bluuebunny/crossref_metadata_2025 huggingface dataset:
Chaotic Circuit and Its Application in Weak Signal Detection
Authors: Xu Xuemei , Wang Xiao , Wen Jun
Published: December 2022

In this work, we design a novel 3D chaotic circuit model and investigate the dynamics of a system without an equilibrium point inspired by Justin’s model. New features are presented by tuning the controlling circuit parameters, including dramatic hysteresis loops, heart bistable hidden attractors, and symmetrical attractors. We surprisingly find that these behaviors indeed lead to switched systems among various oscillators such as “hysteresis loops”, “Van der Pol”, “heart”, “bell” and “butterfly”. Hence, both the voltage’s amplitude and frequency are modulated in proper parameters. It is interesting to find that in the system, it is easy to control the bistable threshold value and the transition trajectory between the chaotic and the periodic states. These characteristics have great potential to dramatically enhance the accuracy and sensitivity of signal detection. A high quality factor circuit is achieved by adjusting the parameters of the chaotic system, so that the influence of noise on the ratio of signal to noise (SNR) of the system is almost negligible. Systematic experiments are carried out to verify the prediction from numerical simulations. To conclude, this system enables a new method to detect weak signals coupled with strong noise.
Reliability Aspects of Quantile-Based Residual Coefficient of Variation
Authors: Aswin I. C. , Sankaran P. G. , Sunoj S. M.
Published: December 2022

In reliability studies, the data is often truncated and hence the residual random variable plays a vital role in the modelling and analysis of lifetime data. Like various reliability measures such as hazard rate, mean residual life function, variance residual life function, the residual coefficient of variation is also an important tool considered by many. In this paper, we study a quantile version of coefficient of variation of residual life, an alternative to the traditional distribution function measure. We study various properties of it and obtain characterizations based on some well-known probability models. We introduce a class of distributions with linear quantile-based residual coefficient of variation and study their basic reliability properties. We also obtain some stochastic comparison and aging properties using quantile-based residual coefficient of variation. The L-moment method of estimation for the class of distributions with linear quantile-based residual coefficient of variation has also been illustrated using two real data sets.
Calculation of Instantaneous Reliability of Grain Structure Under Small Samples
Authors: Du Juan , Li Haibin
Published: December 2022

Under the condition of small sample, the performance parameters’ interval quantization and instantaneous reliability calculation of solid rocket motor grain structure are researched. Two important mechanical properties of material: Relaxation modulus and Poisson’s ratio are obtained by experiments of grain material. Since the data of parameters obtained are a small sample, the method of gray theory is used to mine the experimental data. It realizes the uncertainty quantitative analysis of grain material performance parameters and obtains the quantitative interval of performance parameters. Considering that the evidence theory can directly attribute probabilistic mass to the set or interval, the instantaneous reliability of grain structure is analyzed based on the evidence theory. By establishing the relationship between failure surface and the frame of discernment, and using the belief function and plausibility function to obtain the upper and lower bound probability distribution of structural reliability and failure probability, the instantaneous reliability probability interval of structure is obtained.
A Bayes Analysis of a Dependent Competing Risk Model Based on Marshall–Olkin Bivariate Weibull Distribution
Authors: Gupta Ankita , Ranjan Rakesh , Gupta Akanksha , Upadhyay Satyanshu K.
Published: December 2022

This paper considers a competing risk model defined on the basis of minimum of two dependent failures where the two failures are assumed to jointly follow Marshall–Olkin bivariate Weibull distribution. This paper explores some important features of corresponding likelihood functions and performs a full Bayesian analysis of the model for data resulting from normal as well as accelerated life tests. The accelerated model is described by regressing the scale parameters of the model through inverse power-law relationship. Posterior-based inferences are drawn using the Gibbs sampler algorithm after specifying proper but vague priors for the model parameters. The numerical illustration is provided using real datasets. The performance of the model is assured by Bayesian tools of model compatibility and then the entertained model is compared with the competing risk model based on Marshall–Olkin bivariate exponential assumption.
Behavior Analysis of Feeding Unit of a Paper Industry in Fuzzy Environment
Authors: Sharma Seema , Mamta
Published: December 2022

The feeding unit is an important functional part in the paper industry that requires the development of effective maintenance plan based on its reliability analysis. This study analyzes reliability of the considered unit employing trapezoidal fuzzy numbers in fuzzy [Formula: see text]–[Formula: see text] methodology with Petri net modeling. In this analysis, the collected crisp input data of each subsystem has been fuzzified using trapezoidal fuzzy numbers. Then fuzzy values of several reliability factors of feeding unit, namely, failure rate, repair time, reliability, availability, etc. at ±15%, ±25% and ±40% spread levels have been calculated. Further, the obtained fuzzy values of reliability factors have been defuzzified to analyze system behavior of feeding unit. The analysis is useful for plant maintenance and operation management to understand the behavior of feeding unit in a more realistic manner.
A HYBRID ENSEMBLE LEARNING MODEL FOR EVALUATING THE SURFACE ROUGHNESS OF AZ91 ALLOY DURING THE END MILLING OPERATION
Authors: JHA PANCHANAND , SHAIKSHAVALI G. , SHANKAR M. GOWRI , RAM M. DINESH SAI , BANDHU DIN , SAXENA KULDEEP K. , BUDDHI DHARAM , AGRAWAL MANOJ KUMAR
Published: December 2022

