Search Results (2,668)

To increase productivity, more sophisticated cluster tools are developed. To achieve this, one of the ways is to increase the number of spaces in a process module (PM) and the number of fingers on a robot arm as well, leading to a cluster tool with multi-space PMs and a multi-finger-arm robot. This paper discusses the scheduling problem of cluster tools with four-space PMs and a four-finger-arm robot, a typical tool with multi-space PMs and a multi-finger-arm robot adopted in modern fabs. With two arms in such a tool, one is used as a clean one, while the other is used as a dirty one. In this way, wafer quality can be improved. However, scheduling such cluster tools to ensure the residency time constraints is very challenging, and there is no research report on this issue. This article conducts an in-depth analysis of the steady-state scheduling for this type of cluster tools to explore the effect of different scheduling strategies. Based on the properties, four robot task sequences are presented as scheduling strategies. With them, four linear programming models are developed to optimize the cycle time of the system and find feasible schedules. The performance of these strategies is dependent on the activity parameters. Experiments are carried out to test the effect of different parameters on the performance of different strategies. It shows that, given a group of parameters, one can apply all the strategies and choose the best result obtained by one of the strategies. Full article

In the era of vast information, individuals are immersed in choices when purchasing goods and services. Recommender systems (RS) have emerged as vital tools to navigate these excess options. However, these systems encounter challenges like data sparsity, impairing their effectiveness. This paper proposes a novel approach to address this issue and enhance RS performance. By integrating user demographic data, singular value decomposition (SVD) clustering, and semantic similarity in collaborative filtering (CF), we introduce the UDIS method. This method amalgamates four prediction types—user-based CF (U), demographic-similarity-based (D), item-based CF (I), and semantic-similarity-based (S). UDIS generates separate predictions for each category and evaluates four different merging techniques—the average, max, weighted sum, and Shambour methods—to integrate these predictions. Among these, the average method proved most effective, offering a balanced approach that significantly improved precision and accuracy on the MovieLens dataset compared to alternative methods. Full article

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster–support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud. Full article

The Construction Cost Index (CCI) is an important tool that is widely used in construction cost management to monitor cost fluctuations over time. Numerous studies have been conducted on CCI development and forecasting models, including time series, artificial intelligence, machine learning, and hybrid models. Therefore, this study seeks to reveal the complexity of CCI forecasting and identify the leading indicators, trends, and techniques for CCI prediction. A bibliometric analysis was conducted to explore the landscape in the CCI literature, focusing on co-occurrence, co-authorship, and citation analysis. These analyses revealed the frequent keywords, the most cited authors and documents, and the most productive countries. The research topics and clusters in the CCI forecasting process were presented, and directions for future research were suggested to enhance the prediction models. A case study was conducted to demonstrate the practical application of a forecasting model to validate its prediction reliability. Furthermore, this study emphasizes the need to integrate advanced technologies and sustainable practices into future CCI forecasting models. The findings are useful in enhancing the knowledge of CCI prediction techniques and serve as a base for future research in construction cost estimation. Full article

Species identification of Listeria isolates remained a tedious process still based on culturing methods that, in recent years, have led to the description of many species that are not even part of the genus Listeria. It is advisable to provide new precise techniques since this taxon includes two pathogens that are usually transmitted through the food chain, Listeria monocytogenes and L. ivanovii. The approach, so-called multilocus phylogenetic analysis (MLPA) that uses several concatenated housekeeping gene sequences, provides accurate and affordable classification frameworks to easily identify Listeria species by simple Sanger sequencing. Fragments of seven housekeeping genes (gyrA, cpn60, parE, recA, rpoB, atpA, and gyrB) from 218 strains of all Listeria species currently described were used to build an MLPA of the concatenated sequence, a total of 4375 bp. All isolates subjected to identification were clustered within the species of Listeria sensu stricto, L. monocytogenes, L. innocua, and L. welshimeri, and some reference strains were reclassified as L. ivanovii and L. seeligeri. Housekeeping-gene sequencing has been demonstrated to represent a pragmatic tool that can be firmly considered in food control. Full article

