Search

Facing the escalating global challenge of frequent and severe heatwaves, this study meticulously assesses heatwave dynamics across urban and nonurban areas in central Thailand. It introduces a novel workflow integrating ground-based observations with satellite-derived land surface temperature data over 39 years (1981–2019). Our findings reveal a significant increase in daytime heatwaves in urban and peri-urban areas, with notable rises in the number, frequency, duration, and amplitude of heatwaves. Conversely, nighttime heatwaves intensify mainly in rural areas. Land surface temperature data show distinct patterns: peri-urban regions experience significant daytime increases in heatwave magnitude, amplitude, and frequency, contrasting with varied trends in urban and rural settings. The annual pattern of heatwave characteristics across specific regions reveals that daytime occurrences are more frequent and intense in peri-urban zones such as Pathum Thani and eastern Bangkok, with annual episodes ranging from 2 to 9 and durations of 10 to 39 days. In contrast, urban areas such as downtown Bangkok are more prone to nighttime heatwaves, with a wider occurrence range of 3 to 12 events and longer durations, lasting from 13 to 62 days annually. Overall, this research advances traditional methods by offering a nuanced view of heatwave dynamics and highlighting the potential of remote sensing to identify risk areas. The study demonstrates how this precise technique can identify extreme weather events and support sustainable climate practices, government policy, and decision-making, all of which are crucial for enhancing resilience and addressing the growing threat of heat-related health risks.

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/right leg pocket. The performance of traditional machine learning algorithms (Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), and XGBoost) is compared against deep learning models (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer models) under two sensor configurations. Our findings highlight that the Temporal Convolutional Network (TCN) model consistently outperforms other models, particularly in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. Deep learning models such as LSTM, GRU, and Transformer also demonstrate strong performance, showcasing their effectiveness in capturing temporal dependencies in HAR tasks. Traditional machine learning models, including RF and XGBoost, provide reasonable performance but do not match the accuracy of deep learning models. Additionally, incorporating data from linear accelerometers and gravity sensors led to slight improvements over using accelerometer and gyroscope data alone. This research enhances the recognition of passenger behaviors for intelligent transportation systems, contributing to more efficient congestion management and emergency response strategies.

Urban planning and management increasingly depend on accurate building and population data. However, many regions lack sufficient resources to acquire and maintain these data, creating challenges in data availability. Our methodology integrates multiple data sources, including aerial imagery, Points of Interest (POIs), and digital elevation models, employing Light Gradient Boosting Machine (LightGBM) and Gradient Boosting Decision Tree (GBDT) to classify building uses and morphological filtration to estimate heights. This research contributes to bridging the gap between data needs and availability in resource-constrained urban environments, offering a scalable solution for global application in urban planning and population mapping.

In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature (Tair) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis (p<0.05) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability.

This review addressed the recent progress of sintered reaction‐bonded Si3N4 (SRBSN) ceramics which are expected to be used as ceramic substrates of the next-generation power devices. Following an overview of thermal conductivities of Si3N4 ceramics and their improvement strategies, we described the advantages of the SRBSN routes. Because of the low content of lattice oxygen, the SRBSN ceramics possess significantly high thermal conductivities, compared to Si3N4 ceramics prepared by conventional methods. This material also showed exceptionally high fracture toughness and the strong rising R-curve behavior. The properties of the SRBSN ceramics are largely affected by the nitridation conditions, including the temperature, pressure and heating rate, and the sintering additive ratios. The thermal fatigue tests revealed the excellent thermal fatigue resistance of the SRBSN substrates, compared with those of other ceramic substrates. The strength retention ratio after thermal fatigue was clearly proportional to the fracture toughness. The dielectric breakdown strength (DBS) of the SRBSN ceramics with large fibrous grains showed a thickness dependency reverse to the normal (lower DBS in thinner substrates).

