Search Results (9,716)

The fourth-order nonlinear Boussinesq water wave equation, which describes the propagation of long waves in the intertidal zone, is investigated in this study. The exact wave patterns of the equation were computed using the $tanh$ method. As stability decreased, soliton wave structures were derived using similarity transformations. Numerical simulations supported these findings. The $tanh$ method introduced a Galilean modification, leading to the discovery of several new exact solutions. Subsequently, the fourth-order nonlinear Boussinesq wave equation was transformed into a planar dynamical system using the travelling wave transformation. The quasi-periodic, cyclical, and nonlinear behaviors of the analyzed equation were particularly examined. Numerical simulations revealed that varying the physical parameters impacts the system’s nonlinear behavior. Graphs represent all possible examples of phase portraits in terms of these parameters. Furthermore, the study was proven to be highly beneficial for addressing issues such as shock waves and highly active travelling wave processes. Sensitivity analysis theory and the Lyapunov exponent were employed, offering a wide variety of linear periodic and first-frequency periodic characteristics. Sensitivity analysis and multistability analysis of the Boussinesq water wave equation were thoroughly investigated. Full article

Due to model errors caused by local variations in cloud precipitation processes, there are still significant uncertainties in current predictions and simulations of short-duration heavy rainfall. To tackle this problem, the effects of warming on cloud-precipitation efficiency was analyzed utilizing a weather research and forecasting (WRF) model. The analysis focused on a rainstorm corridor event that took place in July 2020. Rainstorm events from 4–6 July formed a narrow rain belt with precipitation exceeded 300 mm in the middle and lower reaches of the Yangtze River. Temperature sensitivity tests revealed that warming intensified the potential temperature gradient between north and south, leading to stronger upward motion on the front. It also strengthened the southwest wind, which resulted in more pronounced precipitation peaks. Warming led to a stronger accumulation and release of convective instability energy. Convective available potential energy (CAPE) and convective inhibition (CIN) both increased correspondingly with the temperature. The precipitation efficiency increased sequentially with 2 °C warming to 27.4%, 31.2%, and 33.1%. Warming can affect the cloud precipitation efficiency by both promoting and suppressing convective activity, which may be one of the reasons for the enhancement of extreme precipitation under global warming. The diagnostic relationship between upward moisture flux and lower atmospheric stability during precipitation evolution was also revealed. Full article

This research paper attempts to describe the transmission dynamic of zoonotic visceral leishmaniasis with the aid of a mathematical model by considering the asymptomatic stages in humans and animals. The disease is endemic in several countries. Data used in the research are obtained from the literature while some are assumed based on the disease dynamic. The consideration of both asymptomatic and the symptomatic infected individuals is incorporated in both humans and animals (reservoir), as well as lines of treatment for the human population. It is found that the model has two fixed points; the VL-free fixed point and the VL-endemic fixed point. Stability analysis of the fixed points shows that the VL-free fixed point is globally asymptotically stable whenever the basic reproduction number is less than one and the VL-endemic fixed point is globally asymptotically stable whenever the basic reproduction number is greater than one. Sensitivity analysis is conducted for the parameters in the basic reproduction number, and the profile of each state variable is also depicted using the data obtained from the literature and those assumed. The transmission probability from infected sandflies to animals, transmission probability from infected animals to sandflies, per capita biting rate of sandflies of animals, and rate of transfer from symptomatic infected animals to the recovered class are among the most sensitive parameters that have the greatest influence on the basic reproduction number. Moreover, the value of the basic reproduction number is obtained to be 0.98951, which may require further study, as the margin between potential disease control and outbreak is thin. Full article

