An Enhanced Deep Recurrent Neural Network for Autism Spectrum Disorder Diagnosis

Autism Spectrum Disorder is one of the major investigation area in current era. There are many research works introduced earlier for handling the Autism Spectrum Disorders. However those research works doesn’t achieve the expected accuracy level. The accuracy and prediction efficiency can be increased by building a better classification system using Deep Learning. This paper focuses on the deep learning technique for Autism Diagnosis and the domain identification. In the proposed work, an Enhanced Deep Recurrent Neural Network has been developed for the detection of ASD at all ages. It attempts to predict the autism spectrum in the children along with prediction of areas which can predict the autism in the prior level. The main advantage of EDRNN is to provide higher accuracy in classification and domain identification. Here Artificial Algal Algorithm is used for identifying the most relevant features from the existing feature set. This model was evaluated for the data that followed Indian Scale for Assessment of Autism. The results obtained for the proposed EDRNN has better accuracy, sensitivity, specificity, recall and precision.

Business Model Innovation for Circular Economy in Fashion Industry: A Startups' Perspective

Circular Economy (CE) has emerged as a potential strategy for developing business practices based on sustainability concerns, especially in the fashion industry, which presents high environmental and social impacts. Startups are responsible for introducing innovations in business conduction toward CE. As a current theme, research on Business Model Innovation for Circular Economy (BMI4CE) has increased. However, empirical research in the fashion industry and startups is still scarce. This paper aimed to identify the key elements of startups' BMI4CEs, using the fashion industry as the context of the study. We conducted an exploratory and descriptive multiple case study composed of ten early-stage fashion startups from Europe, North America, and Asia. The findings suggest that environmental and economic sustainability dimensions receive priority in the analyzed BMI4CEs. On business type, we found differences between product-based and service-based Business Models (BMs). The Business Models Innovations (BMIs) were based mainly on CE principles of closed-loop and reducing material use and consumption. BMs focus on CE strategies of product reuse and extend resource time by lowering consumption and material use. Findings also demonstrate the role of emerging and digital technologies (e.g., blockchain and artificial intelligence) for BMI4CEs effectiveness. We developed five propositions and a theoretical framework from a triple bottom line perspective. This research highlights new theoretical perspectives under an investigation area still little explored in the literature. Results enable fashion startup managers to understand better the functioning of BMI4CEs and the critical elements needed for their effectiveness.

Assessment of Photosynthetic Pigments and Dust Load in Selected Avenue Plants of Saharanpur and Herbertpur Areas of India

The paper depicts impact of SO2, NO2, ozone and CO in chosen road plants of Saharanpur and Herbertpur regions in Indian dominion. Plant species falling in and around the area of modern territory and street side were chosen for examining purpose. Different morphological attributes and impact of business and vehicular residue on chlorophyll shade were contemplated and noticed the impact of residue molecule on development of the plant species (Ficus religiosa, Mangifera indica). In the examination the impacts of residue on chosen tree species was noticed and which will help in overseeing advancement of green belt to lessen the air contamination in the investigation area. Treatment with dust fundamentally expanded the fixation on leaves and the impacts were more extreme in significant degree of residue medicines. Contrasted with control, chlorophyll and porphyrin in substance were essentially diminished in the residue treated gatherings. This investigation propounds that business and vehicular residue statements affect photosynthetic shades, with broad impacts on development and improvement of plant.

Common-midpoint spatial autocorrelation analysis of seismic ambient noise obtained from spatially unaliased sensor distribution

We introduce a passive surface wave method using seismic ambient noise obtained from dozens of receivers forming spatially unaliased two-dimensional (2D) arrays. The method delineates two- or three-dimensional (2D or 3D) S-wave velocity ( VS) models to depths of several hundreds of meters, without using any sources. Typical data acquisition uses 50 to 100 vertical-component 2 Hz geophones on the surface with 5 to 30 m receiver spacing. Cableless seismographs with GPS record 20 to 60 minutes of ambient noise. We establish a 2D grid covering the investigation area and use a common midpoint spatial autocorrelation (CMP-SPAC) method to calculate phase velocities, resulting in a dispersion curve for each grid point. The method provides dozens of dispersion curves in the investigation area. We use a one-dimensional (1D) non-linear inversion to estimate a 1D VS profile for each grid point, and then construct pseudo 2D or pseudo 3D VS models from the 1D VS profiles. Precision and accuracy of the CMP-SPAC method was tested with a numerical simulation using a 3D finite-difference method. The results of the simulation demonstrated the applicability of the method to complex velocity structures. We applied the method to an active fault investigation in China. Sixty-four cableless seismographs were deployed in an investigation area 330 × 660 m (217,800 m2) with 5 m and 30 m receiver spacings for dense and sparse grids, respectively. A 3D VS model was obtained to a depth of 150 m from CMP-SPAC analysis. The resultant 3D VS model indicates approximately 50 m of vertical displacement on a known fault.

