right transfer access control model by means of combining the Attribute-Based Access Control model (ABAC) and blockchain technology. Owing to the characteristics of ABAC’s attributes and right association, the massive problems of some sensor nodes can be solved. At the same time, for the sake of addressing the dynamic problems such as node access and right transfer, right transfer contract (TS) and access control contract (CS) are employed on the chain to ensure efficient and safe transmission of rights. To solve on-chain storage problems and ensure transparency of the operation, the idea of Rollup in Ethereum expansion is used to upload the final state of protocol policy and right exchange to the chain. Any user can know the policy and current right transfer status at any time. Finally, comparative and security analysis show that the model presented here can solve IoT devices’ massive and dynamic problems more effectively and it is more secure than the traditional models.
ALBRL: Automatic Load-Balancing Architecture Based on Reinforcement Learning in Software-Defined Networking
Authors: Chen Junyan , Wang Yong , Ou Jiangtao , Fan Chengyuan , Lu Xiaoye , Liao Cenhuishan , Huang Xuefeng , Zhang Hongmei
Published: May 2022

Due to the rapid development of network communication technology and the significant increase in network terminal equipment, the application of new network architecture software-defined networking (SDN) combined with reinforcement learning in network traffic scheduling has become an important focus of research. Because of network traffic transmission variability and complexity, the traditional reinforcement-learning algorithms in SDN face problems such as slow convergence rates and unbalanced loads. The problems seriously affect network performance, resulting in network link congestion and the low efficiency of inter-stream bandwidth allocation. This paper proposes an automatic load-balancing architecture based on reinforcement learning (ALBRL) in SDN. In this architecture, we design a load-balancing optimization model in high-load traffic scenarios and adapt the improved Deep Deterministic Policy Gradient (DDPG) algorithm to find a near-optimal path between network hosts. The proposed ALBRL uses the sampling method of updating the experience pool with the SumTree structure to improve the random extraction strategy of the empirical-playback mechanism in DDPG. It extracts a more meaningful experience for network updating with greater probability, which can effectively improve the convergence rate. The experiment results show that the proposed ALBRL has a faster training speed than existing reinforcement-learning algorithms and significantly improves network throughput.
Adaptive Friction Compensation for a Novel Two-Axis Differential Microfeed System
Authors: Wang Anning , Feng Xianying , Wang Dong , Li Peigang , Xu Zhenyu
Published: May 2022

The conventional drive feed system inevitably enters the nonlinear creep region at very low speed. This seriously affects its low-speed performance and tracking accuracy. The new two-axis differential microfeed system (TDMS) can overcome the low-speed crawling and creep phenomenon caused by the inherent characteristics of the traditional electromechanical servo system structure. In this study, the dynamic model of TDMS is first established, and the LuGre friction model is improved according to its motion characteristics. The friction model of the screw nut whole assembly of TDMS is established. On this basis, two nonlinear observers are designed to estimate the internal state of the friction model, and the TDMS adaptive friction compensation controller is designed to estimate the uncertain parameters in TDMS through the parameter adaptive law. The global asymptotic stability of the TDMS controller is proved by Lyapunov theorem. Finally, the experimental verification is carried out by using dSPACE system. The results showed that the proposed TDMS adaptive friction compensation controller has good control accuracy. And it can effectively compensate the friction torque of lead screw nut differential drive. Compared with the application of traditional PID controller in TDMS, the tracking accuracy is improved by an order of magnitude.
Curcumol Targeting PAX8 Inhibits Ovarian Cancer Cell Migration and Invasion and Increases Chemotherapy Sensitivity of Niraparib
Authors: Liu Caihong
Published: May 2022

