Modeling Techniques Research Articles

Privacy Protection Behaviors from a New Angle: Exploratory Analysis on a Russian Sample

To understand why people engage in privacy protection behaviors on the Internet, multiple researchers tried to reveal the underlying factors of the phenomenon. The results were ambiguous. Identifying privacy concerns as a mediating construct, some researchers were able to establish its influence on protection behaviors, others were not. In addition, the structure of hypothesized factors affecting privacy concerns differed from work to work. This paper aims to shed light on the inconsistencies in the previous research. To achieve this, we perform an exploratory analysis of factors affecting privacy protection behavior on the Internet. We conduct a survey on a Russian sample (N=228) and perform the analysis using a technique of structural equation modeling. Our results suggest the following: (a) privacy protection behavior is a multidimensional construct, with each behavior type being affected by its own set of factors; (b) privacy concerns might have a dynamic structure, that is dependent on the context and the environment of the target population. Given these findings, we conclude that future research should consider a specific type of protection behavior together with characteristics of the target population and to refrain from generalization of the results from other domains or protection behavior types.

In-silico Investigation of Ginseng Phytoconstituents as Novel Therapeutics Against MAO-A.

Ginseng (Panax ginseng) is a herb of medicinal and nutritional importance. Ginseng has been used since ancient times for the treatment of numerous ailments as it has many therapeutic properties. Several phytoconstituents are present in Panax ginseng that possess a variety of beneficial pharmacological properties. To explore the potential of phytoconstituents of Panax ginseng in the treatment of depression, a molecular modeling technique was utilized targeting monoamine oxidase-A (MAO-A). A total of sixty-one phytoconstituents of ginseng were drawn with the help of ChemBioDraw Ultra 12.0 software and PDBs for MAO-A enzyme were retrieved from the RCSB PDB database. The prepared ligands were screened for MAO-A properties using the software Molegro Virtual Docker (MVD 2010.4.1.0). All the prepared ligands were evaluated for drug-likeliness properties using Swiss ADME. Among the docking studies of 60 Ginseng phytochemicals including one standard, 15 phytoconstituents with the highest dock score and better binding interactions were selected further for absorption, distribution, metabolism and excretion (ADME) studies. Stachyose (-227.287, 17 interactions), Raffinose (-222.157, 14 interactions), and Ginsenoside Rg1 (-216.593, 10 interactions) were found to possess better interactions as compared to Clorgyline taken as a standard drug. Stachyose was found to be the most potent inhibitor of MAO-A enzyme under investigation and can be a potential lead molecule for the development of newer phytochemical-based treatment of depression.

UNDERSTANDING THE NEXUS BETWEEN ABUSIVE SUPERVISION, KNOWLEDGE HIDING BEHAVIOR, WORK DISENGAGEMENT, AND PERCEIVED ORGANIZATIONAL SUPPORT IN TOURISM AND HOSPITALITY INDUSTRY

Supervisors' dispositions have not received enough attention as potential antecedents to employees' knowledge - hiding behaviors. Based on this, the current study investigates the impact of abusive supervision on knowledge-hiding behaviors, considering the mediating role of work disengagement in this relationship, as well as investigating the moderating role of perceived organizational support in the study model. Data was obtained from frontline service employees of five-star hotels in Sharm El-Sheikh and tourism companies in Cairo, Egypt, by a questionnaire that surveyed 298 employees. The collected data was scrutinized using the Smart PLS-structural equation modeling technique. The PLS-SEM statistics proved the highly positive and significant effect of abusive supervision on knowledge-hiding behavior and employees’ work disengagement, supported the mediating effect of work disengagement, and the moderating effect of perceived organizationa l support. The study highlights some practical implications for hotels, such as implementing integrated knowledge offering a digital library, continuous management and administrative skills training, and behavioral training to facilitate the gaining of knowledge and enhance staff skills, self-confidence, loyalty, and job security. It also strongly suggests adopting a strategy to monitor abusive supervisors through open communication channels, regularly conducting exit interviews to reduce turnover, and enhancing engagement, thereby preventing abusive practices.

