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Based on strategic choice theory, this study examines the impact and mechanisms of intellectual property demonstration city (IPDC) policy in China on corporate ESG performance.

This study uses China’s A-share listed companies’ data from 2009 to 2019 and conducts a difference-in-differences (DID) to explore the causal relationship between IPDC policy and corporate ESG performance.

Baseline regression results indicate that the IPDC policy can significantly improve corporate ESG performance. Mechanism tests reveal that the IPDC policy expands firm green technology innovation, enhances firm human capital investment and increases government innovation subsidies, thereby promoting corporate ESG performance. Moderating effect results show that the promotion impact on corporate ESG performance of the IPDC policy is diminished by government fiscal pressure. Heterogeneity analyses indicate that the IPDC policy has a stronger impact on corporate ESG performance in key cities, firms in high-tech industries, firms with a higher reliance on intellectual property protection (IPP) and state-owned enterprises (SOEs).

The findings enrich the theoretical research on the influencing factors of corporate ESG performance and provide practical references to strengthen IPP and implement a more thorough intellectual property development strategy.

A proper understanding of malware characteristics is necessary to protect massive data generated because of the advances in Internet of Things (IoT), big data and the cloud. Because of the encryption techniques used by the attackers, network security experts struggle to develop an efficient malware detection technique. Though few machine learning-based techniques are used by researchers for malware detection, large amounts of data must be processed and detection accuracy needs to be improved for efficient malware detection. Deep learning-based methods have gained significant momentum in recent years for the accurate detection of malware. The purpose of this paper is to create an efficient malware detection system for the IoT using Siamese deep neural networks.

In this work, a novel Siamese deep neural network system with an embedding vector is proposed. Siamese systems have generated significant interest because of their capacity to pick up a significant portion of the input. The proposed method is efficient in malware detection in the IoT because it learns from a few records to improve forecasts. The goal is to determine the evolution of malware similarity in emerging domains of technology.

The cloud platform is used to perform experiments on the Malimg data set. ResNet50 was pretrained as a component of the subsystem that established embedding. Each system reviews a set of input documents to determine whether they belong to the same family. The results of the experiments show that the proposed method outperforms existing techniques in terms of accuracy and efficiency.

The proposed work generates an embedding for each input. Each system examined a collection of data files to determine whether they belonged to the same family. Cosine proximity is also used to estimate the vector similarity in a high-dimensional area.

As China is on an inevitable march into the digital era, firms have accumulated abundant digital assets, such as algorithms and data. Facing the possibility of using digital assets as a new type input, besides traditional inputs such as capital and labor, would powerful managers perform better? Would managerial power help managers increase the efficiency of how a firm combines traditional and digital inputs and converts them into outputs? Thus, the purpose of this study is to investigate whether powerful managers promotes corporate productivity by using digital assets as a new input.

Using data from listed Chinese firms between 2008 and 2020, the authors constructed panel regressions with three-way fixed effects to examine whether and how managerial power influences corporate productivity in the current digital context, particularly under market uncertainty.

The findings reveal no consistent relationship between managerial power and corporate productivity. The results explain this from two contrasting effects: while managerial power promotes technological change it hinders technical efficiency – two components of total productivity. Moreover, this study identifies market uncertainty as a significant external contingency. In uncertain markets, strong managerial power positively impacts corporate productivity.

The results extend extant theoretical insights in the literature on how managerial power might influence corporate productivity.

This paper aims to prepare high corrosion-resistant chromium-free zinc-aluminum (Zn–Al) coatings reinforced with multi-walled carbon nanotubes (MWCNTs) and nano-ZnO particle composites.

The morphology, composition and corrosion resistance of the coatings were analyzed by electrochemical tests, water contact angle tests, immersion tests, scanning electron microscopy/energy dispersive spectrometer and X-ray diffraction.

The composite coating with 0.3% MWCNTs and 0.5% nano-ZnO particles demonstrated both high shielding performance and cathodic protection performance, which was attributed to the porosity filling of MWCNTs and nano-ZnO particles together with the electrical connection of MWCNTs between the zinc and aluminum powders.

