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Automatic determination of manufacturing process sequences for the physical production of given part designs is key to facilitate on-demand cyber manufacturing. In this work, we propose an integrated framework that (i) identifies manufacturing features from 3D part designs using a Graph Neural Network (GNN), (ii) identifies the manufacturing processes necessary to produce all features in the part using a Convolutional Neural Network (CNN) that considers shape, material properties, and quality information, and (iii) outputs an ordered manufacturing sequence that can produce the designed part with the help of sequence mining. Using these methods, the knowledge required to enable automated manufacturing process selection is easily scalable and updatable without requiring manual population of ad-hoc or rule-based descriptions. We present exemplar implementations of the proposed framework by suggesting manufacturing sequences for discrete parts with multiple features. The suggested manufacturing sequences demonstrate the potential of the proposed framework for use in future on-demand cyber manufacturing applications. [ABSTRACT FROM AUTHOR]

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved

Given the tight relationship between the effective material qualities of honeycomb cellular structures and their geometric cell configurations, these attributes play an important role in isolating specific target sound frequencies to prevent transmission through cellular panels. In this study, we introduce a predictive model to investigate the structural-acoustic performance of sandwich panels featuring honeycomb cellular cores, employing a combination of finite element (FE) analysis and machine learning-based neural networks. Initially, Finite Structured Acoustic Element Analysis (FE) was employed to identify the natural frequencies arising from exposure to high noise levels in the sandwich panel. Subsequently, the outputs of the FE model (comprising 224 configurations) were used to train an artificial neural network (ANN) model, using the Levenberg-Marquardt method. The developed ANN model was then utilized to assess the impact of various parameters, including changes in inner cell angles (−45 to +45°), alterations in material composition, variations in skin thickness, and total mass, on the transmission characteristics of the sandwich structure. The results generated by the ANN model closely align with those obtained from the numerical model, exhibiting a small error tolerance of 0.483 and a remarkable correlation coefficient of 0.99798. [ABSTRACT FROM AUTHOR]

GFRP-concrete-steel composite columns (GCS) are formed by combining different components with excellent mechanical properties. The GCS exhibits good corrosion resistance and can serve as a pile foundation platform for offshore wind turbines. An Artificial Neural Network (ANN) is utilized to swiftly predict the loading capacity of GCS columns with various geometrical parameters. The prediction error is primarily controlled within 10%. The thickness of GFRP tubes (tG), the inner diameter of GFRP tubes (DG,i), the thickness of steel tubes (ts), the outer diameter of steel tubes (DS,o), the strength of GFRP tubes (fG), the strength of concrete (fC), and the strength of steel tubes (fS) were used as independent variables to determine the key factors affecting the loading capacity of GCS columns. The impact of the number of input variables on the final prediction accuracy is analyzed. The proportional influence of different parameters on the loading capacity of GCS column is investigated using the Garson algorithm, among which, the thickness of the GFRP tube (tG) accounts for the largest proportion, approximately 24.57%. The suitability of the artificial neural network for predicting the loading capacity of GCS columns with various geometries is revealed. The results indicate that the ANN can be utilized for high-precision compressive strength prediction, and the thickness of the GFRP tube (tG) is the most influential factor on the loading capacity of GCS columns. [ABSTRACT FROM AUTHOR]

