This pilot study evaluated the visionMC system, a low-cost artificial intelligence system integrating HOG-based facial recognition and voice notifications, for workflow optimization in a family medicine practice. Implemented on a Raspberry Pi 4, the system was tested over two weeks with 50 patients. It achieved 85% recognition accuracy and an average detection time of 3.4 s. Compared with baseline, patient waiting times showed a substantial reduction in waiting time and administrative workload, and the administrative workload decreased by 5–7 min per patient. A satisfaction survey (N = 35) indicated high acceptance, with all scores above 4.5/5, particularly for usefulness and waiting time reduction. These results suggest that visionMC can improve efficiency and enhance patient experience with minimal financial and technical requirements. Larger multicenter studies are warranted to confirm scalability and generalizability. visionMC demonstrates that effective AI integration in small practices is feasible with minimal resources, supporting scalable digital health transformation. Beyond biometric identification, the system’s primary contribution is streamlining practice management by instantly displaying the arriving patient and enabling rapid chart preparation. Personalized greetings enhance patient experience, while email alerts on motion events provide a secondary security benefit. These combined effects drove the observed reductions in waiting and administrative times.
Engineering machinery, tools, and implements, Technological innovations. Automation
Collaboration is expected to play a central role in the transition to a bioeconomy - a central pillar of a green economy. Such collaboration is supposed to connect traditional biomass processing firms with diverse actors in fields where biomass ought to substitute existing or create novel products and processes. This study analyzes the network of technology collaborations among innovating firms in Sweden between 1970 and 2021. The results reveal generally positive associations between direct and indirect ties, with meaningful increases in innovation output for each additional direct collaboration partner. Relationships between brokerage positions and innovation output were statistically insignificant, and cognitive proximity - while following theoretical expectations - materially insignificant. These associations are mostly equal between actors heavily invested in the bioeconomy and those focusing on other innovation areas, indicating that these actors operate under largely similar mechanisms linking collaboration and subsequent innovation output. These results suggest that stimulating collaboration broadly - rather than attempting to optimize collaboration compositions - could result in higher number of significant Swedish innovations, for bioeconomy and other sectors alike.
Mahidur R. Sarker, Maher G. M. Abdolrasol, Saad Mohamad Hanif Md
et al.
Agricultural automation (AA) driven by artificial intelligence (AI) and the Internet of Things (IoT) represents a transformative approach to addressing modern farming challenges, such as resource optimization, animal health monitoring, precision farming, and supply chain efficiency. This study examines the adoption and development of AI and IoT technologies in agriculture over the past decade, focusing on key advancements, trends, and their practical applications in the field. A bibliometric analysis of 3404 publications from 2014 to 2024, revealing a 402% growth in research output over the decade, with 18.21% of contributions originating from China and 13.82% from the United States, highlighting these nations’ leadership in this field. Prominent themes include smart agriculture, precision farming, and AI‐driven decision‐making systems. The findings also show a comparatively lower contribution from European countries, indicating potential areas for collaborative growth. This analysis identifies critical tools and technologies, such as IoT‐enabled sensors and AI‐powered data analytics that address real‐time agricultural issues, such as crop health monitoring and yield prediction. The bibliometric analysis identifies key themes including smart agriculture, precision farming, and AI‐driven decision systems. Performance data from reviewed studies show that Long Short‐Term Memory (LSTM) models achieve up to 97% accuracy in yield prediction based on time‐series data, while convolutional neural networks reach 90%–99% accuracy in image‐based plant disease detection. IoT‐enabled precision irrigation systems demonstrate 20%–30% water savings, and autonomous machinery has been shown to reduce labor requirements by up to 25%. Furthermore, the study anticipates significant future advancements, including enhanced energy‐efficient IoT devices and integration of robotics in farming. By presenting a comprehensive review of the literature and identifying gaps in current research, this work provides valuable insights for policymakers, researchers, and industry stakeholders aiming to accelerate the adoption of AI and IoT in agriculture.
Milan Z. Momčilović, Vladimir Dodevski, Sanja Krstić
et al.
