Search results for: spatiotemporal fusion

Authors: Ma Lei-Lei, Zhou You

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Aiming at the problems of low detection accuracy of deep learning algorithm in traffic sign detection, this paper proposes improved EfficientDet based traffic sign detection algorithm. Multi-head self-attention is introduced in the minimum resolution layer of the backbone of EfficientDet to achieve effective aggregation of local and global depth information, and this study proposes an improved feature fusion pyramid with increased vertical cross-layer connections, which improves the performance of the model while introducing a small amount of complexity, the Balanced L1 Loss is introduced to replace the original regression loss function Smooth L1 Loss, which solves the problem of balance in the loss function. Experimental results show, the algorithm proposed in this study is suitable for the task of traffic sign detection. Compared with other models, the improved EfficientDet has the best detection accuracy. Although the test speed is not completely dominant, it still meets the real-time requirement.

Keywords: convolutional neural network, transformer, feature pyramid networks, loss function

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Authors: Norbert Csizmadia

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The 21st Century's main power centers are the cities. More than 52% of the world’s population lives in cities, in particular in the megacities which have a population over 10 million people and is still growing. According to various research and forecasts, the main economic concentration will be in 40 megacities and global centers. Based on various competitiveness analyzes and indices, global city centers, and city networks are outlined, but if we look at other aspects of urban development like complexity, connectivity, creativity, technological development, viability, green cities, pedestrian and child friendly cities, creative and cultural centers, cultural spaces and knowledge centers, we get a city competitiveness index with quite new complex indicators. The research shows this result. In addition to the megacities and the global centers, with the investigation of functionality, we got 64 so-called ‘fusiononopolis’ (i.e., fusion-polis) which stand for the most decisive economic power centers of the 21st century. In this city competition Asian centers considerably rise, as the world's functional city competitiveness index is being formed.

Keywords: economic geography, human geography, technological development, urbanism

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Authors: Mounir Azzam, Valerie Graw, Andreas Rienow

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The Syrian war, which commenced in 2011, has resulted in significant changes in the real estate market in the Damascus metropolitan area, with rising levels of insecurity and disputes over tenure rights. The quest for spatial justice is, therefore, imperative, and this study performs a spatiotemporal analysis to investigate the impact of the war on real estate differentiation. Using the hedonic price models including 2,411 housing transactions over the period 2010-2022, this study aims to understand the spatial dynamics of the real estate market in wartime. Our findings indicate that war variables have had a significant impact on the differentiation and depreciation of property prices. Notably, property attributes have a more substantial impact on real estate values than district location, with severely damaged buildings in Damascus city resulting in an 89% decline in prices, while prices in Rural Damascus districts have decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google Earth Engine. We monitored 250 samples from hedonic datasets within three different years of the Syrian war (2015, 2019, and 2022). Our findings show that correlation values were highly differentiated, particularly among Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall between 2015 and 2019, they improved slightly after 2019. The findings have valuable implications, not only for investment prospects in setting up a successful reconstruction strategy but also for spatial justice of property rights in strongly encouraging sustainable real estate development.

Keywords: hedonic price, real estate differentiation, reconstruction strategy, spatial justice, urban texture analysis

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Authors: Svilen Dimitrov, Manthan Pancholi, Norbert Schmitz, Didier Stricker

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This paper presents the end-to-end development of a wearable motion sensing harness consisting of computational unit and four inertial measurement units to control three smartphone therapeutic games for children. The inertial data is processed in real time to obtain lower body motion information like knee raises, feet taps and squads. By providing a Wi-Fi connection interface the sensor harness acts wireless remote control for smartphone applications. By performing various lower body movements the users provoke corresponding game state changes. In contrary to the current similar offers, like Nintendo Wii Remote, Xbox Kinect and Playstation Move, this product, consisting of the sensor harness and the applications on top of it, are fully wearable, which means they do not rely on the user to be bound to concrete soft- or hardwareequipped space.

