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This paper examines the mediating effect of R&D alliance network dynamics on the relationship between technology convergence and firm innovation quality, it further investigates the moderating effect of degree assortativity in the inventor network. An empirical analysis is conducted based on a panel negative binomial regression model with data of Chinese high‐tech firms listed on the Shanghai and Shenzhen Stock from 2000 to 2018. The results show that novel technology convergence not only contributes to innovation quality directly but also indirectly through ego‐network expansion. The relationship between reinforced technology convergence and innovation quality is inverted U‐shaped, partially mediated by ego‐network stability. In addition, degree assortativity suppresses the effects of novel technology convergence on ego‐network expansion and innovation quality and steepens the inverted U‐shaped relationships of reinforced technology convergence with ego‐network stability and innovation quality. This paper contributes to the relevant literature by revealing the links between technology convergence, ego‐network dynamics, and innovation quality, and emphasizing the importance of inventor collaboration ties formation mechanism in knowledge searching and convergent innovation. Furthermore, the conclusion provides managerial suggestions for high‐tech firms to coordinate internal and external resources to improve technical impacts. [ABSTRACT FROM AUTHOR]

Binary combinatorial optimization plays a crucial role in various scientific and engineering fields. While deterministic approaches have traditionally been used to solve these problems, stochastic methods, particularly metaheuristics, have gained popularity in recent years for efficiently handling large problem instances. Ant Colony Optimization (ACO) is among the most successful metaheuristics and is frequently employed in non‐binary combinatorial problems due to its adaptability. Although for binary combinatorial problems ACO can suffer from issues such as rapid convergence to local minima, its eminently parallel structure means that it can be exploited to solve large and complex problems also in this field. In order to provide a versatile ACO implementation that achieves competitive results across a wide range of binary combinatorial optimization problems, we introduce a parallel multicolony strategy with an improved cooperation scheme to maintain search diversity. We evaluate our proposal (Binary Parallel Cooperative ACO, BiPCACO) using a comprehensive benchmark framework, showcasing its performance and, most importantly, its flexibility as a successful all‐purpose solver for binary combinatorial problems. [ABSTRACT FROM AUTHOR]

Summary: One of the practical preferences of cloud service providers is to use specialized physical hosts. In other words, the goal is to place homogeneous virtual machines (VMs) on the physical host according to performance criteria such as energy consumption, resource wastage, and utilization. virtual machine placement (VMP) falls into NP‐hard knapsack problems. To overcome the time complexity, the use of heuristic and metaheuristic methods has attracted the attention of researchers. In this paper, we use an entropy‐based method for VMP for the first time. The proposed method tries to place the VMs on physical machines by considering the type of VMs to minimize entropy. Entropy is a measurable property that is more associated with disorder, randomness, or uncertainty. We use one of the most common entropy criteria called the Gini coefficient. In summary, among the different placement combinations of VMs, those that can minimize the Gini coefficient are preferred. We then solve the multi‐objective problem with the non‐dominated sorting genetic algorithm (NSGA‐III). We also combine this method with differential evolution methods to improve the quality of solutions. Recent research in other engineering fields has shown that combining metaheuristic methods with differential evolution methods increases the rate of convergence toward the optimal solution. The simulation results on the CloudSim simulator, along with statistical analysis, show that the entropy‐based method has a significant improvement over the state‐of‐the‐art methods in terms of significant performance criteria such as utilization, resource wastage, and energy consumption. [ABSTRACT FROM AUTHOR]

In light of the challenges surrounding the conceptualization and definition of spatial justice within our increasingly data‐driven society, this article commences an inquiry into the convergence of space, justice, and data within human geography literature and related disciplines, focusing notably on the urban field. The paper outlines theories concerning social justice‐based rights to the city, especially emphasizing the significance of existing literature that bridges such theories with recent scholarship on data justice. It supplements these discussions by deriving a theoretical framework for digital spatial justice rooted in other space‐based theories, exploring the more‐than‐human realms of non‐human entities, affect, and information. [ABSTRACT FROM AUTHOR]

The Kaczmarz algorithm is widely used in image reconstruction. In this paper, we propose an enhanced version of the Kaczmarz algorithm by integrating Nesterov momentum into the Kaczmarz algorithm, denoted as the Kaczmarz algorithm with momentum. Our innovation offers a novel approach to accelerate the convergence rate of the Kaczmarz algorithm significantly. Based on greedy rules for selecting work row, we prove that the Kaczmarz method with momentum can achieve global linear convergence rates. Furthermore, we extend a constrained version of the Kaczmarz algorithm with Nesterov momentum for improved image reconstruction and prove its convergence. Numerical experiments show that the proposed algorithm outperforms the original Kaczmarz algorithm in terms of efficiency for image reconstruction. [ABSTRACT FROM AUTHOR]

Bacteria have distinctive properties that make them ideal for biomedical applications. They can self‐propel, sense their surroundings, and be externally detected. Using bacteria as medical therapeutic agents or delivery platforms opens new possibilities for advanced diagnosis and therapies. Nano‐drug delivery platforms have numerous advantages over traditional ones, such as high loading capacity, controlled drug release, and adaptable functionalities. Combining bacteria and nanotechnologies to create therapeutic agents or delivery platforms has gained increasing attention in recent years and shows promise for improved diagnosis and treatment of diseases. In this review, design principles of integrating nanoparticles with bacteria, bacteria‐derived nano‐sized vesicles, and their applications and future in advanced diagnosis and therapeutics are summarized. [ABSTRACT FROM AUTHOR]

