Local System Research Articles

Smartening Local Energy in Europe: A Comparative Analysis of Three Cases and Their Implications for Supporting Transformative Governance Practices

Achieving global sustainability goals, while maintaining or increasing access to energy services, calls for agile and many-faceted responses. The pursuit of ‘smart local energy systems’ is one such response that involves a paradigm shift, taking the concept of an energy system away from large-scale power plants and fuel stores towards more flexible configurations to harness renewable energy flows, with highly distributed physical assets, actors, skills and controls. Smart local energy requires citizens, industry, utilities and government bodies to collaborate and learn together in order to develop effective modes of governing that meet sustainability goals and provide reliable, accessible energy services. In this article, we introduce practical implications of creating more localised energy systems with the aid of digital technologies. We then analyse three emerging European energy communities in terms of their actors, activities and alliances, to build an understanding of governance practices within and beyond the communities that are capable of fostering transformative change towards sustainability. From this, we argue that maintaining progress towards smart local energy systems requires transformative governance within and beyond initiatives. It requires local governance arrangements that are agile and responsive to new actors and activities as well as to broader external circumstances.

Reduced expression of central innate defense molecules in pancreatic biopsies from subjects with Type 1 diabetes.

Defensins play a crucial role in the innate immune system's first defense against microbial threats. However, little is known about the defensin system in the pancreas, especially in relation to Type 1 diabetes. We explore the expression of defensins in different disease stages of Type 1 diabetes and correlated obtained findings to the degree of inflammation, providing new insights into the disease and the innate immune system. Pancreases from non-diabetic human organ donors of different age groups and donors with Type 1 diabetes with different disease duration were examined. Sections from head, body and tail of the pancreas were stained for eight different defensins and for immune cells; CD3+, CD45+, CD68+ and NES+ (granulocytes). In non-diabetic adult controls the level of expression for defensins Beta-1,Alpha-1, Cathelicidin and REG3A correlated with the level of inflammation. In contrast, individuals with Type 1 diabetes exhibit a reduction or absence of several central defensins regardless of the level of inflammation in their pancreas. The expression of Cathelicidin is present in neutrophils and macrophages but not in T-cells in subjects with Type 1 diabetes. Obtained findings suggest a pancreatic dysfunction in the innate immune system and the bridging to the adaptive system in Type 1 diabetes. Further studies on the role of the local innate immune system in Type 1 diabetes is needed.

TDOA-based UWB indoor 1D localization via weighted sliding window filtering

Accurate localization of a person or object is a significant aspect in the field of industrial internet of things (IoT). Ultra-wideband (UWB) localization system, a promising technology, has been widely studied due to its high accuracy in 2D and 3D scenarios. Moreover, in narrow corridors and deep-well environments, the 1D localization is also an important application with the advantage of resource saving, however which attracts less attention. In this paper, we focus on the problem of 1D localization and propose a practical localization model with time difference of arrivals (TDOA). To solve the nonlinear 1D localization problem, we design an algorithm that integrates the dichotomous method and Newton's iterative method, which realizes an efficient solution. Meanwhile, in order to cope with the random errors in the real scenes, we introduce a sliding window algorithm to filter out the undesirable solutions. By observing the localization error map, we design a weighted sliding window algorithm to achieve further improvement of the localization accuracy. In addition, we derive the Cramér-Rao lower bound (CRLB) for 1D localization of TDOA. Simulations and practical experiments show that our proposed algorithm greatly benefits the localization accuracy of 1D TDOA and provides strong support for its practical application.

Challenges in Optimizing Nanoplatforms Used for Local and Systemic Delivery in the Oral Cavity.

