Search Results (21)

Smart feedlots are increasingly adopting Precision Livestock Farming technologies to enable continuous, individual-animal monitoring and more proactive management in intensive beef production systems. This narrative review synthesises evidence from approximately 350 academic publications, of which 117 are formally cited, complemented by industry deployments and the authors’ experience in smart feedlot system development. We cover enabling digital infrastructure (power, sensing networks, wireless connectivity, and gateways), animal identification and sensing (RFID, automated weighing, wearables, and pen-side sensors), machine vision (RGB, thermal, and multispectral imaging from fixed and mobile platforms), and AI-based analytics and decision support for health, welfare, performance, and environmental management. Across the literature, key components have progressed beyond proof-of-concept toward operation under commercial constraints. Reported outcomes include reduced reliance on routine pen-rider observation and yard handling, earlier triage of emerging morbidity risk and behavioural change, and more standardised welfare auditing. Vision-based methods are repeatedly validated against trained human scorers in both on-farm and abattoir contexts, while automated weighing and image-based liveweight estimation support higher-frequency growth monitoring with low single-digit percentage error in representative studies. Precision feeding and targeted supplementation are associated with improved feed utilisation and reduced resource wastage, although effectiveness and adoption vary across animal classes and production stages. We identify priorities for robust, scalable deployment: resilient communications in harsh environments, appropriate edge–cloud partitioning under intermittent connectivity, and interoperable multi-sensor data fusion to deliver trustworthy alerts and actionable insights. Persistent barriers remain cost, durability, maintenance burden, integration and interoperability, data governance, and workforce capability. Full article

Digital transformation in commercial banks is a critical enabler of modern financial development. While technological advancement and resource allocation are key drivers, managerial attributes also play a decisive role in shaping transformation trajectories. Managerial myopia—often arising from short-term performance pressures, evolving regulatory expectations, and cyclical macroeconomic conditions—warrants particular attention. This study examines how managerial myopia constrains banks’ digital transformation by analyzing its direct impact, underlying behavioral mechanisms, and contingent boundary conditions. Using panel data from 55 Chinese listed commercial banks from 2010 to 2021, we construct a text-based measure of managerial myopia through linguistic analysis of annual reports and employ fixed-effects models for estimation. The results show that a short-term managerial orientation significantly impedes digital transformation, primarily by reducing banks’ propensity for proactive risk-taking. However, this inhibitory effect weakens when managers anticipate longer tenures, management teams exhibit greater diversity in overseas experience and functional expertise, or the average educational level is higher. Moreover, the adverse effects are less pronounced in larger banks and those with stronger corporate governance. Increased external scrutiny and intensified market competition further mitigate this negative influence. These findings offer actionable insights for banking stakeholders aiming to strengthen governance, extend managerial time horizons, and foster an innovation-oriented culture conducive to sustained digital advancement. Full article

This study draws on socio-technical transition theory to examine how multi-actor dynamics among producers, consumers, and the media within an experimental niche—Korea’s regulatory sandbox—shape policy responsiveness and the regulatory speed of governmental responses to emerging technologies, thereby influencing socio-technical transitions. We construct a longitudinal dataset of 2136 sandbox approvals between 2019 and 2025 and 1374 cases in which related legal or administrative adjustments have been completed. Changes in actor couplings before and after sandbox approval are first assessed using Pearson correlation analysis, while temporal lead–lag relationships are identified via vector autoregression (VAR) and Granger causality tests. Building on these dynamic analyses, the study subsequently investigates the determinants of regulatory response speed using ordered logistic regression, incorporating government policy orientation (progressive vs. conservative) as a moderating variable. The results show, first, that the strong producer–consumer coupling observed prior to sandbox approval weakens afterwards, whereas the consumer–media linkage becomes substantially stronger. Second, the time-series analysis of technologies within the regulatory sandbox reveals a typical technology-push pattern and a self-reinforcing feedback loop. Specifically, producer activity initiates the signal sequence, preceding consumer reactions; subsequently, media coverage significantly drives consumer engagement, and the resulting increase in consumer attention, in turn, stimulates further media coverage. Third, in the ordered logit model, media activity accelerates legal and regulatory reform, whereas consumer activity acts as a delaying factor, with producer activity showing no significant direct effect. Finally, government policy orientation systematically moderates the magnitude and direction of these effects. Overall, the study proposes an actor-centered mechanism in which learning generated in the sandbox is externalized through consumer–media channels and translated into regulatory pacing. Based on these findings, we derive practical implications for firms and regulators regarding proactive media engagement, transparent use of evidence, institutionalized channels for consumer input, and robust feedback standards that support sustainable commercialization of emerging technologies. Full article