Abstract Background Malaria is one of the most prevalent and deadliest illnesses in sub-Saharan Africa. Despite recent gains made towards its control, many African countries still have endemic malaria transmission. This study aimed to assess malaria burden at household level in Kongo central province, Democratic Republic of Congo (DRC), and the impact of community participatory Water, Sanitation and Hygiene (WASH) Action programme. Methods Mixed method research was conducted in two semi-rural towns, Mbanza-Ngungu (a WASH action site) and Kasangulu (a WASH control site) in DRC between 1 January 2017 through March 2018, involving 625 households (3,712 household members). Baseline and post-intervention malaria surveys were conducted with the use of World Bank/WHO Malaria Indicator Questionnaire. An action research consisting of a six-month study was carried out which comprised two interventions: a community participatory WASH action programme aiming at eliminating mosquito breeding areas in the residential environment and a community anti-malaria education campaign. The latter was implemented at both study sites. In addition, baseline and post-intervention malaria rapid diagnostic test (RDT) was performed among the respondents. Furthermore, a six-month hospital-based epidemiological study was conducted at selected referral hospitals at each site from 1 January through June 2017 to determine malaria trend. Results Long-lasting insecticide-treated net (LLIN) was the most commonly used preventive measure (55%); 24% of households did not use any measures. Baseline malaria survey showed that 96% of respondents (heads of households) reported at least one episode occurring in the previous six months; of them only 66.5% received malaria care at a health setting. In the Action Research, mean incident household malaria cases decreased significantly at WASH action site (2.3 ± 2.2 cases vs. 1.2 ± 0.7 cases, respectively; p

Abstract Background Procalcitonin (PCT) is a useful marker for pneumonia. However, its clinical usefulness in elderly patients has not been studied extensively. This study aimed to assess the relationship between PCT and prognosis and pneumonia severity in elderly patients with pneumonia acquired outside the hospital. Methods Data considered relevant to pneumonia severity and prognosis were retrospectively obtained from clinical charts of all patients with pneumonia who were admitted to our hospital from 2010 to 2017. The primary outcome was 30-day mortality in elderly patients (aged ≥75 years), and the relationship between PCT levels and pneumonia severity, as determined by the pneumonia severity index (PSI) was also examined. Results Data were collected from 667 patients, of which 436 were elderly patients. Multivariate and receiver operating characteristic curve analysis revealed that albumin, body mass index, and PSI class rather than PCT are important factors related to 30-day mortality in elderly patients. PCT was also not an independent prognostic factor in younger patients. PCT levels significantly differed by pneumonia severity (mild, moderate, and severe) in both younger (p

Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

Cachexia is an intractable metabolic disorder that causes extreme weight loss. It is a symptom of many chronic diseases, including cancer, liver failure, congestive heart failure and chronic kidney disease, and there is as yet no effective treatment. While the mechanisms underlying cachexia are complex, it is often accompanied by elevated angiotensin II (Ang II). Human placental extract (HPE) is a source of numerous biologically active molecules and has been used clinically to treat chronic hepatitis, liver cirrhosis and other chronic diseases. Here, we investigated the effects of HPE in an Ang II-induced cachexia model in mice. HPE treatment preserved both fat mass and lean body mass and suppressed weight loss in the cachexia model, though food intake was unaffected. Ang II infusion also caused cardiac hypertrophy and fibrosis. HPE suppressed these effects as well as Ang II-induced cardiac expression of genes related to heart failure and cardiac remodeling. HPE also reversed Ang II-induced downregulation of mitochondria-related molecules and suppressed cardiac inflammation and oxidative stress. HPE administration may thus be an effective approach to the treatment of cachexia, cardiac hypertrophy and fibrosis.

Hepatic iron deposition is seen in cases of chronic hepatitis and cirrhosis, and is a hallmark of a poorer prognosis. Iron deposition is also found in non-alcoholic steatohepatitis (NASH) patients. We have now developed a mouse model of NASH with hepatic iron deposition by combining a methione- and choline-deficient (MCD) diet with an iron-overload diet. Using this model, we evaluated the effects of human placenta extract (HPE), which has been shown to ameliorate the pathology of NASH. Four-week-old male C57BL/6 mice were fed the MCD diet with 2% iron for 12 weeks. In liver sections, iron deposition was first detected around the portal vein after 1 week. From there it spread throughout the parenchyma. Biliary iron concentrations were continuously elevated throughout the entire 12-week diet. As a compensatory response, the diet caused elevation of serum hepcidin, which accelerates excretion of iron from the body. Accumulation of F4/80-positive macrophages was detected within the sinusoids from the first week onward, and real-time PCR analysis revealed elevated hepatic expression of genes related inflammation and oxidative stress. In the model mice, HPE treatment led to a marked reduction of hepatic iron deposition with a corresponding increase in biliary iron excretion. Macrophage accumulation was much reduced by HPE treatment, as was the serum oxidation-reduction potential, an index of oxidative stress. These data indicate that by suppressing inflammation, oxidative stress and iron deposition, and enhancing iron excretion, HPE effectively ameliorates iron overload-induced liver injury. HPE administration may thus be an effective strategy for treating NASH.

