Search Results (4,159)

The global demand for end-stage hip and knee osteoarthritis surgical treatment is rising, as is the need of optimal postoperative rehabilitation. Patient stratification is key to provide rehabilitation professionals and policy makers with real-life data in support of early discharge planning and continuous care provision. The aim of this retrospective, observational study was to investigate which factors can predict the burden of care at discharge (BCD) and the inpatient rehabilitation length of stay (LOS) based on a set of demographic, societal, clinical and organizational data collected from a high-volume orthopedic hospital. We included 45.306 variables from 1678 patients. All variables were initially tested individually using a linear regression model for inpatient rehabilitation LOS and a logistic regression model for BCD. Variables that resulted significant (p < 0.05) were subsequently considered in a single, comprehensive linear regression model, or a single, logistic regression model, respectively. Age, living with a family, occupational status, baseline Barthel Index and duration of surgery were predictors of inpatient rehabilitation LOS and BCD. Sex, primary or secondary osteoarthritis, American Society of Anesthesiologists score, body mass index, transfusion, biological risk, type of anesthesia, day of surgery, numeric pain rating scale and baseline cognitive function at baseline were not. Including specific patient comorbidities, surgical access technique and chronic use of pharmacological therapy can improve the predictive power of the model. Full article

Using construction and demolition (C&D) waste in concrete production is a promising step toward environmental resilience amid the construction industry’s ecological footprint. The extensive history of using bricks in the construction of buildings has resulted in a considerable amount of waste associated with this commonly used material. This study aimed to assess the quality of concrete by examining the effect of replacing cement with varying percentages of recycled brick powder (RBP—0% to 50%). The primary objectives include evaluating the mechanical properties of concrete and establishing the feasibility of using RBP as a partial cement substitute. The investigation of target concrete can be divided into two phases: (i) laboratory investigation, and (ii) numerical investigation. In the laboratory phase, the performance of concrete with RBP was assessed under short-term dynamic and various static loads. The drop-weight test recommended by the ACI 544 committee was used to assess the short-term dynamic behavior (352 concrete discs). Furthermore, the behavior under static load was analyzed through compressive, flexural, and tensile strength tests. During the numerical phase, artificial neural network models (ANN) and fuzzy logic models (FL) were used to predict the results of 28-day compressive strength. The impact life with different failure probabilities was predicted based on the impact resistance results, by combining the Weibull distribution model. Additionally, an impact damage evolution equation was presented for mixtures containing RBP. The results show that the use of RBP up to 15% caused a slight decrease in compressive, flexural, and tensile strength (about 3–5%). Also, by replacing RBP up to 15%, the first crack strength decreased by 7.15% and the failure strength decreased by 6.46%. The average error for predicting 28-day compressive strength by FL and ANN models was recorded as 4.66% and 0.87%, respectively. In addition, the results indicate that the impact data follow the two-parameter Weibull distribution, and the R² value for different mixtures was higher than 0.9275. The findings suggest that incorporating RBP in concrete can contribute to sustainable construction practices by reducing the reliance on cement and utilizing waste materials. This approach not only addresses environmental concerns but also enhances the quality assessment of concrete, offering potential cost savings and resource efficiency for the construction industry. Real-world applications include using RBP-enhanced concrete in non-structural elements, such as pavements, walkways, and landscaping features, where high strength is not the primary requirement. Full article

The Internet of Things (IoT) is transforming industries by integrating sensors and connectivity into everyday objects, enabling enhanced monitoring, management, and automation through Machine-to-Machine (M2M) communication. Despite these advancements, the IoT faces limitations in accurately predicting environmental conditions and power consumption. This study proposes an advanced IoT platform that combines real-time data collection with secure transmission and forecasting using a hybrid Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) model. The hybrid architecture addresses the computational inefficiencies of LSTM and the short-term dependency challenges of GRU, achieving improved accuracy and efficiency in time-series forecasting. For all prediction use cases, the model achieves a Maximum Mean Absolute Error (MAE) of 3.78%, Root Mean Square Error (RMSE) of 8.15%, and a minimum $R^2$ score of 82.04%, the showing proposed model’s superiority for real-life use cases. Furthermore, a comparative analysis also shows the performance of the proposed model outperforms standalone LSTM and GRU models, enhancing the IoT’s reliability in real-time environmental and power forecasting. Full article

