Search Results (59,458)

Global optimization problems, prevalent across scientific and engineering disciplines, necessitate efficient algorithms for navigating complex, high-dimensional search spaces. Drawing inspiration from the resilient and adaptive growth strategies of moss colonies, the moss growth optimization (MGO) algorithm presents a promising biomimetic approach to these challenges. However, the original MGO can experience premature convergence and limited exploration capabilities. This paper introduces an enhanced bio-inspired algorithm, termed crisscross moss growth optimization (CCMGO), which incorporates a crisscross (CC) strategy and a dynamic grouping parameter, further emulating the biological mechanisms of spore dispersal and resource allocation in moss. By mimicking the interwoven growth patterns of moss, the CC strategy facilitates improved information exchange among population members, thereby enhancing offspring diversity and accelerating convergence. The dynamic grouping parameter, analogous to the adaptive resource allocation strategies of moss in response to environmental changes, balances exploration and exploitation for a more efficient search. Key findings from rigorous experimental evaluations using the CEC2017 benchmark suite demonstrate that CCMGO consistently outperforms nine established metaheuristic algorithms across diverse benchmark functions. Furthermore, in a real-world application to a three-channel reservoir production optimization problem, CCMGO achieves a significantly higher net present value (NPV) compared to benchmark algorithms. This successful application highlights CCMGO’s potential as a robust and adaptable tool for addressing complex, real-world optimization challenges, particularly those found in resource management and other nature-inspired domains. Full article

Uncontrolled, severe asthma remains a significant clinical challenge, affecting a small proportion of asthma patients worldwide. Despite advancements in treatment options, a subset of patients continues to experience frequent exacerbations, uncontrolled symptoms, and impaired quality of life. The advent of biological therapies has revolutionized the management of severe asthma, offering targeted treatments that address specific inflammatory pathways. This review provides a comprehensive overview of the efficacy and response criteria of biological treatments in severe asthma, focusing on clinical, functional, and inflammatory markers used to help in the evaluation of the biologic treatment. Key response criteria include symptom control, reduction in exacerbations, improvement in lung function, and a reduction in or the discontinuation of oral corticosteroids. Biomarkers such as blood eosinophils and exhaled nitric oxide (FeNO) are essential tools in guiding treatment adjustments. Real-world studies underscore the importance of personalized treatment strategies, as variability in response to biological therapies can be significant. The emergence of tools such as the FEOS score and EXACTO questionnaire offer quantitative measures for assessing biological response and guiding clinical decisions. Additionally, predictive factors for better or poorer responses, such as pre-treatment lung function and comorbidities, like obesity and rhinosinusitis, are critical in patient selection. This review highlights the need for ongoing reassessments and potential modifications of therapy in cases of suboptimal response. Practical considerations for switching biological therapies are discussed, emphasizing the importance of tailoring treatments to individual patient profiles and disease phenotypes. With the continued development of personalized medicine, the outlook for patients with severe asthma is improving, selecting specific biomarkers to improve the selection of the biologic treatment. Full article

In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregation operation model is designed to aggregate dispersed demand-side resources into a coordinated aggregated response entity through an aggregator. Second, the demand-side resource aggregation evaluation indexes are established from three dimensions of response capacity, response reliability, and response flexibility, and the multi-objective aggregation optimization model of demand-side resources is constructed with the objective function of the larger potential market revenue and the smallest risk of deviation penalty. Finally, robust optimization theory is adopted to cope with the uncertainty of demand-side resource responsiveness, the robust transaction decision model of demand-side resource aggregator is constructed, and a community in Henan Province is selected for simulation analysis to verify the validity and applicability of the proposed model. The findings reveal that the proposed cluster aggregation optimization method reduces the bias penalty risk of the demand-side resource aggregators by about 33.12%, improves the comprehensive optimization objective by about 18.10%, and realizes the optimal aggregation of demand-side resources that takes into account both economy and risk. Moreover, the robust trading decision model can increase the expected net revenue by about 3.1% under the ‘worst’ scenario of fluctuating uncertainties, which enhances the resilience of demand-side resource aggregators to risks and effectively fosters the involvement of demand-side resources in the electricity market dynamics. Full article

