Search Results (106)

The rapid growth of the sharing economy has improved resource utilization in car-sharing, yet it has also sharpened market competition and diversified user demand. A persistent obstacle is the low coordination efficiency between asset-heavy operating companies and traffic-driven platforms, whose misaligned objectives waste social resources. This paper uses differential game theory to analyze their dynamic coordination strategies and benefit allocation mechanisms. The Nerlove–Arrow model captures the evolution of brand goodwill, while the company’s decisions on station layout, vehicle dispatch, and pricing, together with the platform’s advertising investment, form the core decision variables in a two-party game framework linking the asset side and the traffic side. Compared with the non-cooperative Nash equilibrium, the cooperative mode removes the double marginalization effect, strengthens the investment incentives of both parties, and raises the system’s steady-state goodwill and total profit, achieving a Pareto improvement. To ground the cooperative framework in rigorous theory, we supply a verification theorem confirming that the linear candidate value functions satisfy the Hamilton–Jacobi–Bellman equations over the entire admissible state space. A formal proof of instantaneous rationality ensures that neither party falls into a cooperation trap on the horizon $[0,T]$, and the asymptotic stability of the steady-state goodwill trajectory is established. We further endogenize the revenue-sharing coefficient through a generalized Nash bargaining model that admits asymmetric bargaining structures, and introduce a Stackelberg leadership benchmark as a third comparative regime. Sensitivity analyses with respect to the discount rate and user heterogeneity confirm the robustness of the findings. A dedicated discussion section bridges the gap between idealized parameterization and data-driven calibration, describing practical pathways via A/B testing, user churn metrics, and econometric estimation of demand parameters. The results offer a scientific decision-making reference for strategic cooperation in the car-sharing industry. Full article

This study utilizes a large-scale numerical simulation model to investigate the hydrodynamic behavior and particle transport characteristics of gas–liquid–solid three-phase flow in vertical wellbores featuring multi-source confluence and curved geometries. Simulation results indicate that increasing flow velocity shifts the dominant control mechanism from surface tension to inertial forces, transitioning the flow pattern from slug flow to churn flow. In curved pipe sections, centrifugal phase separation and geometric shielding effects cause significant flow asymmetry and maintain large bubble stability at the inner wall. Additionally, the multi-inlet structure induces shear rate gradients that result in the spatial coexistence of two distinct bubble scales. Furthermore, localized gas concentrations exceeding 70% at the upper inlet can trigger severe gas-locking phenomena and intense pressure pulsations. Full article

Offshore high-water-rate gas wells can often sustain stable production for a considerable period after liquid first appears at the wellhead. Unlike conventional onshore gas wells with relatively low liquid production, these wells can remain in stable production during the middle and late production stages even when the gas velocity in the wellbore has fallen far below the critical value predicted by conventional liquid-carrying criteria. Under such conditions, the wellbore flow pattern commonly shifts from annular mist flow to churn flow and slug flow, and liquid transport becomes governed by a dynamic balance jointly controlled by pressure differential and gas entrainment. As a result, conventional critical liquid-carrying theory alone is no longer sufficient for accurate production state identification. To address this issue, this study proposes a production state identification method for offshore high-water-rate gas wells based on dynamic pressure profile calibration and nodal analysis. In this method, the wellbore pressure profile serves as the key link between outflow capacity and production state evaluation. Measured data from flowing pressure tests are used to calibrate the pressure profile within the selected multiphase flow correlation by introducing two calibration coefficients, namely the liquid holdup calibration coefficient and the two-phase friction calibration coefficient. Gaussian process regression is then applied to model the temporal evolution of the calibration coefficients, generate their fitted trajectories, and predict their values at the next time step. By using the predicted calibration coefficients to recalibrate the pressure profile, dynamic calibration of the wellbore pressure profile is achieved. Field applications to four offshore high-water-rate gas wells show that the calibrated pressure profiles are in closer agreement with the measured pressure points. The accuracy of production-state identification is also significantly improved, and the gas production rates calculated from calibrated nodal analysis are closer to the values reported in daily production records than those obtained before calibration. These results demonstrate that the proposed method effectively improves both wellbore pressure profile prediction and production-state identification for offshore high-water-rate gas wells. The study provides a practical method for production state evaluation and production management of offshore high-water-rate gas wells during the middle and late stages of field development. Full article

