HighTech and Innovation Journal
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Innovation Process and a Model for HighTech Companies within an Industrial District
Full text linkHigh-tech companies innovate through complicated and dynamic interactions with other shareholders willing to form a “Technology Innovation System.” There is extensive literature on the formation of such systems; however, most such studies have not considered their dynamic features, R&D intensity, and other factors. Innovation dynamics should be framed upon the activities of R&D for technological development and then transferred into commercial economic values. This work aims to develop a model to improve the understanding of the dynamic behavior of high-tech companies within an industrial district in İstanbul, Turkey. This research outlines innovative approaches along with other features such as R&D intensity within the companies, the availability of scientific and technological personnel, the collaboration with scientific organizations, and initiatives in intellectual property rights. Extended interviews were conducted with the upper management of 8 high-technology companies using a structured and in-depth interviewing technique. As a result, based on specific indicators and scoring data, this study reveals the importance of a “technology innovation system” within such an industrial district for high-tech companies for the company's business processes and indirectly within the company's management approach. This research outlines innovation systems along with other features such as R&D intensity, the availability of scientific and technological personnel, and their involvement through scientific research collaborations
UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied Environments
Full text linkThis study presents a UAV-based Structural Health Monitoring (SHM) system that combines Lighthouse localization with a two-stage CNN architecture—AlexNet for crack classification and YOLOv4 for segmentation—to enable reliable crack detection and spatial mapping in GNSS-denied environments. This study explores the effectiveness of this combination as a practical and computationally efficient solution for indoor SHM tasks. The UAV was deployed within a 1.5 m × 1.2 m × 1.2 m test volume to inspect synthetic cracks derived from Özgenel’s dataset, as well as a real-world wall crack. Two experiments were conducted: evaluating UAV localization accuracy and assessing the system’s ability to detect cracks and provide corresponding pose data. The system achieved a 1–2 cm margin of error in pose estimation, alongside 100% precision, 83.33% recall, and 91.89% accuracy in crack detection. This level of localization accuracy supports stable autonomous UAV flight and ensures that cracks are detected and spatially localized with minimal deviation. Beyond classification and segmentation, the system returns pose data tied to each detected crack, allowing users to identify defect locations precisely and use this information to guide inspection or maintenance tasks. Future work includes expanding the dataset, generalization, and evaluating scalability via multi-base station setups
Correlation Between Agricultural Product Purchases and Live-Streaming Economy in the Digital Economy
Full text linkObjectives: This paper aims to explore how agricultural product sales via live streaming affects the purchasing behavior in the context of the digital economy and evaluate the correlation between them. Methods: A questionnaire was designed to collect respondent’ personal information and information on their purchases of agricultural products. The correlation between agricultural product purchases and the live-streaming economy was measured. Findings: Most respondents purchased agricultural products on platforms such as Douyin and Taobao, preferred watching live streaming of internet celebrities and farmers, primarily bought fruits, vegetables, whole grains, and coarse cereals, and expressed high satisfaction with their agricultural product purchases. Correlation analysis indicated that the correlation coefficient between agricultural products and purchases in the live-streaming economy was highest at 0.742. Regression analysis found a significant positive correlation between agricultural products, anchors, live streaming, platforms, and agricultural product purchases. Novelty: The research quantifies the relevant information on agricultural product live streaming and purchases through questionnaire analysis. It also reveals the positive influence of the digital economy on agricultural product purchases, providing some references for the further development of agricultural product live streaming
Simulated Annealing Algorithm for Vehicle Routing with Stochastic Travel Times and Soft Time Windows
