Network Innovation Research Articles

Looking for Innovation Opportunity Areas in the Value Chain in the Agri-food Sector

Agricultural producers know their business very well, through daily practice they know the best way to carry out their processes; however, currently, it is necessary to continually seek improvements in its production processes to remain competitive and not only survive but grow. Thus, today we speak of open innovation, which implies the establishment of networks, made up of diverse local, regional, national and international nodes and components through which small or medium-sized producers can learn about experiences from other latitudes, which help them develop innovation projects to stay competitive. Through the review and analysis of cases of some Mexican agricultural producers and other Latin American countries based on the experience and proposals of some international organizations, which use the network innovation model, in this work a simple and synthetic proposal is developed for the opportunity areas to carry out innovation projects in the different links of the value chain of agri-food production processes.

Applications of Artificial Intelligence in Computer Vision and Network Fields

The rapid development of artificial intelligence (AI) technology has sparked a wave of innovation in the fields of computer vision and networks. This paper explores the applications of AI in image recognition, deep learning, network security, and intelligent network management, systematically reviewing the integration of computer vision and network innovations. It also highlights key application scenarios, such as multimodal data fusion and edge computing. Additionally, this paper addresses challenges such as data privacy, computational resource demands, and model optimization, and forecasts the future potential of AI in creating intelligent vision and network systems. This research provides theoretical support and guidance for further exploration of AI’s integration and innovation in computer vision and network fields.

The 30-year evolution of oral cholera vaccines: A case study of a collaborative network alternative innovation model.

Cholera outbreaks have been rapidly increasing around the world. While long-term cholera prevention and control measures rely on improvements in water, sanitation, and hygiene, oral cholera vaccines (OCVs) are used for prevention and control in the short-to-medium term. OCVs lack the market incentives available in other more profitable disease areas. The development of OCVs was made possible through an alternative innovation model, which sustained innovation across multiple generations of the product for more than three decades. To examine how this alternative innovation model worked, we conducted 18 semi-structured interviews with key stakeholders related to the development of several OCVs, including "Dukoral", "Shanchol", and "Euvichol-Plus", as well as additional cholera vaccines currently under development. Interview data was analyzed thematically according to the resources used by organizations (including funding, knowledge, relationships, and manufacturing), and the practices they implemented during each stage of the R&D process (including knowledge management and intellectual property strategies, approaches to transparency, and global access strategies). Next, we created participatory network maps to illustrate the structure of the relationships between stakeholders and how these evolved over time. We found that a core group of stakeholders were able to influence policies to promote the use of OCVs, and successfully develop, finance, and obtain WHO Prequalification for safe, effective, and affordable OCVs for global procurement and distribution. The evolution of OCVs demonstrates how a collaborative network innovation model can successfully develop new pharmaceutical products that are affordable and well-suited for use in context. This model could be applied to other areas of pharmaceutical innovation, such as pandemic preparedness, for more equitable outcomes for global public health.

Guest editorial. Revisiting the Slow Food Movement: Three Cases of Heritage, Innovation, and Sustainability in Alternative Food Networks

The special issue, ‘Revisiting the Slow Food Movement: Three Cases of Heritage Innovation and Sustainability in Alternative Food Networks', explores the integration of Slow Food principles within Alternative Food Networks (AFNs) to enhance sustainability and cultural heritage preservation. By analysing case studies from Afghanistan, Ireland, and Switzerland, the editorial illustrates how AFNs can address food security, local communities' social adherence, economic viability, and environmental sustainability. The Afghan case emphasises collaborative agribusiness for resilience and market access, the Irish study focuses on sustainable lobster fishing practices, and the Swiss example highlights the revival of traditional red berry cultivation. Utilising the Triple Bottom Line framework, this editorial explores the multifaceted benefits of AFNs in fostering sustainable food systems that align with local cultural practices and innovative agricultural techniques. This special issue provides valuable insights for policymakers, practitioners, and scholars aiming to develop more resilient and equitable food systems globally.

Proposed Modbus Extension Protocol and Real-Time Communication Timing Requirements for Distributed Embedded Systems

The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and remains the foundation for the development of numerous advanced communication protocols. In this paper, the conducted research resulted in a major contribution; specifically, it describes the mathematical model for the Modbus protocol and defines the acquisition cycle model that corresponds to incompletely defined protocols in order to provide a timestamp and achieve temporal consistency for proposed Modbus Extension. The derived technical contribution of the authors is to exemplify the functionality of a typical industrial protocol that can be decomposed to improve the performance of data acquisition systems. Research results in this area have significant implications for innovations in industrial automation networking because of increasing distributed installations and Industrial Internet of Things (IIoT) applications.

