Search Results (862)

To address the shortcomings of the Dung Beetle Optimization (DBO) algorithm in ship power-system fault reconfiguration, such as low population diversity and an imbalance between global exploration and local exploitation, the authors of this paper propose an improved Dung Beetle Optimization (LESSDBO) algorithm. The improvements include optimizing the initial population using Latin hypercube sampling and an elite population strategy, optimizing parameters with an improved sigmoid activation function, introducing the sine–cosine algorithm (SCA) for position update optimization, and performing multi-population mutation operations based on individual quality. The LESSDBO algorithm was applied to simulate the fault reconfiguration of a ship power system, and it was compared with the traditional DBO, Genetic Algorithm (GA), and Modified Particle Swarm Optimization (MSCPSO) methods. The simulation results showed that LESSDBO outperformed the other algorithms in terms of convergence accuracy, convergence speed, and global search capability. Specifically, in the reconfiguration under Fault 1, LESSDBO achieved optimal convergence in seven iterations, reducing convergence iterations by more than 30% compared with the other algorithms. In the reconfiguration under Fault 2, LESSDBO achieved optimal convergence in eight iterations, reducing convergence iterations by more than 23% compared with the other algorithms. Additionally, in the reconfiguration under Fault Condition 1, LESSDBO achieved a minimum of four switch actions, which is 33% fewer than the other algorithms, on average. In the reconfiguration under Fault Condition 2, LESSDBO achieved a minimum of eight switch actions, which is a 5.9% reduction compared with the other algorithms. Furthermore, LESSDBO obtained the optimal reconfiguration solution in all 50 trials for both Faults 1 and 2, demonstrating a 100% optimal convergence probability and significantly enhancing the reliability and stability of the algorithm. The proposed method effectively overcomes the limitations of the traditional DBO in fault reconfiguration, providing an efficient and stable solution for the intelligent topology reconfiguration of ship power systems. Full article

Aim: We have previously shown that CRP < 2.9 mg/L is a better predictor of endoscopic remission (ER) than CRP < 5 mg/L in ulcerative colitis (UC). Here, we prospectively evaluate CRP and FCP cut-offs and compare them in predicting ER and histological remission (HR) in UC. Method: One hundred thirty-five steroid-free UC patients were evaluated prospectively. ER was defined as Mayo endoscopic sub-score 0–1. In colonoscopy, the colon was evaluated as seven segments: rectum, sigmoid, descending, proximal-transverse, distal-transverse, ascending colon, and cecum. Two biopsies of each segment were evaluated for histological inflammation and graded using the Nancy and Geboes scores. All segment biopsies with Nancy < 1 and Geboes < 2 were defined as HR. Results: The optimum cut-off values for FCP and CRP were 120 μg/g and 2.75 mg/L for ER, respectively. AUC values of FCP and CRP were similar for ER and Mayo-0 disease in ROC analysis. CRP and FCP also had similar performances with these cut-offs regarding ER. While CRP was a predictor to assess the extensiveness of active UC, FCP was not. ROC analysis showed no difference between CRP and FCP regarding HR. Cut-off values for HR were 2.1 mg/L and 55 μg/g for CRP and FCP, respectively. CRP and FCP, in combination with the mentioned cut-off values, detected ER and HR in nearly 2/3 and ½ of the patients, respectively, with high specificity. Conclusions: Reappraised CRP (ER: 2.75 mg/L, HR: 2.1 mg/L) has as much diagnostic contribution as relevant FCP in predicting ER and HR and contributes more to revealing the proximal extension in active colitis compared to FCP. Relevant CRP and FCP combinations may improve the prediction rates. Full article

