Search Results (161)

Preformed porous media (PPM) technology has emerged as a transformative approach to enhance heat and mass transfer in vacuum freeze-drying (VFD) of agricultural and food products. This review systematically analyzes recent advances in PPM research, with particular focus on spray freeze-drying (SFD) as the dominant technique for precision pore architecture control. Empirical studies confirm PPM’s efficacy: drying time reductions of 20–50% versus conventional VFD while improving product quality (e.g., 15% higher ginsenoside retention in ginseng, 90% enzyme activity preservation). Key innovations include gradient porous structures and multi-technology coupling strategies that fundamentally alter transfer mechanisms through: resistance mitigation via interconnected macropores (50–500 μm, 40–90% porosity), pseudo-convection effects enabling 30% faster vapor removal, and radiation enhancement boosting absorption by 40–60% and penetration depth 2–3 times. While inherent VFD limitations (e.g., low thermal conductivity) persist, we identify PPM-specific bottlenecks: precision regulation of pore structures (<5% size deviation), scalable fabrication of gradient architectures, synergy mechanisms in multi-field coupling (e.g., microwave-PPM interactions). The most promising advancements include 3D-printed gradient pores for customized transfer paths, intelligent monitoring-feedback systems, and multiscale modeling bridging pore-scale physics to macroscale kinetics. This review provides both a critical assessment of current progress and a forward-looking perspective to guide future research and industrial adoption of PPM-enhanced VFD. Full article

Urban flooding and the management of stormwater present significant challenges that necessitate innovative and sustainable solutions. This research examines the effectiveness of green stormwater infrastructure (GSI) for resilient storm sewer systems using the Storm Water Management Model (SWMM), based on customized local climate scenarios. Daily climate data downscaled by four CMIP6 models—CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM—was used. The daily data was disaggregated into 15 min temporal resolution using the HyetosMinute R-package. Two GSI types—bio-retention and rain gardens—were evaluated with a maximum coverage of 30%. The analysis focuses on two future climate scenarios, SSP2-4.5 and SSP5-8.5, predicted under the Shared Socioeconomic Pathways (SSPs) framework. The performance of the stormwater network was assessed for mid-century (2041–2060) and late century (2081–2100), both before and after integration of GSI. Three performance metrics were applied: node flooding volume, number of nodes flooded, and pipe surcharging duration. The simulation results showed an average reduction in flooding volumes ranging between 86 and 98% over the area after integration of GSI. Similarly, reductions ranging between 78 and 89% and between 75 and 90% were observed in pipe surcharging duration and number of nodes vulnerable to flooding, respectively, following GSI. These findings underscore the potential of GSI in fostering sustainable urban water management and enhancement of sustainable development goals (SDGs). Full article

This study investigates the critical determinants of customer retention in casual dining restaurants within the context of the post-pandemic “new normal.” Anchored in service quality and consumer behavior theories, the research examines the influences of food quality, health measures, perceived price, brand image, ambiance, and location on customer decision making. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 336 respondents in the National Capital Region, Philippines were analyzed to assess the relationships among these variables and their effects on restaurant selection and customer retention. The results reveal that food quality (β = 0.698, p < 0.05) exerts the strongest influence on restaurant selection, followed by health measures (β = 0.477, p = 0.001), perceived price (β = 0.378, p < 0.02), and brand image (β = 0.341, p < 0.035). Furthermore, health measures (β = 0.436, p = 0.002) and restaurant selection (β = 0.475, p < 0.05) significantly enhance customer retention, while ambiance and location were not found to be significant predictors. These findings offer theoretical contributions to the service quality and consumer trust literature and provide practical and policy-relevant insights for food establishments adapting to health-driven consumer expectations. The study highlights the need for the strategic integration of safety protocols, pricing value, and brand positioning to foster long-term loyalty and resilience in the evolving food service market. Full article

Background/Objectives: The introduction of composite attachments has greatly improved orthodontic aligner therapy, through better force delivery, more predictable movements, and enhanced retention. This in vitro study aims to present and investigate an innovative digital protocol for aligner attachment fabrication incorporating the latest 3D technology used in dentistry. Methods: A virtual attachment measuring 2.5 × 2 × 2 mm was designed using computer-aided design (CAD) software (Meshmixer, Autodesk Inc., San Francisco, CA, USA) and exported as an individual STL file. The attachments were fabricated using a digital light processing (DLP) 3D printer (model: Elegoo 4 DLP, Shenzhen, China) and a dental-grade biocompatible resin. A custom 3D-printed placement guide was used to ensure precise positioning of the attachments on the printed maxillary dental models. A flowable resin was applied to secure the attachments in place. Following attachment placement, the models were scanned using a laboratory desktop scanner (Optical 3D Smart Big, Open Technologies, Milano, Italy) and three intraoral scanners: iTero Element (Align Technology, Tempe, AZ, USA), Aoral 2, and Aoral 3 (Shining 3D, Hangzhou, China). Results: Upon comparison, the scans revealed that the iTero Element exhibited the highest precision, particularly in the attachment, with an RMSE of 0.022 mm and 95.04% of measurements falling within a ±100 µm tolerance. The Aoral 2 scanner showed greater variability, with the highest RMSE (0.041 mm) in the incisor area and wider deviation margins. Despite this, all scanners produced results within clinically acceptable limits. Conclusions: In the future, custom attachments made by 3D printing could be a valid alternative to the traditional composite attachments when it comes to improving aligner attachment production. While these preliminary findings support the potential applicability of such workflows, further in vivo research is necessary to confirm clinical usability. Full article

