Search Results (157)

This paper introduces a novel and flexible class of continuous probability distributions, termed the Alpha Power Survival Ratio-X (APSR-X) family. Unlike many existing transformation-based families, the APSR-X class integrates an alpha power transformation with a survival ratio structure, offering a new mechanism for enhancing shape flexibility while maintaining mathematical tractability. This construction enables fine control over both the tail behavior and the symmetry properties, distinguishing it from traditional alpha power or survival-based extensions. We focus on a key member of this family, the two-parameter Alpha Power Survival Ratio Exponential (APSR-Exp) distribution, deriving essential mathematical properties including moments, quantile functions and hazard rate structures. We estimate the model parameters using eight frequentist methods: the maximum likelihood (MLE), maximum product of spacings (MPSE), least squares (LSE), weighted least squares (WLSE), Anderson–Darling (ADE), right-tailed Anderson–Darling (RADE), Cramér–von Mises (CVME) and percentile (PCE) estimation. Through comprehensive Monte Carlo simulations, we evaluate the estimator performance using bias, mean squared error and mean relative error metrics. The proposed APSR-X framework uniquely enables preservation or controlled modification of the symmetry in probability density and hazard rate functions via its shape parameter. This capability is particularly valuable in reliability and survival analyses, where symmetric patterns represent balanced risk profiles while asymmetric shapes capture skewed failure behaviors. We demonstrate the practical utility of the APSR-Exp model through three real-world applications: economic (tax revenue durations), engineering (mechanical repair times) and medical (infection durations) datasets. In all cases, the proposed model achieves a superior fit over that of the conventional alternatives, supported by goodness-of-fit statistics and visual diagnostics. These findings establish the APSR-X family as a unique, symmetry-aware modeling framework for complex lifetime data. Full article

Transcatheter aortic valve replacement (TAVR) is a high-risk cardiovascular interventional procedure with a high incidence of postoperative complications, urgently requiring more refined risk identification and mitigation strategies. The main challenges in assessing the risk of TAVR complications lie in the scarcity of real-world data and the co-occurrence of multiple complications. This study developed an adjustment evaluation model that adapts randomised clinical trial (RCT) evidence to real-world data (RWD) and adopted multi-label classification methods that incorporate a LocalGLMnet-like regularization term, enabling data-adaptive parameter shrinkage for more accurate estimation. In the empirical analysis, with real surgical data from a hospital in the United States, a combination of multi-label random sampling and representative multi-label classification algorithms was used to fit the data. The model was compared across multiple evaluation metrics, including Hamming loss, ranking loss, and micro-AUC, to ensure robust results. The model used in this paper bridges the gap between medical risk prediction and insurance actuarial science, provides a practical data modelling foundation and algorithmic support for the future development of post-operative complication insurance products that precisely align with clinical risk. Full article

The use of high-resolution satellite stereo pairs for dense image matching is a core technology for the low-cost generation of large-scale digital surface models (DSMs). However, water areas in satellite imagery often exhibit weak texture characteristics. This leads to serious issues in reconstructing water surface DSMs with traditional dense matching methods, such as significant holes and abnormal undulations. These problems significantly impact the intelligent application of satellite DSM products. To address these issues, this study innovatively proposes a water region DSM reconstruction method, boundary plane-constrained surface water stereo reconstruction (BPC-SWSR). The algorithm constructs a water surface reconstruction model with constraints on the plane’s tilt angle and boundary, combining effective ground matching data from the shoreline and the plane constraints of the water surface. This method achieves the seamless planar reconstruction of the water region, effectively solving the technical challenges of low geometric accuracy in water surface DSMs. This article conducts experiments on 10 high-resolution satellite stereo image pairs, covering three types of water bodies: river, lake, and sea. Ground truth water surface elevations were obtained through a manual tie point selection followed by forward intersection and planar fitting in water surface areas, establishing a rigorous validation framework. The DSMs generated by the proposed algorithm were compared with those generated by state-of-the-art dense matching algorithms and the industry-leading software Reconstruction Master version 6.0. The proposed algorithm achieves a mean RMSE of 2.279 m and a variance of 0.6613 m² in water surface elevation estimation, significantly outperforming existing methods with average RMSE and a variance of 229.2 m and 522.5 m², respectively. This demonstrates the algorithm’s ability to generate more accurate and smoother water surface models. Furthermore, the algorithm still achieves excellent reconstruction results when processing different types of water areas, confirming its wide applicability in real-world scenarios. Full article

