Search Results (113,492)

As a typical extreme environment, a high-geothermal environment poses severe challenges to tunnel construction in western China. In this paper, a thermal–mechanical coupling model was formulated to evaluate the cracking behavior of lining under high-geothermal conditions, considering the early property evolution of concrete. This was further validated by field monitoring and analyzed by adjusting the relevant parameters. Results indicate that the higher cracking risk occurred on the external surface of the lining sidewall after 24 h of casting. With the increase in surrounding rock temperature, the duration of cracking risk in the lining was extended. When the surrounding rock temperature exceeded 68.7 °C, thermal insulation measures were required for the lining structure. Notably, superior thermal insulation was achieved by applying a sandwich structure of rigid polyurethane materials with a thickness of 20–60 mm. In terms of curing conditions, adopting formwork with a larger heat convection coefficient was conducive to reducing the cracking risk of the tunnel lining, with an appropriate removal time of 48 h. This work provides insights into the thermal–mechanical behavior of lining concrete, thereby mitigating its early cracking in a high-geothermal environment. Full article

Background: The accurate staging of multiple myeloma (MM) is essential for optimizing treatment strategies, while predicting the progression of asymptomatic patients, also referred to as monoclonal gammopathy of undetermined significance (MGUS), to symptomatic MM remains a significant challenge due to limited data. This study aimed to develop machine learning models to enhance MM staging accuracy and stratify asymptomatic patients by their risk of progression. Methods: We utilized gene expression microarray datasets to develop machine learning models, combined with various data transformations. For multiple myeloma staging, models were trained on a single dataset and validated across five independent datasets, with performance evaluated using multiclass area under the curve (AUC) metrics. To predict progression in asymptomatic patients, we employed two approaches: (1) training models on a dataset comprising asymptomatic patients who either progressed or remained stable without progressing to multiple myeloma, and (2) training models on multiple datasets combining asymptomatic and multiple myeloma samples and then testing their ability to distinguish between asymptomatic and asymptomatic that progressed. We performed feature selection and enrichment analyses to identify key signaling pathways underlying disease stages and progression. Results: Multiple myeloma staging models demonstrated high efficacy, with ElasticNet achieving consistent multiclass AUC values of 0.9 across datasets and transformations, demonstrating robust generalizability. For asymptomatic progression, both modeling approaches yielded similar results, with AUC values exceeding 0.8 across datasets and algorithms (ElasticNet, Boosting, and Support Vector Machines), underscoring their potential in identifying progression risk. Enrichment analyses revealed key pathways, including PI3K-Akt, MAPK, Wnt, and mTOR, as central to MM pathogenesis. Conclusions: To the best of our knowledge, this is the first study to utilize gene expression datasets for classifying patients across different stages of multiple myeloma and to integrate multiple myeloma with asymptomatic cases to predict disease progression, offering a novel methodology with potential clinical applications in patient monitoring and early intervention. Full article

Algae have great therapeutic value and have attracted a great deal of attention due to the abundance of bioactive compounds they contain, which may be the key to fighting diseases of various origins, such as skin cancer, breast cancer, or osteosarcoma. In this regard, global trends indicate that cancer is likely to become the leading cause of death and the main obstacle to increased life expectancy in the 21st century, which is related to multiple factors, including the various effects of climate change, which will continue to cause afflictions to human health. Then, excess exposure to ultraviolet radiation (UVR) causes damage to DNA, proteins, enzymes, and various cellular structures and leads to the development of cancer, premature aging of the skin (wrinkles, dryness, dilation of blood vessels, and loss of collagen and elastin), or alterations of the immune system. In addition, multidrug resistance (MDR) is characterized by the overexpression of efflux pumps, such as P-glycoprotein or P-gp, that expel chemotherapeutic drugs out of the cancer cell being the main obstacle to their efficacy. Some molecules inhibit efflux pumps when co-administered with antineoplastic agents, such as glycolipids. Mycosporin-like amino acids and glycolipids isolated from Sargassum have shown an important role as potential anticancer agents. The results show that glycolipids and mycosporin-like amino acids present in brown algae of the genus Sargassum exhibit cytotoxic effects on different types of cancer, such as breast cancer, leukemia, and osteosarcoma, which is a key criterion to be considered as a natural anti-cancer strategy; but, more in-depth in vitro studies are needed to represent them at the in vivo level, as well as their validation in preclinical assays. Full article

