Earticle

This paper analyzes the temporal and spatial trends in hazardous material releases in the state of Texas between 2008 and 2013. The analysis was reserved to the top 10% of National Response Center related spills and incorporated the use of basic statistics and spatial analysis tools. A correlation was identified between the number of spills reported and the Bureau of Labor Statistics unemployment rates. Additionally, this research identified Equipment Error as the leading cause of an incident, contrary to previous analysis determining that Human Error was the main cause. Spatially, incidents are more frequently reported near the Gulf of Mexico, with a slight significance to the Dallas-Ft. Worth region in 2011. A positive correlation was identified between the population count of minority census groups per county and the number of chemical releases per county.

This paper applies transmit antenna selection algorithms to spatial-temporal combining-based spatial multiplexing (SM) ultra-wideband (UWB) systems. The employed criterion is based on the largest minimum output signal-to-noise ratio of the multiplexed streams. It is shown via simulations that the bit error rate (BER) performance of the SM UWB systems based on the two-dimensional Rake receiver is significantly improved by antenna diversity through transmit antenna selection on a log-normal multipath fading channel. When the transmit antenna diversity through antenna selection is exploited in the SM UWB systems, the BER performance of the spatial-temporal combining-based zero-forcing (ZF) receiver is also compared with that of the ZF detector followed by the Rake receiver.

The Yellow River Delta (YRD) wetlands are the largest coastal wetlands in China, and it serves to control soil erosion, nourish the climate and protect biodiversity. At present, due to climate change and human activities, the wetlands of the YRD are facing ecological and environmental problems such as species invasion, vegetation degradation, and biodiversity reduction. In order to study the evolution of wetland landscape types more intuitively, this paper proposed a semantic segmentation network based on the encoder and decoder structure of ResNet-18. Then, the wetland landscapes in the YRD were classified into five types by combining the Landsat series of remote sensing images. In addition, this paper used the optical flow algorithm to visualize the identification results, which can represent the evolution pattern of wetland landscape types in different years. The results of this paper have an important significance for the subsequent development planning and protection in the YRD.

Soil pollution resulting from petroleum hydrocarbons (PHCs) arising from industrialization and human activities has emerged as a progressively severe global concern. Establishing an accurate spatial distribution prediction model for PHCs through limited sampling data play an important role in understanding the migration characteristics of PHCs and effectively preventing soil pollution. This article employs soil samples within 8 m of a chemical plant, in conjunction with hydrogeological data, to model the spatial distribution of PHC content using a feedforward neural network (FNN). The prediction outcomes are characterized through three-dimensional visualization. The findings indicate that FNN demonstrates superior estimation accuracy compared to traditional interpolation method. Regarding the horizontal distribution within surface soil, there is pronounced lateral migration of PHC content in both the storage area and manufacturing shop, with migration aligning following the direction of groundwater. Vertically, PHC content exhibits a consistent pattern of increasing and then decreasing with greater depth. It is predominantly enriched in the lower section of the aeration zone and the upper part of the saturated zone, particularly within 4 m, under influence of groundwater. In this study, the prediction model offers an original approach to the spatial distribution of soil pollutants.

This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.

This paper presents the applications of Kriging spatial interpolation methods for meteorologic variables, including temperature and relative humidity, in regions of Vietnam. Three types of interpolation methods are used, which are as follows: Ordinary Kriging, Universal Kriging, and Universal Kriging plus Digital Elevation model correction. The input meteorologic data was collected from 98 ground weather stations throughout Vietnam and the outputs were interpolated temperature and relative humidity gridded fields, along with their error maps. The experimental results showed that Universal Kriging plus the digital elevation model correction method outperformed the two other methods when applied to temperature. The interpolation effectiveness of Ordinary Kriging and Universal Kriging were almost the same when applied to both temperature and relative humidity.

Sensors are deployed to gather physical, environmental data in sensor networks. Depending on scenarios, it is often assumed that it is difficult for batteries to be recharged or exchanged in sensors. Thus, sensors should be able to process users' queries in an energy-efficient manner. This paper proposes a spatial query processing scheme- Minimum Bounding Area Based Scheme. This scheme has a purpose to decrease the number of outgoing messages during query processing. To do that, each sensor has to maintain some partial information locally about the locations of descendent nodes. In the initial setup phase, the routing path is established. Each child node delivers to its parent node the location information including itself and all of its descendent nodes. A parent node has to maintain several minimum bounding boxes per child node. This scheme can reduce unnecessary message propagations for query processing. Finally, the experimental results show the effectiveness of the proposed scheme.