The prediction of food shelf life has become a vital tool for distributors and consumers, enabling them to determine storage and optimal edible time, thus avoiding unexpected food waste. Artificial neural network (ANN) have emerged as an effective, fast and accurate method for modeling, simulating and predicting shelf life in food. ANNs are capable of tackling nonlinear, complex and ill-defined problems between the variables without prior knowledge. ANN model exhibited excellent fit performance evidenced by low root mean squared error and high correlation coefficient. The low relative error between actual values and predicted values from the ANN model demonstrates its high accuracy. This paper describes the modeling of ANN in food quality prediction, encompassing commonly used ANN architectures, ANN simulation techniques, and criteria for evaluating ANN model performance. The review focuses on the application of ANN for modeling nonlinear food quality during storage, including dairy, meat, aquatic, fruits, and vegetables products. The future prospects of ANN development mainly focus on optimal models and learning algorithm selection, multiple model fusion, self-learning and self-correcting shelf-life prediction model development, and the potential utilization of deep learning techniques. HIGHLIGHTS: ANN-based food shelf life prediction methods are reviewed. This paper discusses application of ANN in the food storage process. BPNN is the mainstream ANN architecture used for the prediction of food quality. ANNs are useful for prediction of outputs with high accuracy. Future trends of ANN in the agri-supply chain are evaluated. [ABSTRACT FROM AUTHOR]

In this article, the application of an artificial neural network (ANN)-based machine learning (ML) strategy to predict the coupled frequency of geometrically skewed multiphase magnetoelectric (MME) composite plate exposed to hygrothermal environment is presented. The ANN model is trained using a dataset comprising more than one million simulations conducted using an in-house developed finite element formulation. The underlying multiphysics governing equations are derived using Hamilton's principle and higher-order shear deformation theory (HSDT). The influence of the hygrothermal environment on the elastic stiffness of MME composites is defined by the empirical constants in the constitutive relations. Four prominent combinations of the empirical constants leading to different elastic stiffness relations have been considered in this study. Alongside, the influence of geometrical skewness on the coupled fundamental frequency is also assessed. For the training of the ANN model, the Levenberg–Marquardt optimization algorithm with 30 neurons along with a tangent sigmoid activation function is used. The trained ANN model is tested on an unseen dataset, different from the training data, and it is shown to accurately predict the natural frequency of MME plate with a maximum error of 2.3%. Further, excluding the training time and considering the computational time alone, the ANN model is found to be 6.3 times faster than the FE simulations. It is anticipated that such ML-based reduced order models can be effective in the design process, especially in complex multiphysics problems, such as the one considered in the work, involving a multitude of geometric, loading and material parameters. [ABSTRACT FROM AUTHOR]

This study aims to serve as a performance indicator for the workability and strength of concrete when coarse aggregate, sand, cement, and water are partially substituted with waste plastic bottle caps. The significance of these alternative plastic water bottle caps is to reduce plastic trash that is difficult to lapse and to prevent waste that can be transformed to something that may be employed in the advancement of technology in the future. The principle of "Reduce, Reuse, and Recycle" is used, which not only lowers environmental pollution but also reduces costs. In the building industry, concrete is the most often utilized material. In order to preserve natural resources and minimize the number of materials that end up in landfills, green construction is becoming a more significant worldwide issue. Empty cans and bottle tops from drinking water bottle produce a lot of garbage. The difficulty of biodegrading plastic trash and the need for techniques for recycling or reuse make this a problem for the environment. Such problems are being investigated in this study in order to determine whether it could be possible to partially replace coarse aggregate with 0, 6 and 12% in the manufacturing of concrete using discarded bottle caps. To evaluate the compressive strength, split tensile, and flexural strength test characteristics in a laboratory setting; waste bottle caps were used as replacements for coarse aggregate at different percentages. The highest compressive strength was determined to be 28.80 MPa with 12% replacement of used plastic bottle caps with a water cement ratio of 0.55. Advanced statistical methods, including RSM-CCD (Response Surface Method-Central Composite Design) and machine learning models ANN-LM (Artificial Neural Network- Levenberg Marquardt), were applied in this study to predict concrete performances based on mix design variations. It was found that ANN-LM model displayed more accurate prediction relative to RSM-CCD method. [ABSTRACT FROM AUTHOR]

