Search Results (159)

The rapid growth of the internet and social media has made electronic word of mouth (eWOM) a key element of modern marketing. In the hospitality industry, nowadays, effective eWOM management is essential for developing impactful strategies and fostering customer satisfaction. This paper introduces an enhanced approach to strategic customer base management based on online reviews by extending the Recency, Frequency, and Monetary (RFM) model with three novel dimensions, the Helpfulness, Promoter Score, and Stability of the customer, thereby forming the RFHPS model. It also includes the 2-tuple linguistic model, one of the most popular computing with words models, to improve precision in the RFHPS score’s computation and the findings’ interpretability. Using K-means clustering, customers are segmented across these five dimensions. The data on luxury hotels in Spain gathered from TripAdvisor demonstrate the model’s applicability. By integrating this framework into customer relationship management systems, managers can tailor marketing strategies for distinct segments, facilitating deeper customer understanding and bolstering eWOM generation. Full article

This study investigated post-fire vegetation recovery in Algeria’s Tenira forest using statistical traits (PCA), RFM, and LANDIS-II spatial analysis. The dataset included satellite imagery and environmental variables such as precipitation, temperature, slope, and elevation, spanning over a decade (2010–2020). Tenira forest is composed of Mediterranean species (36.5%); the biological types encountered are dominated by therophytes (39.19%). Ninety fire outbreaks were recorded, resulting in a loss of 1400.56 ha of surface area. Following the PCA results, precipitation, temperature, slope, and elevation were the main drivers of recovery (PC1 explained 43% alone, with the first five principal components accounting for 90% of observed variance, reflecting significant environmental gradients). Based on these components, an RFM predicted the post-fire recovery with an overall accuracy of 70.5% (Cost-Sensitive Accuracy), Quantity Disagreement of 3.1%, and Allocation Disagreement of 76%, highlighting spatial misallocation as the primary source of errors. The evaluation also identified PC4 (species richness) and PC3 (elevation) as significant predictors, collectively accounting for >50% of the variation in post-fire recovery. In the spatial analysis using LANDIS-II, the growth of vegetation, mainly in mid-altitude areas, was shown to be stronger, with the species consisting of those areas being more diverse. As a result, it demonstrated the connection between species richness and recovery capability. These findings can be useful in developing a management and development strategy, as well as proposing actions for species recovery after fire, such as the construction of firebreaks or the introduction of fireproof species, to make the forest more resistant to weather changes in Mediterranean ecosystems. Full article

This study investigates the application of machine learning techniques for predicting volcanic vent locations based on aeromagnetic geophysical data. Magnetic data, known to reflect subsurface geological structures, presents a valuable source of information for understanding volcanic activity. Leveraging this data, we aim to develop and validate predictive models capable of discerning the presence of volcanic vents. Through a comprehensive data analysis, feature engineering, and model training, we explore the intricate relationships between magnetic variations and volcanic vent locations. Various machine learning algorithms were evaluated for their efficacy in binary classification, with a focus on identifying areas with a high likelihood of volcanic vent presence. The Random Forest model (RFM) was adopted given its high performance metrics, achieving a prediction accuracy of 92%. Our results demonstrate the successful prediction of volcanic vent locations, with a significant correlation of 86% between the actual and predicted vent locations and a high Degree of Certainty (DC) at 97%. This research contributes to the advancement of geospatial data analysis within the field of geoscience, showcasing the potential of machine learning in interpreting and utilizing magnetic data for volcanic hazard assessment and early warning systems. The findings represent a significant step towards enhancing our understanding of volcanic dynamics and improving the predictive tools available for volcanic hazard assessment. Full article

Fine particulate matter (PM_2.5) poses significant health risks, prompting public health organizations to recommend the use of respirators and facemasks (RFMs) to mitigate exposure. Consequently, interest in their usage has increased, leading to several studies assessing the efficiency of these personal-level interventions against various fractions of ambient particulate matter (PM). We conducted a comprehensive literature search across PubMed, Web of Science, and Scopus to identify relevant studies and address the following objectives: (1) explore the efficiency of RFMs in reducing ambient PM; (2) discuss discrepancies in efficiencies reported; (3) critique the experimental setups used to evaluate the efficiency of RFMs; and (4) propose recommendations for future research. Five relevant studies we reviewed reported significantly lower RFM effectiveness against ambient PM, with a size-dependent efficiency that decreases for smaller PM fractions. Variations in the reported efficiencies were primarily attributed to design-related factors, resulting in poor facial fit. Therefore, it is crucial to consider standardizing and properly designing these products. These studies overlooked essential factors, such as using dummy heads with flexible textures that mimic human skin. The use of rigid-textured dummy heads, as seen in previous studies, may fail to accurately represent real-world conditions. We recommend researchers take into account diverse facial profiles in their experiments. Moreover, it is essential to consider facial characteristics in the design of RFMs. We believe the evidence supports the increasing need for the adoption of appropriate guidelines and regulations to govern RFM suppliers at both national and international levels. Full article

