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Fatigue Analysis of Sustainable Bituminous Pavements with Artificial and Recycled Aggregates
No full textThe circular economy represents a significant opportunity to enhance the mechanical properties of bituminous mixtures, thereby contributing to sustainable development. This research compares the behaviour of traditional bituminous mixtures with sustainable ones that reuse recycled materials, industrial waste products, or additives that improve mechanical or rheological properties. The methodology employed comprised the acquisition of fatigue resistance laws from 4-point bending tests on prismatic specimens. This facilitated the analytical determination of the number of axles of 13 tons that the section of pavement with sustainable material can support for comparison with the axles supported in the conventional mix. The findings corroborate the utilization of sustainable bituminous mixtures in pavement sections, employing the maximum circularity criterion. The fatigue laws calculated must permit the use of different calculation methods or other applications in green infrastructures, such as cycling lanes or pedestrian areas. On sections with an AADT of between 800 and 25 HV/day, all of the analyzed bituminous mixtures with sustainable materials prolong the service life of the road. There were increases in service life of between 25.5% and 6.6%, respectively, which satisfactorily achieved an increase in pavement service life based on the criterion of maximum circularity
Hydrochemical Appraisal of Groundwater Quality for Managed Aquifer Recharge (MAR) in Southern Punjab, Pakistan
No full textWater quality assessment is crucial for the sustainable use and management of groundwater resources. This study was carried out in the irrigated plains of Vehari District, Punjab, Pakistan, to evaluate groundwater suitability for a managed aquifer recharge (MAR) project. Twenty groundwater samples were collected in June 2021 from an area of 1522 km2 and analysed for major physicochemical parameters including electrical conductivity (EC), total dissolved solids (TDS), pH, turbidity, calcium (Ca), magnesium (Mg), chloride (Cl), alkalinity (Alk), bicarbonate (HCO3−), hardness, potassium (K), sulphate (SO42−), sodium (Na), and nitrate (NO3−). Water quality was assessed using WHO and PID standards, alongside derived hydrochemical indices such as sodium percentage (%Na), Kelly’s ratio (KR), sodium adsorption ratio (SAR), residual sodium carbonate (RSC), and the water quality index (WQI). The dataset was interpreted using geo-statistical, geospatial, multivariate, and correlation analyses. Cations and anion dominance followed the order Na+ > Ca2+ > Mg2+ > K+ and HCO3− > SO42− > Cl− > NO3−. According to the WQI analysis, 35% of the water samples are classified as “poor,” half (50%) as “very poor,” and the remaining 15% as “unsuitable” for drinking purposes. However, irrigation suitability indices confirmed that groundwater is generally acceptable for agricultural use, though unfit for drinking. The outcomes of this study provide essential insights for groundwater management in the region, where the Punjab Irrigation Department (PID) has initiated a MAR project. Considering that the irrigation sector is the major groundwater consumer in the area, the compatibility of groundwater and surface water quality supports the implementation of MAR to enhance agricultural sustainability
Beyond Physical Upgrades: Reconfiguring Original Residents’ Belongingness in Informal Settlements with a Network–Node–Subject Lens
No full textInformal settlements are widely present and important urban spaces, providing valuable living spaces for many migrant populations, low-income groups, and indigenous peoples. However, urbanization faces a common challenge: the transformation of informal settlements often destroys social structures and belongingness. Despite its importance, few studies have examined how redevelopment is correlated with original residents’ belongingness. To address this gap, this study proposes a research framework of belongingness structured along the logical chain of “network–node–subject”. Social network analysis (SNA) is employed to identify the existing outdoor activity space network and its key nodes. Exploratory factor analysis (EFA) is then conducted to determine the key environmental features of outdoor activity spaces that influence original residents’ belongingness. Furthermore, K-means clustering is applied to explore the correlation mechanism between space and belongingness across different age groups. This study identifies differentiated and universal elements by analyzing the clustered conflict factors, in order to provide precise policy insights. The findings provide actionable insights for enhancing residents’ belongingness during the redevelopment of informal settlements in cities
Research on Hyperspectral Remote Sensing Prospecting Model for Porphyry Copper Deposits: A Case Study of the Qulong–Jiama Ore District