Needle insertion simulations play an important role in medical training and surgical planning. Most simulations require boundary conforming meshes, while the diffuse domain approach, currently limited to stiff needles, eliminates the need for meshing geometries. In this article the diffuse domain approach for needle insertion simulations is first extended to the use of flexible needles with bevel needle tips, which are represented by an Euler‐Bernoulli beam. The model parameters are tuned and the model is evaluated on a real‐world phantom experiment. Second, a new method for the relaxation of the needle‐tissue system after the user releases the needle is introduced. The equilibrium state of the system is determined by minimizing the potential energy. The convergence rate of the coupled Laplace equations for solving the Euler‐Bernoulli beam is 1.92 ± 0.14 for decreasing cell size. The diffuse penalty method for the application of Dirichlet boundary conditions results in a convergence rate of 0.73 ± 0.21 for decreasing phase field width. The simulated needle deviates on average by 0.29 mm compared to the phantom experiment. The error of the tissue deformation is below 1 mm for 97.5% of the attached markers. Two additional experiments demonstrate the feasibility of the relaxation process. The simulation method presented here is a valuable tool for patient‐specific medical simulations using flexible needles without the need for boundary conforming meshing. To the best of the authors' knowledge this is the first work to introduce a relaxation model, which is a major step for simulating accurate needle‐tissue positioning during realistic medical interventions. [ABSTRACT FROM AUTHOR]

Multiple kernel k$$ k $$‐means (MKKM) clustering has been an important research topic in statistical machine learning and data mining over the last few decades. MKKM combines a group of prespecified base kernels to improve the clustering performance. Although many efforts have been made to improve the performance of MKKM further, the present works do not sufficiently consider the potential structure of the partition matrix. In this paper, we propose a novel sparse multiple kernel k$$ k $$‐means (SMKKM) clustering by introducing a ℓ1$$ {\ell}_1 $$‐norm to induce the sparsity of the partition matrix. We then design an efficient alternating algorithm with curve search technology. More importantly, the convergence and complexity analysis of the designed algorithm are established based on the optimality conditions of the SMKKM. Finally, extensive numerical experiments on synthetic and benchmark datasets demonstrate that the proposed method outperforms the state‐of‐the‐art methods in terms of clustering performance and robustness. [ABSTRACT FROM AUTHOR]

Summary: This paper considers the problem of convergence for the optimal linear filter with time‐correlated fading channel and channel noise (TCFCCN) proposed in Liu et al. A novel time‐varying random vector is defined where the vector includes the state error, the innovation, the fading state error and a small portion of the new measurement's noise. Based on studying the convergence of the time‐varying matrix obtained from the defined random vector, the optimal linear filter's convergence conditions are established. It is pointed out that, under proper conditions, the optimal linear filter is convergent and a set's each element converges to a unique fixed point. Two numerical examples are given to demonstrate the obtained theoretical results's effectiveness. [ABSTRACT FROM AUTHOR]

Novel technology systems, such as "fiber optics" and "printed electronics," increasingly emerge at the interface of hitherto unrelated technology areas. As such, new technology systems often arise through technology convergence, characterized by integrating technology components and knowledge from different technology systems, resulting in a novel system architecture. This phenomenon is of utmost societal relevancy but simultaneously poses tremendous challenges for firms' technology strategies. Firms must not only cope with unrelated knowledge rooted in hitherto different technologies but also have to decide deliberately how systemic (i.e., complete technology system) versus focused (i.e., single component of the technology system) their engagement in technology development in the converging technology system ought to be. In addition, firms need to decide strategically to what extent to develop specialized or design knowledge. Extant concepts of technology strategy fall short of capturing this complexity inherent in converging technology systems. Therefore, to address how technology strategies co‐evolve along with the emergence of new technology systems, this study adds a systems perspective to technology strategy by developing the concept of technology system coverage. This novel dimension of technology strategy is formed by the scope (i.e., focused vs. systemic coverage of the technology system) and type of technological knowledge (i.e., specialized or design knowledge). We empirically apply this novel angle of technology strategy to the convergence field of printed electronics. Based on a longitudinal set of 828 patents over 30 years, 74 relevant corporate actors are identified. The underlying taxonomy enables us to reveal four technology strategies and develop five propositions. The results indicate that all firms build design knowledge over time, whereas not all firms build specialized knowledge, no matter what technology strategy is pursued. In sum, this work advances literature by understanding technology strategy in emerging complex technology systems, introducing a systems perspective. [ABSTRACT FROM AUTHOR]

Summary: System identification is the powerful tool to establish the mathematical models of practical systems. This article considers the parameter identification for bilinear systems with colored noise. First, the bilinear system is transformed into an input‐output form by eliminating the state variables in the model. Then, based on the data filtering technique, the measured input‐output data is filtered using an estimated noise transfer function, and a filtering based generalized extended stochastic gradient (F‐GESG) algorithm is proposed to enhance the parameter estimation accuracy. Furthermore, the multi‐innovation identification theory is employed to improve the convergence speed of the proposed F‐GESG algorithm, and a filtering based multi‐innovation generalized extended stochastic gradient (F‐MI‐GESG) algorithm is developed to identify the parameters of bilinear systems. The GESG algorithm is given for comparison. Finally, a numerical simulation example is provided to illustrate the effectiveness and excellent performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

