Research In Field Research Articles

Abstract 477: Tumor treating fields (TTFields) targeted self assembling nanoparticles for pancreatic cancer treatment: In vitro and in vivo assessment

Abstract Tumor Treating Fields (TTFields) is a novel, non-invasive and safe treatment modality, which is prescribed to be used in conjunction with classical anticancer chemotherapeutic drugs. TTFields when used alone are safe, but their use in combination with classical chemotherapy continues to offer drug related adverse reactions which ultimately impacts the patient compliance. To address this challenge, we earlier reported meticulously optimized self-assembling cationic-anionic polymer nanoparticles (S-CAP NPs) with average particle size of about 200 nM, encapsulation efficiency of 55-65 % and sustained drug release over 60 h. We also reported that, TTFields were capable of destabilizing the S-CAP NPs leading to targeted drug release. The results were also corroborated with in vitro cytotoxicity and colony formation studies in presence of TTFields using multiple pancreatic cancer cell lines. After establishing the proof-of-concept, the objective was to assess the in vitro cellular uptake of S-CAP NPs. For this, fluorescent dye loaded S-CAP NPs were developed and subjected to cellular uptake analysis. The in vitro cellular uptake studies confirmed the enhanced uptake of S-Cap NPs by passive diffusion in the pancreatic cancer cell lines compared to normal pancreatic epithelial cell line. The studies corroborated preferential cellular uptake of S-Cap NPs by the pancreatic tumor. Among all the formulations, the chitosan- bovine serum albumin S-CAP NPs was most optimum and was used for in vivo safety studies. For this, in vivo acute and repeated dose 28-day toxicity of the optimum placebo S-CAP NPs was conducted in Sprague Dawley rats (Western University of Health Sciences Protocol #R20IACUC031) and OECD guidelines. The in vivo studies showed a LD50 value above 2000 mg/kg and NOAEL value of 1000 mg/kg. These high doses safety of S-CAP NPs confirmed the potential of this approach for future preclinical efficacy studies followed by clinical translation. Lastly, the in vitro stability studies were conducted as per the ICH guidelines for a period of 36 months and the formulations were concluded to be stable. The tumor targeting potential, in vivo safety and stability of S-CAP NPs, establish their translational potential for TTFields triggered targeted drug release in cancer treatment. This approach will not only reduce the adverse effect associated with combination of TTFields and chemotherapy but can also be explored as a platform technology for management of other cancers. This study is supported by AACR-Novocure tumor treating fields research fellowship. Citation Format: Preshita Prafulla Desai, Sunil Prabhu. Tumor treating fields (TTFields) targeted self assembling nanoparticles for pancreatic cancer treatment: In vitro and in vivo assessment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 477.

Research of Innovation and Digital Transformation in Justice: A Systematic Review

Objective: To develop a mapping of studies on innovation and digital transformation in the justice sector, publishes from 2001 to 2022. Five research questions were defined: 1) How to define innovation and digital transformation introduced in the justice sector? 2) What types of innovations and digital transformations are implemented in the justice sector? 3) What are the objectives of introducing innovation and digital transformation in the justice sector? 4) What are the antecedents that influence the process of innovation and digital transformation in the justice sector? 5) What are the results of the innovation and digital transformation process in the justice sector?Methods: The systematic review of sources (scientific articles, conference proceedings, monographs and institutional documents) presented in this paper was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. Selected by “research field”, “topic”, “research design”, “year of publication” and the keywords “public administration”, “public sector”, “e-justice”, “digital transformation”, and “innovation”, the sources were analyzed and evaluated according to five main aspects: (1) definition of innovation; (2) types of innovation; (3) objectives of innovation; (4) antecedents of innovation; and (5) results of innovation.Results: The heuristic model for studying innovation in the public sector developed by Vries et al. (2015) was adapted and applied to the research field of justice. The adaptation of the heuristic model allowed forming the following areas of analysis: contextual antecedents in justice; institutional antecedents in justice; characteristics of innovation and digital transformation in justice; individual antecedents in justice; types of innovation and digital transformation in justice; results of the process of innovation and digital transformation in justice.Scientific novelty: a comprehensive review of the literature in the field of innovation and digital transformation in justice is presented by adapting the approach to conducting systematic studies of the literature in the social sciences. It helped to identify gaps and define directions for further research in the given field, including the activation of comparative legal research, expansion of the methodological base, reliance on the theories of public administration, etc.Practical significance: the obtained results allow forming a heuristic map of innovation and digital transformation in justice, create antecedents (contextual, institutional and individual) and the basis for future empirical research in the field of justice, analysis, evaluation and improvement of public policy in the field of innovation and digital transformation in this area.