No full textThe Qulong–Jiama polymetallic ore concentration area, located in the eastern segment of the Gangdese metallogenic belt, is one of China’s most significant copper resource production zones. With the growing demand for copper resources, this area has become a key target for mineral exploration. The current study aims to explore the application potential of multispectral and hyperspectral remote sensing technologies in porphyry copper deposit prospecting, establish a hyperspectral remote sensing prospecting model tailored to this region, and provide technical support for prospecting prediction and resource exploration of similar deposits. Sentinel-2 and Landsat 8 data were used to outline major alteration anomalies at the regional scale, while GF-5 hyperspectral data enabled precision mineral mapping. Results show clear porphyry-style alteration zoning. Hyperspectral mineral identification reveals 33 mineralization- and alteration-related minerals, including muscovite, biotite, pyrophyllite, dickite, chlorite, epidote, and limonite. The ore concentration area exhibits a well-developed inner–middle–outer alteration sequence: (1) an inner potassic–silicic zone locally accompanied by skarn; (2) a middle phyllic and argillic zone dominated by quartz–sericite–pyrite assemblages; and (3) an outer propylitic zone of chlorite–epidote–carbonate with supergene iron oxides. These alteration patterns spatially coincide with known deposits and metallogenic structures such as faults, annular features, and intrusive contacts. Based on these spatial relationships, a hyperspectral remote sensing prospecting model was constructed. The model defines diagnostic mineral assemblages for each zone, highlights structurally altered overlapping areas as priority targets, and effectively predicts the distribution of ore-related alteration belts. The strong correspondence between remote sensing-derived anomalies and existing deposits demonstrates that hyperspectral alteration information is a reliable indicator of ore-forming systems. The proposed model not only provides a scientific basis for further prospecting and exploration in the Qulong–Jiama area but also serves as a reference for copper exploration in the Gangdese metallogenic belt and other similar porphyry–epithermal metallogenic systems
Lint Cleaning Performance of a Pneumatic Fractionator: Impacts on Fiber Quality and Economic Value of Saw- and Roller-Ginned Upland Cotton
No full textCurrent saw- and pin-type lint-cleaning systems used by the ginning industry have challenges retaining lint quality. The objective of the research was to test a novel pneumatic fractionator for the lint cleaning of an Upland cotton variety that was both saw- and roller-ginned. The process variables tested were initial lint moisture content in the range of 5.5–15% w.b., line pressure in the range of 276–552 kPa, and residence time in the range of 15–45 s. Experiments were conducted based on a central composite design. Models based on response surface methodology (RSM) were developed for final lint moisture, total trash extracted during lint cleaning, and High-Volume Instrument (HVI) fiber quality. The RSM models adequately described the pneumatic fractionation process, as indicated by the coefficient of determination, predicted vs. observed plots, and residual values. The results indicated that the interactions among initial lint moisture content, residence time, and line pressure significantly affected lint quality. At the optimized pneumatic fractionator process conditions, the predicted lint quality attributes were better for both roller- and saw-ginned lint compared to lint cleaned with saw- and pin-type lint cleaners. The upper half mean length increased by 1 mm, the uniformity index was higher by 0.5–1 percentage points, the strength was 1–2 g/tex higher, and the short fiber content was reduced by more than one percentage point. Color grades were better for pneumatic fractionated lint compared to saw- and pin-type lint cleaning methods. Lint value was approximately 4 cents/kg higher for both saw- and roller-ginned pneumatic fractionated lint, compared to lint cleaned using saw- and pin-type lint cleaners. The novel pneumatic fractionator, when compared to industry-standard saw- and pin-type lint cleaners, effectively cleaned lint while retaining fiber quality and removing most of the motes and trash
Optimizing Energy Storage Systems with PSO: Improving Economics and Operations of PMGD—A Chilean Case Study