A comprehensive review on crack detection schemes and modelling techniques in high-speed rotor systems

ABSTRACT The combination of a rotor and bearings plays a pivotal role in various industrial applications, including gas turbines, compressors, turbochargers, aircraft engines, and wind generators. Ensuring the structural integrity of the rotor-bearing assembly during static and dynamic conditions is most important, as any failure could lead to unpredictable losses. Due to their continuous operation, high-speed rotors are more susceptible to faults such as misalignment, bow, eccentricity, and cracks. Among these faults, cracks are the most predominant fault that affects the system’s reliability. Despite significant advancements in rotor dynamics, cracks still threaten the integrity and performance of the rotor-bearing assembly. This review paper aims to thoroughly review the challenges and issues associated with crack formation in high-speed rotor systems. Various crack detection methods are also explored using vibration signals obtained from the rotor-bearing unit and their associated processing techniques. This study also gives an extensive overview of different crack modelling and analysis techniques. Furthermore, a novel rotor-bearing model is modelled using the finite element method, and the numerical results obtained from this model are validated with the existing literature model. This review paper will serve as a valuable resource for researchers that offers insights into the latest advancements and emphasises the importance of crack detection for ensuring the reliability of high-speed rotor systems.

Mapping and modeling of water erosion in the Revubué river Sub-Basin, Moatize, Mozambique

This study investigated water erosion in the Revubué River Sub-Basin, Moatize, Mozambique, utilizing advanced mapping and modeling techniques. Through detailed land use surveys and application of the Potential Erosion Model (EPM), we identified areas with moderate to severe erosion rates (φ = 0.54), particularly in zones of intensive agriculture (15% of the study area) and mining (1% of the study area). The results highlighted the influence of undulating topography (average slope of 9%) and intensive agricultural practices on erosion rates (0 – 25 ton/ha). Urgent implementation of soil conservation practices, such as contour plowing and terracing, was recommended to mitigate soil loss, improve agricultural productivity, and promote environmental sustainability. Agricultural sustainability was emphasized, focusing on investments in sustainable agricultural practices to preserve long-term soil health. Raising awareness among farmers about the impacts of soil erosion and implementing effective management practices were considered crucial. Extension services were recognized as key agents in disseminating knowledge for promoting eco-friendly agricultural practices.

Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization

Abstract The worldwide transition to cleaner, sustainable energy sources, prompted by population growth and industrialization, responds to uncertain fossil fuel prices and environmental concerns, highlighting the substantial benefits of renewable energy in reducing greenhouse gas emissions and addressing climate change. Derived from non-fossilized organic materials, biomass emerges as a significant and sustainable contributor to renewable energy. Its diverse nature is complemented by a range of conversion technologies, encompassing combustion, pyrolysis, gasification, and liquefaction, providing versatile avenues for biomass energy transformation. Gasification, the transformative process of converting organic matter into combustible gases under controlled oxygen levels, is accomplished through direct oxygen supply or pyrolysis. This method yields a dependable gaseous fuel versatile for heating, industrial processes, power generation, and liquid fuel production. Machine learning employs advanced statistical techniques for modeling across diverse industries, showcasing particular efficacy in optimizing thermochemical processes by precisely identifying the optimal operating conditions required for achieving desired product properties. These models utilize proximate biomass data to predict the elemental compositions of N2 and H2. Assessment of both single and two hybrid models indicated that the introduced optimizers significantly enhanced the estimation of N2 and H2 when combined with Decision Tree (DT), with Decision Tree Coupled with Artificial Hummingbird Algorithm (DTAH) proving particularly effective. Notably, DTAH demonstrated outstanding performance with remarkable R 2 values of 0.990 for N2 and 0.992 for H2. Additionally, the minimal Root Mean Square Error (RMSE) values of 1.291 and 1.550 for N2 and H2 predictions respectively underscore the precision of DTAH, establishing it as a suitable choice for practical real-world applications.

Exploring arteriolar atherosclerosis: laminar blood flow across stenosis with fluid-structure interaction and gravitational effects

Abstract In response to the unanswered relevant questions surrounding atherosclerosis, it becomes imperative to investigate arterioles using sophisticated mathematical modelling techniques to shed light on critical stress and strain patterns influenced by gravity. The primary objective of this study is to scrutinize flow characteristics and probe stress and strain distributions experienced by the intima layer of arterioles, encompassing coronary, renal, cerebral, mesenteric, and pulmonary arteries, under gravitational forces. This investigation employs a fluid-structure interaction methodology utilizing arbitrary Eulerian–Lagrangian formulation. The study delves into blood flow characteristics within coronary, renal, cerebral, mesenteric, and pulmonary arterioles using the fluid-structure interaction technique, employing an arbitrary Eulerian–Lagrangian formulation. It thoroughly examines various biomechanical parameters such as the Cauchy–Green stress tensor, Principal strain, Piola–Kirchoff stress tensor, deformation tensor, and volume strain along the intima layer under the gravitational influence, elucidating vulnerable regions prone to endothelial dysfunction. Higher values of δV are found at the left shoulder and in the intima’s post stenosis area due to the pressure gradient along the flow channel, whereas other intima regions show a null volume strain. A thorough understanding of stress distribution is essential to create focused therapies to lessen vascular health problems. The stress in the post-stenosis region seems to affect the endothelial layer to a significant extent.