Review on line detection of wood panel images

In industrial, the modern intelligent degree of board counting is relatively low. Some small manufacturers still count by hand, but for large factories, manual counting needs a lot of human resources, and the accuracy of counting is low. With the rapid development of modern intelligence, image recognition is becoming more mature, some traditional algorithms keep emerging, such as Hough Transform, Fast Line Detector (FLD), Line Segment Detector (LSD) and other line detection algorithms, they have their own advantages and disadvantages, and are summarized and tested, compare which algorithm has higher accuracy and better effect in the field of board linear detection and counting. Finally, the operation mechanism, advantages and disadvantages are summarized, and the process and trend of the further optimization and development of the traditional algorithm line detection technology in the future are prospected, which provides some reference for the research in related fields.

Triplet-triplet annihilation photon upconversion-mediated photochemical reactions.

Photon upconversion is a method for harnessing high-energy excited states from low-energy photons. Such photons, particularly in the red and near-infrared wavelength ranges, can penetrate tissue deeply and undergo less competitive absorption in coloured reaction media, enhancing the efficiency of large-scale reactions and in vivo phototherapy. Among various upconversion methodologies, the organic-based triplet-triplet annihilation upconversion (TTA-UC) stands out - demonstrating high upconversion efficiencies, requiring low excitation power densities and featuring tunable absorption and emission wavelengths. These factors contribute to improved photochemical reactions for fields such as photoredox catalysis, photoactivation, 3D printing and immunotherapy. In this Review, we explore concepts and design principles of organic TTA-UC-mediated photochemical reactions, highlighting notable advancements in the field, as well as identify challenges and propose potential solutions. This Review sheds light on the potential of organic TTA-UC to advance beyond the traditional photochemical reactions and paves the way for research in various fields and clinical applications.

Comparing Differences in Jogging Support across Various Land Use Types in Urban Built-Up Areas Using User-Recommended Routes

Land use types other than specialized athletic fields provide a variety of jogging environments, addressing the shortage of urban fitness facilities and promoting urban health as well as sustainability. Currently, there is limited research comparing the differences in jogging support among various land use types, which can assist decision-makers in setting priorities and targeted strategies for urban renewal, especially in urban built-up areas with limited land resources. Initially, spatial information, statistical data, and recommendation reason text were extracted from recommended routes in mobile fitness apps and categorized into six land use types. Subsequently, spatial potential was measured through descriptive statistics, buffer area analysis, spatial autocorrelation analysis, and line density analysis. Environmental preferences were gauged by text analysis using jieba word segmentation and grouped word frequency calculation. Finally, the measurement results of different land uses were compared, including scale differences, spatial differentiation, environmental perception, and environmental elements. The research found that streets, residential areas, campuses, parks, and greenways possess significant potential to support jogging, particularly streets. These types of land use exhibit varying spatial potentials and attractions in environmental preferences. Targeted recommendations have been proposed to support the renewal of urban built-up areas and research in related fields.

Analysis of bistatic multiphoton quantum radar cross section for the cylindrical surface.

A closed-form model of bistatic multiphoton quantum radar cross section (QRCS) for the cylindrical surface, the main structure of typical aircraft, especially missiles, is established to analyze the system and scattering characteristics. The influence of curvature of the three-dimensional target on QRCS is analyzed. By comparing and analyzing the bistatic multiphoton QRCS for a cylinder and a rectangular plate, we find that the QRCS for the convex surface target is the extension of the QRCS for the planar target with inhomogeneous atomic arrangement intervals and patterns. The characteristics of cylindrical QRCS are discussed by combining the transceiver system and the photon number of the transmitted signal, and the influences of the cylindrical radius, cylindrical length, and incident photon number on QRCS are analyzed. The bistatic results provide guidance on potential strong scattering directions for the target under various directions of photon incidence. Compared with the plane target, the cylindrical target amplifies scattering intensity near the target surface at the scattering angle side in the bistatic system. A bistatic multiphoton quantum radar system can achieve sharpening and amplification of the main lobe of the QRCS for a cylinder in an extensive scattering angle range. Bistatic multiphoton quantum radar has better visibility for the cylinder with a smaller length. These characteristics will provide prior information for research in many fields, such as photonic technology, radar technology, and precision metrology.

