Post-analysis of predictive models fosters their application in practice, as domain experts want to understand the logic behind them. In epidemiology, methods explaining sophisticated models facilitate the usage of up-to-date tools, especially in the...
This paper presents a comprehensive review of emerging innovations and future research directions leveraging Large Language Models (LLMs) for urban data analytics, examining how cities generate, structure, and use information to support planning and...
The efficiency and reliability of business processes in commercial banks are critical to financial stability and compliance. However, traditional evaluation methods that rely on retrospective qualitative assessments and static frameworks struggle to...
The growing focus on enhancing ship performance over the past decades has driven the invention of various energy-saving devices. Conducting performance analyses of these devices is essential to substantiate the claimed energy savings. However, this p...
The paper is devoted to the digital transformation of the core engineering maintenance processes involved in medium-voltage overhead distribution network infrastructure inspection. The study presents an analysis of digital twin utilization for inspec...
This study proposes a data-driven framework with post hoc interpretability analysis for one-step-ahead crystal diameter prediction in the Czochralski (CZ) silicon single-crystal growth process. To address the strong multivariable coupling, nonlinear...
Laser beam welding (LBW) involves complex and rapid interactions between the laser and material, often resulting in defects such as pore formation. Emissions collected during the process offer valuable insight but are difficult to interpret directly...
Global climate change poses many threats, with significant consequences for crop productivity and food security. The agricultural sectors in India and Japan face multiple problems, such as pre-harvest problems (volatility in input prices), post-harve...
In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) f...
Small- and medium-sized enterprises (SMEs) increasingly rely on digital technologies to sustain innovation, yet limited empirical evidence explains how business intelligence capabilities translate into superior innovation outcomes, particularly in em...
The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have gro...
In this paper, we present a curated dataset derived from continuous multi-object tracking observations over a two-year period from a signalized urban intersection in Salzburg, Austria. The dataset includes time-resolved trajectories of multimodal roa...
Wind turbine power upgrades have recently become a debated topic in wind energy research. Their assessment poses some challenges and calls for devoted techniques: some reasons are the stochastic nature of the wind and the multivariate dependency of w...
The strong heterogeneity in and complex engineering conditions of deep shale gas reservoirs make productivity prediction challenging, especially in nascent blocks where data is scarce. This scarcity constitutes a critical research gap for the applica...
Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders...
Financial markets react continuously to corporate disclosures, yet stock prices may not adjust immediately to new information. One of the most widely studied anomalies associated with post-announcement return behavior is post-earnings announcement dr...
Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring a prompt and functional recovery of the built environment. Monitoring-based approaches have the potential to significantly improve upon current visual inspection...
To ensure the safe repurposing of post-mining landscapes, understanding and managing geotechnical risks, particularly ground movements such as settlements on opencast mining dump surfaces, is critical. Satellite-based radar interferometry (InSAR) tec...
The advancement of generative artificial intelligence (AI) has accelerated the progress of intelligent design methods for building structures. However, generative AI models struggle to design inclined shear wall components due to their limited genera...
This study examines the pull-out behaviour of power-actuated fasteners (PAFs) in concrete through a combined analytical, experimental, and probabilistic approach. An experimental programme with 40 power-actuated fasteners (PAFs) installed in plain co...
This study evaluates the effectiveness of combining remote sensing techniques with the Random Forest algorithm for estimating the Periodic Annual Increment (PAI) in a dry tropical forest located within the Caatinga biome in northeastern Brazil. The a...
This study investigates model-based emergency strategies for damage prevention following cable breaks in six-degree-of-freedom cable-driven parallel robots. By relaxing real-time constraints, the empirical performance limit of existing and novel reco...
The integration of a high proportion of renewable energy has significantly reduced the grid inertia level and markedly increased the risk of transient frequency instability in power systems. Meanwhile, the large-scale integration of diverse heterogen...
In the cities of post-industrialized countries, renovation is the main part of building construction activity and has a major urban impact. Measuring this ongoing phenomenon and its distribution is of great usefulness for municipality urban planning...
This study addresses the challenge of consolidating heterogeneous railway infrastructure data into a unified framework to support advanced analysis and data-driven asset management. The primary objective is the development of a spatial–temporal...
The increasing availability of automated development workflows and data-driven methods raises the question of when approaches based on artificial intelligence (AI) provide potential benefits over established engineer-driven workflows in lightweight s...
