Search Results (175)

The demand for concrete has led to increased use of raw materials and significant waste generation. Recycled aggregate concrete (RAC) offers a viable approach to sustainable concrete; however, the use of weakly bonded mortar on aggregate leads to low strength and crack formation. Fiber reinforcement, specifically hybrid fiber reinforcement combining steel, glass, basalt, and polypropylene fibers, can increase the tensile and flexural properties of RAC. This study developed machine learning models to enable the prediction of hybrid fiber-reinforced RAC’s compressive, splitting tensile, and flexural strength performance; these new models overcome the limitations of previous research, which relied on only one fiber type and regular methods of optimization. Two models (a deep neural network (DNN) and an XGBoost model) were trained and optimized using bald eagle search (BES), particle swarm optimization (PSO), and the Bayesian optimization (BO) algorithm to improve performance. Among the three optimization analyses, PSO-XGBoost achieved the highest accuracy for compressive strength and splitting tensile strength, while BES-XGBoost achieved the highest accuracy for flexural strength. The most significant influences on the compressive strength were curing age and silica fume, while the main drivers of splitting tensile strength and flexural strength were fiber volume and fiber characteristics. The use of SHAP-based methodology with a user-friendly interface further improved the design of RAC mixtures, reducing waste from raw materials, enhancing the structural performance of RAC, and enabling data-driven decision-making in the manufacturing of eco-friendly concrete products. Full article

With the expansion of power grids, the increasing peak-valley load difference threatens grid security, a challenge addressed by time-of-use pricing which incentivizes load shifting. Since central air-conditioning systems account for over 60% of building energy use, optimizing them for efficiency and cost under time-of-use pricing is crucial. This study presents an integrated optimization framework that coordinates photovoltaic generation, battery storage, and grid power. The approach develops a BES-LSTM forecasting model by using the Bald Eagle Search (BES) algorithm to tune Long Short-Term Memory (LSTM) network parameters for accurate cooling-load prediction. A central air-conditioning water-system energy-minimization model is then formulated and solved with an improved BES algorithm that incorporates adaptive opposition-based learning, logistic chaotic mapping, and Lévy flight. Finally, a daily schedule is optimized by partitioning time according to time-of-use price intervals and treating generation output, battery charge/discharge, and grid draw as decision variables. Simulations demonstrate that the framework reduces the central air-conditioning water system’s total energy consumption by an average of 28.7% and lowers energy costs under time-of-use pricing by 22.38%, achieving both significant energy savings and economic benefits. Full article

Male pattern baldness (MPB) is a highly prevalent condition with a complex, poorly understood molecular basis that limits therapeutic innovation. This study aimed to bridge the gap between statistical genetic associations and biological function by identifying and prioritizing causal proteins and pathways involved in MPB. Using data from 24,069 men in the UK Biobank, we performed a proteome-wide association study of 2911 plasma proteins with self-reported MPB severity via multivariable ordinal logistic regression, adjusting for age, Body Mass Index (BMI), ethnicity, lifestyle, socioeconomic factors, and testosterone levels. Significant proteins underwent pathway enrichment analysis. Genetic integration included MAGMA for gene-level aggregation and tissue prioritization, transcriptome-wide association studies (TWAS) with GTEx models, conditional fine-mapping, and validation in an independent scalp biopsy transcriptomics dataset (GSE90594). Druggability and pleiotropy were evaluated using databases and phenome-wide association studies. Forty-seven proteins were significantly associated with MPB severity, enriched in pathways involving epidermis development, hair cycle regulation, and cell adhesion. Multi-omic integration prioritized five independent candidate genes: CD38, FGF5, TACSTD2, DPEP1, and PLB1. Transcriptomic validation confirmed differential expression in balding scalp for CD38 (upregulated) and TACSTD2, PLB1 (downregulated). CD38 was identified as druggable with low pleiotropic risks. This study elucidates the molecular architecture of MPB, revealing novel biological pathways beyond canonical androgen signaling. By prioritizing promising non-hormonal targets like CD38, our findings provide a robust, evidence-based framework to guide the development of future therapeutic interventions for this common condition. Full article

