Analysis about Planning and Modification of the Upper and Lower Structures of the 38 Floor Apartment SkyHouse Project with Equivalent Static Analysis

The modeled building structure is a regular building, with the number of levels being varied. The structural model is divided into 38-level portals. This research uses the help of the SAP2000 v21 program to facilitate the earthquake analysis process. The results of the study that will be compared are displacements between levels and base shear that occur due to earthquake forces. The results of the analysis have shown that static analysis produces greater results for the structural models compared to dynamic analysis. The difference in displacement between levels produced by the two methods in the three structural models is still included in the displacement limits between levels of permission required in SNI 1726-2012, so that the three models can still be analyzed by static analysis and dynamic analysis. Because the results of displacement and base shear in static analysis are greater than dynamic analysis, static analysis is safer if used for earthquake force loading in general structural calculations. Although in earthquake analysis, dynamic analysis is a more accurate analysis because the analysis process is closer to the actual situation.

OrganoID: a versatile deep learning platform for organoid image analysis

Organoids are three-dimensional in vitro tissue models that closely represent the native heterogeneity, microanatomy, and functionality of an organ or diseased tissue. Analysis of organoid morphology, growth, and drug response is challenging due to the diversity in shape and size of organoids, movement through focal planes, and limited options for live-cell staining. Here, we present OrganoID, an open-source image analysis platform that automatically recognizes, labels, and tracks single organoids in brightfield and phase-contrast microscopy. The platform identifies organoid morphology pixel by pixel without the need for fluorescence or transgenic labeling and accurately analyzes a wide range of organoid types in time-lapse microscopy experiments. OrganoID uses a modified u-net neural network with minimal feature depth to encourage model generalization and allow fast execution. The network was trained on images of human pancreatic cancer organoids and was validated on images from pancreatic, lung, colon, and adenoid cystic carcinoma organoids with a mean intersection-over-union of 0.76. OrganoID measurements of organoid count and individual area concurred with manual measurements at 96% and 95% agreement respectively. Tracking accuracy remained above 89% over the duration of a four-day validation experiment. Automated single-organoid morphology analysis of a dose-response experiment identified significantly different organoid circularity after exposure to different concentrations of gemcitabine. The OrganoID platform enables straightforward, detailed, and accurate analysis of organoid images to accelerate the use of organoids as physiologically relevant models in high-throughput research.

Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation (TD_MLE) and that flimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical.

Review of machine learning applications to power systems studies

The complexity of electric power networks from generation, transmission and distribution stations in modern times has resulted to generation of big and more complex data that requires more technical and mathematical analysis because it deals with monitoring, supervisory control and data acquisition all in real time. This has necessitated the need for more accurate analysis and predictions in power systems studies especially under transient, uncertainty or emergency conditions without interference of humans. This is necessary so as to minimize errors with the aim targeted towards improving the overall performance and the need to use more technical but very intelligent predictive tools has become very relevant. Machine learning (ML) is a powerful tool which can be utilized to make accurate predictions about the future nature of data based on past experiences. ML algorithms operate by building a model (mathematical or pictorial) from input examples to make data driven predictions or decisions for the future. ML can be used in conjunction with big data to build effective predictive systems or to solve complex data analytic problems. Electricity generation forecasting systems that could predict the amount of power required at a rate close to the electricity consumption have been proposed in several works. This study seeks to review machine learning applications to power system studies. This paper reviewed applications of ML tools in power systems studies.

Hybrid Metaheuristic-Based Spatial Modeling and Analysis of Logistics Distribution Center

The location analysis of logistics distribution centers is one of the most critical issues in large-scale supply chains. While a number of algorithms and applications have been provided for this end, comparatively fewer investigations have been made into the integration of geographical information. This study proposes logistic distribution center location analysis that considers current geographic and embedded information gathered from a geographic information system (GIS). After reviewing the GIS, the decision variables and parameters are estimated using spatial analysis. These variables and parameters are utilized during mathematical problem-based analysis stage. While a number of existing algorithms have been proposed, this study applies a hybrid metaheuristic algorithm integrating particle swarm optimization (PSO) and genetic algorithm (GA). Using the proposed method, a more realistic mathematical model is established and solved for accurate analysis of logistics performance. To demonstrate the effectiveness of the proposed method, Korea Post distribution centers were considered in South Korea. Through tests with several real-world scenarios, it is proven experimentally that the proposed solution is more effective than existing PSO variations.