A low-cost and eco-friendly recombinant protein expression system using copper-containing industrial wastewater.

The development of innovative methods for highly efficient production of recombinant proteins remains a prominent focus of research in the biotechnology field, primarily due to the fact that current commercial protein expression systems rely on expensive chemical inducers, such as isopropyl β-D-thiogalactoside (IPTG). In our study, we designed a novel approach for protein expression by creating a plasmid that responds to copper. This specialized plasmid was engineered through the fusion of a copper-sensing element with an optimized multiple cloning site (MCS) sequence. This MCS sequence can be easily customized by inserting the coding sequences of target recombinant proteins. Once the plasmid was generated, it was introduced into an engineered Escherichia coli strain lacking copA and cueO. With this modified E. coli strain, we demonstrated that the presence of copper ions can efficiently trigger the induction of recombinant protein expression, resulting in the production of active proteins. Most importantly, this expression system can directly utilize copper-containing industrial wastewater as an inducer for protein expression while simultaneously removing copper from the wastewater. Thus, this study provides a low-cost and eco-friendly strategy for the large-scale recombinant protein production. To the best of our knowledge, this is the first report on the induction of recombinant proteins using industrial wastewater.

The concept of the principles of law in the field of criminal law

The article is dedicated to analyzing the role and position of legal principles within the framework of criminal law. The author emphasizes the importance of researching the principles of law both from a theoretical perspective - aiming to understand the content and directions of the development of criminal law as a branch of law, and from a practical perspective, manifested during the direct application of measures of criminal legal influence on certain subjects of criminal legal relations. Legal principles express the essence and social purpose of law, so it is difficult to overestimate their significance. They reflect the fundamental values and ideals of society at a particular stage of its development. As these values and ideals change, the principles of law also evolve. The relevance of the research topic is driven by the lack of a unified approach among scholars in defining the principles of criminal law, as well as insufficient attention to them from the legislator. The author's goal is to formulate their own definition of the concept of criminal law principles. To achieve this goal, the author examines the fundamental approaches taken by various scholars in defining legal principles as a whole and specifically those in context of criminal law. Emphasizing the diversity of such approaches, the author focuses on defining legal principles within the framework of broad and narrow types of legal understanding. The article highlights key features of criminal law principles that correspond to the specific characteristics of criminal law as a legal field. Trends of incorporating legal principles into legal norms are also analyzed. Furthermore, the article separately explores the issue of incorporating criminal law principles in legal norms. The article is intended for further research in the given field, that hold prospects in the specified thematic area.

COOPERATION MECHANISM AND CHINESE-FOREIGN COOPERATION IN RUNNING SCHOOLS IN CHINA: RESOURCE SHARING AND QUALITY ASSURANCE

Chinese-foreign cooperative education is an important form of foreign cooperation in Chinese education, which provides an effective way to promote the internationalization of Chinese higher education, improve the quality of education, and promote international exchanges and cooperation. With the rapid development of China's society and economy, Chinese-foreign cooperative education is gradually showing new opportunities and challenges in the field of higher education. Effectively introducing foreign advanced educational resources and management experience, making up for some shortcomings of domestic higher education, improving the quality of education as well as enhancing students' cross-cultural communication ability, and improving their own competitiveness are the opportunities for the rapid development of Chinese-foreign cooperative education; and the challenge is that Chinese-foreign cooperative education is not only the cooperation between schools, but also the exchange and integration of culture, technology, talents, and other fields. Therefore, the establishment and improvement of the collaboration mechanism becomes particularly important. In this study, a quantitative research method was used to distribute questionnaires to faculty and staff of Chinese higher education institutions with a background of Chinese-foreign collaborative schooling, and their teaching experiences and views were pooled. Data processing and model validation were conducted through data analysis software such as SPSS and AMOS to derive the impact of resource sharing and quality assurance on the effectiveness of Chinese-foreign cooperation and student learning outcomes in a collaborative mechanism. It was found that in Chinese-foreign collaborative education, resource sharing not only optimizes the teaching environment and improves the education level, but also promotes cultural exchange and academic cooperation. Quality assurance, on the other hand, is a key factor in guaranteeing the sound development of Chinese-foreign cooperative programs, and only by ensuring the quality of education can more international recognition and support be achieved. The findings of this study demonstrate the importance of collaboration mechanisms in Chinese-foreign cooperative education, providing strong theoretical support and practical guidance for the future development of Chinese-foreign cooperative education. It also provides ideas and methods that can be drawn on for future research in related fields and injects momentum for sustainable development to establish a more flexible and efficient collaboration mechanism, strengthen international exchanges, promote intercultural understanding and integration, and realize a win-win situation for Chinese-foreign cooperative education.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0758/a.php" alt="Hit counter" /></p>

A State-of-the-Art Review on Structural Strengthening Techniques with FRPs: Effectiveness, Shortcomings, and Future Research Directions.