3D printing (3DP) enables the development of highly customizable skincare solutions, offering precise control over formulation, structure, and aesthetic properties. Therefore, this study explores the impact of patches’ microstructure on hydration efficacy using conventional and advanced chemical/morphological confocal techniques. Moreover, it advances to the personalization of under-eye 3D-printed skincare patches and assesses consumer acceptability through emotional sensing, providing a comparative analysis against a non-3D-printed market option. The results indicate that increasing the patches’ internal porosity enhances water retention in the stratum corneum (53.0 vs. 45.4% µm). Additionally, patches were personalized to address individual skin needs/conditions (design and bioactive composition) and consumer preferences (color and fragrance). The affective analysis indicated a high level of consumer acceptance for the 3D-printed option, as evidenced by the higher valence (14.5 vs. 1.1 action units) and arousal (4.2 vs. 2.7 peaks/minute) scores. These findings highlight the potential of 3DP for personalized skincare, demonstrating how structural modifications can modulate hydration. Furthermore, the biometric-preference digital approach employed offers unparalleled versatility, enabling rapid customization to meet the unique requirements of different skin types. By embracing this advancement, a new era of personalized skincare emerges, where cutting-edge science powers solutions for enhanced skin health and consumer satisfaction. Full article

Churn prediction has become one of the core concepts in customer relationship management within the insurances, telecom, and internet service provider industries, which is essential in customer retention. Therefore, this study attempts to analyze the effectiveness of the advanced machine learning and deep learning models for churn prediction in the evaluation of the models’ performance across different sectors. This would help conclude whether the varied patterns of the churn throughout different sectors to the level that affects the model performance and to what extent. The work includes three datasets: namely, insurance churn, internet service provider customer churn, and Telecom churn datasets. The implementation and comparison conducted in this study of models include XGBoost, Convolutional Neural Networks (CNNs), and Ensemble Deep Learning with the pre-trained hybrid approach. The results show that the ensemble deep learning model outperforms other models in terms of accuracy and F1-score, achieving accuracies of up to 95.96% in the insurance churn dataset and of 98.42% in the telecom churn dataset. Moreover, traditional machine learning models like XGBoost also produced competitive results for selected datasets. The proposed deep learning ensembles reveal the strength and possibility for churn prediction and provide a benchmark for future research relevant to customer retention strategies. Also, the proposed ensemble deep learning model shows stable performance across different sectors, which reflects its ability to capture the varied churn patterns of different sectors. Full article

Background/Objectives: The percutaneous insertion of an external ventricular drain (EVD) is a common neurosurgical procedure that is crucial in managing acute brain injuries because of the drain’s role in monitoring intracranial pressure and draining cerebrospinal fluid. The primary indication is acute hydrocephalus, which often results from subarachnoid hemorrhage, intracranial hemorrhage, traumatic brain injury, stroke, or infection. Standard EVD placement targets the frontal horn of the lateral ventricle. However, complications such as hemorrhage, infection, and catheter occlusion frequently arise, with occlusion rates ranging from 19% to 47%. Occlusion can lead to increased intracranial pressure, necessitating interventions such as saline flushes or fibrinolytic drug administration. The placement of an EVD is a very specific choice that must be tailored to the individual patient, often in scenarios in which multiple interpretations of the data are possible: the question of which patient is eligible for EVD placement may be subjective. Intraventricular fibrinolysis (IVF) with urokinase-type plasminogen activator (uPA) or tissue-type plasminogen activator is used with the aim of lysing intraventricular clots and preventing EVD occlusion. Despite numerous studies, conclusive evidence on their efficacy is lacking. The CLEAR III trial confirmed the safety of IVF but showed uncertain benefits in neurological outcomes. Given the limited literature on uPA, this study evaluates its intrathecal administration for the prevention of EVD occlusion. Not all therapies are appropriate for all patients, and customizing strategies is often the right way to get the best result. Methods: This retrospective study analyzed 20 patients with EVDs receiving intrathecal uPA. The patients had a mean age of 56.4 years, with 95% presenting with hydrocephalus and 80% presenting with intraventricular hemorrhage. uPA dosages varied (25,000–100,000 IU), with an average of 3.9 doses per patient. Results: IVF effectively maintained EVD patency in 95% of cases. One patient experienced asymptomatic bleeding, while four (20%) developed post-treatment infections, the development of which was potentially influenced by the prolonged duration of EVD retention (>21 days). Analysis of Graeb scores showed faster clot resolution with early uPA administration. A higher initial Graeb score correlated with increased total uPA load but not with mortality or discharge outcomes. Although infection rates were slightly higher than in CLEAR III, multiple confounding factors, including duration of EVD retention and bilateral placement, were present. Conclusions: This study supports the feasibility and safety of intrathecal uPA administration for management of EVD occlusion in certain contexts. The appropriate choice in the context of ‘personalized medicine’ must necessarily consider the risk–benefit ratio. Full article