In metal-cutting operations, the surface roughness of the end product plays a significant role. It not only affects the aesthetic appearance of the end product but also signifies the product’s performance in the long run. Products with a high surface finish have higher endurance limits with negligible local stresses. On the other hand, products with rough surfaces are subjected to high stresses when they are engaged in various mechanical operations with varying loads. Surface roughness depends on various machining factors such as feed rate, depth of cut, cutting speed, or spindle speed. Therefore, it is required to predict surface roughness for the given machining parameters to reduce the cost and increase the life of the end product. In this work, an attempt has been made to evaluate the surface roughness of AZ91 alloy during the end milling operation. In this regard, various state-of-the-art ensemble learning models have been adopted and compared with the proposed hybrid ensemble model. The proposed hybrid ensemble model is the integration of random forest, gradient boosting, and a deep multi-layered neural network. In order to evaluate the performance of the proposed model, comparative analyses have been made in terms of mean square error, mean average error, and [Formula: see text] score. The result shows that the proposed hybrid model gives minimum error for surface roughness.
EXPERIMENTAL STUDY ON THE SURFACE ROUGHNESS AND OPTIMIZATION OF CUTTING PARAMETERS IN THE HARD TURNING USING BIOCOMPATIBLE TiAlN-COATED AND UNCOATED CARBIDE INSERTS
Authors: DIKSHIT MITHILESH K. , PATHAK VIMAL K. , AGRAWAL REEYA , SAXENA KULDEEP K. , BUDDHI DHARAM , MALIK VINAYAK
Published: December 2022

Machining of difficult-to-cut materials has always been a focus of research. In terms of surface roughness, it is one of the most important machinability indicators used to evaluate the performance of machining processes. This research aims to investigate the effect of biocompatible TiAlN-coated and uncoated carbide inserts, as well as the effect of cutting parameters such as feed, rotational speed, and depth of cut on surface roughness in the hard turning of M2 tool steel at 64 HRC. The central composite design is used to create the experimental layout. Surface roughness values are measured using separate experiments for coated and uncoated inserts. A quadratic model is selected, and an analysis of variance (ANOVA) is performed to test the adequacy of the developed model. From the ANOVA, it is found that feed and rotational speed are the most significant parameter while hard turning with TiAlN-coated and uncoated inserts, respectively. Cutting parameters are ranked according to their importance using the Pareto chart. The composite desirability function is employed to determine the optimal setting of cutting parameters to minimize the surface roughness and a confirmation experiment is conducted to validate the optimization results. Confirmation results are very close to the predicted value and the error between experimental and predicted results are 7.93% and 9.36% with TiAlN-coated and uncoated carbide inserts, respectively. TiAlN-coated carbide insert gives better surface roughness compared to an uncoated carbide insert.
Wireless Sensor Network Security Analysis for Data and Aggregation
Authors: Manoharan Maravarman , Babu S. , Pitchai R.
Published: December 2022

Data security is critical in wireless sensor networks (WSNs) because communication signals are highly available due to data transmission in free space. Attacks ranging from passive eavesdropping to active snooping are more common on these networks. This paper proposes secure data transfer using data encryption based on the improved Rivest–Shamir–Adleman (RSA) with Diffie–Hellman (DH) key exchange algorithm (IRSA-DH). For this purpose, the adaptive distance-based agglomerative hierarchical (ADAH)-based clustering method is used. Then the cluster head (CH) is selected using the improved weight-based rain optimization (IWRO) to improve the network’s lifespan. This study aims to design a secure group communication method for WSNs. In order to generate and distribute the key to the group, the RSA and DH and key exchange algorithm had been hybridized with the Key Management Center (KMC). For safe communication between users, the key exchange technique is investigated. The performance measures such as throughput, packet loss ratio (PLR), packet delivery ratio (PDR), latency, energy consumption, end-to-end delay (EED) and network lifetime are analyzed and compared with the existing approaches.
Average preserving variation processes in view of optimization
Authors: Lassalle Rémi
Published: December 2022

In this paper, we investigate specific least action principles for laws of stochastic processes within a framework which stands on filtrations preserving variations. The associated Euler–Lagrange conditions, which we obtain, exhibit a deterministic process in the dynamics aside the canonical martingale term. In particular, taking specific action functionals, extremal processes with respect to those variations encompass specific laws of continuous semi-martingales whose drift characteristic is integrable with independent increments. Then, we relate extremal processes of classical cost functions, in particular of specific entropy functions, to a class of forward-backward systems of Mckean–Vlasov stochastic differential equations.
A Novel Crowdsourcing Task Recommendation Method Considering Workers’ Fuzzy Expectations: A Case of ZBJ.COM
Authors: Yang Biyu , Li Longxiao , Wang Xu , Tan Guangzhu
Published: December 2022