Background/objectives: Pseudomonas aeruginosa can cause community-acquired infections affecting various body sites. The present retrospective study investigated the genetic diversity of 173 isolates (166 clinical, 7 environmental) of P. aeruginosa collected from clinical pathology laboratories in Abidjan, Côte d’Ivoire (2001–2011). Methods: Multiple-Locus Variable Number of Tandem Repeats (VNTR) Analysis (MLVA) using 13 loci was applied to all isolates and compared to published MLVA data. The antibiotics status of the isolates was compiled when available and compared to published profiles. Results: Among 95 isolates analyzed for their antibiotics status, 14 displayed concerning resistance profiles: five multidrug-resistant (MDR) and nine extensively drug-resistant (XDR). MLVA typing revealed a high genetic diversity (>130 genotypes), with many genotypes represented by a single strain. Notably, thirteen clusters (≥4 related isolates) were observed. Some clusters displayed close genetic relatedness to isolates from France, Korea, and well-studied strains (ST560, LES and PA14). Comparative analysis suggested the presence of international high-risk MDR clones (CC233, CC111) in Côte d’Ivoire. Importantly, MLVA clustering revealed a close relationship of CC235-MDR strains with a locally identified cluster (group 9). Conclusions: These findings support MLVA as a reliable and cost-effective tool for low-resource settings, allowing the selection of relevant strains for future whole genome sequence analyses. This approach can improve outbreak investigations and public health interventions aimed at curbing MDR P. aeruginosa transmission within hospitals and at the national level. Full article

The rapid growth of the aviation industry highlights the need for strong safety management. Analyzing data on unsafe aviation events is crucial for preventing risks. This paper presents a new method that integrates the Transformer network model, clustering analysis, and feature network modeling to analyze Chinese text data on unsafe aviation events. Initially, the Transformer model is used to generate summaries of event texts, and the performance of three pre-trained Chinese models is evaluated and compared. Next, the Jieba tool is applied to segment both summarized and original texts to extract key features of unsafe events and prove the effectiveness of the pre-trained Transformer model in simplifying lengthy and redundant original texts. Then, cluster analysis based on text similarity categorizes the extracted features. By solving the correlation matrix of these features, this paper constructs a feature network for unsafe aviation events. The network’s global and individual metrics are calculated and then used to identify key feature nodes, which alert aviation professionals to focus more on the decision-making process for safety management. Based on the established network and these metrics, a data-driven hidden danger warning strategy is proposed and illustrated. Overall, the proposed method can effectively analyze Chinese texts of unsafe aviation events and provide a basis for improving aviation safety management. Full article

The deposition of agglomerated hydroxyapatite (HAp) powders by low-pressure cold spray has been a topic of interest in recent years. Key parameters influencing the deposition of HAp powders include particle morphology and impact kinetic energy. This work examines the deposition of HAp powders on various metal surfaces to assess the impact of substrate properties on the formation of HAp deposits via cold spray. The substrates studied here encompass metals with varying hardness and thermal conductivities, including Al6061, Inconel alloy 625, AISI 316 stainless steel, H13 tool steel, Ti6Al4V, and AZ31 alloy. Single-track experiments offer insights into the initial interactions between HAp particles and different substrate surfaces. In this study, the results indicate that the ductility of the substrate may enhance HAp particle deposition only at the first deposition stages where substrate/particle interaction is the most critical factor for deposition. Features on the substrate associated with the first deposition sprayed layer include localized substrate deformation and the formation of clusters of HAp agglomerates, which aid in HAp deposition. Furthermore, after multiple spraying passes on the various metallic surfaces, deposition efficiency was significantly reduced when the build-up process of HAp coatings shifted from ceramic/metal to ceramic/ceramic interactions. Overall, this study achieved agglomerated HAp deposits with high deposition efficiencies (30–60%) through single-track experiments and resulted in the preparation of HAp coatings on various substrates with thickness values ranging from 24 to 53 µm. These coatings exhibited bioactive behavior in simulated body fluid. Full article

