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Due to their simplicity and global convergence properties, the conjugate gradient (CG) methods are widely used for solving unconstrained optimization problems, especially those of large scale. To establish the global convergence and to obtain better numerical performance in practice, much effort has been devoted to develop new CG methods or even to modify well- known methods. In 2012, Rivaie et al., have proposed a new CG method, called RMIL which has good numerical results and globally convergent under the exact line search. However, in 2016, Dai has pointed out a mistake in the steps of the proof of global convergence of RMIL and hence to guarantee the global convergence he suggested a modified version of RMIL, called RMIL+. In this paper, we present another modified version of RMIL, which is globally convergent via the exact line search. Furthermore, to support the theoretical proof of the global convergence of the modified version in practical computation, a numerical experiment based on comparing it with RMIL, RMIL+, and CG-DESCENT was done. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) printing technology has led to transformative shift in prosthodontics. This review summarizes the evolution, processing techniques, materials, integration of digital plan, challenges, clinical applications and future directions of 3D printing in prosthodontics. It appraises from the launch of 3D printing to its current applications in prosthodontics. The convergence of printing technology with digital dentistry has facilitated the creation of accurate, customized prostheses, redefining treatment planning, design, and manufacturing processes. The progression of this technology is from generating models to prosthesis like-fixed dental prosthesis (FDP), implants, and splints. Additionally, it exhibits more wide capabilities. The exploration of materials for 3D printing provides various options like polymers, ceramics, metals, and hybrids, each with distinctive properties that are applicable to different clinical scenarios. The combination of 3D-printing technology and digital workflow simplifies the processes of data transfer, computer-aided design (CAD) design to fabrication, decreasing errors and chairside time. The clinical benefits include enhanced accuracy, comfort, conservative lab procedures, and economics. Challenges in the technology involve significant aspects like initial investment, material availability, and skill requirements. Future trends emphasize on research for improved materials, bioprinting integration, artificial intelligence (AI) application, regularization efforts to ensure safe and common use of the technology. 3D printing offers promise in prosthodontics, addressing challenges through research. The material improvements will promote its broader adoption and revolutionize the future of dental rehabilitation. [ABSTRACT FROM AUTHOR]

Smart grids are two-way communications grids that converge Information Technology (IT) and Operational Technology (OT) to transfer energy-related information between different industry components within the grid. Smart grids have changed the energy sector by increasing sustainability, efficiency and integrating renewable energy sources. However, smart grids are vulnerable to IT-related attacks because they rely on Information and Communication Technology (ICT). By surveying relevant papers and evaluating accessible statistics, this study explores cybersecurity in smart grids by examining current communication protocols and standards. We carefully compile various datasets with general information about four of the most smart grid-related datasets. Our study and conclusions address the key components of a smart grid and offer information that can help create cybersecurity plans specifically for smart grids. This research contributes to the discourse on smart grid security, which is important for preserving the stability of contemporary energy systems. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of the import of convergence arguments in theory assessment. A first part is devoted to making the point of the different types of strategies based on convergence, providing new distinctions with respect to the existing literature. Specific attention is devoted to robustness vs consilience arguments and one representative example for each category is then discussed in some detail. These are: (a) Perrin's famous robustness argument on behalf of the atomic hypothesis on the grounds of the concordance of thirteen different procedures to the same result for the Avogadro number; (b) the consilience argument motivating the trust in the viability of the extra-dimension conjecture in the context of early string theory. These two cases are expressly chosen in order to highlight possible differences, also including whether the convergence obtains in terms of empirical or theoretical procedures. Notwithstanding these various differences, in both cases the evaluation of the assessment strategy similarly depends, in a significant way, on how the convergence argument is interpreted, as shown in the final part of the paper. [ABSTRACT FROM AUTHOR]

Digital technology development and convergence within the ICT field has led to increased significance of technology platforms and ecosystems organizing around them. Even the corporate IT systems are migrated more and more to the cloud platforms. The current explosive growth of artificial intelligence and big data utilization will accelerate that trend also for the industrial value network partners, where the amount of data produced, and low latency requirements force the processing more towards the edge of the cloud. Utilizing technology platform analyses, theory of technology and market disruptions, Value Capture Model, and scenario building, this study investigates Industry 4.0 drivers towards generic digital platforms and ecosystems, and the scenarios to which this development can lead. In addition, the strategic position of different kind of players related to the presented scenarios are examined. The perspectives provided in this study help the players in directing their strategic cooperation and competition efforts in the current early phase of the Industry 4.0 ecosystems formation. [ABSTRACT FROM AUTHOR]

Safe and flexible hydrogen storage technology (HST) emerges as a crucial element in driving the industrialization of hydrogen energy. Consequently, HSTs are being extensively investigated globally, with an increasing number diffusing beyond national boundaries through transnational patent applications. However, the absence of an evolutionary analysis constrains the exploration of technological opportunities in HST. In this study, we unveil the evolutionary trajectory and innovation trends of HST by analyzing its global diffusion and convergence network, utilizing patent family and IPC co-occurrence data spanning from 1993 to 2022. Our results indicate that the transnational diffusion of HST is concentrated in core countries. Japan and France emerged as dominant source countries, while China, Europe, Korea, and Canada were identified as main target countries for HST diffusion. Notably, in the past decade, China and France have experienced significant expansion in filing HST patents. However, China filed a relatively limited number of patent families, suggesting a varied level of engagement in transnational HST diffusion. Hydrogen fuel cells, storage alloys, vessels, and distribution systems constitute the primary technological domains of HST. In the initial stages, fuel cells and alloy materials dominated HST development, often converging with metal powders and heat treatment. However, since 2003, F17C, related to storage vessels, has evolved as a focal point of convergence, involving the integration of pressure tanks, filling and discharging systems, liquid transportation, gas purification, and rare earth materials. Overall, this research offers novel insights for strategizing future HST innovations. • The evolution of HST was evaluated through patent analysis. • Diffusion and convergence trajectory study provides a new perspective to understand HST's innovation trend. • The global diffusion network of HST tends to concentrate in few source and target countries. • Storage vessels and alloys are emerging directions of HST convergence. [ABSTRACT FROM AUTHOR]