Crowdsourcing recommendation is necessary to surmount information overload and assist workers to identify tasks of interest efficiently. However, existing studies mainly concentrate on investigating workers’ qualifications or requirements, neglecting their fuzzy expectations and psychological behavior. Besides, workers can only passively accept the results and do not have any right to change them even if they are not satisfactory. As workers will pay different attention to task aspects, we regard the crowdsourcing recommendation as a multi-criteria decision-making (MCDM) problem. In this paper, we propose an MCDM-based method for automated and interactive crowdsourcing task recommendations while comprehensively considering workers’ fuzzy expectations and psychological behavior. Given a large volume of available tasks on a crowdsourcing platform, the requirement of massive human efforts in traditional MCDM is not applicable in such situation. Therefore, we formulate a set of quantitative task evaluation criteria for automated task assessments based on the attributes that have the most impact on workers’ participation behavior. Further, to capture workers’ fuzziness in expectations, an intuitionistic fuzzy 2-tuple linguistic (IF2L) term is introduced, which are further leveraged as reference points to evaluate their “gains” and “losses” based on the prospect theory. Finally, the D-S evidence theory is adopted to gather workers’ uncertain prospect values. A case of ZBJ.COM, a popular crowdsourcing platform in China, shows that the proposed method is efficient and effective in recommending suitable and personalized tasks to workers.
Entanglement recovery in noisy binary quantum information protocols via three-qubit quantum error correction codes
Authors: Morea Alessio , Notarnicola Michele N. , Olivares Stefano
Published: December 2022

The task of preserving entanglement against noises is of crucial importance for both quantum communication and quantum information transfer. To this aim, quantum error correction (QEC) codes may be employed to compensate, at least partially, the detriments induced by environmental noise that can be modeled as a bit-flip or a phase-flip error channel. In this paper, we investigate the effects of the simple three-qubit QEC codes to restore entanglement and nonlocality in a two-qubit system and consider two practical applications: superdense coding and quantum teleportation. Though the considered three-qubit QEC codes are known to perfectly work in the presence of very small noise, we show that they can avoid the sudden death of entanglement and improve the performance of the addressed protocols also for larger noise amplitudes.
Assessment of long-term sensor performance based on a large THM experiment in the HADES URL
Authors: Verstricht Jan , Nackaerts Dries , Li Xiang Ling , Leonard Didier , Levasseur Séverine , Van Geet Maarten
Published: December 2022

A monitoring plan is an important part of a disposal programme. Monitoring a deep geological repository for the disposal of radioactive waste faces several challenges. These may arise from the technically demanding environment in which the monitoring equipment must operate or from the potentially long period of time during which they must operate. Over the past decades, a lot of experience has been gained in monitoring experiments in underground research laboratories (URL).

Since the HADES URL became operational in the 1980s, thousands of sensors have been installed. To document the experience gained in this context, ONDRAF/NIRAS launched a research project to evaluate the performance of the monitoring equipment implemented in the HADES URL. This required developing a method to assess the performance of sensors in a consistent way. The methodology is explained in this paper and illustrated for the instruments installed to monitor the THM response of Boom Clay to the large-scale PRACLAY
in situ
experiment.
A Linearizability-based Hierarchy for Concurrent Specifications
Authors: Castañeda Armando , Rajsbaum Sergio , Raynal Michel
Published: December 2022

Two linearizability-style correctness conditions that can be used to argue safety properties of progressively more concurrent behaviors of objects.
FlexCNN: An End-to-end Framework for Composing CNN Accelerators on FPGA
Authors: Basalama Suhail , Sohrabizadeh Atefeh , Wang Jie , Guo Licheng , Cong Jason
Published: December 2022

With reduced data reuse and parallelism, recent convolutional neural networks (CNNs) create new challenges for FPGA acceleration. Systolic arrays (SAs) are efficient, scalable architectures for convolutional layers, but without proper optimizations, their efficiency drops dramatically for reasons: (1) the different dimensions within same-type layers, (2) the different convolution layers especially transposed and dilated convolutions, and (3) CNN’s complex dataflow graph. Furthermore, significant overheads arise when integrating FPGAs into machine learning frameworks. Therefore, we present a flexible, composable architecture called FlexCNN, which delivers high computation efficiency by employing dynamic tiling, layer fusion, and data layout optimizations. Additionally, we implement a novel versatile SA to process normal, transposed, and dilated convolutions efficiently. FlexCNN also uses a fully pipelined software-hardware integration that alleviates the software overheads. Moreover, with an automated compilation flow, FlexCNN takes a CNN in the ONNX

1

representation, performs a design space exploration, and generates an FPGA accelerator. The framework is tested using three complex CNNs: OpenPose, U-Net, and E-Net. The architecture optimizations achieve 2.3× performance improvement. Compared to a standard SA, the versatile SA achieves close-to-ideal speedups, with up to 5.98× and 13.42× for transposed and dilated convolutions, with a 6% average area overhead. The pipelined integration leads to a 5× speedup for OpenPose.
Dilated Convolution-based Feature Refinement Network for Crowd Localization
Authors: Gao Xingyu , Xie Jinyang , Chen Zhenyu , Liu An-An , Sun Zhenan , Lyu Lei
Published: December 2022