This paper addresses the challenge of objectively determining a website’s personality by developing a methodology based on automated quantitative analysis, thus avoiding the biases inherent in human surveys. Utilizing a database of 3000 websites, data extraction tools gather relevant data, which are then analyzed using Artificial Intelligence (AI) techniques, including machine learning (ML) and natural language processing. Four ML algorithms—K-means, Expectation Maximization, Hierarchical Agglomerative Clustering, and DBSCAN—are implemented to assess and classify website personality traits. Each algorithm’s strengths and weaknesses are evaluated in terms of data organization, cluster flexibility, and handling of outliers. A software tool is developed to facilitate the research process, from database creation and data extraction to ML application and results analysis. Experimental validation, conducted with identical training and testing datasets, achieves a success rate of up to 94% (with an Error of $\le 50\%$) in accurately identifying website personality, which is validated by subsequent surveys. The research highlights significant relationships between website attributes and personality traits, offering practical applications for website developers. For instance, developers can use these insights to design websites that align with business goals, enhance customer engagement, and foster brand loyalty. Additionally, the methodology can be applied to creating culturally resonant websites, thus supporting New Zealand’s cultural initiatives and promoting cross-cultural understanding. This research lays the groundwork for future studies and has broad applicability across various domains, demonstrating the potential for automated, unbiased website personality classification. Full article

Family planning (FP) services are crucial interventions for improving maternal and child health outcomes and promoting gender equity. However, ensuring equitable access to these services remains a significant challenge, particularly in countries like Pakistan, where sociocultural norms, economic disparities, and geographic barriers hinder FP uptake. This study utilized spatial analysis techniques, including hotspot analysis, to investigate geographic disparities in FP uptake in Pakistan using data from Pakistan Demographic and Health Surveys (PDHS) conducted in 2006–2007 and 2017–2018. ArcMap 10.1 was used for spatial analysis and Stata 12.0 for statistical analysis. Results revealed significant spatial variations in FP uptake, with urban areas exhibiting higher uptake rates than rural regions. Hotspot analysis identified dynamic changes in contraceptive prevalence rates (CPR), with significant clustering in some regions and dispersion in others. It also identified areas with high unmet need, low intention to use FP services, and preference for family size (>3 children), highlighting the need for targeted behavioral change interventions. This innovative spatial approach provides nuanced insights for policymakers and program planners to develop targeted interventions based on localized data to improve FP service delivery, mitigate disparities, and ultimately advance efforts to improve maternal and child health outcomes. The application of geospatial analysis is an effective tool for enhancing program planning, evaluation, and resource allocation in diverse geographical contexts. Full article

Powdery mildew infection is a significant challenge in butternut squash (Cucurbita moschata) production during winter in Hainan, China. The tropical climate of Hainan promotes powdery mildew infection, resulting in substantial yield losses. By utilizing transcriptome and genome sequencing data, SNPs and potential genes associated with powdery mildew resistance in butternut squash were identified. The analysis of differentially expressed genes (DEGs) following powdery mildew infection revealed several genes involved in resistance, with particular focus on a resistance (R) gene cluster that may be linked to the observed resistance. Two MLO genes in clade V from Cucurbita moschata may not be directly associated with resistance in the two genotypes studied. These findings are expected to contribute to the development of genetic tools for improving powdery mildew resistance in Cucurbita crops, thereby reducing yield losses and enhancing the sustainability of butternut squash production in Hainan and other regions. Full article

Bio-based insulation materials (BbIMs) represent a potential alternative to conventional insulations, with their characteristics that favor a negative-carbon built environment. However, their use may face challenges that could prevent them from being used on a large scale in certain countries. The current study aims to provide focused insights into the practical difficulties and market opportunities for the application of BbIMs in Europe through a systematic literature review (SLR). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used as the basis for the conduct and reporting of this review. A keyword search was performed in Web of Science, Scopus, and ScienceDirect databases to select peer-reviewed English-language articles. HubMeta web tool was used to organize the selection process. The quantitative visualization of the literature was made by the Bibliometrix R package V4.1.4. Data were manually extracted and clustered in an Excel sheet. The review included 28 studies that have revealed interrelated insights. Difficulties range from regulatory and policy limitations and variability in performance, such as microbial growth and inconsistency in the behavior of materials under different conditions, to cost barriers. However, there are promising opportunities, including policy incentives and material performance benefits such as improved energy efficiency and indoor air quality. This research contributes to the literature by providing focused insights into the practical difficulties and market opportunities for the application of BbIMs in Europe. Research gaps and future perspectives point to the need for more field validation experiments, exploration of alternative production processes, and expanding life cycle assessment scopes to optimize their integration and performance. Stakeholder perceptions were conducted with a small sample in some countries, so insights from stakeholders are needed to confirm or correct current findings. Full article