Background: Endometriosis is one of the most common gynecological diseases that can lead to infertility. The aim of this quantitative, descriptive, and cross-sectional study was to analyze the prevalence and the annual nationwide health insurance treatment cost of endometriosis in Hungary in 2010 and 2019. Methods: The data used in this study were sourced from publicly funded, national, real-world datasets administered by the National Health Insurance Administration (NHIFA). The total number of cases of endometriosis in the Hungarian population was determined by ICD codes and all types of care. The total prevalence, age-specific prevalence, and annual health insurance expenditure by age group were evaluated. Results: The highest numbers of patients and prevalence (2010: 101.9/100,000 women; 2019: 197.3/100,000 women) were found in outpatient care. Endometriosis, regardless of its type, mainly affects patients in the 30–39-year age group (number of patients—2010: 6852; 2019: 11,821). The NHIFA spent a total of EUR 1,639,612 on endometriosis treatment in 2010 and EUR 1,905,476 in 2019. The average annual health insurance expenditure per capita was EUR 574 in 2010 and EUR 426 in 2019. There was a significant correlation between length of stay and mean age of patients in both years (2010 r = 0.856, p < 0.001; 2019 r = 0.877, p < 0.001). Conclusions: The number endometriosis cases is increasing. Early diagnosis and targeted treatment would reduce endometriosis symptoms and therefore improve patients’ quality of life and reduce health insurance costs. This would be helped by the establishment of endometriosis centers. Full article

Lung cancer represents one of the most significant malignant tumors in terms of its threat to the health and life of the population, exhibiting the fastest growing incidence and mortality rates. The utilization of natural language processing methodologies for the analysis of lung cancer data can facilitate the detection, diagnosis and treatment of this disease. Given the sensitive nature of patient data and the difficulty in obtaining a substantial quantity of reliable information, the majority of previous studies have utilized publicly accessible datasets on lung cancer. However, publicly available datasets lack detailed descriptions of patients’ symptoms and personal information. Furthermore, the quality and authenticity of the generated text are difficult to ensure, which presents challenges for lung cancer-related research. To address the aforementioned issues, this paper proposes a controlled text generation method for lung cancer symptom descriptions in the context of small sample data. The method involves two key steps: firstly, the small sample dataset is expanded through an unsupervised learning approach, and secondly, compliant texts are generated by a generator. The method was found to be superior to other unsupervised methods in terms of ROUGE value and other indexes through experimental comparison. Furthermore, the generated text was found to be more closely aligned with the symptom descriptions of patients in real cases through manual evaluation, which provides valuable insights for lung cancer and related research. Full article

Electric vehicle (EV) battery technology is at the forefront of the shift towards sustainable transportation. However, maximising the environmental and economic benefits of electric vehicles depends on advances in battery life cycle management. This comprehensive review analyses trends, techniques, and challenges across EV battery development, capacity prediction, and recycling, drawing on a dataset of over 22,000 articles from four major databases. Using Dynamic Topic Modelling (DTM), this study identifies key innovations and evolving research themes in battery-related technologies, capacity degradation factors, and recycling methods. The literature is structured into two primary themes: (1) “Electric Vehicle Battery Technologies, Development & Trends” and (2) “Capacity Prediction and Influencing Factors”. DTM revealed pivotal findings: advancements in lithium-ion and solid-state batteries for higher energy density, improvements in recycling technologies to reduce environmental impact, and the efficacy of machine learning-based models for real-time capacity prediction. Gaps persist in scaling sustainable recycling methods, developing cost-effective manufacturing processes, and creating standards for life cycle impact assessment. Future directions emphasise multidisciplinary research on new battery chemistries, efficient end-of-life management, and policy frameworks that support circular economy practices. This review serves as a resource for stakeholders to address the critical technological and regulatory challenges that will shape the sustainable future of electric vehicles. Full article

