Efficient System Research Articles

From the Belly of the Beast, the Monsters We Can Create from the Zombies and the Hyenas amongst us!

The purpose of this article is to address what has always been discussed as the three main elements of the social world. That is, the structure, culture and agency in the management, leadership and governance systems in university education. Such elements, as analytically distinct, arise from the assumptions that their roles, respectively, are values free exercise, asocial, apolitical and, therefore, neutral. Citing a particular case that has been a subject of deep debates and extensive conversations in previous ECMLG 2021, ECMLG 2022 and as part one of ECMLG 2024, this paper subjects, as an immanent critique, the claims that the management of organisations’ resources, which generally make for the interface between senior management and the formal governance of the organisation, with all things being equal, can make for forms of efficiency and effectiveness in organisational and management systems. With the title of the paper, From the belly of the beast, the monsters we can create from the zombies and the hyenas amongst us, the paper thus adopts a disruptive and transgressive stance about what can always appear as the structural and cultural norms. As such, the paper will argue, by drawing from the post-modernist theories, for example, the social realist theory as anchored on the critical realist philosophy, that it takes the properties and powers both of the knowledge and knower structures for the ideal of university education as the public good, as the case will illustrate, to be a realist reality. By means of a social realist methodology, research data was drawn from organizational records, which was then subjected to critical discourse analysis for identifying the structural mechanisms that could generate the crisis events and processes, and further to how the latter could be experienced and observed over time. Such a methodology therefore foregrounds the value of morphogenesis/morphostasis in a social realist oriented project. Based on what ought to be the theory-methodology-practical program chain, the value of the article is, in itself, and for generalised contexts, thus related to what ought to lie behind the shine of leadership practices, of the management systems, and of the governance structures. That refers to the case of what can be the three headed monstrous situations, and further to the value of the practices as the nexus of research and development, of policy and implementation, and of strategy and leadership.

Deep learning-based spectrum sensing and modulation categorization for efficient data transmission in cognitive radio

Abstract One prominent feature of cognitive radio (CR) involves spectrum sensing (SS), which allows licensed primary users to remain unaffected by secondary users’ ability to discover and exploit unoccupied frequency bands. Spectrum sensing enhances the use of spectrum in CR devices, increasing their adaptability and efficiency in wireless communication systems. The rise of wireless equipment and the advent of IoT technologies compound this need for flexibility. Over time, the fixed allocation of frequencies has led to inefficiencies and underutilization as bandwidth needs increase. Deep learning and artificial intelligence have improved spectrum sensing by increasing detection probability of primary users’ presence under noisy environments, enabling cognitive radio systems to respond intelligently to fluctuations in RF environments. This article is concerned with deep learning techniques for spectrum sensing and modulation categorization with CBRT structure, which combines convolutional neural networks (CNNs), bidirectional recurrent neural networks (BRNNs), and transformer networks (TNs) to improve spectrum sensing. CNNs are responsible for performing spectrum feature extraction; BRNNs are used to capture temporal dependencies; and TNs are good at long range dependencies. Better performance for this model is aimed by integrating the three architectures described. In the proposed work, six digital modulation schemes were considered, for spectrum sensing. The sensing of spectrum in this model is performed using the RadioML2016.10B open-source dataset and performance metrics like the Jaccard Index (JI), Fowlkes’s Mallows Index, and F1 Score. Modulation classification has been performed using MIGOU-MOD open-source dataset. The proposed model exhibits good detection probability and low sensing error, unlike other methods at lower SNR.

Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants

One of the main goals of the International Energy Agency (IEA) is to manage and utilize clean energy to achieve net zero emissions by 2050. Hydropower plants can significantly contribute to this goal as they are vital components of the global energy infrastructure, providing a clean, safe, and sustainable power source. Accordingly, there is great interest in developing methods to prevent errors and anomalies and ensure full operational availability. With modern hydropower plants equipped with sensors that capture extensive data, machine learning algorithms utilizing these data to detect and predict anomalies have gained research attention. This paper demonstrates that deep learning algorithms are particularly powerful in predicting time series. Three well-known deep learning networks are examined and compared to previous approaches, followed by the introduction of a new, innovative hybrid network. Using real-world data from two hydropower plants, the hybrid model outperforms individual deep learning models by achieving more accurate fault detection, reducing false positives, offering early fault prediction, and identifying faults several weeks before occurrence. These results showcase the hybrid network’s potential to enhance maintenance planning, reduce downtime, and improve operational efficiency in energy systems.

Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach

Outdated, oversized variable speed pump drives (VSDPs) in industry lead to sub-optimal energy efficiency and considerable energy losses. This paper proposes methods to develop 2D efficiency maps for motors, converters, and pumps using polynomial surface fitting, which enables efficiency evaluation in a wide operating range. The method was applied to an oversized VSDP in an industrial chilled water supply system, comparing the original system with five alternative VSDP combinations with high-efficiency motors and pumps. The five VSDP variants demonstrated average energy savings of around 30%, with the synchronous reluctance motor (SRM) configurations outperforming the induction motor (IM) configurations by up to 7 percentage points, particularly at low loads. The high-efficiency SRM-based 252-IE5 variant delivered the best overall energy performance, highlighting the benefits of optimised system sizing and motor selection for energy savings. The proposed method can be used in both industrial and residential applications and offers great advantages in process systems that require variable flow and pressure of water or other fluids during operation, such as HVAC, water supply and wastewater treatment, district heating, etc. The development of a VSDP drive with efficient energy optimisation is an interdisciplinary problem of mechanical and electrical engineering, and without the interaction of engineers from both fields the result will not be optimal. We try to present our method so that it can be a reliable tool for mechanical, electrical, and other engineers or researchers to assist them in finding possible energy savings, performing energy audits, and selecting the most suitable components when modernising existing or developing new systems.

Innovative Strategies for Thermal Energy Optimization and Renewable Energy Integration in Net-Zero-Energy Buildings: A Comprehensive Review

The thermal performance and energy efficiency of buildings are critical factors in achieving sustainable energy systems as energy needs for heating and cooling are expected to represent more than 50% of global final energy consumption. This study analyzes conventional renewable energy systems for heating and cooling in buildings, focusing on strategies for developing net-zero-energy buildings. This review covers the integration of renewable energy, the use of intelligent energy management systems, and the optimization of thermal processes. It also compares various systems based on their advantages and limitations and analyzes emerging trends in the thermal management of buildings in different climate zones. The synthesis of recent literature highlights practical recommendations for achieving high thermal performance in buildings, including the importance of selecting appropriate energy systems based on local climatic conditions, optimizing system efficiency, and taking advantage of new materials and advanced technologies. This review aims to contribute to promoting sustainable construction practices with the integration of renewable energy sources and improving the energy efficiency of buildings.

Energy management strategy for fuel cell hybrid tractor considering demand power frequency characteristic compensation.

The application of fuel cell tractors is expected to drive technological upgrades and sustainable development in agricultural machinery. However, fuel cell hybrid systems face issues such as slow dynamic response, low efficiency, and short lifespan. This paper proposes an energy management strategy based on signal reconstruction methods, including Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to achieve optimal energy utilization and system efficiency based on frequency response characteristics. First, we collected data on the tractor's traction force and operating speed, and calculated the required traction power using a full-machine dynamics model built in MATLAB software. We conducted frequency response characteristic analysis of the fuel cell hybrid system based on EMD and VMD, establishing an energy management controller to sequentially meet the average power demand of the fuel cell under plowing load operations, the instantaneous acceleration power demand of the power battery, and the real-time compensation power demand of the supercapacitor. The results show that the EMD strategy exhibits good stability, while the VMD strategy performs better in terms of hydrogen consumption. Under the VMD strategy, the hybrid system achieves a maximum output efficiency of 55.0% with a total hydrogen consumption of 750g. Compared to the EMD strategy, the maximum efficiency of the system increases by 27.31%, and hydrogen consumption decreases by 3.49%. This study provides a new theoretical foundation and technological route for the application of fuel cell hybrid systems in the field of agricultural machinery.

Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities

Optimizing urban water distribution systems is essential for reducing economic losses, minimizing water wastage, and addressing resource access gaps, particularly in drought-prone regions impacted by climate change. We apply advanced artificial intelligence (AI) techniques and the Internet of Things (IoT) to optimize water networks in Spain using simulation. By employing EPANET for hydraulic modeling and a linear regression-based algorithm for optimization, we achieved up to 96.62% system efficiency with a mean absolute error of 0.049. Our approach demonstrates the potential to conserve up to 648,000 L of water daily at high-demand nodes, contributing to substantial resource savings across urban water networks. We propose a global architecture utilizing Low Power Wide Area Network and Low Earth Orbit solutions for widespread deployment. This study underscores the potential of AI in water network optimization and suggests future research avenues for implementing the proposed architecture in real urban water systems.

Parking Assistant with the Simulation of Application

Smart Parking can be used for any city. It aims to improve the efficiency and sustainability of urban transportation systems by using technology to manage parking systems. This article provides an overview of the different components of smart parking systems. The increasing vehicular population in urban areas has led to significant challenges related to parking management, exacerbating traffic congestion, environmental impact, and user dissatisfaction. The app facilitates real- time information on available parking spaces, reservation capabilities, and payment integration, thereby enhancing user experience and operational efficiency. We summarize the methodologies applied in developing the app, which include sensor integration for dynamic parking space monitoring and mobile application development for user interaction. The article concludes with a consideration of the future of smart parking and its role in the overall development of smart cities. In conclusion, the findings serve as a basis for future advancements in intelligent parking systems, ensuring improved service delivery for urban commuters.

Analysis of Water Dynamics at the Effluent Treatment Station of Cooperoque: Evaluation of Precipitation, Evaporation, and Infiltration of Effluent from a Milk Refrigeration Station

Purpose: The study aims to analyze water dynamics at the Cooperoque Effluent Treatment Station, focusing on the interactions between precipitation, evaporation, and effluent infiltration to enhance water resource management. Theoretical reference: The research references various studies and methodologies related to the quantification of evaporation from lakes and reservoirs, particularly in semi-arid regions, utilizing the Jensen-Haise Method for evaporation estimation. The research aligns with previous studies, such as Mallmann (2014), on water infiltration in soil management, emphasizing the importance of understanding water dynamics for sustainable resource management. Method: The study employs the Jensen-Haise Method to calculate evaporation rates, covering the period from January 2023 to October 2024. It examines the effects of precipitation, evaporation, and effluent infiltration on tank levels. Results and conclusion: The results reveal that tank levels are influenced by precipitation, evaporation, and effluent infiltration, with more significant declines during dry months and smaller reductions during periods of high precipitation. Evaporation rates, calculated using the Jensen-Haise Method, are higher in warmer months, contributing to lower tank levels, while these rates decrease in colder months. A notable anomaly occurred in May 2024 when effluent infiltration exceeded the volume of generated effluents and rainfall due to the activation of an irrigation system. Key findings indicate that lake levels drop more during dry months, with a significant negative water balance observed in May 2024 due to excessive infiltration during heavy rainfall. The study concludes that adaptive water management strategies are necessary to address climatic variations and improve the efficiency of the effluent treatment system. Implications of research: The research provides valuable insights for developing adaptive water management strategies to address climatic variations and improve the efficiency of effluent treatment systems. Continuous monitoring is emphasized as crucial for sustainable water resource management. Originality/value: The study highlights the complex interactions between precipitation, evaporation, and effluent dynamics, offering a foundation for improved water management practices and contributing to the preservation of regional water resources. The study contributes to the understanding of water dynamics at effluent treatment stations, offering practical insights for managing water resources effectively. It highlights the importance of adaptive strategies and continuous monitoring to maintain pond levels and optimize the treatment process.