No full textThis work develops a methodology for operating Battery Energy Storage Systems (BESSs) in distribution networks, connected in parallel with a medium- and small-scale photovoltaic Distributed Generator (PMGD), focusing on a real project located in the O’Higgins region of Chile. The objective is to increase energy sales by the PMGD while ensuring compliance with operational constraints related to the grid, PMGD, and BESSs, and optimizing renewable energy use. A real distribution network from Compañía General de Electricidad (CGE) comprising 627 nodes was simplified into a validated three-node, two-line equivalent model to reduce computational complexity while maintaining accuracy. A mathematical model was designed to maximize economic benefits through optimal energy dispatch, considering solar generation variability, demand curves, and seasonal energy sales and purchasing prices. An energy management system was proposed based on a master–slave methodology composed of Particle Swarm Optimization (PSO) and an hourly power flow using the successive approximation method. Advanced optimization techniques such as Monte Carlo (MC) and the Genetic Algorithm (GAP) were employed as comparison methods, supported by a statistical analysis evaluating the best and average solutions, repeatability, and processing times to select the most effective optimization approach. Results demonstrate that BESS integration efficiently manages solar generation surpluses, injecting energy during peak demand and high-price periods to maximize revenue, alleviate grid congestion, and improve operational stability, with PSO proving particularly efficient. This work underscores the potential of BESS in PMGD to support a more sustainable and efficient energy matrix in Chile, despite regulatory and technical challenges that warrant further investigation
Stability-Driven Osteoporosis Screening: Multi-View Consensus Feature Selection with External Validation and Sensitivity Analysis
No full textBackground/Objectives: Osteoporosis is a major global health concern, and early risk assessment plays a crucial role in fracture prevention. Although demographic, clinical, and lifestyle factors are commonly incorporated into screening tools, their relative importance within data-driven prediction frameworks can vary substantially across datasets. Rather than aiming to identify novel predictors, this study evaluates the stability and behavior of established osteoporosis risk factors using statistical inference and machine learning-based feature selection methods across heterogeneous data sources. We further examine whether simplified and near-minimal models can achieve predictive performances comparable to that of full-feature configurations. Methods: An open-access Kaggle dataset (n = 1958) and a retrospective clinical dataset from the University of Phayao Hospital (n = 176) were analyzed. Feature relevance was assessed using logistic regression, likelihood ratio testing, MRMR, ReliefF, and unified importance scoring. Multiple predictor configurations, ranging from full-feature to minimal and near-minimal models, were evaluated using decision tree, support vector machine, k-nearest neighbor, naïve Bayes, and efficient linear classifiers. External validation was performed using hospital-based records. Results: Across all analyses, age consistently emerged as the dominant predictor, followed by corticosteroid use, while other variables showed limited incremental predictive contributions. Simplified models based on age alone or age combined with medication-related variables achieved performances comparable to full-feature models (accuracy ≈91% and AUC ≈ 0.95). In addition, near-minimal models incorporating gender alongside age and medications demonstrated a favorable balance between discrimination and computational efficiency under external validation. Although overall performance declined under distributional shift, naïve Bayes and efficient linear classifiers showed the most stable external behavior (AUC = 0.728–0.787). Conclusions: These findings indicate that stability-driven feature selection primarily reproduces well-established epidemiological risk patterns rather than identifying novel predictors. Minimal and near-minimal models—including those incorporating gender—retain acceptable performances under external validation and are methodologically efficient. Given the limited size and single-center nature of the external cohort, the results should be interpreted as preliminary methodological evidence rather than definitive support for clinical screening deployment. Further multi-center studies are required to assess generalizability and clinical relevance
Natural Essential Oils as Promising Antimicrobial Agents to Improve Food Safety: Mechanistic Insights Against Multidrug-Resistant Campylobacter jejuni and Campylobacter coli Isolated from Tunisia