Objective. To investigate the effects of Curcumol on invasion, migration and epithelial-mesenchymal transformation of IGROV-1 and OVCAR-3 cells in ovarian cancer and its potential mechanism. Meanwhile, the effect of Curcumol on the antitumor activity of Niraparib was analyzed. Methods. Cell Counting Kit 8 (CCK-8) was used to detect the effects of Curcumol on the activity of IGROV-1 and OVCAR 3 cells. In vitro invasion assay (Transwell) was used to test the invasiveness of cells. Cell migration was detected by scratch assay. The inhibitory effect of Curcumol on PAX8 was detected by QRT-PCR. To evaluate the antitumor effect of Curcumol in subcutaneous tumor-bearing animal model. Results. Knockdown of PAX8 could inhibit the proliferation, invasion and migration of ovarian cancer cells. After Curcumol treated IGROV-1 and OVCAR-3 cells, the cell proliferation ability was decreased, the number of invasive cells was significantly reduced, and the scratch closure rate was significantly reduced, in a dose-dependent manner. Mechanism studies showed that Curcumol increased the antitumor activity of Niraparib by inhibiting PAX8. Conclusion. Curcumol can inhibit the invasion, migration and epithelial-mesenchymal transformation of IGROV-1 and OVCAR-3 cells in ovarian cancer, and its mechanism is related to the targeted inhibition of PAX8. Curcumol also increased the sensitivity of Niraparib chemotherapy by inhibiting PAX8.
Construction of Game Model between Carbon Emission Minimization and Energy and Resource Economy Maximization Based on Deep Neural Network
Authors: Ma Lan , Wang Dalei
Published: May 2022

Under this background, this paper tries to find countermeasures and ways for carbon reduction by observing and analyzing the influencing factors of carbon emissions, designing ways to minimize carbon emissions and maximize resources and energy. In view of the above problems, the carbon emission prediction research is closely combined with the research of deep neural network, the carbon emission prediction models based on deep neural network are established, respectively, and the game theory is introduced to maximize the resource economy. Based on the analysis of the cost of energy resources, this paper puts forward a model based on game theory and makes an overall planning of the bidding online auxiliary decision-making system in combination with the actual market demand. Build a big data analysis platform based on the Internet of things, collect the data related to carbon emission for normalization, analyze the influencing factors related to carbon emission by using the principal component analysis method, select the data with higher connection value, and take the time series data as the input of the deep neural network for simulation verification. The simulation results show that the game model of carbon emission minimization and energy resource economic maximization based on deep neural network can effectively improve the economic maximization of energy resources and reduce carbon emissions.
A Framework for Formal Analysis of Anonymous Communication Protocols
Authors: Yang Ke , Xiao Meihua
Published: May 2022

Anonymity is the property of keeping secret the identity of the user performing a certain action. The need for anonymity may arise in a wide range of situations, like electronic voting, Web browsing, and so on. In order to verify the anonymity of security protocols, a framework for formal analysis of anonymous communication protocols is proposed. In this framework, we define operational semantics for security protocols using a labelled transition system; the transition relation is defined by the transition rules, which include create rule, send rule, and receive rule. In addition, the formal description of intruder model in this framework is given. The proposed intruder model specifies the capabilities of the intruder and is weaker than the Dolev–Yao model. Moreover, the concepts of mapping and trace equivalence are proposed; the sender anonymity is formally defined. To illustrate the applicability of proposed framework, we explore the use of probabilistic model checking tool PRISM to analyze the sender anonymity of Crowds protocol. The experimental results show the relationship between sender anonymity and the number of nodes, path reformulations, and forwarding probability, which provides a good way about how to protect the sender anonymity of anonymous communication protocols.
A Precise Medical Imaging Approach for Brain MRI Image Classification
Authors: Siddiqi Muhammad Hameed , Alsayat Ahmed , Alhwaiti Yousef , Azad Mohammad , Alruwaili Madallah , Alanazi Saad , Kamruzzaman M. M. , Khan Asfandyar
Published: May 2022

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.
Relapsing De Novo Membranous Nephropathy
Authors: Ali Ala , Al-Taee Huda , Kadhim Thaer J.
Published: May 2022

Allograft membranous glomerulopathy can be a recurrent or de novo disease. Both instead have different underlying immune pathophysiology and disease pattern. While the introduction of ANTI-PLAR2 and THS7A brought new insights into the management of Immune/primary MN, the treatment of de novo MN is not clear. Relapsing de novo MN in a kidney transplant was rarely reported. Here, we present a case of relapsing de novo MN without evidence of rejection and a gratifying response to rituximab.
E-Commerce Credit Network Control Strategy from a Critical Perspective
Authors: Hebing Zhang , Xiaojing Zheng
Published: May 2022