Highly Active Oxygen Evolution Reaction of NiMoO4 Sub-1nm Nanowires Boosts Luminol Electrochemiluminescence.

In recent years, there has been an increasing research focus on the luminol-H2O electrochemiluminescence (ECL) system due to its ability to address the instability and toxicity of H2O2, which are common issues associated with the conventional luminol-H2O2 ECL system. To enhance the ECL efficiency of the luminol-H2O system, researchers have developed electrocatalytic materials with exceptional oxygen evolution reaction (OER) properties to facilitate water electrolysis into O2 to produce reactive oxygen species (ROS) and act as co-reactant promoters. However, most of these materials are characterized by their nanoscale or microscale dimensions, resulting in relatively large sizes and low specific surface areas, which hinder the application of the luminol-H2O system. To address this challenge, nickel molybdate sub-1nm nanowires (NiMoO4 S1 NWs) with a large specific surface area is synthesized that can offer many active sites to enhance the performance of the OER to boost the ECL of luminol. This study demonstrates that the large amount of ROS generated by the OER of NiMoO4 S1 NWs play a crucial role in enhancing the ECL intensity of luminol. Finally, a NiMoO4 S1 NWs-based ECL biosensor for the highly sensitive detection of the nucleocapsid proteins of SARS-CoV-2 is successfully constructed.

Health Monitoring and Prediction Using Non-invasive Sensors in IoT Environment

Introduction: IoT's blend with non-invasive sensors has made health state prediction possible and constant real-time monitoring transformed healthcare. Using photoplethysmography (PPG) and electrocardiograms (ECG), this device evaluates important parameters including heart rate, blood oxygen saturation, and blood pressure. As healthcare systems face increasing demands, this technology provides a patient-centered and efficient method for long-term health tracking—needed for early intervention and management of chronic diseases. Objectives: The main goal is to evaluate using time-series data from PPG and ECG sensors the efficiency of an IoT-enabled health monitoring system coupled with machine learning models in predicting health conditions including arrhythmias and hypoxia. Methods: This system detects events based on temporal pattern recognition by use of Long Short-Term Memory (LSTM) networks and Random Forest classifiers. Noise reduction is preprocessed and wearable device data is labeled for training of machine learning models. The design combines wearable sensors, a smartphone app, and cloud-based analysis for continuous monitoring and real-time input. Results: Experimental data shows that the LSTM model attained a 95% accuracy while the Random Forest classifier attained a 92.5% accuracy. Both models displayed remarkable accuracy and recall even while the LSTM model excels in identifying anomalies in time-series data and the Random Forest classifier skillfully detects health events depending on certain criteria. Conclusions: This study demonstrates the capability of IoT-based health monitoring systems to enhance patient outcomes through continuous, non-invasive monitoring and predictive analytics. Despite its potential, difficulties persist, including data security, sensor reliability, and energy efficiency, necessitating additional research for broad use and clinical integration.

Development of surface-texturized black silicon through metal-assisted chemical etching and its application in the thermophotovoltaic field: a review and recommendation