Hydraulic fracturing is a widely employed technique for stimulating unconventional shale gas reservoirs. Supercritical CO₂ (SC-CO₂) has emerged as a promising fracturing fluid due to its unique physicochemical properties. Existing theoretical models for calculating breakdown pressure often fail to accurately predict the outcomes of SC-CO₂ fracturing due to the complex, nonlinear interactions among multiple influencing factors. In this study, we conducted fracturing experiments considering parameters such as fluid type, flow rate, temperature, and confining pressure. A fully connected neural network was then employed to predict breakdown pressure, integrating both our experimental data and published datasets. This approach facilitated the identification of key influencing factors and allowed us to quantify their relative importance. The results demonstrate that SC-CO₂ significantly reduces breakdown pressure compared to traditional water-based fluids. Additionally, breakdown pressure increases with higher confining pressures and elevated flow rates, while it decreases with increasing temperatures. The multi-layer neural network achieved high predictive accuracy, with R, RMSE, and MAE values of 0.9482 (0.9123), 3.424 (4.421), and 2.283 (3.188) for training (testing) sets, respectively. Sensitivity analysis identified fracturing fluid type and tensile strength as the most influential factors, contributing 28.31% and 21.39%, respectively, followed by flow rate at 12.34%. Our findings provide valuable insights into the optimization of fracturing parameters, offering a promising approach to better predict breakdown pressure in SC-CO₂ fracturing operations. Full article

Integrating low-carbon design into products is crucial for reducing carbon emissions throughout their life cycle and promoting sustainable development. Addressing the uncertainty in the carbon footprint resulting from the unknown choice of product material solutions. This paper considers ABC (activity-based costing) along with the components’ carbon footprint and scrap return issues to illustrate the above challenge in a two-stage production-inventory system with imperfect processes. We determine the optimal production and sales strategies that maximize total profit per unit time. An algorithm is developed to identify these optimal solutions. To illustrate the effectiveness of the proposed model and algorithm, two numerical examples from the Taiwan die casting industry are presented. Additionally, a sensitivity analysis is conducted to provide valuable managerial insights. Full article

Two charge transfer compounds based on pyridazinium ylids were studied by electronic absorption spectroscopy in binary and ternary solutions, with the purpose of evaluating their descriptors of the first singlet excited state and to estimate the strength of the intermolecular interactions in protic solvents. The molecular descriptors of the excited state were comparatively estimated using the variational method and the Abe model of diluted binary solutions. Analysis of electronic properties using density functional theory was performed for several key solvents, in order to understand the solvatochromic behavior. The DFT calculations revealed that, in the polar and strongly interacting solvents, the carbanion and the terminal group become a stronger electron acceptor. The bathochromic shift of the ICT band was confirmed using DFT calculus. The ability of the two ylids to recognize and discriminate the solvents was analyzed with principal component analysis and with cluster analysis. Although the study was performed in 24 solvents, the results showed that the ylids were most sensitive to alcohols, so they can be a useful tool to identify and classify different types of low-alcoholic solvents. Full article

It is often difficult for a structural safety design method based on deterministic analysis to fully and reasonably reflect the randomness of mechanical parameters, while the traditional reliability analysis method has a large calculation cost and low accuracy. In this paper, based on the seepage–stress coupling numerical model, the random variables affecting the reliability of the collaborative bearing of surrounding rock and lining structures are successfully identified. Then, the improved sparrow search algorithm (ISSA) is used to optimize the hyper-parameters of the Kriging surrogate model, in order to improve the computational efficiency and accuracy of the reliability analysis model. Finally, the ISSA-Kriging-MCS model is used to quantitatively evaluate the reliability of the surrounding rock-reinforced concrete lining structure under multiple failure modes, and the sensitivity of each random variable is discussed in depth. The results show that the high-pressure tunnel structure has high safety and reliability. The reliability indexes of each failure mode decrease with the increase in the coefficient of variation (COV) of random variables. In addition, the same random variable also exhibits varying degrees of influence in different failure modes. Full article