Keywords: wearable harness, inertial measurement units, smartphone therapeutic games, motion tracking, lower-body activity monitoring

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Authors: Farzam Fatolazadeh, Kalifa Goita, Mehdi Eshagh, Shusen Wang

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The Gravity Recovery and Climate Experiment (GRACE) is a satellite mission with twin satellites for the precise determination of spatial and temporal variations in the Earth’s gravity field. The products of this mission are monthly global gravity models containing the spherical harmonic coefficients and their errors. These GRACE models can be used for estimating terrestrial water storage (TWS) variations across the globe at large scales, thereby offering an opportunity for surface and groundwater storage (GWS) assessments. Yet, the ability of GRACE to monitor changes at smaller scales is too limited for local water management authorities. This is largely due to the low spatial and temporal resolutions of its models (~200,000 km2 and one month, respectively). High-resolution GRACE data products would substantially enrich the information that is needed by local-scale decision-makers while offering the data for the regions that lack adequate in situ monitoring networks, including northern parts of Canada. Such products could eventually be obtained through downscaling. In this study, we extended the spectral combination theory to simultaneously downscale spatiotemporally the 3o spatial coarse resolution of GRACE to 0.25o degrees resolution and monthly coarse resolution to daily resolution. This method combines the monthly gravity field solution of GRACE and daily hydrological model products in the form of both low and high-frequency signals to produce high spatiotemporal resolution TWSA and GWSA products. The main contribution and originality of this study are to comprehensively and simultaneously consider GRACE and hydrological variables and their uncertainties to form the estimator in the spectral domain. Therefore, it is predicted that we reach downscale products with an acceptable accuracy.

Keywords: GRACE satellite, groundwater storage, spectral combination, terrestrial water storage

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Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

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Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

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Authors: Yagya Raj Pandeya, Joonwhoan Lee

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The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network.

Keywords: multimodal, 3D convolution, music-video feature extraction, generalized mean

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Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

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The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph

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Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

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A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

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Authors: Miguel Ramirez, Ivan Lizarazo

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The emergence of various interpretations of sustainability, including weak and strong paradigms, can be traced back to the definition of sustainable development provided in the 1987 Brundtland report and the subsequent evolution of the sustainability concept. However, there has been limited scholarly attention given to clarifying the concept of sustainability within the theoretical and conceptual framework of geography. The discipline has predominantly been focused on understanding the diverse conceptions of sustainability within its epistemological boundaries, resulting in tensions between sustainability paradigms and their associated dimensions, including the incorporation of political perspectives, with particular emphasis on environmental geography's epistemology. In response to this gap, a conceptual framework for sustainability is proposed, effectively integrating spatial and territorial concepts. This framework aims to enhance geography's role in contributing to sustainability by utilizing the land system theory, which is based on the dynamics of land use change. Such an integrated conceptual framework enables incorporating methodological tools such as remote sensing, encompassing various earth observations and fusion methods, and supervised classification techniques. Additionally, it looks for better integration of socioecological information, thereby capturing essential population-related features.

Keywords: geography, sustainability, land change science, territorial sustainability

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Authors: Mosaad Khadr

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Knowledge of the temporal and spatial variability of rainfall trends has been of great concern for efficient water resource planning, management. In this study annual, seasonal and monthly rainfall trends over the Sinai Peninsula were analyzed by using absolute homogeneity tests, nonparametric Mann–Kendall (MK) test and Sen’s slope estimator methods. The homogeneity of rainfall time-series was examined using four absolute homogeneity tests namely, the Pettitt test, standard normal homogeneity test, Buishand range test, and von Neumann ratio test. Further, the sequential change in the trend of annual and seasonal rainfalls is conducted using sequential MK (SQMK) method. Then the trend analysis based on discrete wavelet transform technique (DWT) in conjunction with SQMK method is performed. The spatial patterns of the detected rainfall trends were investigated using a geostatistical and deterministic spatial interpolation technique. The results achieved from the Mann–Kendall test to the data series (using the 5% significance level) highlighted that rainfall was generally decreasing in January, February, March, November, December, wet season, and annual rainfall. A significant decreasing trend in the winter and annual rainfall with significant levels were inferred based on the Mann-Kendall rank statistics and linear trend. Further, the discrete wavelet transform (DWT) analysis reveal that in general, intra- and inter-annual events (up to 4 years) are more influential in affecting the observed trends. The nature of the trend captured by both methods is similar for all of the cases. On the basis of spatial trend analysis, significant rainfall decreases were also noted in the investigated stations. Overall, significant downward trends in winter and annual rainfall over the Sinai Peninsula was observed during the study period.