Abstract The Shockley-Queisser limit poses a significant challenge in solar technology research, limiting the theoretical efficiency to around 30%. Thermophotovoltaic (TPV) systems have emerged as a solution by incorporating a thermal absorber in traditional solar cell setups to achieve total efficiency beyond the limits. The efficiency of the overall system heavily depends on the performance and quality of the thermal absorber, which absorbs photons from the heat source and transfers them to the TPV cell. However, complex and expensive fabrication processes have hindered widespread adoption of TPV technology. The well-established metal assisted chemical etching (MACE) method could be the best choice to mitigate these as it is a cost-effective, scalable, and mass-production-friendly process, which is widely used for surface texturization, creating nanostructures like nanopores, pyramids, and nanowires. MACE
technique is also suitable for producing highly efficient silicon-based thermal absorbers with over 90% absorption rate, which can contribute to increased total conversion efficiency. However, it does not come without challenges such as maintaining control over the etch rate in order to achieve uniformity. This paper comprehensively reviews the utilization of MACE for fabricating silicon-based thermal absorbers in TPV systems with the range of effective wavelengths of 600 – 2000 nm which corresponds to the energy level of 0.55 – 1.85 eV. The advantages and challenges of MACE, along with characterization techniques, are extensively discussed. By providing valuable insights, this paper aims to support researchers interested in advancing TPV technology.

Research on Hydraulic Characteristics of Water Leakage Phenomenon of Waterproof Hammer Air Valve in Water Supply Pressure Pipeline Based on Sustainable Utilization of Water Resources in Irrigation Areas

To investigate the causes of water leakage in the waterproof hammer air valve and its impact on sustainable water resource management, the DN100 waterproof hammer air valve was taken as the research object. By using the overset grid solution method of ANSYS Fluent 2021 R1 software, the flow field simulation of the waterproof hammer air valve was carried out. The transient action during the ascent phase of the key structural component floating ball, and the velocity and pressure distribution of the flow field inside the air valve are analyzed. The results showed that by giving different inlet flow velocities, the normal flow velocity range for the floating ball to float up was below 35 m/s and above 50 m/s. When the inlet flow velocity was between 35 m/s and 50 m/s, the growth rate of the pressure difference above and below the floating ball increased from 1.48% to 5.79% and then decreased to 0.4%. The floating ball would not be able to float up due to excessive outlet pressure above, which would cause the DN100 waterproof hammer air valve to leak water and fail to provide water hammer protection. When the inlet flow rate is 5 m/s, the velocity and pressure inside the valve body increase with time during the upward movement of the floating ball inside the waterproof hammer air valve and tend to stabilize at 400 ms. Through the generated pressure and velocity cloud maps, it can be observed that the location of maximum pressure is at the bottom of the buoy, directly below the floating ball, and at the narrow channels on both sides of the outflow domain. The location of the maximum velocity is at the small inlet of the bottom of the buoy. When the inlet speed of the valve is constant, a large amount of water flow is blocked by the floating ball, reducing the flow velocity and forming partial backflow below the floating ball, with an obvious vortex phenomenon. A small portion of the water flow passes through the air valve at a high velocity from both ends of the channel, and the water flow below the floating ball is in an extremely unstable state under the impact of high-speed water flow, resulting in a large gradient of water flow velocity passing through the valve. The research results not only help to improve the operational efficiency of water resource management systems but also reduce unnecessary water resource waste, thereby supporting the goal of sustainable water resource management.

Solar-based nighttime electric power generator based on radiative cooling

This study focuses on developing and investigating a hybrid nighttime electric power generator that integrates photovoltaic (PV) cells with thermoelectric generators (TEG) to provide continuous power generation during both day and night. During the day, PV cells efficiently capture solar energy and convert it into electricity. At night, radiative cooling lowers the surface temperature of the PV panels, creating a temperature differential between the ambient air and the cooled panels. This temperature difference drives the TEG modules, which generate electricity based on the Seebeck effect. The experimental results reveal that the size and configuration of TEG modules significantly affect power output. A single 3 cm×3 cm TEG produced up to 0.9 mW of power with a 55°C temperature difference, while a larger 4 cm×4 cm TEG generated up to 3.8 mW. Furthermore, connecting two 4 cm×4 cm TEGs in series resulted in a peak output of 7.7 mW, nearly double that of the single TEG setup. This hybrid system demonstrates potential for moderate nighttime power generation, suitable for small household applications such as LED lights, laptops, phone chargers, and wireless routers. The ability to generate power both during the day and night enhances the efficiency and reliability of renewable energy systems, offering a continuous, sustainable power supply that mitigates the limitations of conventional solar panels. The results underscore the feasibility of scaling up TEG modules to achieve higher power outputs, making this system a promising solution for addressing nighttime energy demands in off-grid and low-power applications.