The research examines the impact of nonlinear energy sinks (NES) on the reduction in the nonlinear vibratory responses of eccentrically stiffened functionally graded (ESFG) panels exposed to hydrodynamic loads. To simulate real marine environments, hydrodynamic forces, such as lift and drag that change with velocity, have already been determined experimentally using Matveev’s equations for a particular ship. The material composition of both the panel and the stiffeners varies across their thickness. The stiffeners are modeled using Lekhnitskii’s smeared stiffener approach. Additionally, analytical approaches implement the classical shell theory (CST) with considerations for geometric nonlinearity, along with the Galerkin method for calculations. The P-T method is subsequently employed to determine the nonlinear vibratory behavior of ESFG panels. In this method, the piecewise constant argument is used jointly with the Taylor series expansion, which is why it is named the P-T method. The findings reveal that NES can effectively dissipate vibrational energy, contributing to the extended service life of marine structures while reducing the need for frequent maintenance. This study supports sustainability objectives by increasing energy efficiency, lessening structural fatigue, and improving the overall environmental impact of marine vessels and infrastructure. Full article

Biofilms are structured microbial communities that adhere to various abiotic and biotic surfaces, where organisms are encased in an exo-polysaccharide matrix. Organisms within biofilms use various mechanisms that help them resist external challenges, such as antibiotics, rendering them more resistant to drugs. Therefore, researchers have attempted to develop suitable laboratory models to study the physical features of biofilms, their resistance mechanisms against antimicrobial agents, and their gene and protein expression profiles. However, current laboratory models suffer from various limitations. In this comprehensive review, we have summarized the various designs that have been used for laboratory biofilm models, presenting their strengths and limitations. Additionally, we have provided insight into improving these models to more closely simulate real-life scenarios, using newly developed techniques in additive manufacturing, synthetic biology, and bioengineering. Full article

Background/Objectives: Urinary tract infections (UTI) represent a highly frequent and debilitating disease. Immunoactive prophylaxis, such as the polyvalent bacterial whole-cell-based sublingual vaccine MV140, have been developed to avoid antibiotic use. However, the effectiveness of this tool in the Portuguese population is still unknown. This study aims at assessing the effectiveness of treatment with MV140 in a cohort of Portuguese patients presenting with recurrent UTIs. Methods: Prospective observational real-life study of 125 patients with complicated and uncomplicated recurrent UTIs treated with MV140. The primary outcome was a reduction in frequency and severity of UTIs after a follow-up of 12 months. Overall satisfaction, adverse events, and assessment of the effectiveness of MV140 in subgroups of patients with specific risk factors for UTIs were secondary outcomes. Results: In the 12 months after treatment outset, 38% of patients were UTI-free, 34% reported 1 or 2 UTI episodes, and the remaining 28% presented 3 or more UTIs, corresponding to a mean reduction of 3.20 (2.87–3.53, 95% C.I.; p < 0.001) UTI episodes per year per patient. The effectiveness of MV140 was the same regardless of sex, BMI, regular sexual activity, hypertension, diabetes mellitus, depression, paraplegia, performance of intermittent self-catheterization, indwelling bladder catheter, or previous use of other UTI-preventing vaccines. We observed a higher effectiveness in post-menopausal women compared to pre-menopausal (74.7% vs. 59.4%, respectively, p = 0.029). A total of 73% of patients reported a reduction in symptom severity or days of disease, and the mean global satisfaction was 7.52/10. Conclusions: MV140 demonstrated to be effective in the reduction rate of recurrent UTIs in a cohort of adult Portuguese patients. Full article

