Search Results (1,515)

This study, conducted in Mexico, evaluates the agricultural potential of three compost formulations BFS1, BFS2, and BFS3 produced from mining tailings and thermophilic microbial mats and collected from geothermal environments. The physicochemical characterization included pH, electrical conductivity (EC), macronutrients (N, P, K, Ca, Mg, and S), micronutrients (Fe, Zn, B, Cu, Mn, Mo, and Ni), organic matter (OM), and the carbon-to-nitrogen (C/N) ratio. All composts exhibited neutral pH values (7.38–7.52), high OM content (38.5–48.4%), and optimal C/N ratios (10.5–13.9), indicating maturity and chemical stability. Nitrogen ranged from 19 to 21 kg·t⁻¹, while potassium and calcium were present in concentrations beneficial for crop development. However, EC values (3.43–3.66 dS/m) and boron levels (>160 ppm) were moderately high, requiring caution in saline soils or with boron-sensitive crops. A semi-quantitative Compost Quality Index (CQI) ranked BFS3 highest due to elevated OM and potassium content, followed by BFS1. BFS2, while rich in nitrogen, scored lower due to excessive boron. One-way ANOVA revealed no significant difference in nitrogen (p > 0.05), but it did reveal significant differences in potassium (p < 0.01) and boron (p < 0.001) among formulations. These results confirm the potential of mining tailings—microbial mat composts are low-cost, nutrient-rich biofertilizers. They are suitable for field crops or as components in nursery substrates, particularly when EC and boron are managed through dilution. This study promotes the circular reuse of geothermal and industrial residues and contributes to sustainable soil restoration practices in mining-affected regions through innovative composting strategies. Full article

Evaluating the renewable energy potential, such as that of solar photovoltaics (PV), is important for developing renewable energy policies. This study investigated rooftop solar PV potential in Thailand based on open-source geographic information system (GIS) building footprints, solar PV power output, and the most recent land use data (2022). GIS-based overlay analysis, buffering, fishnet modeling, and spatial join operations were applied to assess rooftop availability across various building types, taking into account PV module installation parameters and optimal panel orientation. Economic feasibility and sensitivity analyses were conducted using standard economic metrics, including net present value (NPV), internal rate of return (IRR), payback period, and benefit–cost ratio (BCR). The findings showed a total rooftop solar PV power generation potential of 50.32 TWh/year, equivalent to 25.5% of Thailand’s total electricity demand in 2022. The Central region contributed the highest potential (19.59 TWh/year, 38.94%), followed by the Northeastern (10.49 TWh/year, 20.84%), Eastern (8.16 TWh/year, 16.22%), Northern (8.09 TWh/year, 16.09%), and Southern regions (3.99 TWh/year, 7.92%). Both commercial and industrial sectors reflect the financial viability of rooftop PV installations and significantly contribute to the overall energy output. These results demonstrate the importance of incorporating rooftop solar PV in renewable energy policy development in regions with similar data infrastructure, particularly the availability of detailed and standardized land use data for building type classification. Full article

Background: Pleural mesothelioma (PM) is a type of cancer that is difficult to diagnose and treat. Patients may have vastly varying prognoses, and prognostic factors may help guide the clinical approach. As a recently identified biomarker, the pan-Immune-Inflammation-Value (PIV) is a simple, comprehensive, and peripheral blood cell-based biomarker. Methods: The present study represents a retrospective observational analysis carried out within a single-center setting. Ninety-five patients with PM stages I–IV were enrolled in the study. We analyzed the correlation between patients’ demographic characteristics, clinicopathological factors such as histological subtypes, surgery status, tumor thickness, blood-based parameters, and treatment options with their prognoses. PIV was calculated by the following formula: (neutrophil count × monocyte count × platelet count)/lymphocyte count. Additionally, blood-based parameters were used to calculate the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and systemic immune inflammation index (SII). Results: We categorized the patients into two groups, low PIV group (PIV ≤ 732.3) and high PIV group (PIV > 732.3) according to the determined cut-off value, which was defined as the median. It was revealed that high PIV was associated with poor survival outcomes. The median follow-up period was 15.8 months (interquartile range, IQR, 7.1 to 29.8 months). The median overall survival (OS) was significantly longer in patients in the low PIV group (median 29.8 months, 95% confidence interval (CI), 15.6 to 44) than the high PIV group (median 14.7 months, 95% CI, 10.8 to 18.6 p < 0.001). Furthermore, the study revealed that patients with low PIV, NLR, and SII values were more likely to be eligible for surgery and were diagnosed at earlier stages. Additionally, these markers were identified as potential predictors of disease-free survival (DFS) in the surgical cohort and of treatment response across the entire patient population. Conclusions: In addition to well-established clinical factors such as stage, histologic subtype, resectability, and Eastern Cooperative Oncology Group (ECOG) performance status (PS), PIV emerged as an independent and significant prognostic factor of overall survival (OS) in patients with PM. Moreover, PIV also demonstrated a remarkable independent prognostic value for disease-free survival (DFS) in this patient population. Additionally, some clues are provided for conditions such as treatment responses, staging, and suitability for surgery. As such, in this cohort, it has outperformed the other blood-based markers based on our findings. Given its ease of calculation and cost-effectiveness, PIV represents a promising and practical prognostic tool in the clinical management of pleural mesothelioma. It can be easily calculated using routinely available laboratory parameters for every cancer patient, requiring no additional cost or complex procedures, thus facilitating its integration into everyday clinical practice. Full article