R&D strategies of companies with low and high technological levels are discussed based on the concept of technology convergence and divergence. However, empirically detecting enterprise technology convergence in the distribution of enterprise technology (total productivity increase) over time and identifying key change factors are challenging. This study used a novel statistical indicator that captures the internal technology distribution change with a single number to clearly measure the technology distribution peak as a change in critical bandwidth for enterprise technology convergence and presented it as evidence of each technology convergence or divergence. Furthermore, this study applied the quantitative technology convergence identification method. Technology convergence appeared from the separation of total corporate productivity distribution of 69 IT companies in Korea in 2019–2020 rather than in 2015–2016. Results indicated that when the total technological level was separated from the technology leading and technology catch‐up, IT companies were found to be pursuing R&D strategies for technology catch‐up. [ABSTRACT FROM AUTHOR]

Current biomaterial‐based strategies explored to treat articular cartilage defects have failed to provide adequate physico‐chemical cues in order to guide functional tissue regeneration. Here, it is hypothesized that atmospheric‐pressure plasma (APPJ) treatment and melt electrowriting (MEW) will produce microfiber support structures with covalently‐immobilized transforming growth factor beta‐1 (TGFβ1) that can stimulate the generation of functional cartilage tissue. The effect of APPJ operational speeds to activate MEW polycaprolactone meshes for immobilization of TGFβ1 is first investigated and chondrogenic differentiation and neo‐cartilage production are assessed in vitro. All APPJ speeds test enhanced hydrophilicity of the meshes, with the slow treatment speed having significantly less CC/CH and more COOH than the untreated meshes. APPJ treatment increases TGFβ1 loading efficiency. Additionally, in vitro experiments highlight that APPJ‐based TGFβ1 attachment to the scaffolds is more advantageous than direct supplementation within the medium. After 28 days of culture, the group with immobilized TGFβ1 has significantly increased compressive modulus (more than threefold) and higher glycosaminoglycan production (more than fivefold) than when TGFβ1 is supplied through the medium. These results demonstrate that APPJ activation allows reagent‐free, covalent immobilization of TGFβ1 on microfiber meshes and, importantly, that the biofunctionalized meshes can stimulate neo‐cartilage matrix formation. This opens new perspectives for guided tissue regeneration. [ABSTRACT FROM AUTHOR]

The advancement of the Internet of Things/5G infrastructure requires a low‐cost ubiquitous sensory network to realize an autonomous system for information collection and processing, aiming at diversified applications ranging from healthcare, smart home, industry 4.0 to environmental monitoring. The triboelectric nanogenerator (TENG) is considered the most promising technology due to its self‐powered, cost‐effective, and highly customizable advantages. Through the use of wearable electronic devices, advanced TENG technology is developed as a core technology enabling self‐powered sensors, power supplies, and data communications for the aforementioned applications. In this review, the advancements of TENG‐based electronics regarding materials, material/device hybridization, systems integration, technology convergence, and applications in healthcare, environment monitoring, transportation, and smart homes toward the future green earth are reported. [ABSTRACT FROM AUTHOR]

Achieving environmental equity is important for ensuring resource utilization efficiency and realizing sustainable development. Using panel data of 286 cities in China from 2011 to 2018, this paper analyzes whether financial support in the form of digital finance can mitigate environmental inequality. The findings indicate that (1) digital finance has a significant mitigating effect on environmental inequality. Digital finance development has a stronger industrial pollution emission reduction effect on high‐polluting regions than on low‐polluting regions and can reduce the environmental pollution gap between regions. The results of robustness tests such as an endogeneity test support this finding. (2) Digital finance development can have a stronger mitigating effect on environmental inequality in regions with a stronger local government governance capacity and environmental preferences. Digital finance can be a more significant inhibitor of environmental inequality in areas where industries transfer out due to their stronger needs for industrial transformation and finance. (3) Residential income and green technology innovation are important factors affecting industrial pollution emissions and reductions. Digital finance development can narrow the income gap between regions and promote the convergence of green technology innovation capacity, in turn alleviating environmental inequality. In the future, while continuing to promote the development of digital finance, it is necessary to improve the governance capacity of local governments, to increase the efficiency of resource utilization in places where industrial transfer is undertaken and to strengthen the role of financial support in industrial pollution control. [ABSTRACT FROM AUTHOR]

This paper evaluates how the convergence (integration) of green characteristics in multifunctional technological products/services affects consumers' decision making, evaluating intention to purchase convergent products in these specific scenarios with green attributes. Three studies were developed. The first two focused on when the inclusion or exclusion of green attributes changes consumers' perspectives related to (1) utilitarian or hedonic characteristics in the convergence, and (2) selfish or altruistic characteristics of technology usage. The last study incorporated a change in the independent variable from the inclusion or exclusion of green attributes to the inclusion of green attributes as a benefit. Therein, both research scenarios included green characteristics, one related to the periphery and other to the centrality of the product, with a moderating effect of (3) high or low brand trust according to the green convergence design. The results demonstrate that green characteristics are better suited to scenarios in which most of the convergence relates to utilitarian attributes in products, but egoistic characteristics in services, the latter of which reflects a hedonic perspective. Concerning both scenarios of a product with green characteristics, the green benefit is better suited to the utilitarian context, in which the brand is perceived as important, leading to a high level of trust. [ABSTRACT FROM AUTHOR]