Systematic reviews are a powerful tool to summarize the existing evidence in medical literature. However, identifying relevant articles is difficult, and this typically involves structured searches with keyword-based strategies, followed by the painstaking manual selection of relevant evidence. A.I. may help investigators, for example, through topic modeling, i.e., algorithms that can understand the content of a text. We applied BERTopic, a transformer-based topic-modeling algorithm, to two datasets consisting of 6137 and 5309 articles, respectively, used in recently published systematic reviews on peri-implantitis and bone regeneration. We extracted the title of each article, encoded it into embeddings, and input it into BERTopic, which then rapidly identified 14 and 22 topic clusters, respectively, and it automatically created labels describing the content of these groups based on their semantics. For both datasets, BERTopic uncovered a variable number of articles unrelated to the query, which accounted for up to 30% of the dataset—achieving a sensitivity of up to 0.79 and a specificity of at least 0.99. These articles could have been discarded from the screening, reducing the workload of investigators. Our results suggest that adding a topic-modeling step to the screening process could potentially save working hours for researchers involved in systematic reviews of the literature. Full article

Galling wear, a severe form of wear, is a known problem in sheet metal forming. As the wear state is not directly observable in closed tribosystems, such as in industrial sheet metal forming processes, indirect tool wear monitoring techniques for inferring the wear state of the tool from suitable signal characteristics are the subject of intense research. The analysis of acoustic emissions is a promising technique for tool condition monitoring. This research has explored feature selection using t-tests, linear regression models, and cluster analysis of the data. This analysis has been conducted both with and without the inclusion of control variables, friction, and roughness to discriminate between the behavior of the acoustic emissions during different stages of galling wear. Scratch testing at slow sliding speed (1 mm/s) has been used to produce the galling wear between a tool steel indenter and aluminum sheet at 10 N applied load, for which the acoustic emissions were recorded. The bursts of the acoustic emission signal were processed and investigated to observe how the bursts changed with increasing galling damage (increasing material removal and transfer). Novel parameters in the field of galling wear have been identified, and novel models for observing the change in galling wear have been identified, thus furthering the development of acoustic emissions analysis as a non-invasive condition monitoring system, particularly for sheet metal forming processes. Full article

This study investigates the hydrological dynamics of the Pativilca Basin in the Southern Hemisphere using the SWAT (Soil and Water Assessment Tool) model. Seventy-seven watersheds across a mountainous region were analyzed using elevation data, land cover, soil type, and gridded meteorological products (RAIN4PE and PISCO) for hydrological simulations. Watershed delineation, aided by a Digital Elevation Model, enabled the identification of critical drainage points and the definition of Hydrological Response Units (HRUs). The model calibration and validation, performed using the SWAT-CUP with the SUFI-2 algorithm, achieved Nash–Sutcliffe Efficiency (NSE) values of 0.69 and 0.72, respectively. Cluster analysis categorized the watersheds into six distinct groups with unique hydrological and climatic characteristics. The results showed significant spatial variability in the precipitation and temperature, with pronounced seasonality influencing the daily flow patterns. The higher-altitude watersheds exhibited greater soil water storage and more effective aquifer recharge, whereas the lower-altitude watersheds, despite receiving less precipitation, displayed higher flows due to runoff from the upstream areas. These findings emphasize the importance of incorporating seasonality and spatial variability into water resource planning in mountainous regions and demonstrate the SWAT model’s effectiveness in predicting hydrological responses in the Pativilca Basin, laying the groundwork for future research in mountain hydrology. Full article