Background and Objectives: Lower limb defects often present significant reconstructive challenges due to limited soft tissue availability and exposure of critical structures. Perforator-based flaps offer reliable solutions, with minimal donor site morbidity. This study aimed to evaluate the efficacy of infrared thermography (IRT) in preoperative planning and postoperative monitoring of perforator-based flaps, assessing its accuracy in identifying perforators, predicting complications, and optimizing outcomes. Materials and Methods: A prospective observational study was conducted on 76 patients undergoing lower limb reconstruction with fascio-cutaneous perforator flaps between 2022 and 2024. Perforator mapping was performed concurrently with IRT and Doppler ultrasonography (D-US), with intraoperative confirmation. Flap design variables and systemic parameters were recorded. Postoperative monitoring employed thermal imaging on days 1 and 7. Outcomes were correlated with thermal, anatomical, and systemic factors using statistical analyses, including t-tests and Pearson correlation. Results: IRT showed high sensitivity (97.4%) and positive predictive value (96.8%) for perforator detection. A total of nine minor complications occurred, predominantly in patients with diabetes mellitus and/or elevated glycemia (p = 0.05). Larger flap-to-defect ratios (A/C and B/C) correlated with increased complications in propeller flaps, while smaller ratios posed risks for V-Y and Keystone flaps. Thermal analysis indicated significantly lower flap temperatures and greater temperature gradients in flaps with complications by postoperative day 7 (p < 0.05). CRP levels correlated with glycemia and white blood cell counts, highlighting systemic inflammation’s impact on outcomes. Conclusions: IRT proves to be a reliable, non-invasive method for perforator localization and flap monitoring, enhancing surgical planning and early complication detection. Combined with D-US, it improves perforator selection and perfusion assessment. Thermographic parameters, systemic factors, and flap design metrics collectively predict flap viability. Integration of IRT into surgical workflows offers a cost-effective tool for optimizing reconstructive outcomes in lower limb surgery. Full article

Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily reliant on rural transport systems, using both motorised but mainly alternative means of transport. However, rural roads often suffer from poor construction due to the use of low-strength, in situ soils and limited financial resources, leading to premature failures and subsequent traffic disruptions with significant economic losses. This study investigates the use of rice husk ash (RHA), a waste byproduct from rice production, as a sustainable supplement to Ordinary Portland Cement (OPC) for soil stabilisation in order to increase durability and sustainability of rural roads, hence limit recurrent maintenance needs and associated transport costs and challenges. To conduct this study, soil samples collected from Mulungushi, Zambia, were treated with combinations of 6–10% OPC and 10–15% RHA by weight. Laboratory tests measured maximum dry density (MDD), optimum moisture content (OMC), and California Bearing Ratio (CBR) values; the main parameters assessed to ensure the quality of road construction soils. Results showed that while the MDD did not change significantly and varied between 1505 kg/m³ and 1519 kg/m³, the OMC increased hugely from 19.6% to as high as 26.2% after treatment with RHA. The CBR value improved significantly, with the 8% OPC + 10% RHA mixture achieving the highest resistance to deformation. These results suggest that RHA can enhance the durability and sustainability of rural roads and hence improve transport systems and subsequently improve socioeconomic factors in rural areas. Full article

