Search

This paper first presents six (6) types of benchmark amplitude design specifications, including variable low-pass specification, variable high-pass specification, variable bandpass specification, variable bandstop specification, variable notch-frequency specification, and variable notch-width specification. The design specifications can be taken as typical benchmark specifications targeted at testing as well as validating the effectiveness of various newly developed algorithms for designing various typical variable filters (VFs). Then, the paper provides a novel unity-bounded (UB) function developed for ensuring the stability of those VFs with recursive structures. Finally, a wide variety of simulation results are included for validating the design performance as well as the assured stability by using both the benchmark design specifications and the novel UB function. [ABSTRACT FROM AUTHOR]

This paper presents a survey of machine learning (ML) methods used in applications dedicated to the building and construction industry. A building information modeling (BIM) model, being a database system for civil engineering data, is presented. A representative selection of methods and applications is described. The aim of this paper is to facilitate the continuation of research efforts and to encourage bigger participation of database system researchers in the field of civil engineering. [ABSTRACT FROM AUTHOR]

Actor-network theory (ANT) represents a research paradigm applicable to innovation management (IM) research. Its unique ontology of second-degree objectivity through symmetry can be combined with many research contexts, methods, and concepts. This paper summarises the insights from a literature review of 299 Web of Science articles on both ANT and IM. The meta-features of all articles are analysed to identify 25 articles for in-depth analysis. Three ANT literature streams are identified: descriptive, managerial proactive, and participatory proactive. These three streams are found to differ in terms of their specific methods, concepts, and research contexts. An additional subset of the 10 most cited articles is used to validate the findings. We suggest that IM researchers should select a specific ANT approach based on the context and the theorised hypothesis of their research. Knowledge of the options within ANT and how they can be applied to different IM contexts can help IM researchers in maximising research outcomes. [ABSTRACT FROM AUTHOR]

This editorial paper presents the papers published in a special issue devoted to the modeling and simulation of mutating virus pandemics in a globally connected world. The presentation is proposed in three parts. First, motivations and objectives are presented according to the idea that mathematical models should go beyond deterministic population dynamics by considering the multiscale, heterogeneous features of the complex system under consideration. Subsequently, the contents of the papers in this issue are presented referring to the aforementioned complexity features. Finally, a critical analysis of the overall contents of the issue is proposed, with the aim of providing a forward look to research perspectives. [ABSTRACT FROM AUTHOR]

This paper studies the biomechanics of muscles after swimming. This paper uses exercise biomechanics and deep learning models to process surface EMG signals after muscle injury. In this way, the relationship between the EMG signal and muscle strength after muscle injury after swimming can be obtained. Studies have found a proportional relationship between muscle strength and the strength of EMG signals during muscle injury recovery after swimming. The conclusions of this study can provide us with some clinical experience in the recovery of muscle damage during physical exercise. [ABSTRACT FROM AUTHOR]

In this paper, we design a natural scene text detection and recognition model based on deep learning by model construction and in-depth study of wild scene text detection and recognition. This paper proposes a scene text recognition method based on connection time classification and attention mechanism for the situation where natural scene text is challenging to recognize due to the high complexity of text and background. The method converts the text recognition problem in natural scenes into a sequence recognition problem, avoiding the drawback of overall recognition performance degradation due to the difficulty of character segmentation. At the same time, the attention mechanism introduced can reduce the network complexity and improve the recognition accuracy. The performance of the improved PSE-based text detection algorithm in this paper is tested on the curved text datasets SCUT-ctw1500 and ICDAR2017 in natural scenes for comparison. The results show that the proposed algorithm achieves 88.5%, 77%, and 81.3% in the three indexes of accuracy, recall, and F1 value, respectively, without adding the pre-training module. The algorithm can detect text in any direction well without adding the pre-training module; the improved text recognition algorithm based on CRNN in this paper is tested on the natural scene dataset ICDAR2017, and the results show that the accuracy rate reaches 94.5% under the condition of no constraint, which is a good performance. [ABSTRACT FROM AUTHOR]

Gait recognition based on acceleration sensor signals is more suitable for complex environments than other signal-based methods, which can be used for data acquisition for gait recognition. However, most traditional methods only consider the position matching of multiple accelerometers, ignoring the comfort of the collector. In recent years, support vector machine (SVM) has been widely used in portrait recognition, language, and video processing. It has a flexible and powerful processing ability for nonlinear sequence input, and is a sparse and robust classifier. In this paper, we propose a multi-feature SVM algorithm ME-SVM, which is classified after multi-feature fusion. Usually, in the research of action recognition based on acceleration sensors, researchers classify from a single eigenvalue, but this paper optimizes this and further improves the recognition performance. Simulation and experimental results show that the algorithm has high accuracy and robustness. [ABSTRACT FROM AUTHOR]

Analyzing eye movement data to evaluate learning status has become crucial in intelligent education. The eye movement scanning path can directly or indirectly reflect changes in thinking patterns and psychological states. By analyzing the scanning path, we can explore the commonality and differences in learners' eye movement behaviors and provide essential references for improving visual content and giving guidance. This paper first studies the time series representation and clustering of the learner's scanning path under the same task. Then, the three learning states of concentration, mind-wandering, and information wandering are evaluated through the clustering results. Specifically, the improved DBA algorithm (iDBA) is proposed to extract group eye movement patterns, combined with the dynamic time warping (DTW) algorithm to calculate the similarity of scanning paths and determine the clustering seeds, while the distance density clustering (DDC) algorithm is used for clustering. Experiments show that time series-based eye movement pattern mining can identify group viewing behaviors. Meanwhile, clustering reveals different reading strategies and provides the ability to assess learning status. [ABSTRACT FROM AUTHOR]