Global strategies for ensuring access to clean and safe drinking water are increasingly shifting toward a preventive approach based on risk assessment and risk management of the entire water supply and production chain. However, many developing countries, including South Africa, still lag in adopting advanced real-time water monitoring technologies aligned with Water 4.0 principles. To transition to these innovative technologies, it is essential to understand current gaps in water monitoring and the challenges to adopting these systems. This systemic review aims to assess current monitoring practices, identify implementation challenges, and explore strategic pathways for adopting smart water infrastructure in eThekwini Municipality, South Africa. This review identifies critical gaps in eThekwini’s water quality monitoring, including limited real-time surveillance, fragmented data systems, budgetary constraints, cybersecurity vulnerabilities, uneven rural–urban access, slow commercialization of academic innovations, policy misalignment, and insufficient technical capacity. It emphasizes the potential of real-time monitoring systems, automation, and artificial intelligence (AI) to address existing water quality monitoring challenges. Additionally, special focus is given to the role of electronic sensors in measuring physicochemical parameters like turbidity, pH, and dissolved oxygen as cost-effective indicators for detecting microbial contaminants. Implementing Water 4.0 strategies provides eThekwini and similar municipalities an opportunity to develop a more proactive, resilient, and sustainable approach to water quality management. Full article

The responsibility for risk assessment and user safety in forested and recreational areas lies with the property owner. This study shows that unmanned aerial vehicles (UAVs), combined with remote sensing and GIS analysis, effectively support the identification of high-risk trees, particularly those with reduced structural stability. UAV-based surveys successfully detect 78% of dead or declining trees identified during ground inspections, while significantly reducing labor and enabling large-area assessments within a short timeframe. The study covered an area of 6.69 ha with 51 reference trees assessed on the ground. Although the multispectral camera also recorded the red-edge band, it was not included in the present analysis. Compared to traditional ground-based surveys, the UAV-based approach reduced fieldwork time by approx. 20–30% and labor costs by approx. 15–20%. Orthomosaics generated from images captured by commercial multispectral drones (e.g., DJI Mavic 3 Multispectral) provide essential information on tree condition, especially mortality indicators. UAV data collection is fast and relatively low-cost but requires equipment capable of capturing high-resolution imagery in specific spectral bands, particularly near-infrared (NIR). The findings suggest that UAV-based monitoring can enhance the efficiency of large-scale inspections. However, ground-based verification remains necessary in high-traffic areas where safety is critical. Integrating UAV technologies with GIS supports the development of risk management strategies aligned with the principles of precision forestry, enabling sustainable, more proactive and efficient monitoring of tree-related hazards. Full article

The effective integration of energy storage systems is paramount for the widespread deployment of renewable energy technologies. Selection of a specific storage system is typically dictated by the primary challenge it aims to mitigate, such as intermittency, grid stability, or power quality. The optimization of overall system efficiency and longevity is increasingly achieved through hybrid storage systems that integrate supercapacitors into their designs. This research introduces a novel circuital equivalent for a commercial supercapacitor, optimized for precise simulations within the frequency range of power electronics applications. A key distinction of this circuital equivalent lies in its rigorous foundation: its comprehensive characterization across a broad frequency spectrum, specifically from 0.01 Hz to 300 kHz, employing a commercial frequency response analyzer. This precise circuital representation offers substantial utility in simulation, analysis, and design of high-frequency circuits, particularly for switched-power converter design and control. It enables the anticipation of undesirable phenomena, such as significant voltage ripple and operational instability. This predictive capability is crucial for experimental preparation, facilitating the proactive integration of necessary filters and protective measures within sensing circuits, thereby underscoring its value prior to physical implementation. In addition, the developed circuital equivalent exhibits broad compatibility, allowing seamless implementation within commercial circuit simulators. Finally, the proposed methodology was illustrated with a commercial supercapacitor, but it can be applied to other supercapacitor types or manufacturers. Full article