We introduce a novel portfolio optimization framework—Distributionally Robust Multivariate Stochastic Cone Order (DR-MSCO)—which integrates partial orders on random vectors with Wasserstein-metric ambiguity sets and adaptive cone structures to model multivariate investor preferences under distributional uncertainty. Grounded in measure theory and convex analysis, DR-MSCO employs data-driven cone selection calibrated to market regimes, along with coherent tail-risk operators that generalize Conditional Value-at-Risk to the multivariate setting. We derive a tractable second-order cone programming reformulation and demonstrate statistical consistency under empirical ambiguity sets. Empirically, we apply DR-MSCO to 23 Borsa Istanbul equities from 2021–2024, using a rolling estimation window and realistic transaction costs. Compared to classical mean–variance and standard distributionally robust benchmarks, DR-MSCO achieves higher overall and crisis-period Sharpe ratios (2.18 vs. 2.09 full sample; 0.95 vs. 0.69 during crises), reduces maximum drawdown by 10%, and yields endogenous diversification without exogenous constraints. Our results underscore the practical benefits of combining multivariate preference modeling with distributional robustness, offering institutional investors a tractable tool for resilient portfolio construction in volatile emerging markets. Full article

Sweet potato is a valuable root due to its nutritional benefits, health-promoting properties, and technological applications. The peel, often discarded during food processing, can be employed in the food industry, supporting a circular economy. Purple sweet potato peel (PSPP) is rich in anthocyanins, which can be used as natural colourants and antioxidants. Optimising their extraction can enhance yield and reduce costs. The current work aimed to optimize anthocyanin and antioxidant recovery from PSPP using a Box-Behnken design and sonotrode ultrasound-assisted extraction (sonotrode-UAE). Three independent variables were analysed: extraction time (2–6 min), ethanol concentration (35–85%), and liquid-to-solid ratio (10–30 mL/g). The dependent variables included total monomeric anthocyanin content (TMAC), individual anthocyanins, and antioxidant activity. TMAC in 15 extracts ranged from 0.16 to 2.66 mg/g PSPP. Peonidin-3-caffeoyl-p-hydroxybenzoyl sophoroside-5-glucoside was the predominant anthocyanin. Among four antioxidant assays, Ferric-reducing antioxidant power (FRAP) showed the highest value. Ethanol concentration significantly influenced anthocyanin and antioxidant recovery (p < 0.05). The model demonstrated adequacy based on the coefficient of determination and variation. Optimal extraction conditions were 6 min with 60% ethanol at a 30 mL/g ratio. Predicted values were validated experimentally (coefficient of variation <10%). In conclusion, PSPP is a promising matrix for obtaining anthocyanin-rich extracts with antioxidant activity, offering potential applications in the food industry. Full article

Over recent decades, phase-change materials (PCMs) have gained prominence as latent-heat thermal energy storage systems in building envelopes because of their high energy density. However, only PCMs that complete a full daily charge–discharge cycle can deliver meaningful energy and carbon-emission savings. This simulation study introduces a methodology that simultaneously optimizes PCM integration for storage efficiency, indoor thermal comfort, and energy savings. Two new indicators are proposed: overall storage efficiency (EC_n), which consolidates heating and cooling-efficiency ratios into a single value, and the performance factor (PF), which quantifies the PCM’s effectiveness in maintaining thermal comfort. Using EnergyPlus v8.9 coupled with DesignBuilder, a residential ASHRAE 90.1 mid-rise apartment was modeled in six warm-temperate (Cfb) European cities for the summer period from June 1 to August 31. Four paraffin PCMs (RT-22/25/28/31 HC, 20 mm thickness) were tested under natural and controlled ventilation strategies, with windows opening 50% when outdoor air was at least 2 °C cooler than indoors. Simulation outputs were validated against experimental cubicle data, yielding a mean absolute indoor temperature error ≤ 4.5%, well within the ±5% tolerance commonly accepted for building thermal simulations. The optimum configuration—RT-25 HC with temperature-controlled ventilation—achieved PF = 1.0 (100% comfort compliance) in all six cities and delivered summer cooling-energy savings of up to 3376 kWh in Paris, the highest among the locations studied. Carbon-emission reductions reached 2254 kg CO₂-e year⁻¹, and static payback periods remained below the assumed 50-year building life at a per kg PCM cost of USD 1. The EC_n–PF framework, therefore, provides a transparent basis for selecting cost-effective, energy-efficient, and low-carbon PCM solutions in warm-temperate buildings. Full article