• The physical-digital convergence of a programmable robot integrated into play has effects that enhance both spatial reasoning and computational thinking. • The teacher remained a key actor, providing verbal and nonverbal scaffolding to promote children's understanding and mastery of spatial and computational concepts. • Talking about spatial concepts combined with the interaction with a robot enhanced children's acquisition of spatial ideas. • The quantity of children's verbal expressions reflected the complexity of their applied spatial skills and computational thinking. Early evidence suggests that robot programming offers an important foundation for spatial reasoning and computational thinking; however, researchers have debated how to introduce young children to these skills. We explored how preschoolers develop understanding of spatial concepts and computational thinking as the children took turns navigating a path during guided play with a programmable robot. Our analysis of the data included a multimodal visualization of the exchanges between the teacher, children, and a programmable robotic device. The findings highlight the effects that arose from the physical-digital convergence of technology integrated into play including: (a) the robot evoked a chain of actions, enacting pedagogic forces that transformed children's learning; (b) children used their own bodies as spatial referents, co-locating themselves in space with the robot; (c) the teacher remained a key actor, providing verbal and nonverbal scaffolding to promote children's understanding and mastery of spatial and computational concepts; and (d) talking with the teacher about spatial concepts combined with the interactions with the robot enhanced children's acquisition of spatial ideas relative to the quantity of children's verbal expressions and complexity of their applied spatial skills and computational thinking. [ABSTRACT FROM AUTHOR]

• Green technology progress (GTP) of yellow river economic belt is measured by slacks-based DEA model. • Effects of industrial restructuring and environmental regulation on GTP are analyzed by convergence model. • Industrial restructuring and environmental regulation respectively have positive effect on GTP. • The positive effect of industrial restructuring reduces with the increase of environmental regulation. • Fiscal decentralization is main transmission channel for above results. Using 57 city-level panel data of Yellow River Economic Belt from 2004 to 2018, this paper investigates the effect of industrial restructuring and environmental regulation on dynamic change of green technology progress (GTP) by convergence models. Industrial restructuring is divided into rationalization and advancement industrial structure. Concretely, δ convergence results present that GTP values of different cities do not converge to steady state, but absolute β convergence results show that GTP growth rate of different cities will reach to a certain steady-level. In addition, the estimation results verify the existence of GTP conditional β convergence. Rationalization industrial structure, advancement industrial structure, and environmental regulation respectively have significantly positive effect on GTP, but the positive effects of rationalization and advancement industrial structure reduce with the increase of environmental regulation. Moreover, fiscal revenue decentralization and fiscal expenditure decentralization are important transmission channels through which industrial restructuring and environmental regulation affect GTP. [ABSTRACT FROM AUTHOR]

• This paper studies the dynamics of labor productivity convergence and technology catch-up within Africa. • It examines how African countries are catching-up with the local technology frontier from a nonparametric and structural perspectives. • The analysis shows that productivity convergence within Africa is primarily driven by technology catch-up (i.e., efficiency change). • The paper explores the special role of technological catch-up by decomposing it into within-sector convergence, between-sector convergence, and initial specialization. • The results highlight the special role of structural change in closing the productivity gap with the local frontier. This paper studies the dynamics of labor productivity convergence and technology catch-up within Africa, shedding light on two important and inter-related issues that are central to Africa's growth: (i) convergence of relative productivity among African countries and (ii) the role of technological change and technological catch-up in driving productivity change across and within African countries. We do this by using a nonparametric method to estimate an African production frontier. Productivity change in Africa is decomposed into two components: technological change and technological catch-up. Our results show that Botswana and Mauritius are the only two countries in Africa that have converged to the productivity as well as the efficiency level of the frontier. This successful convergence is driven more by technological catch-up and less by technological change. We explore the special role of technological catch-up by decomposing it into within-sector convergence, between-sector convergence and initial specialization using a structural model (Shift and Share catch-up decomposition). The results highlight the special role of structural change in closing the productivity gap with the frontier. This paper contributes to recent evidence suggesting that countries can climb up the income ladder at a faster rate through a two-pronged transformation – i.e., structural change and technological catch-up. [ABSTRACT FROM AUTHOR]

In the current paper, we use backward difference and local discontinuous Galerkin (BD-LDG) methods for temporal and spatial discretization of fourth-order partial integro-differential equations (PIDEs) with memory terms containing weakly singular kernels. This work provides a stability analysis of the proposed method, and at the end, by presenting some numerical experiments, we demonstrate the stability of the resulting scheme and show numerically that the optimal convergence rate is O (h k + 1) in the discrete L 2 norm. [ABSTRACT FROM AUTHOR]

With the advent of the Third computing platform of Social Mobility Analytics and Cloud (SMAC), data is getting generated in huge amounts. This huge amount of data is collected for domain-specific information to process them to get required domain-specific information as in real-time health analytics, financial frauds, real-time automated car driving, vital information of patients undergoing robotic surgery, handling cyber threats etc. This huge data, also known as Big Data, is highly unstructured and imbalanced that is not possible for traditional techniques to handle and process. Advancements in computing power, speedy data storage and convergence of SMAC technologies have also contributed to the swift acceptance of the technology. This led to innovative analytical techniques that are data as well as computation intensive. One such technique is Deep Learning which originated from the artificial neural network and found its use in handling many real-life problems involving multidimensional features. The advantage of Feature Learning or Representational Learning makes Deep Learning a wonderful tool for big data analytics. The previous level of hierarchy transfers the feature learning to the next levels and thus complex features are learned through the learning of simpler features at different levels of abstraction. For efficient learning of these features, tuning of hyper-parameters is a mandatory step. The current work incorporates Grid Search for classification to find the best classifier for the classification of Medicare beneficiaries based on two scenarios. The first Scenario is beneficiaries who are affected by cancer and the Second Scenario is where Medicare beneficiaries are provided Gender wise (being a Female beneficiary). By experimenting using these algorithms at 10-fold cross-validation, the best results were achieved in the sensitivity of 99.17 %, Specificity of 97.68 % and accuracy of 98.8 % with Deep Learning Neural Network with Dropout for First Scenario and achieved the best results in the sensitivity of 82.97 %, Specificity of 68.71 % and accuracy of 75.05 % with Random Forest for Second Scenario. [ABSTRACT FROM AUTHOR]

Industry has entered a new age of industrial change which is revolutionising how products are manufactured. Driving this industry change are newly developing digital technologies such as robotics, artificial intelligence, advanced analytics and the industrial internet. This convergence of technology and manufacturing is driving the digital transformation of every industrial segment from operations to logistics, and from aeronautics to retail product manufacture. The technology advances evident from Industry 4.0 will result in industry disruption and build competitive advantages for the organisations who can master the technologies in their manufacturing processes. However, organisations are struggling to develop a tactical methodology to introduce these technologies while simultaneously transforming their organisation cultures and organisations and maximising the benefits. One of the key reasons for the Fourth Industrial Revolution called Industry 4.0 is the need to strengthen the competitiveness of Western European economies, which as a result of the progressing globalization process and rising labor and business costs [1]. A key benefit of the introduction of Industry 4.0 initiatives in a manufacturing plant is to help overcome current operations management limitations, such as, the lack of knowledge on how the process is performing at a specific point in time. This paper will investigate the impact of deploying a range of Industry 4.0 technologies to access machine and operations performance data, to improve equipment reliability and to reduce the costs associated with the maintenance of equipment. With the introduction of the operational data analytics as a result of data extraction from manufacturing equipment, along with integrating other data sources, the business has realised improvements in key metrics like OTIF (On Time in Full), OEE (Overall Equipment Effectiveness), MTBF (Mean Time Between Failure), MTTR (Mean Time to Return), CuC (Consumable Unit Cost), and Lead Time. This paper discusses this project and the results obtained at Zimmer Biomet while also discussing the research carried out for ZOML to begin its journey on manufacturing digitalisation. The paper starts with a background in Industry 4.0. The next section provides a background on the company where the problem statement and project have come from with some detail on their digitalisation journey. The following section details the problem and the project that the research is derived from and the final section is in relation to the results of the research and the project. [ABSTRACT FROM AUTHOR]