This paper studies the relationship between corporate social responsibility (CSR) and stock price volatility based on data of Chinese A-share listed companies from 2010 to 2018, and further analyses the path and underlying mechanism of CSR that affects stock price volatility. With China's gradual transition to a sustainable development model, both public and the government are paying increasing attention to CSR performance. At the same time, the Chinese government takes a more serious attitude towards systemic financial risks, emphasising the importance of controlling systemic risks such as an abnormal stock price on many public occasions. In this context, CSR and stock price volatility have received unprecedented attention, and it's of great value for both industry and the government to explore the impact of CSR on stock price volatility. [ABSTRACT FROM AUTHOR]

Currently, the prevalence of various Internet intrusion technologies has brought much challenge to the enterprise management. For many core documents, the information leakage may lead to the loss of secrets of enterprises. Therefore, some core official documents in enterprises are in the format of papers, rather than electronic format. As a consequence, it is of significance to develop automatic information processing techniques for official documents in the format of papers, so as to improve the working efficiency of enterprises. In this paper, a novel business scheduling approach for enterprises via vision sensing-based automatic documental information extraction is proposed. For the first stage, the vision sensing-based optical character recognition (OCR) technique is utilized to extract textual information from official documents in the format of papers. For the second stage, the deep neural network is utilized to output business scheduling results on the basis of digital recognition contents from the first stage. Finally, the experimental simulation is also carried out to verify efficiency of the proposal. [ABSTRACT FROM AUTHOR]

Over the years, the humanities community has increasingly requested the creation of artificial intelligence frameworks to help the study of cultural heritage. Document Layout segmentation, which aims at identifying the different structural components of a document page, is a particularly interesting task connected to this trend, specifically when it comes to handwritten texts. While there are many effective approaches to this problem, they all rely on large amounts of data for the training of the underlying models, which is rarely possible in a real-world scenario, as the process of producing the ground truth segmentation task with the required precision to the pixel level is a very time-consuming task and often requires a certain degree of domain knowledge regarding the documents at hand. For this reason, in this paper, we propose an effective few-shot learning framework for document layout segmentation relying on two novel components, namely a dynamic instance generation and a segmentation refinement module. This approach is able of achieving performances comparable to the current state of the art on the popular Diva-HisDB dataset, while relying on just a fraction of the available data. [ABSTRACT FROM AUTHOR]

As information technology expands across industries in the age of deep learning, companies face new changes in their credit assessment methods. One of the difficulties in financing food enterprises stems from the complexity of investment in reviewing enterprises' credit. Therefore, this paper proposes a deep learning-based credit prediction and evaluation model for food enterprises, which performs well on the dataset and achieves 85.73% and 88.56% accuracy in verifying the performance and default test samples, respectively. In addition, the model was confirmed to have good robustness through ablation experiments. Finally, the paper concludes with relevant recommendations for food companies based on the study's findings, offering new methods to improve their corporate credit assessment. [ABSTRACT FROM AUTHOR]

Image defect detection of casting parts is a key part of the production process in the machinery manufacturing industry. The traditional methods are ineffective because traditional computer image processing methods require a large number of manual features to be set artificially, and the detection time is too long. In order to save human resources and improve the efficiency of image defect detection, this paper proposes a deep learning-based defect detection method for automobile parts. This paper selects EfficientNetB0 as the backbone framework of the target detection network, which significantly reduces the memory usage of the model and shortens the model inference time, while improving the model detection accuracy. Facing the problem of small samples of defect image dataset, we analyze the image characteristics of the dataset and introduce shape transformation and scale scaling as the basic online data enhancement method according to the industrial field image projection law. Then, it is expected to combine the traditional image processing algorithms according to the characteristics of casting parts with different depth distribution and multiple morphological changes, and develop a special image defect data enhancement method. This further improves the performance of the model and increases the detection accuracy of the algorithm by 22.3% without increasing the data. [ABSTRACT FROM AUTHOR]

CFD-modeling for numerical investigation is used in a wide range of applied tasks, e.g. in fluid mechanics. To better understand the effect of operational parameters on the final results, some tasks are associated with carrying out monotonous, repetitive calculations for a wide range of operational parameters such as velocity, flow direction and temperature. In this paper, a Python-based code for automation of the repeating calculations in CFD-modeling was developed and described. The automation code was tested for CFD-modeling in Ansys Fluent for two flow dynamic tasks: a simple 2D-geometry — NACA0018 airfoil, and a complex 3D-geometry — packed bed with heat transfer. Three different computers with various computational power were used for the comparison. The results of CFD-modeling were compared with the experimental data. The efficiency of using Python-based code was evaluated through comparison with the results of manual (without automation) calculation. It was established that the application of the Python-based code does not affect the accuracy of numerical results. At the same time, utilization of the Python-based code can save up to 25% of computation time for the simple 2D-geometries with a moderately low number of elements in the mesh, and up to 15% for the complex 3D-geometries with a number of elements in several millions. The compiled Python-based code is attached as supplementary material to this paper. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a Knowledge Management (KM) model in order to investigate and monitor the performance of HEIs in Oman. In this paper, a KM model of five KM components based on the relative performance factor and a critical set of 22 indicators from an original set of 180 indicators are proposed and outlined; in terms of the use of the KM categories and selected indicators, the performance of 30 Higher Education Institutions was measured and studied. In this paper, the literature and related approaches were studied. A KM model which is based on the five main components of KM, relative performance factor, indicators, weights, and data collection was proposed. Three types of data were collected from institutions to calculate the indicators/the relative performance factor, and several tests were carried out. In addition, the conceptualisation and operationalisation processes of the model are discussed. The proposed KM model was implemented in all the private institutions and the obtained results show the applicability and accuracy of the technique, and the proposed technique was compared with the classical approach. The results of the proposed approach have been tested and analysed comprehensively: these results of the statistical tests of the aforementioned proposed approach show a high level of accuracy of the results, and that the results of institutions differ significantly one from another, and also that the differences between institutions are not due to any random factor. The discussion of the numerical results shows that the performance of HEIs was placed in three categories. The findings of the paper highlight the performance of HEIs and they give significant evidence to individual HEIs and to the stakeholders so as to allow them to act on the findings and distinguish between them in terms of the actual performance. [ABSTRACT FROM AUTHOR]