In lifetime testing, the failure times of highly reliable products under normal usage conditions are often impractically long, making direct reliability assessment impractical. To overcome this, step-stress partially accelerated life testing is employed to reduce testing time while preserving data quality. This paper develops a Bayesian model based on Type II censored data, assuming that item lifetimes follow the Topp–Leone inverted Kumaraswamy distribution, a flexible alternative to classical lifetime models due to its ability to capture various hazard rate shapes and to model bounded and skewed lifetime data more effectively than traditional models observed in real-world reliability data. Bayes estimators of the model parameters and acceleration factor are derived under both symmetric (balanced squared error) and asymmetric (balanced linear exponential) loss functions using informative priors. The novelty of this work lies in the integration of the Topp–Leone inverted Kumaraswamy distribution within the Bayesian step-stress partially accelerated life testing framework, which has not been explored previously, offering improved modeling capability for complex lifetime data. The proposed method is validated through comprehensive simulation studies under various censoring schemes, demonstrating robustness and superior estimation performance compared to traditional models. A real-data application involving COVID-19 mortality data further illustrates the practical relevance and improved fit of the model. Overall, the results highlight the flexibility, efficiency, and applicability of the proposed Bayesian approach in reliability analysis. Full article

This study introduces a new and flexible class of probability distributions known as the novel alpha-power X (NAP-X) family. A key development within this framework is the novel alpha-power half-logistic (NAP-HL) distribution, which extends the classical half-logistic model through an alpha-power transformation, allowing for greater adaptability to various data shapes. The paper explores several theoretical aspects of the proposed model, including its moments, quantile function and hazard rate. To assess the effectiveness of parameter estimation, a detailed simulation study is conducted using seven estimation techniques: Maximum likelihood estimation (MLE), Cramér–von Mises estimation (CVME), maximum product of spacings estimation (MPSE), least squares estimation (LSE), weighted least squares estimation (WLSE), Anderson–Darling estimation (ADE) and a right-tailed version of Anderson–Darling estimation (RTADE). The results offer comparative insights into the performance of each method across different sample sizes. The practical value of the NAP-HL distribution is demonstrated using two real datasets from the metrology and engineering domains. In both cases, the proposed model provides a better fit than the traditional half-logistic and related distributions, as shown by lower values of standard model selection criteria. Graphical tools such as fitted density curves, Q–Q and P–P plots, survival functions and box plots further support the suitability of the model for real-world data analysis. Full article

Probability models are instrumental in a wide range of applications by being able to accurately model real-world data. Over time, numerous probability models have been developed and applied in practical scenarios. This study introduces the AAP-X family of distributions—a novel, flexible framework for continuous data analysis named after authors Aadil Ajaz and Parvaiz. The proposed family effectively accommodates both symmetric and asymmetric characteristics through its shape-controlling parameter, an essential feature for capturing diverse data patterns. A specific subclass of this family, termed the “AAP Exponential” (AAPEx) model is designed to address the inflexibility of classical exponential distributions by accommodating versatile hazard rate patterns, including increasing, decreasing and bathtub-shaped patterns. Several fundamental mathematical characteristics of the introduced family are derived. The model parameters are estimated using six frequentist estimation approaches, including maximum likelihood, Cramer–von Mises, maximum product of spacing, ordinary least squares, weighted least squares and Anderson–Darling estimation. Monte Carlo simulations demonstrate the finite-sample performance of these estimators, revealing that maximum likelihood estimation and maximum product of spacing estimation exhibit superior accuracy, with bias and mean squared error decreasing systematically as the sample sizes increases. The practical utility and symmetric–asymmetric adaptability of the AAPEx model are validated through five real-world applications, with special emphasis on cancer survival times, COVID-19 mortality rates and reliability data. The findings indicate that the AAPEx model outperforms established competitors based on goodness-of-fit metrics such as the Akaike Information Criteria (AIC), Schwartz Information Criteria (SIC), Akaike Information Criteria Corrected (AICC), Hannan–Quinn Information Criteria (HQIC), Anderson–Darling (A*) test statistic, Cramer–von Mises (W*) test statistic and the Kolmogorov–Smirnov (KS) test statistic and its associated p-value. These results highlight the relevance of symmetry in real-life data modeling and establish the AAPEx family as a powerful tool for analyzing complex data structures in public health, engineering and epidemiology. Full article