The ability to gather real‐time and near real‐time data on marine mammal distribution, movement, and habitat use has advanced significantly over the past two decades. These advances have outpaced their adoption into a meaningful, risk‐based assessment framework so critically needed to support society's growing demands for a transition to increased reliance on renewable energy. Marine acoustics have the capacity to detect, identify, and locate vocalizations over broad areas. Photogrammetric and image processing increases the ability to visually detect animals from surface or aerial platforms. Ecological models based on long‐term observational data coupled with static and remotely sensed oceanographic data are able to predict daily and seasonal habitat suitability. Extensive monitoring around anthropogenic activities, combined with controlled experiments of exposure parameters (i.e., sound), supports better informed decisions on reducing effects. Population models and potential consequence modeling provide the ability to estimate the significance of individual and population exposure. The collective capacities of these emerging technical approaches support a risk ranking and risk management approach to monitoring and mitigating effects on marine mammals related to development activities. The monitoring paradigm related to many offshore energy‐related activities, however, has long been spatially limited, situationally myopic, and operationally uncertain. A case evaluation process is used to define and demonstrate the changing paradigm of effective monitoring aimed at protecting living resources and concurrently providing increased certainty that essential activities can proceed efficiently. Recent advances in both technologies and operational approaches are examined to delineate a risk‐based paradigm, driven by a diversity of regional data inputs, that is capable of meeting the imperative for timely development of offshore wind energy. Integr Environ Assess Manag 2022;18:939–949. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points: This paper makes the case that the national policy and mandate for development of offshore wind resources can be facilitated by the updating of marine mammal monitoring approaches through the incorporation of emerging technologies and a regional risk‐based approach.There are currently a number of existing and developing efforts to collect information on marine mammals in the Atlantic Outer Continental Shelf that can be updated and incorporate into a regional monitoring effort through collaboration and integration.The regulatory construct is adaptable to a regional risk‐based approach, the implementation of which would require acceptance by regulators and stakeholders and relief for project proponents. [ABSTRACT FROM AUTHOR]

Land use information is the basis of various geo‐spatial applications. Traditionally, land use patterns are predicted with agent‐based simulation, suffering from a long convergence process. Deep learning techniques have recently been used for land use classification but not prediction, due to the lack and difficulty of collecting enough training data. This paper proposes a novel paradigm for land use data generation with a randomized simulation strategy. We also design a tailored deep land use prediction model, LUPnet, to demonstrate the usage of the paradigm. Experimental results reveal the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper proposes a Gaussian Distance Intersection over Union (GDIoU) loss function‐based YOLOv4 deep learning network to solve the problem of slow speed and low accuracy insulator location in power facilities health inspection. In the scheme, A GDIoU loss function is designed to accelerate the convergence speed of the YOLOv4 deep learning network; at the same time, the GDIoU loss is added as one part of the network propagation loss, and the insulator's location accuracy is accordingly improved. Moreover, a re‐location scheme for tilt insulators correction is proposed to enhance the location accuracy of the insulators in different spatial angle states. Large amounts of field insulator images were gathered as training and testing samples to evaluate the performance of the proposed scheme. The experimental results have demonstrated that the GDIoU‐based YOLOv4 deep learning network combined with the tilt correction scheme can improve the insulator location speed by three times compared with the peer schemes, and the average precision is increased by 7.37% compared with the naive YOLOv4 network. The performance of the proposed scheme meets the requirement of online insulator location adequately. [ABSTRACT FROM AUTHOR]

Development of advanced grid‐synchronization technique for unbalanced and distorted grid is considered in this paper. In this context, self‐tuning filter (STF) is considered to extract the fundamental component from the measured unbalanced and distorted voltages. Standard STF considers balanced grid voltages which is not always possible to ensure in the actual power grid. To mitigate this issue, an extended STF is proposed by analyzing the standard STF in the state‐space framework. To make the proposed ESTF grid‐following, a robust open‐loop frequency estimator is also applied. The closed‐loop system enjoys excellent filtering benefit from the extended STF side while taking advantage of the fast convergence speed property of the open‐loop frequency estimation technique. Numerical simulation and experimental results with double second‐order generalized integrator phase‐locked loop are provided to validate the theoretical developments. [ABSTRACT FROM AUTHOR]

Summary: Scientists are creating high‐fidelity, physiologic simulation platforms that enable the testing of various artificial organs, which may offer a valuable alternative to animal testing in the future [ABSTRACT FROM AUTHOR]

Regional technology characters (TCs) are studied in this paper to find whether innovation in Chinese cities leads to technology converging, or technology divergence, in both the intra‐city and inter‐city scales, with which regional economic development strength may be better explained. Based on a literature review on the subject, a two‐dimensional research framework and relevant technical lens are designed to clarify TC in cities composed of parameters on Technology Strength (TS) versus Technology Angle (TA). By applying Principle Component Analysis, a technical lens is made possible based on extracted parameters on three integrated technology dimensions for measuring TSs and TAs over 117 Chinese sample cities with invention patent data covering 21 technology field between 2005/2006 and 2015/2016. Technology convergence versus divergence are examined for 28/24 sample cities beyond average score, and the research findings indicate that technology divergence in Chinese cities can be confirmed at the inter‐city level, even partially at the intra‐city level. This finding holds for most sample cities with fairly large and modest TSs, while intra‐city technology converging is significant, especially for larger sized TS samples, however they also differ on different dimensions. This may imply that urban economic development in China is technical based and differentiated. [ABSTRACT FROM AUTHOR]

Micro‐LED displays are now emerging rapidly towards commercialization. This novel compound semiconductor display technology has the potential to interface very effectively to advanced electronics, in particular Si CMOS, to create new forms of display which also interrogate and communicate with their environment in sophisticated ways. [ABSTRACT FROM AUTHOR]