Automated grasp labeling and detection framework with pixel-level precision

Due to the differences in shape, material, and color of objects, the detection of planar grasps by robots remains challenging. Traditional methods rely on discrete grasp configurations for annotation, ignoring many possible grasp configurations. This leads to poor network generalization, making it challenging to handle a diverse range of grasped objects. Manual reannotation to continuous labels can effectively address this issue but comes with a significant cost. Therefore, this paper proposes a Pixel-level Grasp framework. Firstly, APGLG can automatically generate pixel-level grasp labels and discrete labels into continuous labels, effectively increasing the information content of individual data and improving the network generalization performance. Then, we propose the Max-Grasp-Net, built on the Multi-axis Vision Transformer and Dynamic Convolution Decomposition, to construct a U-shaped Network structure. A grasp decoder is incorporated, and deep supervision is applied to enhance network generalization. On the Cornell dataset, we achieve the best results, a grasping accuracy of 99.55%, an average success rate of 97.92% in single-object grasping, and 95.83% in multi-object grasping. We verified the effectiveness of our label generation algorithms and network innovation through actual grasp experiments.

P.O.L.A.R. Star: A New Framework Developed and Applied by One Mid-Sized Pharmaceutical Company to Drive Digital Transformation in R&D.

Digital transformation has become a cornerstone of innovation in pharmaceutical research and development (R&D). Pharmaceutical companies now have an imperative to embrace transformation, including mid-sized and small-sized companies despite resource limitations that do not allow economies of scale compared with larger organizations. This article describes the journey undertaken by Chiesi to develop an efficient framework to drive digital transformation along its R&D value chain with the objective of building and refreshing a clear roadmap and relevant priorities, together with identifying and enabling new digital capabilities and skills within R&D, defining tools and processes that will guide Chiesi activities in the space up to mid-long term. This work has led so far to five main achievements, which align with the steps in the framework: a strategically aligned roadmap with key focus areas for digital transformation and a dedicated team to lead the effort; a common language for data across the R&D value chain; an internal mindset that's open to innovation and participation in key external networks and consortia; a set of quick-win use cases for the new framework; and a defined set of Key Performance Indicators (KPIs) and monitoring tools for digital transformation. The work presented here demonstrates that R&D digital transformation should represent an ongoing process to enable cross-functional collaboration and integration within complex corporate environments that face an ever-growing volume of diverse data, to efficiently support business needs, and to ensure a positive impact on patient care.

LOGISTICS INFRASTRUCTURE MANAGEMENT IN THE SYSTEM OF DIGITAL TRANSFORMATION OF THE ECONOMY OF UKRAINE

The article examines the main aspects of the digitalization of logistics processes in the context of their impact on the efficiency and competitiveness of enterprises. It explores technological innovations such as the use of big data, the Internet of Things, and blockchain, which are changing approaches to supply chain management. The role of government policy in supporting digital logistics initiatives is analyzed, and the industry’s key challenges are identified. The main directions for implementing digital innovations in global logistics networks have been determined. Factors that negatively affect the implementation of digital solutions in the activities of logistics enterprises are considered. The necessity of implementing digital technologies to optimize logistics processes and increase the stability of Ukraine’s economy in the face of global changes has been proven.

Impact of Metro Rail Transit Systems in Metropolitan Cities: A Bibliometric Analysis

The research investigates the evolving context of Metro Rail Transit (MRT) systems, using a thorough bibliometric analysis to identify patterns, trends, and significant contributors impacting scholarly debate. The study focuses on the effects of MRT systems in metropolitan cities, looking at both existing studies and new areas for future research. The primary goals are to examine the evolving publication scene, identify prominent individuals and entities, analyze keyword networks, and understand current research subjects. The study aims to provide a complete understanding of the effects of MRT systems to guide future research and inform urban planning decisions. Using bibliometric approaches, the study examines a varied dataset of scientific publications about MRT systems. The examination includes advancements in publication trends, identification of significant contributors, and analysis of keyword networks, titles, and abstracts to help untangle the complexity of MRT systems’ impact. The study uncovers dynamic shifts in scholarly contributions, emphasizing research themes and identifying significant contributors, organizations, and countries fostering creative discussions. Transit-oriented development, environmental sustainability, and socioeconomic ramifications are key topics that match global goals. The study meets its aims, offering significant insights into the various effects of MRT systems in metropolitan areas. By analyzing growing trends, identifying major contributors, and explaining key topics, the research lays the groundwork for future investigations, directing educated urban planning decision-making. The study’s future implications stem from its ability to drive further research, emphasizing the continued importance of investigating real-world consequences, ethical considerations, and fair access to emerging technologies. As urban mobility advances, this study offers the framework for informed decision-making and continuous innovation in metropolitan transit networks.