Durable plastics are a sustainability challenge for healthcare products. Orthopedic products are regulated with strict specifications for human tissue interactions. Healthcare engineers and managers select plastic to meet the full range of material properties. Plastic is plentiful, low cost, and reliable, with established supply chains. Used plastic products can be discarded using existing waste management systems with low externality costs for orthopedic businesses. However, plastic is produced from fossil petroleum, raising issues for sustainability commitments of healthcare product companies. Barriers to the transition away from single-use plastic toward circular systems and bio-based healthcare products have been studied, but the transition is a goal that has yet to be realized. This research article reports on a transition engineering design sprint with a medium-sized orthopedic company specializing in orthoses for children and teenagers. The design sprint process engages company experts with systems perspectives on the role of unsustainable plastic in orthopedic healthcare and illuminates opportunities for capturing value in business transition. Two system transition project concepts were co-developed. The first concept is a plastics value map that aims to converge the satisfaction of essential needs with the usefulness of plastics under the limitations of a biophysically constrained future economy. The second concept is an orthopedics library data system concept that would allow reusing of fit-for-purpose used products and to inform the refurbishment of used products. In addition to an explanation of the design of the two concepts, the article presents reflections of co-design stakeholders on the usefulness and usability of the concepts. The article provides a real-world application of the co-design processes in transition engineering and the reflection by the company on the value of the results. The results indicate that the co-designed concepts could enable the company to address its sustainability aspirations and potentially resolve the dissonance of sustainability and business viability. Full article

The application of CAD/CAM technologies in modern production has revolutionized manufacturing processes, leading to significant improvements in precision, efficiency, and flexibility. These technologies enable the design and manufacturing of complex geometries with high accuracy, reducing errors and material waste. CAD/CAM integration streamlines workflows, enhances productivity, and facilitates rapid prototyping, accelerating the time-to-market for new products. Additionally, it supports customization and scalability in production, allowing for cost-effective small-batch and large-scale manufacturing. Without a 3D model of the product, it is not possible to use the advantages of applying advanced CAD/CAM technologies. Recognizing 3D models from engineering drawings is essential for modern production, especially for outsourcing companies in fluctuating market conditions, where the production process is organized with 2D workshop drawings on paper. This paper proposes a novel methodology for reconstructing 3D models from 2D engineering drawings, specifically those in DXF file format, leveraging a genetic algorithm. A core component of this approach is the representation of the 2D drawing as a symmetric adjacency matrix. This matrix serves as the foundational data structure for the genetic algorithm, enabling the evolutionary process to effectively optimize the 3D reconstruction. The experimental evaluation, conducted on multiple engineering drawing test cases (including both polyhedral and cylindrical geometries), demonstrated consistent convergence of the proposed GA-based method toward topologically valid and geometrically accurate 3D wireframe models. The approach achieved successful reconstruction in all cases, with fitness scores ranging from 1.1 to 112.2 depending on model complexity, and average execution times from 2 to 100 s. These results confirm the method’s robustness, scalability, and applicability in real-world CAD environments, while establishing a new direction for topology-driven 3D reconstruction using evolutionary computation. Full article

This study investigates sustainable competitiveness in the wine industry using Romania and Portugal as comparative case studies within the conceptual frameworks of Industry 5.0 and the Triple Bottom Line (TBL). While sustainability, robotics, and performance indicators are explored directionally, the core empirical contribution focuses on evaluating key wine industry metrics and their impact on export value. Using data from the International Organisation of Vine and Wine (OIV) and the World Trade Map, we perform a one-way ANOVA to examine differences between the two countries across five variables: vineyard area, wine production volume, grape production, consumption, and export value. The results reveal statistically significant differences in all indicators except vineyard area, with Portugal significantly outperforming Romania in production, consumption, and exports (p < 0.001). To assess the drivers of export performance, we construct a Structural Equation Model (SEM) using SmartPLS. The model confirms that wine production volume and domestic consumption are the strongest positive predictors of export value (loading factors 1.003 and 0.909, respectively), while vineyard area has minimal influence. The model exhibits strong fit indices (e.g., SRMR = 0.009; NFI = 0.971), supporting the robustness of the results. The findings suggest that internal market strength and production efficiency, rather than land size, are critical for export competitiveness. Romania can enhance its performance by aligning production strategies with TBL principles and selectively adopting Industry 5.0 technologies in viticulture. Full article