As an emerging computer vision task, crowd localization has received increasing attention due to its ability to produce more accurate spatially predictions. However, continuous scale variations in complex crowd scenes lead to tiny individuals at the edges, so that existing methods cannot achieve precise crowd localization. Aiming at alleviating the above problems, we propose a novel Dilated Convolution-based Feature Refinement Network (DFRNet) to enhance the representation learning capability. Specifically, the DFRNet is built with three branches that can capture the information of each individual in crowd scenes more precisely. More specifically, we introduce a Feature Perception Module to model long-range contextual information at different scales by adopting multiple dilated convolutions, thus providing sufficient feature information to perceive tiny individuals at the edge of images. Afterwards, a Feature Refinement Module is deployed at multiple stages of the three branches to facilitate the mutual refinement of feature information at different scales, thus further improving the expression capability of multi-scale contextual information. By incorporating the above modules, DFRNet can locate individuals in complex scenes more precisely. Extensive experiments on multiple datasets demonstrate that the proposed method has more advanced performance compared to existing methods and can be more accurately adapted to complex crowd scenes.
Getting a Handle on Handles
Authors: Roberts Alexandra J.
Published: December 2022

Navigating moniker management.
Are Software Updates Useless against Advanced Persistent Threats?
Authors: Massacci Fabio , di Tizio Giorgio
Published: December 2022

Considering the conundrum of software updates.
Achieving Human Parity on Visual Question Answering
Authors: Yan Ming , Xu Haiyang , Li Chenliang , Tian Junfeng , Bi Bin , Wang Wei , Xu Xianzhe , Zhang Ji , Huang Songfang , Huang Fei , Si Luo , Jin Rong
Published: December 2022

The
Visual Question Answering (VQA)
task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topic with an increasing number of real-world applications in the last decade. This paper introduces a novel hierarchical integration of vision and language
AliceMind-MMU (ALIbaba’s Collection of Encoder-decoders from Machine IntelligeNce lab of Damo academy - MultiMedia Understanding)
, which leads to similar or even slightly better results than a human being does on VQA. A hierarchical framework is designed to tackle the practical problems of VQA in a cascade manner including: (1) diverse visual semantics learning for comprehensive image content understanding; (2) enhanced multi-modal pre-training with modality adaptive attention; and (3) a knowledge-guided model integration with three specialized expert modules for the complex VQA task. Treating different types of visual questions with corresponding expertise needed plays an important role in boosting the performance of our VQA architecture up to the human level. An extensive set of experiments and analysis are conducted to demonstrate the effectiveness of the new research work.
SoK: DAG-based Blockchain Systems
Authors: Wang Qin , Yu Jiangshan , Chen Shiping , Xiang Yang
Published: December 2022

Limitations on high latency and low scalability of classical blockchain systems retard their adoptions and applications. Reconstructed blockchain systems have been proposed to avoid the consumption of competitive transactions caused by linear sequenced blocks. These systems, instead, structure transactions/blocks in the form of
Directed Acyclic Graph (DAG)
and consequently rebuild upper layer components. The promise of DAG-based blockchain systems is to enable fast confirmation (complete transactions within million seconds) and high scalability (attach transactions in parallel) without significantly compromising security. However, this field still lacks systematic work that summarises DAG techniques. To bridge the gap, this
Systematization of Knowledge (SoK)
provides a comprehensive analysis of ever-existing and ongoing DAG-based blockchain systems. We abstract a general model to capture the main features and identify six types of design patterns. Then, we evaluate these systems from the perspectives of structure, consensus, property, security, and performance. We further discuss the trade-off between different factors, open challenges, and the potentiality of DAG-based solutions, indicating their promising directions for future research.
FastSpline: Automatic Generation of Interpolants for Lattice Samplings
Authors: Horacsek Joshua , Alim Usman
Published: December 2022

Interpolation is a foundational concept in scientific computing and is at the heart of many scientific visualization techniques. There is usually a tradeoff between the approximation capabilities of an interpolation scheme and its evaluation efficiency. For many applications, it is important for a user to navigate their data in real time. In practice, evaluation efficiency outweighs any incremental improvements in reconstruction fidelity. We first analyze, from a general standpoint, the use of compact piece-wise polynomial basis functions to efficiently interpolate data that is sampled on a lattice. We then detail our automatic code-generation framework on both CPU and GPU architectures. Specifically, we propose a general framework that can produce a fast evaluation scheme by analyzing the algebro-geometric structure of the convolution sum for a given lattice and basis function combination. We demonstrate the utility and generality of our framework by providing fast implementations of various box splines on the Body Centered and Face Centered Cubic lattices, as well as some non-separable box splines on the Cartesian lattice. We also provide fast implementations for certain Voronoi-splines that have not yet appeared in the literature. Finally, we demonstrate that this framework may also be used for non-Cartesian lattices in 4D.
A Personality-based Model of Emotional Contagion and Control in Crowd Queuing Simulations
Authors: Xue Junxiao , Zhang Mingchuang , Yin Hui
Published: December 2022

Queuing is a frequent daily activity. However, long waiting lines equate to frustration and potential safety hazards. We present a novel, personality-based model of emotional contagion and control for simulating crowd queuing. Our model integrates the influence of individual personalities and interpersonal relationships. Through the interaction between the agents and the external environment parameters, the emotional contagion model based on well-known theories in psychology is used to complete the agents’ behavior planning and path planning function. We combine the epidemiological SIR model with the cellular automaton model to capture various emotional modelling for multi-agent simulations. The overall formulation involves different emotional parameters, such as patience, urgency, and friendliness, closely related to crowd queuing. In addition, to manage the order of the queue, governing agents are added to prevent the emotional outbreak. We perform qualitative and quantitative comparisons between our simulation results and real-world observations on various scenarios. Numerous experiments show that reasonably increasing the queue channel and adding governing agents can effectively improve the quality of queues.
Illness Experiences of Adolescents with Type 1 Diabetes
Authors: Kim Ji Eun
Published: December 2022

