High-resolution human settlement maps provide detailed delineations of where people live and are vital for scientific and practical purposes, such as rapid disaster response, allocation of humanitarian resources, and international development. The increased availability of high-resolution satellite imagery, combined with powerful techniques from machine learning and artificial intelligence (AI), has spurred the creation of a wealth of settlement datasets. The agreement and alignment between these datasets has not been studied in detail. We compare three settlement maps developed by Google (Open Buildings), Meta (High Resolution Population Density Maps) and Microsoft (Global Building Footprints), and uncover which factors drive mismatch. Our study focuses on 44 African countries. We build a global machine learning model to predict where datasets agree, and find that geographic and socio-economic factors considerably impact overlap. However, we also find there is great variability across countries, suggesting complex interactions between country morphology and dataset overlap. It is vital to understand the shortcomings of AI-derived settlement layers as international organizations, governments, and NGOs are already experimenting with incorporating these into programmatic work. We anticipate our work to be a starting point for more critical and detailed analyses of AI derived datasets for humanitarian, policy, and scientific purposes.

Africa's schools will educate the majority of the 21st century's working population, influencing the global economy. Through combined spatial analysis techniques on over 500,000 schools, we estimate a 2 billion EUR cost to power unelectrified schools with decentralized solar photovoltaic systems. Given the positive effect on children's food security and the growing need for digitalization, ensuring clean electricity access includes both electricity demand for internet connectivity and electric cooking. Our analysis reveals that 32% of African school-aged children live near unelectrified schools, with the nearest electrified school often too far away. The electrification of these facilities would reduce education-seeking trips by an average 45 min by motorized transport or 6 h on foot. This significant time savings, combined with the broader benefits of decentralized energy, can significantly enhance educational access, economic development, and environmental sustainability in Africa.

Novel digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development and can contribute to monitoring and mitigating humanitarian problems. The potential of applying novel technologies to solving some of humanity’s most pressing issues has garnered interest outside the traditional disciplines studying and working on international development. Today, scientific communities in fields like Computational Social Science, Network Science, Complex Systems, Human Computer Interaction, Machine Learning, and the broader AI field are increasingly starting to pay attention to these pressing issues. However, are sophisticated data driven tools ready to be used for solving real-world problems with imperfect data and of staggering complexity? We outline the current state-of-the-art and identify barriers, which need to be surmounted in order for data-driven technologies to become useful in humanitarian and development contexts. We argue that, without organized and purposeful efforts, these new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.

Extensive forest restoration is a key strategy to meet nature-based sustainable development goals and provide multiple social and environmental benefits. Yet achieving forest restoration at scale requires cost-effective methods. Tree planting in degraded landscapes is a popular but costly forest restoration method, which often results in less biodiverse forests when compared to natural regeneration techniques under similar conditions. Here, we assess the current spatial distribution of pantropical natural forest (from 2000-2016) and use this information to present the first model of the potential for natural regeneration across tropical forested countries and biomes at 30-meter spatial resolution. We estimate that 215 million hectares - an area greater than the entire country of Mexico - have potential for natural forest regeneration, representing an above-ground carbon sequestration potential of 23.4 Gt CO2 (range 21.1-25.7 Gt) over 30 years. Five countries (Brazil, Indonesia, China, Mexico, and Colombia) account for 52% of this estimated potential, showcasing the need for targeting restoration initiatives that leverage natural regeneration potential. Our results facilitate broader equitable decision-making processes that capitalise on the widespread opportunity for natural regeneration to help achieve national and global environmental agendas.

Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model’s qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health.

This paper proposes a method to enhance the immunity to board-level charged device model (CDM) induced electrostatic discharge (ESD) in dual in-line memory modules (DIMM) assembled with dynamic random-access memory (DRAM) components. In the industry, CDM control specification exists only for individual integrated circuit (IC) components including DRAM. However, there are instances where failures occur at levels much lower than the pre-charge voltage guaranteed by the component due to the large capacitance of modules assembled with IC components. The approach of modifying the printed circuit board (PCB) layout of the module to safely ground the discharge current is adopted as a solution. The effectiveness of this strategy is verified through the attach-detach-assessment (ADA) evaluation method, which simulates the real user environment, and actual improvements are confirmed.

Lately, there has been a notable surge in the defense industry's efforts to develop highly advanced intelligent systems. These systems encompass sophisticated computing platforms that boast an impressive level of autonomy. However, it's important to acknowledge that these very systems are not impervious to vulnerabilities stemming from both hardware and software tampering. Within the context of this discourse, our focus of the survey is directed towards the hardware security module. This component stands out for its capability to offer a significantly heightened level of protection when compared to conventional software-based techniques. Through the lens of this paper, we embark on a comprehensive survey of Trusted Platform Module (TPM), a hardware security module, shedding light on its potential to fortify the defense against threats that emerge from various vectors of attack.