Water vapor-impermeable AlON/HfO_x bilayer films were constructed through a hybrid high-power impulse magnetron sputtering (HiPIMS) and radio-frequency magnetron sputtering process (RFMS), applied as an encapsulation of flexible electronics such as organic photovoltaics. The deposition of monolithic and amorphous AlON films through HiPIMS was investigated by varying the duty cycles from 5% to 20%. At an accelerated test condition, 60 °C, and 90% relative humidity, a 100 nm thick monolithic AlON film prepared using a duty cycle of 20% exhibited a low water vapor transmission rate (WVTR) of 0.0903 g m⁻² day⁻¹ after testing for 336 h. Furthermore, after introducing a nanocrystalline HfO_x film through RFMS, a 214 nm thick AlON/HfO_x bilayer film reached the lowest WVTR of 0.0126 g m⁻² day⁻¹. Full article

Artificial intelligence has revolutionized retail by enhancing business decision-making. This research applies the RFM (Recency, Frequency, Monetary) framework for customer segmentation, promoting sustainable consumer behaviour and eco-friendly products. Mobility issues, such as efficient goods movement and customer access, are also pivotal in sustainable retail supply chains. A systematic literature review (SLR) and Python-based clustering techniques (K-Means, hierarchical, DBSCAN) are employed to analyse a four-year dataset of customer data. The SLR identified six key areas from 71 articles. Clustering results varied: RFM binning found four clusters, K-Means and Mean Shift found three, and hierarchical and DBSCAN found two. The study emphasizes a data-centric retail strategy and the transformative impact of machine learning on customer engagement. Full article

Since Kraljic’s strategic matrix was applied to supply chain management, classification of items, suppliers, and customers has become of increasing interest to research and companies. The aim of this research is to develop an easily interpretable multicriteria classification matrix method and validate it in real-world scenarios with a robustness analysis. This method assigns alternatives to one of four classes defined by critical dimensions that integrate several evaluation criteria. Initially, a global search pre-classifies the alternatives using the PROMETHEE net flows. Then, two local searches are carried out that make use of the discriminant properties of the net flow signs to improve the quality of the assignments. This approach is specifically applied to pre-classified alternatives near the boundary between two or more categories. The method has been validated by segmenting thousands of customers. Four customer segments were identified: strategic, collaborative, transactional, and non-preferred. A comparison was made between the results and those derived from an alternative method. Through an extensive sensitivity analysis, the proposed method was shown to be robust to parameter variation, highlighting its reliability in real dynamic contexts. The method provides valuable, easily interpretable information, which constitutes the basis for developing personalised strategies to enhance customer relationship management. Full article

(1) Background: Retained fetal membranes (RFM) in cattle negatively impact reproduction, calving intervals, and health. This study examined OS markers and fatty acid profiles in Romanian Spotted cattle, comparing cows with normal parturition to those with RFM. Over 9 weeks, serum samples were collected from 22 cows (7 with RFM, 15 normal) at intervals before and after parturition. Placental tissues were also analyzed. The aim was to identify OS biomarkers that predict RFMs, track changes over time, and assess their impact on the placental fatty acid profile. (2) Methods: Samples were analyzed for superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA), and total antioxidant capacity (TAC). Placental fatty acids were profiled using gas chromatography–mass spectrometry. (3) Results: SOD and CAT activities increased in cows with retained fetal membranes (RFM) before parturition (SOD: p < 0.001, RFM 404.601 ± 20.941 vs. NP 339.101 ± 44.911; CAT: p < 0.01, RFM 121.132 ± 14.831 vs. NP 96.070 ± 2.397), indicating OS. However, significant decreases during labor suggested weakened antioxidant defenses. Total antioxidant capacity (TAC) peaked during parturition in RFM cows (p < 0.0001, 38.780 ± 3.727 vs. 11.150 ± 1.555), signaling heightened stress. Additionally, MDA levels increased before parturition (p < 0.001, RFM 8.424 ± 1.894 vs. NP 3.807 ± 0.484), confirming lipid peroxidation. RFM cows also exhibited higher levels of saturated fatty acids and lower monounsaturated fatty acids, pointing to metabolic stress. (4) Conclusions: This study highlights the role of OS and fatty acid imbalances in RFMs, suggesting potential strategies to improve reproductive outcomes by managing OS. Full article