Diagnostic Tools for Autism Spectrum Disorders by Gender: Analysis of Current Status and Future Lines

Studies on the prevalence of autism spectrum disorders have shown gender disproportion. In recent years, there has been an increasing interest in this investigation area. There are two main research lines; the first is focused mostly on gender-related biological reasons that could account for low ASD prevalence in women (i.e., related to some protective factors related to hormones or the immune system, among others), and the second research line studies possible diagnostic biases. In the present study, a review of the latter line of research is made based on two main objectives: (a) analysis of possible biases in diagnostic tools and (b) other nonbiological ASD prevalence explained by gender differences. As a result of our theoretical review, we found that the articles reviewed showed contradictory results and possible diagnostic biases, not only in their design but also in their assessment standards. We concluded that specific or complementary diagnostic tools and procedures differentiated by gender should be developed in order to reduce these biases.

Investigation of water quality from Damanga estuary through the water quality indexing Gujarat

The seasonal investigation about the water quality from Damanganga river estuary on two habitats downstream and upstream was carried out from January to December 2019 containing three major seasons: winter, summer and monsoon. For this monitoring activity total 29 parameters (24 physico-chemical parameters and 5 heavy metals) were analyzed. Multivariate analyses suggested inter dependency among these studied parameters. Water Quality Index is computed based on the major fluctuated and affected parameters. The calculated values of WQI for all three seasons ranged from 122.84 to 173.82 which suggested poor water quality of the water body. WQI values of the investigation area proposed that the estuarine water quality is deteriorated due to high value of presented heavy metals (Aluminum, Iron, Manganese, Boron and Zinc), Chloride, Ammonium and Sulfate in water sample. In this case, the downstream station is having accessional pollutant contaminations while the upstream station is having diminutive pollutant contaminants. Temporally, the dominant frailty found during the winter followed by summer and monsoon. This study field exhibited poor quality of water; the reason behind this might be the impressive surrounding industrial zone as well as other anthropogenic activities. There is quite normal probability distribution expressed by the represented water quality data at the both habitats. The Bray-Curtis cluster analysis shows different percentage similarity level between the water quality parameters.

Localization of Deep Voids through Geophysical Signatures of Secondary Dewatering Features

Discrete deep targets are a significant challenge for most surface-based geophysical techniques, even when considering high property contrasts. Generally, surface-based geophysical methods lose lateral and vertical resolution with depth as a result of poor measurement geometry and increased signal attenuation. The poor measurement geometry can be overcome through use of cross-borehole methods, but lateral localization is still needed for optimal borehole placement. As such, a relatively small, deep void located near the maximum depth of investigation is very unlikely to be detected. Yet, secondary features associated with these voids can be exploited for enhanced detection performance. When voids are located below the groundwater table a significant amount of dewatering and pumping is required to make them a functional passageway. This dewatering not only removes water from the void space but also the surrounding formation, resulting in a much larger, if more diffuse, secondary target: an induced groundwater table gradient. Many geophysical sensing methods are sensitive to subsurface moisture content. Here we implement a two-dimensional (2D) joint-petrophysical mixing-model, using inverted electrical resistivity tomography and inverted seismic refraction models to sense changes in the groundwater table gradient. Results are validated using depth to bedrock, groundwater-surface water information, ground-penetrating radar, and time-domain reflectometry methods. Our initial proof of concept is applied to a shallow area with a significant soil moisture gradient, through different surface soil types and bedrock. The 2D joint-petrophysical mixing-model results were used to generate an estimate of air, moisture, and matrix percent fractions in the investigation area, providing a clear delineation of the groundwater surface.