This paper addresses the problem of onboard computer application of dynamic low-orbit satellite network routing algorithms. In low-orbit satellite networks, the satellite topology changes in real time, and satellite disconnection occurs frequently. The problem of routing algorithms for low-orbit satellites can be solved by reinforcement learning algorithms. However, the inference process based on deep reinforcement learning models suffers from excessive computation due to the operation of multiple convolutional layers. In this paper, we propose a method to accelerate convolutional layer operations by parallelizing them using heterogeneous processors. This approach is compared to the traditional single-processor-based convolutional operation method, commonly used in dynamic low-orbit satellite network routing algorithms. Our evaluation, conducted on an actual heterogeneous processor-based onboard computer, demonstrates that the proposed method not only matches the accuracy of the conventional single-processor-based approach, but also significantly reduces the execution time.

EDITORIAL article Front. Big Data, 21 November 2023Sec. Data Analytics for Social Impact Volume 6 - 2023 | https://doi.org/10.3389/fdata.2023.1301903

Novel digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development and can contribute to monitoring and mitigating humanitarian problems. The potential of applying novel technologies to solving some of humanity's most pressing issues has garnered interest outside the traditional disciplines studying and working on international development. Today, scientific communities in fields like Computational Social Science, Network Science, Complex Systems, Human Computer Interaction, Machine Learning, and the broader AI field are increasingly starting to pay attention to these pressing issues. However, are sophisticated data driven tools ready to be used for solving real-world problems with imperfect data and of staggering complexity? We outline the current state-of-the-art and identify barriers, which need to be surmounted in order for data-driven technologies to become useful in humanitarian and development contexts. We argue that, without organized and purposeful efforts, these new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.

To investigate the clinical features of ocular myasthenia gravis (OMG) in ophthalmology. A total of 28 patients with ptosis or diplopia who were followed for at least 6 months between March 2016 and February 2022 were included in this study. The clinical symptoms of the patients and test results were analyzed. According to the positivity of serologic or electrophysiologic test, these patients were divided into 2 groups (positive and negative OMG results) and according to the clinical symptoms of diplopia or ptosis for comparison. Ptosis, diplopia, and both ptosis and diplopia were present in 6 (21.43%), 14 (50.0%), and 8 (28.57%) patients, respectively. Acetylcholine receptor auto-antibody (AchR Ab) was positive in 16 (57.14%) of 28 patients and the ice test was positive in 13 (92.86%) of 14 patients with ptosis. Abnormal thymic lesions were presented in 7 (25.0%) patients, and a definite improvement in response to pyridostigmine was observed in 27 (100.0%) patients. Both ptosis and diplopia were significantly higher in the group with positive results than that in the negative results group (P = .025). In addition, both horizontal and vertical diplopia was significantly higher in the group with AchR Ab titer > 5.0 than that in the group with AchR Ab titer < 5.0 (P = .041). After excluding cranial nerve palsy, if there is ptosis and diplopia, especially vertical diplopia, the possibility of OMG should be considered.

In this study, crystalline Ni @ nickel hydroxide (cNNH) and Co-doped cNNH were obtained using a hydrothermal method. We then systematically analyzed the formation mechanism by investigating the effects of reagents on the synthesis of cNNH. To improve the electrochemical performance, cNNH was doped with Co ions. Detailed morphological study shows that both nanostructures consist of a Ni metal core and ~5 nm Ni(OH) 2 shell layer. Co-doped cNNH showed a specific capacitance of 1238 F/g at a current density of 1A/g, significantly higher than that of cNNH. The enhanced performance of the Co-doped cNNH is attributed to the reduced path length of the electrons caused by the decrease in the size of the nanostructure and the increased conductivity owing to Co ions substituting Ni ions. The reported synthesis method and electrochemical behavior of cNNH and Co-doped cNNH affirm their great potential as electrochemically active materials for applications such as batteries, supercapacitors, gas sensing, and photocatalysis.

Across the tropics, recent agricultural shifts have led to a rapid expansion of tree plantations, often into intact forests and grasslands. However, this expansion is poorly characterized. Here, we report tropical tree plantation expansion between 2000 and 2012, based on classifying nearly 7 million unique patches of observed tree cover gain using optical and radar satellite imagery. The resulting map was a subsample of all tree cover gain but we coupled it with an extensive random accuracy assessment (n = 4,269 points) to provide unbiased estimates of expansion. Most predicted gain patches (69.2%) consisted of small patches of natural regrowth (31.6 ± 11.9 Mha). However, expansion of tree plantations also dominated increases in tree cover across the tropics (32.2 ± 9.4 Mha) with 92% of predicted plantation expansion occurring in biodiversity hotspots and 14% in arid biomes. We estimate that tree plantations expanded into 9.2% of accessible protected areas across the humid tropics, most frequently in southeast Asia, west Africa and Brazil. Given international tree planting commitments, it is critical to understand how future tree plantation expansion will affect remaining natural ecosystems. Changes in agricultural practices have led to the expansion of tree plantations across the tropics, but this expansion is poorly characterized. Nearly 7 million unique patches of observed tree cover gain are classified through satellite imagery to report on tropical tree plantation expansion between 2000 and 2012.