Credit problems are the main bottleneck in the development of e-commerce. Both the time and degree of e-commerce credit control directly affect its economic benefits. From the perspective of the criticality of complex systems, this paper explores the control node of e-commerce credit behaviour. Implementing control in this node can not only ensure the stable development of the credit network but also optimise the control cost. This paper constructs a credit behaviour model for the four regulatory behaviours of the transaction subject, analyses the evolution law of the credit network, and determines the critical state of the network. Finally, the paper’s empirical analysis and simulation experiments prove that when the false information in the credit network in the empirical data is 32%, the implementation of credit control has an efficient control effect. Nevertheless, 32% is not a universal result; the specific critical point value needs to be recalculated according to the theoretical derivation of the critical point combined with the actual network. These research results can help regulators obtain the highest regulatory return with the lowest regulatory investment.
EPPSA: Efficient Privacy-Preserving Statistical Aggregation Scheme for Edge Computing-Enhanced Wireless Sensor Networks
Authors: Tao Yunting , Kong Fanyu , Yu Jia , Xu Qiuliang
Published: May 2022

In edge computing-enhanced wireless sensor networks (WSNs), multidimensional data aggregation can optimize the utilization of computation resources for data collection. How to improve the efficiency of data aggregation has gained considerable attention in both academic and industrial fields. This article proposes a new efficient privacy-preserving statistical aggregation scheme (EPPSA) for WSNs, in which statistical data can be calculated without exposing the total number of sensor devices to control center. The EPPSA scheme supports multiple statistical aggregation functions, including arithmetic mean, quadratic mean, weighted mean, and variance. Furthermore, the EPPSA scheme adopts the modified Montgomery exponentiation algorithms to improve the aggregation efficiency in the edge aggregator. The performance evaluation shows that the EPPSA scheme gets higher aggregation efficiency and lower communication load than the existing statistical aggregation schemes.
The Drivers of the Behavioral Adoption Intention of BITCOIN Payment from the Perspective of Chinese Citizens
Authors: Mensah Isaac Kofi , Mwakapesa Deborah Simon
Published: May 2022

The bitcoin payment innovation has gained wider interest around the world, but its adoption among the general population has been a challenge. Bitcoin as a peer-to-peer technology works with no central authority or banks, and the transaction management and issuing of bitcoin is carried out collectively by the network. One major debate on bitcoin development and diffusion is the critical matter of nongovernment intervention through adequate policy and regulatory framework and thus hinders people’s active participation (acceptance) in the bitcoin market. Consequently, this study is purposed to examine the role government policy and regulations (moderating impact) can have in driving the acceptance of bitcoin payment from the Chinese perspective. The UTAUT was used as the theoretical basis from which a model was developed for testing. The structural equation model (SEM) through the use of SmartPLS was employed to undertake the analysis. The results have demonstrated that government regulation moderates (significant) the influence of both performance expectancy and infrastructure support on the behavioral acceptance of bitcoin payment. However, government regulation contrary to expectations was not significant in moderating the influence of effort expectancy (EE) and security on the acceptance of bitcoin payments. Additionally, the study discovered that performance expectancy, security, EE, and infrastructure support were significant in encouraging bitcoin behavioral adoption. The practical and theoretical implications of these findings on the development and diffusion of bitcoin technology systems are dissected meticulously.
Computation of M-Polynomial and Topological Indices of Phenol Formaldehyde
Authors: Kamran Muhammad , Salamat Nadeem , Khan Riaz Hussain , ur Rehman Asghar Ubaid , Alam Md. Ashraful , Pandit M. K.
Published: May 2022