An Element Free Galerkin (EFG) meshless formulation and solutions using higher order shear deformation theory with nine degrees of freedom for the static analysis of Functionally Graded Material (FGM) plates are provided. This technique estimates the shape function using Moving Least Squares (MLS) method. The proposed method is validated by comparing the present findings with those in the literature. A novel Artificial Neural Network (ANN) model is developed to forecast the deflection of FGM plates within less computational time. Detailed parametric and convergent studies reveal that the proposed EFG solution and the ANN technique are more efficient than their conventional counterparts. The validation and comparison of the generated results in the present investigation with the other analysis methods revealed that the EFG method and ANN model give more accurate results than the FEM and other meshless methods. The current EFG-ANN model reduces computing time by 99.94% when compared to the EFG approach. Also, the accuracy is enhanced using the EFG approach with HSDT9 for the FGM plate. [ABSTRACT FROM AUTHOR]

In the present study, the machine learning based probabilistic model is proposed to perform the vibration analysis of functionally graded (FG) porous plates reinforced with graphene nanoplatelets (GNP). The plate is considered with the statistical variation in material properties such as elastic moduli of metal matrix and GNP, porosity index, and weight fraction of GNP while sampling via normal distribution. The effective material properties, i.e. elastic modulus and density of the GNP reinforced porous composite are respectively obtained using Halpin-Tsai and the rule of mixture models. The size and pore density are varied along the thickness direction using continuous cosine functions. The equation of motion based on higher-order shear deformable theory (HSDT) is derived using the variational principle which is further simulated by employing the finite element method (FEM) to obtain natural frequency. The database is generated based on selective input features and natural frequency i.e. output, and is further divided into 70% training dataset and 30% for testing and validation. A regression-based artificial neural network (ANN) model is adopted to learn the underlying function that maps the natural frequency to the inputs. The mapped model is further coupled with Monte Carlo simulation (MCS) to efficiently find the statistical variation in the natural frequency concerning variation in material properties (inputs) at reduced cost and time. [ABSTRACT FROM AUTHOR]

Background: A high portal pressure gradient (PPG) is associated with an increased risk of failure to control esophagogastric variceal hemorrhage and refractory ascites in patients with decompensated cirrhosis. However, direct measurement of PPG is invasive, limiting its routine use in clinical practice. Consequently, there is an urgent need for non-invasive techniques to assess PPG. Aim: To develop and validate a deep learning model that predicts PPG values for patients with decompensated cirrhosis and identifies those with high-risk portal hypertension (HRPH), who may benefit from early transjugular intrahepatic portosystemic shunt (TIPS) intervention. Methods: Data of 520 decompensated cirrhosis patients who underwent TIPS between June 2014 and December 2022 were retrospectively analyzed. Laboratory and imaging parameters were used to develop an artificial neural network model for predicting PPG, with feature selection via recursive feature elimination for comparison experiments. The best performing model was tested by external validation. Results: After excluding 92 patients, 428 were included in the final analysis. A series of comparison experiments demonstrated that a three-parameter (3P) model, which includes the international normalized ratio, portal vein diameter, and white blood cell count, achieved the highest accuracy of 87.5%. In two distinct external datasets, the model attained accuracy rates of 85.40% and 90.80%, respectively. It also showed notable ability to distinguish HRPH with an AUROC of 0.842 in external validation. Conclusion: The developed 3P model could predict PPG values for decompensated cirrhosis patients and could effectively distinguish HRPH. [ABSTRACT FROM AUTHOR]

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors. [ABSTRACT FROM AUTHOR]

This study employs advanced data mining techniques to investigate the DASS‐42 questionnaire, a widely used psychological assessment tool. Administered to 680 students at Necmettin Erbakan University's Ahmet Kelesoglu Faculty of Education, the DASS‐42 comprises three distinct subscales—depression, anxiety and stress—each consisting of 14 items. Departing from traditional statistical methodologies, the study harnesses the power of the WEKA data mining program to analyse the dataset. Employing Naive Bayes (NB), Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) algorithms, the research unveils novel insights. The ANN method emerges as a standout performer, achieving remarkable distinctiveness scores for all subscales: depression (99.26%), anxiety (98.67%) and stress (97.35%). The study highlights the potential of data mining in enhancing psychological assessment and showcases the ANN's prowess in capturing intricate patterns within complex psychological dimensions. By charting a course beyond conventional statistical methods, this research pioneers a new frontier for employing data mining within the realm of social sciences. As a result of the study, it is recommended that teacher candidates in the teacher education process should have knowledge about depression, anxiety and stress, and relevant courses on these topics should be added to the curriculum of teacher education programs. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field. [ABSTRACT FROM AUTHOR]