To provide accurate and efficient forecasting of fluvial flooding assessment in the river basin, the present study links the well-known CA-based urban inundation modeling (2D-OFM-CA) with a one-dimensional river flow model (1D-RFM) as a coupled 1D-2D river–overland modeling. Rules to delineate the geometric linking between the 1D-RFM and 2D-OFM-CA along embankments are developed. The corresponding exchanged water volume across an embankment is then computed by using the free and submerged weir flow formulas. The applicability of the proposed coupled model on fluvial flooding assessment is then assessed and compared with a well-recognized commercial software (HEC-RAS model) through an idealized fluvial case and an extensively studied real-scale fluvial case in the Severn River Basin. Based on the simulated results concerning the numerical accuracy, the coupled model is found to give similar results in the aspects of the river flow and overland flow modeling in both two study cases, which demonstrates the effectiveness of the linking methodology between the 1D-RFM and 2D-OFM-CA. From the viewpoint of numerical efficiency, the coupled model is 47% and 41% faster than the HEC-RAS model in the two cases, respectively. The above results indicate that the coupled model can reach almost the same accuracy as the HEC-RAS model with an obvious reduction in its computational time. Hence, it is concluded that the coupled model has considerable potential to be an effective alternative for fluvial flooding assessment in the river basin. Full article

In order to promote the sustainable development of crowdsourcing logistics and control the cost of crowdsourcing logistics while improving the quality of crowdsourcing services, this paper proposes a courier crowdsourcing task allocation model that considers delivery methods under customer classification, with the optimization objective of minimizing the total cost of the crowdsourcing platform. This model adopts two delivery modes: home delivery by crowdsource couriers and pickup by customers. Customers can freely choose the express delivery method according to their actual situation when placing orders, thus better meeting their needs. Based on the customer’s historical express-consumption data, the entropy weight RFM model is used to classify them, and different penalty functions are constructed for different categories of customers to reduce the total delivery cost and improve the on-time delivery of efficient and potential customers. And a Customer Classification Genetic Algorithm (CCGA) was designed for simulation experiments, which showed that the algorithm proposed in this study significantly improved the local search ability, thereby optimizing the delivery task path of express crowdsourcing. This improvement not only improves the delivery timeliness for efficient and potential customers, but also effectively reduces the total delivery cost. Therefore, the research on parcel crowdsourcing task allocation based on customer classification reduces the cost of crowdsourcing delivery platforms and improves customer satisfaction, which has certain theoretical research value and practical-application significance. Full article

In the field of high-end equipment, the synergistic effect of friction-reducing agents plays an important role in the performance study of gel grease. Exploring its tribological and rheological properties can not only significantly reduce the coefficient of friction of mechanical components and enhance its viscosity at high temperatures but also effectively reduce energy consumption, thus improving the service life of high-end equipment. In this study, Schaeffler Load 460 gel grease was mixed with polysiloxane viscosity modifier (PV611) and molybdenum dialkyl dithiocarbamate (RFM3000) according to (3:1, 1:1, and 1:3), and its tribological properties and rheological properties were investigated by the MRS-10G friction and wear tester, MCR302 rotational rheometer, and crossover test. Comparative analyses of tribological and rheological properties were carried out. The results showed that the average coefficient of friction of Schaeffler Load 460 grease was reduced by 57.2%, 60%, and 71.9%, respectively, with the addition of two different ratios of friction reducers; the average diameter of abrasive spots was reduced by 44.5%, 55.4%, and 61.3%; and the shear stress and viscosity were increased by 117.94 Pa and 1295.02 mPa∙s, respectively, compared with that of the original grease, which is a good example for the lubrication of gel grease in the high-end equipment industry. This study provides a new direction and idea for the lubrication research of gel grease in the high-end equipment industry. Full article

