Search Results (794)

Anaerobic digestion (AD) of lignocellulosic biomass is often constrained by biomass recalcitrance, limiting methane recovery. This study investigated whether low-temperature dilute-acid hydrothermal pretreatment could enhance methane production from pine nut shells (PNSs), a lignin-rich and underutilized agro-industrial residue, and whether derivative-based kinetic landmarks could provide a more systematic characterization of batch AD performance. Methane production was significantly improved by dilute sulfuric acid and hydrothermal pretreatments. The highest methane yield (201.8 mL CH₄ g⁻¹ VS) was achieved under the combined 100 °C hydrothermal and 2.5% H₂SO₄ condition, representing approximately 1.8-fold and 3.3-fold increases compared with hydrothermal-only and untreated PNSs, respectively. Enhanced performance was attributed to hemicellulose solubilization, lignin disruption, and improved substrate accessibility. In contrast, excessive acid severity resulted in process instability, associated with total volatile fatty acid accumulation and pH reduction. The Modified Logistic Model (MLM) was further used to derive five kinetic landmarks (PAA, PAM, PI, PDM, and PDA) describing phase-specific features of cumulative methane production curves. While these landmarks provide a model-based framework for comparing batch AD kinetics, their nearly constant normalized yields primarily reflect the geometry of the fitted logistic function rather than independent biological invariants. Overall, the results identify 100 °C hydrothermal pretreatment with 2.5% H₂SO₄ as an effective moderate-severity strategy for enhancing methane recovery from PNSs and demonstrate the utility of MLM-derived landmarks as comparative descriptors of phase-resolved methane production. Full article

Anaerobic digestion (AD) of agro-industrial residues supports the green energy transition by converting organic matter into renewable biogas. Sugar beet pulp is a highly fermentable feedstock, although its process response may vary with chemical composition. This study examined how chemical composition affects mesophilic biogas-production kinetics of sugar beet pulp prepared under laboratory conditions from surplus sugar beet roots. The roots represented ten sugar beet varieties (A–J), and the prepared pulp was characterised for pH, dry matter, organic dry matter, mineral composition, and the relative shares of simple sugars, polysaccharides, protein, and fibre. Batch digestion tests were performed at 39 °C for 30 days. Production curves were analysed using complementary kinetic models (modified Gompertz and a two-fraction first-order model) to capture the lag phase and the contributions of rapidly and slowly degradable substrate pools. Biogas yields ranged from 126 to 141 m³ Mg⁻¹ fresh matter with 50–55% CH₄, corresponding to 64.3–76.1 m³ CH₄ Mg⁻¹ organic dry matter, while organic matter conversion reached 71.2–82.4%. Varieties enriched in simple sugars exhibited a higher share of the fast-degradable fraction and shorter lag phases, indicating faster onset and stronger methane formation. In contrast, higher fibre contents reduced the slow-fraction rate constant and lowered overall conversion, consistent with hydrolysis-limited degradation of the structural carbohydrate matrix. The mineral ion background, particularly K and Na, indicated moderate ionic buffering and stable operation without inhibition. The novelty of this work lies in integrating detailed compositional profiling with dual kinetic modelling to translate chemical fingerprints into tentative process-relevant implications. These implications include feeding strategy, organic loading control and hydraulic retention time selection, and they require further validation in continuous or semi-continuous AD systems. Full article