Storm-runoff processes in a mainly waterlogged low mountain range catchment in the national park Hunsrück-Hochwald, SW-Germany

<p>This study investigates potential effects of wetland restoration on storm flow dynamics in a mainly waterlogged low mountain range catchment located in SW-Germany. Here, wetland drainage networks are being sealed, aiming to achieve rising soil water tables and reestablished peat vegetation. With the help of hydrograph separation, multiple linear regression (MLR) and covariance analysis (ANCOVA), runoff-governing storm properties and sealing influences were analyzed. Results show, that not only natural storm parameters (precipitation sum, rainfall intensity, antecedent precipitation and temperature) exert influence on storm-runoff, but sealings also led to significantly altered processes: On the one hand, storm-runoff coefficients increased in sealed catchments, resulting most likely from more saturated soils, providing a smaller infiltration capacity. This is a desired effect of rewetting but coincidently a downside regarding storm flood prevention. On the other hand, lag times, meaning the timespan between rainfall occurrence and the hydrograph starting to rise, were noticeably prolonged. This effect can be potentially beneficial when it comes to storm flood prevention. Overall, statistical models including sealings showed more satisfactory results describing stormflow variance compared to models without sealings. Therefore, sealings do exert – statistically proven – an effect on storm runoff. The heterogeneity of the results, representing a dense gauge network spread over an investigation area of roughly 7.5 km² shows, that a high-resolution sampling, both spatially and temporally, is vital. That is since runoff processes in waterlogged low mountain range catchments are still poorly understood.</p>

LOOP SOURCE TRANSIENT ELECTROMAGNETICS IN AN URBAN NOISE ENVIRONMENT: A CASE STUDY IN SANTIAGO DE CHILE

We present a transient electromagnetic (TEM) study in the noisy urban environment of the megacity Santiago de Chile. Our investigation characterizes the electrical conductivity structure of the Santiago basin down to 300 m depth, providing key information about the sedimentary infill, hydrogeological aspects, and geomorphological units. Fifty-two TEM-soundings were recorded over roughly 900 km2. We identified different transient characteristics and noise patterns, both spatially correlated to the investigation area. This step allowed classification of the soundings into three groups: highly distorted data, transients severely affected by cultural electromagnetic noise sources, and good quality data with low noise levels. Conventional 1D inversion techniques were then employed to derive resistivity depth models. The models were interpreted using sedimentary units and alluvial fan information, static groundwater level observations, and verified using borehole data. Based on 1D models, different minimum sedimentary thicknesses are observed, ranging between 50 m and 300 m depth. The average derived sedimentary thickness is ∼210 m. In addition, seven soundings indicated the basement. These are mostly located towards the periphery of the Santiago basin. We found a high conductive zone towards the north and a low conductive area towards the south of the basin. The southern area is characterized by the absence of conductive layers at a depth between 100 m and 150 m depth. This is likely related to the presence of a thick gravel layer and the absence of shallow clay layers acting as aquitards. The resistivity distribution and the sedimentary thickness obtained for this noisy and urban environment are a key input for groundwater research as water supply becomes more limited, specifically in central Chile. The overall consistency of the derived subsurface models highlights the suitability of the TEM method for investigating urban noise environments.

INVESTIGATION OF WATER QUALITY FROM DAMANGANGA ESTUARY THROUGH THE WATER QUALITY INDEXING, GUJARAT

The seasonal investigation about the water quality from Damanganga river estuary on two habitats downstream and upstream was carried out from January to December 2019 containing three major seasons: winter, summer and monsoon. For this monitoring activity total 29 parameters (24 physico-chemical parameters and 5 heavy metals) were analyzed. Multivariate analyses suggested inter dependency among these studied parameters. Water Quality Index is computed based on the major fluctuated and affected parameters. The calculated values of WQI for all three seasons ranged from 122.84 to 173.82 which suggested poor water quality of the water body. WQI values of the investigation area proposed that the estuarine water quality is deteriorated due to high value of presented heavy metals (Aluminum, Iron, Manganese, Boron and Zinc), Chloride, Ammonium and Sulfate in water sample. In this case, the downstream station is having accessional pollutant contaminations while the upstream station is having diminutive pollutant contaminants. Temporally, the dominant frailty found during the winter followed by summer and monsoon. This study field exhibited poor quality of water; the reason behind this might be the impressive surrounding industrial zone as well as other anthropogenic activities. There is quite normal probability distribution expressed by the represented water quality data at the both habitats. The Bray-Curtis cluster analysis shows different percentage similarity level between the water quality parameters.