Keywords: trend analysis, rainfall, Mann–Kendall test, discrete wavelet transform, Sinai Peninsula

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Authors: Yichin Chen

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Taiwan is an island characterized by an active tectonics and high erosion rates. Monitoring the dynamic landscape of Taiwan is an important issue for disaster mitigation, geomorphological research, and watershed management. Long-term and high spatiotemporal landform data is essential for quantifying and simulating the geomorphological processes and developing warning systems. Recently, the advances in unmanned aerial vehicle (UAV) and computational photogrammetry technology have provided an effective way to rebuild and monitor the topography changes in high spatio-temporal resolutions. This study rebuilds the 30-years landform change in the Aiyuzi watershed in 1986-2017 by using the aerial photogrammetry-based techniques. The Aiyuzi watershed, located in central Taiwan and has an area of 3.99 Km², is famous for its frequent landslide and debris flow disasters. This study took the aerial photos by using UAV and collected multi-temporal historical, stereo photographs, taken by the Aerial Survey Office of Taiwan’s Forestry Bureau. To rebuild the orthoimages and digital surface models (DSMs), Pix4DMapper, a photogrammetry software, was used. Furthermore, to control model accuracy, a set of ground control points was surveyed by using eGPS. The results show that the generated DSMs have the ground sampling distance (GSD) of ~10 cm and ~0.3 cm from the UAV’s and historical photographs, respectively, and vertical error of ~1 m. By comparing the DSMs, there are many deep-seated landslides (with depth over 20 m) occurred on the upstream in the Aiyuzi watershed. Even though a large amount of sediment is delivered from the landslides, the steep main channel has sufficient capacity to transport sediment from the channel and to erode the river bed to ~20 m in depth. Most sediments are transported to the outlet of watershed and deposits on the downstream channel. This case study shows that UAV and photogrammetry technology are useful for topography change monitoring effectively.

Keywords: aerial photogrammetry, landslide, landform change, Taiwan

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Authors: Athina Puype, Lorenzo Malerba, Nico De Wispelaere, Roumen Petrov, Jilt Sietsma

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Reduced activation ferritic/martensitic (RAFM) steels like EUROFER97 are primary candidate structural materials for first wall application in the future demonstration (DEMO) fusion reactor. Existing steels of this type obtain their functional properties by a two-stage heat treatment, which consists of an annealing stage at 980°C for thirty minutes followed by quenching and an additional tempering stage at 750°C for two hours. This thermal quench and temper (Q&T) treatment creates a microstructure of tempered martensite with, as main precipitates, M23C6 carbides, with M = Fe, Cr and carbonitrides of MX type, e.g. TaC and VN. The resulting microstructure determines the mechanical properties of the steel. The ductility is largely determined by the tempered martensite matrix, while the resistance to mechanical degradation, determined by the spatial and size distribution of precipitates and the martensite crystals, plays a key role in the high temperature properties of the steel. Unfortunately, the high temperature response of EUROFER97 is currently insufficient for long term use in fusion reactors, due to instability of the matrix phase and coarsening of the precipitates at prolonged high temperature exposure. The objective of this study is to induce grain refinement by appropriate modifications of the processing route in order to increase the high temperature strength of a lab-cast EUROFER RAFM steel grade. The goal of the work is to obtain improved mechanical behavior at elevated temperatures with respect to conventionally heat treated EUROFER97. A dilatometric study was conducted to study the effect of the annealing temperature on the mechanical properties after a Q&T treatment. The microstructural features were investigated with scanning electron microscopy (SEM), electron back-scattered diffraction (EBSD) and transmission electron microscopy (TEM). Additionally, hardness measurements, tensile tests at elevated temperatures and Charpy V-notch impact testing of KLST-type MCVN specimens were performed to study the mechanical properties of the furnace-heated lab-cast EUROFER RAFM steel grade. A significant prior austenite grain (PAG) refinement was obtained by lowering the annealing temperature of the conventionally used Q&T treatment for EUROFER97. The reduction of the PAG results in finer martensitic constituents upon quenching, which offers more nucleation sites for carbide and carbonitride formation upon tempering. The ductile-to-brittle transition temperature (DBTT) was found to decrease with decreasing martensitic block size. Additionally, an increased resistance against high temperature degradation was accomplished in the fine grained martensitic materials with smallest precipitates obtained by tailoring the annealing temperature of the Q&T treatment. It is concluded that the microstructural refinement has a pronounced effect on the DBTT without significant loss of strength and ductility. Further investigation into the optimization of the processing route is recommended to improve the mechanical behavior of RAFM steels at elevated temperatures.