Objective. This study is aimed at exploring the illness experiences of adolescents with type 1 diabetes (T1D). Methods. Using semistructured questions, 12 adolescents with T1D in Korea were interviewed regarding their illness experiences. Data were analyzed using grounded theory methodology. Results. Participants described their experiences in the core category, “becoming myself again,” which resulted in 130 concepts, 36 subcategories, 13 categories, and five themes. The themes included tied, overwhelmed, running away, struggling, and conciliating. Tied, the first process, entailed three categories: “confined to blood sugar control,” “feeling constrained,” and “supervised by parents.” The overwhelmed process included “self-banished” and “surrounded by stigma.” Running away included three categories—“growing up,” “folding,” and “withdrawal.” Struggling entailed “seeking for solution to overcome,” “developing response skills,” and “accepting help.” The last process, conciliating, included “redefining my perspective” and “reconciling with myself.” Conclusions. The findings indicate that the illness experience of adolescents with T1D should be understood in terms of both physical and psychosocial dimensions, considering the sociocultural and developmental context. The results of this study provide valuable information on diabetes education. Moreover, these findings encourage nurses to develop intervention programs and strategies to help adolescents with T1D.
A Look at Vygotsky’s Sociocultural Theory (SCT): The Effectiveness of Scaffolding Method on EFL Learners’ Speaking Achievement
Authors: Sarmiento-Campos Noemí-Victoria , Lázaro-Guillermo Juan Carlos , Silvera-Alarcón Edwin-Neil , Cuellar-Quispe Sergio , Huamán-Romaní Yersi-Luis , Apaza Oscar Apaza , Sorkheh Abdolsatar
Published: December 2022

Speaking is an English ability that learners should acquire. Nevertheless, learners had several speaking difficulties, one of which was a fear of making errors when speaking in English. Therefore, this study investigated the effects of the scaffolding method on enhancing Iranian EFL learners’ speaking achievement. To meet this end, a sample of 61 Iranian EFL learners were chosen from a total of 87 participants based on their Oxford Placement Test’s (OPT, 2007) performance. They were picked out of a private English language school. Their English proficiency was at the intermediate level. The target participants were then randomly divided into two groups: one control group (conventional group) and one experimental group (scaffolding group). Intermediate proficiency level was the primary criterion that had to be met for the learners to be chosen as respondents in this study. Their speaking competence was calculated by speaking section of PET as pretest. This pretest speaking contained different parts like interviews. In the next phase, the treatment was administered. For the scaffolding group, the instructor offered flexible occasions to exercise their skills, knowledge, and learning methods in diverse settings and for different targets. Carrying out the dialogs and designing questions from the texts, making conversations based on the picture and the question cards, were applied as scaffolding techniques. The control group, on the other hand, took part in typical speaking courses using customary methods of instruction. The teacher provided the pupils with a piece of recent news and a video episode of recent events in order to employ authentic language—language as it is used in a real situation. Finally, the two groups’ performances were posttested by PET speaking section. The performance of the two groups was compared, also the results of their pretest and posttest were contrasted. The inferential statistics including independent samples t-test and paired samples t-tests findings showed that there was a substantial difference between the experimental groups and the control group posttests. The results indicated that the experimental group significantly outperformed the control group (

p
<
0.05

) in the speaking posttest, showing that applying scaffolding to experimental group was effective. The study has several educational implications that are highlighted.
Numerical Investigation on ELF Electromagnetic Field Distribution of Pipeline Robot Tracking and Positioning System Using UAV
Authors: Tian Bao , Yang Zhaolin , Ji Shouhong , Chen Xiang , Zhao Kun
Published: December 2022

Pipeline robot, as a new type of equipment for pipeline operations such as pigging and detection, will play an increasingly important role in the operation and maintenance of oil and gas pipeline networks. The tracking and positioning technology during its operation process is one of the essential topics to improve the operating performance of pipeline robots and eliminate potential pipeline accidents. This paper presents the overall design of the wireless tracking and positioning system for pipeline robots based on extremely low frequency (ELF) electromagnetic method using the unmanned aerial vehicle (UAV). Starting from the classical electromagnetic theory, a mathematical model for the distribution of ELF electromagnetic field in buried metal pipeline environment is established. According to the characteristics of ELF electromagnetic wave transceiver and the model based on classical theory, the equivalent magnetic dipole transmission model is deduced. Based on the equivalent model, the attenuation characteristics of the ELF electromagnetic wave in the external space of the pipeline are analyzed by numerical simulation. The influence of the geometrical dimensions, environmental and working parameters, and other factors on the electromagnetic field intensity outside the pipeline is given at the same time. Finally, the distribution of ELF electromagnetic field in pipeline environment is discussed for horizontal-laying pipeline and inclined-laying pipeline, and the tracking method of pipeline robot is proposed on this basis. The proposed scheme is practical and effective, and it is suitable for real-time tracking and positioning of robot working in the pipelines whose slope is no more than 60 degrees with known or unknown distribution.
Direction-of-Arrival Estimation in Time-Modulated Linear Arrays Based on the MT-BCS Approach
Authors: Zhang Qing-He , Zhang En-Yu , Zhang Shi-Hui
Published: December 2022