Using a fractional-order replication dynamic equation to describe the process of learning and modifying strategies, this paper tries to develop a fractional-order game model of green and low-carbon (GLC) innovation evolution in manufacturing enterprises. The uniqueness, local stability, and global stability of the evolutionary dynamics are discussed to determine the evolutionary stable strategy (ESS) of the game. The discretized form of the fractional-order game model, the ESS of the discretized game, and the sensitivity analysis of parameters are studied. Then, the influence of variable parameters on the ESS is analyzed using phase diagrams and bifurcation diagrams. The results show that: manufacturing industry can develop into a stable situation where all enterprises adopt GLC innovative technologies if their incomes exceed their investment; decreasing the fractional order of the model, the convergence rate to ESS will increase, but the stability of the system will decrease. [ABSTRACT FROM AUTHOR]

The paper examines the dynamics of rural electrification in India and explores the transition to a complementary convergence of decentralised and centralised electricity systems for sustainable rural electricity services. A novel socio-technical framework of analysis for assessment of cases of rural electricity systems is developed and applied in Indian villages to explore the interplays of different elements of rural electricity systems, enabling and hindering factors, and actors and institutions that affect the governance and sustenance of rural electrification. A mixed research method, along with document analysis, was applied to study the socio-technical perspectives, using 21 indicators representing nine different interplays of elements of rural electricity systems. The analysis reveals there is a significant effect of governance on technology performance, which in turn affects customer practices. There is also a significant correlation between customer practices and governance. Policy enablers along with strong institutions and actors - both at the subnational and local level - are key drivers to effective governance. Furthermore, irrespective of centralised or decentralised technology, adequacy and reliability of electricity supply and effective customer service delivery at the local level are important i.e., both supply reliability and quality, and customer service quality are equally important. The findings have clear policy implications for the governance of the electricity sector in India. • A novel sociotechnical framework for assessing village electrification is developed. • Novel framework tests interplays of various elements of the electricity system. • There is a significant correlation between governance & performance of technology. • There is also a significant effect of technology performance on customer practices. • Policy enablers, strong institutions & actors are drivers to effective governance. • A mix of decentralised & centralised electric system will have a better outcome. [ABSTRACT FROM AUTHOR]

The progress of modern research is constantly fueled by the convergence of multiple technologies. Despite the enormous potential of both fluorescence (FL) and photoelectrochemical (PEC) technologies, the development of synergistic PEC-FL sensing platforms that combine the advantages of both is still in its early stages due to their relatively recent inception. Hydrogen sulfide (H 2 S), possessing dual irritant and asphyxiating traits, poses challenges for environmental preservation and human health. The development of the PEC-FL detection methodology for H 2 S in complex environmental settings is imperative. Combining FL and PEC sensing techniques, this work presented a new concept of photoinduced electron-transfer (PET) effect grafting for dual-mode fluorescence and PEC analysis. Briefly, a well-designed fluorescent molecule (BTFM-DNP) featuring the PET effect was synthesized and implemented to modulate the photoelectric response of the indium tin oxide (ITO)/BiOI photocathode electrode. After reacting with H 2 S, the thiolysis of dinitrophenyl ether eliminated the intramolecular PET effect and recovered the significant fluorescence of the probe. Remarkably, the newly formed 2,4-dinitrobenzenethiol (DBT) with strong electron-withdrawing groups was then grafted to the ITO/BiOI photoelectrode and achieved the successful transfer of the PET process, resulting in a sharp decrease in photocurrent. The as-developed dual-mode protocol exhibited good performance in terms of ultra-sensitivity, high selectivity, fast response, and a wide detection range from 1 pM to 80 μM. The newly developed PEC-FL sensing platform can be applied to detect H 2 S levels in both the environment and food. This study demonstrates a promising synergy between fluorescent probes and PEC sensors, offering a novel perspective on the advancement of multi-mode analysis techniques. This approach has the potential to significantly enhance detection accuracy and reliability. [Display omitted] • A dual-mode strategy was proposed for H 2 S-sensitive detection. • The PET effect was grafted into an innovative fusion of fluorescence and PEC. • Dual-mode analysis showed ultra-sensitivity and a wide detection range. [ABSTRACT FROM AUTHOR]

Technology convergence is considered one of the cornerstones of technological innovation as a phenomenon emerging at the intersection of two previously unrelated fields of technology. The new technological system is a new combination of knowledge types, technology components and intersections. For this matter, analyzing patents is an essential part for strategic decision making. However, the manual analysis of large amounts of patent semantics is often time-consuming, extensive, and difficult even for experts. To enhance manual patent analyses, new machine learning-based techniques are gaining increasing interest. This study aims to enrich this methodological research by developing and evaluating an unsupervised text-mining approach to automatically cluster patents of two knowledge types into four technology components. To this end, this study presents a five-step method including the comparison between different algorithms and design choices. This method is applied to printed electronics-relevant patents extracted from the Derwent World Patent Index and enables to draw recommendations for automated patent analyses. The findings show different significances for types of components: while components of the specialized knowledge type could be predicted with significance, components of the design knowledge types could not provide significant results. [ABSTRACT FROM AUTHOR]