This work laid an experimental foundation for the preparation and corrosion mechanism of high corrosion-resistant chromium-free Zn–Al coating reinforced with MWCNTs and nano-ZnO particles.

The purpose of this research is to identify and analyse the perceived risk factors affecting the safety of electric two-wheeler (E2W) riders in urban areas. Given the exponential growth of the global E2W market and the notable challenges offered by E2W vehicles as compared to electric cars, the study aims to propose a managerial framework, to increase the penetration of E2W in the emerging market, as a reliable, and sustainable mobility alternative.

The perceived risk factors of riding E2Ws are relatively scanty, especially in the context of emerging economies. A mixed-method research design is adopted to achieve the research objectives. Four expert groups are interviewed to identify crucial safety risk E2W factors. The grey-Delphi technique is used to confirm the applicability of the extracted risk factors in the Indian context. Next, the Grey-Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique is employed to reveal the causal-prominence relationship among the perceived risk factors. The dominance and prominence scores are used to perform Cause and Effect analysis and estimate the triggering risk factors.

The finding of the study suggests that reckless adventurism, adverse road conditions, individual characteristics and distraction caused by using mobile phones, as the topmost triggering risk factors that impact the safety of E2Ws drivers. Similarly, reliability on battery performance low velocity and heavy traffic conditions are found to be some of the critical safety factors.

E2Ws are anticipated to represent the future of sustainable mobility in emerging nations. While they provide convenient and quick transportation for daily urban commutes, certain risk factors are contributing to increased accident rates. This research analyses these risk factors to offer a comprehensive view of driver and rider safety. Unlike conventional measures, it considers subjective quality and reliability parameters, such as battery performance and reckless adventurism. Identifying the most significant causal risk factors helps policymakers focus on the most prominent issues, thereby enhancing the adoption of E2Ws in emerging markets.

We have proposed an integrated framework that uses grey theory with Delphi and DEMATEL to analyse the safety risk factors of driving E2W vehicles considering the uncertainty. In addition, the amalgamation of Delphi and DEMATEL helps not only to identify the pertinent safety risk factors, but also bifurcate them into cause-and-effect groups considering the mutual relationship between them. The framework will enable practitioners and policymakers to design preventive strategies to minimize risk and boost the penetration of E2Ws in an emerging country, like India.

This study aims to present a novel topology optimization method for effectively minimizing the frequency response over a given frequency interval considering anisotropic features and fiber angles simultaneously.

The variable thickness sheet (VTS) method is used to obtain a free material distribution under the specified volume constraint. The anisotropic equivalent stiffness matrix based on the material fiber angles is considered in the orthotropic material properties model, which ensures a sufficiently large design space to minimize the frequency response. To lessen the computational burden, the quasi-static Ritz vector (QSRV) method is integrated to approximate the structural response.

Compared to considering only one element, the optimization process simultaneously considers the spatially-varying fiber angles and the material distribution, allowing for a broader design space to minimize the frequency response of additive manufacturing (AM) structures. The orthotropic properties play an important role in determining optimal material distribution of the structure. Moreover, the QSRV method makes the frequency response analysis more efficient.

The anisotropic stiffness and spatially-varying angles of the fiber materials induced by the layer-by-layer printing process of carbon fiber reinforced plastics (CFRP) are simultaneously considered to further minimize the frequency response of AM structures, which improves the performance of AM-CFRP structures.

Torque is one of the main parameters reflecting the operation status and detection of a mechanical rotation system. The use of quartz pillar to design torque sensors has advantage over the use of quartz disk, but research into the torsional effect of quartz pillar is rare. This paper aims to investigate a novel type of torque sensor based on piezoelectric torsional effect.

Based on the theory of anisotropic elasticity and the Maxwell electromagnetism, the torsion stress and distribution of surface charge of a rectangular quartz pillar are calculated. Using finite element analysis, the polarized electric field of the piezoelectric pillar is solved. According to the theoretical calculation of torsional effect of piezoelectric quartz pillar, detection electrodes are mounted on the surface of the quartz pillar and a new type of torque sensor is designed.

The calibration experimental results show that the bound charges are proportional to the torque applied, and the torque sensor has fully reached the dynamometer standard.