This article proposes a novel approach for the estimation of the direction-of-arrival (DoA) of multiple signals impinging on time-modulated arrays (TMAs). The algorithm transforms DoA estimation into compressive sensing (BCS) formulation to tackle the sparse signal problem. Based on the voltage outputs of the TMAs at multiple times instants, a strategy using multitasks BCS (MT-BCS) is applied to recover the DoA and improve the accuracy. The comparison with the existing algorithms of DoA estimation in TMAs verifies the effectiveness and feasibility of the proposed method.
Magnesium Bioresorbable Scaffold (BRS) Magmaris vs Biodegradable Polymer DES Ultimaster in NSTE-ACS Population—12-Month Clinical Outcome
Authors: Rola Piotr , Włodarczak Adrian , Włodarczak Szymon , Barycki Mateusz , Szudrowicz Marek , Łanocha Magdalena , Furtan Łukasz , Woźnica Katarzyna , Kulczycki Jan Jakub , Jaroszewska-Pozorska Joanna , Kędzierska Michalina , Doroszko Adrian , Lesiak Maciej
Published: December 2022

Background. Percutaneous coronary intervention (PCI) in the acute coronary syndrome (ACS) setting is associated with a greater probability of device failure. The currently ongoing development of new scaffold technologies has concentrated an effort on improving the PCI outcomes, including the use of new biodegradable materials. This pilot study evaluates the performance of a magnesium bioresorbable scaffold (Magmaris, Biotronik, Germany) in comparison to the sirolimus‐eluting bioresorbable polymer stents (BP-SES) (Ultimaster, Terumo, Japan) in the NSTE-ACS setting. Methods. The population of this pilot comprised 362 patients assigned to one of two arms (193-Magmaris vs 169-Ultimaster). The data regarding the primary outcome comprised of death from cardiac causes, myocardial infarction, and stent thrombosis, along with target-lesion failure (TLF) and other clinical events was collected in the 1-yearfollow-up. Results. There were no statistically significant differences in clinical outcomes in the short term (30 days) or in the 1-yearfollow-up between both groups. Conclusion. At 12 months, there were no statistically significant differences between the Magmaris and Ultimaster for composed endpoints or the TLF.
Catalytic and Photocatalytic Degradation Activities of Nanoscale Mn-Doped ZnCr2O4
Authors: Sankudevan P. , Sakthivel R. V. , Gopal Ramalingam , Raghupathi C. , Ambika S. , Mujahid Alam Mohammed , Rangasamy Baskaran
Published: December 2022

In the present work, the effect of Mn doping in Zinc Chromite (ZnCr2O4) and particle size reduction on catalytic and photocatalytic degradation performance have been evaluated. The pristine Zn1−xMnxCr2O4 (x = 0 to 0.03) nanoscale samples are synthesized through a hydrothermal approach. The synthesized catalysts are characterized by XRD, HR-SEM, HR-TEM, catalytic, and photocatalytic degradation analyses. X-ray diffraction analysis results confirmed the formation of the ZnCr2O4 structure and its phase purity, crystallite size, and Mn dopant effect. The surface morphology and particle size of Zn1−xMnxCr2O4 samples are evaluated by SEM and TEM measurements. The textural properties of ZnCr2O4 samples are identified by the surface area analysis. The catalytic performance of Mn-doped ZnCr2O4 samples reveals superior catalytic performance compared to pristine ZnCr2O4 in benzaldehyde and carbonyl compound productions. Under UV irradiation, an excellent photocatalytic degradation efficiency of 89.66% for Zn0.97Mn0.03Cr2O4 catalyst with methylene blue has been obtained.
Online Superficial Gas Velocity, Holdup, and Froth Depth Sensor for Flotation Cells
Authors: Leiva Claudio , Acuña Claudio , Bergh Luis , Luukkanen Saija , da Silva Cristóbal
Published: December 2022

In flotation process, the efficiency and selectivity depend on mineralogy, particle size distribution and liberation, reagents added, mixing, and particle coverage. However, the kinetics of particle recovery is highly dependent on cell hydrodynamic and circuit configuration and operational strategy. Controlling froth depth and gas flow rate, measured as superficial gas velocity, is a straightforward alternative related to kinetics in the froth and collection zones. However, these parameters are not measured accurately. Froth depth measurement is based on a floating device coupled with a sonic sensor; this configuration presents hysteresis and deviation due to variation in the gas holdup and pulp density. In self-aspirated machines, there is no technology to measure gas velocity. To address this problem, the intelligent online gas dispersion sensor based on two concentric HDPE cylindres is proposed. The intelligent online gas dispersion sensor is based on two concentric HDPE cylinders. The methodology improves the accuracy of gas velocity calculation with a new algorithm. Froth depth measurement is based on two pressure transducers, reducing the uncertainty of the floating sonic sensor to 1 cm. Pulp bulk density is directly measured, and gas holdup can be estimated. Experimental results and industrial device validation indicate that the new intelligent system can measure superficial gas velocity (Jg) online and self-calibrate, with a 2% error, the froth depth error being ±1 cm. Therefore, a multiparameter sensor for measuring gas dispersion in industrial flotation cells was experimentally designed and validated in an industrial environment (TRL 8). In this context, the proposed online gas dispersion sensor emerges as a robust technology to improve the operation of the flotation process.
Antecedents of Continuance Intention of Social Networking Services (SNS): Utilitarian, Hedonic, and Social Contexts
Authors: Jo Hyeon
Published: December 2022

