Search Results (64)

This paper introduces an unsupervised machine learning approach for student clustering and personalized recommendations in education. We employ the K-means clustering algorithm to identify distinct student groups based on behavioral engagement metrics. Unlike previous studies that relied on predefined categories, our methodology validated the number of clusters using both the elbow method and silhouette analysis, which ensured an optimal grouping structure. The clustering phase served as a foundation for deriving insights into student learning behaviors. To assess the clustering quality, we applied the silhouette score to quantify intra-cluster cohesion and inter-cluster separation, which provided statistical validation for our approach. Following the clustering process, we developed a recommendation system based on the user-based nearest neighbors collaborative filtering approach. This system tailors educational strategies to the unique characteristics of each cluster, enhancing student engagement and learning outcomes. Furthermore, we compared our methodology against alternative clustering and recommendation techniques to demonstrate its robustness and effectiveness. Our findings suggest that this combined clustering and recommendation framework offers a data-driven approach to personalized education, which can be extended beyond the KALBOARD360 dataset to other educational contexts. The overarching goal was to refine adaptive learning models that cater to the diverse needs of students, improving their academic success and participation. Full article

Ensuring consistent progress toward cities’ net-zero emission goals requires understanding key dimensions of urban climate governance—particularly the motivations driving municipalities toward net zero and the critical barriers and enablers along this pathway. Current knowledge on these critical aspects is fragmented, lacking a holistic framework and empirical prioritization of key factors. We developed an integrated analytical framework and empirically distilled the most salient motivations, barriers, and enablers through a large-scale survey targeting 489 net-zero-committed municipalities—known as “Zero Carbon Cities”—across Japan. With responses from 309 municipalities, we deliver the first systematic mapping of factors perceived as most influential by Japanese local authorities. The results indicate that municipalities are primarily motivated by seizing local economic development opportunities (enhanced local energy conditions, financial gains and savings, and local industry revitalization), future-proofing communities against disasters, and enhancing the local quality of life. Key barriers and enablers were identified across four categories: municipal resources and authority (budgets, dedicated staff, and empowered climate agencies), knowledge and expertise (staff climate competence), institutional coherence (cross-departmental coordination and stakeholder involvement), and political will and leadership (the presence of climate champions and awareness within city halls and among residents). Accordingly, we discuss implications and derive recommendations toward strengthened local action in Japan and beyond. Full article

Many important practical problems connected to energy efficiency in buildings, ecology, metallurgy, the development of wireless communication systems, the optimization of radar technology, quantum computing, pharmacology, and seismology are described by large-scale mathematical models that are typically represented by systems of partial differential equations. Such systems often involve numerous input parameters. It is crucial to understand how susceptible the solutions are to uncontrolled variations or uncertainties within these input parameters. This knowledge helps in identifying critical factors that significantly influence the model’s outcomes and can guide efforts to improve the accuracy and reliability of predictions. Sensitivity analysis (SA) is a method used efficiently to assess the sensitivity of the output results from large-scale mathematical models to uncertainties in their input data. By performing SA, we can better manage risks associated with uncertain inputs and make more informed decisions based on the model’s outputs. In recent years, researchers have developed advanced algorithms based on the analysis of variance (ANOVA) technique for computing numerical sensitivity indicators. These methods have also incorporated computationally efficient Monte Carlo integration techniques. This paper presents a comprehensive theoretical and experimental investigation of Monte Carlo algorithms based on “symmetrized shaking” of Sobol’s quasi-random sequences. The theoretical proof demonstrates that these algorithms exhibit an optimal rate of convergence for functions with continuous and bounded first derivatives and for functions with continuous and bounded second derivatives, respectively, both in terms of probability and mean square error. For the purposes of numerical study, these approaches were successfully applied to a particular problem. A specialized software tool for the global sensitivity analysis of an air pollution mathematical model was developed. Sensitivity analyses were conducted regarding some important air pollutant levels, calculated using a large-scale mathematical model describing the long-distance transport of air pollutants—the Unified Danish Eulerian Model (UNI-DEM). The sensitivity of the model was explored focusing on two distinct categories of key input parameters: chemical reaction rates and input emissions. To validate the theoretical findings and study the applicability of the algorithms across diverse problem classes, extensive numerical experiments were conducted to calculate the main sensitivity indicators—Sobol’ global sensitivity indices. Various numerical integration algorithms were employed to meet this goal—Monte Carlo, quasi-Monte Carlo (QMC), scrambled quasi-Monte Carlo methods based on Sobol’s sequences, and a sensitivity analysis approach implemented in the SIMLAB software for sensitivity analysis. During the study, an essential task arose that is small in value sensitivity measures. It required numerical integration approaches with higher accuracy to ensure reliable predictions based on a specific mathematical model, defining a vital role for small sensitivity measures. Both the analysis and numerical results highlight the advantages of one of the proposed approaches in terms of accuracy and efficiency, particularly for relatively small sensitivity indices. Full article