This study explores integrating Building Information Modeling (BIM) technology into risk management practices for construction projects, aiming to enhance project performance through improved risk identification, assessment, and mitigation. The research employs the Analytical Hierarchy Process (AHP) to prioritize BIM-based strategies across multiple risk management dimensions, including technical, financial, sustainability, and time management. The findings demonstrate that BIM-based financial strategies rank highest among BIM-driven risk management, followed by sustainability and time. In contrast, technical, operation, and maintenance capabilities have the lowest rank. Given the high priority of BIM financial strategies, they have been applied to conduct sensitivity analysis; the sensitivity analysis results demonstrate the dynamic nature of a BIM sub-criteria strategy in response to changes in the weight of financial considerations. As financial concerns diminish, the shift towards sustainability, health, safety, and time efficiency underscores the importance of a more balanced approach in BIM strategy prioritization. BIM-based risk management improves project outcomes by enabling real-time data-driven decision-making, enhancing stakeholder collaboration and optimizing resource use, cost control, and sustainability. This research contributes to theoretical and practical advancements in construction risk management, suggesting that BIM can be a transformative tool for optimizing project performance while addressing the complexities and uncertainties inherent in the construction industry. Full article

Objectives: To construct an optimal magnetic resonance imaging (MRI) texture model to evaluate histological patterns and predict prognosis in patients with osteosarcoma (OS). Methods: Thirty-four patients underwent pretreatment MRI and were diagnosed as having OS by surgical resection or biopsy between September 2008 and June 2018. Histological patterns and 3-year survival were recorded. Manual segmentation was performed in intraosseous, extraosseous, and entire lesions on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted images to extract texture features and perform principal component analysis. A support vector machine algorithm with 3-fold cross-validation was used to construct and validate the models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate diagnostic performance in evaluating histological patterns and 3-year survival. Results: Eight patients were chondroblastic and the remaining twenty-six patients were non-chondroblastic patterns. Twenty-seven patients were 3-year survivors, and the remaining seven patients were non-survivors. In discriminating chondroblastic from non-chondroblastic patterns, the model from extraosseous lesions on the T2-weighted images showed the highest diagnostic performance (AUCs of 0.94 and 0.89 in the training and validation sets). The model from intraosseous lesions on the T1-weighted images showed the highest diagnostic performance in discriminating 3-year non-survivors from survivors (AUCs of 0.99 and 0.88 in the training and validation sets) with a sensitivity, specificity, positive predictive value, and negative predictive value of 85.7%, 92.6%, 75.0%, and 96.2%, respectively. Conclusions: The texture models of extraosseous lesions on T2-weighted images can discriminate the chondroblastic pattern from non-chondroblastic patterns, while the texture models of intraosseous lesions on T1-weighted images can discriminate 3-year non-survivors from survivors. Full article

Cranberries, native to North America, are known for their nutritional value and human health benefits. One hurdle to commercial production is losses due to fruit rot. Cranberry fruit rot results from a complex of more than ten filamentous fungi, challenging breeding for resistance. Nonetheless, our collaborative breeding program has fruit rot resistance as a significant target. This program currently relies heavily on manual sorting of sound vs. rotten cranberries. This process is labor-intensive and time-consuming, prompting the need for an automated classification (sound vs. rotten) system. Although many studies have focused on classifying different fruits and vegetables, no such approach has been developed for cranberries yet, partly because datasets are lacking for conducting the necessary image analyses. This research addresses this gap by introducing a novel image dataset comprising sound and rotten cranberries to facilitate computational analysis. In addition, we developed CARP (Cranberry Assessment for Rot Prediction), a convolutional neural network (CNN)-based model to distinguish sound cranberries from rotten ones. With an accuracy of 97.4%, a sensitivity of 97.2%, and a specificity of 97.2% on the training dataset and 94.8%, 95.4%, and 92.7% on the independent dataset, respectively, our proposed CNN model shows its effectiveness in accurately differentiating between sound and rotten cranberries. Full article

