Local System Research Articles

Pediatric anesthesia outreach in low and middle-income countries: Models, motives, and moral misalignments.

A lack of anesthesia and surgical capacity leaves approximately 1.7 billion children per annum without access to surgical and anesthetic care. Over the past 50 years, the predominant strategy to address this lack of access has been to provide surgical capacity primarily from high-income countries (HICs) to low and middle-income countries (LMICs) in the form of short-term surgical missions. More recently, the international medical community has recognized the need to build sustainable surgical capacity in resource-constrained settings. This article reviews three models of surgical aid: the vertical model (short-term surgical missions); the horizontal model (system-wide capacity building); and the diagonal model, which is a hybrid of the first two. At their core, medical aid interventions exist on a spectrum ranging from providing surgical capacity to building surgical capacity. The skills, attitudes, and behaviors that drive success in providing medical capacity are fundamentally different from those that drive success in building medical capacity. The root cause of this difference is a shift in the moral duty of the visiting physician from a duty solely to the patient in front of them (based on the primacy of the doctor-patient relationship) to include a duty to the local physicians and the local medical system, and by extension to the next 10 000 patients in need of care. Failure to address the conflicts engendered by this fundamental moral shift risks undermining capacity-building efforts in all models of medical aid.

Experimental study on the punching failure model of soft red rock subgrade by BPT-BVM methods and it’s assessment of the load bearing capacity

The recommended bearing capacity of medium weathering mudstone foundation is less than the capacity of the rock structure to withstand loads in Southwest China. A comprehensive failure characterization of medium weathering mudstone in Chengdu has been performed including bearing plate test (BPT), binocular vision measurement (BVM) test, uniaxial compressive strength test, trial trench test of shallow rock surface and 3D imaging in this paper. Failure behavior of rock has been modeled with 3D imaging algorithm that utilizes Zhang’s calibration method in BVM system combination with trial trench test of shallow rock surface. The bearing capacity of medium weathering mudstone foundation were extracted from uniaxial experiments and BPT-BVM test by fitting relevant material properties to the data. The results revealed that: Bearing capacity of medium weathering mudstone of layered isotropic in Chengdu is undervalued. Specifically, the characteristic load carrying value is in the range 1500–2500 kP, that is 50% higher than in the local standard system. Failure process is different from Hoek–Brown Failure Criterion, presenting a wave peak transfer phenomenon of the increment displacement into the distance. Thus, it can be reduced to that of punching failures for thin bedded structures of Moudstone foundations. Compressive strength of soft rock proves to be main factor limiting the bearing capacity, a clear correlation between the uniaxial compressive strength reduction coefficient and the bearing capacity has been used to establish, leading to the proposal of a load bearing capacity prediction model.

Building an inclusive ecosystem for minority and women entrepreneurs: A case study of Columbus

We all agree that it is essential to build an inclusive entrepreneurship ecosystem for all kinds of people, particularly minority and women entrepreneurs, as the past studies are clear to demonstrate underperformance of those demographic groups. At the same time, we are only beginning to understand what kind of systemic barriers exist for women or minority entrepreneurs at the regional system level. Furthermore, it requires a different set of knowledge about how we can practically create an inclusive ecosystem. This paper applies the concept of equity planning for the context of economic development. This paper examines a case study of the City of Columbus, Ohio, in 2018–2019 in which the City Government actively promoted to build an inclusive ecosystem for women and minority entrepreneurs. We induce five essential steps that we could learn from this inclusionary practice which provide tangible implications for other city governments, nonprofit organizations, foundations, and other stakeholders of local entrepreneurship systems.

SLM-SLAM: A Visual SLAM System Based on Segmented Large-Scale Model in Dynamic Scenes and Zero-shot Conditions

Abstract In the real world, the presence of various dynamic objects can impact the localization accuracy of the majority of typical Visual Simultaneous Localization and Mapping (VSLAM) systems. Simultaneously, many dynamic VSLAM systems based on neural networks require pre-training for specific application scenarios. We present SLM-SLAM, the first VSLAM system that implements zero-shot processing of dynamic scenes. It achieves the capability to handle various dynamic objects without the necessity for pre-training, enabling straightforward adaptation to different application scenarios. Firstly, we designed an open-world semantic segmentation module based on a segmented large-scale model to acquire semantic information in the scene. Subsequently, we devised a label-based strategy for selecting feature points, jointly optimizing poses with the weighted labels provided by both semantic and geometric information. Finally, we refined the keyframe selection strategy of ORB-SLAM3 to prevent matching errors caused by an insufficient number of remaining static feature points in the scene. We conducted experiments on the TUM dataset, the KITTI dataset, and real world scenarios. The results indicate that in dynamic scenes, our SLM-SLAM significantly improves localization accuracy compared to ORB-SLAM3, and its performance is comparable to state-of-the-art dynamic VSLAM systems.

