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Item does not contain fulltext, We report 21 families displaying neurodevelopmental differences and multiple congenital anomalies while bearing a series of rare variants in mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4). MAP4K4 has been implicated in many signaling pathways including c-Jun N-terminal and RAS kinases and is currently under investigation as a druggable target for multiple disorders. Using several zebrafish models, we demonstrate that these human variants are either loss-of-function or dominant-negative alleles and show that decreasing Map4k4 activity causes developmental defects. Furthermore, MAP4K4 can restrain hyperactive RAS signaling in early embryonic stages. Together, our data demonstrate that MAP4K4 negatively regulates RAS signaling in the early embryo and that variants identified in affected humans abrogate its function, establishing MAP4K4 as a causal locus for individuals with syndromic neurodevelopmental differences.

Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the un

This study addressed the complexity involved in integrating the causative risk factors influencing construction profitability. Most of the existing studies cover the individual effects of profitability influencing factors. Very few focus on the systematic impact without incorporating the complexity and associated dynamics, presenting a gap targeted by the current study. The current study aimed to assess causative interrelations and interdependencies between profitability influencing risk factors (PIRF), through systems thinking (ST) and system dynamics (SD) modeling. The SD approach was used to evaluate the integrated impacts on profitability-influencing risk categories (PIRC) in construction projects. The causative influencing factors affecting construction profitability were identified through a comprehensive literature review. These were ranked using content analysis, and categorized into significant issues. Through 250 structured surveys and 15 expert opinion meetings, the path for quantitative and qualitative evaluations was prepared. Following these investigations, a causal loop diagram (CLD) was established using the ST technique, and the integrated effect was quantified using SD modeling. The study finds the rising cost of material, supply chain process, payment issues, planning and scheduling problems, financial difficulties, and effective control of manpower and equipment resources as the most critical PIRFs. The integrated effects of PIRFs on PIRC were quantified using SD modeling. This study helps field professionals with profitability-influencing factors, diagnosing issues, and integrating impacts regarding decision-making and policy formulation. For researchers, it presents a list of factors that can be investigated in detail, and the holistic interrelationships established.

Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research—from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology—from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle. © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.

The current spread out in Big Data analytics and the medical Internet of Things (IoT) originated the recognition of smart health. Smart health is the integration of devices, sensors, cameras, and objects or things embedded with sensors that generate an enormous amount of data known as big data. IoT-enabled smart health management assimilates the digital traces generated by things and humans including teledentistry. The key purpose of teledentistry is to analyze the data and produce valuable insights and hidden patterns to offer services and overcome the existing challenges. This article proposes a data management design using Big Data analytics for smart teledentistry planning. The proposed scheme is the customization of the present parallel platforms to accomplish effective processing. The design offers three different modules which are the data pre-processing, data ingestion, and data processing module. The pre-processing is performed to speed up the real data processing. The recent schemes lack well-organized and effective parallel data loading and proficient communication. The Big Data storage and ingestion are achieved using optimized utility and parallel mechanisms. Optimized RDD-enabled (resilient distribution) Yet Another Resource Negotiator (YARN) based proposal is designed for resourceful cluster administration and computation of medical Big Data. The proposed data design is experimentally simulated with authentic datasets using Apache Spark 3.0 framework. The proposed architecture is compared with existing state-of-the-art proposals. The simulation results reveal the efficacy of the proposed system.

Global disruption and innovation are revolutionising many industries and creating a digitally disruptive environment (DDE); real estate is no exception. Real estate online platforms (REOPs) are getting more attention in the era of COVID-19, where the real estate businesses must run online, and the service users are pushed to a forced digital experience adoption. The users or consumers are not satisfied or happy with the current state of REOPs and often regret their decisions due to the poor quality of the web-based information provided on these REOPs. Based on a mixed approach of integrated KANO-SISQual, the current study develops a comprehensive smart real estate technology adoption model (SRETAM) to model the users’ perception of the REOPs in DDE. Data is collected through a questionnaire survey from 407 Sydney based REOP users. A total of 31 key factors are identified by reviewing 256 articles vetted by 12 local real estate agents. The results highlight 19 factors to be very important to REOPs users: reliability, the accuracy of the information, and credibility of the online platforms are the top three important factors. The hypotheses’ results show that all the SRETAM categories contribute equally to the users’ behavioural intention to use the REOPs and the KANO categories are statistically different. For KANO categories, eight attractive features are identified: graphical statistics, familiar web technology, content structure, web design, immersive content, self-efficacy, information novelty, and presence of 3D interactive models. Displaying the users’ location, learning tutorials, and hyperlinks to external sources on the web portals dissatisfy REOP users. The factors and their classifications can help the users get more features, verified, and transparent information for making better decisions in the DDE, whereas the REOP can enjoy more business due to increased service consumers. This can lay the foundation for transforming the traditional real estate secto

Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91

Water scarcity has become a major problem for many countries, resulting in declining water supply and creating a need to find alternative solutions. One potential solution is rainwater harvesting (RwH), which allows rainwater to be stored for human needs. This study develops an RwH assessment system through building information modeling (BIM). For this purpose, a hydrological study of Cfa-type climate cities is conducted with the example of Islamabad, Pakistan. The monthly rainfall data of three sites were assessed to determine the volume of the accumulated rainwater and its potential to meet human needs. The average number of people living in a house is taken as the household number. Household number or of the number of employees working at a small enterprise, roofing material, and rooftop area are used as the key parameters for pertinent assessment in the BIM. The data simulated by BIM highlight the RwH potential using five people per house as the occupancy and a 90 m2 rooftop area for residential buildings or small enterprises as parameters. The results show that the selected sites can collect as much as 8,190 L/yr of rainwater (48 L/person/day) to 103,300 L/yr of rainwater (56 L/person/day). This much water is enough to fulfill the daily demands of up to five people. Therefore, it is established that the study area has an RwH potential that is able to meet the expected demands. This study presents a baseline approach for RwH to address water scarcity issues for residential buildings and factories of the future.

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetra-tion (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete

Coronavirus Disease 2019 (COVID-19) has emerged as a global pandemic since late 2019 and has affected all forms of human life and economic developments. Various techniques are used to collect the infected patients’ sample, which carries risks of transferring the infection to others. The current study proposes an AI-powered UAV-based sample collection procedure through self-collection kits delivery to the potential patients and bringing the samples back for testing. Using a hypothetical case study of Islamabad, Pakistan, various test cases are run where the UAVs paths are optimized using four key algorithms, greedy, intra-route, inter-route, and tabu, to save time and reduce carbon emissions associated with alternate transportation methods. Four cases with 30, 50, 100, and 500 patients are investigated for delivering the self-testing kits to the patients. The results show that the Tabu algorithm provides the best-optimized paths covering 31.85, 51.35, 85, and 349.15 km distance for different numbers of patients. In addition, the algorithms optimize the number of UAVs to be used in each case and address the studied cases patients with 5, 8, 14, and 71 UAVs, respectively. The current study provides the first step towards the practical handling of COVID-19 and other pandemics in developing countries, where the risks of spreading the infections can be minimized by reducing person-to-person contact. Furthermore, the reduced carbon footprints of these UAVs are an added advantage for developing countries that struggle to control such emissions. The proposed system is equally applicable to both developed and developing countries and can help reduce the spread of COVID-19 through minimizing the person-to-person contact, thus helping the transformation of healthcare to smart healthcare