During the last 30 years, a proliferation of Data Management Maturity Models has been observed. Most of this proliferation was driven by consulting companies that used existing models and tried to differentiate their offerings by renaming the concepts. This has created confusion that led organisations to have difficulty selecting a Maturity Model and applying it. This paper proposes a simpler and more Integrative Framework to help the organisation assess and sustainably enhance its data management. To do so, in-depth academic papers and professional documents have been gathered following a structured approach. The outcome of the paper is an Integrative Framework and a Data Maturity Evaluation and Enhancement Process that tries to simplify Data Management Maturity Models. [ABSTRACT FROM AUTHOR]

Knowledge of every organisation is the most important resource, often not fully leveraged by organisations. Moreover, the value of tacit knowledge of an individual is much less understood and not exploited to the desired levels in organisations. It is quite evident when an experienced employee retires from an organisation, he carries away a very valuable chunk of knowledge with him, which is a strategic resource of the organisation. The COVID-19 pandemic and the great resignation times created a sudden loss of experts which troubled many organisations and made the topic of tacit knowledge management very relevant to current times. While PRISMA framework was used for selection of articles among the vast literature using justifiable validation criteria, a combination of bibliometric and narrative methods is used for the process of analysis and summarisation. The relevant papers are obtained using keywords "Tacit knowledge", "Tacit knowledge management", "Knowledge sharing". Overall, 50 relevant and quality papers are picked after systematic filtration and study. The important keywords from these papers are picked and analysed to arrive at six major themes under which all the papers can be categorised. The six themes are tacit knowledge concept evolution, barriers to knowledge sharing, tacit knowledge acquisition process — various methods, role of context in knowledge sharing, enablers for tacit knowledge sharing, and IT for explicit Knowledge capture and reuse. Each of these themes also has sub-themes under which the papers are categorised. The gist of papers under each of these themes/sub-themes is captured in detail, providing key insights under each theme. While literature review papers focus on a certain domain or industry, this work developed a conceptual model using these themes which represents the current literature in tacit knowledge management as a system model, thus making this work complementary to the current body of knowledge. This model is useful in placing any future literature under one of the themes. Future work also can focus on adding additional themes as well as making this model more insightful through continuous improvement. Each of the themes in the model created can be colour coded to represent high and least researched areas, which can be a valuable guide for the future researchers. Future research directions relevant for the current engineering industries are collated from the literature reviewed, detailed under the above themes in chronological order. [ABSTRACT FROM AUTHOR]

With the continuous exploitation of coal resources, human safety has been seriously threatened during the mining process. Therefore, it is of great significance to establish an efficient human infrared image recognition system. In this paper, three classes of infrared image data of the human body are collected by a thermal imager, namely Human, Human others and None. According to the characteristics of downhole infrared images, a distributed channel feature extraction module (DCFE) is designed, and DCA-Net is proposed based on this module. The experimental results show that the recognition rate of the network reaches 98%. Compared with other networks, this network has better recognition performance. Among them, the recognition rate of DCA-Net50 reaches 98.214%, the amount of parameters and calculations are relatively small, and the cost-effectiveness is the highest. It is suitable for the human body infrared image recognition system that requires high accuracy and high real-time performance. [ABSTRACT FROM AUTHOR]

Intelligent agriculture has become the development trend of agriculture in the future, and it has a wide range of research and application scenarios. Using machine learning to complete basic tasks for people has become a reality, and this ability is also used in machine vision. In order to save the time in the fruit picking process and reduce the cost of labor, the robot is used to achieve the automatic picking in the orchard environment. Cherry target detection algorithms based on deep learning are proposed to identify and pick cherries. However, most of the existing methods are aimed at relatively sparse fruits and cannot solve the detection problem of small and dense fruits. In this paper, we propose a cherry detection model based on YOLOv5s. First, the shallow feature information is enhanced by convolving the feature maps sampled by two times down in BackBone layer of the original network model to the input end of the second and third CSP modules. In addition, the depth of CSP module is adjusted and RFB module is added in feature extraction stage to enhance feature extraction capability. Finally, Soft- Non-Maximum Suppression (Soft-NMS) is used to minimize the target loss caused by occlusion. We test the performance of the model, and the results show that the improved YOLOv5s-cherry model has the best detection performance for small and dense cherry detection, which is conducive to intelligent picking. [ABSTRACT FROM AUTHOR]

Succinic acid, also known as dicarboxylic acid, is one of the biochemical products chemically produced from Escherichia coli (E. coli) metabolism. However, by using conventional methods succinic acid cannot be produced sufficiently and it is costly. Hence, there is a lot of ongoing research on E. coli by using in silico methods. Researchers build computational models of E. coli to analyze and modify their metabolic network. This paper proposes a hybrid of ant colony optimization–genetic algorithm–flux balance analysis (ACOGAFBA) in enhancing the succinic acid production of E. coli by identifying genes to be knocked out. Ant colony optimization (ACO) is a swarm intelligent optimization that is inspired based on the natural foraging behavior of ant colony. Local search technique like genetic algorithm (GA) is applied to solve optimization and search problem by approximation. Flux balance analysis (FBA) is used for fitness calculation after gene knockout. FBA identifies a point (fitness) in flux space by using quadratic programming, which is closest to the wild type point. ACOGAFBA produced three sets of gene knockout lists. The dataset i JR904 is used in this paper. The results show that ACOGAFBA can identify the set of knockout genes to improve succinic acid production in E. coli. [ABSTRACT FROM AUTHOR]