Over the past twenty years, the Middle East has experienced a surge in air pollution and dust, resulting in a range of issues affecting both people and the environment. Monitoring particulate matter (PM10 and PM2.5) has long been essential in assessing air quality. Thus, creating precise and proficient predictive models to estimate particulate matter concentrations is imperative for effectively managing and reducing air pollution. The estimation of seasonal and intra-annual PM concentrations was conducted in this study through the use of MLR and MLP models. A diverse range of meteorological parameters, including evaporation, temperature, wind speed, visibility, precipitation, and humidity, were employed along with aerosol optical depth (AOD). During autumn, the MLR and MLP models exhibited impressive performances. For PM10, the R values were 0.7 and 0.79, whereas for PM2.5, they were 0.7 and 0.81, respectively. The MLP's superior correlation between the observed and estimated seasonal and intra-annual PM concentrations was noteworthy, as it consistently favored PM2.5 and highlighted the superiority of the ANN-MLP model over MLR. The predictive data underscored a correlation between PM concentration and the four seasons, emphasizing the seasonal impact on PM levels. Sensitivity analysis revealed that relative humidity (RH) was the primary factor influencing the intra-annual levels of both PM10 and PM2.5. This study offers valuable insights into comprehending the formation process, implementing effective control measures, and establishing predictive models for PM, all aimed at proficiently managing air quality. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is the main component of soil organic matter (SOM) and constitutes the crucial component of the soil. It supports key soil functions, stabilizes soil structure, aid in plant-nutrient retention and release, and promote water infiltration and storage. Predicting SOC using Sentinel-2 data integrated with machine learning algorithms under zero tillage practice is inadequately documented for developing countries like Zimbabwe. The purpose of this study is to evaluate the performance of support vector machine (SVM), artificial neural network (ANN), and partial least square regression (PLSR) algorithms from Sentinel-2 data for SOC estimation. The SVM, ANN and PLSR models were used with a cross-validation to estimate the SOC content based on 50 georeferenced calibration samples under a zero-tillage practice. The ANN model outperformed the other two models by delivering a coefficient of determination (R2) of between 55 and 60% of SOC variability and RMSE varied between 5.01 and 8.78%, whereas for the SVM, R2 varied between 0.53 and 0.57 and RMSE varied between 6.25 and 11.39%. The least estimates of SOC provided by the PLSR algorithm were, R2 = 0.44–0.49 and RMSE = 7.59–12.42% for the top 15 cm depth. Results with and R2, root mean square error (RMSE) and mean absolute error (MAE) for SVM, ANN and PLSR, show that the ANN model is highly capable for capturing SOC variability. Although the ANN algorithm provides more accurate SOC estimates than the SVM algorithm, the difference in accuracy is not significant. Results revealed a satisfactory agreement between the SOC content and zero tillage practice (R2, coefficient of variation (CV), MAE, and RMSE using SVM, ANN and PLSR for the validation dataset using four predictor variables. The calibration results of SOC indicated that the mean SOC was 15.83% and the validation mean SOC was 17.02%. The SOC validation dataset (34.17%) had higher degree of variation around its mean as compared to the calibration dataset (29.86%). The SOC prediction results can be used as an important tool for informed decisions about soil health and productivity by the farmers, land managers and policy makers. [ABSTRACT FROM AUTHOR]