This study reports the synthesis of activated carbon from dwarf elder, a lignocellulosic precursor, yielding a material with a high specific surface area (500.43 m2/g) and mesoporous structure (median pore radius: 3.88 nm). The physicochemical properties of the obtained carbon were characterized using field-emission scanning electron microscopy (FE-SEM), Brunauer–Emmett–Teller (BET) analysis, and Fourier-transform infrared spectroscopy (FTIR), confirming its suitability for aqueous-phase sorption applications. Batch experiments demonstrated carbon’s efficacy in adsorbing amlodipine besylate (AMB), a model pharmaceutical pollutant, with a maximum capacity of 325.9 mg/g under optimized conditions (pH 10.0, room temperature). Systematic evaluation of key parameters, such as initial AMB concentration, sorbent dosage, pH, and agitation speed revealed that sorption kinetics adhered to pseudo-second-order and Elovich model. The high efficiency of the synthesized carbon material, coupled with its low-cost and eco-friendly synthesis, positions it as a promising candidate for the scalable remediation of AMB and structurally related pharmaceuticals from contaminated water sources.
The article considered key aspects of the development of Arctic deposits, including the introduction of technological innovations, the impact of sanctions restrictions and geopolitical factors, as well as the prospects for import substitution. Special attention is paid to the role of digital technologies such as the Internet of Things (IoT), mixed, augmented and virtual reality, as well as artificial intelligence in improving the safety and efficiency of hydrocarbon production. The paper analyzes priority technological solutions in the oil and gas sector. The article highlights the need to develop domestic technologies to increase the competitiveness of Russian companies in the international arena and reduce the risks associated with the implementation of Arctic projects.
Rising economic activities, industrialization, and production raise energy demand, and the increased use of energy from fossil fuels has a negative impact on environmental quality. Due to their higher energy density, fossil fuels are anticipated to dominate the future energy supply. It is possible for the usage of fossil fuels to be supplanted by the growth of renewable energy sources, investments in low-carbon automation, and the creation of an energy infrastructure. Environmental innovations and technologies may help increase energy efficiency and acquire renewable energy sources, which may lead to a reduction in energy consumption, the achievement of green energy, and an improvement in environmental quality. This study examines the impact of technological innovations on energy efficiency, energy demand, and carbon dioxide emissions in light of economic growth. From 1985 to 2023, panel data from Belt and Road initiative countries is utalized using static and dynamic panel models. The results support the significance of technological innovations in lowering energy consumption, increasing energy efficiency, and enhancing environmental quality. In addition, it was confirmed that the significant effects of energy consumption, economic growth, energy demand, and financial development on carbon dioxide emissions degrade environmental quality. According to the results, increased economic activity is the result of increase in energy demand, while technological innovations and well-established financial institutions were found to be beneficial in reducing energy consumption, increasing energy efficiency, acquiring renewable energy sources, and boosting environmental quality. The results of this study have significant policy implications and recommendations for the sample countries.
Toll roads can play a vital role in supporting mobility, logistics, and regional economic growth. However, issues such as congestion, declining road quality, and limited real-time information systems often hinder service performance. This study aims to update the Toll Road Service Quality (TRSQ) model by integrating artificial intelligence (AI)-based technological innovations as a mediating variable to enhance excellent toll road services. Using a quantitative explanatory approach, data were collected from 480 users of the Pemalang–Batang Toll Road via a questionnaire survey. Data analysis employed SmartPLS to test causal relationships among TRSQ variables, which include information, accessibility, reliability, mobility, safety and security, rest areas, and responsiveness. Results show that all TRSQ variables significantly influence service excellence, both directly and through technology as a mediator. Priority indicators identified include toll gate queue length, availability of road markings and information boards, traffic flow, real-time traffic updates, and service comfort. The study highlights the urgency of applying AI-based intelligent transportation systems—such as innovative CCTV, remote sensors, and automated information management—to optimise toll road performance, improve user satisfaction, and strengthen sustainable transport services. Keywords: artificial intelligence, smartpls, technological innovation, toll road service, TRSQ model, user satisfaction
The Triple Helix model has provided a foundational framework for analyzing National Innovation Systems by highlighting the roles of universities, industries, and government research institutes. However, increasing heterogeneity within these actor groups limits the explanatory power of typological approaches. This study introduces a capability-based network methodology that maps the structural relationships among innovation actors based on the similarity of their research and development (R&D) capabilities. Drawing on Economic Complexity Theory, we measure each actor's revealed comparative advantage (RCA) across scientific and technological fields and construct an R&D Actor Space - a proximity-based network that reflects the relational configuration of innovation capacities. Applying this method to Korean R&D data, we uncover a stratified system in which central, highly diversified universities coexist with more specialized firms and government institutes. Network analysis reveals assortative and unequal structures, and hierarchical clustering further highlights layered subgroupings. By moving beyond categorical classification, this capability-based network approach provides a scalable and generalizable tool for analyzing structural complexity within national innovation systems.