In this paper, we propose a methodology designed to deliver actionable insights that help businesses retain customers. While Machine Learning (ML) techniques predict whether a customer is likely to churn, this alone is not enough. Explainable Artificial Intelligence (XAI) methods, such as SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), highlight the features influencing the prediction, but businesses need strategies to prevent churn. Counterfactual (CF) explanations bridge this gap by identifying the minimal changes in the business–customer relationship that could shift an outcome from churn to retention, offering steps to enhance customer loyalty and reduce losses to competitors. These explanations might not fully align with business constraints; however, alternative scenarios can be developed to achieve the same objective. Among the six classifiers used to detect churn cases, the Balanced Random Forest classifier was selected for its superior performance, achieving the highest recall score of 0.72. After classification, Diverse Counterfactual Explanations with ML (DiCEML) through Mixed-Integer Linear Programming (MILP) is applied to obtain the required changes in the features, as well as in the range permitted by the business itself. We further apply DiCEML to uncover potential biases within the model, calculating the disparate impact of some features. Full article

Online marketing environments are rapidly being transformed by Artificial Intelligence (AI). This represents the implementation of Machine Learning (ML) that has significant potential in content personalization, enhanced usability, and hyper-targeted marketing, and it will reconfigure how businesses reach and serve customers. This systematic examination of machine learning in the Digital Marketing (DM) industry is also closely examined, focusing on its effect on human–computer interaction (HCI). This research methodically elucidates how machine learning can be applied to the automation of strategies for user engagement that increase user experience (UX) and customer retention, and how to optimize recommendations from consumer behavior. The objective of the present study is to critically analyze the functional and ethical considerations of ML integration in DM and to evaluate its implications on data-driven personalization. Through selected case studies, the investigation also provides empirical evidence of the implications of ML applications on UX/customer loyalty as well as associated ethical aspects. These include algorithmic bias, concerns about the privacy of the data, and the need for greater transparency of ML-based decision-making processes. This research also contributes to the field by delivering actionable, data-driven strategies for marketing professionals and offering them frameworks to deal with the evolving responsibilities and tasks that accompany the introduction of ML technologies into DM. Full article

This work presents a novel, compact (six pieces), low-cost (<$500 USD), and easy-to-manufacture metallography mounting device. The device is designed to produce high-quality polymer encapsulated samples that rival those obtained from commercial equipment ($5000–$10,000 USD). Utilizing the House of Quality (HoQ) framework within Quality Function Deployment (QFD), the device prioritizes critical customer requirements, including safety (validated via finite element method, FEM), affordability, and compatibility with standard hydraulic presses. FEM analysis under 29 MPa pressure revealed a maximum Von Mises stress of 80 MPa, well below the AISI 304 stainless steel yield strength of 170 MPa, yielding a static safety factor of 2.1. Fatigue analysis under cyclic loading (mean stress ${\sigma }_m$ = 40 MPa, amplitude stress ${\sigma }_a$ = 40 MPa) using the Modified Goodman Criterion demonstrated a fatigue safety factor of 3.75, ensuring infinite cycle durability. The device was validated at 140 °C (413.15 K) with a 5-min dwell time, encapsulating samples in a cylindrical configuration (31.75 mm diameter) using a 200 W heating band. Benchmarking confirmed performance parity with commercial systems in edge retention and surface uniformity, while reducing manufacturing complexity (vs. conventional 100-piece systems). This solution democratizes access to metallography, particularly in resource-constrained settings, fostering education and industrial innovation. Full article