This paper shows that the torsional effect of the developed piezoelectric quartz pillar can be used to create a new type of piezoelectric torque sensor.

This study aims to use the knowledge management perspective to examine the mechanism through which entrepreneurship drives firms’ technological innovation in the digital age. The objective is to develop a multi-stage integrated theoretical model to explain how entrepreneurship exerts its influence on firms’ technological innovation with a particular focus on the knowledge management perspective. The findings can be used for the cultivation of entrepreneurship and for the promotion of continuous technological innovation activities.

This study uses a case-based qualitative approach to examine the relationship between entrepreneurship and technological innovation. The authors first analyze the case of SANY and then explore the mechanism of how entrepreneurship can promote a firm’s technological innovation from the perspective of knowledge management based on the technology-organization-environment framework. An integrated theoretical model is then developed in this study.

Based on a case study, the authors propose that there are three main processes of knowledge management in firms’ technological innovation: knowledge acquisition, knowledge integration and knowledge creation. In the process of knowledge acquisition, the joint effects of innovation spirit, learning spirit, cooperation spirit and global vision drive the construction and its healthy development of firms’ innovation ecosystem. In the process of knowledge integration, the joint effects of innovation spirit, cooperation spirit and learning spirit help complete the integration of knowledge and further the accumulation of firms’ core knowledge resources. In the process of knowledge creation, the joint effects of mission spirit, learning spirit and innovation spirit encourage the top management team to establish long-term goals and innovation philosophy. This philosophy can promote the establishment of a people-oriented incentive mechanism that helps achieve the transformation from the accumulation of core knowledge resources to the research and innovation of core technologies. After these three stages, firms are passively engaged in the “reverse transfer of knowledge” step, which contributes to other firms’ knowledge management cycle. With active knowledge acquisition, integration, creation and passive reverse knowledge transfer, firms can achieve continuous technological innovation.

This study has important theoretical implications in entrepreneurship research. This study helps advance the understanding of entrepreneurship and literature on the relationship between entrepreneurship and technological innovation in the digital age, which can broaden the application of knowledge management theories. It can also help better understand how to develop healthy firm-led innovation ecosystems to achieve continuous optimization of knowledge and technological innovation in the digital age.

This study proposes an integrated theoretical model to address the issues of entrepreneurship and firms’ technological innovation in the digital age, and it is also one of few studies that focuses on entrepreneurship and innovation from a knowledge management perspective.

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

Point-of-interest (POI) recommendation techniques play a crucial role in mitigating information overload and delivering tailored services. To address limitations in conventional POI recommendation systems, constrained by sparse user-POI interactions and incomplete consideration of temporal dynamics, POI recommendation based on the spatial-temporal graph (STG-POI) is proposed.

Spatial-temporal sequence graphs from geographical locations and user interaction history data are constructed, which are used to mine spatial-temporal sequence information. Using the data filtered by the band-pass filter, graph neural networks with distance-awareness and sequence-awareness are applied to capture high-order spatial-temporal connections within diverse graph topologies. The model leverages contrastive learning for self-supervised disentanglement of graph representations, providing self-supervised signals for sequential and geographical intent perception, thereby achieving more precise POI personalization.

Compared to the baseline model GSTN, experiments on the Foursquare and Gowalla data sets reveal that STG-POI improves testing AUC by 2.0%, 2.1%, 2.0% and decreases logloss by 1.9%, 3.3%, 0.3%, respectively. These results indicate the model’s effectiveness in capturing spatial-temporal information, surpassing mainstream POI recommendation baseline models.

This approach constructs a dual graph from user interaction data, harnessing sequential and geographical information as self-supervised signals. It yields decoupled representations of these influences, offering a comprehensive insight into user behaviors and preferences within location-based social networks, thus enhancing recommendation accuracy and interpretability. This approach addresses the challenge in graph convolutional network where only rough and smooth features are conducive to recommendation by using band-pass filters to significantly reduce computational complexity, thereby enhancing recommendation speed by filtering out noise data that does not contribute to recommendation performance. Experimental results indicate that this model surpasses current mainstream approaches in POI recommendation tasks, effectively integrating both geographical and temporal features.