To address challenges encountered in traditional Elman neural networks (ENNs), such as low convergence accuracy, difficulties in hyperparameter selection, and issues with gradient disappearance, a new ENN based on the Gaussian kernel and an improved seagull optimization algorithm (SOA) named GSENN is proposed. First, the principle of the ENN is introduced, and the effects of the network structure, parameters, and gradient descent algorithm on its output are analyzed. Building upon the analysis results, an input layer for the ENN using the Gaussian kernel is designed. The outstanding local feature extraction capability of the Gaussian kernel is used to enhance convergence accuracy, and the predicted trends are more in line with real data. In addition, we propose an SOA incorporating a nonlinear factor to optimize the weights and thresholds of the ENN. This approach aims to resolve the challenges in hyperparameter selection and problems of gradient vanishing in the ENN. Seven commonly used neural network structures and the latest improved techniques were chosen for comparative experiments across different datasets. The experimental results show that GSENN achieves a mean relative error rate of 4.286 %. The MSE results indicate a 45.600 % improvement over the original ENN and a 37.142 % improvement over BP neural networks. The experimental results demonstrate that GSENN exhibits efficient forecasting capability and offers reliable and effective forecasting for the relevant departments. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) facilitates patients with all-time-connected medical devices through cost-effective solutions and a feeling of comfort with round-the-clock hospital support. The patients who cannot visit hospitals for routine checkups prefer the usage of IoMT devices. The healthcare facilities also rely on the real-time statistics of IoMT machines and diagnose problems and their solutions within a small interval of time. This could only be possible when a robust convergence of technologies is used with IoMT devices. This review study discusses how we may apply the convergence of IoMT devices with trending technologies and focuses on delivering a new dimension of novel IoMT usage through broad multi-homing dense networks. It also discusses various applied machine learning algorithms available in the healthcare industry. It compares a variety of disease predictions and the accuracy of the equipment and decision recommendations. The review study also discusses various IoMT convergence open challenges and opportunities through the labor industry healthcare system. [ABSTRACT FROM AUTHOR]

Hotspot detection in infrared thermal (IRT) images obtained by unmanned aerial vehicles (UAVs) is essential for the reliability, safety and efficiency of photovoltaic (PV) plants. We observe that some objects in the complicated environment, which are outside the region of PV arrays, might significantly interfere with the performance of automatic hotspot detection. This work focuses on the problem of PV array extraction from complex scene, which has not been solved in previous literature. Existing studies have attempted image processing methods to extract PV arrays. However, these approaches rely on manually designed features and parameters, which are impractical for complex scene. In this paper, a deep learning model, namely modified U-Net, is proposed to solve the problem. The proposed model can learn features and parameters automatically. Base on the classical U-Net, several innovations are provided: (1) batch normalization layers are adopted to alleviate internal covariate shift problems; (2) "He initialization" is used to increase the robustness and speed up the convergence; and (3) "RMSprop" is adopted to update parameters adaptively. 1211 IRT images of a PV plant containing complex background are collected to verify the effectiveness of the proposed method. Extensive experiments are conducted to compare the modified U-Net with five deep learning models and three image processing methods. The results demonstrate that the proposed method performs better than existing methods. Cooperating with the modified U-Net for PV array extraction from complex scene, even a simple hotspot detection method can achieve the accuracy of 99.79% and the F1 score of 0.9548. [Display omitted] • U-Net is modified to extract photovoltaic array from complex scene. • Batch normalization layers are adopted to alleviate internal covariate shift problems. • "He initialization" is used to increase the robustness and speed up the convergence. • An adaptive optimization method, namely "RMSprop" is adopted to update parameters. • Extensive experiments verify the effective of the method. [ABSTRACT FROM AUTHOR]

• A novel modeling methodology of monorail train and large-scale bridge interaction is developed and validated with a field test. • Convergence, accuracy, and computational efficiency of the proposed methodology are all discussed. • Comparison with traditional model illustrates the advantages and significance of the proposed methodology. • Effects of the varying bridge profiles on the vehicle-bridge interaction are elaborately evaluated. High–efficiency solutions for extremely long bridge structures are a challenge in vehicle-bridge interaction modelling. In the present work, a novel modelling methodology for a monorail train and large-scale bridge interaction system is developed based on moving element technology. First, the governing equations for the monorail train subsystem are derived by adopting the Newton-Euler method, and an extremely long bridge is modelled based on an improved component mode synthesis method in which a bridge unit with finite length is updated and moved as the train moves forward. The train and bridge subsystems are coupled together by a nonlinear wheel–track interaction model. Subsequently, the convergence of the proposed method is discussed in detail, and reasonable values of several crucial model parameters are determined. On this basis, the simulation results obtained by the proposed methodology are compared with those of the finite element method (FEM) and field test data, validating the reliability of the proposed methodology and revealing its remarkable advantages. Finally, by applying the proposed methodology, the dynamic performance of the monorail train-bridge system is evaluated under different operating conditions. Results indicate that the proposed methodology can achieve good calculation accuracy for the vehicle-bridge dynamic responses; additionally, its computational efficiency is much higher than that of the FEM. The variation of the bridge section profile has a notable effect on the dynamic performance of the monorail vehicle-bridge system, which becomes more significant with increasing train speed. [ABSTRACT FROM AUTHOR]

Our study matches multiple rounds of China Family Panel Studies (CFPS) data from 2012 to 2020 with regional macro fiscal and economic data, and explores the impact of regional fiscal decentralization on citizens' satisfaction with public services from the subjective perception of individual respondents. The study found that the increase in regional fiscal decentralization has a significant positive effect on improving the satisfaction of citizens with public services. However, due to differences in individual characteristics among the surveyed individuals, this effect has obvious heterogeneity. Mechanism analysis showed that the enhancement of local government's awareness of public service supply and the increase of regional fiscal transparency have a promoting effect on the improvement of citizens' satisfaction with public services. From the perspective of the convergence in the perception of public service satisfaction among citizens within the same province, the increase of fiscal decentralization has not effectively promoted the equalization development of public services at the provincial level in China. • Explored the impact of fiscal decentralization on citizens' satisfaction with public services from subjective perception • The increase of fiscal decentralization has a positive effect on improving the satisfaction of citizens with public services • The enhancement of government's awareness and fiscal transparency can promote increase of satisfaction with public services • The increase of fiscal decentralization has not effectively promoted equalization of public services at the provincial level [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) is a rapidly evolving technology that attracts interest from active research communities such as Internet of things, data security and Artificial Intelligence. The proposed review critically and comprehensively analyses the current research in the Internet of Vehicles domain particularly focusing on its key components V2X scenario, evidence management, incident reporting with warning and Trust in IoV with emphasis on the convergence of the major technologies Blockchain, Artificial Intelligence and IoT. The proposed framework derives from the fact that the decisions in an IoV ecosystem are driven by analyzing the complex data collected from several Internet of Things (IoT) devices related to vehicles, infrastructure, and road users followed by applying ML algorithms for intelligent decision making and advanced algorithms for ensuring security and trust. The review will be an eyeopener for researchers in the connected vehicles domain, shedding light into the recent advancements in each of the major components along with the identification of critical challenges and future research recommendations. [ABSTRACT FROM AUTHOR]

