Search Results (185)

Background/Objectives: Improving sparse testing is essential for enhancing the efficiency of genomic prediction (GP). Accordingly, new strategies are being explored to refine genomic selection (GS) methods under sparse testing conditions. Methods: In this study, a sparse testing approach was evaluated, specifically in the context of predicting performance for tested lines in untested environments. Sparse testing is particularly practical in large-scale breeding programs because it reduces the cost and logistical burden of evaluating every genotype in every environment, while still enabling accurate prediction through strategic data use. To achieve this, we used training data from CIMMYT (Obregon, Mexico), along with partial data from India, to predict line performance in India using observations from Mexico. Results: Our results show that incorporating data from Obregon into the training set improved prediction accuracy, with greater effectiveness when the data were temporally closer. Across environments, Pearson’s correlation improved by at least 219% (in a testing proportion of 50%), while gains in the percentage of matching in top 10% and 20% of top lines were 18.42% and 20.79%, respectively (also in a testing proportion of 50%). Conclusions: These findings emphasize that enriching training data with relevant, temporally proximate information is key to enhancing genomic prediction performance; conversely, incorporating unrelated data can reduce prediction accuracy. Full article

Understanding the structural behaviour of flat plate systems during fire exposure is critical for ensuring the safety of occupants and emergency personnel. Flat slabs, a widely used structural system, undergo significant thermal deformations in fire, which increase demands on supporting columns and reduce the stiffness and strength of concrete and steel. While experimental fire tests have provided valuable data to understand the behaviour of isolated components of flat slabs, numerical analysis is the only route to comprehending the structural behaviour of full-scale flat plate structures during fire exposure. ABAQUS is commonly used for modelling reinforced concrete (RC) structures under fire, with two prevailing techniques: (1) solid element modelling for concrete and truss reinforcement and (2) shell element modelling with embedded steel layers and line-column elements. However, uncertainties remain regarding the influence of modelling parameters such as dilation angle and concrete tensile stress, and the impact of surface fire exposure has not been comprehensively studied. This study presents a novel contribution by conducting a detailed numerical investigation of a full-scale flat plate structure exposed to fire using both modelling approaches. The shell-element model was validated against experimental data and used to evaluate the effect of dilation angle and tensile strength assumptions. A unique aspect of this work is the assessment of fire exposure on different slab surfaces, including bottom, top, and both, which provides insights into slab deflections and column displacements under different surface fire exposure scenarios. The structure was then modelled using solid elements to systematically compare modelling techniques. The results highlight key differences between approaches and guide for selecting the most suitable modelling strategies for fire-exposed flat plate systems. Full article

Commonly, accelerometers are used to determine the tension force in cables through an indirect process; however, it is necessary to know the mechanical parameters of each element, such as mass and length. Typically, obtaining or measuring these parameters is not feasible. Therefore, this research proposed an alternative methodology to indirectly estimate them based on historical information about the so-called classic instruments (accelerometers and hydraulic jack). This case study focused on the Rio Papaloapan Bridge located in Veracruz, Mexico, a structure that has experienced material casting issues due to inadequate heat treatment in some cable top anchor over its lifespan. Thirteen cables from the structure were selected to evaluate the proposed methodology, yielding results within 3.8% of difference compared to direct tension estimation generated by a hydraulic jack. Furthermore, to enhance data collection, this process was complemented using a computer vision methodology. This involved remotely measuring the vibration frequency of cables from high-resolution videos recorded with a smartphone. The non-contact method was validated in a laboratory using a vibrating table, successfully estimating oscillation frequencies from video-recording with a fixed camera. A field test on eight cables of a bridge was also conducted to assess the performance and feasibility of the proposed method. The results demonstrated an RMS Error of approximately 2 mHz and a percentage difference in the tension force estimation below 3% compared to an accelerometer measurement approach. Finally, it was determined that this composed methodology for indirect tension force determination is a viable option when: (1) cables are challenging to access; (2) there is no line of sight between the camera and cables outside the bridge; (3) there is a lack of information about the mechanical parameters of the cables. Full article

Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate breeding cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials. Genomic (G) data were combined with hyperspectral (H) and multispectral + thermal (M) imaging across the 2022 and 2023 growing seasons at the Plant Science Research and Education Unit, Citra, Florida. A panel of 312 wheat genotypes was analyzed using GBLUP-based models, integrating G + H and G + M data from SP to predict BP yield. SP models demonstrated promising predictive ability, with G + H models achieving moderate within-year (0.43 to 0.51) and across-year (0.43) prediction accuracies, while G + M models reached 0.53 to 0.58 and 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy of 0.47 when M data from the 2022 SP, combined with G, was used to predict BP yield in 2023. Additionally, the top 25% specificity (coincide index) was evaluated, with models showing up to 47–51% within a year and 43–45% between years overlap in the highest predicted-yielding lines between SP and BP trials, further emphasizing the potential of SP data for early selection. These findings suggest that SP trials can provide meaningful predictions for BP yields, enabling earlier selection and faster breeding cycles. Full article

Background/Objectives: Checkpoint inhibitors (ICIs) are key therapies for NSCLC, but current selection criteria, such as excluding mutation carriers and assessing PD-L1, lack sensitivity. As a result, many patients receive costly treatments with limited benefit. Therefore, this study aimed to predict which NSCLC patients would achieve durable survival (≥24 months) with immunotherapy. Methods: A comprehensive ensemble radiomics approach was applied to pretreatment CT scans to prognosticate overall survival (OS) and predict progression-free survival (PFS) in a cohort of 220 consecutive patients with inoperable NSCLC treated with first-line ICIs (pembrolizumab or atezolizumab, nivolumab or prolgolimab) as monotherapy or in combination. The radiomics pipeline evaluated four normalization methods (none, min-max, Z-score, mean), four feature selection techniques (ANOVA, RFE, Kruskal–Wallis, Relief), and ten classifiers (e.g., SVM, random forest). Using two to eight radiomics features, 1680 models were built in the Feature Explorer (FAE) Python package. Results: Three feature sets were evaluated: clinicopathological (CP) only, radiomics only, and a combined set, using 6- and 12-month PFS and 24-month OS endpoints. The top 15 models were ensembled by averaging their probability scores. The best performance was achieved at 24-month OS with the combined CP and radiomics ensemble (AUC = 0.863, accuracy = 85%), followed by radiomics-only (AUC = 0.796, accuracy = 82%) and CP-only (AUC = 0.671, accuracy = 76%). Predictive performance was lower for 6-month (AUC = 0.719) and 12-month PFS (AUC = 0.739) endpoints. Conclusions: Our radiomics pipeline improved selection of NSCLC patients for immunotherapy and could spare non-responders unnecessary toxicity while enhancing cost-effectiveness. Full article

Power line detection (PLD) is a crucial task in the electric power industry where accurate PLD forms the foundation for achieving automated inspections. However, recent top-performing power line detection methods tend to generate thick and noisy edge lines, adding to the difficulties of subsequent tasks. In this work, we propose a multi-scale feature-based PLD method named LUM-Net to allow for the detection of power lines in a crisp and precise way. The algorithm utilizes EfficientNetV1 as the backbone network, ensuring effective feature extraction across various scales. We developed a Coordinated Convolutional Block Attention Module (CoCBAM) to focus on critical features by emphasizing both channel-wise and spatial information, thereby refining the power lines and reducing noise. Furthermore, we constructed the Bi-Large Kernel Convolutional Block (BiLKB) as the decoder, leveraging large kernel convolutions and spatial selection mechanisms to capture more contextual information, supplemented by auxiliary small kernels to refine the extracted feature information. By integrating these advanced components into a top-down dense connection mechanism, our method achieves effective, multi-scale information interaction, significantly improving the overall performance. The experimental results show that our method can predict crisp power line maps and achieve state-of-the-art performance on the PLDU dataset (ODS = 0.969) and PLDM dataset (ODS = 0.943). Full article

Background/Objectives: Continuous manufacturing is gaining importance in the nanopharmaceutical field, offering improved process efficiency and product consistency. To fully leverage its potential, the integration of Process Analytical Technology (PAT) tools is essential for real-time quality control and robust process monitoring. Among the critical quality attributes (CQAs) of nanosystems, particle size plays a key role in ensuring product consistency and performance. However, real-time size monitoring remains challenging due to complex process dynamics and nanosystem heterogeneity. Methods: This study evaluates the applicability of conventional Dynamic Light Scattering (DLS) and spatially resolved DLS (SR-DLS) using the NanoFlowSizer (NFS) as PAT tools in a temperature-regulated top-down nano-production line. Various lipid-based nanosystems, including solid lipid nanoparticles (SLN), nanostructured lipid carriers (NLC), and nanoemulsions (NEs), were investigated. To ensure reliable implementation, key factors such as sample dilution, viscosity, focus position, measurement angle and temperature effects were systematically assessed for offline and at-line DLS using the Litesizer 500, as well as for offline, inline, and online SR-DLS using the NFS. Results: Offline screening confirmed that selecting the appropriate dilution medium and rate ensures measurement reliability. At-line methods provided an efficient alternative by enabling rapid final product control with minimal manual intervention. Inline and online monitoring further enhanced process efficiency by enabling real-time tracking of size, reducing waste, and allowing immediate process adjustments. Conclusions: This study demonstrates that integrating offline, at-line, in-line, and online DLS techniques allows for comprehensive product monitoring throughout the entire production line. This approach ensures a streamlined process, enables real-time adjustments, and facilitates reliable quality control after production and during storage. Full article