Full text linkIn the context of urban logistics, travel time uncertainty is a critical challenge for efficient route planning. Thus, this study proposes an algorithm based on Simulated Annealing (SA) that addresses this problem through a dual approach. On the one hand, the effectiveness of the algorithm is validated in deterministic VRPTW scenarios, using classical Solomon instances and achieving an average GAP of 3.42% in the most complex cases. On the other hand, a stochastic model fed with empirical data from Google Maps is integrated, designed to capture real-time traffic variability, thus addressing the VRPSTTW problem. The results show that the algorithm not only maintains a standard deviation of 2,048 energy units, consolidating its robustness to fluctuations in the optimal parameters, but also stands out for its ability to generate robust solutions in urban contexts with high temporal uncertainty. This proposal, being based on real data and not on theoretical simulations, positions the algorithm as a strategic tool to optimize logistic operations in dynamic and volatile environments
Optimizing Green Business Information Management Systems Through Carbon-Neutral Digital Transformation Pathway Design
Full text linkThis research develops a comprehensive framework for optimizing green business information management systems to achieve carbon neutrality goals through digital transformation. The study conducted cross-sector carbon footprint assessments of information systems across six industries, analyzing emission patterns based on operational scales, industry characteristics, and technological architectures. A multi-tiered optimization model was developed targeting infrastructure, data management, and application layers, validated through empirical data from enterprises undergoing digital transformation. Results reveal a strong negative correlation (r = -0.73) between digital maturity indices and emission intensity, with organizations implementing comprehensive digital transformation achieving average carbon reductions of 31% over five years. The proposed multi-tiered optimization approach enabled 42.6% emission reductions, with technology companies achieving 68% reductions. Economic analysis demonstrates return on investment ranging from 132-278% over five-year periods, with payback periods of 14-36 months. This study advances information management theory by integrating technological architecture with environmental performance governance, providing quantifiable carbon assessment methodologies across system layers and practical implementation matrices for industry-specific applications. The framework enables organizations to balance carbon reduction objectives with operational efficiency, addressing the critical gap between theoretical potential and practical implementation in carbon-neutral transformations
Evaluating the Performance of NoSQL Databases for Big Data in Cloud Computing Environments
Full text linkThis study aims to evaluate the performance of NoSQL databases in distributed cloud computing environments, addressing the lack of comprehensive benchmarking in this domain. Specifically, it investigates MongoDB and Riak KV, two widely used NoSQL systems, across diverse cloud platforms, including Google Cloud, DigitalOcean, and OpenStack. Using the Yahoo Cloud Serving Benchmark, we designed and implemented a benchmarking model to measure key performance indicators, including latency, throughput, and scalability, under varying workloads and data sizes. The analysis revealed that MongoDB integrated with Google Cloud consistently outperformed other configurations, demonstrating superior throughput and lower latency in read and write operations. In contrast, Riak Key Value generally exhibited higher latency, especially in scan-intensive workloads. To support practical decision-making, a decision tree model was developed based on empirical findings to guide optimal selection of cloud computing platforms and databases. The proposed benchmarking framework is modular and extensible, allowing adaptation to other NoSQL technologies, cloud providers, and performance metrics. This research presents a novel, systematic methodology for evaluating NoSQL database performance in cloud environments, providing actionable insights for selecting high-performing, scalable solutions in big data applications. This modular design enables the addition of more database technologies, deployment options, and performance standards in the future, thereby supporting broader research and real-world applications in distributed systems and cloud computing
Interpretable and Uncertainty-Aware Multi-Modal Spatio-Temporal Deep Learning Framework for Regional Economic Forecasting
Full text linkThe objective of this study is to improve the accuracy, interpretability, and reliability of regional economic forecasting, a task essential for effective policy-making, infrastructure planning, and crisis management. Existing econometric and machine learning models often suffer from linear assumptions, limited use of heterogeneous data, and a lack of transparent uncertainty quantification. To address these limitations, we propose a unified multi-modal spatio-temporal deep learning framework that integrates satellite imagery, structured economic indicators, and policy documents through an adaptive cross-modal attention mechanism. The methodology incorporates a spatio-temporal cross-attention module to capture dynamic inter-regional dependencies and temporal patterns, along with a Bayesian neural prediction head to quantify uncertainty. Applied to a 13-year dataset from 75 Chinese cities, the model demonstrates substantial improvements, reducing mean absolute error by 37% compared to XGBoost and achieving 92% PICP (Prediction Interval Coverage Probability) under a 90% confidence threshold. Case studies further validate its ability to trace pandemic-induced economic shocks and reveal latent propagation pathways. The novelty of this work lies in its integrative architecture that jointly advances multi-modal fusion, interpretability, and uncertainty quantification, offering both methodological innovation and practical utility. This framework provides policymakers with transparent, risk-aware predictions and establishes a scalable foundation for next-generation economic forecasting