Very heterogeneous patterns of firm employment growth characterize the Chinese provinces, suggesting the existence of a relationship between these and regional factors. This empirical work explores this topic by providing novel evidence of the role played by regional institutional and regional factors in firm employment growth in China during the decade 1998–2007, an important period in the Chinese transition towards a market economy. The results show that controlling for the internal determinants of firm performance, the firms located in provinces converging faster towards a market economy, exhibiting relatively better patterns (in terms of agglomeration, endowment of human capital, innovation and environmental protection) tend to expand their employment relatively more. These findings confirm the existence of a macro–micro‐connection as to what firm heterogeneity is concerned, as they reveal a differential growth channel along the regional dimension. In particular, local progress in government–market relationships appears as the most relevant dimension of marketization process. Given the regionally decentralized institutional system in China and considering its process of transformation, these findings inform local authorities concerned with the promotion of regional employment. [ABSTRACT FROM AUTHOR]

The problem of robust control applied to adjust the configuration of an ankle prosthesis based on disturbance estimation has been addressed in this study. Active disturbance rejection control was the paradigm used for controlling the robotic prosthesis by means of a direct active estimation. Based on this active estimation, the robust controller implemented the disturbance cancellation providing a fast converge to the origin of the tracking error. The uncertainties affecting the prosthesis dynamics were identified by a high‐order extended state high gain observer. This identification was used to force the tracking between the actual position and force needed in the ankle prosthesis and some reference values obtained by a biomechanical gait cycle analysis. Therefore, the estimated states were used to implement a robust output feedback controller that was effective to reject actively the perturbations. This rejection implemented within the controller forced the trajectory tracking to a small vicinity of the origin. A strategy based on composite Lyapunov function served to prove that tracking problem for the prosthesis was successfully solved despite the switching nature of the gait cycle. The controller was implemented in numerical simulations for showing the convergence of the tracking error. The convergence of this tracking error to the region around the origin was obtained within the first second of simulation. [ABSTRACT FROM AUTHOR]

Companies increasingly seek to foster collaborative innovation through the design of innovation spaces such as incubators, accelerators, studios, and fab labs. Innovation spaces bring together multiple actors for collaborative practices to generate new products and processes. Despite their growing popularity, many innovation spaces fail to deliver on their promises and are subsequently shut down. How can innovation spaces foster effective collaborative innovation? This article illustrates the role of space and boundary objects to facilitate collaborative innovation. Based on illustrative examples from the context of business studios, the findings show that innovation spaces enable the four affordances of convergence, generativity, socialization, and collaborative learning. Managers who design and run innovation spaces need to leverage these affordances to propel collaborative innovation. [ABSTRACT FROM AUTHOR]

This paper aims to develop a new finite‐time convergent guidance law for intercepting maneuvering targets accounting for second‐order autopilot lag. The guidance law is applied to guarantee that the line of sight (LOS) angular rate converges to zero in finite time and results in a direct interception. The effect of autopilot dynamics can be compensated based on the finite‐time backstepping control method. The time derivative of the virtual input is avoided, taking advantage of integral‐type Lyapunov functions. A finite‐time disturbance observer (FTDOB) is used to estimate the lumped uncertainties and high‐order derivatives to improve the robustness and accuracy of the guidance system. Finite‐time stability for the closed‐loop guidance system is analyzed using the Lyapunov function. Simulation results and comparisons are presented to illustrate the effectiveness of the guidance strategy. [ABSTRACT FROM AUTHOR]

The paper investigates the sampled‐data state feedback control (SDSFC) for set stabilization of Boolean control networks (BCNs). Set stabilization means that a system converges to a subset of the state space under certain controllers. Assume that the given subset is M, where |M|=q and sampling period is τ. We consider two conditions q ≤ τ;q > τ and for any given subset M, calculate the corresponding largest control invariant subset (LCIS). Moreover, a design procedure to calculate all possible SDSFCs for set stabilization of BCNs is obtained. Ultimately, we provide an example to demonstrate the efficiency of the results. [ABSTRACT FROM AUTHOR]

Although modern path tracers are successfully being applied to many rendering applications, there is considerable interest to push them towards ever‐decreasing sampling rates. As the sampling rate is substantially reduced, however, even Monte Carlo (MC) denoisers–which have been very successful at removing large amounts of noise–typically do not produce acceptable final results. As an orthogonal approach to this, we believe that good importance sampling of paths is critical for producing better‐converged, path‐traced images at low sample counts that can then, for example, be more effectively denoised. However, most recent importance‐sampling techniques for guiding path tracing (an area known as "path guiding") involve expensive online (per‐scene) training and offer benefits only at high sample counts. In this paper, we propose an offline, scene‐independent deep‐learning approach that can importance sample first‐bounce light paths for general scenes without the need of the costly online training, and can start guiding path sampling with as little as 1 sample per pixel. Instead of learning to "overfit" to the sampling distribution of a specific scene like most previous work, our data‐driven approach is trained a priori on a set of training scenes on how to use a local neighborhood of samples with additional feature information to reconstruct the full incident radiance at a point in the scene, which enables first‐bounce importance sampling for new test scenes. Our solution is easy to integrate into existing rendering pipelines without the need for retraining, as we demonstrate by incorporating it into both the Blender/Cycles and Mitsuba path tracers. Finally, we show how our offline, deep importance sampler (ODIS) increases convergence at low sample counts and improves the results of an off‐the‐shelf denoiser relative to other state‐of‐the‐art sampling techniques. [ABSTRACT FROM AUTHOR]