Fault diagnosis based on the partial discharge (PD) recognition has been widely applied on a gas-insulated line breaker (GILB) and gas-insulated switchgear (GIS) as a reliable online condition monitoring method. This paper dealt with insulation defect diagnosis based on a Random Forest (RF) algorithm with an optimized feature selection method. Four different types of insulation defect models, such as the free-moving particle (FMP) defect, the protrusion-on-conductor (POC) defect, the protrusion-on-enclosure (POE) defect, and the delamination defect, were prepared to simulate representative PD single pulses and PRPD patterns generated from the GILB. The PD signals generated from defect models were detected using the PRPD sensor which can detect phase-synchronized PD signals with the applied high-voltage (HV) signals without the need for additional equipment. Various statistical PD features were extracted from PD single pulses and PRPD patterns according to four kinds of PD defect models, and optimized features were selected with respect to variance importance analysis. Two kinds of PD datasets were established using all statistical features and top-ranked features. From the experimental results, the RF algorithm achieved accuracy rates exceeding 92%, and the PD datasets using only half of the statistical PD features could reduce the computational times while maintaining the accuracy rates. Full article

Breast cancer is a prevalent type of cancer worldwide, leading to both high incidence and mortality, and hence, effective and safe drugs are needed. Because of this, the use of natural products and their derivatives has become a popular strategy for developing new chemotherapeutic agents. In this study, 17 new sesquiterpene-aryl derivatives were synthesized using (−)-drimenol as the starting material. The cytotoxicity of these semi-synthetic derivatives was determined in MCF-7 cells, a breast cancer model, and in a non-tumor cell line, MCF-10, to evaluate selectivity. The results show that five of these sesquiterpene derivatives had IC₅₀ values between 9.0 and 25 µM. Of these, compound 14c stands out for its higher cytotoxicity in MCF-7 cells but lower cytotoxicity in MCF-10 cells, being more selective than daunorubicin (selective index values of 44 and 28, respectively). In addition, compound 14c induced oxidative stress in MCF-7 cells, activated caspases-3/7, and selectively inhibited topoisomerase II (TOP2) versus topoisomerase I (TOP1) in MCF-7 cells. In silico studies allowed us to propose a binding mode for 14c to the TOP2 DNA complex to validate the experimental results. Therefore, this study demonstrated the importance of aryl-sesquiterpene structures and their promising profiles in the search for new bioinspired antitumor drugs in natural products. Full article

This study presents a two-step artificial neural network (ANN) approach for detecting mooring failures in a spread-moored floating production storage and offloading (FPSO) vessel using platform motion data. Synthetic statistical data generated from time-domain simulations were utilized as input features. The first-step ANN determines whether the mooring system is intact or a failure has occurred within a specific mooring group. If a failure is detected, the second-step ANN identifies the exact failed mooring line within the group. Hyperparameter optimization was performed using Bayesian and random search methods, and multiple input variable sets were evaluated. The results indicate that the mean values of platform motions, particularly surge and yaw, play a crucial role in accurately identifying mooring failures. Additionally, selecting the top 10 features based on mutual information can be a way to improve detection accuracy. The proposed two-step ANN approach outperformed the single-step ANN method, achieving higher classification accuracy and reducing misclassification between mooring lines. These findings demonstrate the potential of machine learning for near-real-time mooring integrity monitoring, offering a practical and efficient alternative to traditional inspection methods. Full article