This paper presents a thorough examination of the technical requirements for a new Distribution System Operation (DSO) scheme in Colombia. This study contextualizes these requirements to consider local particularities by looking at national and international standards and models relevant to DSO. This study aims to align the technical requirements to the DSO technologies that offer the greatest advantages (real-time data readings to automate commercial cycle, suspension and reconnection of the service, improving reliability and quality of power supply, and environmental benefits) and the fewest implementation obstacles. Today, an electrical operator can become more proactive by integrating technologies such as advanced metering infrastructure (AMI), distributed energy resources (DER), microgrids, and advanced distribution automation (ADA). This study will provide a structured framework for the implementation of a cutting-edge DSO technology in order to assist Colombia’s energy sector in becoming more dynamic and efficient with a smarter and more active electricity distribution system. Full article

Background/Objectives: Quality by Design (QbD) has revolutionized pharmaceutical development by transitioning from reactive quality testing to proactive, science-driven methodologies. Rooted in ICH Q8–Q11 guidelines, QbD emphasizes defining Critical Quality Attributes (CQAs), establishing design spaces, and integrating risk management to enhance product robustness and regulatory flexibility. This review critically examines QbD’s theoretical frameworks, implementation workflows, and industrial applications, aiming to bridge academic research and commercial practices while addressing emerging challenges in biologics, advanced therapies, and personalized medicine. Methods: The review synthesizes regulatory guidelines, case studies, and multidisciplinary tools, including Design of Experiments (DoE), Failure Mode Effects Analysis (FMEA), Process Analytical Technology (PAT), and multivariate modeling. It evaluates QbD workflows—from Quality Target Product Profile (QTPP) definition to control strategies—and explores advanced technologies like AI-driven predictive modeling, digital twins, and continuous manufacturing. Results: QbD implementation reduces batch failures by 40%, optimizes dissolution profiles, and enhances process robustness through real-time monitoring (PAT) and adaptive control. However, technical barriers, such as nonlinear parameter interactions in complex systems, and regulatory disparities between agencies hinder broader adoption. Conclusions: QbD significantly advances pharmaceutical quality and efficiency, yet requires harmonized regulatory standards, lifecycle validation protocols, and cultural shifts toward interdisciplinary collaboration. Emerging trends, including AI-integrated design space exploration and 3D-printed personalized medicines, promise to address scalability and patient-centric needs. By fostering innovation and compliance, QbD remains pivotal in achieving sustainable, patient-focused drug development. Full article

Free-range egg production plays a key role in the global food system, and current market trends suggest that consumer demand for free-range eggs will continue to rise. Free-range egg production is susceptible to a wide range of factors, including climatic conditions, management practices, and disease presence. These factors can cause variability in the laying rate of a flock over time, leading to fluctuations in egg production. The main purpose of this study was to investigate the risk of short-term free-range egg production losses using data derived from a combination of sensing technologies and management activities. Production and environmental data were collected from a commercial farm comprising seven flocks of laying hens. The variables studied included laying rate, feed intake, water intake, solar radiation, humidity, precipitation, and indoor/outdoor temperature. These were processed into a set of aggregate features calculated across a 14-day moving window. Generalized estimating equations were used to analyze the association between the derived production and environmental features and the probability of a short-term drop in egg production, expressed through deviations in the laying rate on the day immediately following the data window. Odds ratios were used to express the relative risk of a production drop by comparing the features for window periods where production drops occur to the window periods where production drops did not occur. The results demonstrated that a range of data features based on the laying rate, feed intake, water intake, and indoor/outdoor temperatures all had significant associations with the odds of a production drop. Key findings from the study show that an increase in feed intake and laying rate measured across the 14-day data window were correlated with a lower risk of a sudden drop in egg production. Conversely, a low mean indoor temperature (x < 16.1 °C group), measured through environmental sensing data, was correlated with a higher risk of a sudden drop in egg production. This study quantifies the link between data features derived from production and environmental monitoring and egg production issues, thereby providing useful insights on the most important data items captured through day-to-day monitoring, which can be used for proactive management. Further research should be carried out to investigate how technologies such as machine learning and analytics platforms can be applied for the task of forecasting production interruptions using the data features explored in this study. Full article