Green purchase behaviour of Arab millennials towards eco-friendly products: the moderating role of eco-labelling

Purpose This study aims to examine millennial consumers’ intentions towards purchasing eco-friendly products in the emerging Arab nation of Oman, focusing on the moderating role of eco-labelling (EL). Design/methodology/approach Environmental knowledge (EK) and environmental concern (EC) are integrated into the theory of planned behaviour (TPB) framework as independent variables, along with EL as a moderating variable. The data was collected through a self-administered survey and consisted of 705 random responses that was tabulated and analysed through the structural equation modelling technique. Findings ECs, subjective norms (SNs) and perceived behavioural control (PBC) significantly influence consumer attitudes towards green eco-friendly products. Consumers’ EK and attitudes positively influence the purchase intention (PI) to make green purchases. SNs and PBC substantially influence consumer attitudes towards adopting green products. However, the findings reveal a non-significant moderating influence of EL on the relationship between EC and PI for millennial Omani consumers. Research limitations/implications The findings are limited to only one Arab country, Oman, which limits the scope of the study. This study contributes to the TPB by using additional dimensions to examine the green purchasing behaviour of millennials. Practical implications Marketers can use these insights to develop strategies that emphasise the environmental benefits of products, leverage social influences and empower consumers to make environmentally conscious choices. Incorporating EL and green packaging can enhance the visibility and attractiveness of green products. Social implications Promoting environmental education, awareness campaigns and policy interventions that highlight the importance of eco-friendly product choices can foster more sustainable consumption patterns among Arab consumers. Originality/value To the best of the authors’ knowledge, this is the first study to examine the green PIs of millennials in an Arab context, specifically focusing on the moderating role of EL.

Impact of Political Instability on Tourism in West Bengal.

This study aimed to investigate the effects of political instability on tourism-related business performance, incoming tourist flows in West Bengal, and overall tourism-based earnings. The nonlinear effects of political risk and economic growth on tourism receipts are examined in this research. Nevertheless, the secondary data that is now accessible with the unobserved component model technique detection only provides a broad picture of the issue. Experts such as managers of lodging establishments, staff members of local tourism development offices, and tourism researchers were interviewed as a result. Expert interviews exposed West Bengal's tourism industry's prospects, challenges, and potential developments in the face of the region's five years of political unrest. The findings confirm earlier research showing that, in the near run, political unrest lowers tourism. However, the long-term viewpoint has yielded various results from the expert interviews with main province cities. Political instability benefits cities with developed tourism businesses that are located outside of crisis areas. Places that relied on visitors from the countries or continents like Europe, Germany, China, USA are mostly affected. The political unrest in eastern West Bengal did not significantly affect cities with low levels of tourism development.

Multi-scale hybrid modeling of plant growth in response to environmental conditions and soil nutrients availability

Crop modeling plays a crucial role in agriculture, aiding our understanding and prediction of crop growth and yield in diverse environmental conditions. This study aims to develop a comprehensive mathematical model describing plant growth in response to environmental conditions and soil nutrient availability. To achieve this, we relied on a field experiment with lettuce plants under varying environmental conditions. Employing growth models such as logistic, Gompertz, Aikman & Scaife, and Scaife, Cox & Morris, we assessed the influence of time, day-degrees, and effective-day-degrees across different plant densities and during distinct periods throughout the year. In general, describing plant growth in terms of day-degrees or effective-day-degrees yielded an improved model fit and more precise estimations of growth parameters. As a result, we described the growth of plant length in terms of effective-day-degrees instead of time in the equations of the Bessonov-Volpert system. Additionally, we modified the equation describing plant length growth using previously fitted functions. By incorporating these adjustments, we characterized the one-dimensional growth of plant weight under varying environmental conditions without branching, using the Bessonov-Volpert model. This study contributes valuable insights into crop modeling techniques, refining our understanding of optimizing plant growth under different environmental conditions.

