Target Area Research Articles

Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations

Background The accurate provision of weather information holds immense significance to many disciplines. One example corresponds to the field of air traffic management, in which one basis for weather detection is set upon recordings from sparse weather stations on ground. The scarcity of data and their lack of precision poses significant challenges to achieve a detailed description of the atmosphere state at a certain moment in time. Methods In this article, we foster the use of physics-informed neural networks (PINNs), a type of machine learning (ML) architecture which embeds mathematically accurate physics models, to generate high-quality weather information subject to the regularization provided by the Navier-Stokes equations. Results The application of PINNs is oriented to the reconstruction of dense and precise wind and pressure fields in areas where only a few local measurements provided by weather stations are available. Our model does not only disclose and regularize such data, which are potentially corrupted by noise, but is also able to precisely compute wind and pressure in target areas. Conclusions The effect of time and spatial resolution over the capability of the PINN to accurately reconstruct fluid phenomena is thoroughly discussed through a parametric study, concluding that a proper tuning of the neural network’s loss function during training is of utmost importance.

Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction

ABSTRACT The difficult, time-consuming, and imbalanced acquisition of landslide inventories in complex and heterogeneous large areas often results in limited predictive performance for most statistical landslide spatial prediction methods. Although partial transfer learning methods have produced reliable predictive results and successfully implemented knowledge transfer between data-rich and data-scarce areas, most of these methods generally only extract inadequate environmental features and lack the ability to interpret when, what, and how to effectively transfer knowledge to other regions with limited data. In this paper, a heterogeneous transfer learning method considering feature representations and environmental consistency, which features robustness, similarity, and transferability, is proposed for landslide spatial prediction. Specifically, we trained a stacked autoencoder (SAE) to extract more nonlinear features among environmental factors, and added an environmental similarity criterion to the transfer adaptation boosting (TrAdaBoost) algorithm to minimize feature dissimilarities and avoid negative transfer in different scenarios. To evaluate the robustness of the proposed method, we first selected two target areas (Lushan County and Luding County, China) and a source area (Wenchuan County, China) as case study areas. Then, we directly combined the source area and target area as an additional dataset without considering transfer learning to validate the significance and necessity of the proposed method. The area under the receiver operating characteristic curve (AUC) of the two target regions for the proposed method were 0.920 and 0.972, respectively, which were greater than those of the traditional TrAdaBoost (0.909 and 0.969, respectively), SAE (0.790 and 0.937, respectively), and random forest (0.915 and 0.966, respectively) methods. Furthermore, the AUC values of the SAE (0.851 and 0.900) and random forest (0.890 and 0.935) models based on the expanded datasets were also lower than those of the proposed method. Therefore, the experimental results show that the proposed method can be generalized well due to its efficient utilization and high adaptability. Moreover, the proposed method can not only be applied to emergency rescue and disaster prevention but can also offer a promising way to improve landslide predictions of models with incomplete landslide inventories.

Computed tomography myocardial perfusion imaging to detect myocardial ischemia in patients with anxiety and obstructive coronary heart disease post-exposure to mental stressors

This study aims to measure myocardial blood flow (MBF) using dynamic CT- myocardial perfusion imaging (CT-MPI) combined with mental stressors in patients with obstructive coronary artery disease (OCAD) and in patients with anxiety and no obstructive coronary artery disease (ANOCAD). A total of 30 patients with OCAD with 30 patients with ANOCAD were included in this analysis. Using the 17-segment model, the rest and stress phase MBF of major coronary arteries in participants were recorded respectively. Compared with ANOCAD patients, OCAD patients were more likely to have localized reduction of MBF (p < 0.05). For patients with ANOCAD, both global MBF and MBF of the main coronary arteries in the stress phase were lower than those in the rest phase (all p < 0.05), but there was no significant difference in MBF among the main coronary arteries in the rest or stress phase (p = 0.25, p = 0.15). For patients with OCAD, the MBF of the target area was lower than that of the non-target area in both the rest and stress phase, and the MBF of the target area in the stress phase was lower than that in the rest phase (all p < 0.05). However, there was no significant difference in MBF between the rest or stress phase in the non-target area (p = 0.73). Under mental stress, the decrease in MBF in ANOCAD patients was diffuse, while the decrease in MBF in OCAD patients was localized. Dynamic CT-MPI combined with mental stressors can be used to detect MBF changes in anxiety patients.