Whey, a cheese production byproduct, can be anaerobically digested to reduce pollution and generate energy. Yet, stability is challenging due to organic content sensitivity and influent fluctuations. The present work attempts to implement the mechanistic model and machine learning (ML) models (support vector regression (SVR) and artificial neural networks (ANNs)) together to predict the concentration dataset of substrate 1 (S1) (i.e., carbohydrates and proteins), substrate 2 (S2) (i.e., glucids and amino acids), VFA, and methane (CH4) as a function of input independent variables, namely time and organic loading rate (OLR). The R2 values for S1, S2, VFA, and CH4 obtained through the mechanistic model remained as 0.953, 0.918, 0.84, and 0.976, respectively; for ANN models, 0.982, 0.928, 0.958, and 0.99; and for SVR models, 0.984, 0.939, 0.938, and 0.999, respectively. ML models have been discovered to be among the most precise and versatile compared to the mechanistic model. Moreover, other performance metrics, such as RMSE (0.022–2.177), MRE (0.007–0.100), and AARE (0.008–0.104) for ANN and RMSE (0.083–1.961), MRE (0.021–0.091), and AARE (0.037–0.089) for SVR, are obtained, indicating good prediction performances for both ML models. SVR and ANN models excel, aligning concentration curves to the optimum line when input parameters are adjusted, unlike the subpar traditional-based mechanistic model. Therefore, ML methods offer a tool to predict anaerobic digestion more effectively, enhancing design and operations. [ABSTRACT FROM AUTHOR]

In this work, a dimensionless group-incorporating artificial neural network (DI-ANN) model is established to determine the relative influence of factors affecting single-track depth for laser-directed energy deposition (L-DED), specifically as it correlates with two types of porosity (keyhole mode and lack-of-fusion voids). The DI-ANN model's input features include process parameters, process signatures acquired from experiments, and multiphysics simulation-based dimensionless groups. Experimental data were acquired for SS316L straight single-track samples, prepared using a continuous-wave laser with a top-hat profile. The feature influence on single-track depth was analysed using Spearman's correlation coefficient before training the DI-ANN model, which was tested using 5-fold cross-validation. Laser power, scanning speed, powder feed rate, melt pool width, melt pool area, maximum temperature, maximum Marangoni number, mean and maximum Grashof number, and mean solidification time are selected for training the model. The predictive quality of this model for single-track depth is 6.1% mean absolute percent error (MAPE) and 11.7% maximum absolute percent error (MXAPE). Compared with recent literature, this study shows an approximately 30% improvement in the MAPE and a 70% improvement in MXAPE. The presented model can be used for process parameter optimisation for porosity reduction and be a valuable foundation for feedback control algorithms for heat penetration. [ABSTRACT FROM AUTHOR]

In this study, ginger was dried using the microwave infrared cooperative vibrating drying (MICVD) technique. Drying kinetics, moisture evolution, cell morphology and sample structure change, mechanical properties, and physicochemical properties of the sample during drying were investigated. The ginger dried at the microwave power of 0.4 W/g and infrared temperature of 50 °C (M0.4I50) exhibited the highest water diffusion rate. Significant moisture evolution in the sample was observed during the drying. The free water in the sample was removed rapidly, the immobilized water increased in the early drying stage and then decreased, and the bound water increased slowly throughout the drying. Removing moisture caused cell collapse in the ginger and the formation of moisture microchannels and pores. These structural changes resulted in increased fractal dimension, porosity, and volume shrinkage in the final dried product. Moisture evolution also significantly affected the rheological behavior of the ginger and its color profile. The correlation analysis result suggested that the key quality attributes of ginger, such as fractal dimension, porosity, color, volume shrinkage ratio, and rheological characteristic, were significantly correlated with moisture content and evolution. Moreover, backpropagation in the artificial neural network (BP-ANN) model was proved effective in predicting these key quality attributes based on its moisture content parameters. This study aimed to interpret the moisture evolution of ginger during MICVD drying and provide insight to future researchers on the development and application of this novel drying technique. [ABSTRACT FROM AUTHOR]

Precise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990–2016. Six scenarios (Sc1–Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners. [ABSTRACT FROM AUTHOR]