Keywords: ductile-to-brittle transition temperature (DBTT), EUROFER, reduced activation ferritic/martensitic (RAFM) steels, thermal treatments

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Authors: Fengqing Li, Petra Schneider

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Failure of tailings management facilities (TMF) of radioactive residues is an enormous challenge worldwide and can result in major catastrophes. Particularly in transboundary regions, such failure is most likely to lead to international conflict. This risk occurs in Kyrgyzstan and Uzbekistan, where the current major challenge is the quantification of impacts due to pollution from uranium legacy sites and especially the impact on river basins after natural hazards (i.e., landslides). By means of GoldSim, a probabilistic simulation model, the amount of tailing material that flows into the river networks of Mailuu Suu in Kyrgyzstan after pond failure was simulated for three scenarios, namely 10%, 20%, and 30% of material inputs. Based on Muskingum-Cunge flood routing procedure, the peak value of uranium flood wave along the river network was simulated. Among the 23 TMF, 19 ponds are close to the river networks. The spatiotemporal distributions of uranium along the river networks were then simulated for all the 19 ponds under three scenarios. Taking the TP7 which is 30 km far from the Kyrgyzstan-Uzbekistan border as one example, the uranium concentration decreased continuously along the longitudinal gradient of the river network, the concentration of uranium was observed at the border after 45 min of the pond failure and the highest value was detected after 69 min. The highest concentration of uranium at the border were 16.5, 33, and 47.5 mg/L under scenarios of 10%, 20%, and 30% of material inputs, respectively. In comparison to the guideline value of uranium in drinking water (i.e., 30 µg/L) provided by the World Health Organization, the observed concentrations of uranium at the border were 550‒1583 times higher. In order to mitigate the transboundary impact of a radioactive pollutant release, an integrated framework consisting of three major strategies were proposed. Among, the short-term strategy can be used in case of emergency event, the medium-term strategy allows both countries handling the TMF efficiently based on the benefit-sharing concept, and the long-term strategy intends to rehabilitate the site through the relocation of all TMF.

Keywords: Central Asia, contaminant transport modelling, radioactive residue, transboundary conflict

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Authors: Vistorina Amputu, Katja Tielboerger, Nichola Knox

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Primary productivity in dry savannahs is constraint by moisture availability and under increasing anthropogenic pressure. Thus, considering climate change and the unprecedented pace and scale of rangeland deterioration, methods for assessing the status of such rangelands should be easy to apply, yield reliable and repeatable results that can be applied over large spatial scales. Global and local scale monitoring of rangelands through satellite data and labor-intensive field measurements respectively, are limited in accurately assessing the spatiotemporal heterogeneity of vegetation dynamics to provide crucial information that detects degradation in its early stages. Fortunately, newly emerging techniques such as unmanned aerial vehicles (UAVs), associated miniaturized sensors and improving digital photogrammetric software provide an opportunity to transcend these limitations. Yet, they have not been extensively calibrated in natural systems to encompass their complexities if they are to be integrated for long-term monitoring. Limited research using drone technology has been conducted in arid savannas, for example to assess the health status of this dynamic two-layer vegetation ecosystem. In our study, we fill this gap by testing the relationship between UAV-estimated cover of rangeland functional attributes and field data collected in discrete sample plots in a Namibian dryland savannah along a degradation gradient. The first results are based on a supervised classification performed on the ultra-high resolution multispectral imagery to distinguish between rangeland functional attributes (bare, non-woody, and woody), with a relatively good match to the field observations. Integrating UAV-based observations to improve rangeland monitoring could greatly assist in climate-adapted rangeland management.

Keywords: arid savannah, degradation gradient, field observations, narrow-band sensor, supervised classification

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Authors: Lingaraju Dumpala, Narasa Raju Gosangi

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Friction Stir Welding (FSW) is relatively new, environmentally friendly, versatile, and widely used joining technique for soft materials such as aluminum. FSW has got a lot of attention as a solid-state joining method which avoids many common problems of fusion welding and provides an improved way of producing aluminum joints in a faster way. FSW can be used for various aerospace, defense, automotive and transportation applications. It is necessary to understand the friction stir welded joints and its characteristics to use this new joining technique in critical applications. This study investigated the mechanical properties of friction stir welded aluminum 6063 alloys. FSW is carried out based on the design of experiments using L16 mixed level array by considering tool rotational speeds, tool feed rate and tool tilt angles as process parameters. The optimization of process parameters is carried by Taguchi based regression analysis and the significance of process parameters is analyzed using ANOVA. It is observed that the considered process parameters are high influences the mechanical properties of Al6063.