Purpose: We aimed to evaluate the accessibility and equity of automated external defibrillators (AEDs)in areas with a high concentration of older adults and to propose evidence-based policy directions usinggeographic information systems (GIS). Methods: For the secondary analysis, multiple datasets were integrated from the Korea's NationalGeographic Information Institute, the Ministry of the Interior and Safety, the Suwon Fire Department,and the Suwon Southern Fire Station, including continuous digital topographic maps, demographicstatistics, AED locations, road networks, and out-of-hospital cardiac arrest incidence data. The clusteringand spatial heterogeneity of AED deployment were examined using kernel density estimation andspatial autocorrelation analysis. Service areas reflecting the walking speeds of older adults weredelineated, and equity was assessed using the Gini coefficient. A two-step floating catchment areaanalysis was used to provide supply-to-demand ratios and accessibility indices. Results: AEDs exhibited clustered patterns centered on public locations, and several areas were identified as potential service blind spots, revealing inequities at the neighborhood level. Several AED siteswere insufficient for the local older-adult population, and only a subset of areas demonstrated relativelyhigh accessibility indices. Conclusion: As the population age structure shifts toward older age groups, AED policy criteria should beredesigned to reflect the mobility and accessibility of older adults. Implementing mixed relocationstrategies that incorporate age-friendly criteria, conducting periodic policy performance evaluations,and enhancing AED visibility and tailored education that accounts for the cognitive characteristics ofolder adults will contribute to improving the health of older populations.

Purpose: This study aimed to analyze the spatial distribution of hypertension prevalence and identify the demographic, behavioral, and community environmental factors associated with regional variation across 153 eup-myeon-dong administrative units in Chungcheongbuk-do, South Korea.Methods: Secondary data analysis was performed using Community Health Survey data from 62,411 adults aged ≥ 30 years (2017-2021). The prevalence of hypertension was defined as the proportion of individuals diagnosed with hypertension by a physician. Spatial statistical indicators were obtained from the Statistical Geographic Information Service (SGIS). Descriptive statistics, Pearson’s correlation, choropleth mapping, global Moran’s I, local indicators of spatial association (LISA), and ordinary least-squares (OLS) regression analyses were performed.Results: The prevalence of hypertension showed statistically significant spatial clustering (global Moran’s I=0.442, p<.001), with high-high clusters concentrated in rural eup-myeon areas and low-low clusters in urban dong areas. Despite confirmed clustering, the LM lag and error tests indicated no statistically significant spatial dependence in the regression residuals. The regression analysis showed that the prevalence of hypertension increased in areas with a higher proportion of older adults, greater obesity prevalence, and higher satisfaction with public transportation, whereas stronger trust among neighbors and greater satisfaction with healthcare access were associated with a lower prevalence.Conclusion: These findings support a shift toward tailored nursing and public health interventions at the sub-district level, prioritizing rural high-risk areas and reflecting the distinct demographic, behavioral, and environmental contexts of each community.

This paper presents a robust vehicle detection method for Low Earth Orbit satellite imagery degraded by environmental factors. To enhance feature representation from RGB and IR images, a denoising encoder and a multimodal fusion (MF) module with multi-head squeeze excitation are used. To compensate for texture loss in small vehicles at low resolution, a spatial attention module (SAM) is embedded into the YOLOv5 backbone, and a modified wide activation super-resolution (WDSR) branch is attached for learning finer-grained representations. Experiments on degraded images show that the proposed model outperforms the YOLO series and SuperYOLO in terms of mean average precision (mAP).

This research aims to determine the contents, cooking, and serving styles of local Turkish coffee kinds in Turkiye and to reveal their differences from traditional Turkish coffee. In addition, the distribution of local Turkish coffee kinds determined according to cities and regions was determined. Document analysis was applied to determine the features of local Turkish coffee kinds. In the document analysis, local Turkish coffee kinds are divided into three basic categories according to the codes of “coffee content,” “serving style,” “cooking method,” and “difference from traditional Turkish coffee.” Turkish coffee in the first category is prepared without using coffee powder, and local Turkish coffee kinds in the second category differ from traditional Turkish coffee with their features such as the coffee beans are roasted and ground, the presentation of the coffee, the degree of cooking, and the cooking method of the coffee. Different products (local products) are mixed into the coffees in the last local Turkish coffee category. Additionally, local Turkish coffees specific to cities were collected under the “city/region authenticity” code. Black Cumin Coffee, Mırra Coffee, Menengic Coffee, Kenger Coffee, Turkish coffee cooked in a cup, and Dibek Coffee are common local Turkish coffee kinds in some cities and regions.