Wind energy is a vital renewable resource with substantial economic and environmental benefits, yet its spatial variability poses significant optimization challenges. This study advances wind farm layout optimization by employing a systematic genetic algorithm (GA) tuning approach using the design of experiments (DOE) method. Specifically, a full factorial 2² DOE was utilized to optimize crossover and mutation coefficients, enhancing convergence speed and overall algorithm performance. The methodology was applied to a hypothetical wind farm with unidirectional wind flow and spatial constraints, using a fitness function that incorporates wake effects and maximizes energy production. The results demonstrated a 4.50% increase in power generation and a 4.87% improvement in fitness value compared to prior studies. Additionally, the optimized GA parameters enabled the placement of additional turbines, enhancing site utilization while maintaining cost-effectiveness. ANOVA and response surface analysis confirmed the significant interaction effects between GA parameters, highlighting the importance of systematic tuning over conventional trial-and-error approaches. This study establishes a foundation for real-world applications, including smart grid integration and adaptive renewable energy systems, by providing a robust, data-driven framework for wind farm optimization. The findings reinforce the crucial role of systematic parameter tuning in improving wind farm efficiency, energy output, and economic feasibility. Full article

Bamboo is known as the “world’s second largest forest”. The bamboo industry has become a globally recognized green industry, and the research and development of bamboo-based products have huge economic, ecological, and cultural values. In this study, a biosorbent with magnetically sensitive properties was developed based on natural bamboo powders (BPs) for the removal of methylene blue (MB) dye from aqueous solution. The selected BPs with 60 mesh were magnetized by loading Fe₃O₄ using an in situ co-precipitation process. The adsorption–desorption equilibrium was nearly established after 30 min, achieving a removal efficiency of 97.7% for 5.0 g/L BPs/Fe₃O₄ in a 20 mg/L MB solution. The removal efficiency of MB by 5.0 g/L BPs/Fe₃O₄ exhibited a remarkable enhancement, escalating from 33.9% at pH = 5 to an impressive 93.9% at pH = 11 in a 50 mg/L MB solution. The linear fitting method demonstrated greater suitability for characterizing the adsorption process compared to the nonlinear fitting method, which encompassed both adsorption isotherms and kinetics studies. Among these approaches, the adsorption isotherms were well-fitted to the Langmuir model, while the kinetics were accurately represented by the pseudo-second-order model. The removal efficiency by the recycled BPs/Fe₃O₄ adsorbent remained at 97.3% over five consecutive cycles, proving that BPs/Fe₃O₄ has a high potential for being used as a highly efficient biosorbent. Moreover, the BPs/Fe₃O₄ biosorbent had superparamagnetism with strong magnetic sensitivity, which could facilitate the sustainable removal of hazardous dye from the aqueous solution in practical applications. Full article

Background: Seasonal influenza currently remains a major public health concern for the community and, in particular, the health care worker (HCW). According to the World Health Organization, HCWs are among the high-risk categories for which vaccination is recommended, due to the derived absenteeism, productivity loss, and high probability of transmitting the disease to vulnerable individuals or patients. Therefore, an HCW vaccination policy should be adopted by every health care provider. There is growing evidence that a time effect of the vaccination event is probable, which may influence vaccine effectiveness. We designed and conducted an observational study to investigate the time to anti-influenza vaccination event of different categories of HCWs belonging to different occupational settings in a tertiary hospital during three seasons in order to retrieve some insight about HCW prioritization when designing vaccination campaigns. Materials and Methods: We retrospectively analyzed the results of two HCW anti-influenza vaccination campaigns (2022 and 2023) to assess any difference regarding job typology and unit typology (critical care, surgical, medical, service). We first fitted a classic Cox proportional hazard model and then an AI random forest model to assess variable importance. We used R, RStudio, and the survex package. Results: Overall, other HCWs reported a lower vaccination rate compared to nurses (HR 0.77; 95%CI 0.62–0.97), and service unit personnel appeared to more likely be vaccinated (HR 1.42; 95%CI 1.01–1.99) compared to those belonging to the critical care units. As expected, older workers tended to be vaccinated more frequently (HR 1.70 for the (46, 65] category compared to the younger one; 95%CI 1.39–2.09). The variable importance analysis showed consistent superiority of the ward typology and age category variables with respect to time. During the entire timeline, the ward typology appeared to be more important than the HCW typology. Conclusions: Our results suggest a prioritization policy based firstly on the unit typology followed by the job typology for HCW anti-influenza campaigns. Full article