This study investigated the effects of construction design and seasonal climatic conditions on bedding dynamics in bedded-pack dairy systems with contrasting composting functionality. The study intentionally included systems representing both composting bedded-pack barns (CBP), characterized by active management (regular turning and ventilation), and non-composting bedded-pack barns (BPB), which lacked aeration and did not promote active composting, resulting in limited or absent composting activity. Nine farms were divided into three groups: CONV (large, full-time CBP), ADAP (adapted, full-time CBP), and PART (partially used BPB). Evaluations were conducted during both cold and hot seasons. Composting dynamics were assessed over 24 h by measuring bedding temperature and moisture at eight points. During daytime, additional measurements at twenty points allowed for spatial distribution analysis using the inverse distance weighting method. Bedding attributes—including pH, density, depth, and particle size—were also measured in eight points. A 2 × 3 factorial design (two seasons, three barn types) was applied, and data were analyzed using Tukey’s test and Pearson correlation. Microclimate conditions were monitored through air temperature and humidity. Bedding temperature was significantly higher in the hot season (36.55 °C) compared to the cold season (32.12 °C), and was highest in the ADAP group (40.01 °C), followed by CONV (37.39 °C) and PART (26.18 °C) (p < 0.05). The 24 h temperature curve indicated favorable composting conditions only in the CONV and ADAP groups. Spatial temperature distribution varied significantly across locations in most barns (p < 0.05). Moisture content was lower in the hot season (46.91% and 41.41%) than in the cold season (57.03% and 51.97%) for CONV and ADAP, respectively. Moisture and temperature were significantly correlated with key bedding characteristics (p ≤ 0.05). Overall, a greater combination of characteristics associated with more favorable composting conditions was observed in ADAP barns, particularly during the hot season, whereas PART systems showed conditions incompatible with active composting. Full article

Rock bolt corrosion can weaken support systems and affect the long-term stability of water-flooded roadways. This study investigates the symmetry evolution of roadway deformation induced by bolt deterioration in alkaline water-flooded roadways, using Sanxin Coal Mine, Xinjiang, as a case. Electrochemical accelerated corrosion tests were conducted in 10% Na₂SO₄ solutions at pH = 9, 11, and 13 for 3, 6, and 9 d, followed by uniaxial tensile tests and FLAC3D numerical simulations. Under the controlled accelerated electrochemical conditions, the mass loss rate and corrosion rate generally increased with corrosion duration, with the greatest deterioration observed in the pH = 13 group after 9 d. The tensile curves of corroded bolts still exhibited elastic deformation, yielding, strain hardening, and post-peak softening stages. However, the yield load decreased with increasing mass loss rate, with fitted slopes of −0.1842, −0.07531, and −0.04998 kN/% for pH = 9, 11, and 13, respectively. Numerical results showed that bolt deterioration intensified roadway deformation and stress redistribution. Under severe corrosion, the horizontal displacement of the two sidewalls reached approximately −153.7 mm and 155.4 mm, while the maximum roof subsidence and floor heave reached about −188.7 mm and 191.3 mm, respectively. The shallow stress release zone expanded, and the deep stress concentration became more pronounced. Moreover, bolt deterioration intensified the roadway response while largely preserving its left–right symmetry. The numerical results incorporating the experimentally derived bolt deterioration showed increased roadway deformation and stress redistribution, indicating that bolt-capacity degradation can adversely affect roadway stability. These findings provide a reference for evaluating residual support performance and designing reinforcement measures for water-flooded roadways. Full article

Small punch fatigue tests (SPFTs) were conducted on three high-strength stainless steels: X17CrNi15-2, 15-5PH, and PH13-8Mo. The SPFT valley displacement-versus-SPFT life curves for the three stainless steels exhibited three different stages. Power-law relationships were obtained to characterize the maximum forces with SPFT lives for different stainless steels. The fracture mechanisms of the tested SPFT specimens were characterized via scanning electron microscopy, which was dependent on the materials and applied loads. Finite element analyses were performed to obtain the equivalent local stresses and strains. A simplified critical plane-strain energy density (SED) criterion was used for the SPFT life assessment by correlating the FE-obtained strain energy density with the SPFT life. The SED values for the SPFT life were in the following order: PH13-8Mo > 15-5PH > X17CrNi15-2. Full article