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

The efficient and timely identification of oil spill areas is crucial for ocean environmental protection. Synthetic aperture radar (SAR) is widely used in oil spill detection due to its all-weather monitoring capability. Meanwhile, existing deep learning-based oil spill detection methods mainly rely on the classical U-Net framework and have achieved impressive results. However, SAR images exhibit high noise, blurry boundaries, and irregular shapes of target areas, as well as speckles and shadows, which lead to the loss of performance in existing algorithms. In this paper, we propose a novel network architecture to achieve more precise segmentation of oil spill areas by reintroducing rich semantic contextual information before obtaining the final segmentation mask. Specifically, the proposed architecture can re-fuse feature maps from different levels at the decoder end. We design a multi-convolutional layer (MCL) module to extract basic feature information from SAR images, and a feature extraction module (FEM) module further extracts and fuses feature maps generated by the U-Net decoder at different levels. Through these operations, the network can learn rich global and local contextual information, enable sufficient interaction of feature information at different stages, enhance the model’s contextual awareness, and improve its ability to recognize complex textures and blurry boundaries, thereby enhancing the segmentation accuracy of SAR images. Compared to many U-Net based segmentation networks, our method shows promising results and achieves state-of-the-art performance on multiple evaluation metrics.

Terrorism and education: Evidence from instrumental variables estimators

SummaryThis paper estimates the effect of exposure to terrorist violence on education. Since terrorists may choose targets endogenously, we construct a set of novel instruments. To that end, we leverage exogenous variation from a local terrorist group's revenues and its affiliation with al‐Qaeda. Across several Kenyan datasets, we find that attacks suppress school enrolment more than predicted by difference‐in‐differences‐type estimators. This indicates that terrorists target areas experiencing unobserved, positive shocks. Evidence suggests fears and concerns as mechanisms of impact, rather than educational supply.

[Spatial and Temporal Distribution Characteristics of Ozone Concentration and Health Benefit Assessment in the Beijing-Tianjin-Hebei Region from 2015 to 2020].

To evaluate the spatial and temporal distribution characteristics of ambient ozone (O3) in the Beijing-Tianjin-Hebei (BTH) Region, the land use regression (LUR) model and random forest (RF) model were used to simulate the ambient O3 concentration from 2015 to 2020. Meanwhile, all-cause, cardiovascular, and respiratory mortalities as well as economic losses attributed to O3 were also estimated. The results showed that upward trends with fluctuation were observed for ambient O3 concentration, mortalities, and economic losses attributable to O3 exposure in the BTH Region from 2015 to 2020. The areas with high O3 concentration and great changes were concentrated in the central and southwestern regions, whereas the concentration in the northern region was low, and the change degree was small. The spatial distribution of the mortalities was also consistent with the spatial distribution of O3 concentration. From 2015 to 2020, the economic losses regarding all-cause mortality and cardiovascular mortality increased in 13 cities of the BTH Region, whereas the economic losses of respiratory mortality decreased in 4 cities in the BTH Region. The results indicated that the priority areas for O3 control were not uniform. Specifically, Beijing, Tianjin, Hengshui, and Xingtai were vital areas for O3 pollution control in the BTH Region. Differentiated control measures should be adopted based on the characteristics of these target areas to decline O3 concentration and reduce health impacts and economic losses associated with O3 exposure.