Phenol formaldehyde (phenolic resin) has a wide range of moldings. However, it has immense consumption in manufacturing electrical equipment due to its insulating property. Phenolic resin retains properties at the freezing point, and also its age cannot be determined. Due to its insulator property, it has wide use in electrical equipment. In this article, degree-based topological indices of phenol formaldehyde are determined with the help of M-polynomial. We calculate the Zagreb index, Randić index, K-Banhatti indices, modified K-Banhatti indices, atom-bond connectivity index, geometric arithmetic index, symmetric index, inverse sum index, and harmonic index.
Multiple Spacecraft Formation Flying Control around Artificial Equilibrium Point Using Propellantless Approach
Authors: Zhao Lei , Yuan Changqing , Li Xiaoming , He Jingjiu
Published: May 2022

This paper demonstrates a detailed analysis of the feasibility for compact formation system around an L2-type artificial equilibrium point by means of continuous low-thrust propulsion in the hybrid form of solar sail and Coulomb force propulsion. Firstly, in view of non-ideal solar sail, the position of L2-type artificial equilibrium point and numerical periodic orbits around L2 utilized as leader’s nominal trajectory are given. Secondly, considering the external disturbances in the deep space environment, the nonlinear dynamic model of the spacecraft formation system based on the circular restricted three-body problem (CRTBP) is derived, under the assumption that the leader covers the nominal trajectory and each follower adjusts its propulsive acceleration vector (that is, both its sail attitude and electrostatic charge) in order to track a desired relative trajectory. Thirdly, based on a new double power combination function reaching law, a fast integral terminal sliding mode control methodology (MFITSM) is ameliorated to achieve orbital tracking rapidly, which has better robustness against external disturbances and the buffeting effect during spacecraft propulsion simultaneously. To properly allocate control inputs, a novel optimal allocation scheme is designed to calculate the charge product of the spacecrafts and sail attitude angles, which can make the magnitude of the acceleration required from the Coulomb propulsion system minimum and avoid formation geometry instabilities by balancing electrostatic interaction between adjacent spacecraft. Finally, several numerical examples are conducted to validate the superiority of the proposed control algorithm.
Tumor Suppressor Role and Clinical Significance of the FEV Gene in Prostate Cancer
Authors: Liang Yu-Xiang , Liang Ying-Ke , Zou Zhi-Hao , Zhuo Yang-Jia , Ye Jian-Heng , Zhu Xue-Jin , Cai Zhou-Da , Lin Zhuo-Yuan , Mo Ru-Jun , Wu Shu-Lin , Zhang Yan-Qiong , Zhong Wei-De
Published: May 2022

Background. In our previous research, we developed a 32-gene risk index model that may be utilized as a robust prognostic method for predicting prostate cancer (PCa) recurrence after surgery. Among the 32 genes, the Fifth Ewing Variant (FEV) gene was one of the top downregulated genes in relapsed PCa. However, current understanding of the FEV gene and its involvement in PCa is limited. Methods. FEV mRNA expression was analyzed and correlated to clinical outcomes in PCa patients who underwent prostatectomy at the Massachusetts General Hospital. Specimens from tissue microarray (TMA) including 102 prostate cancer patients were analysis for the expression of FEV. Meanwhile, FEV expression profiles were also assessed in PCa cell lines and in BPH-1 prostate epithelial cells using western blotting and quantitative reverse transcription-PCR (qRT-PCR). Furthermore, we transfected LNCaP and PC-3 cells with either an empty vector or full-length FEV gene and performed in vitro cell functional assays. The part FEV plays in tumor xenograft growth was also assessed in vivo. Results. Of the 191 patients included in this study base on the DASL dataset, 77 (40.3%) and 24 (13.6%), respectively, developed prostate-specific antigen (PSA) relapse and metastasis postradical prostatectomy. Significant FEV downregulation was observed in PCa patients showing PSA failure and metastasis. The protein expression of FEV was significantly negatively correlated with the Gleason score and pathological stage in prostate cancer tissues. Similarly, FEV expression significantly decreased in all PCa cell lines relative to BPH-1 (all

P
<
0.05

). Functional assays revealed that FEV expression markedly inhibited PCa cell growth, migration, and invasion, which in turn significantly repressed the growth of tumor xenografts in vivo. Conclusion. The results of this study suggest an association between downregulated FEV expression and PSA relapse in PCa patients. In addition, FEV may act as a tumor suppressor in PCa.