Ball milling can improve protein recovery from defatted rice bran, but the links among milling conditions, particle attributes, and extraction transport remain insufficiently defined. This study evaluated the effects of milling time (30–90 min) and rotational speed (30–120 rpm) on powder properties and alkaline protein extraction at pH 11 for 30–180 min at 24, 37, and 50 °C. Powders were characterized by laser diffraction, SEM image analysis, X-ray diffraction, and extraction-relevant indices describing the interfacial area and diffusion time scale. Extraction curves were fitted to first-order, pseudo-second-order, Peleg, and apparent Fick diffusion models. Milling reduced median particle size from 145 to 61 µm, increased fines (<45 µm) from 1.86% to 32.09%, and raised surface area proxies by about 30- to 40-fold. Compared with the control sample, milled samples generally showed faster extraction and higher protein recovery, with maximum endpoint recoveries of 89.91 mg g⁻¹ at 24 °C, 90.06 mg g⁻¹ at 37 °C, and 86.10 mg g⁻¹ at 50 °C. Late-stage extraction data collapsed onto a Fickian master curve, indicating diffusion-limited behavior, and apparent effective diffusivity increased with temperature. At 37 °C, the radius–shape–circularity model explained nearly all the between-powder variation in $\ln D_e\left(R^2=0.998;\mathrm{adjusted} R^2=0.996\right)$, and the shape factor remained significant after accounting for particle radius $\left(p=0.0179\right)$. Overall, ball milling improved extraction primarily by reducing diffusion length and altering particle morphology, providing practical guidance for optimizing rice bran protein recovery. Full article

One of the major determinants of crop productivity and sustainable agricultural practices is soil fertility. Proper soil assessment helps farmers make informed decisions about nutrients and fertilizers. This study utilizes 16 machine learning classifiers for soil fertility prediction, including learner-based, ensemble-based, instance-based, and probabilistic-based models. The model’s performance is assessed using accuracy, precision, recall, and F1-score. This paper presents a machine learning model for predicting soil fertility based on soil physicochemical characteristics. The data used in the research comprise vital soil parameters: nitrogen, phosphorus, potassium, pH, organic carbon, electrical conductivity, and micronutrients. Missing-value imputation, label encoding, and feature standardization are among the data preprocessing methods used to enhance data quality. Correlation analysis, ANOVA F-score, and mutual information were used to assess feature importance and determine the most significant soil characteristics. The experimental observation reveals that the RF model achieves an accuracy of 90.91% compared to the other models. Additional assessment using multi-class Receiver Operating Characteristic (ROC) and Precision–Recall (PR) curves showed excellent discriminative ability across the dominant soil fertility, which was of high quality. The findings show that machine learning models, especially ensemble-based models, are effective at estimating soil fertility levels. The proposed framework provides a data-driven, reliable decision-support system to assess soil fertility, enabling farmers and agricultural experts to enhance nutrient management and crop production. Full article

Background: Tricuspid regurgitation (TR) is a common valvular heart condition associated with significantly increased mortality. It is often underdiagnosed and undertreated due to limited insight into patient-specific risk prediction and optimal timing of intervention. Machine learning (ML) methods offer the potential to address these gaps by identifying high-risk patients, estimating survival probabilities, and uncovering key risk markers that influence outcomes. Methods: We developed and evaluated models to predict survival curves for a cohort of 949 patients with moderate or severe TR. Three modeling approaches were compared: Cox proportional hazards (Cox PH), Random Survival Forests (RSF), and DeepSurv (a deep learning-based survival model). Models were trained on clinical and imaging features extracted from cardiac magnetic resonance (CMR) studies and patient records. Performance was assessed using the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC). Kaplan–Meier analysis and multivariable Cox regression were used to identify significant predictors of mortality. Results: RSF achieved the best predictive performance with a C-index of 78% and AUC of 82%, followed by DeepSurv (C-index 72%, AUC 78%) and Cox PH (C-index 66%, AUC 76%). Predicted survival curves for low- and high-risk groups demonstrated clear separation, underscoring the models’ ability to distinguish patient risk. Key predictors of poor survival included older age, tobacco exposure, right ventricular dilation and hypertrophy, right atrial enlargement, and the presence of non-ischemic myocardial fibrosis. These features were independently associated with elevated mortality risk and showed distinct survival differences in Kaplan–Meier analysis. Conclusions: Machine learning-based survival models, particularly RSF and DeepSurv, offer beneficial tools for individualized risk stratification in patients with advanced TR. Structural abnormalities of the right heart and myocardial fibrosis were among the most significant predictors of mortality, highlighting the importance of early detection and timely intervention. Integrating AI-driven survival prediction into clinical workflows could potentially benefit decision-making and enable more personalized management of TR. Full article