Federated learning avoids centralizing data in a central server by distributing the model training process across devices, thus protecting privacy to some extent. However, existing research shows that model updates (e.g., gradients or weights) exchanged during federated learning may still indirectly leak sensitive information about the original data. Currently, single-key homomorphic encryption methods applied in federated learning cannot solve the problem of privacy leakage that may be caused by the collusion between the participant and the federated learning server, whereas existing privacy-preserving federated learning schemes based on multi-key homomorphic encryption in semi-honest environments have deficiencies and limitations in terms of security and application conditions. To this end, this paper proposes a privacy-preserving federated learning scheme based on multi-key fully homomorphic encryption to cope with the potential risk of privacy leakage in traditional federated learning. We designed a multi-key fully homomorphic encryption scheme, mMFHE, that encrypts by aggregating public keys and requires all participants to jointly participate in decryption sharing, thus ensuring data security and privacy. The proposed privacy-preserving federated learning scheme encrypts the model updates through multi-key fully homomorphic encryption, ensuring confidentiality under the CRS model and in a semi-honest environment. As a fully homomorphic encryption scheme, mMFHE supports homomorphic addition and homomorphic multiplication for more flexible applications. Our security analysis proves that the scheme can withstand collusive attacks by up to $N-1$ users and servers, where N is the total number of users. Performance analysis and experimental results show that our scheme reduces the complexity of the NAND gate, which reduces the computational load and improves the efficiency while ensuring the accuracy of the model. Full article

The single-phase titanium ß-alloy Ti10V2Fe3Al (Ti-1023) has been widely used in the aerospace industry due to its unique mechanical properties, which include high fatigue strength and fracture toughness, as well as high corrosion resistance. On the other hand, these unique properties significantly hinder the cutting processes of this material, especially those characterized by a closed machining process area, such as drilling. This paper is devoted to the study of the short hole drilling process of the above-mentioned titanium alloy using direct measurements and numerical modeling. Measurements of the cutting force components in the drilling process and determination of the resultant cutting force and total cutting power were performed. The macro- and microstructure of chips generated during drilling were analyzed, and the dependence of the chip compression ratio and the distance between neighboring segments of serrated chips on cutting speed and drill feed was determined. Experimental studies were supplemented by determining the temperature on the lateral clearance face of the drill’s outer cutting insert in dependence on the cutting modes. For the modeling of the drilling process using the finite element model, the parameters of the triad of component submodels of the numerical model were determined: the machined material model, the model of contact interaction between the tool and the machined material, and the fracture model of the machined material. The determination of these parameters was performed through the DOE sensitivity analysis. The target values for performing this analysis were the total cutting power and the distance between neighboring chip segments. The maximum deviation between the simulated and experimentally determined values of the resulting cutting force is no more than 25%. At the same time, the maximum deviation between the measured values of the temperature on the lateral clearance face of the drill’s outer cutting insert and the corresponding simulated values is 26.1%. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by progressive impairment of neuronal transmission due to focal demyelination. The most common form is RRMS (relapsing-remitting multiple sclerosis), which, under the influence of certain factors, can progress to SPMS (secondary progressive multiple sclerosis). Our study aimed to validate the criteria proposed by a working group of the Romanian Society of Neurology versus the criteria proposed by a group of experts from Spain, Karolinska, and Croatia concerning the progression from RRMS to SPMS. Methods: This was done by gathering epidemiological data (age, gender) and by applying clinical tests such as the 9HPT (9-hole peg test), 25FWT (25-foot walk test), and EDSS (expanded disability status scale) tests and the SDMT test (symbol digit modalities test). The present research is a cohort study that included a number of 120 patients diagnosed with MS according to the McDonald Diagnostic Criteria 2017. The study was carried out between January 2023 and April 2024, including patients hospitalized in the Neurology Clinic of the Clinical Rehabilitation Hospital from Iasi, Romania. The data were collected at baseline (T0) and at a 12-month interval (T1). Results: The statistical analysis was conducted using Kaiser–Meyer–Olkin analysis, which indicated a value of 0.683, thus validating the clinical tests used. The correlation matrix and the linear regression for all the tests showed highly significant statistical results. Furthermore, the ROC curve analysis of the criteria suggested by the working group of the Romanian Society of Neurology demonstrated that the EDSS, 9HPT, and 25FWT are highly sensitive in diagnosing SPMS, an opinion that is shared with the Spanish experts, but not with the Karolinska expert panel. Using the criteria given by the Croatian expert group in the ROC curve analysis showed that only the EDSS was strongly significant for the progression to the SPMS phase. Conclusions: In conclusion, all clinical methods used demonstrated that they are valid and can contribute to identifying patients with an increased risk of progression. The model proposed by the Romanian Society of Neurology working group is similar to other countries’ expert opinions and can be used to detect the risk of disease progression and establish a more tailored therapeutic management of SPMS. Full article