Information technology diffusion (ITD) is essential for promoting the sustainable development of technologies and helping technology owners gain a competitive advantage. The web-based development of technology and the digital transformation needs of multidisciplinary markets simultaneously drive the increasing convergence and expansibility of information technology (IT). However, whether these two technological characteristics can contribute to the diffusion of early-stage IT and how they influence the further diffusion of IT that has already diffused on a large scale are yet to be thoroughly investigated. We argue that the impact of technology convergence on ITD is heterogeneous when distinguishing between low- and high-ITD scenarios, as is technology expansibility. Our quantile regression estimates use a dataset about Internet of Things (IoT) patents authorized by the Chinese Patent Office from 2009 to 2019. The results show that increased technology convergence benefits ITD in high-ITD scenarios but does not affect ITD in low-ITD scenarios. Technology expansibility negatively impacts ITD in high-ITD scenarios and positively impacts ITD in low-ITD scenarios. This study broadens the theoretical paradigm of "technological characteristics and ITD" from the domain perspective. It inspires information technology owners to formulate sustainable strategies related to ITD according to technology convergence and expansibility. • Enriching the study of IT management and technology diffusion through the dual lens of convergence and application domain. • Increased technology convergence is beneficial for IT diffusion in high-diffusion scenarios, but has no effect in low-diffusion scenarios. • Increased technology expansibility promotes IT diffusion in low-diffusion scenarios while acting conversely in high-diffusion scenarios. • A quantile regression model is built and tested by data from the citation network of IoT patents, relevant patents' and companies' information. [ABSTRACT FROM AUTHOR]

This study aims to explore the association between the digital economy (DE) and corporate total factor productivity (TFP), as well as to analyzing the potential moderating influence of industry environment. The sample comprises Chinese A-share listed manufacturing enterprises, and the analysis utilizes the knowledge-based view of firms. The results reveal an inverted U-shaped association between the digital economy and corporate total factor productivity, with the industry environment playing a moderating role in this association. Specifically, the inverted U-shaped association is flatter in industries with higher levels of munificence and dynamism, while it is steeper in the higher complexity industries. These findings underscore the impact of the digital economy on firm productivity and provide guidance on how to effectively utilize the digital economy to improve productivity and competitiveness under different industry environments. • We present new and detailed evidence supporting the productivity paradox within the knowledge-based view. • We empirically discover the inverted U-shaped relationship between digital economy and corporate total factor productivity. • We examine the moderating effects of industry environments on this relationship. • We contribute to a deeper understanding of digital economy, especially in emerging economies. [ABSTRACT FROM AUTHOR]

Recognized as essential for firms, a systematic methodology for identifying potential technology convergence opportunities has been the focus of many studies attempting to predict the emergence of new convergences between technology classes. However, because technology classes cover a fairly broad range of technology properties, the practical implications of the identified opportunities are not clearly articulated. Motivated to address this problem, this study develops supervised learning models to predict potential technology convergence between semantically coherent technology topics using cluster likelihood, link possibility, and technological similarity as input features. Patent data is utilized to generate technology topics and explore technology convergence between them, and a case analysis on wearables is conducted. Wearables, at the forefront of innovation, offer a dynamic environment converging disciplines like electronics and health sciences, making them an ideal subject for validating the proposed approach practically. The case analysis leads to the development of six individual models and one voting classifier that ensembles them together. The models perform favorably based on traditional metrics and indicate feasible and rational opportunities across various technologies, affirming the validity of our approach. This study's primary contribution lies in enhancing firms' R&D planning capabilities, enabling them to seize opportunities within specific technology topics. • A supervised learning model for anticipating technology convergence is proposed. • This study uses technology topics to explore opportunities with specific meanings. • The performance of the individual models and voting classifier is quite respectable. • The in-depth discussion of the predicted future opportunities validates this study. • This study contributes to enhancing the R&D planning capabilities of firms. [ABSTRACT FROM AUTHOR]

Numerous studies document the acceleration of technology and industry convergence and point out the role of digitalisation adding further impetus to these processes. However, the issue of how digitalisation engenders the blurring of industry boundaries has received little attention. This paper addresses this research gap and contributes to the conceptual understanding of blurring industry boundaries. We integrate previously disparate theories and constructs into a single conceptual framework to elucidate the mechanisms by which digital technologies make industry boundaries increasingly permeable. The proposed framework is illustrated with references to recent developments in the automotive industry. [ABSTRACT FROM AUTHOR]

In this paper, a novel fixed-time controller (FTC) strategy based on leader–follower mechanism and finite-time disturbance observer (FDO) is proposed for surface vehicles (SVs) formation suffering from complex unknowns. The excellent features of designed strategy are shown below: (1) A fixed-time tracking control (FTTC) approach combining with integral sliding mode (ISM) technology is devised for a nominal leader SV such that fixed-time stability can be ensured; (2) To achieve formation efficiently, a fixed-time formation controller (FTFC) strategy incorporating with backstepping technology is proposed for coordinating follower SVs; (3) Considering complex disturbances in the entire formation system, finite-time disturbance observers (FDOs) are injected into the FTFC framework which in turn contributes to accurate formation control with fixed-time convergence. Finally, simulation results demonstrate remarkable performance of the proposed FDO-FTFC scheme. [ABSTRACT FROM AUTHOR]

The stochastic gradient descent (SGD) method plays a central role in training deep convolutional neural networks (DCNNs). The recent advances in the field of optimization methods for DCNNs follow the direction of gradients. The innovations mainly lie in adopting different techniques to manage the history of gradients or automatically adapt the step size. In contrast, in this paper we propose a novel optimization approach for training the top dense layer of DCNN. It primarily utilizes the orientation directly pointing to the optimal values of parameters instead of the direction of gradients to navigate the parameters updates. The Moore–Penrose inverse method has been adopted to determine the difference between the current parameters and the optimal parameters, and the parameter updates are driven along this direction to compensate such a difference. Subsequently, the parameters have been fine-tuned along the direction of the classical gradient. Experiments have been conducted on extensively selected benchmark datasets. The results indicate that the proposed approach obtains a higher convergence rate and lower minimum loss compared to other state-of-the-art optimization methods. Furthermore, with the same DCNN architectures, the performance improvement margin between the proposed optimization method and other state-of-the-art optimization approaches is highly significant. [ABSTRACT FROM AUTHOR]

