An IoT-Based Framework for Enhanced Construction Material Management and Tracking

Effective inventory management is essential for determining a project's growth and success in the construction industry. Recent studies have revealed that conventional techniques for managing construction materials and components still depend on human capacities, despite the fact that they account for 50%–60% of the entire cost of a typical project. This results in error-prone scenarios including product misplacement, stock replenishment, inappropriate handling, and counterfeiting. Because of this, real-time data collecting using developing technology like RFID (Radio Frequency Identification) is highly desired. In this paper, an Internet of Things (IoT)-based framework for managing and tracking construction materials is presented. It is equipped with a GSM-based GPS module and a number of sensors, including the DHT 11 sensor, and can effectively synthesize all dynamic data regarding the real-time location, ambient temperature, and humidity along the supply chain associated with various types of resources. The prototype of the system uses the aforementioned components to collect the real-time location data. The information gathered using this prototype model enables us to monitor the materials effectively from a distance and provides us with a viable method of deployment for related tasks

Implementation of Synthesize GAN Model to Detect Outlier in National Stock Exchange Time Series Multivariate Data

This research work explores a novel approach for identifying outliers in stock related time series multivariate datasets, using Generative Adversarial Networks (GANs). The proposed framework harnesses the power of GANs to create synthetic data points that replicate the statistical characteristics of genuine stock related time series. The use of Generative Adversarial Networks to generate tabular data has become more important in a number of industries, including banking, healthcare, and data privacy. The process of synthesizing tabular data with GANs is also provided in this paper. It involves several critical steps, including data collection, preprocessing, and exploration, as well as the design and training using Generator and Discriminator networks. While the discriminator separates genuine samples from synthetic ones, the generator is in charge of producing synthetic data. Generating high quality tabular data with GANs is a complex task, but it has the potential to facilitate data generation in various domains while preserving data privacy and integrity. The results from the experiments confirm that the GAN framework is useful for detecting outliers.  The model demonstrates its proficiency in identifying outliers within stock-related time series data. In comparison, our proposed work also examines the statistics and machine learning models in related application fields

Design of Improved Soft Computing based Maximum Power Point Tracking System for Operational Efficiency Enhancement of Solar Photovoltaic Energy System

The most promising renewable energy source is solar energy, which has enormous potential but has not yet been fully investigated or converted into useful power. The process of converting solar radiation into electrical power is marked by fluctuations in output and waste. These losses are associated with the processes of conversion, transformation, and usage. Temperature, irradiance, and shade are the main operating conditions that affect how well a solar photovoltaic system performs. The efficiency with which power is converted from the panel to the load determines the operational efficiency. Charge controllers are made to convert solar photovoltaic power into electricity for an external circuit. The goal of the research is to look into efficient algorithms that can improve the solar photovoltaic energy system's operating efficiency. In order to increase the operational efficiency of solar photovoltaic systems, research is concentrated on developing maximum power point tracking systems (MPPT) under a variety of operating scenarios, including partial shade and fluctuating irradiance. The performance of the system under various operating conditions has been investigated through the simulation of an equivalent mathematical model. A unique method for charge controller duty cycle control and solar system cooling has been developed. It is based on hybridization of PV-T System and cuckoo search optimization. Simulations of the suggested system have been run under standard, complex, and changing shading pattern and operating conditions. Simulations have shown that the devised method performs better in terms of tracked power, tracking time, and tracking stability. Comparing the suggested research to heuristic approaches based on conventional and soft computing, the solar photovoltaic system's operational performance is much improved

A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology

Background: Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods: We have developed Results: By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions

A reference tissue atlas for the human kidney

Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes

Shank2 identifies a subset of glycinergic neurons involved in altered nociception in an autism model

BACKGROUND: Autism Spectrum Disorders (ASD) patients experience disturbed nociception in the form of either hyposensitivity to pain or allodynia. A substantial amount of processing of somatosensory and nociceptive stimulus takes place in the dorsal spinal cord. However, many of these circuits are not very well understood in the context of nociceptive processing in ASD. METHODS: We have used a Shank2 RESULTS: We determined that Shank2 LIMITATIONS: Our investigation is limited to male mice, in agreement with the higher representation of ASD in males; therefore, caution should be applied to extrapolate the findings to females. Furthermore, ASD is characterized by extensive genetic diversity and therefore the findings related to Shank2 mutant mice may not necessarily apply to patients with different gene mutations. Since nociceptive phenotypes in ASD range between hyper- and hypo-sensitivity, diverse mutations may affect the circuit in opposite ways. CONCLUSION: Our findings prove that Shank2 expression identifies a new subset of inhibitory interneurons involved in reducing the transmission of nociceptive stimuli and whose unchecked activation is associated with pain hypersensitivity. We provide evidence that dysfunction in spinal cord pain processing may contribute to the nociceptive phenotypes in ASD