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Surface defects in bearings not only affect the appearance but also impact the service life and performance. Therefore, it is imperative for bearing manufacturers to conduct quality inspections before bearings leave the factory. However, traditional visual inspection methods exhibit shortcomings such as high omission rates, insufficient feature fusion and oversized models when dealing with multiple target defects in bearings. To address these challenges, this paper proposes a surface defect detection method for bearings based on an improved Yolov8 algorithm (G-Yolov8). Firstly, a C3Ghost convolutional module based on the Ghost module is constructed in YOLOv8 to simplify model computational costs. Secondly, a global attention mechanism module is designed at the end of the backbone network to increase sensitivity to implicit small target area features and optimize feature extraction efficiency. Subsequently, a deep deformable convolution feature pyramid network is constructed by introducing the deformable convolutional networks version 2 (DCNv2) and the lightweight content-aware reassembly of features upsampling operator to reduce sampling information loss and improve the fusion of multi-scale target defects. Finally, different attention mechanisms are embedded in the detection network to construct a multi-attention detection head to replace the decoupled head, refining classification and localization tasks, reducing feature confusion, and improving the model’s detection accuracy. Experimental results demonstrate that the improved algorithm achieves a 3.5% increase in mean average precision on a self-made small-scale train bearing surface defect dataset, with a 17.3% reduction in model size. This improvement not only enhances accuracy but also addresses the requirement for lightweight deployment in subsequent stages.

Automated Plant Disease Detection Using Deep Learning

Traditional methods of diagnosing plant diseases are mainly based on expert diagnosis which easily causes delay in crop disease control and crop management. Due to the problems of many target areas and similar target types in the process of plant disease detection, the identification accuracy and speed are required to be high. Therefore, it is necessary to optimize and improve the existing methods (CNN, RCNN, Fast RCNN, Faster RCNN, and SSD) to meet the detection needs. So, we came up with a deep learning-based approach to identify the plant leaf diseases and classify the diseases using an object detection model called YOLO. There are different versions of YOLO, proposed in recent times, in that YOLOv5 model is considered one of the best models and the other is the recent version, YOLOv8 which was proposed in 2022. So, in this paper we compared the two models of YOLO, YOLOv5 and YOLOv8 on the same dataset, and found that for the dataset used the YOLOv5 model was found to be the better model with a mAP of 0.641, while the YOLOv8 model has mAP of 0.516. This study proposes that the YOLOv5 model is suitable for plant disease identification tasks by comparing it with the latest version of the YOLOv8 model which was proposed in 2022. To make the YOLO model to be better it can be optimized and the transfer learning ability of the model can be used to expand the application scope in the future. Keywords— Deep Learning, CNN, RCNN, Fast RCNN, Faster RCNN, SSD, YOLO, mAP, Transfer Learning, Disease Classification, Detection.

Közúti közlekedési balesetben megsérült gyalogosok számának csökkentési lehetőségei

Összefoglalás. A közlekedési balesetekben sérült személyek, valamint a baleset miatti anyagi károk jelentős mértékű társadalmi-gazdasági veszteségértéket képviselnek. Kutatásommal célom a közlekedés biztonságosabbá tételéhez szükséges kulcsterületek feltárása és a lehetséges potenciális beavatkozási területek meghatározása volt. Munkám során a 2016–2020 közötti közlekedési balesetek adatait elemeztem leíró statisztikai módszerrel. Megállapítottam, hogy a leginkább kritikus pont a gyalogosok közlekedése. Azokban a balesetekben, ahol volt gyalogos sérült, a balesetek két okcsoportja dominál: gyalogosok hibája (34,3%), valamint a járművezetők gyalogosok részére történő elsőbbségadásának elmaradása (38,1%). Az eredmények lehetőséget adnak a további célirányos vizsgálatokra, valamint a hatékony beavatkozásokra. Summary. Transport is an essential part of everybody lives. The aim of transport users is to make their journeys without accidents in the shortest possible time, while optimising their individual resources and inputs. The number of people injured in road traffic accidents with personal injury and the material damage caused by a significant socio-economic loss. By preventing accidents, it is clear that the costs and resources spent on road accidents can be reduced. Therefore, I started my research by determining the share of road user groups in the number of accidents, the number of people injured and the amount of economic loss value associated with accidents. I analysed road accidents between 2016 and 2020. By the accident analysis, I found that among vulnerable road users, the proportion of pedestrians and cyclists injured was extremely high. Injuries in these two transport modes accounted for more than half of the injuries in cars. By calculating the loss value, I found that the damage to the national economy caused by pedestrian injuries was significant. In my research, by analysing the causes of accidents, I found that in accidents where there was a pedestrian injured, two groups of causes dominate: pedestrian error (34.3%) and drivers not giving priority to pedestrians (38.1%). Examining the accident data, it was evident that the number of pedestrian accidents due to pedestrian error is very high, so I continued my research in the area of other activities of pedestrians while walking. I measured pedestrians in 10 locations in Budapest. During the measurements, I recorded and analysed data of 11536 persons. The average rate of distraction was 54.3%, which means that one in two pedestrians is doing some other activity while walking. The most frequent activity was talking to another pedestrian (29.8%), but phone use (15.7%) and head/earphone use (13.5%) were also very significant. My research results so far show that if we want to improve pedestrian safety, we need to reduce accidents caused by pedestrians’ own fault and increase drivers’ willingness to give priority to pedestrians. My research plans also include investigating ways to reduce pedestrian errors, increasing pedestrian awareness and examining pedestrian-driver interactions in more depth. I already have results in this area, but my aim is to identify target areas (including human, infrastructure, vehicle field) for my research that can be implemented in practice, thus helping to improve pedestrian safety and road safety.