Correlative electron microscopy of discontinuous precipitation

This paper presents a comprehensive study on discontinuous precipitation (DP) in Al-22 at.% Zn alloy, leveraging the correlative and synergic effects of advanced microscopy and modelling techniques. The study is structured into three pivotal steps: in-situ scanning electron microscopy combined with an energy backscattered diffraction analysis for real-time monitoring and an analysis of DP reaction kinetics and grain boundary misorientations; transmission electron microscopy and an energy X-ray dispersive microanalysis for detailed observation of solute-depleted α and solute-rich β lamellae, offering insights into the kinetics of the reaction front (RF) and solute concentration profiles; and a sophisticated modelling approach addressing the limitations of direct observation methods by simulating solute concentration changes within αphase lamellae during RF go-and-stop cycles. This multifaceted approach elucidates the nuances of DP mechanisms, from the nucleation and growth of precipitates to the estimation of instantaneous RF velocity and the impact of grain boundary misorientations. The integration of these methodologies enhances our understanding of DP kinetics and morphology at both meso- and nanoscales, highlighting the significant role of correlative microscopy in metallurgical research. The findings not only deepen our grasp of the fundamental mechanisms driving DP reactions but also provide a valuable comparison framework for experimental data, potentially guiding the development and optimization of metallurgical processes.

Data-driven classification and explainable-AI in the field of lung imaging

Detecting lung diseases in medical images can be quite challenging for radiologists. In some cases, even experienced experts may struggle with accurately diagnosing chest diseases, leading to potential inaccuracies due to complex or unseen biomarkers. This review paper delves into various datasets and machine learning techniques employed in recent research for lung disease classification, focusing on pneumonia analysis using chest X-ray images. We explore conventional machine learning methods, pretrained deep learning models, customized convolutional neural networks (CNNs), and ensemble methods. A comprehensive comparison of different classification approaches is presented, encompassing data acquisition, preprocessing, feature extraction, and classification using machine vision, machine and deep learning, and explainable-AI (XAI). Our analysis highlights the superior performance of transfer learning-based methods using CNNs and ensemble models/features for lung disease classification. In addition, our comprehensive review offers insights for researchers in other medical domains too who utilize radiological images. By providing a thorough overview of various techniques, our work enables the establishment of effective strategies and identification of suitable methods for a wide range of challenges. Currently, beyond traditional evaluation metrics, researchers emphasize the importance of XAI techniques in machine and deep learning models and their applications in classification tasks. This incorporation helps in gaining a deeper understanding of their decision-making processes, leading to improved trust, transparency, and overall clinical decision-making. Our comprehensive review serves as a valuable resource for researchers and practitioners seeking not only to advance the field of lung disease detection using machine learning and XAI but also from other diverse domains.

A Study on the Impact of Vertical Grid Parameter Perturbations in the Regional Ocean Modeling System

In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid distribution difference with high resolution at the surface and bottom. Two types are considered in the model, including removing the Coriolis force (type 1) and employing a different Coriolis force (type 2). According to the experiments, the velocity of the current in type 1 yields uncertainty, and wind energy could penetrate the upper ocean and reach the abyss. The surface velocity in type 2 is fundamentally compatible with the empirical relationship constructed by Ekman, and the curved lines of the vertical distribution of horizontal currents nearly match. For type 1, the velocity is very strong from the sea surface to the bottom. When comparing type 1 and type 2 cases, the Coriolis force obstructs the wind energy transfer into the deep ocean. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF)’s global surface wind distribution indicates that the realistic ocean upper wind boundary is similar to the numerical experiment in the Pacific and Atlantic oceans, where the wind direction is along the latitude line at the equator. In order to make the experimental situation as close as possible to the real ocean, validation experiments are conducted in this study to consider the uncertainty in the current profile at the equator. The simulation results of type 1 differ significantly from the data obtained from the real ocean. This uncertainty may transfer the signal to higher latitudes, causing incorrect simulation results, especially in the critical region. Overall, this research not only makes discoveries in physical ocean theory but also guides predictive and forecasting techniques for ocean modeling.