Information propagation of focus wave mode localized waves in anisotropic turbulent seawater

The evolution of the information transfer capability of an optical system for underwater focused wave mode localized wave (FWMLW) in anisotropic weakly turbulent absorbing seawater is studied. By developing the probability distribution function as well as the detection probability of the vortex modes carried by the FWMLW and the average bit error rate of the FWMLW underwater system, the information capacity of the FWMLW system with a pointing error is modeled. Through a numerical analysis of the effects of turbulent seawater and optical system parameters on the built light intensity, the detection probability, and the information capacity models, we find that the FWMLW system has an optimal delay time determined by the spectrum bandwidth when the spectrum bandwidth is greater than 1. The information capacity of the FWMLW system is higher than that of the X localized wave system under the same turbulent seawater channel condition, and FWMLW is a better optical signal source for vortex mode division multiplexing underwater systems than a Bessel–Gaussian beam.

A Vision/Inertial Navigation/Global Navigation Satellite Integrated System for Relative and Absolute Localization in Land Vehicles

This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual–inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual–inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.

Driving innovation in energy and telecommunications: next-generation energy storage and 5G technology for enhanced connectivity and energy solutions

Driving innovation in energy and telecommunications involves leveraging next-generation energy storage and 5G technology to enhance connectivity and energy solutions. This review explores the intersection of these two domains, highlighting the importance of advancements in energy storage and 5G technology for a sustainable and connected future. Energy storage is crucial for balancing the supply and demand of electricity in modern power systems. Traditional energy storage methods, such as batteries and pumped hydro, have limitations in terms of scalability, efficiency, and cost-effectiveness. Next-generation energy storage technologies, including advanced batteries, hydrogen storage, and thermal storage, offer promising solutions to overcome these limitations. These technologies enable efficient energy storage at scale, facilitating the integration of renewable energy sources like solar and wind into the grid. By storing excess energy generated during periods of low demand, next-generation energy storage systems ensure a reliable and stable power supply, reducing the reliance on fossil fuels and lowering greenhouse gas emissions. In parallel, the evolution of telecommunications technology, particularly the advent of 5G networks, is revolutionizing connectivity and communication. 5G technology offers significantly higher data transfer speeds, lower latency, and increased network capacity compared to its predecessors. These capabilities are essential for supporting emerging technologies such as the Internet of Things (IoT), autonomous vehicles, and smart grids. With 5G-enabled IoT devices, utilities can monitor energy consumption in real-time, optimize grid operations, and detect and respond to faults more efficiently. Moreover, 5G connectivity enhances the efficiency and reliability of energy storage systems by enabling seamless communication between distributed energy resources and grid operators. The convergence of next-generation energy storage and 5G technology presents numerous opportunities for driving innovation in both energy and telecommunications sectors. One of the key areas of innovation is the development of smart energy storage systems equipped with 5G connectivity. These systems can autonomously adjust their operation based on grid conditions, weather forecasts, and energy demand patterns, optimizing energy storage and distribution in real-time. Furthermore, advanced energy management algorithms leveraging artificial intelligence (AI) and machine learning (ML) algorithms can optimize energy usage and storage, further improving the efficiency and reliability of the grid. Another area of innovation lies in the integration of renewable energy resources with 5G-enabled microgrids. Microgrids are localized energy systems that can operate independently or in conjunction with the main grid. By combining renewable energy sources with energy storage and 5G-enabled communication, microgrids can provide reliable, clean, and resilient power to remote or urban areas. These microgrids can also facilitate peer-to-peer energy trading, allowing consumers to buy and sell excess energy within their communities, fostering energy independence and sustainability. Furthermore, advancements in battery technology, such as solid-state batteries and flow batteries, are enhancing the performance and reliability of energy storage systems. Solid-state batteries offer higher energy density, faster charging rates, and improved safety compared to conventional lithium-ion batteries. Flow batteries, on the other hand, provide scalability and long-duration storage capabilities, making them suitable for grid-scale applications. Integrating these advanced battery technologies with 5G-enabled monitoring and control systems enhances the overall resilience and flexibility of the energy infrastructure. In addition to technological advancements, driving innovation in energy and telecommunications requires collaboration among various stakeholders, including policymakers, regulators, industry players, and research institutions. Policies and regulations should incentivize the deployment of next-generation energy storage and 5G infrastructure, promote interoperability standards, and ensure data privacy and security. Public-private partnerships can facilitate the investment and deployment of innovative solutions, while research and development initiatives can spur further technological advancements. Driving innovation in energy and telecommunications through next-generation energy storage and 5G technology is essential for building a sustainable, connected, and resilient future. By leveraging advanced energy storage systems, smart grids, and 5G-enabled communication networks, we can optimize energy usage, reduce carbon emissions, and enhance the reliability and efficiency of our energy infrastructure. Collaboration and investment across various sectors are key to unlocking the full potential of these transformative technologies and achieving a brighter, more sustainable future for generations to come. Keywords: Innovation, Energy, Telecommunications, Next-Generation, 5G technology, Enhanced connectivity.