In this paper, we propose a PatchMatch‐based Multi‐View Stereo (MVS) algorithm which can efficiently estimate geometry for the textureless area. Conventional PatchMatch‐based MVS algorithms estimate depth and normal hypotheses mainly by optimizing photometric consistency metrics between patch in the reference image and its projection on other images. The photometric consistency works well in textured regions but can not discriminate textureless regions, which makes geometry estimation for textureless regions hard work. To address this issue, we introduce the local consistency. Based on the assumption that neighboring pixels with similar colors likely belong to the same surface and share approximate depth‐normal values, local consistency guides the depth and normal estimation with geometry from neighboring pixels with similar colors. To fasten the convergence of pixelwise local consistency across the image, we further introduce a pyramid architecture similar to previous work which can also provide coarse estimation at upper levels. We validate the effectiveness of our method on the ETH3D benchmark and Tanks and Temples benchmark. Results show that our method outperforms the state‐of‐the‐art. [ABSTRACT FROM AUTHOR]

Geo Week 2024 is an event that combines the International Lidar Mapping Forum (ILMF), SPAR 3D Expo & Conference, and AEC Next. It aims to address the changing needs of professionals in the built world and geospatial fields, as well as the increasing convergence of technology. The conference and trade show will showcase commercial applications of 3D technologies, innovations, and case studies in the built environment, as well as advanced remote sensing solutions. The event will take place from February 11-13, 2024, in Denver, Colorado, USA. For more information, interested individuals can visit the website or contact info@geo-week.com. [Extracted from the article]

This paper investigates club convergence in per capita income across 81 NUTS‐III regions in Turkey over the 1987–2017 period using the procedure suggested by Phillips and Sul. Based on a nonlinear factor model that allows for transitional heterogeneity, our econometric approach enables us to test the presence of convergence clusters and to examine their transitional behavior. We obtain strong evidence that there is no absolute or conditional convergence but convergence clubs across Turkish regions: five clubs in the first period covering 1987–2001 and six clubs in the second period covering 2004–2017. The geographical distribution of clubs suggests a clear separation between the eastern and the western regions of Turkey for both periods. While geography may be destiny, results from an ordered logit model reveal that initial income per capita, human capital, and total credits are the most important determinants of convergence clubs in Turkey. [ABSTRACT FROM AUTHOR]

The Nadaraya–Watson estimator is among the most studied nonparametric regression methods. A classical result is that its convergence rate depends on the number of covariates and deteriorates quickly as the dimension grows. This underscores the "curse of dimensionality" and has limited its use in high‐dimensional settings. In this paper, however, we show that the Nadaraya–Watson estimator has an oracle property such that when the true regression function is single‐ or multi‐index, it discovers the low‐rank dependence structure between the response and the covariates, mitigating the curse of dimensionality. Specifically, we prove that, using K‐fold cross‐validation and a positive‐semidefinite bandwidth matrix, the Nadaraya–Watson estimator has a convergence rate that depends on the number of indices rather than on the number of covariates. This result follows by allowing the bandwidths to diverge to infinity rather than restricting them all to converge to zero at certain rates, as in previous theoretical studies. [ABSTRACT FROM AUTHOR]

As a consequence of the convergence between manufacturing technology and the foundation technologies of Industry 4.0, it is becoming more important for firms to formulate an innovation strategy for their technological capabilities. In this context, the present study measures firm‐level technology convergence (TC) capability using patent network analysis. A firm's TC capabilities are measured using three centrality indices pertaining to a patent network, which is constructed based on the relationship between patents and their international patent classification. For the empirical analysis, panel regression is conducted to observe the effect of TC capabilities on innovation for the top 30 firms in four manufacturing industries. We find that the TC degree positively influences the firms' overall innovation, namely their total number of patents, and negatively influences their convergent innovation, calculated as the ratio between the number of TC patents and the total number of patents, while the effect of TC betweenness is the opposite. These findings imply that while concentrating on similar technologies may promote quick technology application, it could hamper the enhancement of a TC's potential. To promote TC, a firm should thus develop technologies more likely to be involved in TC. [ABSTRACT FROM AUTHOR]

This paper focuses on the adaption challenge that confronts the top management team (TMT) of science incubators in situations of substantial technological uncertainty. To do that, we draw on the three‐year longitudinal analysis of a major bioscience catalyst in the United Kingdom. Through the lens of 'prospective sensemaking', we follow the TMT as they work with stakeholders in their ecosystem to make sense of a significant technological shift: the convergence of life sciences, IT and other sciences in the health care environment. Our analysis reveals how prospective sensemaking resulted in the launch of a new strategy to exploit these emerging opportunities. However, stakeholders' increasingly fragmented interpretation of the term convergence and the anticipation of legitimacy challenges in the wider ecosystem resulted in the repositioning of the incubator. Our findings contribute to extant research on science incubation. In particular, the paper sheds light on the complex interactions of incubator TMT's with stakeholders in situations of technological change and uncertainty. Moreover, responding to technological change does not only affect the structural conditions of an incubator. Rather, it may also require changes to the positioning of the incubator in order to maintain legitimacy in the wider ecosystem. The paper also suggests managerial as well as policy‐level implications. [ABSTRACT FROM AUTHOR]