Keywords: FSW, aluminum alloy, mechanical properties, optimization, Taguchi, ANOVA

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Authors: Yinsheng Zhang

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Changes in the extent and amount of snow cover in Eurasia are of great interest because of their vital impacts on the global climate system and regional water resource management. This study investigated the spatial and temporal variability of the snow cover extent (SCE) and snow water equivalent (SWE) of continental Eurasia using the Northern Hemisphere Equal-Area Scalable Earth Grid (EASE-Grid) Weekly SCE data for 1972–2006 and the Global Monthly EASE-Grid SWE data for 1979–2004. The results indicated that, in general, the spatial extent of snow cover significantly decreased during spring and summer, but varied little during autumn and winter over Eurasia in the study period. The date at which snow cover began to disappear in spring has significantly advanced, whereas the timing of snow cover onset in autumn did not vary significantly during 1972–2006. The snow cover persistence period declined significantly in the western Tibetan Plateau as well as the partial area of Central Asia and northwestern Russia but varied little in other parts of Eurasia. ‘Snow-free breaks’ (SFBs) with intermittent snow cover in the cold season were mainly observed in the Tibetan Plateau and Central Asia, causing a low sensitivity of snow cover persistence period to the timings of snow cover onset and disappearance over the areas with shallow snow. The averaged SFBs were 1–14 weeks in the Tibetan Plateau during 1972–2006 and the maximum intermittence could reach 25 weeks in some extreme years. At a seasonal scale, the SWE usually peaked in February or March but fell gradually since April across Eurasia. Both annual mean and annual maximum SWE decreased significantly during 1979–2004 in most parts of Eurasia except for eastern Siberia as well as northwestern and northeastern China.

Keywords: Eurasia, snow cover extent, snow cover persistence period, snow-free breaks, onset and disappearance timings, snow water equivalent

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Authors: Majid Aldalbahi

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In 2009, over 3.4 billion people in the world resided in urban areas as a result of rapid urban growth. This figure is estimated to increase to 6.5 billion by 2050. This urban growth phenomenon has raised challenges for many countries in both the developing and developed worlds. Urban growth is a complicated process involving the spatiotemporal changes of all socio-economic and physical components at different scales. The socio-economic components of urban growth are related to urban population growth and economic growth, while physical components of urban growth and economic growth are related to spatial expansion, land cover change and land use change which are the focus of this research. The interactions between these components are complex and no-linear. Several factors and forces cause these complex interactions including transportation and communication, internal and international migrations, public policies, high natural growth rates of urban populations and public policies. Urban growth has positive and negative consequences. The positive effects relates to planned and orderly urban growth, while negative effects relate to unplanned and scattered growth, which is called sprawl. Although urban growth is considered as necessary for sustainable urbanization, uncontrolled and rapid growth cause various problems including consumption of precious rural land resources at urban fringe, landscape alteration, traffic congestion, infrastructure pressure, and neighborhood conflicts. Traditional urban planning approaches in fast growing cities cannot accommodate the negative consequences of rapid urban growth. Microsimulation programme, and modelling techniques are effective means to provide new urban development, management and planning methods and approaches. This paper aims to use these techniques to understand and analyse the complex interactions for the case study of Riyadh city, a fast growing city in Saudi Arabia.

Keywords: policy implications, urban planning, traffic congestion, urban growth, Suadi Arabia, Riyadh

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Authors: Bilge Demir, Ahmet Durgutlu, Mustafa Acarer

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In this study, the butt welding of the commercial AZ31 magnesium alloy sheets have been carried out by using Tungsten Inert Gas (TIG) welding process with alternative and pulsed current. Welded samples were examined with regards to hardness and microstructure. Despite some recent developments in welding of magnesium alloys, they have some problems such as porosity, hot cracking, oxide formation and so on. Samples of the welded parts have undergone metallographic and mechanical examination. Porosities and homogeneous micron grain oxides were rarely observed. Orientations of the weld microstructure in terms of heat transfer also were rarely observed and equiaxed grain morphology was dominant grain structure as in the base metal. As results, fusion zone and few locations of the HAZ of the welded samples have shown twin’s grains. Hot cracking was not observed for any samples. Weld bead geometry of the welded samples were evaluated as normal according to welding parameters. In the results, conditions of alternative and pulsed current and the samples were compared to each other with regards to microstructure and hardness.

Keywords: AZ31 magnesium alloy, microstructures, micro hardness TIG welding

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Authors: Aussara Panya, Nunghathai Sawasdee, Mutita Junking, Chatchawan Srisawat, Kiattawee Choowongkomon, Pa-Thai Yenchitsomanus