Background: Artificial intelligence has become a valuable tool for diagnosing and detecting postoperative complications early. Through imaging and biochemical markers, clinicians can anticipate the clinical progression of patients and the risk of long-term complications that could impact the quality of life or even be life-threatening. In this context, artificial intelligence is crucial for identifying early signs of complications and enabling clinicians to take preventive measures before problems worsen. Materials and methods: This observational study analyzed medical charts from the electronic archive of the Clinical Emergency Hospital in Galați, Romania, covering a four-year period from 2018 to 2022. A neural network model was developed to analyze various socio-demographic and paraclinical data. Key features included patient demographics, laboratory investigations, and clinical outcomes. Statistical analyses were performed to identify significant risk factors associated with deep venous thrombosis (DVT). Results: The analysis revealed a higher prevalence of female patients (60.78%) compared to male patients, indicating a potential gender-related risk factor for DVT. The incidence of DVT was highest among patients aged 71 to 90 years, affecting 56.86% of individuals in this age group, suggesting that advanced age significantly contributes to the risk of developing DVT. Additionally, among the DVT patients, 15.69% had a body mass index (BMI) greater than 30, categorizing them as obese, which is known to increase the risk of thrombotic events. Furthermore, this study highlighted that the highest frequency of DVT was associated with femur fractures, occurring in 52% of patients with this type of injury. The neural network analysis indicated that elevated levels of direct bilirubin (≥1.5 mg/dL) and prothrombin activity (≤60%) were strong predictors of fracture-related complications, with sensitivity and specificity rates of 78% and 82%, respectively. These findings underscore the importance of monitoring these laboratory markers in at-risk populations for early intervention. Conclusions: This study identified critical risk factors for developing DVT, including advanced age, high BMI, and femur fractures, which necessitate longer recovery periods. Additionally, the findings indicate that elevated direct bilirubin and prothrombin activity play a significant role in predicting DVT development. These results suggest that AI can effectively enhance the anticipation of clinical evolution in patients, aiding in early intervention and management strategies. Full article

With the continuous penetration of flexible resources, the distribution network is gradually forming a two-way interactive supply and demand relationship with the transmission network and users. The deployment of soft open point (SOP) and energy storage represents a crucial strategy for voltage regulation and power flow control in distribution networks. This article puts forth a methodology for optimizing the configuration of SOP and energy storage based on the characteristics of the snowflake-shaped grid and sensitivity analysis. Firstly, the location of the SOP is determined based on the characteristics of the interconnection nodes between snowflake websites. Secondly, the voltage sensitivity analysis is employed to identify nodes that have a significant impact on the system voltage distribution, thereby enabling the selection of an optimal energy storage site. Subsequently, a multi-objective optimization configuration model for SOP and energy storage is established, taking into account the economic efficiency and load balancing of the power grid. Finally, the method is verified using a snowflake-shaped grid in Tianjin. In comparison with the plan that solely considers the economic aspects of the power grid, the method proposed in this article can reduce the degree of load balancing by 50.93% while simultaneously increasing the annual comprehensive cost by only 24.35%. Full article