Myanmar, also known as Burma, has a rich cultural heritage, and its historical tourist attractions well known around the world. Therefore, we designed and developed an interactive spatial augmented reality (iSAR) book on the cultural heritage of Myanmar. This iSAR book has total of 18 pages with rich media content including videos, animations, audio, and images featuring the cultural heritage of Myanmar in a digital format. In addition to virtual content, navigational features such as virtual buttons and touch-based hand gestures were implemented using Leap Motion and VVVV. Therefore, the developed iSAR book allows virtual content and navigational features to merge seamlessly into a physical book. Five participants were recruited to evaluate the prototype iSAR book, and interviews were conducted to gather their feedback based on its immersive qualities. Thus, the developed iSAR book on Myanmar effectively shares the cultural heritage of Myanmar, and ultimately allows users to explore and gain more insight into the country.

Three-dimensional (3D) human pose reconstruction from single-view image is a difficult and challenging topic. Existing approaches mostly process frame-by-frame independently while inter-frames are highly correlated in a sequence. In contrast, we introduce a novel spatial-temporal 3D human pose reconstruction framework that leverages both intra and inter-frame relationships in consecutive 2D pose sequences. Orthogonal matching pursuit (OMP) algorithm, pre-trained pose-angle limits and temporal models have been implemented. Several quantitative comparisons between our proposed framework and recent works have been studied on CMU motion capture dataset and Vietnamese traditional dance sequences. Our framework outperforms others by 10% lower of Euclidean reconstruction error and more robust against Gaussian noise. Additionally, it is also important to mention that our reconstructed 3D pose sequences are more natural and smoother than others.

The exceptional development of electronic device technology, the miniaturization of mobile devices, and the development of telecommunication technology has made it possible to monitor human biometric data anywhere and anytime by using different types of wearable or embedded sensors. In daily life, mobile devices can collect wireless body area network (WBAN) data, and the co-collected location data is also important for disease analysis. In order to efficiently analyze WBAN data, including location information and support medical analysis services, we propose a geohash-based spatial index method for a location-aware WBAN data monitoring system on the NoSQL database system, which uses an R-tree-based global tree to organize the real-time location data of a patient and a B-tree-based local tree to manage historical data. This type of spatial index method is a support cloud-based location-aware WBAN data monitoring system. In order to evaluate the proposed method, we built a system that can support a JavaScript Object Notation (JSON) and Binary JSON (BSON) document data on mobile gateway devices. The proposed spatial index method can efficiently process location-based queries for medical signal monitoring. In order to evaluate our index method, we simulated a small system on MongoDB with our proposed index method, which is a document-based NoSQL database system, and evaluated its performance.

In this study, we consider visible light communication in an indoor line-of-sight environment. It has been proved that among the multiple input multiple output (MIMO) techniques, spatial modulation (SM) performs better than repetition coding (RC) and spatial multiplexing (SMP). On the basis of a combination of SM and pulse amplitude modulation (PAM), here, we propose an enhanced SM algorithm to improve the bit error rate. Traditional SM activates only one light-emitting diode (LED) at one time, and the proposed enhanced SM activates two LEDs at one time and reduces the intensity levels of PAM by half. Under the condition of a highly correlated channel, power imbalance is used to improve the algorithm performance. The comparison between the two schemes is implemented at the same signal-to-noise ratio. The simulation results illustrate that the enhanced SM outperforms the traditional SM in both highly correlated and lowly correlated channels. Furthermore, the proposed enhanced SM scheme can increase the transmission rate in most cases.