Baculoviruses, the largest studied insect viruses, are highly pathogenic to host insects. Bombyx mori nucleopolyhedrovirus (BmNPV) is the main cause of nuclear polyhedrosis of silkworm, a viral disease that causes significant economic losses to the sericulture industry. The anti-BmNPV mechanism of the silkworm has not yet been characterized. Carboxypeptidase is an enzyme that is involved in virtually all life activities of animals and plants. Studies have shown that the carboxypeptidase family is related to insect immunity. There are few reports on the role of carboxypeptidase in the defense of silkworms against pathogen invasion. In this study, we identified the homologous gene Bombyx mori metal carboxypeptidases12 (BmMCP12) related to mammalian carboxypeptidase A2 (CPA2) and found that BmMCP12 had a Zn-pept domain. The BmMCP12 gene was primarily located in the cytoplasm and was highly expressed in the midgut of silkworms, and the expression level in BmN-SWU1 cells was upregulated after infection with BmNPV. After overexpression of the BmMCP12 gene, quantitative real-time (qRT)-PCR and Western blots showed that BmMCP12 could inhibit BmNPV replication, whereas knockout of the gene had the opposite effect. In addition, we constructed transgenic silkworm strains with a knockout of BmMCP12, and the transgenic strains had reduced resistance to BmNPV. These findings deepen the functional study of silkworm carboxypeptidase and provide a new target for BmNPV disease prevention in silkworms. Full article

This study explores the application of generalized conformable derivatives in modeling hotel demand dynamics in Mexico, using the Gompertz-type model. The research focuses on customizing conformable functions to fit the unique characteristics of the Mexican hotel industry, considering the Tourist Area Life Cycle (TALC) model and aiming to enhance forecasting accuracy. The parameter adjustment in all cases was made by designing a convex function, which represents the difference between the theoretical model and real data. Results demonstrate the effectiveness of the generalized conformable derivative approach in predicting hotel demand trends, showcasing its potential for improving decision-making processes in the Mexican hospitality sector. The comparison between the logistic and Gompertz models, in both integer and fractional versions, provides insights into the suitability of these modeling techniques for capturing the dynamics of hotel demand in the studied regions. Full article

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection. Full article

The simplified tax system is the most common special tax regime in the Russian Federation in terms of the number of taxpayers. Tax revenues from the simplified tax system account for 6% of the structure of tax revenues of the consolidated budgets of the constituent entities of the Russian Federation and more than 93% of the structure of tax revenues from special tax regimes. The purpose of this study is to identify and assess the factors influencing tax revenues from the tax levied in connection with applying the simplified system of taxation (taxable object—income reduced by the amount of expenses). The objective of this study is to determine a set of factors used by economists to model the level of tax revenues and to conduct a corresponding econometric analysis of the influence of the selected factors on the dependent variable to identify characteristics of the simplified taxation system functioning in the Russian Federation. The object of this study is the per capita tax revenue from the tax levied in connection with applying the simplified system of taxation (the object of taxation is income reduced by expenses) in the Russian Federation. The subject of the research is a set of economic relations, which arise because of tax-legal relations between tax authorities and taxpayers in relation to the calculation of the tax levied in connection with the application of the simplified taxation system. This study’s hypothesis is that the amount of tax revenues is influenced by factors characterizing the economic situation and development of small and medium businesses in the constituent territories of the Russian Federation. This study was conducted in 83 constituent territories of the Russian Federation in 2020–2022. The research methods are statistical analysis and econometric modeling on panel data. During this study, six econometric models were constructed. Based on the results of specification tests, the least squares dummy variables model was selected. The results of the modeling show that the tax rate, the number of taxpayers, and the real average per capita monetary income of the population have a statistically significant impact on the per capita tax revenue under the simplified tax system (the object of taxation is income reduced by the number of expenses). As a result, the focus of economic policy at both macro and meso levels should be on the support of small and medium-sized enterprises in the early stages of their life cycle, as well as on the increase of the purchasing power of the population. Based on the results obtained, it is possible to forecast the revenue side of the budgets of the constituent entities of the Russian Federation. Full article