Uplift modeling optimizes intervention-based campaigns by identifying customers whose behavior changes exclusively due to specific treatments, moving beyond standard baseline risk predictions. However, in real-world deployments, algorithms that maximize traditional causal ranking metrics (e.g., the Qini coefficient) often fail to be optimal in practice. The inherent variance of Conditional Average Treatment Effect (CATE) estimators exposes critical trade-offs between expected economic value, algorithmic stability, and policy interpretability. To address this gap, this study proposes a stability-aware, value-driven computational framework for selecting an uplift policy. The pipeline evaluates multiple causal and non-causal algorithmic families, including traditional baselines, multimodel approaches, and transformed-outcome variants, within a repeated-run validation protocol. Candidate policies are assessed primarily through incremental revenue and target-set stability, whereas a post hoc surrogate tree distillation step is used to translate the selected policy into interpretable rule-based customer segments. An empirical evaluation of the publicly available Telco Customer Churn dataset under two distinct regimes (a causally controlled semisynthetic scenario and an observational proxy scenario) reveals that the highest-yielding causal policy frequently suffers from severe targeting instability, inducing a clear risk–return trade-off. Furthermore, uplift models outperform traditional baselines in the causally controlled regime, whereas traditional baselines remain economically superior in the confounded proxy settings. Overall, this study establishes that jointly assessing economic utility, algorithmic stability, and transparent segmentation is essential for deploying robust and defensible causal machine learning in production environments. Full article

This study examines educator staffing instability in New Mexico by analyzing certified staffing rosters from the New Mexico Public Education Department (2014–2019) alongside a statewide Teacher Working Conditions Survey (N = 4481). The goal was to identify which working conditions districts can influence and to highlight practical strategies for improving teacher retention. Headcount and vacancy analyses show that instability persisted even during periods of workforce growth: vacancies remained high despite increases in educator numbers, reflecting replacement churn and role-specific shortages rather than an overall teacher supply deficit. Vacancy patterns also fluctuated year to year, indicating a labor market responsive to shocks rather than moving toward stability. Turnover estimates further show that educator loss is structural and cumulative across districts, not episodic. The survey findings indicate that job satisfaction varies by grade band, while years of experience do not, suggesting turnover risk is driven more by organizational context than career stage. District-level regression models support this: compensation, leadership instability, student behavior and discipline conditions, and class size predict both annual and long-term turnover. Time for planning, preparation, and collaboration uniquely predicts long-term retention, while administrative discipline support is more strongly associated with annual exits. Overall, the findings highlight retention—not supply—as the central challenge. Full article

With the deep integration of AI and IoT technologies, smartwatches have become core terminals for health management. However, research on the use mechanisms among “digital native” college students remains limited. Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and selected constructs from the Health Action Process Approach (HAPA), this study uncovers the drivers and barriers of youths’ smartwatch health function adoption to propose targeted design strategies. A mixed-methods approach was employed, collecting semi-structured questionnaire data from 226 Chinese college students. Quantitative analysis was conducted (n = 106) using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by qualitative text mining of open-ended feedback from non-users and churned users. The model demonstrated robust predictive power, supporting five hypotheses. Habit and action planning emerged as core antecedents of use intention, which significantly promoted actual use behavior. Effort expectancy acted as a baseline hygiene factor positively influencing performance expectancy. Qualitative findings confirmed that insufficient sensor accuracy and “health data anxiety” are critical psychological barriers. Validating the integrated model’s effectiveness, we propose three strategic interventions: enhancing data precision to build trust, implementing tiered pricing, and designing anxiety-alleviating visual interfaces, offering theoretical and empirical foundations for optimizing smart health products. Full article

Customer churn refers to the termination of a customer’s business relationship with a bank, representing a direct loss of future revenue. Product downsell manifests as a reduction in the number of financial products held or a downgrade in service tier, often signaling early customer disengagement. Accurately identifying customers at risk of these two behaviors has become a cornerstone of profitable growth in the competitive retail banking industry as downsell frequently serves as a precursor to total churn. However, the existing research typically treats these highly correlated behaviors as independent prediction tasks, overlooking their intrinsic link and failing to address the critical challenges of class imbalance and regulatory demands for model interpretability. To tackle these problems, we propose an enhanced multi-task learning network (EMTL-Net), a deep learning framework specifically designed to capture the nuanced interplay between churn and downsell behaviors. EMTL-Net introduces an explicit feature interaction module to enhance the modeling of high-order feature relationships and utilizes a shared representation layer to extract universal customer risk patterns, enabling the joint prediction of churn and downsell. Furthermore, we employ Focal Loss as the training objective to dynamically adjust sample weights, effectively mitigating the class imbalance problem. Critically, to meet financial compliance requirements, we implement a SHAP-based interpretation mechanism that is compatible with multi-task outputs, providing preliminary insights into feature importance. Formal validation of interpretability claims remains an important direction for future research. The experimental results on a publicly available pedagogical bank customer benchmark dataset demonstrate that EMTL-Net achieves excellent performance on both tasks. For churn prediction, the model achieves an AUC of 0.8259, an accuracy of 0.8361, and an F1-score of 0.6235, significantly outperforming the existing baseline models. For downsell prediction (noting that the downsell label is rule-derived from the number of products held), the model achieves an AUC of 0.8932, an accuracy of 0.8571, and an F1-score of 0.7504. Ablation studies confirm the critical contributions of the explicit feature interaction module, Focal Loss, and the residual structure to model performance. Crucially, the interpretability analysis corroborates business intuition by identifying customer age, account balance, and product holdings as dominant churn drivers—a consistency that reinforces the model’s credibility and practical utility in high-stakes financial environments. Full article