In this study, we focused on innovative approaches to improve drug administration in oral pathology, especially by transmucosal and transdermal pathways. These improvements refer to the type of microneedles used (proposing needles in the saw), to the use of certain enhancers such as essential oils (which, besides the amplifier action, also have intrinsic actions on oral health), to associations of active substances with synergistic action, as well as the use of copolymeric membranes, cemented directly on the tooth. We also propose a review of the principles of release at the level of the oral mucosa and of the main release systems used in oral pathology. Controlled failure systems applicable in oral pathology include the following: fast dissolving films, mucoadhesive tablets, hydrogels, intraoral mucoadhesive films, composite wafers, and smart drugs. The novelty elements brought by this paper refer to the possibilities of optimizing the localized drug delivery system in osteoarthritis of the temporomandibular joint, neuropathic pain, oral cancer, periodontitis, and pericoronitis, as well as in maintaining oral health. We would like to mention the possibility of incorporating natural products into the controlled failure systems used in oral pathology, paying special attention to essential oils.

Experimental Investigation Of Coastal Foredune Erosion

Low lying coastal foredunes can provide an initial buffer against near-shore wave attack, while higher elevated dunes can form a sheltering barrier against flooding of an adjacent hinterland (Temmerman et al. 2013). The Eiderstedt peninsula in Northern Germany harbors a dune system spanning roughly 8 km in north-south direction, consisting of an up to 2 km wide beach, lined by an elongated foredune of varying dune dimensions (Mehrtens et al. 2023). An established secondary dune chain closes a 1.2 km gap in the local dike system (see Fig. 1a and 1b). This work presents novel large-scale experiments on the influence of a foredune in front of a secondary dune during storm surge situations. Working hypothesis is that the foredune acts as a sediment buffer, damping incoming waves and reducing overall erosion on the main dune.

Understanding caregiver burden and quality of life in Kerala’s primary palliative care program: a mixed methods study from caregivers and providers’ perspectives

BackgroundFamily caregivers are vital for long-term care for persons with serious health-related suffering in Kerala. Long-term caregiving and ageing may become burdensome and detrimental to patients and caregivers. We compared the caregiver burden and quality-of-life of ageing caregivers with younger caregivers. We also explored the palliative care nurses’ perceptions of the family caregivers’ issues.MethodsWe did a mixed method study focusing on two groups: (i) three in-depth interviews and a cross-sectional survey among 221 caregivers of palliative care patients in five randomly selected panchayats (most peripheral tier of three-tier local self-government system in India concerned with governance of a village or small town) of Kollam district, Kerala, as part of development and validation of the Achutha Menon Centre Caregiver Burden Inventory; (ii) five in-depth interviews with purposively selected primary palliative care nurses as part of a study on local governments and palliative care. We used a structured interview schedule to collect cross-sectional data on sociodemographic and caregiving-related characteristics, caregiver burden, and health-related quality of life using the EuroQol EQ5D5L and interview guidelines on caregiver issues tailored based on participant type for qualitative interviews.ResultsOlder caregivers comprised 28.1% of the sample and had significantly poorer health and quality-of-life attributes. More senior caregivers experiencing caregiver burden had the lowest mean scores of 0.877 (Standard deviation (SD 0.066, 95% confidence intervals (CI) 0.854–0.899) followed by younger caregivers with high burden (0.926, SD 0.090, 95% CI 0.907–0.945), older caregivers with low burden (0.935, SD 0.058, 95% CI 0.912–0.958) and younger caregivers with low burden (0.980, SD 0.041, 95% CI 0.970–0.990). Caregivers faced physical, psychological, social, and financial issues, leading to a caregiver burden. The relationships between the palliative care nurses and family caregivers were complex, and nurses perceived caregiver burden, but there were no specific interventions to address this.ConclusionIn our study from Kollam, Kerala, three out of ten caregivers of palliative care patients were 60 years of age or older. They had significantly lower health-related quality of life, particularly if they perceived caregiver burden. Despite being recognized by palliative care nurses, caregiver issues were not systematically addressed. Further research and suitable interventions must be developed to target such problems in the palliative care programme in Kerala.