Unlocking the Potential of 5G: Innovations and Challenges in Next-Generation Telecommunication Networks in Nigeria

This study investigates the unlocking of 5G potential, focusing on innovations and challenges in next-generation telecommunication networks within Nigeria. Three research questions guided the study, employing a survey research design. The study population consisted of 38 respondents, comprising 17 Technical Unit Heads from the National Information Technology Development Agency (NITDA) and 21 Heads of Telecommunication and Innovation Units from the Nigerian Communications Commission (NCC) across six regional offices in Nigeria. Due to the small population size, the entire population was included, eliminating the need for sampling. Data collection utilized a structured questionnaire developed by the researchers, titled "Innovations and Challenges in Fifth-Generation Telecommunication Networks Questionnaire (ICFTNQ)," achieving a reliability index of 0.87 through the Cronbach Alpha reliability method. Mean statistics were utilized to address the research questions. The findings revealed the significant enhancements brought by 5G to telecommunication infrastructure in Nigeria, particularly in speed, reliability, innovation, and economic development. Key innovative strategies crucial for 5G deployment were identified, including network optimization, collaboration, infrastructure development, and regulatory support. The study recommends collaborative efforts among government, industry stakeholders, and international partners to overcome challenges and establish a robust foundation for successful 5G deployment in Nigeria.

An evolutionary analysis of the diffusion of low-carbon technology innovation in supply networks

In an era increasingly defined by the urgent need to address energy sustainability and environmental degradation, the advancement of low-carbon technologies stands at the forefront of global governmental agendas. Despite China's pioneering efforts in the adoption and development of low-carbon initiatives, its status as the preeminent global carbon emitter highlights the significant challenges impeding the widespread adoption of these innovations. This study delves into these complexities by employing a sophisticated complex network evolutionary game model that intricately weaves together consumer preferences and carbon pricing as pivotal determinants of utility. By examining the influence of corporate strategic decisions on the diffusion of low-carbon technologies, our research provides an insightful micro-level analysis that diverges from the macro-level orientation typically dominating the field. This nuanced approach not only sheds light on the mechanics of low-carbon technology dissemination but also contributes to a more comprehensive understanding of how to effectively navigate and surmount the barriers to low-carbon technological progress. Through this exploration, our study seeks to augment the discourse on the propagation of low-carbon technologies, offering vital insights for policymakers, industry stakeholders, and the broader academic community on fostering a more sustainable and low-carbon future.

Mapping lock-ins and enabling environments for agri-food sustainability transitions in Europe

European agri-food systems must overcome structural lock-ins to achieve more sustainable modes of production and consumption. Yet European regions are highly diverse, and we lack understanding of how different regional characteristics may enable or inhibit sustainability transitions. This hinders the development of context-tailored governance strategies. In this paper, we identify and apply sets of spatial indicators to map the regional potentials for agri-food transitions. We first analyse the strength of lock-in to the incumbent agro-industrial paradigm. We then map the enabling environments for two alternative agri-food networks—multifunctional value chains and civic food networks—that each embed distinct social–ecological qualities of agriculture and food. Results demonstrate a large spatial diversity in transition potential, with stronger lock-ins throughout North and Western Europe and stronger enabling environments for agri-food transitions in Italy, France, Switzerland, and Southwest Germany. We find that lock-ins are strongest in livestock-dominated regions and are associated with higher GHG emissions and excess nitrogen levels. Our study demonstrates the need for coordinated public policies that (1) leverage region-specific transition potentials and (2) enable complementary innovations in market-based and community-led networks.