This research article explores the optimization of aluminium extrusion processes through advanced line balancing techniques, focusing on maximizing marginal profit by increasing melting and casting outputs. By employing mixed integer linear programming (MILP), we identify strategies to minimize idle costs and enhance production efficiency. The study demonstrates that increasing the daily cycle rate from 2 to 4.36 cycles results in a significant rise in daily marginal profit, calculated at USD67,786, after accounting for additional labour costs. This optimization is achieved by expanding the workforce from 8 to 12 operators across two shifts, leading to a 50% increase in labour expenses. The findings reveal a remarkable 117.6% growth in marginal daily profit, underscoring the potential of automation and intelligent manufacturing in transforming the aluminium extrusion industry. Insights from cross-industry research, including Lean Methodology in the Modern Garment Industry, further illustrate the broader applicability of these advancements. This study highlights the critical role of automation in driving productivity and profitability in manufacturing sectors, paving the way for future innovations in aluminium extrusion and beyond.
The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automates simulation evaluation for swift adaptations. This paper presents a dynamic manufacturing line reconfiguration framework to handle disturbances that result in operation time changes. The framework incorporates a system process digital twin for monitoring disturbances and triggering reconfigurations, a capability-based ontology model capturing available agent and resource options, a configuration optimizer generating optimal line configurations, and a simulation generation program initializing simulation setups and evaluating line configurations at approximately 400x real-time speed. A case study of a battery production line has been conducted to evaluate the proposed framework. In two implemented disturbance scenarios, the framework successfully recovers system throughput with limited resources, preventing the 26% and 63% throughput drops that would have occurred without a reconfiguration plan. The reconfiguration optimizer efficiently finds optimal solutions, taking an average of 0.03 seconds to find a reconfiguration plan for a manufacturing line with 51 operations and 40 available agents across 8 agent types.
Technological convergence refers to the phenomenon where boundaries between technological areas and disciplines are increasingly blurred. It enables the integration of previously distinct domains and has become a mainstream trend in today's innovation process. However, accurately measuring technological convergence remains a persistent challenge due to its inherently multidimensional and evolving nature. This study designs an Technological Convergence Index (TCI) that comprehensively measures convergence along two fundamental dimensions: depth and breadth. For depth calculation, we use IPC textual descriptions as the analytical foundation and enhance this assessment by incorporating supplementary patent metadata into a heterogeneous graph structure. This graph is then modeled using Heterogeneous Graph Transformers in combination with Sentence-BERT, enabling a precise representation of knowledge integration across technological boundaries. Complementing this, the breadth dimension captures the diversity of technological fields involved, quantified through the Shannon Diversity Index to measure the variety of technological combinations within patents. Our final TCI is constructed using the Entropy Weight Method, which objectively assigns weights to both dimensions based on their information entropy. To validate our approach, we compare the proposed TCI against established convergence measures, demonstrating its comparative advantages. We further establish empirical reliability through a novel robustness test that regresses TCI against indicators of patent quality. These findings are further substantiated through comprehensive robustness checks. Our multidimensional approach provides valuable practical insights for innovation policy and industry strategies in managing emerging cross-domain technologies.
The transition from 5G to 6G mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for CICIDS2017 datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.