Tensor network representation and entanglement spreading in many-body localized systems: a novel approach

Tensor network representation and entanglement spreading in many-body localized systems: a novel approach

NLOS Identification and Mitigation for Time-based Indoor Localization Systems: Survey and Future Research Directions

One hurdle to accurate indoor localization using time-based networks is the presence of Non-Line-Of-Sight (NLOS) and multipath signals, affecting the accuracy of ranging in indoor environments. NLOS identification and mitigation have been studied over the years and applied to different time-based networks, with most works considering NLOS links with WiFi and UWB channels. In this paper, we discuss the effects and challenges of NLOS conditions on indoor localization and present current state-of-the-art approaches to NLOS identification and mitigation in literature. We survey these approaches and classify them under different categories together with their merits and demerits. We further categorize approaches to tackle NLOS effects into single and hybrid measurement-based approaches in this work. Lessons learnt from the survey with future directions are also presented in this paper.

Intelligent Localization System for Rescue during the Accident in Construction Work

A significant development in assuring the security and well-being of laborers in the construction sector is the Intelligent Localization System for Construction Accident Rescue employing RSSI for Person Identification and Health Monitoring System. During emergencies or accidents, this system is intended to precisely find and identify individuals within a building site by utilizing the power of Radio Signal Strength Indicator (RSSI) technology. The system precisely locates each worker by evaluating the strength of the RSSI signal, facilitating quick and effective rescue efforts. Additionally, the system has health monitoring features that offer workers real-time insights into their well-being. This entails keeping an eye on critical indicators like body temperature and heart rate in order to recognize crisis signals early and seek medical attention quickly if necessary. The combination of individuals.

Sustainable Water Management and Indigenous Socio-Technical Heritage in Marrakech, Morocco

Sustainable Water Management and Indigenous Socio-Technical Heritage in Marrakech, Morocco

A Study on Scan Trajectory Based Effective Localization Method Using Mobile Anchor in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a collection of different nodes working together in a networked fashion in an ordered way. For many applications, sensor location in wireless sensor networks (WSN) is crucial. It is expensive to equip each sensor node with a GPS receiver. Numerous methods, including range-based and range-free methods, have been put forth in the past to determine the locations of sensor nodes that are randomly distributed. The majority of them make use of unique nodes known as anchor nodes, which are thought to be aware of their own positions. These anchor nodes supply the information that other sensors use to calculate their locations. Massive numbers of sensor nodes are used in wireless sensor networks (WSNs) to gather data about the immediate environment. However, this data is useless until the precise location of the data collection points is known. Sensor node localization in wireless sensor networks (WSNs) is important for a number of applications. The current research study develops a path planning technique to apply mobile anchors to the localization systems that are currently in use. The two mobile anchors supported by the effective trajectories, or effective SCAN, is the focus of the proposed research project. To localise the sensor node and transmit data, this method involves setting the mobile anchors to move in the opposite direction. The advantages of this dual movable anchor are demonstrated by the research study in terms of decreased localization time, localization error, and other factors.

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

Abstract Autonomous navigation has been a long-standing research topic, and researchers have worked on many challenging problems in indoor and outdoor environments. One application area of navigation solutions is material handling in industrial environments. With Industry 4.0, the simple problem in traditional factories has evolved into the use of autonomous mobile robots within flexible production islands in a self-decision-making structure. Two main stages of such a navigation system are safe transportation of the vehicle from one point to another and reaching destinations at industrial standards. The main concern in the former is roughly determining the vehicle’s pose to follow the route, while the latter aims to reach the target with high accuracy and precision. Often, it may not be possible or require extra effort to satisfy requirements with a single localization method. Therefore, a multi-stage localization approach is proposed in this study. Particle filter-based large-scale localization approaches are utilized during the vehicle’s movement from one point to another, while scan-matching-based methods are used in the docking stage. The localization system enables the appropriate approach based on the vehicle’s status and task through a decision-making mechanism. The decision-making mechanism uses a similarity metric obtained through the correntropy criterion to decide when and how to switch from large-scale localization to precise localization. The feasibility and performance of the developed method are corroborated through field tests. These evaluations demonstrate that the proposed method accomplishes tasks with sub-centimeter and sub-degree accuracy and precision without affecting the operation of the navigation algorithms in real time.