This study aims to identify the key factors that influence the continuance intentions of social network service (SNS) users through an integrative framework. It suggests a theoretical model that contains utilitarian, hedonic, and social contexts. Data were collected from 286 SNS users in South Korea. This study analyzed the data through partial least squares structural equation modeling (PLS-SEM). The results indicate that both perceived ease of use and perceived usefulness are not related to continuance intention. The findings show that perceived enjoyment has a strong and significant impact on continuance intention. The analysis results reveal that communication positively affects continuance intention. Moreover, system quality serves as the key factor of perceived ease of use, even though it has no significant effect on perceived usefulness. Information quality was found to have a significant impact on perceived usefulness but not on perceived ease of use. The results of the study intimated that service quality is significantly related to both perceived ease of use and perceived usefulness. Positive affect significantly affects both perceived enjoyment and communication. Negative affect significantly impacts perceived enjoyment while having no significant impact on communication. Theoretical contributions and practical implications are elaborated.
Actin-Binding LIM 1 (ABLIM1) Inhibits Glioblastoma Progression and Serves as a Novel Prognostic Biomarker
Authors: Liu Danping , Wang Xiaoping , Liu Ying , Li Chunliu , Zhang Zhen , Lv Peng
Published: December 2022

Background. Glioma is the most prevalent malignant brain tumor in adult humans, and glioblastoma (GBM) is the most malignant type. The actin-binding LIM 1 (ABLIM1) protein can modulate actin polymerization, which is essential for the cell proliferation and migration. We aim to investigate ABLIM1 expression, function, and clinical significance in GBM. Methods. The ABLIM1 mRNA level was extracted from the TCGA and GTEx online databases. The ABLIM1 protein expression level was explored using immunohistochemistry staining in a GBM cohort enrolled in our hospital (

n
=
104

). The patient survival and prognostic factors were determined using the Kaplan-Meier method and multivariate Cox hazard proportional analysis, respectively. Two human GBM cell lines, U87 and U251 cells, were utilized for ABLIM1 overexpression and cell proliferation analyses. A subcutaneous xenograft model was generated using nude mice to validate the tumor-related effect of ABLIM1 in vivo. Results. ABLIM1 exhibited a significantly lower mRNA level in GBM than in other glioma or normal brain tissues. Higher ABLIM1 protein level was correlated with smaller GBM tumor size and better cancer-specific survival (CSS). Multivariate analysis identified ABLIM1 as a novel independent prognostic factor for GBM prognosis. ABLIM1 overexpression significantly inhibits U87 and U251 cell proliferation and colony formation. Consistently, ABLIM1 exerted tumor-suppressing functions in mice models. Conclusion. ABLIM1 plays antitumor roles in GBM progression and could be served as a novel biomarker to help predict GBM prognosis.
Improving Lung Cancer Diagnosis with CT Radiomics and Serum Histoplasmosis Testing
Authors: Marmor Hannah N. , Deppen Stephen A. , Welty Valerie , Kammer Michael N. , Godfrey Caroline M. , Patel Khushbu , Maldonado Fabien , Chen Heidi , Starnes Sandra L. , Wilson David O. , Billatos Ehab , Grogan Eric L.
Published: December 2022

Background:

Indeterminate pulmonary nodules (IPN) are a diagnostic challenge in regions where pulmonary fungal disease and smoking prevalence are high. We aimed to determine the impact of a combined fungal and imaging biomarker approach compared with a validated prediction model (Mayo) to rule out benign disease and diagnose lung cancer.

Methods:

Adults ages 40 to 90 years with 6–30 mm IPNs were included from four sites. Serum samples were tested for histoplasmosis IgG and IgM antibodies by enzyme immunoassay and a CT-based risk score was estimated from a validated radiomic model. Multivariable logistic regression models including Mayo score, radiomics score, and IgG and IgM histoplasmosis antibody levels were estimated. The areas under the ROC curves (AUC) of the models were compared among themselves and to Mayo. Bias-corrected clinical net reclassification index (cNRI) was estimated to assess clinical reclassification using a combined biomarker model.

Results:

We included 327 patients; 157 from histoplasmosis-endemic regions. The combined biomarker model including radiomics, histoplasmosis serology, and Mayo score demonstrated improved diagnostic accuracy when endemic histoplasmosis was accounted for [AUC, 0.84; 95% confidence interval (CI), 0.79–0.88; P < 0.0001 compared with 0.73; 95% CI, 0.67–0.78 for Mayo]. The combined model demonstrated improved reclassification with cNRI of 0.18 among malignant nodules.