This study proposes an integrated framework for sustainable tropical agriculture by combining biochemical waste valorization with spatial carbon footprint estimation in ‘Phulae’ pineapple production. Peel and eye residues from fresh-cut processing were enzymatically converted into rare sugar, achieving average conversion efficiencies of 35.28% for peel and 37.51% for eyes, with a benefit–cost ratio of 1.56 and an estimated unit cost of USD 0.17 per gram. A complementary zero-waste pathway produced functional gummy products using vinegar fermented from pineapple eye waste, with the preferred formulation scoring a mean of 4.32 out of 5 on a sensory scale with 158 untrained panelists. For spatial carbon modeling, the Bare Land Referenced Algorithm (BRAH) and Otsu thresholding were applied to multi-temporal Sentinel-2 and THEOS imagery to estimate plantation age, which strongly correlated with field-measured emissions (r = 0.996). This enabled scalable mapping of plot-level greenhouse gas emissions, yielding an average footprint of 0.2304 kg CO₂ eq. per kilogram of fresh pineapple at the plantation gate. Together, these innovations form a replicable model that aligns tropical fruit supply chains with circular economy goals and carbon-related trade standards. The framework supports waste traceability, resource efficiency, and climate accountability using accessible, data-driven tools suitable for smallholder contexts. By demonstrating practical value addition and spatially explicit carbon monitoring, this study shows how integrated circular and geospatial strategies can advance sustainability and market competitiveness for the ‘Phulae’ pineapple industry and similar perennial crop systems. Full article

Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination (${\mathrm{R}}^2$) of 0.87. Full article

Melissa officinalis L. (Lamiaceae) is a perennial plant species widely used in the pharmaceutical and food industries, particularly valued for its sedative properties. This study investigates the impact of synthetic mulch film and planting density as two experimental factors on agronomic performance, raw material quality, and economic efficiency in lemon balm production. The experiment was conducted at three locations in Serbia (L1: Bačko Novo Selo, L2: Bavanište, L3: Vilandrica) from 2022 to 2024, using two planting densities on synthetic mulch film (F1: 8.3 plants m⁻²; F2: 11.4 plants m⁻²) and a control treatment without mulch (C). The synthetic mulch film used was a synthetic black polypropylene film (Agritela Black, 90 g/m²), uniformly applied in strips across the cultivation area, covering approximately 78% of the soil surface. The results showed consistent increases in morphological parameters and yield across the years. Plant height in F1 and F2 treatments ranged from 65 to 75 cm, while in the control it reached up to 50 cm (2022–2024). Fresh biomass yield varied from 13.4 g per plant (C) to 378.08 g per plant (F2), and dry biomass yield from 60.3 g (C) to 125.4 g (F2). The highest essential oil content was observed in F2 (1.2% in 2022), while the control remained at 0.8%. The F2 treatment achieved complete weed suppression throughout the experiment without the use of herbicides, demonstrating both agronomic and ecological advantages. Economic evaluation revealed that F2 generated the highest cumulative profit (€142,164.5) compared to the control (€65,555.3). Despite higher initial investment, F2 had the most favorable cost–benefit ratio in the long term. This study highlights the crucial influence of mulching and planting density on optimizing lemon balm production across diverse climatic and soil conditions, while also underscoring the importance of sustainable, non-chemical weed management strategies in lemon balm cultivation. Full article

Background/Objectives: Upper tract urothelial carcinoma (UTUC) is often complicated by intravesical recurrence and cancer progression following radical nephroureterectomy (RNU). Identifying reliable prognostic biomarkers remains crucial for optimizing postoperative surveillance. The goal of this study was to assess the prognostic value of the De Ritis ratio (AST/ALT) in predicting bladder recurrence and oncologic outcomes in patients with clinically localized UTUC undergoing RNU. Methods: This retrospective study analyzed 87 patients treated with RNU between 2018 and 2025. Preoperative De Ritis ratios were calculated, and an optimal cut-off value of 1.682 was determined using ROC analysis. Recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS) were analyzed using the Kaplan–Meier and Cox regression methods. Logistic regression was used to identify independent predictors of bladder recurrence. Results: A high De Ritis ratio was significantly associated with increased bladder recurrence and worse RFS and CSS, but not OS. Multivariate analysis confirmed that an elevated De Ritis ratio, current smoking, positive surgical margins, and synchronous bladder cancer were the independent predictors of bladder recurrence. The De Ritis ratio demonstrated strong discriminatory performance (AUC: 0.807), with good sensitivity and specificity for predicting recurrence. Conclusions: The De Ritis ratio is a simple, cost-effective preoperative biomarker that may aid in identifying UTUC patients at higher risk for intravesical recurrence and cancer-specific mortality. Incorporating this ratio into clinical decision-making could enhance risk stratification and guide tailored follow-up strategies. Full article

Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost counter-tube detector. Since this detector type provides a bulk GR response across a wide energy range, EGR signals are influenced by several confounding factors, e.g., soil radon emanation, biomass. To what extent these confounding factors deteriorate the accuracy of SM estimates obtained from EGR is not fully understood. Therefore, the aim of this study was to compare the accuracy of SM estimates from EGR with those from reference ⁴⁰K GR (1460 keV) measurements which are much less influenced by these factors. For this, a Geiger–Mueller counter (G–M), which is commonly used for EGR monitoring, and a gamma spectrometer were installed side by side in an agricultural field equipped with in situ sensors to measure reference SM and a meteorological station. The EGR_G–M and spectrometry-based ⁴⁰K measurements were related to reference SM using a functional relationship derived from theory. We found that daily SM can be predicted with an RMSE of 3.39 vol. % from ⁴⁰K using the theoretical value of α = 1.11 obtained from the effective ratio of GR mass attenuation coefficients for the water and solid phase. A lower accuracy was achieved for the EGR_G–M measurements (RMSE = 6.90 vol. %). Wavelet coherence analysis revealed that the EGR_G–M measurements were influenced by radon-induced noise in winter. Additionally, biomass shielding had a stronger impact on EGR_G–M than on ⁴⁰K GR estimates of SM during summer. In summary, our study provides a better understanding on the lower prediction accuracy of EGR_G–M and suggests that correcting for biomass can improve SM estimation from the bulk EGR data of operational radioactivity monitoring networks. Full article

Gas turbine (GT) modeling and optimization have been widely studied at the design level but still lacks focus on real-world operational cases. The concept of a digital twin (DT) allows for the interaction between operation data and the system dynamic performance. Among many DT studies, only a few focus on GT for thermal power plants. This study proposes a digital twin prototype framework including the following modules: process modeling, parameter estimation, and performance optimization. Provided with real-world power plant operational data, key performance parameters such as turbine inlet temperature (TIT) and specific fuel consumption (SFC) were initially unavailable, therefore necessitating further calculation using thermodynamic analysis. These parameters are then used as a target label for developing artificial neural networks (ANNs). Three ANN models with different structures are developed to predict TIT, SFC, and turbine power output (GTPO), achieving high R² scores of 94.03%, 82.27%, and 97.59%, respectively. Physics-informed neural networks (PINNs) are then employed to estimate the values of the air–fuel ratio and combustion efficiency for each time index. The PINN-based estimation resulted in estimated values that align with the literature. Subsequently, an unconventional method of detecting alarms by using conformal prediction were also proposed, resulting in a significantly reduced number of alarms. The developed ANNs are then combined with particle swarm optimization (PSO) to carry out performance optimization in real time. GTPO and SFC are selected as the primary metrics for the optimization, with controllable parameters such as AFR and a fine-tuned inlet guide vane position. The results demonstrated that GTPO could be optimized with the application of conformal prediction when the true GTPO is detected to be higher than the upper range of GTPO obtained from the ANN model with a conformal prediction of a 95% confidence level. Multiple PSO variants were also compared and benchmarked to ensure an enhanced performance. The proposed PSO in this study has a lower mean loss compared to GEP. Furthermore, PSO has a lower computational cost compared to RS for hyperparameter tuning, as shown in this study. Ultimately, the proposed methods aim to enhance GT operations via a data-driven digital twin concept combination of deep learning and optimization algorithms. Full article

The quantification of solid debris in used lubricating oil is essential for assessing transmission system wear and optimizing maintenance strategies. This study introduces a low-cost capacitive proximity sensor for monitoring total solid particle contamination in lubricants, with a focus on ferrous (Fe), non-ferrous (Al), and non-metallic (SiO₂) debris. Controlled tests were performed using five mixing ratios of large-to-small particles (100:0, 75:25, 50:50, 25:75, and 0:100) at a fixed debris mass of 0.5 g per 25 mL of SAE 85W-140 automotive gear oil. Cubic regression analysis yielded high predictive accuracy, with average R² values of 0.994 for Fe, 0.943 for Al, and 0.992 for SiO₂. Further dimensionality reduction using Principal Component Analysis (PCA), along with Linear Discriminant Analysis (LDA) of multivariate statistical analysis, effectively classifies debris types and enhances interpretability. These results demonstrate the potential of capacitive sensing as an offline, non-invasive alternative to traditional techniques for wear debris monitoring in transmission systems. These results confirm the potential of capacitive sensing, supported by statistical modeling, as a non-invasive, cost-effective technique for offline classification and monitoring of wear debris in transmission systems. Full article