Output feedback control for hypersonic flight vehicles via distributed high-gain observer

During the hypersonic flight process of rigid hypersonic flight vehicles (HFVs), the flight-path angle and angle of attack are usually small and sensitive to flight condition variation, the accurate measurements are difficult, subsequently the formulation of states observers is an urgent issue. A distributed high-gain observer is proposed to reconstruct the admissible system state dynamics based on limited measured output. First, a distributed observer is proposed for the cascade strict-feedback system to ensure the global convergence of the state estimation errors between the estimated state in each local observer and system states, for the prescribed system initial values. Second, the distributed high-gain observer-based nonlinear control is investigated on the longitudinal dynamics of HFVs with partial measurable states. The rigid body system dynamic is divided into the altitude subsystem and the velocity subsystem. In the altitude subsystem, the proposed distributed high-gain observer is formulated to exact estimate the flight-path angle and angle of attack, and then the back-stepping design is proposed for constructing the controllers. The nonlinear dynamic inversion controller is introduced to accomplish the velocity tracking. Finally, simulation examples are served to verify that the proposed output feedback control possesses the superior properties of high tracking performance, fast convergence of state estimation error and easy-implementation.

Integrating interventions supported by development assistance for health into local health system: evidence from a China–World Bank–UK rural health system strengthening project (1998–2007)

IntroductionTo empirically investigate sustainability of development assistance for health (DAH), we conducted a retrospective case study on the Basic Health Services Project (BHSP) for rural health system strengthening, supported by...

Governing the reterritorialization of agricultural activities: An assessment of food planning policies in France

This paper examines how food planning policies address the reterritorialization of agricultural activities (RAA), a crucial component of local food systems. While food planning as an integrated local policy promoting local food systems has gained increasing research attention, most of the research has been urban-centric, resulting in a limited understanding of how it includes RAA. This paper fills this research gap by assessing 39 food planning projects in France, where the state defines food planning by national law and supports local projects. Through document analysis and semi-structured interviews, RAA-associated food planning policy goals, instruments, and agri-food professional actors’ involvement are identified. The empirical findings highlight the central role of RAA in French food planning projects; it either serves as the primary motivation behind these projects or evolves into a substantial component as the projects develop. A wide range of policy instruments with local innovation to support RAA are identified, with more frequent use of informational and economic than regulatory and coercive instruments, and more focus on economic development than on ecological transition. Minority and majority farmers’ organizations are involved in food planning processes with varying degrees of engagement across territories, reflecting local governance strategies. The analytical methods in this study may contribute to future research to better comprehend RAA in local food policymaking. The systematic overview of RAA-associated food planning measures also offers insights to policymakers in other contexts regarding food policy design. The paper concludes by arguing that food planning extends beyond urban food supply; it also presents an opportunity to leverage RAA for rural revitalization and transformation, in terms of production models, rural-urban links, and local governance.

A generalized Benders decomposition approach for the optimal design of a local multi-energy system

A local multi-energy system (LMES) is a decentralized energy system producing energy under multiple forms to satisfy the energy demand of a set of buildings located in its neighborhood. We study the problem of optimally designing an LMES over a multi-phase horizon. This problem is formulated as a large-size mixed-integer linear program with a block-decomposable structure involving mixed-integer sub-problems. We propose a new way to adapt a recently published framework for generalized Benders decomposition to our problem. This is done by exploiting the fact that the constraint matrix appearing in front of the first-stage variables in the coupling constraints is non-negative. The obtained generalized Benders decomposition algorithm relies on the use of a new type of non-convex Benders cuts involving indicator functions. We first prove that, under the assumption that all first-stage decision variables are integer and bounded, the finite and optimal convergence of our algorithm is guaranteed in theory. We then investigate how to obtain a good numerical performance in practice. Finally, we report the results of a computational study carried out on a real-life case study. These results show that the proposed algorithm clearly outperforms both a mathematical programming solver directly solving the problem as a whole and a state-of-the art hierarchical decomposition algorithm.

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.