Important nodes can determine the internal structure of complex networks and reveal the internal relationships of real-world systems, and identifying key nodes in complex networks is one of the important research areas of complex network science. As the king of commodities, changes in the price of gold significantly impact the economic development of various countries. Especially in the early stages of the outbreak of war between Russia and Ukraine, the price of gold futures has been greatly impacted, and the systemic risks are gradually spreading. In this paper, a gold future price series is mapped into a visibility graph (VG), the characteristics of the gold price time series and key points-in-time, have been explored from the perspective of complex network. First, according to the data structure characteristics of gold futures, this paper converts the closing prices of gold futures of the New York Mercantile Exchange into a complex network through the VG model. Then, by using the complex network model to further delve into the price of gold futures, it is found that the degree distribution of the gold futures network follows a power-law distribution, and has obvious scale-free characteristics. Finally, this paper uses the visual network node shrinking algorithm and the technique for order preference by similarity to ideal solution (TOPSIS) analysis method to identify the key nodes of the gold futures visual map to find the key time nodes in the timeline of gold futures market. Analysis of the key time nodes of this market by four methods reveals that the repetition rate of the key time nodes in the methods' top 10 ranking is as high as 82.5%, indicating that the results obtained in this paper are robust. This study introduces a new model to describe the characteristics of gold futures price series, one which can find key time nodes in gold futures prices and provide potential help for predicting gold futures prices. [ABSTRACT FROM AUTHOR]

This document is a guest editorial introducing a special issue of the International Journal of Semantic Computing. The special issue focuses on the IEEE Multimedia Big Data conference held in Naples, Italy in December 2022. The conference aims to bring together researchers in the fields of multimedia and big data to share findings and explore new directions. The special issue includes four selected papers that underwent a thorough review and revision process. The papers cover topics such as mobile gait analysis, class-aware pseudo labeling in semi-supervised learning, hierarchical time-aware summarization for video captioning, and supporting multidimensional big data analytics over big web knowledge bases. The editorial expresses gratitude to the authors and reviewers and promotes interdisciplinary research in the field of multimedia and big data computing. [Extracted from the article]

The present brief paper offers a multi-dimensional work readiness inventory for measuring future graduates' work readiness. It also highlights the importance of career success and employability by identifying the 21 dimensions of work readiness. This paper provides a summary of how the study developed and validated the work readiness assessment inventory (WRAI) using a total of 1,076 university students in Singapore. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to validate the WRAI. Using factorial analyses, this study examined the reliability and convergent validity of this inventory, suggesting that the WRAI could be used in universities and organisations to bridge the expectations of employees and employers. As there are limited numbers of studies which have developed and validated an inventory relating to work-ready attributes of graduates, the findings in this study can contribute significantly to research and practice areas regarding workforce development. Only by equipping our graduates with work-ready attributes, can they be competent and be confident in finding career success. This brief paper also contributes to labour policy by informing the key stakeholders that relevant work-ready attributes are important for career success and workplace transition which in turn relates to lifelong employability. [ABSTRACT FROM AUTHOR]

"Fake news" refers to the deliberate dissemination of news with the purpose to deceive and mislead the public. This paper assesses the accuracy of several Machine Learning (ML) algorithms, using a style-based technique that relies on textual information extracted from news, such as part of speech counts. To expand the already proposed styled-based techniques, a new method of enhancing a linguistic feature set is proposed. It combines Named Entity Recognition (NER) with the Frequent Pattern (FP) Growth association rule mining algorithm, aiming to provide better insight into the papers' sentence level structure. Recursive feature elimination was used to identify a subset of the highest performing linguistic characteristics, which turned out to align with the literature. Using pre-trained word embeddings, document embeddings and weighted document embeddings were constructed using each word's TF-IDF value as the weight factor. The document embeddings were mixed with the linguistic features providing a variety of training/test feature sets. For each model, the best performing feature set was identified and fine-tuned regarding its hyper parameters to improve accuracy. ML algorithms' results were compared with two Neural Networks: Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM). The results indicate that CNN outperformed all other methods in terms of accuracy, when companied with pre-trained word embeddings, yet SVM performs almost the same with a wider variety of input feature sets. Although style-based technique scores lower accuracy, it provides explainable results about the author's writing style decisions. Our work points out how new technologies and combinations of existing techniques can enhance the style-based approach capturing more information. [ABSTRACT FROM AUTHOR]

The method of six-sigma and the index of process capability are both commonly used tools in the industrial community. Process engineers can follow five improvement steps of the six-sigma method, including "define", "measure", "analyze", "improve", and "control" (DMAIC), aiming to improve and enhance the process quality. However, none of these five improvement steps have a clear corresponding approach. This paper considered process capability indices not only a process quality evaluation tool widely used in the industrial community but also a process quality evaluation and analysis tool adopted by internal engineers. Therefore, this paper applied the method integrating process capability indices and statistical testing to develop execution models for the five improvement steps, DMAIC, of the six-sigma method. First, this paper, based on the concept of yield, not only deduced the relationship between the required value of the process capability index for the product and the process capability index value of the individual quality characteristic but also discussed the definition of the quality level of six-sigma as well as its relationship with the process capability index. Next, according to these results, five improvement execution models of the six-sigma method were developed and served as a reference for the process engineers in the industry to promote the performance of the six-sigma project. The proposed method in this paper applied various normal processes and combined the six-sigma method and process capability indices, both of which are tools commonly used in the industrial community. It also has taken into account the advantages of theoretical contribution and industrial acceptance. [ABSTRACT FROM AUTHOR]