Unlocking carbon finance: Empowering energy communities for mutual benefit

This study investigates the potential of utilizing carbon credits to finance innovative local energy communities in Europe, addressing a significant gap in academic literature. Despite the growing interest in voluntary carbon credits as a mechanism for offsetting carbon emissions, there is limited evidence connecting local energy systems to voluntary carbon markets. The study explores whether carbon credits could serve as a practical tool for funding the development of energy communities, contributing to sustainable energy transitions and climate mitigation strategies. The research employs a comprehensive methodology to assess the quality of carbon credit projects and examines ten innovative energy community projects in Europe. Key findings reveal there is potential for using carbon credits to finance energy community initiatives. However, challenges persist, particularly regarding financing barriers and the volatile nature of carbon markets. The study underscores the importance of policy support in establishing clear standards for voluntary markets and promoting the accessibility of carbon offsets. Furthermore, it suggests potential solutions such as pre-financing agreements and the utilization of carbon market derivatives to enhance project viability. Overall, the study offers insights into the intersection between voluntary carbon markets and local energy initiatives, providing a foundation for further research and policy development in the field of local sustainable development and climate change mitigation.

Dose Optimization of Intravenous Indocyanine Green for Malignant Lung Tumor Localization

Background: Intravenously administered indocyanine green (ICG) accumulates in lung tumors, facilitating their detection via a fluorescence spectrum measurement. This method aids in identifying tumor locations that are invisible to the naked eye. We aim to determine the optimal ICG dose and administration method for accurate tumor identification during lung resection surgeries, utilizing a novel ICG fluorescence spectroscopy system for precise tumor localization. Materials and Methods: ICG should be dissolved in the provided solution or distilled water and administered intravenously approximately 24 h before surgery, beginning with an initial dose of 0.5 mg/kg. If the tumor detection rate is insufficient, the dose may be gradually increased to a maximum of 5.0 mg/kg to determine the optimal dosage for effective tumor detection. This fluorescence spectroscopy during surgery may reveal additional lesions that remain undetected in preoperative assessments. The primary endpoint includes the correct diagnostic rate of tumor localization. The secondary endpoints include the measurement of the intraoperative ICG fluorescence spectral intensity in lung tumors, the assessment of the operability and safety of intraperitoneal ICG administrations, the measurement of the ICG fluorescence spectral intensity in surgical specimens, the comparison of the spectral intensity in lung tissues during collapse and expansion, the correlation between ICG camera images and fluorescence spectral intensity, and the comparison of fluorescence analysis results with histopathological findings. The trial has been registered in the jRCT Clinical Trials Registry under the code jRCTs011230037. Results and Conclusions: This trial aims to establish an effective methodology for localizing and diagnosing malignant lung tumors, thereby potentially improving surgical outcomes and refining treatment protocols.

The Impact of COVID-19 on Consumer Perceptions of Local Food Market Channels

The COVID-19 pandemic had a profound impact on food systems. Despite disrupting conventional markets—such as grocery stores—farmers that focused on short supply chains found new market opportunities. As consumers increasingly became concerned with the safety of in-person shopping, some turned to purchasing directly from farmers and markets that carried products marketed as local, organic, and/or sustainable. With these changes in mind, I ask how consumer perceptions of local food systems (LFSs) changed in the two years after COVID-19. If consumers are more aware of local markets, farmers might reorient production to focus on local supply chains and sustainable production methods. To evaluate consumer perceptions, I use survey responses from residents in five communities in the US South in 2019 and 2022. Using a treatment effects model with propensity score matching, I show that residents became more aware of most local market channels, such as specialty retail stores and Community Supported Agriculture (CSA). Some residents were also more likely to have a positive perception of markets that provide fresh vegetables to low-income residents. Finally, residents wanted more investment into markets that make fresh, local products more available, such as community-owned groceries and CSA.

Translating government policy into practice: How new UK medical schools enact widening participation.