This study, based on situational crisis communication theory and set in the context of the Syrian refugee crisis, seeks first to conduct a comparative analysis of the management of online citizen engagement by local governments and non‐governmental organisations (NGOs). Second, it aims to examine the relationship between certain factors pertaining to the types of responses submitted by citizens via the social media of the aforementioned actors. The sample is composed of several Spanish local governments and NGOs belonging to Red de Municipios de Acogida de Refugiados (Local Government Network for Refugee Allocation). The main findings are that NGOs' online engagement with citizens is more than that of local governments. Notably, NGOs are much more active on their Facebook pages than are local governments. The two actors converge, though, in terms of disseminating instructive information and paying less attention to 'basic crisis response options'. Moreover, the factors 'content type', 'reputation', and 'woman' affect the type of response messages sent by citizens. [ABSTRACT FROM AUTHOR]

In this paper, we study decomposition of functions in Hardy spaces Hp(T)(1

Using multiple indices of family policy efforts – work/family reconciliation, gender equality and income protection, this study offers a nuanced understanding of family policy expansion in 14 OECD countries across three welfare regimes from 1990 to 2010. Findings suggest an overall convergence of family policies, particularly in gender equality and work/family reconciliation. Convergence has occurred simultaneously with distinctive family policy changes across welfare regimes. Conservative welfare states have experienced the most profound family policy alterations, shifting from a traditional gender model to one that encourages women's employment, but have, nevertheless, maintained policy efforts that sustain the traditional gender role. Despite an increase in their family policy efforts, liberal welfare states have continued to maintain minimal state action regarding family policy. Changes in social democratic welfare states have been less substantial, as they seem to continue to pursue a dual‐earner model with high degree of gender equality and work/family reconciliation. [ABSTRACT FROM AUTHOR]

The extraction of partition lines for long and narrow patches (LN patches) is an important yet difficult problem in the generalization of thematic data. When current methods are used to process polygons with irregular shapes or complex branch convergence zones, the extracted line structural features tend to be inaccurate and topologically erroneous. In this article, we propose an improved partition lines extraction algorithm of constrained Delaunay triangulation to counter these issues. The proposed method aims to maintain consistency between the extracted line structure characteristics and the actual object structure, especially for complex branch convergence zones. First, we describe three types of aggregation patterns (Type A, B, and C aggregation zones) that occur in partition line extractions for LN patches of complex branch convergence zones using Delaunay triangulation. Then, a partition line extraction algorithm that accounts for the direction between the edges of triangles and the distance of nodes in aggregation zones is proposed. Finally, we test our method for a dataset relating to Guizhou Province, China. Compared with the current method that uses quantitative indicators and visualization, the results indicate that our method not only has applicability for simple situations but also is superior for preserving structural features of complex branch convergence zones. [ABSTRACT FROM AUTHOR]

We consider a process that is observed as a mixture of two random distributions, where the mixing probability is an unknown function of time. The setup is built upon a wavelet‐based mixture regression. Two linear wavelet estimators are proposed. Furthermore, we consider three regularizing procedures for each of the two wavelet methods. We also discuss regularity conditions under which the consistency of the wavelet methods is attained and derive rates of convergence for the proposed estimators. A Monte Carlo simulation study is conducted to illustrate the performance of the estimators. Various scenarios for the mixing probability function are used in the simulations, in addition to a range of sample sizes and resolution levels. We apply the proposed methods to a data set consisting of array Comparative Genomic Hybridization from glioblastoma cancer studies. [ABSTRACT FROM AUTHOR]

Similarity measures play a significant role in adaptive filtering. Previous work such as correntropy and kernel risk-sensitive loss (KRSL), has successfully improved the technology of adaptive filtering in terms of robustness against outliers, fast convergence speed and high filtering accuracy. Based on KRSL, a newly raised similarity measure, complex KRSL (CKRSL), was proposed by extending KRSL to the complex domain. It successfully gains superior performance than other similarity measures in adaptive filtering algorithms. However, the minimum CKRSL (MCKRSL) algorithm may result in poor performance when the parameters are not properly chosen. In this Letter, an adaptive parameter selection is proposed to help the MCKRSL algorithm improve performance while overcoming the uncertainty in artificial selection. The proposed MCKRSL with variable parameters (MCKRSL-VP) algorithm updates the risk-sensitive parameter and kernel width by making the iteratively squared bias as small as possible. A moving average scheme is further used to smoothly update the risk-sensitive parameter and kernel width. Finally, the authors verify that MCKRSL-VP performs better than other algorithms by simulations. [ABSTRACT FROM AUTHOR]

Abstract: This paper mainly focuses on the development of a hybrid gravitational search algorithm and the pattern search (hGSA‐PS) algorithm for the optimal tuning of a unified power flow controller (UPFC) ‐based damping controller. The optimal parameter assessment for a damping controller craves a decisive tuning method, which can reduce the system error, thus improves the system stability. The design problem associated with the proposed controllers is formulated as an optimization problem and the hGSA‐PS is used to find the UPFC‐based damping controller parameters. Thus, by achieving the optimal settings of the variables of the damping controller, the damping of the system is reduced, and concurrently stability of the system is maintained. A detailed investigation is carried out considering the 4 alternatives UPFC‐based damping controller. As evidence, the numerous system response curves confirm that the planned hybrid GSA‐PS algorithm achieves improved performance compared with the conventional algorithms and the recent hybrid GA‐GSA algorithm. The proposed work basically compares the hGSA‐PS algorithm with the recent developed hybrid GA‐GSA algorithm and the conventional GSA algorithm by considering the convergence rate. Finally, the stability performance of a Simulink‐based 2 area 4‐machine power system is analyzed by applying the hGSA‐PS algorithm. From the simulation results, it can be presumed that the proposed hybrid approach is the best method for outlining the UPFC‐based damping controller. [ABSTRACT FROM AUTHOR]