This study introduces a novel Recency, Monetary, and Duration (RMD) model for customer classification in the hospitality industry. Using a hybrid approach that integrates data mining with multi-criteria decision-making techniques, this study aims to identify valuable customer segments and optimize marketing strategies. This research applies the K-means clustering algorithm to classify customers from a hotel in Iran based on RMD attributes. Cluster validation is performed using three internal indices, and hidden patterns are extracted through association rule mining. Customer segments are prioritized using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and Customer Lifetime Value (CLV) analysis. The outcomes revealed six distinct customer clusters, identified as new customers; loyal customers; collective buying customers; potential customers; business customers, and lost customers. This study helps hotels to be aware of different types of customers with particular spending patterns, enabling hotels to tailor services and improve customer retention. It also provides managers with appropriate tools to allocate resources efficiently. This study extends the traditional Recency, Frequency, and Monetary (RFM) model by incorporating duration, an overlooked dimension of customer engagement. It is the first attempt to integrate data mining and multi-criteria decision-making for customer segmentation in Iran’s hospitality industry. Full article

Programmatic buying has attracted growing interest from manufacturers and has become a driving force behind the growth of digital advertising. Among various formats, mobile advertisements (ads) have emerged as a preferred choice over traditional ones due to their advanced automation, adaptability, and cost-effectiveness. Despite their increasing adoption, academic research on mobile ads remains relatively limited. Unlike conventional statistical analysis techniques, the proposed feature selection methods eliminate the need for assumptions related to data properties such as independence, normal distribution, and constant variance in regression. Additionally, feature selection techniques have recently gained traction in big data analysis, addressing the limitations inherent in traditional statistical approaches. Consequently, this study aims to determine the key success factors of mobile ads in fostering customer loyalty, offering advertisers valuable insights for optimizing mobile ad design. This study begins by identifying potential factors influencing mobile advertising effectiveness. Then, it applies Support Vector Machine Recursive Feature Elimination (SVM-RFE), correlation-based selection, and consistency-based selection methods to determine the key drivers of customer retention. The findings reveal that “Price” and “Preference” are the most significant contributors to enhancing repurchase intention. Moreover, factors such as “Language”, “Perceived Usefulness”, “Interest”, “Mobile Device”, and “Informativeness” are also essential in maximizing the effectiveness of mobile advertising. Full article

The hospitality and tourism (H&T) sector, marked by intense employee–customer interactions, dynamic labor shifts, and high physical and emotional labor demands, faces chronic talent acquisition and retention. Therefore, tailored employer branding (EB) strategies that address the unique characteristics of the H&T sector are essential for improving the current situation. Despite the critical need for tailored solutions, a clear and unified approach for H&T companies to effectively manage their EB strategies, including the development of a compelling employee value proposition (EVP) that resonates with targeted professionals, has yet to be established. Following the PRISMA reporting guidelines, a systematic literature review of 30 peer-reviewed articles from the Scopus and Web of Science databases was conducted to synthesize existing research on EB practices in the H&T sector. The results reveal a fragmented literature that lacks a cohesive framework for categorizing and measuring EVP. The use of varied and inconsistent EVP models and scales across studies hampers comparative analysis and limits the development of generalizable insights. Furthermore, the review highlights a concentration of research within the hotel industry, leaving other important H&T industries, such as the restaurant and cruise industries, underexplored. This SLR emphasizes the urgent need for a unified approach to EB in H&T. Based on these results, promising research avenues are suggested to further advance EB research in H&T, along with managerial implications for enhancing talent attraction and retention in the sector. Full article

Customer loyalty and retention are crucial for digital platforms, yet systematic studies integrating technological innovation and loyalty strategies remain scarce. This study addresses this gap by conducting a bibliometric analysis of key factors influencing customer retention in the digital environment. Our research employs a quantitative bibliometric approach using the Biblioshiny and Bibliometrix tools (RStudio 2022.02), analyzing 300 scientific articles from the Web of Science database (2021–2024). This study applies bibliometric techniques such as descriptive metrics, bibliographic coupling, co-citation, and scientific collaboration mapping to identify trends and thematic clusters. Our findings indicate that emerging technologies, including artificial intelligence and big data, significantly impact customer experience, trust, and engagement. Personalization and digital innovation emerge as fundamental drivers of customer retention, offering strategic insights for companies aiming to strengthen competitiveness in the global digital market. Full article

The banking industry faces significant challenges, from high customer churn rates to threatening long-term revenue generation. Traditionally, churn models assess service quality using customer satisfaction metrics; however, these subjective variables often yield low predictive accuracy. This study examines the relationship between customer attrition and account balance using decision trees (DT), random forests (RF), and gradient-boosting machines (GBM). This research utilises a customer churn dataset and applies synthetic oversampling to balance class distribution during the preprocessing of financial variables. Account balance service is the primary factor in predicting customer churn, as it yields more accurate predictions compared to traditional subjective assessment methods. The tested model set achieved its highest predictive performance by applying boosting methods. The evaluation of research data highlights the critical role of financial indicators in shaping effective customer retention strategies. By leveraging machine learning intelligence, banks can make more informed decisions, attract new clients, and mitigate churn risk, ultimately enhancing long-term financial results. Full article