In protected agriculture, accurately identifying the key growth stages of tomatoes plays a significant role in achieving efficient management and high-precision production. However, traditional approaches often face challenges like non-standardized data collection, unbalanced datasets, low recognition efficiency, and limited accuracy. This paper proposes an innovative solution combining generative adversarial networks (GANs) and deep learning techniques to address these challenges. Specifically, the StyleGAN3 model is employed to generate high-quality images of tomato growth stages, effectively augmenting the original dataset with a broader range of images. This augmented dataset is then processed using a Vision Transformer (ViT) model for intelligent recognition of tomato growth stages within a protected agricultural environment. The proposed method was tested on 2723 images, demonstrating that the generated images are nearly indistinguishable from real images. The combined training approach incorporating both generated and original images produced superior recognition results compared to training with only the original images. The validation set achieved an accuracy of 99.6%, while the test set achieved 98.39%, marking improvements of 22.85%, 3.57%, and 3.21% over AlexNet, DenseNet50, and VGG16, respectively. The average detection speed was 9.5 ms. This method provides a highly effective means of identifying tomato growth stages in protected environments and offers valuable insights for improving the efficiency and quality of protected crop production. Full article

Lin28b and let-7 miRNA regulate mammalian pubertal initiation and Gonadotropin-releasing hormone (GnRH) production. However, it remains unclear which signaling pathways Lin28b regulates to modulate GnRH production. In this study, the mRNA expression levels of Lin28b and let-7 in the pubertal and juvenile goat hypothalamus and pituitary gland were detected, and Lin28b expression in the pubertal hypothalamus decreased significantly compared with that in juvenile tissues. It was predicted that Lin28b might inhibit GnRH1 expression, which was verified in the GnRH-producing cell model GT1-7 cells. Lin28b inhibited GnRH1 expression and promoted Kiss1/Gpr54 signaling. The pyruvate content and the expression of Hif1a and Hk2, which were related to glycolysis, were also promoted by Lin28b overexpression. Additionally, 77 differentially expressed miRNAs (DEMIs) in Lin28b-overexpressed GT1-7 cells were identified. Bioinformatics analysis and mRNA expression of the target genes of DEMIs revealed that the MAPK and PI3K-AKT-mTOR signaling pathways were key pathways that involved the regulatory effect of Lin28b on GnRH. In GT1-7 cells, GnRH1 expression was suppressed by blocking mTOR signaling with rapamycin, which was rescued by Lin28b overexpression. These results indicate that Lin28b-let-7 regulates GnRH1 expression through several pathways, including the Kiss1/Gpr54, MAPK, and mTOR signaling pathways, which are all related to glucose metabolism and provide new insights into the molecular mechanism of the regulatory role of Lin28b on GnRH production. Full article

The current process of embryo selection in in vitro fertilization is based on morphological criteria; embryos are manually evaluated by embryologists under subjective assessment. In this study, a deep learning-based pipeline was developed to classify the viability of embryos using combined inputs, including microscopic images of embryos and additional features, such as patient age and developed pseudo-features, including a continuous interpretation of Istanbul grading scores by predicting the embryo stage, inner cell mass, and trophectoderm. For viability prediction, convolution-based transferred learning models were employed, multiple pretrained models were compared, and image preprocessing techniques and hyperparameter optimization via Optuna were utilized. In addition, a custom weight was trained using a self-supervised learning framework known as the Simple Framework for Contrastive Learning of Visual Representations (SimCLR) in cooperation with generated images using generative adversarial networks (GANs). The best model was developed from the EfficientNet-B0 model using preprocessed images combined with pseudo-features generated using separate EfficientNet-B0 models, and optimized by Optuna to tune the hyperparameters of the models. The designed model’s F1 score, accuracy, sensitivity, and area under curve (AUC) were 65.02%, 69.04%, 56.76%, and 66.98%, respectively. This study also showed an advantage in accuracy and a similar AUC when compared with the recent ensemble method. Full article