Wei-Loon Koe, Noorain Mohd Nordin, Nurul Ezaili Alias
Practising sustainability is no longer a new phenomenon in today's business world. However, many micro, small and medium enterprises (MSMEs) face various challenges in exercising sustainable practices. In addition, the stakeholders' roles in driving their sustainable practices still remained unknown. Therefore, this study was conducted to determine sustainable practices exercised by MSMEs and the driving factors of sustainable practices. This study employed a qualitative research method in which a semi-structured interview was performed with 12 micro and small manufacturing MSMEs. The participants were selected using the purposive sampling method. Based on the interview findings, it concluded that MSMEs practised three aspects of sustainability, namely environment, social and governance, although they practised sustainability in different ways and to different extend. Regarding the driving factors of sustainable practices, it concluded that stakeholders such as government, owners and customers played a crucial role. Specifically, the government played a significant role by providing various financial and non-financial support. The owners of MSMEs were able to initiate sustainability practices by sharing their sustainable beliefs among employees. Customers could also spark sustainable practices in MSMEs due to their changed preferences towards environmentally friendly products. The implications of this study include supporting the current sustainability model, shedding light on the importance of stakeholders in driving sustainable practices, and providing new insights into developing strategies for sustainable practices.
O objetivo do estudo foi determinar a taxa de sobrevivência de abelhas Apis mellifera L., após o contato com produtos de origem botânica (azadiractina e alicina), utilizados como inseticidas naturais na agricultura ecológica. Testaram-se quatro tratamentos: sacarose + água (solução testemunha - ST); óleo de Neem a 1,2% + solução testemunha (ON1); óleo de Neem a 2,0% + solução testemunha (ON2) e Alicina + solução testemunha (AL). Foram feitas quatro repetições por tratamento, sendo cada repetição formada por um grupo de 20 abelhas, as quais foram acomodadas em gaiolas, em ambiente controlado. Após o contato com os tratamentos, as avaliações foram realizadas de hora em hora até a 24ª hora e, posteriormente, na 48ª hora. Nas primeiras 24 horas, foram observadas taxas de sobrevivência de 83,75; 87,50; 20,00 e 48,75% nos tratamentos ST; ON1; ON2 e AL, respectivamente. Após 48 horas, as taxas de sobrevivência observadas decresceram para 53,75; 47,50; 3,75 e 38,75%, para ST, ON1, ON2 e AL, respectivamente. Os resultados obtidos nesse estudo mostraram que os produtos de origem botânica, dependendo da concentração utilizada, podem ser tóxicos para as abelhas.
Manuel Miranda, María Pereda, Angel Sánchez
et al.
A fundamental feature for understanding the diffusion of innovations through a social group is the manner in which we are influenced by our own social interactions. It is usually assumed that only direct interactions, those that form our social network, determine the dynamics of adopting innovations. Here, we put this assumption to the test by experimentally and theoretically studying the role of direct and indirect influences in the adoption of innovations. We perform experiments specifically designed to capture the influence that an individual receives from their direct social ties as well as from those socially close to them, as a function of the separation they have in their social network. The results of 21 experimental sessions with more than 590 participants show that the rate of adoption of an innovation is significantly influenced not only by our nearest neighbors but also by the second and third levels of influences an adopter has. Using a mathematical model that accounts for both direct and indirect interactions in a network, we fit the experimental results and determine the way in which influences decay with social distance. The results indicate that the strength of peer pressure on an adopter coming from its second and third circles of influence is approximately 2/3 and 1/3, respectively, relative to their closest neighbors. Our results strongly suggest that innovation adoption is a complex process in which an individual feels significant pressure not only from their direct ties but also by those socially close to them.
Abstract Following the reform and opening up of China, the Pearl River Delta (PRD) region became a center of foreign investment due to its comparative advantages of cheap labor costs and low land use prices. The tide of migrant workers, comprising a large surplus rural labor force that flooded into the PRD region, caused a rapid increase in the urban population. From the 1980s to the 2000s, migrant workers were a key force that drove urbanization in China. The utilization of automation technology in production since the 2010s has increased the number of unemployed laborers and shifted the dynamics of urbanization. This study investigated how automation is applied in production processes and its effects on different industries, namely, those related to textiles, electronic information, and home electrical appliance manufacturing; specifically we sought to examine the complex relationship among automation, the labor forces, and urbanization by illustrating the implementation of automation in production processes and its influence on labor forces and urbanization. This study revealed that companies in different industries implement automation to differing degrees and through varied upgrading paths. All industries can ultimately achieve technological transformation and cross-industry development. For labor forces, automation exerts two simultaneous folded effects, namely, the direct replacement of low- to middle-skilled workers and the creation of new jobs. The penetration of automation into manufacturing industries has changed the dynamics of urbanization and the social spatial structure of cities, leading to a polarization of the labor forces and the emergence of “dual cities”.