Simulation is a cornerstone of planning and facilitating the transition towards electric mobility in sub-Saharan Africa’s informal public transport. The primary objective of this study is to validate and refine the electro-kinetic model used to simulate electric versions of the sector’s minibuses. A systematic simulation methodology is also developed to correct the simulation parameters and improve the high-frequency GPS data used with the model. A retrofitted electric minibus was used to capture high-frequency GPS mobility data and power draw from the battery. The method incorporates key refinements such as corrections for gross vehicle mass, elevation and speed smoothing, radial drag, hill-climb forces, and the calibration of propulsion and regenerative braking parameters. The refined simulation demonstrates improved alignment with measured power draw and trip energy usage, reducing error margins and enhancing model reliability. Factors such as trip characteristics and environmental conditions, including wind resistance, are identified as potential contributors to observed discrepancies. These findings highlight the importance of precise data handling and model calibration for accurate energy simulation and decision making in the transition to electric public transport. This work provides a robust framework for future studies and practical implementations, offering insights into the technical and operational challenges of electrifying informal public transport systems in resource-constrained regions. Full article

This article used control theory to derive a non-linear exergoeconomic model for a bench-scale pyrolysis unit. A combination of an autoregressive model with an exogenous input model was involved to investigate the energy system. The economic prospects of the unit were also examined by assigning the cost to the exergy content of the energy stream. The analysis covered the detailed evaluation of the design and performance of an updraft system. Thermally processed pine waste was used as a feedstock for the reactor. The developed model fits well with the validation data extracted through the experimental findings. The exergy cost flow rate of processed pine waste was estimated to be 0.027 ¢/s⁻¹. The exergoeconomic factor was the highest for pyrolysis oil and charcoal generated as the end products of the thermal decomposition of processed pine waste. Full article

Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle to effectively capture nonlinear degradation patterns and long-term dependencies. To tackle these challenges, we introduce an innovative framework that combines evolutionary learning with deep learning for RCP. This framework integrates Temporal Convolutional Networks (TCNs), Bidirectional Gated Recurrent Units (BiGRUs), and an attention mechanism to extract comprehensive time-series features and improve prediction accuracy. Additionally, we introduce a hybrid optimization algorithm that combines the Sparrow Search Algorithm (SSA) with Bayesian Optimization (BO) to enhance the performance of the model. The experimental results validate the superiority of our framework, demonstrating its capability to achieve significantly improved prediction accuracy compared to existing methods. This study provides researchers in battery management systems, electric vehicles, and renewable energy storage with a reliable tool for optimizing lithium-ion battery performance, enhancing system reliability, and addressing the challenges of the new energy industry. Full article

Motion planning is critical for ensuring precise and efficient operations of unmanned aerial vehicles (UAVs). While polynomial parameterization has been the prevailing approach, its limitations in handling complex trajectory requirements have motivated the exploration of alternative methods. This paper introduces a finite Fourier series (FFS)-based trajectory parameterization for UAV motion planning, highlighting its unique capability to produce piecewise infinitely differentiable trajectories. The proposed approach addresses the challenges of fixed-time minimum-snap trajectory optimization by formulating the problem as a quadratic programming (QP) problem, with an analytical solution derived for unconstrained cases. Additionally, we compare the FFS-based parameterization with the polynomial-based minimum-snap algorithm, demonstrating comparable performance across several representative trajectories while uncovering key differences in higher-order derivatives. Experimental validation of the FFS-based parameterization using an in-house quadrotor confirms the practical applicability of the FFS-based minimum-snap trajectories. The results indicate that the proposed FFS-based parameterization offers new possibilities for motion planning, especially for scenarios requiring smooth and higher-order derivative continuity at the expense of minor increase in computational cost. Full article

In the field of agricultural robotics, robotic grippers play an indispensable role, directly influencing the rate of fruit damage and handling efficiency. Currently, traditional agricultural robotic grippers face challenges such as high damage rates and high requirements for position control. A robotic gripper for stable spherical fruit handling with high positional offset tolerance and a low fruit damage rate is proposed in this paper. It adopts a three-finger structure. A flexible active roller is configured at the end of each finger, allowing fruit translation with just a gentle touch. An integrated pressure sensor within the active roller further enhances the gripper’s compliance. To describe the effect of the gripper on the fruit, the interaction model was derived. Taking the tomato as a typical soft and fragile spherical fruit, three experiments were conducted to evaluate the performance of the proposed gripper. The experimental results demonstrated the handling capability of the gripper and the maximum graspable weight reached 2077 g. The average failure rate for the unilateral offset of 9 mm was only 1.33%, and for the bilateral offset of 6-6 mm was 4%, indicating the high positional offset tolerance performance and a low fruit damage rate of the gripper. The preliminary tomato-picking capability of the proposed gripper was also validated in a simplified laboratory scenario. Full article