The highly competitive nature of the online food delivery (OFD) market faces a serious retention problem, with acquiring new users typically being much more expensive than retaining existing users. Traditional prediction methods that rely primarily upon static transactional metrics such as recency and frequency are often unable to capture the psychological ‘disconfirmation’ which occurs prior to churn. To fill this gap, this study proposes a framework based on Expectation-Confirmation Theory (ECT). Unsupervised K-Means clustering was employed to classify a simulated and filtered dataset with 1500 customer records containing behaviour, geography, etc. This framework also couples sentiment analysis from BERT, allowing it to identify psychological “silent” attrition. Heterogeneous cohorts, which exhibit different psychological antecedents (utilitarian versus hedonic), were identified. The empirical results of our analyses demonstrated that Random Forest Classifiers with segment-specific features outperform baseline transactional models (F1 = 0.76) with an F1 Score of 0.89. The visual analytic interface developed provides a holistic view of the consumption process than traditional prediction models, including prescriptive, automated segment-based mitigation strategies. Our findings contradict the assumption that the “frequency–loyalty” model applies to all users. High-frequency discretionary users are found to be elastic in terms of retention and will experience significant churn. By utilising the automated action log, managers can plan targeted, highly efficient retention strategies rather than blanket discounting approaches. Full article

Objective neural network-based two-phase flow regime classifiers are developed for vertical, narrow, rectangular channels and a 1 inch round pipe using Kohonen Self-Organizing Maps. In the rectangular channel, the classifier uses five geometric inputs obtained from a two-sensor droplet-capable conductivity probe (DCCP-2): the bulk gas void fraction ${\alpha }_g$, ligament void fraction ${\alpha }_{\mathrm{lig}}$, normalized ligament chord length $y_{\mathrm{lig}}$, normalized large bubble chord length $y_{\ell ,\mathrm{bb}}$, and a droplet indicator. These parameters allow for the objective identification of bubbly/distorted bubbly, cap-turbulent, churn-turbulent, annular, rolling wispy, and wispy flow regimes, and yield quantitative transition boundaries in the $(j_f,j_g)$ plane for a densely populated test matrix. In the round pipe, a four-sensor droplet-capable conductivity probe (DCCP-4) provides the mean and standard deviation of droplet, bubble, and ligament chord length distributions, which are used as inputs to a Self-Organizing Map (SOM) classifier that separates rolling annular and wispy annular regimes at high void fractions. The resulting regime maps are discussed in terms of the associated phase geometries and their impact on interfacial area, drag, and entrainment, providing regime-dependent geometric inputs that can be used to improve Two-Fluid Model closures for reactor downcomers, core channels, and other nuclear thermal–hydraulic applications. Full article

This study investigates the use of DFLSS to mitigate customer churn in a prominent telecommunications provider facing challenges from competitive pricing, regulatory changes, and evolving customer expectations. Employing the DMADV methodology, the research developed a proactive retention strategy using techniques such as propensity modeling, customer segmentation, and predictive analytics to identify churn drivers. Targeted interventions, which include future-dated loyalty discounts, outbound retention campaigns, and process optimization through DOEs were implemented and pilot-tested. The pilot involved approximately 5000 high-risk customers per month, resulting in a 6% increase in customers under contract, a 2% improvement in rates, and a 6% reduction in repeat call rates, equating to 2880 fewer calls annually. Financially, the strategy preserved an estimated 10% in revenue over 12 months, while operational enhancements delivered a 2% cost reduction annually through reduced repeat calls. These findings highlight the importance of proactive outreach and continuous improvement in managing churn. Limitations of this study include the narrow market scope and the need for broader validation. The research contributes to the limited literature on LSS in Western telecom markets and provides a replicable model for practitioners. Future work may explore integrating artificial intelligence to enhance churn prediction and retention strategies. Full article