This study proposes and validates a data-driven statistical process control (SPC) framework for monitoring and reducing particle contamination in automotive aluminium die-casting, addressing a documented gap in the structured application of inferenti...
As suicide rates increase globally, there is a growing need for effective, data-driven methods in mental health monitoring. This study leverages advanced artificial intelligence (AI), particularly natural language processing (NLP) and machine learnin...
Feature prioritization in early-stage software startups is a critical yet poorly structured challenge, as prevailing frameworks rely predominantly on expert intuition and fail to exploit patterns latent in historical delivery data and labor market dy...
Robotic machining is a promising technology for post-processing large additively manufactured parts. However, the applicability and efficiency of robot-based machining processes are restricted by dynamic instabilities (e.g., due to external excitatio...
Consumer technology has been enormously boosted by the COVID-19 pandemic, with one of the primary consumers being the elderly. In this scenario, it is necessary to consider the impact of technologies on different older generational cohorts to underst...
Big data analytics has become a cornerstone of modern industries, driving advancements in business intelligence, competitive intelligence, and data-driven decision-making. This study applies Neural Topic Modeling (NTM) using the BERTopic framework an...
Full-scale wind turbine is a mature technology and therefore several retrofitting techniques have recently been spreading in the industry to further improve the efficiency of wind kinetic energy conversion. This kind of interventions is costly and, f...
The rapid convergence of artificial intelligence, machine learning, and financial and risk analytics has produced a large and fragmented body of scientific literature, making it difficult to identify its thematic structure and evolutionary trends. Th...
Artificial intelligence (AI) has emerged as a transformative tool for plant health monitoring, offering new opportunities for scalable, timely, and data-driven pest and disease management in agriculture. This review provides a comprehensive synthesis...
Breast cancer (BC) has overtaken lung cancer as the most common cancer in the world and the projected incidence rates show a further increase. Early detection through population screening remains the cornerstone of BC control, but a progressive chang...
This paper examines the farming systems and operational structures in the Region of Western Macedonia (RWM), Greece and constructs a typology of farms based on structural, operational, and socio-economic characteristics. Agriculture remains a vital p...
Color distortion is a common issue in Jilin-1 KF01 series satellite imagery, a phenomenon caused by the instability of the sensor during the imaging process. In this paper, we propose a data-driven method to correct color distortion in Jilin-1 KF01 i...
COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (...
Transforming the global legacy of abandoned industrial landscapes into vibrant, sustainable urban assets presents a critical yet complex opportunity, requiring solutions that simultaneously honor heritage and meet evolving urban demands. As multifunc...
Background: Post-transplant immune heterogeneity may influence kidney allograft outcomes, yet the clinical relevance of circulating T-cell phenotypes remains incompletely defined. We aimed to identify 12-month post-transplant data-driven T-cell clust...
The increasing adoption of electronic medical records (EMRs) presents a unique opportunity to enhance trauma care through data-driven insights. However, extracting meaningful and actionable information from unstructured clinical text remains a signif...
Fly ash-based geopolymer concrete (GPC) is a sustainable alternative to conventional cementitious materials; however, its compressive strength is governed by complex and highly correlated mixture parameters, making experimental optimization expensive...
This study addresses the challenge of predicting runout distance of rainfall-induced shallow landslides by integrating deep learning and explainable machine learning. Using the June 2024 landslide disaster at the Fujian-Guangdong-Jiangxi border as a...
Recent decades in the Lancang River Basin have witnessed extensive construction of hydropower dams, profoundly impacting the local environment. Utilizing high-precision satellite data, we conducted a comprehensive analysis of vegetation cover and car...
The COVID-19 pandemic disrupted tuberculosis (TB) control, increasing mortality and potentially worsening disparities. This study aimed to analyze the temporal trends of TB mortality in Brazil and to trace the COVID-19 pandemic’s effect using a...
In recent years, the emergence of the smart city concept has garnered attention as a promising innovation aimed at addressing the multifactorial challenges arising from the concurrent trends of urban population growth and the climate crisis. In this...
Accurately predicting the fatigue life of long-distance natural gas pipelines with internal corrosion defects is essential to ensure structural integrity and operational safety. While data-driven models offer potential in this regard, many lack inter...
The growing adoption of cycling as part of the transition toward sustainable urban mobility, driven by climate change concerns and increasing congestion, has heightened the need to ensure cyclist safety in metropolitan areas. This study proposes an i...
The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible...
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