The two coin sides of bacterial extracellular membrane nanovesicles: atherosclerosis trigger or remedy.

Among the numerous driving forces that cause the atherosclerotic cardiovascular disease (ASCVD), pathogenic bacterial extracellular membrane nanovesicles (BEMNs) containing toxins and virulence factors appear to be the key trigger of inflammation and atherogenesis, the major processes involved in the pathogenesis of ASCVD. Since BEMNs are the carriers of nanosized biomolecules to distant sites, they are now being considered as a novel drug delivery system. Nowadays, many therapeutic strategies are used to treat ASCVD. However, the conventional anti-atherosclerotic therapies are not effective enough. This primarily due to the inefficiency of non-targeted drug delivery systems to tissue affected areas, which, in turn, leads to numerous side effects, as well as faulty pharmacokinetics. In this regard, nanomedicine methods using nanoparticles (NPs) as targeted drug delivery vehicles proved to be extremely useful. Bioengineered BEMNs equipped with disease-specific ligand moieties and loaded with corresponding drugs represent a promising tool in nanomedicine, which can be used as a novel drug delivery system for a successful therapy of ASCVD. In this review, we outline the involvement of pathogenic BEMNs in the triggering of ASCVD, the conventional therapeutic strategies for the treatment of ASCVD, and the recent trends in nanomedicine using BEMNs and NPs as a vehicle for targeted drug delivery.

CHARACTERISTICS OF THE DEVELOPMENT OF THE GAS SUPPLY SYSTEM IN THE REPUBLIC OF ARMENIA

Gas distribution and transportation networks make up the gas supply system. Providing natural gas to the public and the economy is the primary objective of the gas delivery system. Every nation's gas supply network is strategically vital since the cost of natural gas directly impacts the goods and services that are produced and rendered there. It is true that developed nations are looking for innovative technology for natural gas extraction because it is a non-renewable resource. Founded during the Soviet era and throughout multiple phases of development, the Republic of Armenia's gas supply system is a component of the worldwide network. The Public Services Regulatory Commission and the Armenian government, through the Ministry of Territorial Administration and Infrastructure, oversee the country's gas supply industry. The article discusses the Republic of Armenia's domestic natural gas market's supply and sales, its background and present state, investment initiatives, and features that increase the system's operational efficiency.

Design Study for a Superconducting High-Power Fan Drive for a Long-Range Aircraft

New aerodynamic aircraft concepts enable the storage of volumetric liquid hydrogen (LH2). Additionally, the low temperatures of LH2 enable technologies such as the superconductivity of electrical fan drives and power distribution components. An increased power density of the onboard wiring harness and the electrical machine can be expected. The highest system efficiency and the smallest fuel and tank weight will be achieved with a highly efficient energy conversion by the fuel cell from LH2 to electrical energy. This publication shows a comprehensive study for cryogenic fan drives based on experimental-driven tape superconductor investigations, mission profile-based considerations, design analyses of superconducting electrical machines, and studies of the cooling concepts. A cryogenic system cannot be considered without a feasible cooling concept. Here, an approach with a safe He-based cooling system is proposed, using the LH2 flow to the fuel cell as a heat sink for the losses in the electrical system.