In the pursuit of creating more sustainable and resilient structures, the exploration of construction materials and strengthening methodologies is imperative. Traditional methods of relying on steel for strengthening proved to be uneconomical and unsustainable, prompting the investigation of innovative composites. Fiber-reinforced polymers (FRPs), known for their lightweight and high-strength properties, gained prominence among structural engineers in the 1980s. This period saw the development of novel approaches, such as near-surface mounted and externally bonded reinforcement, for strengthening of concrete structures using FRPs. In recent decades, additional methods, including surface curvilinearization and external prestressing, have been discovered, demonstrating significant additional benefits. While these techniques have shown the enhanced performance, their full potential remains untapped. This article presents a comprehensive review of current approaches employed in the fortification of reinforced cement concrete structures using FRPs. It concludes by identifying key areas that warrant in-depth research to establish a sustainable methodology for structural strengthening, positioning FRPs as an effective replacement for conventional retrofitting materials. This review aims to contribute to the ongoing discourse on modern structural strengthening strategies, highlight the properties of FRPs, and propose avenues for future research in this dynamic field.

Personal Bests and Gender

Abstract We connect two large bodies of scientific inquiry. First, important theories in the social sciences establish that human preferences are reference-dependent. Second, a separate field of research documents substantial differences in preferences and attitudes across genders. Specifically, we examine the universe of official classic chess games (more than 250,000 subjects and 22 million games). This allows us to study differences across genders both in cognitive performance (intensive margin) and in competitive participation (extensive margin), using the fact that personal bests act as reference points. We find that males and females behave very differently around their personal bests in both margins.

Mechanisms of obesogens and their impact on adipose tissue, hormones, and inflammation

The complex interactions of genetic, environmental, and behavioral factors that contribute to obesity, a pervasive global health issue, continue to be a severe concern for people all over the world. This manuscript examines the field of obesogen research, seeking to understand the mechanisms by which certain environmental chemicals contribute to the development of obesity. We explore the obesogenic effects by focusing on pathways such as inflammation, hormone interference, and the activation of peroxisome proliferator-activated receptors (PPARs). The text focuses on the significance of PPAR isoforms, especially PPARγ, and how they play a role in adipose tissue growth. We examine how obesogens such as tributyltin (TBT) and bisphenol A (BPA) influence these receptors. Additionally, we examined the impact of obesogens on hormonal regulation, including disruptions to leptin and adiponectin, and investigated the intricate relationship between chronic inflammation and obesity. In the methodology of our study, we utilized a systematic search to identify peer-reviewed articles of relevance. This search spanned various model systems, including in vitro, in vivo, and epidemiological studies, providing insights into the distinct advantages and limitations associated with each. Epigenetic modifications and the influence of obesogens on the development of adipose tissue, metabolism, and appetite control further enrich our understanding of this complex field. Finally, we assess the role of endocrine disruptors in amplifying the risk of obesity, emphasizing the heightened susceptibility during crucial developmental periods. This comprehensive review aims to contribute to the ongoing discourse surrounding obesogens, paving the way for targeted interventions and a more profound comprehension of the global obesity epidemic.

Application of sensor-based speech data mining in E-commerce operations data analysis

With the rapid development of the e-commerce industry, enterprises need to effectively manage and analyze various operational data to support decision making and optimize business processes. Sensor technology is widely used in e-commerce environment, which can collect a lot of voice data and extract valuable information through data mining technology. The study presents the current background and trends of the e-commerce industry, with a focus on the importance and challenges of operational data analytics. A sensor-based voice data mining framework is proposed. Sensors are first used to collect voice data and turn it into a digital signal that can be used for analysis. Then the data mining algorithm is used to process and analyze the voice data, and a series of results about e-commerce operation are obtained. Analyze voice data to help companies develop effective marketing strategies, improve product quality, and optimize supply chain management. This study proves the role of sensor-based voice data mining in e-commerce operational data analysis. By harnessing the full potential of voice data, businesses can better understand and meet customer needs, improve competitiveness and performance. This research result has practical significance for the development of e-commerce industry, and provides guidance and reference for the research and practice in related fields.