Androgen receptor (AR) signaling plays a key role in male pattern baldness. We investigated whether targeting Dickkopf 1 (DKK1) and Secreted frizzled-related protein 1 (SFRP1), two AR-regulated genes, offers a novel therapeutic strategy for hair loss. AR expression was validated in freshly frozen human scalp hair follicles (HFs). AR knockdown was induced in human HFs using AR spherical nucleic acid (SNA). DKK1 and SFRP1 siRNA treatment were performed in HEK293 cells, human dermal papilla cells (hDPC), and human HFs ex vivo. Functional effects of single and combined DKK1 and SFRP1 knockdown were analyzed in human HFs ex vivo by quantitative (immuno)histomorphology. AR knockdown decreased SFRP1 and DKK1 expression. We found reciprocal mRNA upregulation between DKK1 and SFRP1 following their siRNA knockdown in HEK293 and hDPC. We therefore applied a single and combined treatment of DKK1 and SFRP1 siRNA in HFs ex vivo. SFRP1 knockdown prolonged anagen, increased hair matrix keratinocyte proliferation, reduced apoptosis, and increased DKK1 levels in HFs ex vivo, whereas DKK1 knockdown had no effect, and combined knockdown did not enhance SFRP1’s benefits. The culture-dependent compensatory regulation of SFRP1 and DKK1 underscores Wnt-signaling complexity in hair growth and strengthens the rationale for SFRP1 based therapies in anagen maintenance and hair loss. Full article

Coastal waters worldwide are increasingly threatened by excessive nutrient inputs, a key driver of eutrophication. Dissolved inorganic nitrogen (DIN) serves as a vital indicator for assessing the eutrophic status of nearshore marine environments, underscoring the necessity for precise monitoring to ensure effective protection and restoration of marine ecosystems. To address the current limitations in DIN retrieval methods, this study builds on MODIS satellite imagery data and introduces a novel one-dimensional convolutional neural network (1D-CNN) model synergistically co-optimized by the Bald Eagle Search (BES) and Bayesian Optimization (BO) algorithms. The proposed BES-BO-CNN framework was applied to the retrieval of DIN concentrations in the coastal waters of Shandong Province from 2015 to 2024. Based on the retrieval results, we further investigated the spatiotemporal evolution patterns and dominant environmental drivers. The findings demonstrated that (1) the BES-BO-CNN model substantially outperforms conventional approaches, with the coefficient of determination (R²) reaching 0.81; (2) the ten-year reconstruction reveals distinct land–sea gradient patterns and seasonal variations in DIN concentrations, with the Yellow River Estuary persistently exhibiting elevated levels due to terrestrial inputs; (3) correlation analysis indicated that DIN is significantly negatively correlated with sea surface temperature but positively correlated with sea level pressure. In summary, the proposed BES-BO-CNN framework, via the synergistic optimization of multiple algorithms, enables high-precision DIN monitoring, thus providing scientific support for integrated land–sea management and targeted control of nitrogen pollution in coastal waters. Full article

During rotary tillage operations, the draft force is jointly affected by operating parameters and soil conditions, exhibiting pronounced nonlinearity, time-varying behavior, and historical dependence, which all impose higher requirements on tractor operating parameter matching and traction performance analysis. A draft force prediction method that is based on a long short-term memory (LSTM) neural network jointly optimized by a genetic algorithm (GA) and the bald eagle search (BES) algorithm, termed GABES-LSTM, is proposed to address the limited prediction accuracy and stability of traditional empirical models and single data-driven approaches under complex field conditions. First, on the basis of the mechanical characteristics of rotary tillage operations, a time-series mathematical description of draft force is established, and the prediction problem is formulated as a multi-input single-output nonlinear temporal mapping driven by operating parameters such as travel speed, rotary speed, and tillage depth. Subsequently, an LSTM-based draft force prediction model is constructed, in which GA is employed for global hyperparameter search and BES is integrated for local fine-grained optimization, thereby improving the effectiveness of model parameter optimization. Finally, a dataset is established using measured field rotary tillage data to train and test the proposed model, and comparative analyses are conducted against LSTM, GA-LSTM, and BES-LSTM models. Experimental results indicate that the GABES-LSTM model outperforms the comparison models in terms of mean absolute percentage error, mean relative error, relative analysis error, and coefficient of determination, effectively capturing the dynamic variation characteristics of draft force during rotary tillage operations while maintaining stable prediction performance under repeated experimental conditions. This method provides effective data support for draft force prediction analysis and operating parameter adjustment during rotary tillage operations. Full article