Due to the open-source and versatility of the Android operating system, Android malware has exploded, and the malware detection of Android IoT devices has become a research hotspot in recent years. Static analysis technology cannot effectively analyze obfuscated malware. Without decomposing, the existing detection methods are mainly based on grayscale images and single files without analyzing and verifying their anti-obfuscation performance. In addition, the current detection of Android malware using deep learning is concentrated in the field of binary classification. This paper proposes a multi-classification method of the Android malware family based on multi-class feature files and RGB images to solve these problems. The method proposed in this paper does not need to decompile the Android APK installation package. However, it extracts the DEX file and XML file in batch from the APK installation package. Then, it converts the file into an RGB image using the conversion algorithm that converts Android software into images. Finally, the deep neural network automatically obtains the RGB image texture features to realize the multiple classifications of the Android malware family. Experimental data show that the proposed method has high detection performance, and the accuracy of multiple classifications of the Android malware family is as high as 99.84%. In addition, the method based on RGB image is better than the grayscale image in detection accuracy, and the effect of RGB image combined with DEX and XML is better than that of separate DEX file image and separate XML file image. Therefore, the method proposed in this paper can effectively detect the obfuscated Android malware, and the detection accuracy of 99.23% can be achieved for the obfuscated sample data. Furthermore, this method has good anti-obfuscation ability. The proposed method is compared with those based on Multi-Layer Perceptron, Long Short-Term Memory, bidirectional Long Short-Term Memory and Deep Belief Network. The experimental results show the proposed method's effectiveness and high generalization performance. [ABSTRACT FROM AUTHOR]

Automatic and accurate segmentation of tumor area from rectal CT image plays an extremely key role in the treatment and diagnosis of rectal cancer. This paper proposes the MR-U-Net network model. The improvement is that a pair of encoder and decoder is added longitudinally to the U-shaped structure, which is the network structure of the fifth layer, and a residual module is added horizontally to the encoder and decoder of each layer. This model is used to conduct targeted research on the automatic segmentation method of rectal cancer. [H. Gao et al., Rectal tumor segmentation method based on U-Net improved model, J. Comput. Appl.40(8) (2020) 2392–2397] also improved U-Net and used the same dataset as this paper, but the Dice coefficient of all targets was only 83.15%, and the Dice coefficient of small targets was only 87.17%. This paper evaluates the improved MR-U-Net network model with the three indicators of precision, recall and Dice coefficient, and finds that in comparison to Ref. 4 the precision is 95.13%, 2.29% higher than the former work, recall is 94.28%, higher than the former work by 0.34%, Dice coefficient of all targets is 88.45%, increased by 5.3% compared with the former work, and the small targets Dice coefficient is increased by 1.28%, which is the best optimization state of this paper. Experiments show that for datasets with extremely skewed positive and negative samples, the MR-U-Net network structure after improving the hyperparameters in the optimizer can more accurately segment the rectal CT tumor lesion area. [ABSTRACT FROM AUTHOR]

The article presents the discussion on considering uncertain and imprecise data in the decision-making process. Topics include MADM methods being the most frequently applied method on strategy management and supply chain; and enhancing inventory management using BlockChain Technology under cloud sector enabled by Hybrid Multi-Verse with Whale Optimization Algorithm.

In this paper, we investigate a (3+1)-dimensional integrable fourth-order nonlinear equation that can model both the right- and left-going waves in a fluid. Based on the Hirota method, we derive three sets of the bilinear auto-Bäcklund transformations along with some analytic solutions. Through the extended homoclinic test approach, we obtain some breather solutions. We find that the breather propagates steadily along a straight line, with one hole and one peak in each period. Graphical investigation indicates that the coefficients in that equation affect the location and shape of the breather. Moreover, we construct some mixed lump–kink solutions. Fusion and fission between a lump wave and a kink soliton are analyzed graphically. The solutions addressed in this paper may be applied to mimic some complex waves in fluids. [ABSTRACT FROM AUTHOR]

This paper deals with the host–parasitoid model, where the logistic equation governs the host population growth, and a proportion of the host population can find refuge. The equilibrium points' existence, number, and local character are discussed. Taking the parameter regulating the parasitoid's growth as a bifurcation parameter, we prove that Neimark–Sacker and period-doubling bifurcations occur. Despite the complex behavior, it can be proved that the system is permanent, ensuring the long-term survival of both populations. Furthermore, it was observed that the presence of the proportional refuge does not significantly influence the system's behavior compared to the system without a proportional refuge. [ABSTRACT FROM AUTHOR]

This work investigates a prey–predator model featuring a Holling-type II functional response, in which the fear effect of predation on the prey species, as well as prey refuge, are considered. Specifically, the model assumes that the growth rate of the prey population decreases as a result of the fear of predators. Moreover, the detection of the predator by the prey species is subject to a delay known as the fear response delay, which is incorporated into the model. The paper establishes the preliminary conditions for the solution of the delayed model, including positivity, boundedness and permanence. The paper discusses the existence and stability of equilibrium points in the model. In particular, the paper considers the discrete delay as a bifurcation parameter, demonstrating that the system undergoes Hopf bifurcation at a critical value of the delay parameter. The direction and stability of periodic solutions are determined using central manifold and normal form theory. Additionally, the global stability of the model is established at axial and positive equilibrium points. An extensive numerical simulation is presented to validate the analytical findings, including the continuation of the equilibrium branch for positive equilibrium points. [ABSTRACT FROM AUTHOR]