Light Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (Gossypium hirsutum L.), but standardized data acquisition and processing guidelines are lacking. Accurate canopy height estimation is crucial in cotton for optimizing growth regulator application and maximizing yield. The main goal of this study was to determine the optimal unmanned aerial vehicle flight settings—altitude and speed—and assess specific processing parameters’ impact on data accuracy, processing time, and file size. Nine flight settings comprising three altitudes (12.2 m, 24.4 m, and 48.8 m) and three speeds (4.8 km/h, 9.6 km/h, and 14.4 km/h) were tested. LiDAR data were processed using DJI Terra software (v. 4.1.0), where two user-defined processing steps were examined: point-cloud thinning via grid size sub-sampling (0, 10, 20, 30, 40, and 50 cm) and slope classification (flat, gentle, and steep). The optimal flight altitude was 24.4 m, with no effect of flight speed. Grid sub-sampling up to 20 cm produced balanced accuracy, processing time, and file size. The choice of slope category had no significant effect on LiDAR-derived canopy height. These findings contribute to the development of standardized LiDAR data acquisition and processing guidelines for cotton to support crop management decision. Full article

Rapid urbanization and the consequent alteration in land use and land cover (LULC) significantly change the natural landscape and adversely affect hydrological cycles, biological systems, and various ecosystem services, especially in the developing world. Thus, it is vital to study the environmental conditions of a region to mitigate the negative impacts of urbanization. Out of a wide array of parameters, the Environmental Criticality Index (ECI), a relatively new concept, was used in this study, which was conducted over the Kolkata Metropolitan Area (KMA). It was derived using Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to quantify heat-related impact. An increase in the percentage of land area under high ECI categories, from 23.93% in 2000 to 32.37% in 2020, indicated a progressive increase in criticality. The Spatio-temporal Thermal-based Environmental Criticality Consistency Index (STTECCI) and hotspot analysis identified the urban and industrial areas in KMA as criticality hotspots, consistently recording higher ECI. The correlation analysis between ECI and LULC features revealed that there exists a negative correlation between ECI and natural vegetation and agriculture, while built-up areas and ECI are positively correlated. Bare lands, despite being positively correlated with ECI, have an insignificant relationship with it. Also, the designed built-up index extracted the built-up areas with an accuracy of 89.5% (kappa = 0.78). The future scenario of ECI in KMA was predicted using Modules for Land Use Change Evaluation (MOLUSCE) with an accuracy level above 90%. The percentage of land area under low ECI categories is expected to decline from 50.02% in 2000 to 35.6% in 2040, while the percentage of land area under high ECI categories is expected to increase from 23.93% in 2000 to 36.56% in 2040. This study can contribute towards the development of tailored management strategies that foster sustainable growth, resilience, and alignment with the Sustainable Development Goals, ensuring a balance between economic development and environmental preservation. Full article

Water scarcity presents one of the greatest challenges of our time. Especially in naturally water-scarce regions, the need for additional water resources is rising, requiring innovative and site-adapted technologies. The decision for a specific technology is mostly associated with high investment costs and a long life cycle time, which requires a conscientious and transparent decision-making process. In this review, such a framework is developed for selected non-conventional water technologies and strategically evaluated with the goal to support a sustainable technology application based on specific boundary conditions. This is achieved by a matrix-based assessment and the development of key indicators respecting the availability, applicability, environmental impact, scalability, and economic viability of the selected technologies. Based on a wide literature review, the developed methodology involves a systematic comparison of technologies for desalination, water reuse, groundwater utilization, agricultural reuse, and unconventional approaches like cloud seeding, dew water, and fog water harvesting. The developed indicators cover most parameters of the respective categories based on the individual designs. Subsequently, the different technologies are analyzed by a matrix-based evaluation, highlighting various strengths and weaknesses and providing insights into technology application based on regional conditions. The discussion interprets the findings, deriving implications for dry environments, acknowledging limitations, and suggesting pathways for future research. The matrix-based evaluation is illustrated by an example from the Jordan Valley for a brackish water desalination plant. Through this analytical framework, this study contributes to the discourse on sustainable water solutions and a transparent decision-making process, as well as offers valuable insights for policymakers, researchers, and industries during a decision-making progress. Full article