Urban tree planting is widely promoted for its benefits, but the long-term condition of trees is poorly documented, especially as changing and often incompatible conditions, intensified by climate change, affect their ability to deliver those benefits. A case study in Topčider Park (since 1836) was conducted during 2025 through the evaluation of diversity, growth parameters, ornamental value, vitality, and total fresh biomass and the identification of tree taxa with high carbon-sequestration potential in Belgrade (Serbia). The data were statistically processed using descriptive statistics, the Shannon diversity and the Pielou evenness index, PCA, Spearman rank and Chi-square tests. The results indicated a wide distribution and high homogeneity of taxa, greater stability within Angiospermae and moderate stability within Gymnospermae, with PCA showing no correlations between growth parameters, vitality, and ornamental value, confirming the close proximity of all taxa. At the taxon level, London plane, English oak, Ginkgo and Bald cypress stood out in growth parameters, while the assessment of total fresh biomass for all 51 taxa highlighted London plane, Scots pine and Bald cypress as particularly productive and adaptive. Carbon sequestration and CO₂ reduction varied with total fresh biomass. The study offers evidence-based recommendations for selecting urban tree taxa to enhance ecosystem services and support climate-adaptation efforts in urban planning. Full article

Keeping the levels of soil major nutrients (total nitrogen, TN; available phosphorous, AP; and available potassium, AK) in optimum condition is important to achieve the goals of precision agriculture systems. To address the issues of slow speed and low accuracy in soil nutrient detection, this study developed a prediction model for soil major nutrients content based on an improved Extreme Learning Machine (ELM) algorithm. This model utilizes a soil major nutrients detection system integrating pyrolysis and artificial olfaction. First, the Bootstrap Aggregating (Bagging) ensemble strategy was introduced during the model integration phase to effectively reduce prediction variance through multi-submodel fusion. Second, Generative Adversarial Networks (GAN) were employed for sample augmentation, enhancing the diversity and representativeness of the dataset. Subsequently, a multi-scale convolutional and Efficient Lightweight Attention Network (ELA-Net) was embedded in the feature mapping layer to strengthen the representation capability of soil gas features. Finally, adaptive hyperparameter tuning was achieved using the Adaptive Chaotic Bald Eagle Optimization Algorithm (ACBOA) to enhance the model’s generalization capability. Results demonstrate that this model achieves varying degrees of performance improvement in predicting total nitrogen (R² = 0.894), available phosphorus (R² = 0.728), and available potassium (R² = 0.706). Overall prediction accuracy surpasses traditional models by 8–12%, with significant reductions in both RMSE and MAE. These results demonstrate that the method can rapidly, accurately, and non-destructively estimate key soil nutrients, providing theoretical guidance and practical support for field fertilization, soil fertility assessment, and on-site decision-making in precision agriculture. Full article

Background: Regenerative therapies have emerged in recent years. In particular, their utility in managing androgenetic alopecia—the most prevalent hair loss condition worldwide, affecting up to half of adults—is an active area of research. Navigating this space can be challenging for physicians due to widespread commercialization, lack of high-quality evidence, and an evolving regulatory landscape. Objective: To critically review recently published evidence (2020–2025) on platelet-rich plasma, photobiomodulation, stem cells, and exosomes for the treatment of androgenetic alopecia. Methods: A scoping review was conducted using PubMed, Embase (Ovid) and the Cochrane Controlled Register of Trials in February and November of 2025. Combination therapies were excluded. Results and Conclusions: Platelet-rich plasma is the most studied modality, with emerging investigations into newer formulations such as leukocyte-rich and pure platelet-rich plasma. However, recent studies are limited by inconsistent reporting of cellular composition, short follow-up durations, and a lack of comparative data on treatment protocols. The efficacy of photobiomodulation as a monotherapy remains debated, with inconsistent reporting of device parameters. Stem cells and exosomes show promising, though still limited, clinical evidence in inducing hair regrowth. Standardization of these therapies is crucial, with emphasis on transparency, reproducibility, and patient safety. Full article