Critical Review on Water Quality Analysis using IoT and Machine Learning Models

Water quality and its management are the most precise concerns confronting humanity globally. This article evaluates the various sensors used for water quality monitoring and focuses on the water quality index considering the multiple physical, chemical, and biological parameters. A Review of Internet of Things (IoT) research for water quality monitoring and analysis, sensors used for water quality can help remote monitoring of the water quality parameters using various IoT-based sensors that convey the assembled estimations utilizing Low-Power Wide Area Network innovations. Overall, the IoT system was 95% accurate in measuring pH, Turbidity, TDS, and Temperature, while the traditional method was only 85% accurate. Also, this study reviewed the different A.I. techniques used to assess water quality, including conventional machine learning techniques, Support Vector Machines, Deep Neural Networks, and K-nearest neighbors. Compared to traditional methods, machine learning and deep learning can significantly increase the accuracy of measurements of groundwater quality. However, various variables, such as the caliber of the training data, the water quality metrics' complexity, and the monitoring frequency, will affect the accuracy. The geographical information system (GIS.) is used for spatial data analysis and managing water resources. The quality of its data is also reviewed in the paper. Based on these analyses, the study has forecasted the future sensors, Geospatial Technology, and machine learning techniques for water quality monitoring and analysis.

Revolutionizing Network Management: The Impact of Software-Defined Networking

The transformational potential of Software- Defined Networking (SDN) in reshaping the typical network infrastructure is profound. As organizations grapple with the increasing complexity and dynamic nature of modern network requirements, the limitations of traditional networking approaches have become glaringly apparent. SDN emerges as a pivotal innovation, offering unprecedented flexibility, control, and efficiency. This article delves into how SDN revolutionizes network management by centralizing control, enhancing security, optimizing performance, and facilitating network innovation and scalability. By embracing SDN, organizations can significantly improve their network management practices, aligning them with the demands of today's digital landscape.

Enhanced HAR using Dynamic STGAT

Action recognition has seen significant advancements with the integration of spatio-temporal representations, particularly leveraging skeleton-based models and cross-modal data fusion techniques. However, existing approaches face challenges in capturing long- range dependencies within the human body skeleton and effectively balancing features from diverse modalities. To address these limitations, a novel framework, the Dynamic Spatio-Temporal Graph Attention Transformer (D-STGAT), is proposed, which seamlessly integrates the strengths of dynamic graph attention mechanisms and transformer architectures for enhanced action recognition. The framework builds upon recent innovations in graph attention networks (GAT) and transformer models. First, the Spatial-Temporal Dynamic Graph Attention Network (ST-DGAT) is introduced, extending traditional GAT by incorporating a dynamic attention mechanism to capture spatial- temporal patterns within skeleton sequences. By reordering the weighted vector operations in GAT, the approach achieves a global approximate attention function, significantly enhancing its expressivity and capturing long-distance dependencies more effectively than static attention mechanisms. Furthermore, to address the challenges of cross-modal feature representation and fusion, the spatio-temporal Cross Attention Transformer (ST-CAT) is introduced. This model efficiently integrates spatio-temporal information from both video frames and skeleton sequences by employing a combination of full spatio-temporal attention (FAttn), zigzag spatio-temporal attention (ZAttn), and binary spatio-temporal attention (BAttn) modules. Through the proper arrangement of these modules within the transformer encoder and decoder, ST-CAT learns a multi-feature representation that effectively captures the intricate spatiotemporal dynamics inherent in action recognition tasks. Experimental results on the Penn- Action, NTU-RGB+D 60, and 120 datasets showcase the efficacy of the approach, yielding promising performance improvements over previous state-of-the-art methods. In summary, the proposed D-STGAT and ST-CAT frameworks offer novel solutions for action recognition tasks by leveraging dynamic graph attention mechanisms and transformer architectures to effectively capture and fuse spatiotemporal features from diverse modalities, leading to superior performance compared to existing approaches.

Knowledge Sharing and Cumulative Innovation in Business Networks

How can we explain the success of cooperative networks of firms which share innovations, such as Silicon Valley or the Open Source community? This paper shows that if innovations are cumulative, making an invention publicly available to a network of firms may be valuable if the firm expects to benefit from future improvements made by other firms. A cooperative equilibrium where all innovations are made public is shown to exist under certain conditions. Furthermore, such an equilibrium does not rest on punishment strategies being followed after a deviation: it is optimal not to deviate regardless of another firm’s actions following a deviation. A cooperative equilibrium is more likely to arise the greater the number of firms in the network. When R&D effort is endogenous, cooperative equilibria are associated with strategic complementarities between firms’ research effort, which may lead to multiple equilibria.