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Dengue virus (DENV) infection is a global public health problem with approximately 100 million infected cases a year. Presently, there is no approved vaccine or effective drug available; therefore, the development of anti-DENV drug is urgently needed. The clinical reports revealing the positive association between the disease severity and viral titer has been reported previously suggesting that the anti-DENV drug therapy can possibly ameliorate the disease severity. Although several anti-DENV agents showed inhibitory activities against DENV infection, to date none of them accomplishes clinical use in the patients. The surface envelope (E) protein of DENV is critical for the viral entry step, which includes attachment and membrane fusion; thus, the blocking of envelope protein is an attractive strategy for anti-DENV drug development. To search the safe anti-DENV agent, this study aimed to search for novel peptide inhibitors to counter DENV infection through the targeting of E protein using a structure-based in silico design. Two selected strategies has been used including to identify the peptide inhibitor which interfere the membrane fusion process whereby the hydrophobic pocket on the E protein was the target, the destabilization of virion structure organization through the disruption of the interaction between the envelope and membrane proteins, respectively. The molecular docking technique has been used in the first strategy to search for the peptide inhibitors that specifically bind to the hydrophobic pocket. The second strategy, the peptide inhibitor has been designed to mimic the ectodomain portion of membrane protein to disrupt the protein-protein interaction. The designed peptides were tested for the effects on cell viability to measure the toxic to peptide to the cells and their inhibitory assay to inhibit the DENV infection in Vero cells. Furthermore, their antiviral effects on viral replication, intracellular protein level and viral production have been observed by using the qPCR, cell-based flavivirus immunodetection and immunofluorescence assay. None of tested peptides showed the significant effect on cell viability. The small peptide inhibitors achieved from molecular docking, Glu-Phe (EF), effectively inhibited DENV infection in cell culture system. Its most potential effect was observed for DENV2 with a half maximal inhibition concentration (IC50) of 96 μM, but it partially inhibited other serotypes. Treatment of EF at 200 µM on infected cells also significantly reduced the viral genome and protein to 83.47% and 84.15%, respectively, corresponding to the reduction of infected cell numbers. An additional approach was carried out by using peptide mimicking membrane (M) protein, namely MLH40. Treatment of MLH40 caused the reduction of foci formation in four individual DENV serotype (DENV1-4) with IC50 of 24-31 μM. Further characterization suggested that the MLH40 specifically blocked viral attachment to host membrane, and treatment with 100 μM could diminish 80% of viral attachment. In summary, targeting the hydrophobic pocket and M-binding site on the E protein by using the peptide inhibitors could inhibit DENV infection. The results provide proof of-concept for the development of antiviral therapeutic peptide inhibitors to counter DENV infection through the use of a structure-based design targeting conserved viral protein.

Keywords: dengue virus, dengue virus infection, drug design, peptide inhibitor

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Authors: Muneesh Kumar, Rajesh Kumar, Sangeeta Devi

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Heavy metals are a major problem because they are toxic and tend to accumulate in living organisms. This study was carried out with the aims of studying on histopathology of Zn(SO4) toxicity on gill and liver tissues of catfish (Clarias batrachus) within the period of 96 h. Totally, 140 fishes with mean weight 50±10 g were stocked in 12 aquariums with capacity of 200 L water and divided in to 3 trails including control, 4 ppm and 8 ppm of Zn with 3 replicates. Tissue samples were fixed by bouin’s solution and sectioned in 7 μm based on histological regular method and stained with Hematoxylin and Eosin (H&E) method for microscopic study within the period of 96 h. Results showed some damaged such as hyperplasia, telangiectasis and edema, necrosis of second filaments, jerky movement, aneurism, hyperemia and fusion of second filaments in gills; and cell atrophy, necrosis, fatty degeneration, hyperemia and bile stagnation at different treatments in comparison with control. Gill and liver tissue damages were severed with the increase of Zn concentration and days. Therefore, Zn had acute toxicity effects on gill and liver tissues in Catfish at 5 and 10 ppm concentrations.

Keywords: gill, liver, histopathology, zinc, Clarias batrachus

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Authors: Zeina Merabi, Arij Dao

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The temporal order of information processing in the brain is an important code in many acoustic signals, including speech, music, and animal vocalizations. Despite its significance, surprisingly little is known about its underlying cellular mechanisms and network manifestations. In the songbird telencephalic nucleus HVC, a subset of neurons shows temporal combination sensitivity (TCS). These neurons show a high temporal specificity, responding differently to distinct patterns of spectral elements and their combinations. HVC neuron types include basal-ganglia-projecting HVCX, forebrain-projecting HVCRA, and interneurons (HVC¬INT), each exhibiting distinct cellular, electrophysiological and functional properties. In this work, we develop conductance-based neural network models connecting the different classes of HVC neurons via different wiring scenarios, aiming to explore possible neural mechanisms that orchestrate the combination sensitivity property exhibited by HVCX, as well as replicating in vivo firing patterns observed when TCS neurons are presented with various auditory stimuli. The ionic and synaptic currents for each class of neurons that are presented in our networks and are based on pharmacological studies, rendering our networks biologically plausible. We present for the first time several realistic scenarios in which the different types of HVC neurons can interact to produce this behavior. The different networks highlight neural mechanisms that could potentially help to explain some aspects of combination sensitivity, including 1) interplay between inhibitory interneurons’ activity and the post inhibitory firing of the HVCX neurons enabled by T-type Ca2+ and H currents, 2) temporal summation of synaptic inputs at the TCS site of opposing signals that are time-and frequency- dependent, and 3) reciprocal inhibitory and excitatory loops as a potent mechanism to encode information over many milliseconds. The result is a plausible network model characterizing auditory processing in HVC. Our next step is to test the predictions of the model.