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness. Full article

To investigate the effect of hot and humid environmental factors on the mechanical properties of asphalt mixtures research, in this paper, the dynamic modulus of asphalt mixtures under the effects of aging, dry–wet cycling, and coupled effects of aging and dry–wet cycling were measured by the simple performance tester (SPT) system, and the dynamic modulus principal curves were fitted based on the sigmoidal function. The results show that under the aging effect, the dynamic modulus of asphalt mixture increases with the aging degree; the dynamic modulus of short-term aged, medium-term aged, long-term aged, and ultra-long-term aged asphalt mixtures increased by 9.3%, 26.4%, 44.8%, and 57%, respectively, compared to unaged asphalt mixtures at 20 °C and 10 Hz; the high-temperature stability performance is enhanced, and the low temperature cracking resistance performance is enhanced; under the dry–wet cycle, the aging effect of asphalt water is more obvious in the early stage, and dynamic modulus of resilience of the mixture is slightly increased. In the long-term wet–dry cycle process, water on the asphalt and aggregate erosion increased, the structural bearing capacity attenuation, and the dynamic modulus of rebound greatly reduced at 20 °C and 10 Hz. For example, the dynamic modulus of asphalt mixtures with seven wet and dry cycles increased by 3% compared to asphalt mixtures without wet and dry cycles, and the dynamic modulus of asphalt mixtures with 14 cycles of wet and dry cycles and 21 cycles of wet and dry cycles decreased by 10.8% and 16.5%, respectively, compared to asphalt mixtures without wet and dry cycles. The main curve as a whole shifted downward; the high-temperature performance decreased significantly; in the aging wet–dry cycle coupling, the aging asphalt mixture is more susceptible to water erosion, and the first wet–dry cycle after the mix by the degree of water erosion is relatively small, along with the dynamic modulus of rebound. The dynamic modulus of resilience is relatively larger, and the high-temperature performance is relatively better, while the low-temperature performance is worse. Full article

The mechanical behaviour of soils subjected to any stress path in which deviatoric stresses are present is heavily characterised by non-linearity, irreversibility and is strongly dependent on the initial state of stress. The latter, for the majority of geotechnical applications, is normally determined by the at-rest earth pressure coefficient K₀, even though this state is valid, strictly speaking, for axisymmetric conditions and for zero-lateral deformations only. Many expressions are available in the literature for the determination of this coefficient for cohesive and granular materials both for normal consolidated and over-consolidated conditions. These relations are available for low to medium stress levels. Results of an extensive experimental investigation on two sands of different mineralogy up to very high stress (120 MPa) are reported in the paper. For reach very high vertical stresses, a special oedometer has been realised. In the loading phase (normal consolidated sands), the coefficient K_0n depends on the stress level. It passes from values of about 0.8 to values of about 0.45 in the range of effective vertical stress σ′_v = 0.5–4 MPa. Subsequently, K_0n is about constant and varies between 0.45 to 0.55 up to very high vertical effective stresses (120 MPa). For the sands employed in the tests, Jaki’s relation did not lead to reliable results at relatively low pressures, while at high pressures, the same relationship seems to lead to reliable predictions if it refers to the constant volume angle of shear strength. For the over-consolidated sands, K_0C strongly depends on the OCR, and for very high values of OCR, K_0C could be greater than Rankine’s passive coefficient of earth pressure, K_p. This result is due to the very locked structure of the sands caused by the grain crushing, with intergranular contact of sutured and sigmoidal, concavo-convex and inter-penetrating type, that confer to the sand a sort of apparent cohesion and make it similar to weak sandstone. Full article

Accurate monitoring of estuarine turbidity patterns is important for maintaining aquatic ecological balance and devising informed estuarine management strategies. This study aimed to enhance the prediction of estuarine turbidity patterns by enhancing the performance of the multilayer perceptron (MLP) network through the introduction of stochastic gradient descent (SGD) and momentum gradient descent (MGD). To achieve this, Sentinel-2 multispectral imagery was used as the base on which spectral radiance properties of estuarine waters were analyzed against field-measured turbidity data. In this case, blue, green, red, red edge, near-infrared and shortwave spectral bands were selected for empirical relationship establishment and model development. Inverse distance weighting (IDW) spatial interpolation was employed to produce raster-based turbidity data of the study area based on field-measured data. The IDW image was subsequently binarized using the bi-level thresholding technique to produce a Boolean image. Prior to empirical model development, the selected spectral bands were calibrated to turbidity using multilayer perceptron neural network trained with the sigmoid activation function with stochastic gradient descent (SGD) optimizer and then with sigmoid activation function with momentum gradient descent optimizer. The Boolean image produced from IDW interpolation was used as the base on which the sigmoid activation function calibrated image pixels to turbidity. Empirical models were developed using selected uncalibrated and calibrated spectral bands. The results from all the selected models generally revealed a stronger relationship of the red spectral channel with measured turbidity than with other selected spectral bands. Among these models, the MLP trained with MGD produced a coefficient of determination (r²) value of 0.92 on the red spectral band, followed by the MLP with MGD on the green spectral band and SGD on the red spectral band, with r² values of 0.75 and 0.72, respectively. The relative error of mean (REM) and r² results revealed accurate turbidity prediction by the sigmoid with MGD compared to other models. Overall, this study demonstrated the prospect of deploying ensemble techniques on Sentinel-2 multispectral bands in spatially constructing missing estuarine turbidity data. Full article