Maritime communication networks are critical for supporting the increasing demands of oceanic and coastal activities, including shipping, fishing, and offshore operations. However, traditional systems face significant challenges in providing reliable, high-throughput connectivity due to dynamic sea environments, mobility, and non-line-of-sight (NLoS) conditions. Reconfigurable intelligent surfaces (RISs) have been proposed as a promising solution to overcome these limitations by enabling programmable control of electromagnetic wave propagation in next-generation mobile communication networks, such as beyond fifth generation and sixth generation ones (B5G/6G). This paper presents a deep learning-based (DL) scheme for beam selection in RIS-aided maritime next-generation networks. The proposed approach leverages deep learning to optimize beam selection dynamically, enhancing signal quality, coverage, and network efficiency in complex maritime environments. By integrating RIS configurations with data-driven insights, the proposed framework adapts to changing channel conditions and potential vessel mobility while minimizing latency and computational overhead. Simulation results demonstrate significant improvements in both machine learning (ML) metrics, such as beam selection accuracy, and overall communication reliability compared to traditional methods. More specifically, the proposed scheme reaches around 99% Top-$K$ Accuracy levels while jointly improving energy efficiency (ee) and spectral efficiency (SE) by approx. 2 times compared to state-of-the-art approaches. This study provides a robust foundation for employing DL in RIS-aided maritime networks, contributing to the advancement of intelligent, high-performance wireless communication systems for advanced maritime applications, such as autonomous mooring, the autonomous approach, and just-in-time arrival (JIT). Full article

Background/Objectives: There has been a prominent rise in the use of GLP-1 RAs recently, particularly semaglutide, for the treatment of T2DM with or without obesity. Subcutaneous injections of semaglutide have demonstrated beneficial effects on cardiovascular risk factors. However, several factors hinder the use of subcutaneous administration. Therefore, the oral route is preferred; yet, it remains unclear whether oral semaglutide provides cardiovascular protection comparable to its subcutaneous counterpart. Methods: A systematic review in line with the PRISMA guidelines was performed based on eight databases (Scopus, Proquest, Science Direct, PubMed, Google Scholar, EBSCOHost, Clinical Key, and The Cochrane Library) to identify clinical studies that assessed the effects of oral semaglutide on cardiovascular risk factors, especially blood pressure and lipid or cholesterol profile in T2DM patients. Inclusion criteria included studies that used oral semaglutide on top of a mainstay treatment for T2DM compared to the placebo control group, assessed cardiovascular risk factors, and were conducted prospectively or in an RCT design. Case reports, ongoing studies with incomplete results, reviews, animal studies, and retrospective studies were excluded. The Newcastle-Ottawa scale and Jadad scale were used to assess the risk of bias in the included studies. Data extracted from the selected studies included patient characteristics, study design, research methodology, intervention regimen, and cardiovascular risk factors: SBP, DBP, TC, HDL, LDL, and TG. Data were presented in a table format to compare and synthesize the results of each study. Results: Five clinical studies were selected (two were randomized trials and three were observational, prospective studies). All five studies reported a consistent trend in the reduction in SBP (ranging from −2.60 to −12.74 mmHg) after oral semaglutide treatment. However, its effect on DBP was found to be less consistent. Lipid profile results show the most consistent trend in total cholesterol reduction (−8.80 to −22.19 mg/dL). Four studies reported a favorable reduction in LDL cholesterol (−7.6 to −18.0 mg/dL) and triglycerides (−11.00 to −40.13 mg/dL). HDL cholesterol shows the least consistent findings where three studies reported an increasing trend, yet this was not statistically significant; one study reported a mild increase in HDL (+0.90 ± 0.12; p < 0.0001); and one study reported a slight reduction in HDL (55.6 ± 2.5 to 51.6 ± 2.2; p < 0.05). Conclusions: Once-daily oral semaglutide is a promising add-on therapy for the treatment of T2DM with or without obesity in reducing cardiovascular risk factors, potentially lowering cardiovascular-related mortality. Thus, once-daily oral semaglutide may offer cardiovascular benefits comparable to the subcutaneous form, with the advantage of improved adherence. Full article

Musculoskeletal disorders (MSDs) are associated with awkward postures, causing health problems for workers. MSDs impact physical activity levels and decrease professional work capacity. The objective of this study is to investigate the ergonomic risks in a handicraft community enterprise group using Krajood as the main raw material. The sample group consisted of craftsmen who engage in woven bags, and it was selected using inclusion and exclusion criteria. Data were collected with a general information questionnaire, a risk assessment questionnaire for musculoskeletal disorders, and the Rapid Upper Limb Assessment (RULA) worksheet. The results indicate that musculoskeletal disorders were experienced by all the workers during the past year, with pain or discomfort in all 12 body parts. Moreover, most commonly, the pains were in the shoulders, upper back, lower back, and hands/wrists on both the left and the right side. The lower back exhibited a 100% prevalence of symptoms. The risk assessment by RULA indicated that the jobs had the highest possible total risk score at 7 points (45%), which needs to improve immediately. The top three high-risk work processes were the product line hammering steps, using a sewing machine to form the product, and the weaving and forming stage. Therefore, this study provides critical information for the craftsmen and their employers to improve workers’ health and production efficiency. Full article