This study builds on initial ADApp research that identified the factors that influence the intention to use a pharmacy drone app for urgent medication delivery. While previous studies and theories have predominantly focused on user acceptance alone, the present qualitative study introduced a holistic model that integrates user acceptance theories as well as user-centered design principles and technology features. It focused on the user journey to derive core statements from the development of a drone-based application using a qualitative theory synthesis approach (study 1), and explored the perceived participatory collaboration between developers (software and drone developers) and co-developers (core group participants) using final tandem discussions and a qualitative content analysis method (study 2). Study 1 resulted in the identification of eight categories that serve as technical working goals for future participatory technology development. Study 2 identified five critical factors that provide insight into the unique challenges and goals of collaborative development. Both studies contribute to a better understanding of the essential factors that lead to successful participatory processes between developers and co-developers aimed at increasing usability and intention to use. Based on these findings, an integrated model is presented to support participatory design strategies in healthcare technology development. Full article

Phosphorylation is one of the most common protein post-translational modifications. The identification of phosphorylation sites serves as the cornerstone for protein-phosphorylation-related research. This paper proposes a protein-phosphorylation site-prediction model based on graph neural networks named GraphPhos, which combines sequence features with structure features. Sequence features are derived from manual extraction and the calculation of protein pre-trained language models, and the structure feature is the secondary structure contact map calculated from protein tertiary structure. These features are then innovatively applied to graph neural networks. By inputting the features of the entire protein sequence and its contact graph, GraphPhos achieves the goal of predicting phosphorylation sites along the entire protein. Experimental results indicate that GraphPhos improves the accuracy of serine, threonine, and tyrosine site prediction by at least 8%, 15%, and 12%, respectively, exhibiting an average 7% improvement in accuracy compared to individual amino acid category prediction models. Full article

This study conducted a Life Cycle Assessment (LCA) of alternative (electric and hydrogen) and conventional diesel buses in a large metropolitan area. The primary focus was on hydrogen derived from coke oven gas, a byproduct of the coking process, which is a crucial step in the steel production value chain. The functional unit was 1,000,000 km traveled over 15 years. LCA analysis using SimaPro v9.3 revealed significant environmental differences between the bus types. Hydrogen buses outperformed electric buses in all 11 environmental impact categories and in 5 of 11 categories compared to conventional diesel buses. The most substantial improvements for hydrogen buses were observed in ozone depletion (8.6% of diesel buses) and global warming (29.9% of diesel buses). As a bridge to a future dominated by green hydrogen, employing grey hydrogen from coke oven gas in buses provides a practical way to decrease environmental harm in regions abundant with this resource. This interim solution can significantly contribute to climate policy goals. Full article

Microplastics (MPLs) and nanoplastics (NPLs) are smaller particles derived from larger plastic material, polymerization, or refuse. In context to environmental health, they are separated into the industrially-created “primary” category or the degradation derivative “secondary” category where the particles exhibit different physiochemical characteristics that attenuate their toxicities. However, some particle types are more well documented in terms of their fate in the environment and potential toxicological effects (secondary) versus their industrial fabrication and chemical characterization (primary). Fourier Transform Infrared Spectroscopy (FTIR/µ-FTIR), Raman/µ-Raman, Proton Nuclear Magnetic Resonance (H-NMR), Curie Point-Gas Chromatography-Mass Spectrometry (CP-gc-MS), Induced Coupled Plasma-Mass Spectrometry (ICP-MS), Nanoparticle Tracking Analysis (NTA), Field Flow Fractionation-Multiple Angle Light Scattering (FFF-MALS), Differential Scanning Calorimetry (DSC), Thermogravimetry (TGA), Differential Mobility Particle [Sizing] (DMPS), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Scanning Transmission X-ray Microspectroscopy (STXM) are reviewed as part of a suite of characterization methods for physiochemical ascertainment and distinguishment. In addition, Optical-Photothermal Infrared Microspectroscopy (O-PTIR), Z-Stack Confocal Microscopy, Mueller Matrix Polarimetry, and Digital Holography (DH) are touched upon as a suite of cutting-edge modes of characterization. Organizations, like the water treatment or waste management industry, and those in groups that bring awareness to this issue, which are in direct contact with the hydrosphere, can utilize these techniques in order to sense and remediate this plastic polymer pollution. The primary goal of this review paper is to highlight the extent of plastic pollution in the environment as well as introduce its effect on the biodiversity of the planet while underscoring current characterization techniques in this field of research. The secondary goal involves illustrating current and theoretical avenues in which future research needs to address and optimize MPL/NPL remediation, utilizing nanotechnology, before this sleeping giant of a problem awakens. Full article