In dermatological research, accurately identifying different types of skin lesions, such as nodules, is essential for early diagnosis and effective treatment. This study introduces a novel method for classifying skin lesions, including nodules, by combining a unified attention (UA) network with deep convolutional neural networks (DCNNs) for feature extraction. The UA network processes sequential data, such as patient histories, while long short-term memory (LSTM) networks track nodule progression. Additionally, Markov random fields (MRFs) enhance pattern recognition. The integrated system classifies lesions and evaluates whether they are responding to treatment or worsening, achieving 93% accuracy in distinguishing nodules, melanoma, and basal cell carcinoma. This system outperforms existing methods in precision and sensitivity, offering advancements in dermatological diagnostics. Full article

Standard carbon accounting methods and metrics undermine the potential of fast-growing biogenic materials to decarbonize buildings because they ignore the timing of carbon uptake. The consequence is that analyses can indicate that a building material is carbon-neutral when it is not climate-neutral. Here, we investigated the time-dependent effect of using fast-growing fibers in durable construction materials. This study estimated the material stock and flow and associated cradle-to-gate emissions for four residential framing systems in the US: concrete masonry units, light-frame dimensional timber, and two framing systems that incorporate fast-growing fibers (bamboo and Eucalyptus). The carbon flows for these four framing systems were scaled across four adoption scenarios, Business as Usual, Early-Fast, Late-Slow, and Highly Optimistic, ranging from no adoption to the full adoption of fast-growing materials in new construction within 10 years. Dynamic life cycle assessment modeling was used to project the radiative forcing and global temperature change potential. The results show that the adoption of fast-growing biogenic construction materials can significantly reduce the climate impact of new US residential buildings. However, this study also reveals that highly aggressive, immediate adoption is the only way to achieve net climate cooling from residential framing within this century, highlighting the urgent need to change the methods and metrics decision makers use to evaluate building materials. Full article

The amount of irrigation needed can be determined using reference evapotranspiration (ETo), the crop coefficient (Kc), and the water deficit index. Reference evapotranspiration is typically calculated utilizing the Penman–Monteith (PM) model, which necessitates various meteorological parameters, including temperature, humidity, net radiation, and wind speed. In regions where meteorological stations are absent, alternative methods must be employed to estimate these parameters. This study employs a combination of estimated meteorological parameters derived from different methodologies to calculate both reference evapotranspiration and irrigation rates, subsequently evaluating the results through wheat irrigation experiments. The daily irrigation rate for the T1 treatment was computed using real-time meteorological data, resulting in the highest grain yield of 561.73 g/m² and an irrigation water use efficiency of 7.61 kg/m³. The irrigation rate for the T2 treatment was determined based on real-time net radiation alongside monthly average values of temperature, humidity, and wind speed. In comparison to T1, the irrigation amount, yield, and irrigation water use efficiency for T2 decreased by 1.59%, 2.96%, and 1.42%, respectively. For the T3 treatment, the irrigation amount was calculated using monthly average values of temperature, humidity, and wind speed, with net radiation derived from daily light duration. The yield for T3 decreased by 19.4% relative to T1, the irrigation amount decreased by 12.95% relative to T1, and the irrigation water use efficiency decreased by 7.45% relative to T1. In the case of the T4 treatment, monthly average values of temperature, humidity, and wind speed were utilized, while net radiation was calculated using the Hargreaves–Samani (HS) model in conjunction with real-time temperature data. The yield for T4 decreased by 8.75% relative to T1, the irrigation amount decreased by 5.58% relative to T1, and the irrigation water use efficiency decreased by 3.39% relative to T1. For the T5 treatment, similar monthly average values were employed, and net radiation was calculated using HS methodology combined with monthly average temperature data. The yield for T5 decreased by 11.96% relative to T1, the irrigation amount decreased by 6.07% relative to T1, and the irrigation water use efficiency decreased by 6.3% relative to T1. Furthermore, the yield for the CK treatment under conventional irrigation decreased by 20.89% compared to T1, while the irrigation amount increased by 1.57% compared to T1 and the irrigation water use coefficient decreased by 22.14% compared to T1. Above all, this article posits that in areas lacking meteorological stations, monthly mean meteorological data should be utilized for parameters such as temperature, humidity, and wind speed, while the HS model is recommended for calculating net radiation. Full article