This paper describes the fundamental characteristics of cavitation in non-Newtonian liquids and bubble dynamics and then applies them to the domains of bioengineering and biomedicine. The goal of this paper is to examine how Newtonian nanomaterial flows hydromagnetically when subjected to a spinning disc considering such biomedical and bioengineering applications. The vertical axis of the disc rotates with a uniform angular frequency. The fundamental mathematical expressions are governed by the Navier–Stokes equations with the Maxwell equations of magnetism, we obtained ordinary differential equations utilizing Von Kármán's similarity transformations. Additionally, the effects of the magnetic field and radiation restrictions are considered. The RK-4 technique is used to solve the transmuted nonlinear ODEs. The analysis of MATLAB generated flow profiles has looked for changes in the values of key parameters. It is discovered that an increase in the thermal radiation parameter causes a decrease in the nanofluid temperature while an increase in the volume fraction of magnetite nanoparticles causes an increase. The skin-friction and heat-transfer rate at the disc are highly influenced by its rotational, the porosity of the porous media, thermal radiation and nanoparticle size. The rotational parameter, which regulates the disk's rotation, is a result of the rotating phenomenon. The research demonstrates that when the disk's rotation increases, the fluid motion accelerates in both the radial and cross-radial directions. Additionally, increasing the Prandtl number significantly improves heat transport, and a higher value for the rotation parameter shows a lesser concentration phenomenon. Additionally, the Nusselt number shows a decrease curve for a changeable thermal conductivity parameter. Finally, the current research can effectively close a gap in the physique of knowledge. [ABSTRACT FROM AUTHOR]

The influence of conjugate heat transfer based on radiation and magnetohydrodynamic utilization is an attractive research area, with progressive features; it has many applications in thermal engineering, heat exchangers, cooling phenomenon, magnetic cell separation, energy production, hyperthermia, etc. Following the motivating significances of the current research topic, this paper explores the influences of an electrically conducting and radiating fluid with internal friction, heat generation and thermal radiation embedded in a porous medium past a flat permeable plate. The boundary layer equations are dimensionally transformed and solved numerically using implicit finite difference technique called the Keller-box method. The effect of various fluid obeying parameters such as magnetic field, viscous dissipation and heat generation on the flow factors such as velocity and temperature are graphed and discussed in this paper. Growing heat source, energy inside the fluid boosts, thereby increasing the energy of the flow. Increase in generation of energy reduces the viscosity of the flow reduces thereby increasing the velocity of the flow particles. [ABSTRACT FROM AUTHOR]

In this paper, the influence of velocity slip and the Newtonian heating parameters on Williamson Nanofluid across a variable stretching sheet is studied. The basic fluid flow equations are turned into a system of coupled ordinary differential equations with appropriate conditions, which are solved numerically with the Keller-box approach. The outcomes show that, increasing Williamson parameter, due to the viscosity velocity of the fluid slowdown. As a result, it is noticed that the velocity slip parameter reduces Williamson Nanofluid velocity, while increasing shear stress, according to the results. Furthermore, it has been discovered that unlike velocity profiles, the slip influence is more pronounced on temperature and concentration profiles. Furthermore, the Newtonian heating parameter may be shown to have rising functions for velocity and dimensionless temperature. Newtonian heating has uses in the petroleum industry, heat exchangers, conjugate heat transfer around fins and solar radiation. It is discovered that the Joule heating parameter improves the thermal boundary layer thickness and slows down the velocity of the fluid, which aids in maintaining system temperature. Additionally, it has been found that the temperature profile exhibits a stronger slip effect compared with the velocity profile. To prepare toffee, chocolate and other delicacies as efficiently as possible, it is crucial to investigate the flow of Williamson fluids (such as drilling muds, clay coatings, various suspensions and some lubricating oils, thermoplastic melts, and a variety of colloids) in the incidence of heat transfer. The novelty of this paper is the effect of joule heating, Newtonian heating parameter and velocity slip on Williamson fluid velocity and temperature gradient. Furthermore, the heat and mass transfer rates are calculated for various parameters numerically and shown in tabular form. [ABSTRACT FROM AUTHOR]

The bioconvective flows are truly connected to real-life and engineering development. So, the models of biomicrosystems and biocells are considered for the technical analysis in this paper. Our intention for the current analysis was to theoretically examine electrical conduction flow into mass and heat transfer by an extensive gyrotactic microorganism into an inclined magnetic field toward a vertical stretching sheet with nonlinear solar radiation with different solar thermal appliances. The effect on velocity slip and Joule heating was again studied in detail. This classical problem on Navier Stokes equations to the current imitation was decreased to ordinary differential equations by applying the comparison method. The numerical solutions were changed by boundary value problem (BVP) to clarify the subject into finite difference numerical scheme by applying MATLAB. The important results show that the density of motile microorganisms reduces to the bioconvection Lewis number and Peclet number, although reverse performance was noticed for the bioconvection Rayleigh number. Further, solar radiation fosters heat transport. In this paper, complete analysis is provided for potential functions of solar thermoelectric cells, solar ponds, solar thermal power fabrication, etc. [ABSTRACT FROM AUTHOR]

This paper analyzes the theoretical mechanism by which urbanization and consumption structure upgrading exert an impact on carbon emissions in the context of promoting higher quality and efficiency in urbanization, advancing the transition and upgrading of consumption structure, and achieving carbon peaking and carbon neutrality ("dual carbon") goals. By establishing an extended STIRPAT model and an intermediary model, this paper takes nine prefecture-level cities in Fujian Province from 2011 to 2021 as samples to investigate the effects of urbanization and consumption structure upgrading on carbon emission reduction in Fujian Province, revealing the interrelationship and transmission mechanism among the three. The results of this study show that: (i) The relationship between urbanization and carbon emissions in Fujian Province from 2011 to 2021 shows an inverted U-shaped curve, i.e. urbanization accelerated carbon emissions in the early stage and inhibited carbon emissions in the later stage; (ii) the regional heterogeneity analysis shows that under the current urbanization background, the upgrading of consumption structure in western and southern Fujian can significantly reduce carbon emissions, while the relationship between consumption structure upgrading and carbon emission reduction in eastern Fujian is not significant; (iii) the upgrading of consumption structure can help achieve and promote carbon emission reduction through increasing the proportions of the tertiary industry and immaterial consumption and enhancing the level of culture and education, while exerting little effect on carbon emission reduction through consumption in transportation, communication and health care. [ABSTRACT FROM AUTHOR]