Rice (Oryza sativa L.) is a staple crop for nearly half of the global population and one of China’s most extensively cultivated cereals. Heading date, a critical agronomic trait, determines the regional and seasonal adaptability of rice varieties. In this study, a series of mutants (elh5 to elh12) exhibiting extremely late heading under both long-day (LD) and short-day (SD) conditions were identified from an ethyl methanesulfonate (EMS) mutant library. Using MutMap and map-based cloning, the causative gene was identified as a novel allele of Ehd2/OsID1/RID1/Ghd10. Functional validation through CRISPR/Cas9 knockout and complementation assays confirmed its role in regulating heading. The elh6 mutation was found to cause intron retention due to alternative splicing. Ehd2 encodes a Cys-2/His-2-type zinc finger transcription factor with an IDD domain and transcriptional activity in yeast. Its expression peaks in developing leaves before heading and spikes during reproductive conversion. In elh6 mutants, delayed heading resulted from downregulating the Ehd1-Hd3a pathway genes. Salinity stress significantly hampers rice growth and productivity. Transcriptomic analysis of elh10 and ZH8015 seedlings exposed to salt stress for 24 h identified 5,150 differentially expressed genes (DEGs) at the seedling stage, predominantly linked to stress response pathways. Ehd2 was revealed as a modulator of salt tolerance, likely through the regulation of ion transport, enzyme activity, and antioxidant systems. This study establishes Ehd2 as a pivotal factor in promoting heading while negatively regulating salt tolerance in rice. Full article

Noise in 3D photogrammetric point clouds—both close-range and UAV-generated—poses a significant challenge to the accuracy and usability of digital models. This study presents a novel deep learning-based approach to improve the quality of point clouds by addressing this issue. We propose a two-step methodology: first, a variational autoencoder reduces features, followed by clustering models to assess and mitigate noise in the point clouds. This study evaluates four clustering methods—k-means, agglomerative clustering, Spectral clustering, and Gaussian mixture model—based on photogrammetric parameters, reprojection error, projection accuracy, angles of intersection, distance, and the number of cameras used in tie point calculations. The approach is validated using point cloud data from the Temple of Neptune in Paestum, Italy. The results show that the proposed method significantly improves 3D reconstruction quality, with k-means outperforming other clustering techniques based on three evaluation metrics. This method offers superior versatility and performance compared to traditional and machine learning techniques, demonstrating its potential to enhance UAV-based surveying and inspection practices. Full article

Background: Thermal therapy represents a well-established therapeutic approach for chronic musculoskeletal and respiratory conditions. To date, no studies have investigated the clinical effects of treatment interruption in thermal medicine. We aimed to evaluate the clinical impact of COVID-19 lockdown-induced thermal therapy discontinuation through validated patient-reported outcomes. Methods: This single-center observational, retrospective study (March 2020–June 2024) evaluated 97 patients receiving standardized thermal therapy at Saturnia Thermal Springs. Treatment protocols included balneotherapy, mud therapy, and inhalation treatments in cycles of 12–15 sessions, with maintenance protocols every 4–6 months. Primary outcomes were assessed through VAS and SF-36 PCS, with EQ-5D and PSQI as secondary outcomes. Results: Significant clinical deterioration occurred during treatment interruption (p < 0.001) in 77.7% of patients. Recovery patterns were duration-dependent, with the 6–7-year cohort showing faster recovery (mean time to baseline: 2.8 months) compared to the 3–5-year cohort (4.6 months). Effect sizes were substantial across all outcomes (Cohen’s d > 1.0), with EQ-5D scores showing duration-dependent improvement (mean improvement in 6–7-year cohort: 0.27). Conclusions: Thermal therapy interruption precipitates quantifiable clinical deterioration, with recovery patterns significantly influenced by pre-existing treatment duration. These findings support the essential nature of treatment continuity in thermal therapy protocols. Full article

Solar PV inverters require output filters to reduce unwanted harmonics in their output, where LCL filters are a more economical choice than larger inductance-only filters. A drawback of these filters is that they can introduce power quality disturbances, especially at higher frequencies (above 2 kHz). This paper investigates and characterises the resonance phenomenon introduced by different filter types, i.e., LC or LCL, and identifies their behavioural change when combined with multiple parallel grid-tied PV inverter systems. MATLAB/Simulink modelling aspects of PV inverter systems related to resonance phenomenon are presented, including establishing resonance at a specific frequency where potentially large variations in the parameter selection across manufacturers may exist. In addition, a method is developed to establish output filter frequency response through measurements, which is used to develop validated solar PV harmonic models for high-frequency analysis. The low-frequency harmonic models can be used up to the resonant frequency where the current flowing through the filter capacitor is insignificant compared to the current flowing into the electricity network. Full article