In order to explore the effect of pH value of the solution on the growth characteristics of electro-hydro mixed branches of cross-linked polyethylene (XLPE) cables, an electro-hydro mixed branch experimental platform with different pH values was built to accelerate the aging of XLPE cables. The growth characteristics of electro-hydro mixed branches under different pH environments were systematically observed and analyzed by combining macroscopic dielectric properties test with microscopic morphology detection. The macroscopic test results show that the aging degree of the cable is more serious in the acidic or alkaline environment. When there are electrical tree defects in the insulation, acidic or alkaline solutions with different pH values will promote the accelerated aging of mixed branches, and the acceleration effect of acidic environment is more significant. After microscopic detection of sample slices with different acidity and alkalinity, it was found that both acidic and alkaline environments could accelerate the growth of mixed branches. On the basis of electrical trees, the strong acid and strong alkali environment was more suitable for the development of mixed branches than the weak acid and weak alkali environment, and the promotion effect of acidic solution was more prominent. At the same time, this study also deeply analyzed the conversion mechanism of electrical tree to water tree in cables under different pH conditions. Finally, through the correlation analysis between the dielectric performance parameters and the branch density of different groups of samples, the fitting model of the branch density on the macroscopic dielectric performance parameters is obtained by curve fitting, which provides an effective non-destructive testing method for cable multi-branch aging. These results reflect the structure–property relationship of XLPE polymer under acid-base corrosion and electric field coupling and reveal the microstructure degradation mechanism of polyethylene insulation. Full article

The cessation of recurrent anthropogenic activities can promote vegetation succession. In this paper, we report a case study of passive restoration of the herbaceous plant vegetation associated with cork oaks carried out by citizens in collaboration with local farmers in a suburban area of Rome (Italy). A sampling design has been carried out in two comparable patches using replicated plots: (i) a first patch corresponding to the passive restored area, evolving from an uncultivated field towards a cork oak forest, where the mowing activity was stopped in 2017, and (ii) a second patch corresponding to an uncultivated land periodically mowed as a control. We recorded 24 plant species in the restored patch and 9 in the control patch. The Shannon-Wiener diversity index was significantly higher in the restored patch when compared to the control. Whittaker diagrams, graphically representing evenness, showed significant differences among plotted values. The Chao 2 richness estimators evidence the differences between patches (52.17 species vs. 9), graphically observed in the sample rarefaction curves. An analysis in the 2017–2025 period showed a substantial increase in NDVI values in the restored patch (from 0.18 in 2017 to 0.28 in 2025; approximately +54% relative to 2017; mean NDVI increased from 0.181 in 2017 to 0.29 in 2025), indicating an increase in cover/biomass associated with the post-2017 restoration of the area. Suspending mowing, both humidity (due to the reduction in grass cover) and nutrients increase, and the pH is reduced (Ellenberg indices): it is possible that the young oak trees are comparatively more effective cation exchangers. Therefore, only a few years after mowing was suspended, we observed a marked recovery not only of the dominant cork oak component but also of the herbaceous species (Vulpio-Dasypyretum villosi association). Even young, isolated cork oak trees can act as nurse plants (or keystone structures), supporting many species and creating microhabitats for shade-tolerant plants. This passive restoration began when local citizens and a school asked landowners to stop mowing in an area where cork oaks were naturally regenerating, making it an example of autonomous citizen-led environmental management. Full article