A study on the effects of long-term radiation exposure on superconducting magnets in MRI-Guided proton therapy

In this publication, we offer the first assessment of the long-term radiation impact on superconducting magnets utilized in MRI-guided proton therapy. We explore both uniform and non-uniform magnetic fields and create three improved therapy plans for shallow (brain), medium (liver), and deep (prostate) targets. The evaluation of radiation damage in superconducting magnets using therapy plans is carried out through the implementation of the Monte Carlo method, molecular dynamics method, and modified NRT model. The results of the three plans show that a large amount of particles do enter the superconducting magnet during the treatment, with a preponderance of photons and neutrons. For superconducting magnets in MRI systems, the closer the coil is to the target area, the greater the radiation damage, and the extent of radiation damage increases with the energy of the incident proton beam. The PKA with high energy can induce temperature fluctuations locally in the superconducting coils, but does not pose a threat to its mechanical properties. Following 20 years of continuous operation, the DPA of the internal coils is about 2.3097E-17, and the maximum dose to insulating layer is about 1.25 Gy. The results of this paper show that MRI-guided proton therapy poses no threat to the long-term operation of superconducting magnets.

Histidine-Covalent Stapled Alpha-Helical Peptides Targeting hMcl-1.

Several novel and effective cysteine targeting (Cys) covalent drugs are in clinical use. However, the target area containing a druggable Cys residue is limited. Therefore, methods for creating covalent drugs that target different residues are being looked for; examples of such ligands include those that target the residues lysine (Lys) and tyrosine (Tyr). Though the histidine (His) side chain is more frequently found in protein binding locations and has higher desirable nucleophilicity, surprisingly limited research has been done to specifically target this residue, and there are not many examples of His-targeting ligands that have been rationally designed. In the current work, we created novel stapled peptides that are intended to target hMcl-1 His 252 covalently. We describe the in vitro (biochemical, NMR, and X-ray) and cellular design and characterization of such agents. Our findings further suggest that the use of electrophiles to specifically target His residues is warranted.

Graft cell expansion from hiPSC-RPE strip after transplantation in primate eyes with or without RPE damage

Clinical studies using suspensions or sheets of human pluripotent cell-derived retinal pigment epithelial cells (hiPSC-RPE) have been conducted globally for diseases such as age-related macular degeneration. Despite being minimally invasive, cell suspension transplantation faces challenges in targeted cell delivery and frequent cell leakage. Conversely, although the RPE sheet ensures targeted delivery with correct cell polarity, it requires invasive surgery, and graft preparation is time-consuming. We previously reported hiPSC-RPE strips as a form of quick cell aggregate that allows for reliable cell delivery to the target area with minimal invasiveness. In this study, we used a microsecond pulse laser to create a local RPE ablation model in cynomolgus monkey eyes. The hiPSC-RPE strips were transplanted into the RPE-ablated and intact sites. The hiPSC-RPE strip stably survived in all transplanted monkey eyes. The expansion area of the RPE from the engrafted strip was larger at the RPE injury site than at the intact site with no tumorigenic growth. Histological observation showed a monolayer expansion of the transplanted RPE cells with the expression of MERTK apically and collagen type 4 basally. The hiPSC-RPE strip is considered a beneficial transplantation option for RPE cell therapy.