Background/Objectives: Forehead lipomas are benign but cosmetically conspicuous. Direct transcutaneous incision allows easy removal but often leaves visible scars. Hairline approaches conceal scars but are unsuitable for bald or high-hairline patients and lower forehead lipomas. We evaluated a peri-eyebrow approach using a superior brow-margin incision versus conventional methods. Methods: We retrospectively reviewed 176 patients who underwent forehead mass excision between 2008 and 2025. After exclusions, 97 biopsy-proven lipomas were analyzed (peri-eyebrow 22; hairline 38; direct 37). Variables included lipoma location, vertical ratio (lipoma position between the hairline and eyebrow), lipoma size, and incision length. Logistic regression assessed the relationship between vertical ratio and peri-eyebrow incision selection. Multivariate logistic regression identified independent predictors, and bootstrap validation (1000 iterations) assessed the internal stability of the ROC cut-off. Scar quality was evaluated using the Patient and Observer Scar Assessment Scales. Results: Peri-eyebrow incisions were used only for midline or median lipomas. The vertical ratio was highest in the peri-eyebrow group (0.70 ± 0.10) and independently predicted incision choice (adjusted OR per 0.1-unit increase 3.32, p < 0.001). ROC analysis showed excellent discrimination (AUC 0.872), and the bootstrapped AUC (0.802, 95% CI 0.727–0.878) confirmed robust internal validity. The optimal 0.615 cut-off yielded 0.82 sensitivity and 0.79 specificity. Peri-eyebrow and hairline incisions achieved significantly better scar scores than direct incisions (p < 0.001). Conclusions: The peri-eyebrow incision is a safe and cosmetically effective alternative when hairline incisions are unsuitable. It offers concealed scars and outcomes comparable to hairline incisions. Full article

Numerous ingredients in trichological shampoos are advertised as “active against hair loss”; however, the body of evidence behind such claims seems very limited or, in many cases, nonexistent. The aim of this study was to compile an inventory of substances advertised by shampoo manufacturers as “active” against hair loss and systematically review available evidence from clinical trials that would corroborate such claims. We screened declared compositions of trichological shampoos for ingredients advertised as active against hair loss or promoting hair growth. The second step was a systematic review of clinical trials of these substances used topically in the treatment of hair loss. A query in PubMed, Scopus, and Web of Science followed PRISMA and PICO guidelines with the strength of evidence assessed according to GRADE guidelines. We identified 43 trichological shampoos in which 112 individual ingredients were advertised as “active”. Of these, 36 ingredients were indicated as “active” in at least two shampoos and were subject to further study. In the search for evidence, 103,639 articles were screened for relevant information. Ultimately, we identified 29 clinical trials that tested 16 of the 36 ingredients for efficacy against hair loss. Only four ingredients were tested individually: adenosine (four trials; highest strength of evidence: moderate), caffeine (four trials; moderate), placental protein (two trials; low), and melatonin (one trial; moderate). Another 12 ingredients of interest were only tested as parts of complex preparations: Achillea millefolium extract, arginine, biotin, hydrolyzed wheat protein, hydrolyzed soy protein, Panax ginseng, panthenol, piroctone olamine, Prunus amygdalus dulcis, Rosmarinus officinalis, Serenoa serrulata, and Urtica dioica. Such a study design made it impossible to attribute the observed effects to any specific ingredient. No clinical trials of efficacy could be found for the remaining 20 (55.6%) substances repeatedly cited as “active”. At the present stage, scientific evidence for efficacy against hair loss is available only for caffeine, adenosine, placental proteins, and melatonin, but the overall strength of evidence is low. Moreover, a substantial majority of topical ingredients promoted as “active against hair loss” were never actually tested in clinical trials to verify such claims. While unsubstantiated claims of supposed beneficial properties often refer to alleged scientific evidence, there are major gaps to be filled in the field of non-prescription treatments for hair loss. Full article

Background: Androgenetic alopecia, also known as male pattern baldness (MPB), is a common hair loss disorder among middle-aged men. MPB shares similar risk factors with prostate cancer (PrCa), including advancing age, family history, and sex hormones. Several studies have examined the associations between MPB and PrCa; however, the evidence remains unclear. We carried out an updated meta-analysis of epidemiological studies that examined the relationship between age at onset and patterns of MPB (either frontal, vertex, or both) and their associations with risks of total and aggressive PrCa. Methods: A literature search was performed using PubMed and Web of Science databases for epidemiological studies published between 1 January 2000 and 31 December 2024 that examined the associations between MPB and PrCa. From each eligible study, relevant data were extracted on study design and population, sample size, prevalence of MPB at various ages, and their association with PrCa. Pooled relative risks (RR) and corresponding 95% confidence intervals (CI) were calculated using the Der-Simonian and Laird random-effects models. Heterogeneity across studies was assessed by I² statistics, while the quality of studies was evaluated using the Newcastle–Ottawa Scale. Results: A total of 19 observational studies, including 17,810 cases and 146,806 controls/non-cases, were analyzed. The prevalence of MPB increased from 5% to 65% with aging and varied across the studies. Both frontal and vertex MPB were associated with a pooled RR of 1.08 (95% CI 1.02–1.14) for total PrCa, but there was no association with frontal-only MPB. Younger-onset MPB (<40 years old) was also associated with an RR = 1.13 (95% CI 0.96–1.31) for PrCa, although results were not statistically significant. Vertex-only MPB was associated with more aggressive PrCa (pooled RR = 1.14; 95% CI 1.02–1.25); however, there was substantial heterogeneity in the definition of aggressive PrCa across the studies. Conclusions: Men with both frontal and vertex MPB have a modestly elevated risk of PrCa. However, most studies were conducted in Caucasian men and they did not evaluate effect modifications by genetic variations in androgen metabolism pathway genes or changes in serum levels of androgens with aging. Large prospective cohort studies with more accurate longitudinal assessment of hair loss patterns are needed to better understand the complex relationship between genetic susceptibility, endogenous hormones, MPB, and subsequent risk of PrCa. Full article