In this paper, we consider a prototypical convex optimization problem with multi-block variables and separable structures. By adding the Logarithmic Quadratic Proximal (LQP) regularizer with suitable proximal parameter to each of the first grouped subproblems, we develop a partial LQP-based Alternating Direction Method of Multipliers (ADMM-LQP). The dual variable is updated twice with relatively larger stepsizes than the classical region (0 , 1 + 5 2). Using a prediction-correction approach to analyze properties of the iterates generated by ADMM-LQP, we establish its global convergence and sublinear convergence rate of O (1 / T) in the new ergodic and nonergodic senses, where T denotes the iteration index. We also extend the algorithm to a nonsmooth composite convex optimization and establish similar convergence results as our ADMM-LQP. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the evidence for, and to discuss the aspects of the striking similarities that have been identified between backed bladelets recovered in two geographically distant assemblages, one found in Southern Italy (Paglicci, layer 24 horizon A1) and the other in Eastern Europe (Mira, layer II horizon 2). Both assemblages are dated to around 29–28,000 BP and are taxonomically defined as Early Upper Palaeolithic. Detailed comparison of technical and morphological data is impossible because the Eastern European site does not contain an assemblage that lends itself to statistical analysis. The backed bladelets of type PA24A1, found in Paglicci, layer 24 horizon A1-0, and Mira, layer II horizon 2, have no direct analogies in chronologically close Aurignacian and Gravettian sites, in either Southern or Eastern Europe. Taking into account the similar chronological position of the sites, separated by a distance of ca. 2,500 km, it is concluded that the significant similarity of the backed bladelets is most likely explained not by the convergence of development or by trade, but by the direct migration of a group of modern humans who manufactured such specific microliths. The Paglicci (24A 1) and Mira (II/2) industries generally belong to the Early Upper Palaeolithic, being placed chronologically at the transition between the EUP and MUP, being located morphologically and technologically between the Aurignacian and Gravettian. Despite the scarcity of data, the distinctiveness of the backed implements indicates that the sites belong to the same episode of sociocultural development. The issue of the cultural affiliation of the industry with PA24A1 type bladelets remains unanswered, and the search for analogies, either in Eastern or Southern Europe, needs to be continued. [ABSTRACT FROM AUTHOR]

This paper analyzes renewable energy efficiency in Latin American countries (LAC) and investigates the efficiency convergence. Based on the data of the period 2005–2014, Super-efficient Slack-Based Measure (S–U-SBM) model is applied to measure the total factor efficiency of renewable energy, and Malmquist-Luenberger productivity index (ML) is applied to detect the changing trend of efficiency. The S–U-SBM results show that the average value of the total factor efficiency of renewable energy in LAC is 0.808 and has an upward trend, and for individual country, the efficiency value ranges from 0.262 to 1.302 with an unbalanced development and utilization of renewable energy. The ML results show that the average of the total factor efficiency of renewable energy change index is 1.014, and the technical efficiency change is the main factor that drives the growth of the ML in LAC. For two sub-regions, South America, Central-North America and Caribbean, the main factor is technological progress change and technological efficiency change respectively. There are significant conditional convergences in the total factor efficiency growth of renewable energy in the whole Latin American region and the two sub-regions. Technological innovation is the key for Latin American countries to improve the total factor efficiency of renewable energy at this stage. • This article measures total factor efficiency of renewable energy using S–U-SBM model. • Technical efficiency generate more strong impacts on total factor efficiency. • Convergence test is carried out to explore the differential characteristics of ML growth. • There is significant conditional convergence in Latin American countries. [ABSTRACT FROM AUTHOR]

• The convergence of mathematical modeling, nanotechnology, and epidemiology signifies a genomic paradigm shift, enhancing our understanding of infectious diseases at the molecular level. • Mathematical models become precision instruments, shedding light on the intricate molecular dynamics of viral transmission, and providing crucial insights into gene regulation and expression. • Interdisciplinary collaboration, merging genomic precision with mathematical rigor, biology, and epidemiology, promises accelerated discoveries in molecular biology, fostering innovation and exploration in understanding gene regulation and expression during infectious processes. • Global collaboration, bolstered by the predictive power of molecularly-informed mathematical models and nanotechnology, becomes imperative for a comprehensive genomic response to emerging infectious diseases. The intersection of mathematical modeling, nanotechnology, and epidemiology marks a paradigm shift in our battle against infectious diseases, aligning with the focus of the journal on the regulation, expression, function, and evolution of genes in diverse biological contexts. This exploration navigates the intricate dance of viral transmission dynamics, highlighting mathematical models as dual tools of insight and precision instruments, a theme relevant to the diverse sections of Gene. In the context of virology, ethical considerations loom large, necessitating robust frameworks to protect individual rights, an aspect essential in infectious disease research. Global collaboration emerges as a critical pillar in our response to emerging infectious diseases, fortified by the predictive prowess of mathematical models enriched by nanotechnology. The synergy of interdisciplinary collaboration, training the next generation to bridge mathematical rigor, biology, and epidemiology, promises accelerated discoveries and robust models that account for real-world complexities, fostering innovation and exploration in the field. In this intricate review, mathematical modeling in viral transmission dynamics and epidemiology serves as a guiding beacon, illuminating the path toward precision interventions, global preparedness, and the collective endeavor to safeguard human health, resonating with the aim of advancing knowledge in gene regulation and expression. [ABSTRACT FROM AUTHOR]

[Display omitted] • First review to investigate the combination of near-infrared spectroscopy and enrichment technique. • In-sorbent detection of analytes prior to elution. • Advantages and disadvantages of the reported methods. • Outlook into future applications. This comprehensive review paper aims to captivate the applicability of in-sorbent detection, where near-infrared spectroscopy (NIRS) converges with enrichment technologies. For this purpose, we collected and summarized information regarding the combination of several sophisticated analytical enrichment techniques with NIRS to further explore and develop this synergistic approach. Peer-reviewed publications, matching the criteria of in situ NIR measurements prior analyte elution, have been collected, investigated, and concluded within this review. Investigations according to used materials, commercial or self-made, composition, organic or inorganic and applied analytical methodologies have been carried out. Applications extending over a multitude of chemical fields, from environmental to medicinal applications. As this review concludes, the combination of these techniques further expands the applicability of NIRS and moreover tries to solve the long-standing issue of the comparably low sensitivity regarding this vibrational technique. [ABSTRACT FROM AUTHOR]