This paper focuses on the problems encountered in the production process of electronic-grade polycrystalline silicon. It points out that the characterization of electronic-grade polycrystalline silicon is mainly concentrated at the macroscopic scale, with relatively less research at the mesoscopic and microscopic scales. Therefore, we utilize the method of physical polishing to obtain polysilicon characterization samples and then the paper utilizes metallographic microscopy, scanning electron microscopy-electron backscatter diffraction technology, and aberration-corrected transmission electron microscopy technology to observe and characterize the interface region between silicon core and matrix in the deposition process of electronic-grade polycrystalline silicon, providing a full-scale characterization of the interface morphology, grain structure, and orientation distribution from macro to micro. Finally, the paper illustrates the current uncertainties regarding polycrystalline silicon. [ABSTRACT FROM AUTHOR]

Large-size electronic-grade polycrystalline silicon is an important material in the semiconductor industry with broad application prospects. However, electronic-grade polycrystalline silicon has extremely high requirements for production technology and currently faces challenges such as carbon impurity breakdown, microstructure and composition nonuniformity and a lack of methods for preparing large-size mirror-like polycrystalline silicon samples. This paper innovatively uses physical methods such as wire cutting, mechanical grinding and ion thinning polishing to prepare large-size polycrystalline silicon samples that are clean, smooth, free from wear and have clear crystal defects. The material was characterized at both macroscopic and microscopic levels using metallographic microscopy, scanning electron microscopy (SEM) with backscattered electron diffraction (EBSD) techniques and scanning transmission electron microscopy (STEM). The crystal structure changes from single crystal silicon core to the surface of the bulk in the large-size polycrystalline silicon samples were revealed, providing a technical basis for optimizing and improving production processes. [ABSTRACT FROM AUTHOR]

As greenhouse production is considered the most water-conservative solution in the agricultural sector, greenhouse technology has become an integral approach for modern crop production methods. Different types of greenhouse structures are used to produce crops, and each type has advantages for a particular application, having been designed to meet multiple and specific requirements. This paper introduces a quality function deployment (QFD) framework to select two optimal greenhouse types (single-span and multi-span). This study aims to demonstrate greenhouse management and practices and review the development efforts during the last two decades. A market survey, focus group discussion, and personal interviews revealed nine key performance indicators (KPIs) (cost, wind loads, crop loads, roof slope, light transmission, flexibility, maintainability, rapid spread/ease of control, and heating and ventilation). Then, the participating decision-makers identified nine functional requirements, which, if fulfilled, would boost client satisfaction. The framework designed in this paper was developed to select and analytically investigate the optimal system for greenhouse microclimate control based on the retrieved KPIs and functional requirements for client satisfaction. This QFD framework can assist decision-makers, in the preliminary phase, in selecting optimal systems for greenhouse microclimate control based on their individualized situations. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, distributed systems have become an indispensable part of the field of information storage and management. In the process of large-scale data processing, it is an important issue to compress or replay files to ensure their integrity. In order to solve the problem of large-scale computing resources and data storage, the distributed file system emerged as a new system structure. The purpose of this paper to is study the key technologies of the distributed memory file system of high-performance computers is to improve the capability and efficiency of the distributed system. This paper mainly uses the experimental method and the comparative method to analyze the key technology of the distributed memory file system of the high-performance computer. Experimental results show that the maximum bandwidth value of DFMS in file memory processing can reach more than 2000, and the value becomes more stable as the file increases. [ABSTRACT FROM AUTHOR]

The paper analyzes the interaction of economic growth and inflation rate on accounting for both sources of capital investment and global shocks. The analysis method is a time-varying coefficient Bayesian vector autoregression (TVC-BSVAR) model applied in a quarterly data sample of the Vietnam economy over Q2/2001–Q4/2022. The global factors, including the world economic growth and the inflation rate, are the most important drivers of domestic growth and inflation rate. A greater world growth rate raises the domestic growth rate while a higher world inflation rate increases the domestic inflation rate. If the world economy falls into stagflation with declining growth and a rising inflation rate, Vietnam's economic growth reduces while the inflation surges. The monetary and fiscal policy can be employed to recover domestic economic growth but with an associated surge in the domestic inflation rate. [ABSTRACT FROM AUTHOR]

The withdrawal of India from negotiations on the world's largest trade pact, the Regional Comprehensive Economic Partnership (RCEP), in November 2019 was a major setback for proponents of regional economic unity. This paper tries to evaluate India's concerns and also the way forward for India. A review of trade between RCEP countries post-implementation is done to analyze India's stand against joining the block. India decided to opt out of the agreement because of adverse trade balance, impact on the dairy sector, economic slowdown, experience with Free trade agreements (FTAs), China factor, data localization, rules of origin, and the experience of ASEAN countries with Sino-FTA have been some of the factors responsible for this decision. Bilateral trade agreements with Japan, Malaysia, Singapore, Thailand, Korea, Rep.and ASEAN were already in place. So, trade between India and 12 RCEP member countries would not have changed after India's inclusion in RCEP. There was fear of a massive surge in imported manufactures from China and dairy imports from Australia and New Zealand. This paper also evaluates the long-term repercussions of this decision and whether India missed out on becoming part of the global value chain n having greater market access through this block. India's experience following protectionist policies did not yield the desired results in the past. So, an in-depth analysis has been done to validate if India has made a mistake by not joining RCEP or if India's concerns and fears were justified and its regional future. [ABSTRACT FROM AUTHOR]