This paper deals with human spatial cognition using visual and auditory stimulation. More specially, this investigation is to observe the relationship between the head and the eye motor system for the localization of visual target direction in space and to try to describe what is the role of right-side versus left-side pinna. In the experiment of visual stimulation, nineteen red LEDs (Luminescent Diodes, Brightness: $210\;cd/^2$) arrayed in the horizontal plane of the surrounding panel are used. Here the LEDs are located 10 degrees apart from each other. Physiological parameters such as EOG (Electro-Oculography), head movement, and their synergic control are measured by BIOPAC system and 3SPACE FASTRAK. In the experiment of auditory stimulation, one side of the pinna function was distorted intentionally by inserting a short tube in the ear canal. The localization error caused by right and left side pinna distortion was investigated as well. Since a laser pointer showed much less error (0.5%) in localizing target position than FASTRAK (30%) that has been generally used, a laser pointer was used for the pointing task. It was found that harmonic components were not essential for auditory target localization. However, non-harmonic nearby frequency components was found to be more important in localizing the target direction of sound. We have found that the right pinna carries out one of the most important functions in localizing target direction and pure tone with only one frequency component is confusing to be localized. It was also found that the latency time is shorter in self moved tracking (SMT) than eye alone tracking (EAT) and eye hand tracking (EHT). These results can be used in further study on the characterization of human spatial cognition.

Although methods based on supervised learning have demonstrated remarkable performance on fall detection, these existing fall detection algorithms require a substantial quantity of manually labeled training data. In this paper, we combine dilated convolution and LSTM based on auto-encoder, which can be trained on unlabeled data, further saving time and resources, and a novel fall score is computed based on the high-quality reconstructed frame to detect falls. Extensive experimental results indicate that the proposed method further boosts the performance, achieving recognition rate of 97.1%, sensitivity rate of 93.9% and precision rate of 95.1% on the UR dataset.

Deep learning plays a major role in the present video processing tools and algorithms. To? alleviate the limitations of present in-loop filters in the scalable extension of HEVC (SHVC), a combined residual network (CResNet) in-loop filtering is proposed in this paper. The proposed CResNet in-loop filter exploits layer information available in the spatial temporal domain to restrain the visual artifacts like blocking and ringing. Particularly, the block information related to current and co-located blocks of the spatial and temporal base layer reference frames are considered to optimize the in-loop filtering. The proposed architecture has four convolution layers at the base layer and two convolution layers at the enhancement layer that significantly reduces the coding complexity and memory. Additionally to completely train the input content and also to enhance the in-loop filter performance, the on/off level control flag for coding tree Unit (CTU) is sensed using rate distortion optimization (RDO) approach. The experimental results demonstrate that the proposed architecture provides up to 6.2% to 7.2% reduction in bit rate and 1.01 dB improvement in PSNR compared to the standard SHVC codec.

The Netherlands is a human-made country and an extremely well-designed European country as well. The general Dutch spatial planning for the city and environment takes place at a regional level. The local community determines the primary development conditions, and the prospect is included in a legally binding land-use plan. Especially, Rotterdam is a representative port city as the center of world trade and the gateway to western Europe. According to the history of war, the city reconstruction and the movement of the port area have led to a general change in Rotterdam and the regional redevelopment project on the southern port area of Mass river for the expansion of city functions and the balanced development. The research purpose is to understand the spatial development of the Netherlands city-region based on the analysis regarding the Kop van Zuid project, which is a representative implemented case in Rotterdam. The theoretical framework is the five dimensions and twelve indicators of territorial governance from the TANGO research project by the EU. The target case is assessed by planning and social aspect, respectively, and the results are discussed based on the theoretical framework. This research has the possibility to be utilized as advanced research by the European perspective for spatial development in other city-regions with the port area, such as Incheon and Busan in Korea.

Advances in information and mobile technologies have changed the traditional firm ways of working very flexible, collaborative and innovative, resulting in the changes in work place structures and layouts. Despite the growing body of literature examining the novel ways of working, which is called smart work, there is little academic attentions paid to the spatial aspects of new work places, namely smart offices and smart work centers. This research explores the spatial changes of work places that improve business efficiency and collaboration among workers suitable for the novel ways of working. Conducting in-depth field surveys on selected cases, we analyzed the changes in spatial structure and operation policies of smart offices and smart work centers. From this survey, we observed that the new work places under study take various novel spatial forms and they have flexible operating policies such as flexible seating and flexible work hours. We also found that it would be difficult to change existing business practices and typical ways of performing tasks, without changing the bureaucratic spatial designs and layouts. Future studies are suggested to examine how spatial structures and layouts of offices have impacts on space utilization, collaboration, creativity, and job satisfaction.