Computing the dominant eigenpair of an essentially nonnegative tensor is an important topic in tensor analysis and computation because of the important applications in network resource allocations. In this paper, we present an improved homotopy-type method inspired by Chen et al. (2019) to compute the dominant eigenpair of an essentially nonnegative tensor. Based on an approximation technique, the proposed algorithm is suitable for both irreducible and reducible cases and the global convergence analysis is established. Finally, we implement the method via a prediction–correction approach for path following and some numerical results are reported to illustrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Extremum seeking (ES) is a powerful approach to the optimization and stabilization of unknown dynamic systems and is an active field of research in control theory. This paper surveys recent ES algorithm developments. ES has evolved from its origins as a simple but brilliant engineering invention in the 1922 paper of Leblanc to the breakthrough work of Krstić and Wang in 2000 in which the first general stability analysis was given, to the most recent and numerous analytical results which prove convergence for a wide range of general dynamic systems. This survey provides a thorough overview of the dynamic systems for which ES stabilization and optimization results have been provided, the various analytical methods for proving convergence, and an overview of many real world applications for which ES has been utilized in industry and at scientific laboratories. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between Absorptive Capacity (AC) and Knowledge Appropriation (KA), two significant domains in technology transfer, knowledge management, and innovation. While there is an inherent connection between these fields, there remains a gap in understanding their comprehensive convergence. To address this, we reviewed 3412 AC and 368 KA articles, employing techniques such as life-cycle, changepoint, and advanced keyword analyses. We also introduced innovative methods like the H-index, CAGR, and a unique approach comparing keyword frequency over time between AC and KA, spotlighting key convergence areas: innovation, entrepreneurship, and knowledge spillover. Our analysis revealed a parallel evolution of AC and KA, as demonstrated by their shared timelines, thematic overlaps, and mutual influences in the realm of innovation. The life-cycle analysis showed a faster evolutionary pace for AC than for KA but highlighted their shared roots in management during the late 80s. Furthermore, we identified foundational building blocks, including patents, R&D, and knowledge management, that underpin their convergence. Interestingly, some divergent areas were also pinpointed, such as the emphasis on organizational learning in AC but its absence in KA. In summary, this study provides a clear picture of the intertwined relationship between AC and KA, presenting a refined methodology to explore convergence between academic fields, setting the stage for further research in this domain. [ABSTRACT FROM AUTHOR]

• The significance of radical innovations for sustainability is elucidated from the perspective of diverse discourses. • Grassroots innovation (GI) as a radical innovation paradigm with unconventional agents, goals and practices. • A research gap in the systematic analysis and assessment of the sustainability of GIs persists. • A meta-theoretical framework for assessing the sustainability of radical innovation and GI is proposed. • It integrates the culture and tools of diverse discourses and takes a systems thinking view on the evaluation of innovation. Diverse discourses converge into the importance of broadening the focus of sustainability innovations from merely cleaner technologies to more radical, paradigmatic innovations. Here, we focus on grassroots innovation (GI) as a radical innovation paradigm whose agents, goals and practices are fundamentally different from conventional innovation. Researchers typically attribute GIs the potential to influence the transition toward more sustainable production and consumption. Through a systematic literature review, we find that a research gap in the systematic analysis of GIs' sustainability persists. We thus propose a meta-theoretical framework to systematise the sustainability assessment of GIs across three system levels: product, organisational model and socio-technical system. Our framework delineates how the theory and tools from sustainability assessment and sustainable business models may enrich sustainability transitions studies in the analysis of grassroots, and more broadly, radical innovation, providing a systems thinking view and increasing the credibility and reflexivity of sustainability arguments in transition studies. [ABSTRACT FROM AUTHOR]

Label Distribution Learning (LDL) is a new learning paradigm to describe supervision as probability distribution and has been successfully applied in many real-world scenarios in recent years. In LDL applications, the availability of a large amount of labeled data guarantees the prediction performance. In this paper, we cogitate the active learning for LDL to reduce the annotation cost. The center element in practice any active learning strategy is building the criterion that measures the usefulness of the unlabeled data and decides the instances to be selected to label manually. We are probably the first to focus on active instance selecting for label distribution learning. We propose a strategy named Active Label Distribution Learning (ALDL) to select the most informative instances for LDL applications. The fundamental idea of the ALDL strategy is to quantify the degree of disagreement for each unlabeled instance by the committee consisted of selected LDL algorithms, and identify the instances to be labeled manually. ALDL maintains composing the committee with selected LDL algorithms and measure the value of unlabeled instances, and a weight vector is used both parts. Besides, we discuss the convergence and the parameter selecting of ALDL. Finally, compared with other active learning methods, the experimental results on the datasets show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Matrix double splitting iterations are simple in implementation while solving real non-singular (rectangular) linear systems. In this paper, we present two Alternating Double Splitting (ADS) schemes formulated by two double splittings and then alternating the respective iterations. The convergence conditions are then discussed along with comparative analysis. The set of double splittings used in each ADS scheme induces a preconditioned system which helps in showing the convergence of the ADS schemes. We also show that the classes of matrices for which one ADS scheme is better than the other, are mutually exclusive. Numerical experiments confirm that the proposed ADS schemes have several computational advantages over the existing methods. Though the problems are considered in the rectangular matrix settings, the same problems are even new in non-singular matrix settings. [ABSTRACT FROM AUTHOR]

Ocean wave energy has significant technical potential but limited full-scale deployments and technology convergence. Consequently, guidelines for developing wave energy converters (WECs) are still developing themselves, especially around experimental uncertainty analysis (UA). To develop a comprehensive WEC-specific UA methodology, we conducted a 1:30 scale experiment of a case study oscillating water column (OWC) WEC. This paper presents the methodology, which describes UA principles and means to identify parameters causing uncertainty, and demonstrates new WEC-specific UA methods: general uncertainty analysis (GUA), the Monte Carlo method (MCM) for uncertainty propagation, and Type A and Type B uncertainty evaluation. Results show the expanded uncertainty averaged ± 16% for capture width ratio and ± 6% for wave loads; Type B uncertainty tended to be slightly larger than Type A uncertainty; and uncertainty in regular waves slightly larger than irregular waves. The MCM was found to be effective and efficient in uncertainty propagation. In general, given WECs tend to maximise motions, use a power take-off system, and must survive storms, WEC model tests may be especially susceptible to uncertainty due to nonlinearities and modelling complexities. In conclusion, UA should be carried out in WEC model test experiments. We close with recommendations for refining relevant international guidelines. • Uncertainty analysis is key for wave energy converter model test experiments. • Three-stage structure established for general and detailed uncertainty analysis. • Comprehensive examples of Type A and Type B uncertainty evaluation. • WEC-specific procedure developed for Monte Carlo Method of uncertainty propagation. • Uncertainty analysis methodology developed for regular and irregular waves. [ABSTRACT FROM AUTHOR]

• This study presents a novel fractional order adaptive algorithm, called MIFLMS. • The MIFLMS extends the scalar innovation into a vector innovation. • It reveals a faster convergence speed than the FLMS with no noticeable increase in the computational burden. • The proposed algorithm effectively estimates the parameters of nonlinear systems. • The MIFLMS is more robust than the FLMS and provide consistent accurate and convergent performance. The development of procedures based on fractional calculus is an emerging research area. This paper presents a new perspective regarding the fractional least mean square (FLMS) adaptive algorithm, called multi innovation FLMS (MIFLMS). We verify that the iterative parameter adaptation mechanism of the FLMS uses merely the current error value (scalar innovation). The MIFLMS expands the scalar innovation into a vector innovation (error vector) by considering data over a fixed window at each iteration. Therefore, the MIFLMS yields better convergence speed than the standard FLMS by increasing the length of innovation vector. The superior performance of the MIFLMS is verified through parameter identification problem of input nonlinear systems. The statistical performance indices based on multiple independent trials confirm the consistent accuracy and reliability of the proposed scheme. [ABSTRACT FROM AUTHOR]