Unveiling Key Drivers of Citizens' Acceptance of E-Voting: A Quantitative Analysis
Full text linkThe current study examines the broader factors influencing citizens’ trust and adoption of electronic voting (e-voting) systems, extending beyond the conventional focus on trust in government and technology. A conceptual framework was developed by incorporating elements from the TAM, IDT, and trust theory. Data was collected through surveys and investigated using SEM to evaluate the relationships amongst crucial variables. The findings reveal that trust in e-voting is significantly shaped by citizens’ trust in governing bodies, the transparency and reliability of the voting process, trust in the technology, and perceived ease of use. In contrast, perceived public value was not found to significantly impact trust. These results highlight the multifaceted nature of trust in digital governance and underscore the importance of considering both procedural and technological factors in system design. The novelty of this study lies in its broader perspective on trust, emphasizing the role of implementation and process transparency in influencing public perception. The proposed model offers practical insights for policymakers and system developers seeking to improve public confidence and foster wider adoption of e-voting technologies
Performance Assessment of Optimized Link State Routing Protocol on Vehicular Ad Hoc Network Simulation
Full text linkVehicular Ad-hoc Networks (VANETs) are dedicated forms of wireless communication networks designed to handle the challenges of vehicular environments, including high mobility, varying traffic densities, and constantly changing topologies. These factors necessitate the development and evaluation of routing protocols to ensure reliable data communication between vehicles. This study evaluates the performance of the Optimized Link State Routing (OLSR) protocol within Vehicular Ad-hoc Networks (VANETs), focusing on its capability to handle different traffic densities and dynamic environments. Reliable data communication in VANETs is critical due to the high mobility and constantly changing topologies, especially in urban and highway settings. Using NS-3 for network simulation and Simulation of Urban MObility (SUMO) for realistic vehicular mobility modelling, we conducted a series of simulations to assess OLSR's performance in low-density and high-density scenarios across highway and urban environments. Key performance metrics, including packet delivery ratio (PDR), end-to-end delay (E2ED) and throughput were analyzed to capture OLSR's strengths and weaknesses in each setting. The analysis showed that OLSR excels in low-density highway scenarios, achieving a PDR of 100% and low E2ED. However, in high-density urban settings, the protocol encounters performance challenges, with a reduced PDR of 81.40% and a high E2ED of 85.52 seconds, indicating delays in data transmission. These findings emphasize the limitations of OLSR in dense urban environments, highlighting the necessity for adaptive routing protocols that can improve performance in complex, high-density vehicular networks. Doi: 10.28991/HIJ-2025-06-01-019 Full Text: PD
High-Tech Models for Simulating the Wounding Effects of Projectiles of Small Calibres: Benefits for Security Management
Full text linkThe aim of this study is to analyse the effects of projectiles of small calibres on the human femur using an innovative indirect identification method. A heterogeneous physical model was developed that combines ballistic gelatine for soft tissues and porcine femur as an analogue for human bone to simulate gunshot injuries under ethical and economic conditions. The study evaluated three types of ammunition: 9 mm Luger pistol cartridges and two micro-calibre rifle cartridges, 5.56×45 mm (SS 109) and 5.45×39 mm (7H6). Ballistic testing measured impact and exit velocities, assessed bone tissue destruction, soft tissue damage, and the temporary cavity created by projectiles. The findings reveal that micro-calibre rifle projectiles cause up to twice the bone destruction and more extensive soft tissue damage compared to pistol ammunition. The study also highlights the significant role of liquid structures in the medullary cavity in amplifying bone damage. These results improve ballistic testing methodologies, offering valuable insights for crisis management, security operations, and the development of protective equipment. The proposed model serves as a critical tool for understanding the effects on human tissues, aiding in forensic analysis, and advancing experimental ballistics. This research opens new opportunities for applications in the security and health disciplines.
Doi: 10.28991/HIJ-2025-06-01-010
Full Text: PD