Soft tissue is an important operation object in robot-assisted surgery and its mechanical response is of great significance to the precision of surgical operation. Accurate prediction of soft tissue mechanical properties can effectively avoid the potential damage of biological tissue caused by excessive operating force. In this paper, three typical mechanical responses of soft tissue were obtained by no-slip compression experiments of liver tissue. Second, the prediction model of soft tissue mechanical response based on gray prediction was established, and the influence of key parameters of the model on the precision of mechanical response prediction was analyzed. The results show that the gray prediction model can accurately predict the mechanical response of soft tissue, and the prediction accuracy is the highest when the number of historical data is 7. The prediction method of soft tissue mechanical response proposed in this paper will provide important data reference for accurate operation of surgery. [ABSTRACT FROM AUTHOR]

The Republic of Korea has long sought to balance its relationship with the United States and China. While the ROK had once experienced retaliation from China when it tried to align closer with the United States, the Yoon Suk-Yeol administration has made a strategic choice to do so openly without suffering any explicit retaliation of the kind that followed the THAAD dispute in 2016. This paper attempts to explain why Beijing is refraining from explicit retaliatory measures against the ROK by analyzing the factors that have constrained China's choices. It argues that despite evident Chinese concerns over the ROK's strategic alignment with the United States through groupings like the IPEF and collaboration with NATO, China has not explicitly retaliated against the ROK due to Chinese President Xi Jinping's personal preoccupation with China's domestic politics and economic issues in addition to a set of diplomatic priorities that exclude the ROK for the time being and some measure of recognition that retaliation can be counterproductive. This paper concludes by arguing that Beijing will not economically retaliate against the ROK unless it directly infringes on China's core national interests. While there is a broad range of nominally core national interests where China responds rhetorically, there are fewer over which it retaliates with material measures. Therefore, countries facing potential economic retaliation from China over their foreign policy choices must understand the factors that actually compel China to retaliate economically and determine the extent of this retaliation. [ABSTRACT FROM AUTHOR]

This paper addresses a lacuna in the literature concerning how a ruler's ideas shape their attitudes toward the world and others through their personality. It examines the political ideas of Chinese President Xi Jinping as his references to the self-in-relation rather than his schema to assess and treat alters-in-relation. In addition to allowing political actors to assess the world and engage in policymaking, personality may inspire them to use ideas conversely to prepare themselves for the acceptance of their perceived constituencies. In terms of Xi Jinping's evolving personality, an initial need to overcome his individual vulnerability by becoming one with the masses has grown into a quest for transcendence that is informed by the following elements: (1) Buddhism while practicing the Party self; (2) the notion of Confucian harmony for the self-cultivation of Party cadres while practicing the national self; and (3) a socialist sensitivity to the material needs of the disadvantaged while practicing the international self. This has been demonstrated through programs that include his anti-corruption campaigns, his Chinese dream of anti-poverty, and his notion of a shared future of humankind. This paper compares the Kyoto School's use of Buddhism, Mao's use of Confucianism, and Pol Pot's use of Maoism. It also discusses how an autocracy can suffer from involution. [ABSTRACT FROM AUTHOR]

As the U.S.-China relationship tilts toward competition and confrontation, scholars and policymakers have focused on the strategic choices faced by the citizens of small and medium-sized countries caught between the two great powers. However, methodological deficiencies remain in how public preferences for these strategic choices are measured. This paper attempts to fill this gap by developing a new means of measuring public opinion regarding strategic choices. Using survey data collected in Taiwan in March 2023, we demonstrate how this measure is constructed and why it aligns more closely with the theoretical concept of strategic choices. Our findings indicate that preferences for different strategies in Taiwan follow a U-shaped distribution that is centered either on hedging with the United States or with China. Multivariate analyses show that factors such as the threat of China, the U.S. security commitment to Taiwan, skepticism toward the United States, confidence in Taiwan's military, and partisanship are all correlated with public preferences for different strategies. This paper helps us understand these preferences among citizens in small and medium-sized countries and contributes to the literature on public opinion, international relations theory, and policymaking. [ABSTRACT FROM AUTHOR]

The new regulation was introduced to the franchise market of South Korea in that an applicant who wants to be a franchiser must set up direct retail stores and run them for at least one year before recruiting franchisees. Considering the purpose of the regulation, it is inferred that once franchisers run their own stores, their franchisees would be better off than otherwise. This paper addresses one reason that the Korea Fair Trade Commission has no choice but to introduce the regulation. Along the line, this paper investigates whether franchisees would have a more likelihood to make profit in case of franchisers operating their own stores. The result demonstrates that this case could be true since the operation of franchiser-owned stores could be helpful in reducing the cost of franchisees. [ABSTRACT FROM AUTHOR]

Spin pumping and spin-orbit torques play important roles in advancing spintronic device technologies. Our curated special issue focuses on these phenomena in magnetic heterostructures, which are critical for various applications. The high-quality papers1,2,3,4,5,6,7 delve into spin pumping and spin-orbit torques in diverse heterostructures, addressing topics such as insulating/metallic interfaces tailoring spin-to-charge conversion, the influence of HfOx capping layers on perpendicular magnetic anisotropy, anisotropy-controlled spin current generation, sputter pressure-dependent spin Hall angle, the effect of oxygen ion irradiation on spin-orbit torques, and a review paper of emerging two-dimensional/ferromagnetic interfaces. This Editorial discusses the reported articles and the future perspective of these topics. With this special issue, we anticipate significant contributions to both academic research and industrial developments in this direction. [ABSTRACT FROM AUTHOR]