The titer of neutralizing antibodies (NAbs) against viral hemorrhagic septicemia virus (VHSV) has been determined by conventional neutralization assay based on the observation of cytopathic effect (CPE) and plaque formation in cultured cells. However, this method requires several days for the determination and can be affected by operator bias. To develop a rapid and high-throughput neutralization assay against VHSV, we rescued a surrogate chimeric snakehead rhabdovirus, rSHRV-Gvhsv-eGFP, which has the enhanced green fluorescent protein (eGFP) gene between N and P genes and has VHSV G gene instead of SHRV G gene in the genome. The efficacy of rSHRV-Gvhsv-eGFP to determine serum neutralization activity was evaluated using various serum samples derived from New Zealand white rabbits and olive flounder (Paralichthys oliavaceus). Although neutralization titers analyzed using rSHRV-Gvhsv-eGFP were similar to the titers measured using rVHSV-A-eGFP, the time needed for the determination of neutralization titer was much shortened (24 h for rSHRV-Gvhsv-eGFP and 48 h for rVHSV-A-eGFP), proving the usefulness of rSHRV-Gvhsv-eGFP for the neutralization assay against VHSV. In addition, as the neutralization activities using rSHRV-Gvhsv-eGFP could be well-observed without adding fresh serum as a complement source, no preparation is required for the optimization of control fresh serum from naïve fish. The present results suggest that the rapid neutralization assay using rSHRV-Gvhsv-eGFP can be used to investigate neutralization activities against VHSV.

Computer vision for large scale building detection can be very challenging in many environments and settings even with recent advances in deep learning technologies. Even more challenging is modeling to detect the presence of specific buildings (in this case schools) in satellite imagery at a global scale. However, despite the variation in school building structures from rural to urban areas and from country to country, many school buildings have identifiable overhead signatures that make them possible to be detected from high-resolution imagery with modern deep learning techniques. Our hypothesis is that a Deep Convolutional Neural Network (CNN) could be trained for successful mapping of school locations at a regional or global scale from high-resolution satellite imagery. One of the key objectives of this work is to explore the possibility of having a scalable model that can be used to map schools across the globe. In this work, we developed AI-assisted rapid school location mapping models in eight countries in Asia, Africa, and South America. The results show that regional models outperform country-specific models and the global model. This indicates that the regional model took the advantage of having been exposed to diverse school location structure and features and generalized better, however, the global model was the worst performer due to the difficulty of generalizing the significant variability of school location features across different countries from different regions.

Blood blister-like aneurysms (BBAs) are rare vascular lesions and a therapeutic challenge. Although endovascular treatment of BBA is a promising approach, the optimal treatment remains controversial. The purpose of this study was to compare the safety and efficacy of stent-assisted coiling (SAC) and flow diverter (FD) in the management of BBAs. A proportion meta-analysis including a published series of BBAs treated with endovascular approaches from 2009 to 2020 including SAC and FD was performed by searching English language studies via MEDLINE and EMBASE. The 32 studies included 16 based on SAC and 16 involving FD. The long-term complete occlusion rate was higher in FD (89.26

Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in autonomous driving, the ability to detect hazardous situations, which can be seen as an out-of-distribution (OOD) detection problem, becomes crucial. However, conventional datasets often only contain expert driving demonstrations, although some non-expert or uncommon driving behavior data are needed to implement a safety guaranteed autonomous driving platform. To this end, we present a dataset called the R3 Driving Dataset, composed of driving data with different qualities. The dataset categorizes abnormal driving behaviors into eight categories and 369 different detailed situations. The situations include dangerous lane changes and near-collision situations. To further enlighten how these abnormal driving behaviors can be detected, we utilize different uncertainty estimation and anomaly detection methods for the proposed dataset. From the results of the proposed experiment, it can be inferred that by using both uncertainty estimation and anomaly detection, most of the abnormal cases in the proposed dataset can be discriminated. https://rllab-snu.github.io/projects/R3-Driving-Dataset/doc.html

Atomic structure files used for DFT calculation for manuscript "Highly-efficient blue InGaN nanoscale light-emitting diodes"