Spatiotemporal Distributions of Acoustic Propagation in Skull During Ultrasound Neuromodulation.

There is widespread interest and concern about the evidence and hypothesis that the auditory system is involved in ultrasound neuromodulation. We have addressed this problem by performing acoustic shear wave simulations in mouse skull and behavioral experiments in deaf mice. The simulation results showed that shear waves propagating along the skull did not reach sufficient acoustic pressure in the auditory cortex to modulate neurons. Behavioral experiments were subsequently performed to awaken anesthetized mice with ultrasound targeting the motor cortex or ventral tegmental area (VTA). The experimental results showed that ultrasound stimulation (US) of the target areas significantly increased arousal scores even in deaf mice, whereas the loss of ultrasound gel abolished the effect. Immunofluorescence staining also showed that ultrasound can modulate neurons in the target area, whereas neurons in the auditory cortex required the involvement of the normal auditory system for activation. In summary, the shear waves propagating along the skull cannot reach the auditory cortex and induce neuronal activation. Ultrasound neuromodulation-induced arousal behavior needs direct action on functionally relevant stimulation targets in the absence of auditory system participation.

Mutation of the ALS/FTD-associated RNA-binding protein FUS affects axonal development.

Aberrant condensation and localisation of the RNA-binding protein (RBP) fused in sarcoma (FUS) occur in variants of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Changes in RBP function are commonly associated with changes in axonal cytoskeletal organisation and branching in neurodevelopmental disorders. Here, we asked whether branching defects also occur in vivo in a model of FUS-associated disease. We use two reported Xenopus models of ALS/FTD (of either sex), the ALS-associated mutant FUS(P525L) and a mimic of hypomethylated FUS, FUS(16R). Both mutants strongly reduced axonal complexity in vivo. We also observed an axon looping defect for FUS(P525L) in the target area, which presumably arises due to errors in stop cue signalling. To assess whether loss of axon complexity also had a cue-independent component, we assessed axonal cytoskeletal integrity in vitro Using a novel combination of fluorescence and atomic force microscopy, we found that mutant FUS reduced actin density in the growth cone, altering its mechanical properties. Therefore, FUS mutants may induce defects during early axonal development.Significance statement This study demonstrates that mutation of the ALS/FTD (amyotrophic lateral sclerosis/frontotemporal dementia)-associated RNA-binding protein Fused in Sarcoma (FUS) can result in changes in axonal development. These changes occur both axon-autonomously in cytoskeletal organisation during axon extension and context-dependently during axonal branching. This indicates pre-symptomatic, developmental changes in axonal organisation may occur in familial disease variants.

Investigating Earth surface deformation with SAR interferometry and geomodeling in the transborder Meuse–Rhine region

Introduction: This study aims to differentiate local and regional ground uplift, as well as sub-regional subsidence induced by groundwater level drawdown, which are possibly enhanced across fault structures, as monitored by various synthetic aperture radar interferometry (InSAR) processing methods. A buoyant mantle plume under the Eifel may be responsible for the regional ground uplift, including the Weser–Geul (BE) and South Limburg regions (NL), which could negatively affect the area proposed for the future Einstein Telescope.Methods: Different InSAR processing techniques are compared to evaluate their limits in tracking fault structures on a time series of Copernicus Sentinel-1 images while detecting and measuring ground motion based on their phase signature. The results present an overall stable ground for the Euregio Meuse–Rhine region, especially at the Belgian–Dutch border, while tectonic activity is observed along the German side of the Rhine Graben.Results: As the current neotectonic activity in the target area was not well known, we performed a spatiotemporal analysis of ground deformation associated with the presence of NW–SE-trending normal faults where karst also develops, as well as along the Variscan NE–SW-trending thrust faults. This work demonstrates that the identification of deformation hazards using satellite remote sensing (and connected seismological) techniques is challenging mainly due to (very) small regional scale deformation, terrain conditions, and SAR properties.Discussion: Thus, the results mostly indicate ground stability over the area; however, also some agricultural activities were observed, as was deformation along some infrastructure such as railways. Displacements of millimetric order measured along the faults located near the Geul valley (BE) are probably related to old mining activities.