Data-Driven Decision-Making in Transportation Management Using AI

The rapid growth of data generation and the advancements in artificial intelligence (AI) have opened up new opportunities for data-driven decision-making in transportation management. This article presents a comprehensive review of the applications of AI in transportation, focusing on machine learning techniques, optimization algorithms, and predictive analytics. The article proposes a novel AI-based decision-making framework that integrates data preprocessing, AI modeling, and performance evaluation to address complex transportation challenges. A case study on traffic congestion management is conducted to demonstrate the effectiveness of the proposed framework in reducing travel times and improving system efficiency compared to traditional methods. The results highlight the potential of AI in optimizing transportation operations and supporting informed decision-making. However, the article also discusses the limitations and challenges of implementing AI-based decision-making in transportation, such as data quality, privacy concerns, and computational requirements. Future research directions, including transfer learning, integration with emerging technologies, and explainable AI, are identified to facilitate the widespread adoption of AI-based decision-making in transportation management. The findings of this article contribute to the growing body of knowledge on data-driven intelligent transportation systems and provide valuable insights for researchers, practitioners, and policymakers in the field.

Clinical care ratios for, and tasks undertaken by, allied health professionals: A scoping review of the literature.

With the increasing demand on the global health system and the presence of persistent healthcare disparities, it becomes imperative to utilize the full potential of the healthcare workforce, including allied health professionals (AHPs). To date, no review has mapped the literature on clinical care ratios (CCRs) for AHPs. This information is vital in guiding future work-force planning to address healthcare issues such as understaffed and at capacity hospitals. This scoping review mapped available evidence to offer a comprehensive insight into current AHP CCRs, enabling evidence-based decision-making that ensures optimal care and enhanced system efficiency. The scoping review was undertaken using the 'Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews' (PRISMA-ScR). The search was conducted using Ovid (MEDLINE, Embase, Emcare, PsycINFO), CINAHL, the Cochrane Library, Scopus, PEDro, OTseeker and grey literature. Two reviewers independently screened records, following which data extraction was completed for the included studies. Descriptive synthesis was used to summarize the data. Of the 7670 records identified, 18 studies were included in this review. Whilst there was variability between AHPs on direct clinical time spent with patients, considerable amount of time was also spent on activities beyond direct clinical care. To offset this, literature highlights opportunities for allied health assistants and students to undertake tasks which would free-up AHPs to undertake more direct and higher-level tasks. However, limitations of findings (variability in measurement, lack of coverage of some professions) limits generalisability. While the literature highlights considerable variability in CCRs among different AHP disciplines, it does appear nearly a nearly a third of time spent by AHPs are on tasks that are beyond direct clinical care. Future research could explore new workforce models which would free-up AHPs to undertake more direct and higher-level tasks.

Real-Time Data Governance and Compliance in Cloud-Native Robotics Systems

This study investigates the frameworks and challenges of real-time data governance and compliance in cloud-native robotics systems, focusing on data integrity, cloud security, regulatory adherence, and cybersecurity risks. Using extensive datasets from the Amazon AWS Open Data Registry, the EU GDPR Enforcement Tracker, and Kaggle’s IoT dataset, the analysis explores cloud-native systems' data accuracy, security, and governance. Data were extracted through a standardized process: performance metrics, including latency and error rates, were gathered from Amazon AWS to assess system efficiency, GDPR violation records were analyzed from the EU Enforcement Tracker to understand compliance trends, and data volume and governance metrics from Kaggle’s IoT dataset were correlated to identify governance challenges. Together, these data sources provide comprehensive insights into how cloud-native systems can be optimized for real-time operations. The study highlights the cloud security benefits and governance advantages inherent to cloud-native frameworks, such as real-time monitoring, automated threat detection, and data encryption, which collectively reduce unauthorized access risks while supporting operational efficiency. Findings indicate high data accuracy (0.51% error rate) and low latency (mean of 48.96 ms) across systems; however, processing time variability (standard deviation of 28.61 ms) signals a need for further optimization in time-sensitive environments. The regression analysis of GDPR violations reveals a substantial penalty increase of €53,789.41 per violation, emphasizing the financial risks of non-compliance. Correlation analysis (r = 0.083 for data volume and governance failures) suggests that external cybersecurity threats have a greater impact on governance than internal metrics, underscoring the importance of adaptive governance frameworks that support both data integrity and regulatory compliance in cloud-native robotics systems.