Software reliability is a critical factor in assessing the health of software and identifying defects. Software reliability growth models (SRGM) are used to estimate the occurrence of software faults. There are various parameterized and non‐parameterized models of SRGM. These models effectively predict fault occurrence for limited testing conditions. To resolve this problem various neural and artificial neural network (ANN) models are proposed. A problem while using ANN is over‐fitting and under‐fitting. Non‐autoregressive time series models, including ANN variants, offer promising solutions to address under‐fitting issues in SRGM, providing enhanced predictive capabilities for fault occurrence across diverse testing conditions. This study proposes a modified version with a Bayesian regularization technique to address over‐fitting. This modification aims to enhance the suitability of the Bayesian regularization framework for nonlinear autoregressive (NAR) models by carefully adjusting regularization parameters. Comprehensive testing with real‐world software failure datasets is conducted to evaluate the effectiveness of the proposed approach. The results demonstrate that our modified approach improved generalization capabilities and increased prediction accuracy. The NAR‐ANN model exhibits a lower mean squared error of 0.12935 and a higher R2$R^2$ value of 0.99853. [ABSTRACT FROM AUTHOR]

For the risk analysis of spatially distributed structures, the joint prediction of ground motion intensities at multiple sites is required. Therefore, many researchers have come up with spatial correlation models for seismic intensity measures (IMs). The spatial correlation model requires site-specific non-ergodic ground motions. One of the chosen approaches for correlation studies is physics-based simulations (PBS), which account for the complexities related to earthquake source, path and site. The present study evaluates the efficiency of the artificial neural network-based broadband response spectra generator (BBANN) of Sharma et al. (2023) in terms of residual analysis and spatial correlation. The Turkey earthquakes of 6th February 2023 are taken as the case study. The model-predicted values corresponding to a short period are compared with the recorded values for the events. We found that the model was able to capture ground motion trends without any bias in residuals. Also, in the spatial correlation scale, the model predictions are comparable with recorded values. The results highlighted the efficiency of the BBANN model in effectively capturing the spatial pattern of ground motion intensity measures. The study draws attention to the ability of PBS to generate non-ergodic ground motion and, hence, can be useful in seismic hazard and risk frameworks. [ABSTRACT FROM AUTHOR]

The inspection of building components plays a crucial role in reducing maintenance costs throughout the building's life cycle. Many buildings suffer from defects in the building elements, such as surface problems, cracks, and moisture problems. Building Information Modeling (BIM) has become a widely used digital tool for the construction industry, allowing for the virtual visualization, design, and simulation of structures. At the same time, progress in neural networks, a branch of artificial intelligence, presents encouraging potential for classification and evaluation. This article explores the fusion of BIM and neural networks to improve and simplify the building inspection procedure. Neural networks are implemented with BIM to conduct real-time analysis and predictions as part of an inspection framework. A data set consisting of 500 wall samples with defects from different buildings was collected through fieldwork. These data were then classified into five categories, ranging from D1 (No damage) to D5 (Collapse), based on expert opinions. A neural network was used to create a model that can show the severity of degradation of building elements. The BIM platform is utilized in the implementation of the proposed model to facilitate the exchange of information and enhance documentation during inspection. The visualization provided by BIM allows the facility management team and building owners to assess building elements through causality analysis using distinct color codes. A case study of an office building is featured in the paper to showcase the application of the proposed model for asset management. The color-coded system used in this study denotes low-performance conditions as red, medium performance conditions as yellow, and high-performance conditions as green. This article outlines the procedures for categorizing and ranking defects in building elements, specifically focusing on walls. Practical Applications: This study explores innovative methods for assessing the condition of buildings, aiming to improve accuracy, efficiency, and predictive capabilities. Traditional building inspections are often subjective, time-consuming, and labor-intensive, making it challenging to generate consistent and comprehensive evaluations. Our research addresses these limitations by integrating BIM and neural networks. BIM provides detailed digital models of buildings, which include comprehensive information about each component. This technology enables precise and consistent assessments, reducing the subjectivity inherent in manual inspections. Neural networks, a type of artificial intelligence, can analyze complex data patterns and detect potential issues early, allowing for proactive maintenance. By combining these two technologies, our approach offers a more efficient and objective method for building condition assessment. This integration not only saves time and resources but also enhances the ability to predict and prevent future issues, ultimately extending the lifespan of building components and improving overall building performance. These advancements are particularly relevant for property managers, maintenance teams, and construction professionals seeking to optimize building maintenance and management practices. The findings of this study can significantly benefit the construction and facilities management industries by providing a more reliable and advanced method for maintaining building health and safety, thereby ensuring sustainable and cost-effective building management. [ABSTRACT FROM AUTHOR]