Increasing the diversity of medical students, or widening participation (WP), is critical for social justice and healthcare delivery, and many governments are setting policies to encourage WP. However, establishing policy is only the first step in an educational change process: we also need to know "how" policy is enacted or how medical schools interpret and put into practice WP policy. Addressing this gap, the aim of this study was to examine policy enactment in six new UK medical schools with an explicit WP mandate. This qualitative study, underpinned by social constructivism, used semi-structured interviews to explore the experiences of key actors (6 Deans and 14 Admissions staff) of putting policy into practice when setting up a new medical school. Data coding and analysis were initially inductive, using thematic analysis. We then applied Ball's theory of policy enactment to organise the data into four contextual dimensions of 'situation', 'professional', 'material' and 'external'. On the surface, there were many similarities across the six schools in terms of the four dimensions. However, how these dimensions interacted illuminated tensions and differences. For example, some schools found themselves increasingly subjected to local and extra-local rule systems, including pressure to follow host university norms and hosts struggling to accept that medical schools are heavily regulated. There were also tensions between the new medical schools and the medical education "establishment", including lack of power and being judged by overly narrow outcomes. Different contexts seem to influence the enactment of WP to medicine in different places, even in the same country, even in medical schools established at the same time. That policy enactment is a complex, non-linear process of enactment is important to acknowledge: context is critical. Our findings will inform future policies and practices that aim to increase WP in medicine.

OPT-In; Optimized Patient Treatment Outcomes in Plaque Psoriasis: A 3-Year State-Transition Treatment-Sequencing Model in the Italian Setting.

There are several treatment options for plaque psoriasis (PsO), but uncertainty remains around the optimal sequencing of treatments. The aims of this study were to investigate how adopting a best-treatment-first treatment sequence impacts patient outcomes and healthcare systems and to quantify the cost of treatment failure to the healthcare system. A 3-year state-transition treatment-sequencing model which identifies all possible treatment sequences in PsO was adapted to the Italian healthcare setting. Treatments considered in the model are those with European Medicines Agency marketing authorization and reimbursement in Italy as of December 2022. Italian market share data (2019-2021) and list prices (2022) informed the current prescribed sequences; these sequences were compared against all possible sequences to determine opportunities for improvement. Both the national perspective in Italy as well as the local perspective from seven regions were considered. The cost of treatment failure was informed through a questionnaire circulated to Italian dermatologists. Overall, 1284 possible treatment sequences are possible when four lines of treatment are considered for patients with moderate-to-severe PsO in Italy. Within the estimated range of treatment failures across those sequences (0.97-2.56 per patient over 3 years), current prescribing behavior from the national perspective suggests patients will face 1.44 failures on average; this highlights the potential for improvement. For every treatment failure, the cost borne by the Italian National Healthcare Service (NHS) is €676.80. Overall, prescribing more optimized treatment sequences results in a 22.95% reduction in failures with a 2.27% increase in costs. The regional analyses found similar trends. Results suggest that selecting the most effective treatment sequences for incident patients provides the greatest opportunity to reduce treatment failures and maximize patient outcomes with a modest impact on costs. While regional variations exist, there is room for improvement across the board, which could translate to more efficient local healthcare systems.

Arguments for a Community-Based Approach to Geothermal Energy Development

This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. We derive it from the integral characteristics of geothermal energy next to the shift in the energy transition policies to focus on managing green resources locally. This study presents arguments for a framework that approaches geothermal resources as an endogenous factor of community development. To analyse it, we create a model that explains the local economic characteristics of geothermal exploitation beyond its geological conditions. It aims to conceptualise a community-based geothermal development standard referring to the endogeneity principle. Geothermal energy is given attention since the characteristics of this resource determine its use locally. This induces the internalisation of labour and technology in the local economic system, a specific condition for local geothermal projects where a community remains a prime beneficiary. We argue that the role of communities in geothermal exploitation is pivotal in the process of green growth for further expansion of geothermal energy use.

Mapping drivers of land conversion among smallholders: A global systems perspective

CONTEXTAgriculture-driven land conversion poses a significant threat to global biodiversity conservation. Balancing agricultural expansion with conservation is a critical challenge for governments and development partners. Sustainability-oriented agriculture programs such as conservation agriculture and land intensification aim to reduce land conversion and deforestation; however, evidence of their effectiveness remains complex and elusive, especially regarding the role of smallholders. OBJECTIVEThis study addresses this knowledge gap by systematically mapping the drivers of smallholder land conversion and their interconnections across three focal biodiverse contexts including sub-Saharan Africa, Latin America, and Southeast Asia as well as from a global scale. METHODSUsing literature that focuses on the nexus of smallholder farmers and agricultural intensification, this study systematically identified the potential of distinct factors and dynamic processes to serve as system leverage points toward improved outcomes for biodiversity, food security, and smallholder livelihoods, using a combination of structural factor analysis, centrality analysis, and feedback loop analysis. RESULTS AND CONCLUSIONSThe results highlight the importance of strengthening land tenure systems, providing technical support to farmers, improving market access, and prioritizing smallholder livelihoods in mitigating land conversion. SIGNIFICANCEContextual differences emphasize the need for context-specific interventions and further research to explore factors driving land conversion more deeply within specific contextual boundaries. Future studies should map proposed policy interventions onto local systems driving land conversion and incorporate the diverse perspectives of local stakeholders to guide effective and sustainable agriculture interventions.