This study describes the development of an electrochemical sensor for quantitatively measuring acetaminophen (ACOP) in drug tablets. The sensor design is based on the modification of glassy carbon electrode (GCE) using zinc oxide nanoparticles (ZnONPs) embedded in a naturally occurring clay matrix (Sa) functionalized with phenylalanine (Phe). To ensure that the ZnONPs are homogeneously dispersed on the clay surface, the nanocomposite was synthesized using an impregnation approach and low-temperature heat treatment. The amino acid promotes specific interactions with ACOP through hydrogen bonding and π-π stacking, acting as both a stabilizing agent and a molecular recognition moiety. FTIR, UV-Vis, XRD, and FESEM/EDX mapping were employed to fully characterize the developed material (ZnONPs-Sa/Phe). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used for the electrochemical determination of ACOP using the modified electrode GCE/ZnONPs-Sa/Phe. Parameters susceptible to affecting the sensitivity of the developed sensor were optimized, revealing that 5 µL of the suspension ZnONPs-Sa/Phe immobilized on GCE was ideal for the sensing of ACOP in a phosphate buffer solution at pH 2.0. The calibration curve obtained by plotting peak current intensity against ACOP concentration exhibited linear behavior within the concentration range between 0.02 µM and 0.28 µM, enabling determination of the limits of detection (LOD) and quantitation (LOQ) at 8.54 × 10⁻⁹ M and 2.84 × 10⁻⁸ M, respectively. The reproducibility, stability, and selectivity of the sensor were evaluated, followed by its application to the nano-sensing of ACOP in Africure and Doliprane tablets, yielding satisfactory results. The simplicity, affordability, and high analytical sensitivity of the developed sensor make this sensing platform a promising tool for pharmaceutical quality control applications. Full article

(1) Background: Advances in machine learning (ML)-based survival modeling enable the analysis of high-dimensional biomedical data. However, many approaches rely on the proportional hazards (PH) assumption, which is frequently violated in oncology and can limit the interpretability of hazard ratio-based results. Using Estrogen Receptor (ER) status in the METABRIC breast cancer cohort as a case study, we propose a framework that integrates machine learning survival models with Restricted Mean Survival Time (RMST) to provide a more robust and clinically interpretable approach for survival analysis under non-proportional hazards. (2) Methods: Overall survival was analyzed in 1104 patients. PH violations were confirmed using Schoenfeld residuals and Kaplan–Meier inspection. We compared four models: stratified Cox Elastic Net (Cox E-Net), Random Survival Forest (RSF), Gradient Boosting Survival Analysis (GBSA), and DeepHit. Performance was assessed using Harrell’s C-index, time-dependent IPCW C-index, and Integrated Brier Score (IBS). RMST at 180 months was utilized to quantify absolute survival differences between ER subgroups. To improve the stability of the estimates, 200 bootstrap resamples were performed, and 95% confidence intervals were derived from the bootstrap distribution. (3) ER status demonstrated significant PH violation (p < 0.005) with crossing survival curves. Discrimination (C-index 0.664–0.725) and calibration (IBS 0.149–0.169) were comparable across models, with RSF achieving the highest overall performance. Despite similar accuracy, survival curve structures differed substantially. Cox E-Net and RSF reproduced the observed crossing pattern, whereas GBSA generated smoother trajectories and DeepHit showed marked compression of subgroup separation. In the independent test cohort, the empirical RMST difference at 180 months was 16.6 months (ER-positive: 130.4; ER-negative: 113.8). Model-based RMST differences ranged from 1 month (DeepHit) to 27 months (Cox E-Net), with RSF and GBSA (12.8 and 13.8 months) most closely approximating the empirical benchmark. (4) Conclusions: We propose a novel, model-agnostic ML + RMST framework that addresses non-proportional hazards while providing quantifiable, time-specific clinical benefit. Moreover, models with similar discrimination and calibration produced markedly different survival curve behavior and absolute RMST estimates, demonstrating that accuracy metrics alone are insufficient for clinical interpretation. By linking prognostic modeling with absolute survival quantification, this framework advances survival evaluation beyond relative risk ranking toward individualized, clinically meaningful decision support. Full article