In the rapidly evolving landscape of telecommunications, the integration of commercial 5G solutions and the rise of edge computing have reshaped service delivery, emphasizing the customization of requirements through network slices. However, the heterogeneity of devices and technologies in 5G and beyond networks poses significant challenges, particularly in terms of security management. Addressing this complexity, our work adopts the Zero-touch network and Service Management (ZSM) reference architecture to enable end-to-end automation of security and service management in Beyond 5G networks. This paper introduces the ZSM-based framework, which harnesses software-defined networking, network function virtualization, end-to-end slicing, and orchestration paradigms to autonomously enforce and preserve security service level agreements (SSLAs) across multiple domains that make up a 5G network. The framework autonomously manages end-to-end security slices through intent-driven closed loops at various logical levels, ensuring compliance with ETSI end-to-end network slice management standards for 5G communication services. The paper elaborates with an SSLA-triggered use case comprising two phases: proactive, wherein the framework deploys and configures an end-to-end security slice tailored to the security service level agreement specifications, and reactive, where machine learning-trained security mechanisms autonomously detect and mitigate novel beyond 5G attacks exploiting open-sourced 5G core threat vectors. Finally, the results of the implementation and validation are presented, demonstrating the practical application of this research. Interestingly, these research results have been integrated into the ETSI ZSM Proof of Concept #6: ’Security SLA Assurance in 5G Network Slices’, highlighting the relevance and impact of the study in the real world. Full article

Traditionally, freight wagon technology has lacked digitalization and advanced monitoring capabilities. This article presents recent advancements in freight wagon digitalization, covering the system’s definition, development, and field tests on a commercial line in Sweden. A number of components and systems were installed on board on the freight wagon, leading to the intelligent freight wagon. The digitalization includes the integration of sensors for different functions such as train composition, train integrity, asset monitoring and continuous wagon positioning. Communication capabilities enable data exchange between components, securely stored and transferred to a remote server for access and visualization. Three digitalized freight wagons operated on the Nässjo–Falköping line, equipped with strategically placed monitoring sensors to collect valuable data on wagon performance and railway infrastructure. The field tests showcase the system’s potential for detecting faults and anomalies, signifying a significant advancement in freight wagon technology, and contributing to an improvement in freight wagon digitalization and monitoring. The gathered insights demonstrate the system’s effectiveness, setting the stage for a comprehensive monitoring solution for railway infrastructures. These advancements promise real-time analysis, anomaly detection, and proactive maintenance, fostering improved efficiency and safety in the domain of freight transportation, while contributing to the enhancement of freight wagon digitalization and supervision. Full article

The increasing penetration of large-scale Renewable Energy Sources (RESs) has raised several challenges for power grid operation. Power management solutions supporting the integration of RESs, such as those based on energy storage technologies, are generally costly. Alternatively, promoting a more proactive role of the Distribution System Operator (DSO) to successfully manage RESs’ uncertainty, and take advantage of their flexible resources for the provision of ancillary services, can avoid installing expensive devices in the network and reduce costs. In this line, improved coordination between Transmission System Operators (TSOs) and DSOs is highly desirable. In this paper, the feasibility of solving different aspects of the integration of RESs through an improved TSO/DSO coordination is evaluated. In particular, a Systematic Literature Review (SLR) is conducted to study the most relevant TSO/DSO coordination approaches, exclusively focused on integrating distributed RESs, currently available in the literature. Their main operational, managerial, economic, and computational challenges, advantages, and disadvantages are discussed in detail to identify the most promising research trends and the most concerning research gaps to pave the way for future research toward developing a solid TSO/DSO coordination mechanism for integrating RESs efficiently. The main results of the SLR show a clear trend in implementing decentralized TSO/DSO coordination models since they provide efficient facilitation of RESs’ services, while reducing computational burden and communication complexity and, consequently, reducing operative costs. In addition, while different aspects of the TSO/DSO coordination implementation, such as reactive power and voltage regulation, operational cost minimization, operational planning, and congestion management, have been thoroughly addressed in the literature, further research is needed regarding data exchange mechanisms and RESs’ uncertainty modeling and prediction. In this line, the development of standardized communication solutions, based on the Common Grid Model Exchange Standard (CGMES) of the International Electrotechnical Commission (IEC), has shown promising interoperability results, whereas the use of learning-based approaches to predict RESs’ uncertain behavior and distribution networks’ responses, using only historical data, which relieves the need for access to commercially sensitive and proprietary network data, has also shown itself to be a promising research direction. Full article