Colorectal polyps, precursors to colorectal cancer (CRC), require precise identification for appropriate diagnosis and therapy. This study aims to investigate the differences in hematological and inflammatory markers, specifically the CALLY index, HALP score, and immuno-inflammatory indexes, between neoplastic and nonneoplastic polyps. A retrospective cross-sectional study was conducted on 758 patients aged 61.0 ± 11.8 who underwent polypectomy between June 2021 and May 2024. Patients with colorectal adenocarcinoma (n = 22) were excluded. The polyps were classified into neoplastic and nonneoplastic categories based on histopathological evaluation. The study compared the CALLY index, HALP score, and various inflammatory indexes between neoplastic and nonneoplastic polyps. Out of 758 polyps analyzed, 514 were neoplastic, and 244 were nonneoplastic. Neoplastic polyps exhibited significantly lower CALLY and HALP scores (p < 0.05) and higher immuno-inflammatory indexes (p < 0.05) compared to nonneoplastic polyps. Dysplasia status, polyp diameter, and sigmoid colon localization were significant factors in determining neoplastic growth potential. No significant differences were observed in polyp localization in the proximal and distal colon segments or in solitary versus multiple polyps. The CALLY and HALP scores and immuno-inflammatory indexes can serve as valuable markers for distinguishing neoplastic from nonneoplastic polyps. These indexes reflect underlying inflammatory and immune responses, highlighting their potential utility in the early detection and risk stratification of colorectal polyps. Integrating these markers into clinical practice may enhance diagnostic accuracy and improve patient management, leading to timely interventions and better outcomes for individuals at risk of CRC. Full article

Flood-susceptibility mapping (FSM) is crucial for effective flood prediction and disaster prevention. Traditional methods of modeling flood vulnerability, such as the Analytical Hierarchy Process (AHP), require weights defined by experts, while machine-learning and deep-learning approaches require extensive datasets. Remote sensing is also limited by the availability of images and weather conditions. We propose a new hybrid strategy integrating deep learning with the HEC–HMS and HEC–RAS physical models to overcome these challenges. In this study, we introduce a Weighted Residual U-Net (W-Res-U-Net) model based on the target of the HEC–HMS and RAS physical simulation without disregarding ground truth points by using two loss functions simultaneously. The W-Res-U-Net was trained on eight sub-basins and tested on five others, demonstrating superior performance with a sensitivity of 71.16%, specificity of 91.14%, and area under the curve (AUC) of 92.95% when validated against physical simulations, as well as a sensitivity of 88.89%, specificity of 93.07%, and AUC of 95.87% when validated against ground truth points. Incorporating a “Sigmoid Focal Loss” function and a dual-loss function improved the realism and performance of the model, achieving higher sensitivity, specificity, and AUC than HEC–RAS alone. This hybrid approach significantly enhances the FSM model, especially with limited real-world data. Full article