Improved Real-Time Models for Object Detection and Instance Segmentation for Agaricus bisporus Segmentation and Localization System Using RGB-D Panoramic Stitching Images

The segmentation and localization of Agaricus bisporus is a precondition for its automatic harvesting. A. bisporus growth clusters can present challenges for precise localization and segmentation because of adhesion and overlapping. A low-cost image stitching system is presented in this research, utilizing a quick stitching method with disparity correction to produce high-precision panoramic dual-modal fusion images. An enhanced technique called Real-Time Models for Object Detection and Instance Segmentation (RTMDet-Ins) is suggested. This approach utilizes SimAM Attention Module’s (SimAM) global attention mechanism and the lightweight feature fusion module Space-to-depth Progressive Asymmetric Feature Pyramid Network (SPD-PAFPN) to improve the detection capabilities for hidden A. bisporus. It efficiently deals with challenges related to intricate segmentation and inaccurate localization in complex obstacles and adhesion scenarios. The technology has been verified by 96 data sets collected on a self-designed fully automatic harvesting robot platform. Statistical analysis shows that the worldwide stitching error is below 2 mm in the area of 1200 mm × 400 mm. The segmentation method demonstrates an overall precision of 98.64%. The planar mean positioning error is merely 0.31%. The method promoted in this research demonstrates improved segmentation and localization accuracy in a challenging harvesting setting, enabling efficient autonomous harvesting of A. bisporus.

Understanding Wireless ChannelsThrough NeRF2

Despite Maxwell's formulation of the electromagnetic wave laws over a century and a half ago, accurately modeling the transmission of RF signals within electrically complex environments continues to be a formidable challenge. This complexity arises from intricate interactions of RF signals with various obstacles, such as reflections and diffractions. Inspired by the effective utilization of neural networks in computer vision for representing optical fields, this paper introduces NeRF2, a novel neural radio-frequency radiance field. NeRF2 conceptualizes a continuous, volumetric scene function to interpret RF signal propagation. Uniquely, NeRF2, once trained with limited signal measurements, can predict the characteristics and reception of a signal at an arbitrary point, given the transmitter's location. Functioning as a physical-layer neural network, NeRF2 amalgamates learned statistical models with physical ray tracing to create synthetic datasets, tailored for the training needs of application-layer artificial neural networks (ANNs). This integration, termed "turbo-learning," combines authentic and synthetic datasets, significantly augmenting ANNs' training efficacy. Experimental results illustrate that turbo-learning can substantially improve performance, with an estimated 50% enhancement. The efficacy of NeRF2 is further underscored in its applications in indoor localization and 5G MIMO systems.

The Development of the Municipal Registry of People with Diabetes in Porto Alegre, Brazil

Background/Objective: Diabetes registries that enhance surveillance and improve medical care are uncommon in low- and middle-income countries, where most of the diabetes burden lies. We aimed to describe the methodological and technical aspects adopted in the development of a municipal registry of people with diabetes using local and national Brazilian National Health System databases. Methods: We obtained data between July 2018 and June 2021 based on eight databases covering primary care, specialty and emergency consultations, medication dispensing, outpatient exam management, hospitalizations, and deaths. We identified diabetes using the International Classification of Disease (ICD), International Classification of Primary Care (ICPC), medications for diabetes, hospital codes for the treatment of diabetes complications, and exams for diabetes management. Results: After data processing and database merging using deterministic and probabilistic linkage, we identified 73,185 people with diabetes. Considering that 1.33 million people live in Porto Alegre, the registry captured 5.5% of the population. Conclusions: With additional data processing, the registry can reveal information on the treatment and outcomes of people with diabetes who are receiving publicly financed care in Porto Alegre. It will provide metrics for epidemiologic surveillance, such as the incidence, prevalence, rates, and trends of complications and causes of mortality; identify inadequacies; and provide information. It will enable healthcare providers to monitor the quality of care, identify inadequacies, and provide feedback as needed.

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.