No full textThe increasing prevalence of multidrug-resistant (MDR) Campylobacter species poses a serious threat to food safety and public health, highlighting the urgent need for natural antimicrobial alternatives to conventional antibiotics. This study investigated the antibacterial potential and mechanism of action of seven essential oils (EOs), Cymbopogon citratus, Mentha pulegium, Artemisia absinthium, Myrtus communis, Thymus algeriensis, Thymus capitatus, and Eucalyptus globulus, against multidrug-resistant Campylobacter jejuni and Campylobacter coli. The antimicrobial activity was first assessed by the agar disk diffusion and broth microdilution methods to determine inhibition zones, minimum inhibitory concentrations (MICs), and minimum bactericidal concentrations (MBCs). The most active EOs were further evaluated through time–kill kinetics, cell lysis, salt tolerance, and membrane integrity assays to elucidate their bactericidal mechanisms. Results showed that E. globulus, T. algeriensis, and M. communis exhibited the strongest inhibitory effects, particularly against C. jejuni, with MIC values ranging from 3.125% to 6.25%, while C. coli was more resistant. Time–kill and lysis experiments demonstrated rapid bacterial reduction and significant decreases in optical density, indicating cell disruption. Additionally, EO treatments reduced salt tolerance and induced leakage of cytoplasmic materials, confirming membrane damage. Overall, these findings suggest that selected essential oils exert potent antimicrobial effects through membrane disruption and osmotic imbalance, offering promising natural strategies to control MDR Campylobacter in food systems. The application of such bioactive compounds could contribute significantly to improving food quality, extending shelf life, and enhancing food safety
Comparative Study on the In Vitro Gastrointestinal Digestion of Oil Body Suspension from Different Parts of Idesia polycarpa Maxim
No full textThis study provides the first comparative analysis of the physicochemical and functional properties of oil body suspensions derived from different parts—entire fruit (EOB), peel (POB), and seed (SOB)—of Idesia polycarpa Maxim (IPM) during in vitro simulated gastrointestinal digestion. Results demonstrated that the properties of the different suspensions exhibited significant difference during digestion stages. The average particle size of all suspensions decreased, with the most significant reduction observed in POB (91.50%), which was attributable to its lower interfacial protein content and inferior stability. The absolute ζ-potential decreased in the model of gastric digestion (MGD) due to interface disruption but increased in the model of intestinal digestion (MID) following the adsorption of bile salts. Throughout the simulated digestion process, the protein hydrolysis degree, free fatty acid (FFA) release rate, reducing power, and inhibition rates against α-amylase and α-glucosidase all increased, concurrently with a decrease in DPPH radical scavenging activity. Notably, the POB suspension exhibited the highest extent of lipid digestion, with the highest cumulative FFA release rate (27.83%). In contrast, the SOB suspension showed the most significant enhancement in total reducing power (increased by 199.32% after intestinal digestion) and the highest α-glucosidase inhibitory activity. These findings clarify that the part source is a critical factor influencing the digestive properties and functional activities of IPM oil bodies, providing a theoretical foundation for the targeted application in functional foods
A Multi-Stage NLP Framework for Knowledge Discovery from Crop Disease Research Literature
No full textExtracting and organizing knowledge from the agricultural crop disease research literature are challenging tasks because of the heterogeneous terminologies, complicated symptom descriptions, and unstructured nature of scientific documents. In this study, we developed a multi-stage natural language processing (NLP) pipeline to automate knowledge extraction, organization, and integration from the agricultural research literature into a domain-consistent crop disease knowledge graph. The model combines transformer-based sentence embeddings with variational deep clustering to extract topics, which are further refined via facet-aware relevance scoring for sentence selection to be included in the summary. Lexicon-guided named entity recognition helps in the precise identification and normalization of terms for crops, diseases, symptoms, etc. Relation extraction based on a combination of lexical, semantic, and contextual features leads to the meaningful generation of triplets for the knowledge graph. The experimental results show that the method yielded consistently good results at each stage of the knowledge extraction process. Among the combinations of embedding and deep clustering methods, SciBERT + VaDE achieved the best clustering results. The extraction of representative sentences for disease symptoms, control/treatment, and prevention obtained high F1-scores of around 0.8. The resulting knowledge graph has high node coverage and high relation completeness, as well as high precision and recall in triplet generation. The multi-stage NLP pipeline effectively converts unstructured agricultural research texts into a coherent and semantically rich knowledge graph, providing a basis for further research in crop disease analysis, knowledge retrieval, and data-driven decision support in agricultural informatics