The accurate prediction of the spatial distribution of soil organic carbon (SOC) and the identification of the mechanisms underlying its spatial differentiation are of paramount significance for the conservation and utilization of land and regional sustainable development. A total of 512 soil samples were collected from Wuchang and Shuangcheng County in Harbin City, Heilongjiang Province, China, which served as the study area. Six machine learning models, including Random Forest (RF), AdaBoost, Support Vector Regression (SVR), weighted average, Stacking, and Blending, were utilized to predict the spatial distribution of SOC and analyze its spatial differentiation. The result reveals that 12 environmental variables, including soil type, bulk density, pH, average annual precipitation, average annual temperature, net primary productivity (NPP), land use type, normalized difference vegetation index (NDVI), slope, elevation, soil parent material, and distance to rivers, are effective influencing factors on SOC in the study area. It turns out that the Stacking model, with an R² of 0.4327, performed the best in this study, followed by the weighted average, Blending, RF, AdaBoost, and SVR models; a heterogeneous integrated learning model may be more robust than an individual learner. The predicted SOC content is generally lower in the northwestern arable land and higher in the southeastern forest land. In addition, SOC differentiation shows that forest land and grass land with dark brown soil or swamp soil, soil covering igneous and metamorphic rocks with various minerals, higher elevation and slope, and suitable water-thermal and soil intrinsic conditions for aerobic microbial activity benefit the enrichment of SOC in the study area. The enrichment and depletion of SOC are jointly influenced by pedogenesis, microbial activity, and biodiversity. Full article

Generative adversarial networks (GANs) have demonstrated remarkable performance in various fashion-related applications, including virtual try-ons, compatible clothing recommendations, fashion editing, and the generation of fashion items. Despite this progress, limited research has addressed the specific challenge of generating a compatible fashion item with an ensemble consisting of distinct categories, such as tops, bottoms, and shoes. In response to this gap, we propose a novel GANs framework, termed Deep Fashion Designer Generative Adversarial Networks (DFDGAN), designed to address this challenge. Our model accepts a series of source images representing different fashion categories as inputs and generates a compatible fashion item, potentially from a different category. The architecture of our model comprises several key components: an encoder, a mapping network, a generator, and a discriminator. Through rigorous experimentation, we benchmark our model against existing baselines, validating the effectiveness of each architectural choice. Furthermore, qualitative results indicate that our framework successfully generates fashion items compatible with the input items, thereby advancing the field of fashion item generation. Full article

Alpine meadows are vital ecosystems on the Qinghai–Tibet Plateau, significantly contributing to water conservation and climate regulation. This study examines the energy flux patterns and their driving factors in the alpine meadows of the Qilian Mountains, focusing on how the meteorological variables of net radiation (R_n), air temperature, vapor pressure deficit (VPD), wind speed (U), and soil water content (SWC) influence sensible heat flux (H) and latent heat flux (LE). Using the Bowen ratio energy balance method, we monitored energy changes during the growing and non-growing seasons from 2022 to 2023. The annual average daily R_n was 85.29 W m⁻², with H, LE, and G accounting for 0.56, 0.71, and -0.32 of R_n, respectively. Results show that R_n is the main driver of both H and LE, highlighting its crucial role in turbulent flux variations. Additionally, a negative correlation was found between air temperature and H, suggesting that high temperatures may suppress H. A significant positive correlation was observed between soil moisture and LE, further indicating that moist soil conditions enhance LE. In conclusion, this study demonstrates the impact of climate change on energy distribution in alpine meadows and calls for further research on the ecosystem’s dynamic responses to changing climate conditions. Full article