Background/Objectives: Accurate diagnosis is essential to avoid unnecessary procedures for thyroid incidentalomas (TIs). Advances in radiomics and machine learning applied to medical imaging offer promise for assessing thyroid nodules. This study utilized radiomics analysis on F-18 FDG PET/CT to improve preoperative differential diagnosis of TIs. Methods: A total of 152 patient cases were retrospectively analyzed and split into training and validation sets (7:3) using stratification and randomization. Results: The least absolute shrinkage and selection operator (LASSO) algorithm identified nine radiomics features from 960 candidates to construct a radiomics signature predictive of malignancy. Performance of the radiomics score was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). In the training set, the radiomics score achieved an AUC of 0.794 (95% CI: 0.703–0.885, p < 0.001). Validation was performed on internal and external datasets, yielding AUCs of 0.702 (95% CI: 0.547–0.858, p = 0.011) and 0.668 (95% CI: 0.500–0.838, p = 0.043), respectively. Conclusions: These results demonstrate that the selected nine radiomics features effectively differentiate malignant thyroid nodules. Overall, the radiomics model shows potential as a valuable predictive tool for thyroid cancer in patients with TIs, supporting improved preoperative decision-making. Full article

Hair loss is one of the skin conditions that can affect people’s mental health. Plant raw material extracts are of great interest due to their safety. In this study, we utilize reverse network pharmacology to screen for key targets of the Wnt/β-catenin signaling pathway and the TGFβ/BMP signaling pathway, as well as key differential lipids, for plant raw materials selection. The aim is to identify plant raw materials that may have anti-hair loss properties and to validate these findings through cell experiments. Licorice, salvia miltiorrhiza, mulberry leaf, ephedra and curcumae radix were found that may possess anti-hair loss effects. Licorice water extract (LWE), salvia miltiorrhiza water extract (SMWE), mulberry leaf water extract (MLWE), ephedra water extract (EWE) and curcumae radix water extract (CRWE) did not exhibit cytotoxicity on human dermal papilla cells (HDPCs). Through ALP staining, it was found that the expression of ALP in HDPCs treated with LWE, SMWE, MLWE, EWE and CRWE was enhanced. In addition, LWE, SMWE, MLWE, EWE and CRWE have reduced the expression of hair growth inhibitory factor TGF-β1 and inflammatory factor IL-6. Additionally, various water extracts can enhance the secretion of VEGF, with high concentrations of SMWE, EWE and CRWE exhibiting better efficacy. Furthermore, β-catenin, a key factor of the Wnt/β-catenin signaling pathway, was enhanced by LWE, SMWE, MLWE, EWE and CRWE treatment in cultured HDPCs. In conclusion, all five plant raw materials showed some anti-hair loss potential, providing theoretical support for their application in anti-hair loss products. Full article

Rural domestic sewage management is a crucial pathway for achieving Sustainable Development Goal (SDG) 6 targets. Addressing the crucial challenge of prioritizing administrative villages for rural domestic sewage treatment at the county scale requires dedicated planning. However, county-level comprehensive evaluation models designed specifically for this purpose are currently limited. To address this gap, we developed a model based on 13 evaluation indicators encompassing village distribution characteristics, villager demographics, rural economic levels, and sanitation facility conditions. To gauge the varying emphasis on these factors by different groups, a questionnaire survey was conducted among experts, enterprises, and government departments involved in the rural sewage sector in China. Two counties from distinct regions were then chosen to validate these models. The Analytic Hierarchy Process (AHP) coupled with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to rank the importance of the factors and determine the prioritization of rural domestic sewage management in each area. The model results indicated that priority should be given to the county government, township government, ecologically sensitive areas, and administrative villages near tourist attractions in the two selected empirical counties for governance. A sensitivity analysis showed that altitude consistently exhibited high sensitivity in influencing the ranking results across all scenarios (0.4–0.6). In addition, the empirical results obtained were largely consistent with the priorities of local governments. The proposed framework offers a practical application for decision-making systems in rural domestic sewage management at the county level, providing theoretical support and scientific strategies. This holds great significance for achieving SDG 6. Full article