This paper presents an approach to prevent the incorrect transfer of fuel to the wrong tank during refueling. An experimental setup was developed to perform ultrasonic and dielectric measurements on diesel, gasoline, ethanol, and water. The fuel types were determined using an ultrasonic sensor and time-of-flight values were measured at various temperatures. Additionally, the dielectric coefficients of these liquids were measured to determine the liquid types using a dielectric sensor. The results obtained from both the ultrasonic and dielectric methods are systematically compared, and the advantages and disadvantages of each approach are thoroughly discussed. It was observed that dielectric method does not always yield accurate results. The proposed system effectively prevents erroneous transfer of fuel to the tank during refueling. The developed system may be used in practice to distinguish fuel types. In addition, a new approach using machine learning techniques to determine fuel type is presented. Fuel types were classified using 33 machine learning algorithms such as support vector machines, artificial neural networks and K-Nearest neighbors. It seems that artificial neural network with first layer size 25 and quadratic discriminant classifiers have achieved remarkable results with a success rate of 94% in classification. The results highlight the important and effective role of ultrasonic sensors in accurately identifying fuel types, leading to more efficient and safer storage and transportation of fuel. It is also concluded that machine learning techniques can be used effectively in identifying and classifying fuel types. The approach involving ultrasonic and artificial intelligence techniques was particularly innovative in distinguishing fuel types. [ABSTRACT FROM AUTHOR]

Patients with relapsed/refractory (r/r) diffuse large B-cell lymphoma (DLBCL) show varied responses to PD-1 monoclonal antibody (mAb) containing regimens. The mechanisms and predictive biomarkers for the efficacy of this regimen are unclear. This study retrospectively collected r/r DLBCL patients who received PD-1 mAb and rituximab regimens as salvage therapy. Clinical and genomic features were collected, and mechanisms were explored by multiplex immunofluorescence and digital spatial profiling. An artificial neural network (ANN) model was constructed to predict the response. Between October 16th, 2018 and May 4th, 2023, 50 r/r DLBCL patients were collected, 29 were response patients and 21 were non-response patients. CREBBP (p = 0.029) and TP53 (p = 0.015) alterations were statistically higher in non-response patients. Patients with PD-L1 CPS ≥ 5 were correlated with a longer overall survival (OS) than those with PD-L1 CPS < 5 (median OS: not reached vs. 9.7 months, hazard ratio [HR]: 3.8, 95% confidence interval [CI] 0.64–22.44, p = 0.016). Immune-related pathways were activated in response patients. The proportion and spatial organization of tumor-infiltrating immune cells affect the response. PD-L1 CPS level, age, and alterations of TP53, MYD88, CREBBP, EP300, GNA13 were used to build an ANN predictive model that showed high prediction efficiency (training set area under curve [AUC] of 0.97 and test set AUC of 0.94). The proportion and spatial distribution of tumor-infiltrating immune cells may be related to the function of immune-related pathways, thereby influencing the efficacy of PD-1 mAb containing regimens. The ANN predictive model showed potential value in predicting the responses of r/r DLBCL patients received PD-1 mAb and rituximab regimens. [ABSTRACT FROM AUTHOR]