The sustainable development of tourism is a critical issue, and immersive tourism has emerged as a key market trend that significantly contributes to this goal. Experiencescape, a vital component of immersive tourism, plays a crucial role in shaping tourists’ experience and promoting sustainability within the tourism industry. Taking Chang’an Twelve Hours Theme Block as the research object, this paper investigates the composition and impact of immersive tourism experiencescape by utilizing grounded theory and hierarchical regression analysis on data derived from online reviews and tourist surveys. The findings reveal that immersive tourism experiencescape is divided into two main categories: physical and interpersonal. The physical experiencescape consists of three dimensions: functional facilities, thematic atmosphere, and basic environment. The interpersonal experiencescape, on the other hand, includes tourism performances, host-guest interaction, and personal service. The study demonstrates that immersive tourism experiencescape exerts a significant positive influence on tourists’ behavioral intentions, with emotional experience serving as a partial mediator in this relationship. These insights offer valuable theoretical and practical implications. They provide a perspective for enhancing the sustainability of tourism by improving the quality of immersive experiences. Full article

This study introduces an innovative scheme for classifying uncrewed aerial vehicle (UAV)-derived vehicle trajectory behaviors by employing machine learning (ML) techniques to transform original trajectories into various sequences: space–time, speed–time, and azimuth–time. These transformed sequences were subjected to normalization for uniform data analysis, facilitating the classification of trajectories into six distinct categories through the application of three ML classifiers: random forest, time series forest (TSF), and canonical time series characteristics. Testing was performed across three different intersections to reveal an accuracy exceeding 90%, underlining the superior performance of integrating azimuth–time and speed–time sequences over conventional space–time sequences for analyzing trajectory behaviors. This research highlights the TSF classifier’s robustness when incorporating speed data, demonstrating its efficiency in feature extraction and reliability in intricate trajectory pattern handling. This study’s results indicate that integrating direction and speed information significantly enhances predictive accuracy and model robustness. This comprehensive approach, which leverages UAV-derived trajectories and advanced ML techniques, represents a significant step forward in understanding vehicle trajectory behaviors, aligning with the goals of enhancing traffic control and management strategies for better urban mobility. Full article

In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach. Full article

Early and rapid environmental assessment of newly developed aircraft concepts is eminent in today’s climate debate. This can shorten the decision-making process and thus accelerate the entry into service of climate-friendly technologies. A holistic approach within the conceptual aircraft design is taken into consideration in terms of a life cycle assessment (LCA) to properly model and evaluate these concepts. To provide an understanding of how different LCA software affects the assessment, the goals of this study are to establish a baseline metrics definition for comparative evaluation and apply them to two tools. The first tool is an existing simplified derivative of openLCA, while the second, developed in this study, is an automated interface to the same software. The main finding is that researchers and practitioners must carefully consider the intended use of the tool. The simplified tool is suitable for training and teaching purposes and assessments on single score level. In contrast, an advanced tool is required in order to appropriately analyze the overall impact categories requiring high levels of LCA expertise, modeling, and time effort. Full article

In the existing literature, an optimal time-varying toll scheme has been proposed for the Suez Canal to address the inefficiency of numerous ships queuing and waiting at the anchorage area to enter the canal. The primary objective of this tolling strategy is to alleviate the significant issue of ships queuing at the canal’s anchorage area. This stands in contrast to the current tolling system employed by the Suez Canal, which primarily aims to recover the management and operational costs associated with ship passage through the canal. However, the existing literature has yet to explore how the arrival times of ships at the anchorage area will change after implementing the optimal time-varying toll scheme. The goal is to ensure that the equilibrium cost of each tolled ship does not result in losses and achieve maximum efficiency in eliminating queueing at the anchorage area. To address this gap, this paper adopts the principle of cost equilibrium conservation and utilizes the Point-Slope Form to derive two mathematical formulas representing all ships’ post-toll arrival times at the anchorage area of the Suez Canal. These formulas are specifically derived for two categories of tolled ships: those that enter the canal earlier than the latest entry time regulated by the canal authorities and those that enter later. The derived formulas are concise and comparative, strengthening the theoretical underpinnings of the current pricing model for a queuing canal. Furthermore, they serve as valuable references for canal authorities in devising pertinent measures, such as organizing the scheduling of canal pilots, to facilitate the implementation of the optimal time-varying toll scheme. Full article