PREDICTING STOCK MARKET MOVEMENTS USING NEURAL NETWORKS: A REVIEW AND APPLICATION STUDY

In the rapidly evolving landscape of financial markets, the quest for accurate stock market predictions has never been more critical. This paper delves into the transformative potential of neural network models in forecasting stock market movements, offering a comprehensive examination of their effectiveness compared to traditional predictive models. With a focus on the evolution of stock market prediction methodologies, this study aims to uncover the nuanced dynamics of neural networks, their comparative analysis with other models, and the pivotal role of data preprocessing in enhancing prediction accuracy. Employing a qualitative analysis framework, the research meticulously synthesizes findings from selected studies, highlighting the superior performance of neural network models in capturing complex market patterns and adapting to volatility. The results underscore the significant impact of data quality and quantity, architectural nuances of neural networks, and the strategic implications for investors navigating the stock market's unpredictability. Despite the promising outcomes, the study acknowledges inherent challenges in the real-world application of these models, including data imperfections and the complexity of financial ecosystems. Conclusively, the paper advocates for ongoing innovation, interdisciplinary collaboration, and the strategic integration of advanced neural network architectures to overcome existing limitations. Recommendations emphasize the critical need for high-quality, diverse datasets and continuous model refinement to harness the full predictive power of neural networks in stock market forecasting. This study not only illuminates the path forward for investors and financial analysts but also sets the stage for future research in this dynamic field.
 Keywords: Neural Network Models, Stock Market Prediction, Data Preprocessing, Model Accuracy, Financial Markets, Innovation.

Topic Clusters of Scientific Organizations of Moscow Healthcare Department: Distribution and Leaders

Introduction. Studying the evolution and life cycle of topics that form a particular field of knowledge is important in order to predict certain trends in the development of scientific research in any scientific field (including medicine), as these topics are crucial in identifying and developing new directions. The SciVal topic cluster system is one of the effective, appealing, and promising models for topic-based article classification. It is utilized on the SciVal analytical platform using data from the Scopus international scientific citation database.
 Materials and methods. Data collection and uploading were carried out as of January 24, 2023. The time frame of the study was four years, from 2019 to 2022. The goal was to examine a set of medical publications for 15 organizations (4 research institutes and 11 scientific and practical centers) subordinate to Moscow Healthcare Department.
 Results and discussion. Scientific organizations were ranked according to six scientometric indicators that describe the share distribution and quantitative characteristics of medical topic clusters. The study determined the leaders among all the 15 organizations as well as a top three among research institutes and scientific and practical centers. Topic clusters were analyzed based on the maximum and minimum values in the number of publications and citation indexes across 15 organizations and separately for two groups (research institutes and scientific and practical centers).
 Conclusion. One of the primary paths of future development and implementation of the methods presented in this paper appears to be the use of matrix analysis to identify and determine the key features of both individual subject clusters and joint thematic clusters. With the use of matrix analysis, we will be able to establish the most extensive, referenced, and relevant topic clusters based on the quantitative and equity ratios that we calculate not only between the topic clusters themselves but also between organizations that have publications in these clusters.

Harnessing the Power of Stimuli-Responsive Nanoparticles as an Effective Therapeutic Drug Delivery System.

The quest for effective and reliable methods of delivering medications, with the aim of improving delivery of therapeutic agent to the intended location, has presented a demanding yet captivating field in biomedical research. The concept of smart drug delivery systems is an evolving therapeutic approach, serving as a model for directing drugs to specific targets or sites. These systems have been developed to specifically target and regulate the administration of therapeutic substances in a diverse array of chronic conditions, including periodontitis, diabetes, cardiac diseases, inflammatory bowel diseases, rheumatoid arthritis, and different cancers. Nevertheless, numerous comprehensive clinical trials are still required to ascertain both the immediate and enduring impacts of such nanosystems on human subjects. This review delves into the benefits of different drug delivery vehicles, aiming to enhance comprehension of their applicability in addressing present medical requirements. Additionally, it touches upon the current applications of these stimuli-reactive nanosystems in biomedicine and outlines future development prospects.