Given the dominant failure mode of steel bridge deck pavement layers, which is flexural–tensile damage, the dynamic modulus parameters conventionally determined through uniaxial compression testing are found to be inadequate for the design or performance analysis of these layers. In order to simulate the actual stress of a pavement structure under wheel load, the four-point bending fatigue test method and uniaxial compression test method are used to measure the dynamic modulus of an epoxy asphalt mixture, and the differences between the two test methods are analyzed. Furthermore, the four-point bending fatigue test is employed to investigate the dynamic modulus and phase angle properties across varying temperatures and frequencies, facilitating the creation of master curves for these properties and utilizing Sigmoidal models to correlate dynamic modulus data at diverse temperature conditions. This study delves into the influence of epoxy resin content, mixture composition, and aging on the dynamic modulus. The experimental results show that the dynamic modulus measured by uniaxial compression exceeds that obtained from bending fatigue tests, with the difference initially increasing and then decreasing as temperature rises. This discrepancy significantly impacts the mechanical calculations of pavement layers, underscoring the importance of selecting the appropriate testing method. Temperature, frequency, and epoxy resin content have pronounced effects on the viscoelastic properties of the mixtures. Specifically, as temperature increases, the dynamic modulus undergoes a decrease, whereas the phase angle exhibits an increase. Additionally, the dynamic modulus augments with an increase in loading frequency, while the phase angle exhibits varied trends with frequency shifts across different temperatures. Both the WLF and Sigmoidal models are effective in constructing master curve representations for the dynamic flexural modulus and phase angle. The incorporation of epoxy resin transforms asphalt from a primarily viscous to a more elastic material, significantly enhancing the viscoelastic properties of the mixture. Notably, mixtures with 50% and 60% epoxy resin content exhibit comparable dynamic moduli and phase angles, while displaying notably superior performance compared to those with 40% epoxy resin content. For large-scale steel bridge deck pavement, 50% epoxy resin content is recommended. Moreover, epoxy asphalt mixtures demonstrate robust aging resistance, with minimal variations in the dynamic modulus and phase angle before and after aging. The research results can enable the acquisition of dynamic modulus and phase angle data in the whole temperature domain and the whole frequency domain, and provide reliable mixed performance parameters for the study of different application environmental performance of steel bridge deck pavement. Full article

To address the challenges of high experimental costs, complexity, and time consumption associated with pre-mixed combustible gas deflagration experiments under semi-open space obstacle conditions, a rapid temporal prediction method for flame propagation velocity based on Ranger-GRU neural networks is proposed. The deflagration experiment data are employed as the training dataset for the neural network, with the coefficient of determination (R²) and mean squared error (MSE) used as evaluation metrics to assess the predictive performance of the network. First, 108 sets of pre-mixed methane gas deflagration experiments were conducted, varying obstacle parameters to investigate methane deflagration mechanisms under different conditions. The experimental results demonstrate that obstacle-to-ignition source distance, obstacle shape, obstacle length, obstacle quantity, and thick and fine wire mesh obstacles all significantly influence flame propagation velocity. Subsequently, the GRU neural network was trained, and different activation functions (Sigmoid, Relu, PReLU) and optimizers (Lookahead, RAdam, Adam, Ranger) were incorporated into the backpropagation updating process of the network. The training results show that the Ranger-GRU neural network based on the PReLU activation function achieves the highest mean R² value of 0.96 and the lowest mean MSE value of 7.16759. Therefore, the Ranger-GRU neural network with PReLU activation function can be a viable rapid prediction method for flame propagation velocity in pre-mixed methane gas deflagration experiments under semi-open space obstacle conditions. Full article

Background/Objectives: 5-Aminosalicylic acid (5-ASA) is a first-line therapy for ulcerative colitis (UC). This study examined the mucosa-associated microbiota (MAM) in UC patients, distinguishing between those who were 5-ASA tolerant and intolerant. Methods: Brushing samples were collected from the sigmoid and ileal end of patients with UC during endoscopic procedures. The samples were profiled by using the Illumina MiSeq platform. The V3–V4 regions of the 16S rRNA gene (460 bp) were amplified by using tailed PCR. Results: A total of 15 patients with 5-ASA intolerance, 38 patients with 5-ASA tolerance, and 19 healthy controls were recruited in this study. The α-diversity indices were remarkably different among the three groups in the ileum mucosa but not in the sigmoid colon. In the ileum mucosa, Alistipes, Ruminococcaceae, and Odoribacter were less abundant in the 5-ASA-intolerant group than in the control and 5-ASA-tolerant groups. On the contrary, Merdibacter, Brevundimonas, and Porphyromonas were more abundant in the 5-ASA-intolerant group than in other groups. Conclusions: The present study showed that the changes in MAM were characterized by a decrease in mucoprotective bacteria rather than an increase in harmful bacteria. Full article