Perguruan tinggi ialah suatu pendidikan tertinggi yang memiliki tanggung jawab untuk menyedikan sumber daya manusia di Negara Indonesia yang memiliki kemampuan dan keprabadian yang baik dan didukung dengan penguasaan ilmu pengetahuan dan teknologi (Karim, 2020). Seiring perkembangan teknologi dan sistem informasi ditandai dengan Revolusi 4.0 dimana era tersebut terdapat perubahan perilaku manusia yaitu setiap aktivas manuasi dapat dilakukan dengan menggunakan teknologi. Karim, (2020) mengungkapkan bahwa selama tahun 2018-2019 telah banyak kajian seminar di Perguruan Tinggi terdapat berbagai perubahan yang berdampak pada pengelolaan Peindidikan di Indonesia. Beberapa tantangan dinataranya harus mengkobinasikan teknologi cyber otomasi. Dengan demikian, teknologi 4.0 Perguruan tinggi perlu di fokuskan pada penyediaan kebutuhan yang ditunjang dengan IOT (Internet Of Things), Big Data dan Cyber Security. Artinya bahwa kemajuan teknologi informasi berbasis internet supercepat yang dapat dijadikan sebagai penunjang Perguruan Tinggi dalam menyelengarakan pendidikan. Hal tersebut sudah dilakukan oleh Perguruan tinggi dalam meningkatkan karyawan dan saat ini telah monerahkan berbagai hasil positif baik dibidang akademik maupun administratif. Penelitian ini bertujuan mengetahui pengaruh tacit knowledge dan technological capability dengan mediasi innovation behavior terhadap kinerja karyawan Perguruan tinggi. Objek penelitian ini adalah karyawan Perguruang tinggi Universitas Muhammadiyah Ponorogo dan Universitas Muhammadiyah Surakarta berjumlah berjumlah 186 karyawan. Dari hasil penelitian ini tacit knowledge berpengaruh positif terhadap innovation behavior, technological capability berpengaruh positif terhadap innovation behavior, tacit knowledge berpengaruh positif terhadap kinerja karaywan. Innovation behavior memberikan pengaruh positif dalam memediasi tacit knowledge terhadap kinerja karyawan. sedangkan innovation behavior memberikan pengaruh positif dalam memediasi technological capability terhadap kinerja karyawan. Dari hasil penelitian ini disarnkan untuk meningkatkan kinerja karyawan, sebaiknya Perguruan tinggi perlu meningkatkan tacit knowledge karyawan melalui kegiatan diskusi seacara rutin anatara pimpinan dan karyawan. Untuk meningkatkan technological capability, sebaiknya Perguruan tinggi perlu mengadakan pelatihan dibidang teknologi. Higher education is the highest education that has the responsibility to provide human resources in the State of Indonesia that have good abilities and personalities and are supported by mastery of science and technology (Karim, 2020). Along with the development of technology and information systems marked by Revolution 4.0 where this era there are changes in human behavior, namely every human activity can be carried out using technology. Karim, (2020) revealed that during 2018-2019 there have been many seminar studies in Higher Education there are various changes that have an impact on the management of Education in Indonesia. Some of the challenges include having to combine automation cyber technology. Thus, technology 4.0 Higher Education needs to focus on providing needs supported by IOT (Internet Of Things), Big Data and Cyber Security. This means that the advancement of super-fast internet-based information technology can be used as a support for universities in organizing education. This has been done by universities in improving employees and currently has monerahkan various positive results both in the academic and administrative fields. This study aims to determine the effect of tacit knowledge and technological capability with mediation of innovation behavior on the performance of higher education employees. The object of this research is the employees of Perguruang Tinggi Muhammadiyah Ponorogo University and Muhammadiyah Surakarta University totaling 186 employees. From the results of this study, tacit knowledge has a positive effect on innovation behavior, technological capability has a positive effect on innovation behavior, tacit knowledge has a positive effect on employee performance. Innovation behavior has a positive influence in mediating tacit knowledge on employee performance. while innovation behavior has a positive influence in mediating technological capability on employee performance. From the results of this study, it is suggested that to improve employee performance, universities should increase employee tacit knowledge through regular discussions between leaders and employees. To improve technological capability, universities should conduct training in the field of technology.