The authors wish to make the following corrections to this paper [...]

Recently, object detectors based on deep learning have become widely used for vehicle detection and contributed to drastic improvement in performance measures. However, deep learning requires much training data, and detection performance notably degrades when the target area of vehicle detection (the target domain) is different from the training data (the source domain). To address this problem, we propose an unsupervised domain adaptation (DA) method that does not require labeled training data, and thus can maintain detection performance in the target domain at a low cost. We applied Correlation alignment (CORAL) DA and adversarial DA to our region-based vehicle detector and improved the detection accuracy by over 10% in the target domain. We further improved adversarial DA by utilizing the reconstruction loss to facilitate learning semantic features. Our proposed method achieved slightly better performance than the accuracy achieved with the labeled training data of the target domain. We demonstrated that our improved DA method could achieve almost the same level of accuracy at a lower cost than non-DA methods with a sufficient amount of labeled training data of the target domain.

Due to the fact that geospatial information technology is considered necessary for disaster risk management (DRM), the need for more effective collaborations between providers and end users in data delivery is increasing. This paper reviews the following: (i) schemes of disaster risk management and collaborative data operation in DRM; (ii) geospatial information technology in terms of applications to the schemes reviewed; and (iii) ongoing practices of collaborative data delivery with the schemes reviewed. This paper concludes by discussing the future of collaborative data delivery and the progress of the technologies.

In this study, a novel unsupervised method for global urban area mapping is proposed. Different from traditional clustering-based unsupervised methods, in our approach a labeler is designed, which is able to automatically select training samples from satellite images by propagating common urban/non-urban knowledge through the unlabeled data. Two kinds of satellite images, captured by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Phased Array L-band Synthetic Aperture Radar (PALSAR), are exploited here. In this method, spectral features are first extracted from the original dataset, followed by coarse prediction of urban/non-urban areas via weak classifiers. By developing an improved belief-propagation based clustering algorithm, a confidence map is obtained and training data are selected via weighted sampling. Finally, the urban area map is obtained by employing the Support Vector Machine (SVM) classifier. The proposed method can generate urban areamaps at a resolution of 15 m, while the same settings are used for all test cases. Experimental results involving 75 scenes from different climate zones show that our proposed method achieves an overall accuracy of 84.4% and a kappa coefficient of 0.628, which is competitive relative to the supervised SVM method.

Non-alcoholic steatohepatitis (NASH) is a severe form of fatty liver disease that is defined by the presence of inflammation and fibrosis, ultimately leading to cirrhosis and hepatocellular carcinoma. Treatment with human placental extract (HPE) reportedly ameliorates the hepatic injury. We evaluated the effect of HPE treatment in a mouse model of NASH. In the methione- and choline-deficient (MCD) diet-induced liver injury model, fibrosis started from regions adjacent to the sinusoids. We administered the MCD diet with high-salt loading (8% NaCl in the drinking water) to mice deficient in the vasoprotective molecule RAMP2 for 5 weeks, with or without HPE. In both the HPE and control groups, fibrosis was seen in regions adjacent to the sinusoids, but the fibrosis was less pronounced in the HPE-treated mice. Levels of TNF-α and MMP9 expression were also significantly reduced in HPE-treated mice, and oxidative stress was suppressed in the perivascular region. In addition, HPE dose-dependently increased survival of cultured endothelial cells exposed to 100 μM H2O2, and it upregulated expression of eNOS and the anti-apoptotic factors bcl-2 and bcl-xL. From these observations, we conclude that HPE ameliorates NASH-associated pathologies by suppressing inflammation, oxidative stress and fibrosis. These beneficially effects of HPE are in part attributable to its protective effects on liver sinusoidal endothelial cells. HPE could thus be an attractive therapeutic candidate with which to suppress progression from simple fatty liver to NASH.