Improving the energy efficiency of rolling bearings requires a component-resolved understanding of loss mechanisms. While analytical models capture load-dependent losses, load-independent hydraulic losses demand a physics-based approach. This paper presents a computational fluid dynamics (CFD) methodology for the qualification of individual hydraulic loss contributions and to assess their sensitivity to operating conditions. The approach decomposes the total hydraulic loss of the spherical roller bearing 22320 into component-level shares and is benchmarked against dedicated experiments. The simulated results show good agreement with experimental measurements, supporting the validity of the methodology. The discrepancy between the measured and simulated friction torque values averaged at 2–7%, with a single outlier. Furthermore, CFD methods have been demonstrated to be capable of predicting trends in hydraulic losses resulting from variations in speed and temperature. A consistent finding across all investigated conditions is that the rolling elements dominate the hydraulic losses. Churning-induced losses of the rolling elements contribute for more than 50% of the hydraulic losses of the hole bearing in every test. The proposed methodology offers a reproducible way to assign losses individually, compare operating scenarios and guide targeted design measures for loss reduction in rolling bearings. Furthermore, dynamic kinematic simulations of rolling bearings can be equipped with component-resolved hydraulic losses. This is enabling more accurate predictive modelling of the bearing kinematics and detecting effects such as slippage. Full article

Planetary gear is the mainstream deceleration transmission device, and its derivative form of high contact ratio internal gear adopts the structure of full internal meshing. While improving the compactness and efficiency of the transmission, it is necessary to focus on its lubrication characteristics and churning power consumption. In this paper, based on the actual meshing state of high contact ratio internal gear, combined with its geometric parameters, motion speed, and pressure bearing state, the Computational Fluid Dynamics (CFD) model is used to analyze the oil distribution during gear motion. According to the oil state, the oil pressure and viscous force on the gear surface are extracted, the churning loss of the gear is calculated, and the influence of different parameters on the churning loss is analyzed. Finally, based on the influence of the oil churning parameters on the lubrication performance, the representative oil churning parameters are selected for the test. The test results are consistent with the results obtained by the simulation analysis, which provides data support for the study of the lubrication of high contact ratio internal gears. Full article

This paper presents a novel system dynamics-based framework for forecasting market share evolution in the telecommunications sector. The framework conceptualizes market share as flows of subscribers—driven by churn, attraction, and market growth—between interconnected compartments representing providers. It is designed to operate with limited available market data and incorporates stochastic processes to capture market uncertainty, enabling risk-informed forecasts. The framework is applied to the Greek mobile telecommunications market using historical data (2006–2022), with a 5-year hold-back period for validation. Results highlight the dominant role of churn management in market share variability, particularly for the incumbent provider Cosmote, while subscriber attraction parameters show moderate influence for alternative providers Vodafone and Wind Hellas. Sensitivity analysis confirms the model’s robustness and identifies key drivers of forecast variability. The proposed framework provides actionable insights for strategic decision-making, making it a valuable tool for providers and policymakers to address churn, optimize attraction strategies, and ensure long-term competitiveness in dynamic markets. Full article

Machine learning (ML) methods have been successfully employed to support decision-making for Software as a Service (SaaS) providers. While most of the published research primarily emphasizes prediction accuracy, other important aspects, such as cloud deployment efficiency and environmental impact, have received comparatively less attention. It is also critical to effectively use factors such as training time, prediction time and carbon footprint in production. SaaS decision support systems use the output of ML models to provide actionable recommendations, such as running reactivation campaigns for users who are likely to churn. To this end, in this paper, we present a benchmarking comparison of 17 different ML models for churn prediction in SaaS, which include cloud deployment efficiency metrics (e.g., latency, prediction time, etc.) and sustainability metrics (e.g., CO₂ emissions, consumed energy, etc.) along with predictive performance metrics (e.g., AUC, Log Loss, etc.). Two public datasets are employed, experiments are repeated on four different machines, locally and on the cloud, while a new weighted Green Efficiency Weighted Score (GEWS) is introduced, as steps towards choosing the simpler, greener and more efficient ML model. Experimental results indicated XGBoost and LightGBM as the models capable of offering a good balance on predictive performance, fast training, inference times, and limited emissions, while the importance of region selection towards minimizing the carbon footprint of the ML models was confirmed. Full article

This study examines the descriptive trajectories through which service innovation is associated with customer exit dynamics after service failures, drawing on a three-wave panel of 532 online travel agency users and employing partial least squares structural equation modeling with predictive assessment. We analyze how innovation is associated with switching intentions via brand hate and brand distrust over time. Results reveal distinct temporal patterns: service innovation is linked to consistent reductions in both hate and distrust, yet only hate emerges as a salient mediator whose marginal association with switching intensifies over time. In contrast, distrust, although mitigated by innovation, remains relatively stable and behaviorally inert. Rather than asserting a causal explanation, we document temporal associations—labelled here as a “dilution effect”—to indicate that innovation coincides with weakening negative emotions but only partial attenuation of their behavioral correlates. By distinguishing between the fading but influential role of hate and the persistent yet inert nature of distrust, this study clarifies differentiated pathways through which negative states coincide with customer exit. For managers, the results highlight the need for staged innovation strategies to dissipate hate, complemented by long-term trust-repair initiatives to address enduring distrust and reduce customer churn. Full article