Genomic selection (GS) is a predictive methodology that is revolutionizing plant and animal breeding. However, the practical application of the GS methodology is challenging since a successful implementation requires a good identification of the best lines. For this reason, some approaches have been proposed to be able to select the top (or bottom) lines with more Precision. Despite the varying popularity of methods, with some being notably more efficient than others, this paper delves into the fundamentals of these techniques. We used five models/methods: (1) RC, known as the Bayesian Best Linear Unbiased Predictor (GBLUP); (2) R, which is like RC but uses a threshold; (3) RO, Regression Optimum, that leverages the RC model in its training process to fine-tune the threshold; (4) B, Threshold Bayesian Probit Binary model (TGBLUP) with a threshold of 0.5 to classify the cultivars as top or non-top; (5) BO is the TGBLUP but the threshold used is an optimal probability threshold that guarantees similar Sensitivity and Specificity. We also present a benchmark comparison of existing approaches for selecting the top (or bottom) performers, utilizing five real datasets for comprehensive analysis. For methods that necessitate a rigorous tuning process, we suggest a streamlined tuning approach that significantly decreases implementation time without notably compromising performance. Our analysis revealed that the regression optimal (RO) method outperformed other models across the five real datasets, achieving superior results in terms of the F1 score. Specifically, RO was more effective than models R, B, RC, and BO by 60.87, 42.37, 17.63, and 9.62%, respectively. When looking at the Kappa coefficient, the RO model was better than models B, BO, R, and RC by 37.46, 36.21, 52.18, and 3.95%, respectively. In terms of Sensitivity, the RO model outperformed models B, R, and RC by 145.74, 250.41, and 86.20, respectively. The second-best model was the model BO. It is important to point out that in the first stage, the BO and RO approaches train a classification and regression model, respectively, to classify the lines as the top (bottom) or not the top (not the bottom). However, both the BO and RO approaches optimize a threshold in the second stage to perform the classification of the lines that minimize the difference between the Sensitivity and Specificity. The BO and RO methods are superior for the selection of the top (or bottom) lines. For this reason, we encourage breeders to adopt these approaches to increase genetic gain in plant breeding programs. Full article

Background: Although higher-generation TKIs are associated with improved progression-free survival in advanced NSCLC patients with EGFR mutations, the optimal selection of TKI treatment remains uncertain. To address this gap, we developed a web application powered by a reinforcement learning (RL) algorithm to assist in guiding initial TKI treatment decisions. Methods: Clinical and mutational data from advanced NSCLC patients were retrospectively collected from 14 medical centers. Only patients with complete data and sufficient follow-up were included. Multiple supervised machine learning models were tested, with the Extra Trees Classifier (ETC) identified as the most effective for predicting progression-free survival. Feature importance scores were calculated by the ETC, and features were then integrated into a Deep Q-Network (DQN) RL algorithm. The RL model was designed to select optimal TKI generation and a treatment line for each patient and was embedded into an open-source web application for experimental clinical use. Results: In total, 318 cases of EGFR-mutant advanced NSCLC were analyzed, with a median patient age of 63. A total of 52.2% of patients were female, and 83.3% had ECOG scores of 0 or 1. The top three most influential features identified were neutrophil-to-lymphocyte ratio (log-transformed), age (log-transformed), and the treatment line of TKI administration, as tested by the ETC algorithm, with an area under curve (AUC) value of 0.73, whereas the DQN RL algorithm achieved a higher AUC value of 0.80, assigning distinct Q-values across four TKI treatment categories. This supports the decision-making process in the web-based ‘EGFR Mutant NSCLC Treatment Advisory System’, where clinicians can input patient-specific data to receive tailored recommendations. Conclusions: The RL-based web application shows promise in assisting TKI treatment selection for EGFR-mutant advanced NSCLC patients, underscoring the potential for reinforcement learning to enhance decision-making in oncology care. Full article