Successful and sustainable innovation in a health network, the case of a hospital and community health centre in a rural region of Canada

Successful and sustainable innovation in a health network, the case of a hospital and community health centre in a rural region of Canada

A Study on Enhancing Sustainability in Circular Supply Chains through Green Innovations

Redefining product and service lifecycles with eco-friendly and sustainable processes, circular supply chains are green breakthroughs. Minimising waste, environmental effect, and resource efficiency are its goals. In a circular supply chain, green innovations can include product design that facilitates recycling or reuse, process improvements that reduce energy consumption and emissions, logistics solutions that optimise transportation for lower carbon footprints, and service innovations that promote sustainability. These green ideas improve supply chain efficiency and competitiveness while helping the environment. Businesses may reconcile economic growth and environmental stewardship by rethinking and rebuilding linear supply models into circular and sustainable ones, paving the way for a more sustainable future. This study examines how green innovations affect circular supply chains in Andhra Pradesh's durable goods industry. It analyses how product, process, logistical, and service green innovation improves supply chain efficiency. The quantitative research uses structured surveys to obtain data from 150 industry professionals and customers. Convenience sampling was used to acquire varied supply chain performance metrics responses. SPSS is used for rigorous regression analysis in the study. This method lets you study how green innovation affects circular supply chain performance. All green innovations improve supply chain efficiency, but green service innovation has the greatest benefit. This research provides strong evidence for green innovations in durable goods industry circular supply networks. The study emphasises ecologically friendly product creation, logistics, service delivery, and process management.

Knowledge Exchange Networks: Does Gender Matter?

Gender diversity in knowledge exchange networks can be a source of innovation in organizations as it provides new insights that improve the creative process. Diversity literature provides enough evidence of the importance of utilizing mixed-gender teams to gain innovative outcomes. However, in knowledge management literature, gender diversity has been scarcely addressed. Knowledge management literature happens to be dominant, and there is a need to study the impact of gender diversity in knowledge creation processes and networks. This theoretical paper links the diversity literature to knowledge management literature and provides new understandings by considering gender as an important factor in the knowledge creation process. This paper contributes to the literature by emphasizing on exploring knowledge exchange relationships through a feminist perspective. Feminist perspective is also missing in knowledge management literature; thus, by making gender an important consideration in the socialization phase of the knowledge creation process, this paper provides a diverse perspective in knowledge management literature. Furthermore, this paper presents a conceptual model of gender-diverse knowledge exchange networks that shows the inclusion of women in knowledge exchange networks enhances the effectiveness of teams. Women are being neglected when it comes to inclusion in knowledge creating networks because of in-group bias against men and the stereotypical perception of women’s lack of technical and innovative knowledge. This paper sheds light on the importance of women's inclusion in knowledge exchange networks by emphasizing the vitality of diversity in creating knowledge and innovation in collaborative networks and teams in organizations.

Implementing Enhanced Recovery After Surgery for hysterectomy in a hospital network with audit and feedback: A stepped-wedge cluster randomised trial.

To evaluate the effectiveness of implementing the Enhanced Recovery After Surgery (ERAS) protocol in patients undergoing elective hysterectomy in a network of regional hospitals, supported by an intensive audit-and-feedback (A&F) approach. A multi-centre, stepped-wedge cluster randomised trial (ClinicalTrials.gov NCT04063072). Gynaecological units in the Piemonte region, Italy. Patients undergoing elective hysterectomy, either for cancer or for benign conditions. Twenty-three units (clusters), stratified by surgical volume, were randomised into four sequences. At baseline (first 3 months), standard care was continued in all units. Subsequently, the four sequences implemented the ERAS protocol successively every 3 months, after specific training. By the end of the study, each unit had a period in which standard care was maintained (control) and a period in which the protocol, supported by feedback, was applied (experimental). Length of hospital stay (LOS), without outliers (>98th percentile). Between September 2019 and May 2021, 2086 patients were included in the main analysis with an intention-to-treat approach: 1104 (53%) in the control period and 982 (47%) in the ERAS period. Compliance with the ERAS protocol increased from 60% in the control period to 76% in the experimental period, with an adjusted absolute difference of +13.3% (95% CI 11.6% to 15.0%). LOS, moving from 3.5 to 3.2 days, did not show a significant reduction (-0.12 days; 95% CI -0.30 to 0.07 days). No difference was observed in the occurrence of complications. Implementation of the ERAS protocol for hysterectomy at the regional level, supported by an A&F approach, resulted in a substantial improvement in compliance, but without meaningful effects on LOS and complications. This study confirms the effectiveness of A&F in promoting important innovations in an entire hospital network and suggests the need of a higher compliance with the ERAS protocol to obtain valuable improvements in clinical outcomes.