Noncontinuous noise calculated by room response function determined by experimental data

Purpose: Experimental determination of the response room function and its use to estimate the acoustic conditions in rooms with noncontinuous noise sources.Methodology/approach: The detailed parameter calculation of noncontinuous sound fields using the response room function, which is the room response to pulse excitation. The response function can be calculated by analytical or numerical methods and by experimental measurements in production conditions the energy attenuation when a constant noise source is switched off.Findings: Noncontinuous noise has a negative impact on health. The effective noise reduction is determined by the complete and accurate analysis of its energy parameters. The noncontinuous noise estimation based on equivalent levels does not meet the requirements, especially when pulsed noise sources are active. The experimental technique is proposed for the response function calculation and its use in evaluating the noise conditions in rooms with noncontinuous noise sources.Practical implications: The experimental determination of the response function to pulse excitation allows studying the acoustic processes in rooms for the formation of noise conditions when analytical methods cannot be used. The experimentally obtained response function makes it possible to solve problems of changing the noise conditions in rooms with noncontinuous noise sources.

3D MODELING AND VISUALIZATION OF ARCHITECTURE AND LANDSCAPE

The study presents the results of architectural and vegetation survey missions in the UNESCO site of Quinta da Regaleira in the city of Sintra, Portugal. The different types of connecting elements of the epigean and hypogean architectures in the Park are analysed through the disciplinary tools of architectural design. Surveys and models of some of the connecting elements are proposed for an understanding of the site and its subsequent protection and valorisation through digital documentation. Of great interest is the architectural and social relationship that the site establishes with the city of Sintra.The survey of epigean architecture considered the following: the Casa da Renasceça, the Capela, the Cocheiras, the Estufa, the Oficina das Artes, the Loggia dos Pisoes, the Casa dos Ibis, the Torre da Regaleira the Terraço dos Mundos Celestes and the Fonte da Abundância.The survey of underground architecture considered the following architectures: the Gruta do Labirinto, the Gruta da Leda, the Lago da Cascata, the Gruta do Aquario, the Gruta do Oriente, the Portal dos Guardiães, the Poço Imperfeito and the Poço Iniziático.The work constitutes a complete and accurate analysis, represented through technical drawings, in different scales, digital point clouds and 3D modelling for the visualisation of the architecture in the Quinta da Regaleira in Sintra.

Progress of Optical Biosensors for Analysis of Pathogens and Organic Pollutants in Water since 2015

Water contamination by pathogens and organic pollutants is one of the major environmental problems that risk human health. Climate change with extreme weather can promote their prevalence in waters. Environmental monitoring of these pollutants in a fast, continuous, and accurate manner is of increasing demand, especially under the climate change context, but is challenged by their ubiquity and trace concentrations. Optical biosensing is one of the desired solutions owing to its rapid and accurate detection with high sensitivity. Principally, an optical biosensor recognizes these bioactive toxins and contaminants by tailored bioreceptors (e.g., aptamer, enzyme, and cells) and transduces the biological response to optical signals. Research efforts have been made on tailoring bioreceptors and enhancing signal transducing by nanoparticles. This study comprehensively reviewed the mechanisms of optical biosensing and the recent development of bioreceptors and nanomaterials on the enhancement for the rapid, easy, and accurate analysis of emerging contaminants in water. The advantages and challenges on sensitivity, selectivity, and durability of biosensors were discussed along with the opportunities and development strategies.

SHOT-R: A next generation algorithm for particle kinematics analysis

Analysis of live-imaging experiments is crucial to decipher a plethora of cellular mechanisms within physiological and pathological contexts. Kymograph, i.e. graphical representations of particle spatial position over time, and single particle tracking (SPT) are the currently available tools to extract information on particle transport and velocity. However, the spatiotemporal approximation applied in particle trajectory reconstruction with those methods intrinsically prevents an accurate analysis of particle kinematics and of instantaneous behaviours. Here, we present SHOT-R, a novel numerical method based on polynomial reconstruction of 4D (3D+time) particle trajectories. SHOT-R, contrary to other tools, computes bona fide instantaneous and directional velocity, and acceleration. Thanks to its high order continuous reconstruction it allows, for the first time, kinematics analysis of co-trafficked particles. Overall, SHOT-R is a novel, versatile, and physically reliable numerical method that achieves all-encompassing particle kinematics studies at unprecedented accuracy on any live-imaging experiment where the spatiotemporal coordinates can be retrieved.

Treatment of noncognitive neuropsychiatric disorders in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease, which is caused by cerebral amyloidosis. Noncognitive neuropsychiatric disorders (NСNPDs) include emotional, behavioral disorders, as well as psychotic symptoms. NСNPDs are almost an obligatory manifestation of this disease, accompany cognitive impairment and are detected at all stages of the disease – from preclinical to the severe dementia stage. As an example, we present a case report of a female patient with mild dementia in AD in whom Akatinol memantine administration resulted in the stabilization of a cognitive defect within one year and a decrease in the severity of emotional and behavioral disorders. The article discusses the indications and contraindications for antipsychotic administration in this disease, NСNPDs treatment in AD, which includes nonpharmacological and pharmacological methods. Accurate analysis of NСNPDs allows to predict the disease course, optimize the treatment, and thereby improve the quality of life of the patient and his relatives and caregivers.