Keywords: combination sensitivity, songbirds, neural networks, spatiotemporal integration

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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The aim of this study was to improve the effects of human body 3D reconstruction. The MvP algorithm was adopted to obtain key point information from multiple perspectives. This algorithm allowed the capture of human posture and joint positions from multiple angles, providing more comprehensive and accurate data. The study also incorporated the SMPL-X model, which has been widely used for human body modeling, to achieve more accurate 3D reconstruction results. The use of the MvP algorithm made it possible to observe the reconstructed object from multiple angles, thus reducing the problems of blind spots and missing information. This algorithm was able to effectively capture key point information, including the position and rotation angle of limbs, providing key data for subsequent 3D reconstruction. Compared with traditional single-view methods, the method of multi-view fusion significantly improved the accuracy and stability of reconstruction. By combining the MvP algorithm with the SMPL-X model, we successfully achieved better human body 3D reconstruction effects. The SMPL-X model is highly scalable and can generate highly realistic 3D human body models, thus providing more detail and shape information.

Keywords: 3D human reconstruction, multi-view, joint point, SMPL-X

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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Authors: Patricia Shirley Almeida Prado, Liz Brito, Selma Paixão Argollo, Gracie Moreira, Leticia Matos Sobrinho

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Diffuse idiopathic skeletal hyperostosis (DISH) is a degenerative bone disease also known as Forestier´s disease and ankylosing hyperostosis of the spine is characterized by a tendency toward ossification of half the anterior longitudinal spinal ligament without intervertebral disc disease. DISH is not considered to be osteoarthritis, although the two conditions commonly occur together. Diagnostic criteria include fusion of at least four vertebrae by bony bridges arising from the anterolateral aspect of the vertebral bodies. These vertebral bodies have a 'dripping candle wax' appearance, also can be seen periosteal new bone formation on the anterior surface of the vertebral bodies and there is no ankylosis at zygoapophyseal facet joint. Clinically, patients with DISH tend to be asymptomatic some patients mention moderate pain and stiffness in upper back. This disease is more common in man, uncommon in patients younger than 50 years and rare in patients under 40 years old. In modern populations, DISH is found in association with obesity, (type II) diabetes; abnormal vitamin A metabolism and also associated with higher levels of serum uric acid. There is also some association between the increase of risk of stroke or other cerebrovascular disease. The DISH condition can be confused with Heterotopic Ossification, what is the bone formation in the soft tissues as the result of trauma, wounding, surgery, burnings, prolonged immobility and some central nervous system disorder. All these conditions have been described extensively as myositis ossificans which can be confused with the fibrodysplasia (myositis) ossificans progressive. As in the DISH symptomatology it can be asymptomatic or extensive enough to impair joint function. A third confusion osteoarthritis disease that can bring confusion are the enthesopathies that occur in the entire skeleton being common on the ischial tuberosities, iliac crests, patellae, and calcaneus. Ankylosis of the sacroiliac joint by bony bridges may also be found. CASE 1: this case is skeletal remains presenting skull, some vertebrae and scapulae. This case remains unidentified and due to lack of bone remains. Sex, age and ancestry profile was compromised, however the DISH pathognomonic findings and diagnostic helps to estimate sex and age characteristics. Moreover to presenting DISH these skeletal remains also showed some bone alterations and non-metrics as fusion of the first vertebrae with occipital bone, maxillae and palatine torus and scapular foramen on the right scapulae. CASE 2: this skeleton remains shows an extensive bone heterotopic ossification on the great trochanter area of left femur, right fibula showed a healed fracture in its body however in its inteosseous crest there is an extensive bone growth, also in the Ilium at the region of inferior gluteal line can be observed some pronounced bone growth and the skull presented a pronounced mandibular, maxillary and palatine torus. Despite all these pronounced heterotopic ossification the whole skeleton presents moderate bone overgrowth that is not linked with aging, since the skeleton belongs to a young unidentified individual. The appropriate osteopathological diagnosis support the human identification process through medical reports and also assist with epidemiological data that can strengthen vulnerable anthropological estimates.