Focusing on the selection of green and low carbon (GLC) innovation, this paper builds an evolutionary game between manufacturing enterprises, and another model between an enterprise and the government. Based on the models, the dynamic evolution of the selection process was analyzed in details. The results show that the game between manufacturing enterprises will eventually converge, due to the cost advantage of some enterprises in traditional innovation and the spillover effect of GLC innovation results. Some enterprise will choose GLC innovation, but some will adhere to traditional innovation, while sharing part of the revenue of GLC innovation of others. After government intervention, the innovation behaviors of manufacturing enterprises will be compensated for, rewarded, or penalized by means of transfer payment, tax credit, etc. As a result, all enterprises will eventually switch to GLC innovation. [ABSTRACT FROM AUTHOR]

Converging business, sustainability, and technology is a challenge that manufacturing firms face to create value and be competitive. Energy- and raw material-intensive manufacturing industries are particularly aware of environmental issues and circular economy practices due to the large amounts of resources they use. However, manufacturing companies must also be mindful of economic sustainability in order to make their business profitable. For this, appropriate economic evaluation tools are needed, one of which is life cycle costing (LCC). LCC, when applied to the manufacturing context, is often considered as a simple extension of the life cycle assessment (LCA). This is the main limitation of LCC, as it only contributes to determining the economic value of environmental damage. This research aims to overcome this limitation, analyzing the Italian ceramic tile manufacturing sector as a case study in order to conceptually develop, through the abductive methodology, a calculation framework that extends the potential of LCC by including circularity parameters. Subsequently, the conceptual framework is empirically validated using sectoral industrial costs by configuring two scenarios (with and without circularity practices) and building a benchmark for individual firms in this industry. Finally, the research includes some considerations on the positive implications and potential of life cycle costing in an open innovation context. [ABSTRACT FROM AUTHOR]

An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions. Recent efforts have focused on incorporating such constraints in the training of convolutional neural networks (CNN), however this has so far been done within a continuous optimization framework. Yet, various segmentation constraints and regularization priors can be modeled and optimized more efficiently in a discrete formulation. This paper proposes a method, based on the alternating direction method of multipliers (ADMM) algorithm, to train a CNN with discrete constraints and regularization priors. This method is applied to the segmentation of medical images with weak annotations, where both size constraints and boundary length regularization are enforced. Experiments on two benchmark datasets for medical image segmentation show our method to provide significant improvements compared to existing approaches in terms of segmentation accuracy, constraint satisfaction and convergence speed. [ABSTRACT FROM AUTHOR]

The discrete-time fault-tolerant control method for permanent magnet synchronous motors (PMSMs) is investigated for the first time in this paper based on adaptive fuzzy theory, where the designed controllers consider faults containing both loss of effectiveness and bias. Firstly, with the help of Euler method, the discrete-time model of PMSMs is obtained. Secondly, adaptive fuzzy theory is used to realize fault-tolerant control of PMSMs. Next, dynamic surface control is used to resolve the problems of "explosion of complexity" and noncausal in discrete-time systems caused by backstepping. It is proved that the proposed method can guarantee all signals and states in the closed-loop system are semi-global uniform ultimate bounded when the faults occur and the position tracking error can converge to a small neighborhood of the origin. Finally, simulation results show that the proposed control method has strong fault-tolerant performance and robustness. [ABSTRACT FROM AUTHOR]

The algorithm Partial Quicksort, introduced by Conrado Martínez, sorts the l smallest real numbers for a set of n different ones. It uses a splitting like Quicksort, continuing always with the leftmost list. The normalized running time Y n (t) converges with l n → t in distribution to a non degenerate limit. The finite dimensional distributions of the process Y n converge to a limit (Ragab and Roesler (2014)), called the Quicksort process. In this paper we will present the algorithm Quicksort on the fly, a version of Partial Quicksort, showing the almost sure convergence of Y n to the Quicksort process in Skorokhod metric. [ABSTRACT FROM AUTHOR]

With the development of multimedia technology, 3D model has been applied in many fields such as mechanical design, construction industry, entertainment industry, medical treatment and so on. The number of 3D model is becoming more and more in our lives. Therefore, effective automatic management and classification of 3D models become more and more important. In this paper, we propose a dual-meta-learner model based on LSTM to learn the exact optimization algorithm used to train another two learner neural network classifier in the few-shot regime. The parametrization of our model allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while it can also achieve a general initialization of the learner (classifier) network that allows for quick convergence of training. Our method attains state-of-the-art performance by significant margins. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative energy interacting unit ("We-Energy") with the characteristic of full duplex trading mode. In order to manage all the We-Energies in an optimal way, a new integrated energy management framework based on a noncooperative game is performed so as to allocate the energy demands of each WE such that the benefit of each WE can be maximized. To overcome the impact of the randomness and inaccurate information of renewable energy sources, Nash Q-learning algorithm is applied for computation of game equilibrium under the unknown environment. The novelty of the proposed algorithms is related to the incorporation of the continuous action space into the discrete adaptive action set and combined the equilibrium transfer to improve the efficiency of the algorithm. Simulation studies of modified IMS confirm that it has a better performance with the desired equilibrium strategy and convergence speed. [ABSTRACT FROM AUTHOR]

The Active Disturbance Rejection Control (ADRC) is an object of the ever growing interest in the latest years due to its limited requirements concerning knowledge of a plant's mathematical model. In this paper, a problem of system stability in presence of modelling uncertainties is investigated. Precisely, imperfect knowledge of the manipulator dynamics and an inertia matrix is considered by the means of the Lyapunov analysis and conditions are formulated which guarantees convergence of the errors in the system to some finite boundary. Influence of the unknown inertia matrix is thoroughly investigated. It is shown, that if the third derivative of the desired trajectory is small enough then an intentional increase of the input matrix estimation error may lead to a reduction of the tracking error boundary. Obtained results are supported by presented simulations and experiments. • The ADRC is practically stable at least for some range of modelling inaccuracy. • Asymptotical stability can be guaranteed for constant input gain and disturbance. • Overestimation of model's inertia matrix can lead to increase of tracking quality. • High-precision practical experiments confirm previous remarks. [ABSTRACT FROM AUTHOR]