This research explores the intersection of health informatics and behavioral science through the lens of fitness technologies, specifically Fitbit products. Grounded in the Technology Acceptance Model (TAM) and Self-Determination Theory (SDT), this study examines how these technologies influence user acceptance and physical activity motivation. Employing a qualitative approach, the paper analyzed Fitbit user reviews to reveal insights into real-world interactions and perceptions, thereby deepening the understanding of technology adoption behaviors in health contexts. The findings highlight the significance of perceived ease of use and usefulness, as well as the integration of health consciousness in technology acceptance, enriching the TAM framework. Additionally, the study confirms Self-Determination Theory’s proposition of intrinsic motivation being more effective for lasting behavior change, as seen in users’ evolving interactions with Fitbit features. Furthermore, this study contributes to health behavior theories by demonstrating the role of technological devices in altering health routines. Full article

The automotive industry faces continuing challenges with regard to advancing sustainability and reducing energy consumption and vehicle emissions. South Africa accounts for half of the total CO₂ emissions in Africa and is the world’s 12th-largest CO₂ emitter. In this study, we aimed to develop a model combining autoregressive integrated moving averages (ARIMAs) with long short-term memory (LSTM) to determine the best fit for prediction using the lowest root mean square error configuration and enhance energy consumption in automotive component manufacturing. The ARIMA model dissects time-series data into the components of level, trend, and seasonality, while the automatic ARIMA function refines the model parameters. Simultaneously, utilizing historical data, the LSTM model uses specific algorithms to predict future electricity generation and carbon emissions for the automotive component’s manufacturing sector. According to our results, the predicted variables’ interdependence revealed an enhancement in energy intensity for vehicle body part products equal to 29%, a cumulative energy savings of 7.22%, and an increase in energy efficiency equal to 16.25%. Our model’s predictive fitness holds significant potential for allowing automotive component manufacturers to make informed economic and technical decisions toward the development of low-carbon products. Critically, improved energy efficiency in automotive component manufacturing activities is critical for lowering energy consumption, greenhouse gas emissions, sustainable transportation, and production costs. Full article

Water vapor is an active trace component in the troposphere and has a significant impact on meteorology and the atmospheric environment. In order to meet demands for high-precision water vapor and aerosol observations for numerical weather prediction (NWP), the China Meteorological Administration (CMA) deployed 49 Raman aerosol lidar systems and established the first Raman–Mie scattering lidar network in China (CARLNET) for routine measurements. In this paper, we focus on the water vapor measurement capabilities of the CARLNET. The uncertainty of the water vapor Raman channel calibration coefficient (Cw) is determined using an error propagation formula. The theoretical relationship between the uncertainty of the calibration coefficient and the water vapor mixing ratio (WVMR) is constructed based on least squares fitting. Based on the distribution of lidar in regions with different humidity conditions, the method of real-time calibration and quality control based on radiosonde data is established for the first time. Based on the uncertainty requirements of the World Meteorological Organization for water vapor in data assimilation, the calibration and quality control thresholds of the WVMR in regions with different humidity conditions are determined by fitting real-time lidar and radiosonde data. Lastly, based on the radiosonde results, the calibration algorithm established in this study is used to calibrate CARLNET data from October to December 2023. Compared with traditional calibration results, the results show that the stability and detection accuracy of the CARLNET significantly improved after calibration in regions with different humidity conditions. The deviation of the Cw decreased from 12.84~18.47% to 5.41~11.54%. The inversion error of the WVMR compared to radiosonde decreased from 1.05~0.46 g/kg to 0.82~0.34 g/kg. The reliability of the improved calibration algorithm and the CARLNET’s performance have been verified, enabling them to provide high-precision water vapor products for NWP. Full article