Synthesis, anti-HIV activity and molecular modeling study of some new pyrimidine analogues

A new series of 2,6-diamino-5-arylazo-4-chloropyrimidine analogues (6-13) were synthesized from the pyrimidine scaffold 5, via diazotization with various amines. Nucleophilic displacement of compound 5 by ethanethiolate or arylthio nucleophiles, afforded the 4-alkylthio analogues (14-16). Treatment of compound 17 or 18 with thiourea under MWI gave the 4-thione derivatives 19 and 20, respectively. On treatment of compound 20 with 2-mercaptoacetic acid furnished the 4-thio analogue (21). Reaction of compound 19 or 20 with sodium hypochlorite followed by ammonium hydroxide afforded the 4-aminothio analogues 22 and 23, respectively. Oxidation of compound 23 with H2O2 led to the 4-sulphonamide derivative 24. All new compounds were evaluated for their in vitro antiviral activity against the replication of HIV-1 and HIV-2 in MT-4 cells. Compounds 14-16 and 21 showed an EC50 values of > 2.12, 1.99, 1.80 and 1.92 μg/mL, respectively. In addition, preliminary structure-activity relationship and molecular modeling of compound 15 has been studied

Evaluation of plasma biomarkers to predict major adverse kidney events in hospitalized patients with COVID-19

RATIONALE & OBJECTIVE: Patients hospitalized with COVID-19 are at increased risk for major adverse kidney events (MAKE). We sought to identify plasma biomarkers predictive of MAKE in patients hospitalized with COVID-19. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: A total of 576 patients hospitalized with COVID-19 between March 2020 and January 2021 across 3 academic medical centers. EXPOSURE: Twenty-six plasma biomarkers of injury, inflammation, and repair from first available blood samples collected during hospitalization. OUTCOME: MAKE, defined as KDIGO stage 3 acute kidney injury (AKI), dialysis-requiring AKI, or mortality up to 60 days. ANALYTICAL APPROACH: Cox proportional hazards regression to associate biomarker level with MAKE. We additionally applied the least absolute shrinkage and selection operator (LASSO) and random forest regression for prediction modeling and estimated model discrimination with time-varying C index. RESULTS: The median length of stay for COVID-19 hospitalization was 9 (IQR, 5-16) days. In total, 95 patients (16%) experienced MAKE. Each 1 SD increase in soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 was significantly associated with an increased risk of MAKE (adjusted HR [AHR], 2.30 [95% CI, 1.86-2.85], and AHR, 2.26 [95% CI, 1.73-2.95], respectively). The C index of sTNFR1 alone was 0.80 (95% CI, 0.78-0.84), and the C index of sTNFR2 was 0.81 (95% CI, 0.77-0.84). LASSO and random forest regression modeling using all biomarkers yielded C indexes of 0.86 (95% CI, 0.83-0.89) and 0.84 (95% CI, 0.78-0.91), respectively. LIMITATIONS: No control group of hospitalized patients without COVID-19. CONCLUSIONS: We found that sTNFR1 and sTNFR2 are independently associated with MAKE in patients hospitalized with COVID-19 and can both also serve as predictors for adverse kidney outcomes. PLAIN-LANGUAGE SUMMARY: Patients hospitalized with COVID-19 are at increased risk for long-term adverse health outcomes, but not all patients suffer long-term kidney dysfunction. Identification of patients with COVID-19 who are at high risk for adverse kidney events may have important implications in terms of nephrology follow-up and patient counseling. In this study, we found that the plasma biomarkers soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 measured in hospitalized patients with COVID-19 were associated with a greater risk of adverse kidney outcomes. Along with clinical variables previously shown to predict adverse kidney events in patients with COVID-19, both sTNFR1 and sTNFR2 are also strong predictors of adverse kidney outcomes