Keywords: bone disease, DISH, human identification, human remains

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Authors: Wenxiao Liu, Kun Zhang, Yongqing Li

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Bovine herpesvirus 1, a member of the genus Variellovirus of the subfamily Alphaherpesvirinae, has caused severe economic cost to the bovine industry. In this study, BoHV-1 glycerol protein gD was expressed in insect cells, and the purified gD was immunized in the Balb/C mice to generate monoclonal antibodies. Based on hybridoma cell fusion techniques, 20 monoclonal antibodies against Bovine herpesvirus 1 have been obtained. Further, mAb 3F8 with neutralizing activity and gD were applied to develop a blocking enzyme-linked immunosorbent assay (Elisa) for detecting neutralizing antibodies against BoHV-1, which shows a significant correlation between the blocking Elisa and VNT. The sensitivity and specificity of the test were estimated to be 94.59% and 93.42%, respectively. Furthermore, antibody pairing tests revealed that mAb 1B6 conjugated to fluorescence microspheres was used as the capture antibody, and mAb 3F9 was used as the detectable antibody to establish the immunochromatographic assay (ICS). The ICS was conducted to detect BoHV-1 in bovine samples with high sensitivity, specificity, and good stability. Clinical sample testing revealed that the results of ICS and real-time PCR have a coincidence rate of 95.42%. Our research confirmed that the ICS is a rapid and reliable method for the diagnosis of BoHV-1. In conclusion, our results lay a solid foundation for the prevention and control of BoHV-1 infection.

Keywords: bovine disease, BoHV-1, ELISA, ICS assay

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Authors: Hong Bum Kim

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Health technology (HT) area have high growth potential because of global trends such as ageing and economical development. For its high employment effect and capability for creating new business, HT is being considered as one of the major next-generation growth power. Particularly, convergence technologies which are emerged by fusion of HT and other technological area is emphasized for new industry creation in Korea, as a part of Creative Economy. In this study, current status of HT area in Korea is analyzed. The aspect of transition in emphasized technological area of HT-related national R&D enterprise is statistically reviewed. Current level of HT-related technologies such as BT, IT and NT is investigated in this context. Existing research system for HT-convergence technology development such as establishment of research center is also analyzed. Finally, proposed research support system such as system of legislation for developing HT area as one of the main component of Creative Economy in Korea will be analyzed. Analysis of technology trend and policy will help to draw a new direction in progression of R&D enterprise in HT area. Improvement of policy such as legal system reorganization and measure of social agreement for burden of expense could be deduced based on these results.

Keywords: HT, creative economy, policy, national R&D programs

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Authors: Kim Quy Le, Duan Fei, Jia Wei Chew, Jun Zeng, Maria Fabiola Leyva

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In line with the sustainable development goal, molded fiber products play important roles in reducing plastic-based packaging. To fabricate molded fiber products, besides using conventional meshing tools, 3D printing is employed to manufacture the molded fiber screen. 3D printing technique allows printing molded fiber screens with complex geometry, flexible in pore size and shape. The 3D printed molded fiber screens are in the progress of investigation to improve the de-watering efficiency, fiber collection, mechanical strength, etc. In addition, the fiber distribution on the screen is also necessary to access the quality of the screen. Besides using experimental methods to capture the fiber distribution on screen, simulation also offers using tools to access the uniformity of fiber. In this study, the fiber was simulated using the multi-sphere model to simulate the fibers. The interaction of the fibers was able to mimic by employing the discrete element method. The fiber distribution was captured and compared to the experiment. The simulation results were able to reveal the fiber deposition layer upon layer and explain the formation of uneven thickness on the tilted area of molded fiber screen.

Keywords: 3D printing, multi-jet fusion, molded fiber screen, discrete element method

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Authors: Chunying Fang, Haifeng Li, Lin Ma, Mancai Zhang

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Speech intelligibility assessment is an important measure to evaluate the functional outcomes of surgical and non-surgical treatment, speech therapy and rehabilitation. The assessment of pathological speech plays an important role in assisting the experts. Pathological speech usually is non-stationary and mutational, in this paper, we describe a multi-granularity combined feature schemes, and which is optimized by hierarchical visual method. First of all, the difference granularity level pathological features are extracted which are BAFS (Basic acoustics feature set), local spectral characteristics MSCC (Mel s-transform cepstrum coefficients) and nonlinear dynamic characteristics based on chaotic analysis. Latterly, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96-dimensions.The experimental results denote that new features by support vector machine (SVM) has the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.

Keywords: pathological speech, multi-granularity feature, MSCC (Mel s-transform cepstrum coefficients), F-score, radar chart

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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