Enhancing Multi-Junction Solar Cell Performance: Advanced Predictive Modeling and Cutting-Edge CIGS Integration Techniques

Historically, multi-junction solar cells have evolved to capture a broader spectrum of sunlight, significantly enhancing efficiency beyond conventional solar technologies. In this study, we utilized Silvaco TCAD tools to optimize a five-junction solar cell composed of AlInP, AlGaInP, AlGaInAs, GaInP, GaAs, InGaAs, and Ge, drawing on advancements documented in the literature. Our research focused on optimizing these cells through sophisticated statistical modeling and material innovation, particularly examining the relationship between layer thickness and electrical yield under one sun illumination. Employing III-V tandem solar cells, renowned for their superior efficiency in converting sunlight to electricity, we applied advanced statistical models to a reference solar cell configured with predefined layer thicknesses. Our analysis revealed significant positive correlations between layer thickness and electrical performance, with correlation coefficients (R2 values) impressively ranging from 0.86 to 0.96 across different regions. This detailed statistical insight led to an improvement in overall cell efficiency to 44.2. A key innovation in our approach was replacing the traditional germanium (Ge) substrate with Copper Indium Gallium Selenide (CIGS), known for its adjustable bandgap and superior absorption of long-wavelength photons. This strategic modification not only broadened the absorption spectrum but also elevated the overall cell efficiency to 47%. Additionally, the optimization process involved simulations using predictive profilers and Silvaco Atlas tools, which systematically assessed various configurations for their spectral absorption and current–voltage characteristics, further enhancing the cell’s performance. These findings underscore the critical role of precise material engineering and sophisticated statistical analyses in advancing solar cell technology, setting new efficiency benchmarks, and driving further developments in the field.

Navigating Natural Disaster Disruption in the Hospitality Sector: Insights From the September 8, 2023 Earthquake in Morocco

Purpose: This case study delves into the specific hurdles encountered by hotel managers in Marrakech during and after the September 8, 2023 earthquake, alongside the strategies deployed to tackle these challenges. Moreover, it aims to unravel the intricacies faced by risk claims professionals in processing claims and evaluating risks post-earthquake, aiming to formulate suggestions for enhancing disaster risk management within the hospitality domain. Design/Methodology/Approach: Semi-structured interviews were conducted with 27 hotel managers and five insurance professionals in Morocco, all intimately engaged in handling or alleviating the impacts of the earthquake on the hospitality sector. Thematic analysis was utilized to pinpoint key challenges, responses initiated by hotel managers, and insights provided by risk claims professionals. Findings: The study underscores that the earthquake caused significant operational disruptions and financial strains on hotels, resulting in cancellations, revenue loss, and infrastructure damage. Hotel managers responded with prompt evacuation, safety protocols, damage assessment, and customer communication strategies. Risk claims professionals underscored the significance of comprehensive insurance coverage, streamlined claims processes, and advanced risk modeling techniques to mitigate financial risks and bolster disaster resilience within the hospitality sector. Originality/Value: This study furnishes original insights into the challenges faced by hotel managers and risk claims professionals post-seismic event, illuminating the complexities of disaster risk management within the hospitality industry. The recommendations proposed contribute to the formulation of effective strategies and protocols for navigating similar unforeseen events in the future.

Predicting potential habitat distribution of the invasive species Rhynchophorus ferrugineus Olivier in China based on MaxEnt modelling technique and future climate change.

Changes in the distribution of species due to global climate change have a critically significant impact on the increase in the spread of invasive species. An in-depth study of the distribution patterns of invasive species and the factors influencing them can help to better predict and combat invasive alien species. Rhynchophorus ferrugineus Olivier is an invasive species that primarily harms plants of Trachycarpus H. Wendl. The pest invades trees in three main ways: by laying eggs and incubating them in the crown of the plant, on roots at the surface and at the base of the trunk or petiole. Most of the plants in the genus Trachycarpus are taller, and the damage is concentrated in the middle and upper parts of the plant, making control more difficult. In this paper, we combine 19 bioclimatic variables based on the MaxEnt model to project the current and future distributions of R. ferrugineus under three typical emission scenarios (2.6 W m-2 (SSP1-2.6), 4.5 W m-2 (SSP2-4.5) and 8.5 W m-2 (SSP5-8.5)) in the 2050s and 2090s. Among the 19 bioclimatic variables, five variables were screened out by contribution rates, namely annual mean temperature (BIO 1), precipitation of driest quarter (BIO 17), minimum temperature of coldest month (BIO 6), mean diurnal range (BIO 2) and precipitation of wettest quarter (BIO 16). These five variables are key environmental variables that influence habitat suitability for R. ferrugineus and are representative in reflecting its potential habitat. The results showed that R. ferrugineus is now widely distributed in the southeastern coastal area of China (high suitability zone), concentrating in the provinces of Hainan, Guangdong, Fujian, Guangxi and Taiwan. In the future, the area of high and low suitability zones will increase and the area of medium suitability zones will decrease. The area of low suitability zone will still be in the largest proportion. This study aims to provide a theoretical reference for the future control of R. ferrugineus from the perspective of geographic distribution.