The cooperative path following problem of multiple underactuated autonomous underwater vehicles (AUVs) involves two tasks. The first one is to force each AUV to converge to the desired parameterized path. The second one is to satisfy the requirement of a cooperative behavior along the paths. In this paper, both of the tasks have been further studied. For the first one, a simplified path following controller is proposed by incorporating the dynamic surface control (DSC) technique to avoid the calculation of derivatives of virtual control signals. Besides, in order to handle the uncertain dynamics, a new type of neural network (NN) adaptive controller is derived, and then an NN based energy-efficient path following controller is firstly proposed, which consists of an adaptive neural controller dominating in the neural active region and an extra robust controller working outside the neural active region. For the second one, in order to reduce the amount of communications between multiple AUVs, a distributed estimator for the reference common speed is firstly proposed as determined by the communications topology adopted, which means the global knowledge of the reference speed is relaxed for the problem of cooperative path following. The overall algorithm ensures that all the signals in the closed-loop system are globally uniformly ultimately bounded (GUUB) and the output of the system converges to a small neighborhood of the reference trajectory by properly choosing the design parameters. Simulation results validate the performance and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Adaptive feedback based methods in iterative learning control (ILC) have garnered much interest from researchers for some time now. Much as in adaptive feedback control, most of these methods use Lyapunov functions and positive real transfer functions to prove convergence and boundedness of system signals updated through iterative estimations. While Rohrs et al. have motivated further research on the design of robust adaptive feedback controllers, by demonstrating in the early 1980's that the algorithms of the time were not robust in the presence of unmodeled dynamics, the topic of robustness has not been studied much in the adaptive iterative learning control (AILC) literature. Inspired by Rohrs' counterexample, we use a model reference AILC scheme to show the lack of robustness to unmodeled dynamics in AILC. We rigorously define the concept of stability in ILC via L2 space concepts, and demonstrate the existence of unstable learning operators. We put forth linear systems arguments to explain how conditions leading to instability can occur, and support heuristic arguments with simulation examples. Our findings indicate that the shortcomings of AILC in terms of robustness are no different than those of adaptive feedback, with the robustness issue more severe in certain cases, and further research is necessary to design robust AILC schemes. [ABSTRACT FROM AUTHOR]

We present a verification study of three simulation techniques for fluid-particle flows, including an Euler-Lagrange approach (EL) inspired by Jackson's seminal work on fluidized particles, a quadrature-based moment method based on the anisotropic Gaussian closure (AG), and the traditional two-fluid model. We perform simulations of two problems: particles in frozen homogeneous isotropic turbulence (HIT) and cluster-induced turbulence (CIT). For verification, we evaluate various techniques for extracting statistics from EL and study the convergence properties of the three methods under grid refinement. The convergence is found to depend on the simulation method and on the problem, with CIT simulations posing fewer difficulties than HIT. Specifically, EL converges under refinement for both HIT and CIT, but statistics exhibit dependence on the postprocessing parameters. For CIT, AG produces similar results to EL. For HIT, converging both TFM and AG poses challenges. Overall, extracting converged, parameter-independent Eulerian statistics remains a challenge for all methods. © 2017 American Institute of Chemical Engineers AIChE J, 63: 5396-5412, 2017 [ABSTRACT FROM AUTHOR]

A new method to admit large Courant numbers in the numerical simulation of multiphase flow is presented. The governing equations are discretized in time using an adaptive θ-method. However, the use of implicit discretizations does not guarantee convergence of the nonlinear solver for large Courant numbers. In this work, a double-fixed point iteration method with backtracking is presented, which improves both convergence and convergence rate. Moreover, acceleration techniques are presented to yield a more robust nonlinear solver with increased effective convergence rate. The new method reduces the computational effort by strengthening the coupling between saturation and velocity, obtaining an efficient backtracking parameter, using a modified version of Anderson's acceleration and adding vanishing artificial diffusion. © 2016 The Authors. International Journal for Numerical Methods in Fluids Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a complete decomposition and coordination algorithm to solve large-scale hydrothermal optimal power flow (HTOPF) problems. Based on the approximate Newton directions method, which decouples the first-order Karush-Kuhn-Tucker conditions of the original problem, an HTOPF problem with cascaded hydro plants is decomposed into a thermal plant subproblem with independent optimal power flow solutions for each time period and a hydro plant subproblem combined with fixed and variable heads and cascaded plants issues. In order to verify the effectiveness of the proposed algorithm, numerical tests are performed on three large-scale test systems with up to 3120 buses and 7 531 915 primal-dual variables over 168 time periods. Test results show that the proposed algorithm gives excellent performances in convergence and stability. It not only reduces memory usage significantly but also decreases CPU time by about 65-75%. With parallel computing, it is capable of achieving 10-20 times or even 1000 times speed without loss of optimality. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This article discusses media work and the changes that have swept the media industry from the vantage point of professionals working in media companies and organisations. The concept of media work guides towards new understanding about the media industry and media professions under digital transition. Media work indicates a move towards more diversified job tasks, closer cooperation among different media professions, increased commercial thinking, and interaction with audiences. [ABSTRACT FROM AUTHOR]