Conclusions:

Fungal and imaging biomarkers may improve diagnostic accuracy and meaningfully reclassify IPNs. The endemic prevalence of histoplasmosis and cancer impact model performance when using disease related biomarkers.

Impact:

Integrating a combined biomarker approach into the diagnostic algorithm of IPNs could decrease time to diagnosis.
Crizotinib Has Preclinical Efficacy in Philadelphia-Negative Myeloproliferative Neoplasms
Authors: Gurska Lindsay M. , Okabe Rachel , Schurer Alexandra , Tong Meng Maxine , Soto Mark , Choi Daniel , Ames Kristina , Glushakow-Smith Shira , Montoya Allison , Tein Ellen , Miles Linde A. , Cheng Haiying , Hankey-Giblin Pamela , Levine Ross L. , Goel Swati , Halmos Balazs , Gritsman Kira
Published: December 2022

Purpose:

The Philadelphia chromosome–negative myeloproliferative neoplasms (MPN) polycythemia vera, essential thrombocythemia, and primary myelofibrosis are characterized by JAK/STAT pathway activation. JAK inhibitors are approved for MPN treatment, but persistence has been observed, due to JAK/STAT reactivation.

Experimental Design:

Using MPN patient samples, JAK2-mutated cell lines, and MPN mouse models, we examined both the efficacy and mechanism by which crizotinib, the ALK/MET/RON/ROS1 inhibitor approved for the treatment of non–small cell lung cancer, alters MPN cell proliferation and JAK/STAT activation.

Results:

We found that crizotinib suppresses proliferation and activation of JAK/STAT signaling, and decreases the disease burden in the JAK2V617F mouse model of MPN. Furthermore, we found that crizotinib could overcome JAK inhibitor persistence to ruxolitinib. Interestingly, phosphorylation of the crizotinib target RON kinase was enhanced in ruxolitinib-persistent cells. We show that phospho-JAK2 and phospho-RON can physically interact to sustain JAK/STAT signaling, and that the combination of crizotinib and ruxolitinib disrupts this interaction. Furthermore, RON knockdown suppresses proliferation and activation of JAK/STAT signaling in JAK2-mutated cells, and RON deletion in a JAK2V617F mouse MPN model decreases the disease burden. We also observed RON hyperactivation in MPN patient cells, suggesting that RON may be an important target of crizotinib in MPN.

Conclusions:

In summary, we demonstrate that crizotinib has preclinical efficacy in MPN patient cells, JAK2-mutated cell lines, and a JAK2-mutated mouse model, and that the combination of crizotinib with JAK inhibitors suppresses JAK inhibitor persistence. Our work suggests that crizotinib should be investigated for the treatment of patients with MPN.
Unfree Labour and Value Productivity: Challenges for the Marxian Labour Theory of Value
Authors: Parkhurst Bryan
Published: December 2022

This paper explores the question: does unfree labour produce value? The paper does not answer the question. Rather, it contends that, no matter how Marxists answer the question, they end up either (1) relinquishing the view that labour is the only source of value or (2) appealing to an apparently bogus distinction in order to hang on to the view. Both of these alternatives will be unacceptable to the orthodox Marxian economist. For the choice is between jettisoning the labour theory of value and thus giving up on Marxian orthodoxy, or else frankly conceding that one’s orthodoxy is baseless and dogmatic.
The nomenclature of Eurasian giant salamanders (Amphibia, Urodela, Andrias)
Authors: DUBOIS ALAIN , FRÉTEY THIERRY , OHLER ANNEMARIE
Published: December 2022

The history of the taxonomy and nomenclature of the giant Eurasian salamanders of the genus Andrias is provided, with a complete list of available nomina of all extinct and extant species, indicating their current nomenclatural status, their type-specimens and type-localities.
Seasonality and Variability of Snowfall to Total Precipitation Ratio over High Mountain Asia Simulated by the GFDL High-Resolution AM4
Authors: Dong Wenhao , Ming Yi
Published: December 2022

The ratio of snowfall to total precipitation (S/P ratio) is an important metric that is widely used to detect and monitor hydrologic responses to climate change over mountainous areas. Changes in the S/P ratio over time have proved to be reliable indicators of climatic warming. In this study, the seasonality and interannual variability of monthly S/P ratios over High Mountain Asia (HMA) have been examined during the period 1950-2014 based on a three-member ensemble of simulations using the latest GFDL AM4 model. The results show a significant decreasing trend in S/P ratios during the analysis period, which is mainly resulted from reductions in snowfall with increases in total precipitation playing a secondary role. Significant regime shifts in S/P ratios are detected around the mid-1990s, with rainfall becoming the dominant form of precipitation over HMA after the change points. Attribution analysis demonstrates that increases in rainfall during recent decades were primarily caused by a transformation of snowfall to rainfall as temperature warmed. A logistic equation is used to explore the relationship between the S/P ratio and surface temperature, allowing calculation of a threshold temperature at which the S/P ratio equals 50% (i.e., precipitation is equally likely to take the form of rainfall or snowfall). These temperature thresholds are higher over higher elevations. This study provides an extensive evaluation of simulated S/P ratios over the HMA that helps clarify the seasonality and interannual variability of this metric over the past several decades. The results have important socioeconomic and environmental implications, particularly with respect to water management in Asia under climate change.