We developed a ground truth database for urban areas from the Global Rural-Urban Mapping Project (GRUMP) Settlement Points gazetteer of populated place names by visually interpreting 3,734 urban points on satellite images, thus acquiring 2,144 urban and 1,388 non-urban data points. Our database contained many more urban data points than the existing databases, which had only 0 to 11 ground truth data points. We used our database in combination with the Degree Confluence Project database to assess the accuracy of eight satellite-derived urban area maps, among which the MODIS Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid was the most accurate (84% overall accuracy; kappa coefficient, 0.63). Moreover, the most recently published maps were not necessarily the most accurate. We compared the accuracy assessment results of our database with those of another database and found that ours detected more errors of commission but included less chance agreement.

Two patients with granulomatous lung lesions thought to be related to Crohn's disease (CD) are reported. Patient 1 was a 43-year-old man who was diagnosed with CD at age 11 years. He developed a fever in the 38 °C, and a chest X-ray and CT scan showed infiltrates with air bronchograms in the right upper lobe and left lingular segment. Transbronchial lung biopsy (TBLB) revealed granulomatous lesions. Patient 2 was a 76-year-old woman who was diagnosed with CD at age 44 years. Chest CT showed infiltrates and nodular shadows in both lung fields. Video-assisted thoracoscopic surgery (VATS) in June 2012 revealed granulomatous lesions. Tuberculosis, fungal infections, drug-induced lung disorder, and sarcoidosis were ruled out as a cause of the granulomatous lesions in both patients. The aetiology was thought to be CD.

Recently, deep learning techniques have had a practical role in vehicle detection. While much effort has been spent on applying deep learning to vehicle detection, the effective use of training data has not been thoroughly studied, although it has great potential for improving training results, especially in cases where the training data are sparse. In this paper, we proposed using hard example mining (HEM) in the training process of a convolutional neural network (CNN) for vehicle detection in aerial images. We applied HEM to stochastic gradient descent (SGD) to choose the most informative training data by calculating the loss values in each batch and employing the examples with the largest losses. We picked 100 out of both 500 and 1000 examples for training in one iteration, and we tested different ratios of positive to negative examples in the training data to evaluate how the balance of positive and negative examples would affect the performance. In any case, our method always outperformed the plain SGD. The experimental results for images from New York showed improved performance over a CNN trained in plain SGD where the F1 score of our method was 0.02 higher.

Senescence-accelerated mouse prone 8 (SAMP8) and prone 10 (SAMP10) are useful murine model of accelerated aging. SAMP8 shows marked impairment of learning and memory, whereas SAMP10 shows brain atrophy and aging-associated depressive behavior. This study examined the expression of glial cell line-derived neurotrophic factor (GDNF) in SAMP8 and SAMP10 brains, relative to that in SAM resistant 1 (SAMR1) controls, which age normally. Hippocampal GDNF mRNA expression decreased in an age-dependent manner (10- vs 2-month-old animals) in the SAMR1, but not in the SAMP8 or SAMP10 strains. Furthermore, GDNF mRNA expression in 2-month-old SAMP8 and SAMP10 strains was less than in SAMR1 specimens of the same age. The number of surviving neurons in the CA1 region decreased with age in SAMP8 and SAMP10, and also decreased relative to the number of neurons in 10-month-old SAMR1 controls. Immunohistochemistry revealed that cells that were positive for GDNF-like activity in 10-month-old SAMP8 and SAMP10 were diffusely distributed, in part, around the pyramidal cell layer in the hippocampus. These findings suggest that low GDNF expression in young SAMP8 and SAMP10 may be involved in hippocampal dysfunctions, such as age-related learning impairment and neuronal death.

In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE) RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost) and convolutional neural networks (CNN). To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.