Creativity in Project Implementation: An Empirical Study of Project Managers

The construction industry is a dynamic and ever-evolving sector, continuously adapting to societal needs. Within this context, project managers play a pivotal role in steering projects from inception to completion. This study delves into the vital dimension of creativity among project managers in the United Arab Emirates (UAE) and its substantial contribution to the growth of the construction industry in the region. Research in the broader field of construction and project management has traditionally concentrated on factors such as scheduling, cost control, and risk management. However, a noticeable gap exists in the exploration of the relationship between project manager creativity and project success. Hence, the objective of this study is to comprehensively explore various dimensions of project managers’ creativity and evaluate its influence, alongside other criteria, on the outcomes of construction projects. Dimensions and indicators of creativity are derived from a meticulous literature review, and online survey questionnaires were employed to gather insights from individuals engaged in construction projects. The resulting hypothetical model underwent rigorous statistical analysis, employing confirmatory factor analysis and structural equation modeling. Findings indicate a positive impact of tacit knowledge sharing and emotional intelligence on the creativity of construction project managers in the UAE. Moreover, the study establishes that project managers’ creativity, combined with other criteria, significantly contributes to the success of construction projects in the region. These insights are instrumental for fostering creativity among project managers and enhancing overall project success within the construction industry. The study’s originality lies in its distinct contribution to the discourse on creativity in the construction sector.

Understanding Creative Entrepreneurs’ Work Practices: The Varying Conversation between Artistic and Economic Rationales

Research on creative organizations often questions how artistic practices can be squared within the rational decision-making of economic thinking. This paper examines how the relational language, or conversation, between artistic and economic rationales unfolds for creative entrepreneurs. Through ethnographic work with a designer-entrepreneur, this paper presents a fine-grained analysis of the conversation the designer cultivates between artistic and economic rationales through work practices. We contribute to the literature about artistic and economic rationales at work, and more specifically to the concept of conversation. First, we show that high levels of conversing make way for low levels of conversing and vice versa. In the studio, the designer’s engagement with either rationale varies as the creative process progresses. Second, on a more global dynamic, we demonstrate the conversation is continuous. It relies on its variations, which ensure the balance between rationales in the long run. We also contribute to the field of creative entrepreneurship research. We identify here one type of creative entrepreneur, with what we call a ‘small is beautiful’ attitude. Far from the mythical figure of the entrepreneur, this unconventional entrepreneur aims for sustainable use of creative resources rather than growth at all costs.

Emerging Trends in Sustainable Entrepreneurial Behavior: Bibliometric Data Insights

The present study explores the current trends in sustainable entrepreneurial behavior to contribute fundamental knowledge for future academic research in the dynamic field of sustainable entrepreneurship. This study employs a bibliometric analysis using the Scopus database to identify significant changes and emerging topics in academic discourse about the selected keywords. By employing a systematic approach, one can attain a comprehensive understanding of the subject matter and establish a solid foundation for subsequent research. The analysis comprises a total of 666 articles obtained from Scopus. This study utilizes methodological tools like Microsoft Excel, Harzing’s Publish or Perish program, and VOS viewer. These tools were used to quantify and evaluate citation frequencies. This method enables the assessment of the academic output and influence exerted by document type, evolution of published studies, subject area, and prominent keywords. Nevertheless, there are several constraints associated with this study, including the omission of articles published after 2022 and a specific emphasis on English-language publications from 1991 to 2022. Notwithstanding these limitations, there has been a substantial and continuous academic emphasis on the study of sustainable entrepreneurial behavior. Future research should investigate sustainable behavior within the context of social entrepreneurship. The government and policymakers have the ability to significantly contribute to the creation of an ecosystem that promotes sustainable entrepreneurship and sustainable development.

A comprehensive research of deep learning approaches for High Definition map construction in autonomous driving

In the realm of autonomous driving, High Definition Maps (HD maps) are indispensable for safe and precise navigation. Traditional HD map construction methods involving point cloud capture and SLAM have proven effective but labor-intensive. This paper addresses the growing interest in leveraging deep learning techniques to streamline HD map creation. This paper presents a systematic exploration of deep learning methodologies for HD map construction. It categorizes these approaches into two core components: Feature Extraction and Feature Decoding. Feature Extraction involves the transformation of input data, comprising images and LiDAR point clouds, into Bird's Eye View (BEV) representations. Feature Decoding is dissected into rasterized map objectives and vector map objectives. Detailed analysis is conducted on prominent methodologies. The paper provides a nuanced evaluation of these deep learning techniques, highlighting their respective strengths and limitations. Factors such as precision, computational efficiency, and the preservation of fine-grained details are considered when selecting the most suitable method. This comprehensive review summarizes and prospects the research in related fields.