Androgenetic alopecia (AGA) is the most common type of baldness, characterized by progressive miniaturization of the hair follicle and eventually atrophy. Both genetic and androgenic factors play definite roles in the pathophysiology of the disease, including androgens and growth factors, which induce a crosstalk between the dermal papilla and the hair follicle cells. The goal of AGA treatments is to prevent the hair miniaturization process; however, currently there are only two FDA-approved medications to treat AGA: topical Minoxidil (5% and 2%) for men and women, and oral Finasteride (1 mg tablets—Proscar and Propecia) for men. Nevertheless, these are costly, require lifelong treatment, and may have side effects. Thus, there have been many attempts to develop drugs that can harness the mechanisms controlling the pathogenesis of AGA. These pharmacological therapies might achieve more targeted and effective treatment for the disease. In this review, we present various treatments that have demonstrated their ability to induce hair growth by controlling the pathophysiological mechanisms involved in the development of AGA. Interestingly, treatment by a combination of some drugs has resulted in better outcomes than each of the drugs alone, hence demonstrating the advantage of activating different molecular mechanisms simultaneously. Full article

Clean energy applications widely recognize Proton Exchange Membrane Fuel Cells (PEMFCs) for their high efficiency and environmental compatibility. Accurate parameter identification of PEMFC models is essential for enhancing system performance and reliability, particularly under dynamic operating conditions. This paper presents a novel optimization-based approach called Heterogeneous Comprehensive Learning-Bald Eagle Search (HCLBES) with enhanced exploration and exploitation capabilities for the effective modeling of PEMFC. The algorithm combines the exploration strength of the Bald Eagle Search with comprehensive learning and heterogeneity mechanisms to achieve a balanced global and local search space. In this algorithm, the number of agents is divided into two subagents. Each subagent is assigned to focus solely on either exploration or exploitation. The comprehensive learning strategy generates exemplars for both subgroups. In the exploration sub-agent, exemplars are generated using the personal best experiences of agents within that same exploration space. The exploitation subagent generates the exemplars using the personal best experiences of all agents. This separation preserves exploration diversity even if exploitation converges prematurely. The algorithm is applied to optimize parameters of the 250 W and 500 W PEMFC models under varying conditions. Simulation results demonstrate the outperformance of the HCLBES algorithm in terms of convergence speed, estimation accuracy, and robustness compared to recent optimization algorithms. The effectiveness of HCLBES was also verified through statistical metrics and different commercial PEMFC models, including BCS 500 W stacks, Horizon 500, and NedStack PS6. Experimental validation confirms that the proposed algorithm effectively captures the nonlinear behaviours of PEMFCs under dynamic operating conditions. This research aligns with the Sustainable Development Goals (SDGs) by promoting clean and affordable energy (SDG 7) through the enhanced efficiency and reliability of PEMFCs, thereby supporting sustainable industrialization and innovation (SDG 9). Full article

Dents are a common geometric deformation defect in pipelines where the dented section becomes susceptible to local buckling, significantly threatening the integrity and reliability of the pipeline. This paper developed a novel finite element (FE) machine learning (ML)-based approach to analyze and predict the critical buckling moment (CBM) of dented pipelines under combined internal pressure and bending moment (BM) loading. By quantifying the parametric effects on CBM and developing a dataset, an Extreme Learning Machine (ELM) framework through hybrid algorithm integration, combining Bald Eagle Search (BES), Lévy flight, and Simulated Annealing (SA), was proposed to achieve highly accurate CBM predictions. This study offers valuable insights into evaluating the buckling resistance of dented pipelines subjected to complex loading conditions. Full article