Context: Although carcinogenesis is a multi-factorial process, the mutability and the capacity of cells to proliferate are among the major features of the cells that contribute together to the initiation and promotion steps of cancer formation. Particularly, mutability can be quantified by hyper-recombination rate assessed with specific plasmid assay, hypoxanthine-guanine phosphoribosyltransferase (HPRT) mutations frequency rate, or MRE11 nuclease activities. Cell proliferation can be assessed by flow cytometry by quantifying G2/M, G1 arrests, or global cellular evasion. Methods: All these assays were applied to skin untransformed fibroblasts derived from eight major cancer syndromes characterized by their excess of relative cancer risk (ERR). Results: Significant correlations with ERR were found between hyper-recombination assessed by the plasmid assay and G2/M arrest and described a third-degree polynomial ERR function and a sigmoidal ERR function, respectively. The product of the hyper-recombination rate and capacity of proliferation described a linear ERR function that permits one to better discriminate each cancer syndrome. Conclusions: Hyper-recombination and cell proliferation were found to obey differential equations that better highlight the intrinsic bases of cancer formation. Further investigations to verify their relevance for cancer proneness induced by exogenous agents are in progress. Full article

Tau is an intrinsically disordered protein involved in several neurodegenerative diseases where a common hallmark is the appearance of tau aggregates in the brain. One common approach to elucidate the mechanisms behind the aggregation of tau has been to recapitulate in vitro the self-assembly process in a fast and reproducible manner. While the seeding of tau aggregation is prompted by negatively charged cofactors, the obtained fibrils are morphologically distinct from those found in vivo. The Tau AD core fragment (TADC, tau 306–378) has emerged as a new model and potential solution for the cofactor-free in vitro aggregation of tau. Here, we use TADC to further study this process combining multiple amyloid-detecting fluorophores and fibril bioimaging. We confirmed by transmission electron microscopy that this fragment forms fibrils after quiescent incubation at 37 °C. We then employed a panel of eight amyloid-binding fluorophores to query the formed species by acquiring their emission spectra. The results obtained showed that nearly all dyes detect TADC self-assembled species. However, the successful monitoring of TADC aggregation kinetics was limited to three fluorophores (X-34, Bis-ANS, and pFTAA) which yielded sigmoidal curves but different aggregation half-times, hinting to different species being detected. Altogether, this study highlights the potential of using multiple extrinsic fluorescent probes, alone or in combination, as tools to further clarify mechanisms behind the aggregation of amyloidogenic proteins. Full article

Colonic adenomas are considered a precursor of colorectal cancer. A 75-year-old woman had a history of post-operation left breast cancer. She received an excision when the left chest wall recurred. A later FDG PET/CT scan revealed a focal intense FDG accumulation in the sigmoid, a focal mild FDG uptake in the pericolic lymph node, and a focal increased FDG accumulation in the transverse colon. A delayed FDG PET/CT scan after the per-rectal administration of the laxative-augmented contrast medium revealed a filling defect with persistent FDG uptake in the sigmoid and transverse colon and mild FDG uptake in the pericolic lymph node. In addition, more lesions were observed in the rectum and descending colon. The pathology reports showed sigmoid adenocarcinoma with lymph node metastasis, and adenomas in the transverse colon, descending colon, and rectum. Full article

Skin cancer diagnosis greatly benefits from advanced machine learning techniques, particularly fine-tuned deep learning models. In our research, we explored the impact of traditional machine learning and fine-tuned deep learning approaches on prediction accuracy. Our findings reveal significant improvements in predictability and accuracy with fine-tuning, particularly evident in deep learning models. The CNN, SVM, and Random Forest Classifier achieved high accuracy. However, fine-tuned deep learning models such as EfficientNetB0, ResNet34, VGG16, Inception _v3, and DenseNet121 demonstrated superior performance. To ensure comparability, we fine-tuned these models by incorporating additional layers, including one flatten layer and three densely interconnected layers. These layers play a crucial role in enhancing model efficiency and performance. The flatten layer preprocesses multidimensional feature maps, facilitating efficient information flow, while subsequent dense layers refine feature representations, capturing intricate patterns and relationships within the data. Leveraging LeakyReLU activation functions in the dense layers mitigates the vanishing gradient problem and promotes stable training. Finally, the output dense layer with a sigmoid activation function simplifies decision making for healthcare professionals by providing binary classification output. Our study underscores the significance of incorporating additional layers in fine-tuned neural network models for skin cancer classification, offering improved accuracy and reliability in diagnosis. Full article