Highly robust and accurate multi-sensor fusion localization system for complex and challenging scenarios

Highly robust and accurate localization using on-board sensors in the absence of GNSS is a key issue for long-term autonomous navigation of vehicles. However, the limited sensing capabilities of a single sensor cannot resist the effects of feature degradation and high-speed motion in complex and challenging scenarios, which results in the fragile of existing LiDAR-based and visual-based localization algorithms. To solve these problems, we propose a highly robust and accurate localization system by fusing information from multi-sensors. Firstly, we propose a global localization method that leverages deep learning models for processing visual information to provide an accurate initial position for vehicles, which solves the problem of global localization of vehicles in the absence of GNSS. Then, we propose a highly robust and accurate LiDAR-Vision-IMU localization method based on pose graph optimization, which integrates LiDAR-based poses and visual-inertial-based poses. The proposed method detects sensor degradation in real time to dynamically adjust the fusion weights, which solves the problem of low localization performance of a single sensor. Finally, we conduct experiments in complex and challenging scenarios, such as urban, tunnels, and unstructured mountains. Experimental results show that our method can achieve a global localization error of less than 0.5 m in complex scenes. In unstructured mountainous scenarios of length 21.1 km, the root mean square errors (RMSE) of translation and rotation are 0.67 m and 0.048rad, respectively. Compared to most existing algorithms, our method achieves the most robust and accurate localization performance.

Few-shot learning for skin lesion classification: A prototypical networks approach

Prototypical networks (PN) have emerged as one of multiple effective approaches for few-shot learning (FSL), even in medical image classification. This study focuses on implementing a PN for skin lesion classification to assess its performance, generalizability, and robustness when applied across 11 dermoscopic image domains. Unlike conventional FSL scenarios, where the performance is evaluated for unseen classes in the test set, our analysis extends this to evaluate PNs on a complete hold-out dataset with the same classes from a different domain. Differences in a patient’s age, lesion localization, or image acquisition systems variations mimic real-world cross-domain conditions in a clinic. Given the scarcity of medical datasets, this assessment is crucial for potentially translating such systems into real-world clinical settings to support physicians with the diagnosis. Our primary focus is two-fold: investigating whether a PN performs on par with a baseline classifier, even using only a limited number of reference samples from the hold-out test set (in-domain) and whether a PN can generalize to the same classes of unseen domains (cross-domain). Our analysis uncovers that a PN can perform on par with the baseline classifier in an in-domain setting, even with only a few support samples. However, in cross-domain scenarios, a PN exhibits improved performance only on specific domains, while others demonstrate similar or even decreased performance when confronted with a smaller number of images. Our findings contribute to comprehending potential opportunities and limitations of FSL in dermatological practice.

An experimental investigation on the effect of lighting colour of localized radiant panel thermal comfort system

This study investigated the effect of different correlated colour temperature (CCT) lighting on perceived thermal comfort in a simulated domestic environment with radiant heating. A prototype with adjustable CCT lighting (4000K neutral, 1800K warm, 6500K cold) alongside electric radiant panels was installed in an environmental chamber set to 18°C. The investigation involved sixty participants who each underwent 30-minute exposure sessions to one of the lighting scenarios in a blinded setup, during which they filled out thermal comfort questionnaires at predetermined intervals. Overall, the findings revealed satisfactory comfort levels, with average global comfort votes of “good” and 82.32% thermal acceptability, across scenarios. The radiant heating prototype successfully maintained thermal comfort in the less conditioned space. Looking at the different lighting scenarios, the “warm” lighting initially provided the highest thermal comfort, aligning with the hue-heat hypothesis, with global comfort votes of 1.13, compared to 0.96 for “neutral” and 0.78 for “cold” after the first 10 minutes. However, differences became less pronounced over time. Contrary to hypotheses, the “cold” lighting scenario did not reduce comfort compared to “neutral”. Moreover, Participants indicated greater thermal satisfaction under “warm” lighting. Although changes in lighting CCT did not drastically alter thermal perceptions, they did have a subtle influence on comfort, particularly during the initial stages of the experiment. This finding suggests the need for further research into how visual and thermal comfort interactions can be optimized.