A comprehensive study of recent studies on the efficiency of axial flow turbines with an investigation on blade profile modifications in stator and rotor assemblies

Axial flow turbines are crucial in energy production and propulsion across diverse applications. As global energy needs rise, optimizing turbine efficiency is paramount. This review delves into the aerodynamic design of turbine blades, specifically examining blade profile modifications in both stator and rotor assemblies of state-of-the-art models proposed in the studies from 2019 to 2024. The focus is on mitigating performance discrepancies between idealized simulations and real-world applications, which present significant challenges such as inconsistencies in computational fluid dynamics (CFD) predictions and manufacturing constraints. Key findings from this review highlight a potential efficiency gain of 1%–3% through optimal blade designs that reduce incidence losses, secondary flows, and tip vortex formation. Advanced modeling techniques, including high-fidelity, multi-objective optimization with machine learning, are evolving to better predict and enhance turbine performance. However, gaps remain in system-level integration and the limited experimental data restricts comprehensive validation of these new designs. The insights from this review will aid the research community by identifying critical areas for future investigation, such as integrating blade aerodynamics with heat transfer and structural mechanics. Further, it emphasizes the need for exploring combinations of passive and active flow control strategies to fully harness the benefits of blade profile enhancements. These efforts are crucial for achieving substantial improvements in turbine efficiency, which are essential for meeting the growing demands for sustainable and efficient energy production.

Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles

Autonomous vehicles (AVs) have the potential to revolutionize transportation by offering safer, more efficient, and convenient mobility solutions. As AV technology advances, there is a growing need to understand and model traffic dynamics in environments where AVs interact with human-driven vehicles. This review provides a comprehensive overview of the modeling techniques used to simulate and analyze autonomous vehicle traffic. It covers the fundamental principles of AVs, key factors influencing traffic dynamics, various modeling approaches, their applications, challenges, and future directions in AV traffic modeling.

Healthcare Data Analytics and Predictive Modelling: Enhancing Outcomes in Resource Allocation, Disease Prevalence and High-Risk Populations

Purpose: This study aims to explore the role of healthcare data analytics and predictive modeling in enhancing healthcare outcomes, specifically in resource allocation, disease forecasting, and identifying high-risk populations. Methodology: The research employs a comprehensive approach, utilizing various sources of healthcare data such as electronic health records (EHRs) and public health databases. Advanced analytical techniques, including machine learning, artificial intelligence, and big data analytics, are applied to derive actionable insights. Findings: The study reveals that predictive modeling significantly enhances resource optimization, enables accurate disease prevalence forecasting, and improves the identification of high-risk populations. Case studies demonstrate how these technologies lead to more efficient healthcare delivery, cost reduction, and better patient care outcomes. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theoretical understanding of healthcare data analytics by integrating advanced predictive modeling techniques with real-world healthcare applications. It offers valuable insights for policymakers on the importance of investing in data infrastructure and promoting data-driven decision-making. Practically, the study provides healthcare organizations with actionable strategies to implement predictive analytics for improved resource allocation and patient care.

A database of shake-table tests conducted on unreinforced masonry buildings

Experimental results obtained from shake-table tests on complete unreinforced masonry buildings (URM) play a crucial role in enhancing modeling techniques and reducing uncertainties in seismic assessments for this type of building. This paper presents an open-access database comprising 69 shake-table tests on URM buildings with a scale equal to or larger than 1:4 that have been documented in the literature. The database summarizes important geometric parameters, structural characteristics, and experimental results, including maximum shake-table accelerations, story drifts, and evolution of dynamic properties, as well as qualitative information such as observed failure modes and damage progression throughout the experiments. This database is meant to serve the research community as a resource for planning future experimental campaigns, benchmarking numerical models, validating analysis methods, and improving predictive capabilities. As an example application, we generate vulnerability functions (fragility curves) from the experimental data. Our findings reveal that buildings tested in experiments are less vulnerable compared to real buildings, particularly when severe damage is observed and when the ground motion intensity is low to moderate. The ultimate objective is to facilitate the exchange of experimental data and boost collaborative approaches in the seismic assessment of URM buildings.