Introduction: This study assessed the therapeutic effectiveness of a single-pill combination (SPC) of olmesartan/amlodipine plus rosuvastatin for blood pressure (BP) and low-density lipoprotein cholesterol (LDL-C) in patients with hypertension and dyslipidemia. Methods: Adult patients with hypertension and dyslipidemia who were decided to be treated with the study drug were eligible. The primary endpoint was the proportion of patients who achieved BP, LDL-C and both BP and LDL-C treatment goals at weeks 24–48. Secondary endpoints were assessed at weeks 24–48 and included changes in BP and LDL-C levels from baseline; the proportion of patients who achieved treatment goals who were initially classified as uncontrolled at baseline; changes and percent changes in lipid parameters; changes in both BP and LDL-C levels among patients who reached treatment goals who were followed for more than 24 weeks; and the overall safety profile. Results: A total of 5476 patients were enrolled, and 4411 patients comprised the effectiveness evaluation set. The proportions of patients who reached the treatment goals for BP, LDL-C levels, and both BP and LDL-C levels were 67.93% [95% confidence interval (CI) 66.52–69.32], 80.19% [95% CI 78.85–81.49], and 58.07% [95% CI 56.43–59.7], respectively. Secondary endpoints showed statistically significant changes. Overall, the treatment was well tolerated. Conclusions: The treatment of patients with hypertension and dyslipidemia with the olmesartan/amlodipine plus rosuvastatin SPC was associated with significant decreases in SBP/DBP and LDL-C levels, and a high proportion of patients achieved the BP and LDL-C treatment goals. The finding of this study is worthwhile in that this study evaluated the effectiveness and safety in a broad patient population representative of those seen in everyday clinical practice. Full article

Gross primary productivity (GPP), the primary driver of carbon accumulation, governs the sequestration of atmospheric CO₂ into biomass. However, GPP cannot be measured directly, as photosynthesis and respiration are simultaneous. At canopy level in plot-scale studies, GPP can be estimated through the closed chamber-based measurements of net ecosystem exchange (NEE) and ecosystem respiration (Reco). This technique is cost-effective and widely used in small-scale studies with short vegetation, but measurements are periodic-based and require temporal interpolations. The rectangular hyperbolic model (RH) was the basis of this study, developing two temperature-dependent factors following a linear and exponential shift in GPP when the temperature oscillates from the optimum for vegetation performance. Additionally, a water table depth (WTD)-dependent model and an exponential model were tested. In the peak season, modified RH models showed the best performance, while for the rest of the year, the best model varied for each subplot. The statistical results demonstrate the limitations of assuming the light-use efficiency as a fixed shape mechanism (using only one model). Therefore, a multi-model approach with the best performance model selected for each period is proposed to improve GPP estimations for peatlands. Full article

Urban happiness prediction presents a complex challenge, due to the nonlinear and multifaceted relationships among socio-economic, environmental, and infrastructural factors. This study introduces an advanced hybrid model combining a gradient boosting machine (GBM) and neural network (NN) to address these complexities. Unlike traditional approaches, this hybrid leverages a GBM to handle structured data features and an NN to extract deeper nonlinear relationships. The model was evaluated against various baseline machine learning and deep learning models, including a random forest, CNN, LSTM, CatBoost, and TabNet, using metrics such as RMSE, MAE, R², and MAPE. The GBM + NN hybrid achieved superior performance, with the lowest RMSE of 0.3332, an R² of 0.9673, and an MAPE of 7.0082%. The model also revealed significant insights into urban indicators, such as a 10% improvement in air quality correlating to a 5% increase in happiness. These findings underscore the potential of hybrid models in urban analytics, offering both predictive accuracy and actionable insights for urban planners. Full article