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

In recent decades, eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, thus requiring timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), aboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, marked a significant milestone as the world’s inaugural geostationary ocean color observation satellite. Its operational tenure spanned from 1 April 2011 to 31 March 2021. Over ten years, the GOCI has observed oceans, coastal waters, and inland waters within its 2500 km × 2500 km target area centered on the Korean Peninsula. The most attractive feature of the GOCI, compared with other commonly used water color sensors, was its high temporal resolution (1 h, eight times daily from 0 UTC to 7 UTC), providing an opportunity to monitor ICWs, where their water quality can undergo significant changes within a day. This study aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined the GOCI’s strength and performance with different processing methods. These articles reveal that the GOCI played an essential role in monitoring the ecological health of ICWs in its observation coverage (2500 km × 2500 km) in East Asia. The GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by Geostationary Ocean Color Sensors in monitoring water quality and provide suggestions for future Geostationary Ocean Color Sensors to better monitor the ICWs.

The potential advantages of 18F sodium fluoride positron emission tomography-computed tomography for clinical staging and management planning in patients with nasopharyngeal carcinoma.

The staging and treatment planning of nasopharyngeal carcinoma (NPC) face challenges due to limited sensitivity of conventional imaging. 18F-sodium fluoride (18F-NaF) positron emission tomography-computed tomography (PET/CT) offers potential advantages in detecting early bone involvement. This retrospective cohort study aimed to assess the potential advantage of 18F-NaF PET/CT for clinical staging and management planning in patients with NPC and to compare 18F-NaF PET/CT findings with those of conventional imaging modalities. We enrolled a cohort of patients with NPC who underwent 18F-NaF PET/CT at our PET/CT center between July 1, 2017, and June 30, 2021, and analyzed the findings of 18F-NaF PET/CT and conventional imaging modalities. Data from multidisciplinary team discussions on clinical staging and management planning both before and after 18F-NaF PET/CT were recorded. Additionally, any changes in clinical staging and management planning following 18F-NaF PET/CT were documented. A total of 58 patients were included in this study. After 18F-NaF PET/CT imaging, clinical tumor-node-metastasis (TNM) staging was observed to have changed in seven cases (12.1%). Among these, four cases had changes in T stage and three cases in the M stage. Additionally, changes in clinical management plans were observed in eight patients (13.8%). Changes due the results of 18F-NaF PET/CT included three cases with major modification (two cases switched from curative treatment to palliative treatment, and one case switched from palliative treatment to curative treatment) and five cases with minor changes. The minor changes involved alteration to the radiotherapy target volume (three cases with an increased target volume and one case with a reduced target area). Furthermore, one case required an alteration to the radiotherapy strategy for local bone involvement. The use of 18F-NaF PET/CT in patients newly diagnosed with NPC may offer potential advantages for clinical staging and treatment planning, enabling physicians to select a more individualized treatment approach.

Evacuation information methodology that combined a flooded environment and pedestrian behavioral model