Public technology transfer and technology commercialization are attracting worldwide attention, but the research on the commercialization of technology transferred from government-funded research institutes (GRIs) to small- and medium-sized enterprises (SMEs) is scarce. This study aims to identify and prioritize the factors contributing to the commercialization success of technologies transferred from GRIs to SMEs and to quantitatively present their importance. We proposed novel concepts of SMEs’ and GRIs’ technology commercialization proactiveness (SME TCP and GRI TCP) as two main success factors. We conducted hierarchical logistic regression analysis and decision tree analysis for 301 SME cases that adopted technology between 2013 and 2016 from Electronics and Telecommunications Research Institute (ETRI), a representative GRI in Korea. As a result, SME TCP was measured based on technology transfer expenditure (TTE), and frequency (TTF) was confirmed to be the most important factor. In particular, the success rate was higher when TTE exceeded 151 M KRW, or TTF was three or more. In addition, the success rate varied greatly depending on GRI TCP, namely the degree and the type of GRI researchers’ support. These findings can be used as primary data when establishing policies to promote cooperation between SMEs and other GRIs and provide practical implications for both technology providers and adopters. Full article

Over the last decades, we have witnessed the gradual commercialization of the Earth orbit. The exponential development of private space activities makes this distant natural field, with the overcoming of technological difficulties, more and more hospitable to free initiative and entrepreneurship. However, the orbital space is considered global commons. Through the imaginary case method, we intend to ponder on possible ways to legally regulate the exploitation of the orbital space, namely the application of Pigouvian taxes, on the sustainability of the orbital environment, through ethical considerations originating from the application of the Lockean proviso. Although they are designed to cover the damage caused by that particular polluting activity, which is difficult to estimate and, in our case, almost impossible to quantify in the long run, the Pigouvian taxes are the result of a proactive logic. The tension between civilization and nature turns the world outside the Earth into a wilderness destined for humanization, another area of exercise of the liberal self. Non-legal reasons for the sustainability of the orbital environment may arise from observing the Lockean principle of fair ownership. Between the prohibition of an unreasonable destruction of nature’s goods and the equitable access to extra-terrestrial resources, the human desire for appropriation updates the proviso destined for the colonization of America in the twenty-first century. Given that there are currently no plans to clean the technological waste in orbit, adopting the conservation of the orbital environment as an ethical principle could help to formulate a more environmentally responsible liberalism, as part of a long-term agenda of exploitation in the vicinity of our planet. Full article

Although renewable energy investments in developing and emerging economies play a crucial role in accelerating the clean energy transition, investments remain limited. Building on previous research, this study takes a unique approach by analyzing determinants of clean energy investments from investors from one country, the United States, which represents the largest single source of investments. Based on panel data sourced from Bloomberg New Energy Finance (BNEF)’s Climatescope, we analyzed renewable energy investments by investors from the United States between 2008 and 2019. The analysis included four factors (i.e., economic, socio-environmental, political, and proactivity) and covered 61 emerging/developing countries. Our results suggest that the most significant factor that determines renewable energy investment by investors from the United States is commercial ties between the investing and recipient country. Our findings also demonstrate the importance of a strong legal system and clean energy promotion mechanisms, such as feed-in tariffs, in recipient countries. When breaking down investment flows, the effects of different economic factors may vary, depending on whether the renewable technology is solar or wind, which further highlights the importance of understanding determinants of renewable energy investments. Full article