This paper describes a numerically efficient method for optimizing the high power transfer efficiency (PTE) of a resonant cavity-based Wireless Power Transfer (WPT) system for the wireless charging of smart clothing. The WPT system under study unitizes a carbon steel closet intended to store smart clothing overnight as a resonant cavity. The WPT system is designed to operate at 865.5 MHz; however, the operating frequency can be adjusted over a wide range. The main reason behind choosing a resonant cavity-based WPT system is that it has several advantages over the competitive WPT methods. Specifically, in contrast to its Far-field Power Transfer (FPT) and Inductive Power Transfer (IPT) counterparts, resonant cavity-based WPTs do not exhibit path loss and significant PTE sensitivity to the distance between the Tx and Rx coils and misalignment, respectively. The non-uniformity of the fields within the closet is addressed by using an optimized Yagi-like transmitting antenna with an additional element affecting the waveguide mode phases. The changes in the mode phases increase the volume inside the cavity, where the PTE values are higher than 50% (the high PTE region). In the present study, the model decomposition method is adapted to substantially accelerate the process of finding the optimal WPT system parameters. Additionally, the decomposition method explains the mechanism responsible for extending the high PTE region. The generalized scattering matrices are computed using the full-wave simulator Ansys HFSS for three sub-models. Then, the calculated S matrices are combined to evaluate the system’s PTE. The decomposition method is validated against full-wave simulations of the original WPT system’s model for several different parameter value combinations. The simulated results obtained for a sub-optimal model are experimentally verified by measuring the PTE of a real-life closet-based WPT system. The measured and calculated results are found to be in close agreement with the maximum measured PTE, as high as 60%. Full article

Background: In recent decades, the size and passenger capacity of cruise/passenger ships has been associated with noticeable growth; in turn, this has created significant concerns regarding the adequacy of existing evacuation protocols during an “abandon the ship” situation (life threatening emergency). This study provides a systematic overview of related weaknesses and challenges, identifying critical factors that influence evacuation efficiency, and also proposes innovative/interdisciplinary solutions to address those challenges. It further emphasizes the growing complexity of cruise/passenger ship evacuations due to increased vessel size/heavy density of human population, as well as identifying the necessity of addressing both technical and human-centered elements to enhance safety and efficiency of those specific operations. Methods: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, a comprehensive systematic literature search was conducted across academic databases, including Scopus, Science Direct, Google Scholar, and a limited number of academic journals that are heavily maritime-focused in their mission. Emphasis was placed on peer-reviewed articles and certain gray studies exploring the impacts of ship design, human behavior, group dynamics, and environmental conditions on evacuation outcomes. This review prioritized research incorporating advanced simulation models, crowd management solutions (applied in various disciplines, such as stadiums, airports, malls, and ships), real-world case studies, and established practices aligned with contemporary maritime safety standards. Results: The key findings identify several critical factors influencing the overall evacuation efficiency, including ship heeling angles, staircase configurations, and passenger (physical) characteristics (with their mobility capabilities and related demographics clearly standing out, among others). This effort underscores the pivotal role of group dynamics, including the influence of group size, familiarity among the group, and leader-following behaviors, in shaping evacuation outcomes. Advanced technological solutions, such as dynamic wayfinding systems, real-time monitoring, and behavior-based simulation models, emerged as essential tools for optimizing an evacuation process. Innovative strategies to mitigate identified challenges, such as phased evacuations, optimized muster station placements, and tailor made/strategic passenger cabin allocations to reduce congestion during an evacuation and enhance the overall evacuation flow, are also highlighted. Conclusions: Protecting people facing a life-threatening situation requires timely preparations. The need for a holistic evacuation strategy that effectively integrates specific ship design considerations and human factors management, along with inputs related to advanced information technology-related solutions, is the best way forward. At the same time, the importance of real-time adaptive management systems and interdisciplinary approaches to address the challenges of modern cruise/passenger ship evacuations clearly stands out. These findings provide a robust foundation for future research and practical applications, contributing to advancements in maritime safety and the development of efficient evacuation protocols for large-in-size cruise/passenger vessels. Full article