The large lateral displacement of tail ropes increases the risk of their wear and fracture, posing hidden dangers to the safety of the hoisting system. However, no suitable method is available to recognize the lateral swing of tail ropes online. A target-free visual measurement method, which includes the dual-branch SiamSeg, was proposed in this study. Considering the slender characteristics of tail ropes, the receptive field of the feature extraction network was enhanced via the Receptive Field Module (RFM), thereby strengthening the discriminability and integrity of tail rope features. The consistency loss constraints were added to the segmentation loss function to maximize the time sequence information of the video and further improve the accuracy of pixel-level displacement. Compared with other methods, the proposed approach achieved better segmentation effects. Comparison results synchronously measured by sensors revealed the effectiveness of this method and its potential for practical underground applications. Full article

Malnutrition frequently affects patients with alcoholic liver disease (ALD), with important impacts on disease prognosis. Sarcopenia, the clinical phenotype of malnutrition characterized by skeletal muscle loss, is the major component responsible for adverse events in this population. The aim of this study is to assess the use of ultrasound (US) skeletal muscle performance in stratifying ALD disease severity. We recruited 43 patients with ALD and divided them into two groups: alcoholic hepatitis (AH) and alcoholic cirrhosis (AC). We evaluated disease-specific clinical and biological parameters and their relation to US Rectus Femoris muscle (RFM) measurements, including RFM thickness, stiffness (RFMS) and echogenicity (RFE). A thirty-seconds chairs stand test (30sCST) was used as the sarcopenia surrogate test. RMF thickness correlated with platelet count and serum albumin (p < 0.001). Both RFM and RFMS correlated with disease severity (p < 0.001) and 30sCST (p < 0.001, p = 0.002). Patients with AH had more severe US muscle abnormalities compared to AC (RFMS 1.78 m/s vs. 1.35 m/s, p = 0.001) and the highest prevalence of RFE (χ² = 8.652, p = 0.003). Rectus Femoris US assessment could represent a reliable tool in the diagnosis and severity stratification of ALD-induced sarcopenia. Full article

The Rational Function Model (RFM) is composed of numerous highly correlated Rational Polynomial Coefficients (RPCs), establishing a mathematical relationship between two-dimensional images and three-dimensional spatial coordinates. Due to the existence of ill-posedness and overparameterization, the estimated RPCs are sensitive to any slight perturbations in the observation data, particularly when handling a limited number of Ground Control Points (GCPs). Recently, Principal Component Analysis (PCA) has demonstrated significant performance improvements in the RFM optimization problem. In the PCA-based RFM, each Principal Component (PC) is a linear combination of all variables in the design matrix. However, some original variables are noise related and have very small or almost zero contributions to the construction of PCs, which leads to the overparameterization problem and makes the RPC estimation process ill posed. To address this problem, in this paper, we propose an Adaptive Sparse Principal Component Analysis-based RFM method (ASPCA-RFM) for RPC estimation. In this method, the Elastic Net sparsity constraint is introduced to ensure that each PC contains only a small number of original variables, which automatically eliminates unnecessary variables during PC computation. Since the optimal regularization parameters of the Elastic Net vary significantly in different scenarios, an adaptive regularization parameter approach is proposed to dynamically adjust the regularization parameters according to the explained variance of PCs and degrees of freedom. By adopting the proposed method, the noise and error in the design matrix can be reduced, and the ill-posedness and overparameterization of the RPC estimation can be significantly mitigated. Additionally, we conduct extensive experiments to validate the effectiveness of our method. Compared to existing state-of-the-art methods, the proposed method yields markedly improved or competitive performance. Full article

Recent advancements in satellite technology have significantly increased the availability of high-resolution imagery for Earth observation, enabling nearly all regions to be captured frequently throughout the year. These images have become a vast source of big data and hold immense potential for various applications, including environmental monitoring, urban planning, and disaster management. However, obtaining ground control points (GCPs) and performing geometric correction is a time-consuming and costly process, often limiting the efficient use of these images. To address this challenge, this study introduces a Rational Function Model (RFM)-based rigorous bundle adjustment method to enhance the relative geometric positioning accuracy of multiple KOMPSAT-3A images without the need for GCPs. The proposed method was tested using KOMPSAT-3A images. The results showed a significant improvement in geometric accuracy, with mean positional errors reduced from 30.02 pixels to 2.21 pixels. This enhancement ensured that the corrected images derived from the proposed method were reliable and accurate, making it highly valuable for various geospatial applications. Full article