Disaster information influences decision-making. Wireless internet and location-based services, aided by recent technological advances, have laid the foundation for providing personalized disaster information. Despite the provision of simple disaster forecasts and warnings, provision of specific evacuation actions (“where” and “how,” i.e., location and reachability of safe areas) and location-based personalized information that consider real-time disaster situations are a pressing priority. Thus, this study proposed and developed a three-step methodology for providing disaster information during floods that combined a flooded environment and pedestrian behavioral model, namely, 1) prediction of the flooded environment, 2) analysis of evacuation routes based on the movement time of evacuees according to the flood depth, and 3) provision of evacuation information by selecting the optimal evacuation route. This model was demonstrated in the Gangnam Station area in Seoul, Korea. Flooded environment prediction modeling of the target area was performed by applying the 100-year rainfall design to the Storm Water Management Model. The optimal evacuation route, according to the flood depth, was selected by applying the behavioral model (movement time calculation formula) for each evacuee derived through an evacuation behavior experiment. The pre- and post-flooding age- and gender-wise total evacuation distance and time were compared and analyzed. Post-flooding, the evacuation distance and time increased by 168.3% (381.34 m) and 172.3% (3.16 min) on average, respectively, indicating that evacuees must travel farther and longer. The analysis of post-flooding evacuation route change types revealed changes in evacuation distance and time in cases where shelters and evacuation routes and time were significantly different depending on evacuee characteristics (age and gender). This indicated that specific and visual disaster information can be provided according to evacuees’ age and gender using the proposed methodology. This study can serve as the basis for designing an advanced and personalized disaster information system that can provide specific evacuation actions to aid fast evacuation decision-making during floods.

Critical factors for the rehabilitation of Hann Bay (Senegal, West Africa): Solutions for sustainable development

The advanced degradation of Hann Bay (Dakar, Senegal) has reached highly elevated levels, with serious environmental, health, and social consequences. Thus, this study focused on the determinants for rehabilitating Hann Bay and proposed solutions for sustainable development. The target area is currently home to 500,000 inhabitants and hundreds of industrial units, with virtually no suitable wastewater treatment systems. Socio-economic surveys and three rounds of effluent sampling were carried out between 2020 and 2022 at eleven sites in Hann-Bel Air, Dalifort Foirail, Thiaroye sur mer, Sicap Mbao, and Mbao. The study showed increased physicochemical, biological, and bacterial pollution of the heavily contaminated wastewater along the bay. The analytical results of the samples collected for the three series show that almost all sampling points exceed the limit value for TSS (50mg/l) and COD (200mg/l). This indicates that physicochemical and organic pollution are still present in the bay, causing fish to escape into the open sea. BOD measurements are also above the standard (N05-061; 80 mg/l) for series 1 and 2, showing an alteration in bay conditions. Analysis of the microbiological quality of the water in terms of oils and fats showed values well above the current standard (NS 05-061, 20-50 mg/l), which explains the frequent fish kills at the Hann marinas, Hann pêcheurs, and Cité ISRA sites. The same situation was observed in samples of fecal coliforms (CFU/100ml), helminth eggs (eggs/L), nitrogen, phosphorus, zinc, and lead at values outside the norm. Through socio-economic surveys of the populations and workers in the industrial units, the recommended solutions focus on protection and prevention measures, enforcement of regulations, communication and awareness-raising, equipment acquisition, and monitoring and surveillance to rehabilitate the bay.

In Vitro Release from Polymeric Core/Shell Nanoparticles through the Lens of Multiscale Modeling.

The large number of studies involving nanoparticles for cancer therapy is due to their peculiar features: they protect loaded active molecules while extending circulation time and can extravasate from the blood flow to the tumor to deliver drugs directly in the target area. Mathematical modeling can provide a preliminary in silico exploration of design space to optimize an experimental activity that often relies on a trial-and-error approach. However, because of the characteristic size of these vectors (10-1000 nm), numerous phenomena of interest occur at different time and length scales, making a single modeling technique insufficient to fully characterize the system of interest. In this work we employed a multiscale modeling approach, which bridges the phenomena of interest across different scales, to study the in vitro release from polymeric core/shell nanoparticles for cancer therapy loaded with an active compound assembled as a hydrophobic ion pair. The "computational microscope" provided by molecular dynamics simulations was used to track drug molecules through the release process at an atomic scale. The outcomes suggested that the drug is mainly partitioned in the polymer and released as hydrophobic ion pair rather than a free molecule, and that the hydrophobic ion pair is preferentially partitioned in Tween 20 micelles in the release media. A model at macroscale, aimed at describing the release rate and elucidating the release mechanism, was developed according to the results from molecular simulations and validated against experimental data. The outcomes provided insights that are challenging to be obtained experimentally and which supported the development and validation of a release model at macroscale. Overall, the adopted multiscale approach corroborated the experimental findings and provided significant insights into the mechanisms of release.