To reduce food waste and mitigate health risks from accidentally consuming spoiled food, freshness-indicating technologies are increasingly demanded. However, conventional colorimetric-based freshness-indicating packaging is limited by instability, subtle color changes, and complex production processes. This study presents a curcumin-based ink suitable for eco-friendly polylactic acid (PLA) food packaging films enabling real-time shrimp freshness monitoring via integrated intelligent packaging. The ink comprised curcumin as the indicator, ethyl cellulose (EC) and polyvinyl butyral (PVB) as binders, and polyethylene glycol 400 (PEG 400) to regulate permeability. Excellent printability was demonstrated by fineness, initial dryness and fluidity tests. It also demonstrated good thixotropic, viscosity, and flow curve properties. Printing minimally affected the PLA films’ mechanical and barrier properties. The indicator label showed high sensitivity, rapid response, and excellent reversibility to ammonia vapor. Practical application in monitoring shrimp spoilage at 25 °C and 4 °C revealed a strong correlation between the distinct color transition of the label and the increase in total volatile basic nitrogen (TVB-N) content and pH value, providing a reliable visual warning before obvious spoilage signs appeared. This work provides a viable integrated indicator packaging strategy for developing intelligent packaging, offering significant potential to reduce food waste and enhance supply chain transparency for perishable goods. Full article

Background/Objectives: To evaluate the utility of phase angle (PhA) derived from bioelectrical impedance analysis as a screening indicator for pre-frailty in community-dwelling older adults. Methods: This cross-sectional study included 171 participants (36 men and 135 women) in Japan in 2023. PhA at 50 kHz was measured using bioelectrical impedance analysis and evaluated as a potential screening indicator for pre-frailty. Assessments included body composition, physical function tests (maximum walking speed, Timed Up and Go (TUG), grip strength, knee extension strength, and one-leg stance time with eyes open), cognitive function (MoCA-J), and the Motor Fitness Scale (MFS), a questionnaire assessing physical function, along with the Kihon Checklist (KCL). Frailty status was defined using KCL scores (4–7: pre-frailty; ≥8: frailty), and participants were classified into robust and pre-frail/frail groups. Results: PhA was significantly correlated with physical function measures, including grip strength (r = 0.54, p < 0.01), MFS (r = 0.36, p < 0.01), maximum walking speed (r = 0.20, p < 0.05), knee extension strength (r = 0.16, p < 0.05), and TUG (r = −0.17, p < 0.05). In women, logistic regression analysis showed that PhA was independently associated with pre-frailty (age-adjusted odds ratio: 2.38; 95% CI: 1.08–5.23; p < 0.05). ROC analysis yielded an area under the curve of 0.65 (95% CI: 0.56–0.74), indicating modest discriminative ability. Age-adjusted cutoff values of PhA were 4.19° and 4.74°, corresponding to points prioritizing sensitivity and specificity, respectively. Conclusions: PhA is associated with physical function and may serve as a simple, non-invasive indicator for identifying pre-frailty in community settings. However, given its modest discriminative ability, PhA alone may not be sufficient as a standalone screening tool and should be used in combination with other clinical indicators for clinical application. Full article

Background/Objectives: Onion (Allium cepa) peems are an underutilized by-product rich in polyphenols. This study evaluated the physicochemical profile, and bioactive potential (antidiabetic, antimicrobial, antioxidant, and anticoagulant) of Moroccan red onion peels using integrated in vivo, in vitro, and in silico approaches. Methods: Moisture, pH, ash content, and mineral elements were determined, followed by phytochemical screening and three extractions: decoction E0, aqueous Soxhlet E1, and hydroethanolic Soxhlet E2 (70/30; ethanol/water, v/v). The measurement of polyphenols, flavonoids, and tannins was carried out using colorimetric methods, while the molecular profile was studied by high-performance liquid chromatography coupled to ultraviolet detection and electrospray ionization mass spectrometry (HPLC/UV-ESI-MS). Biological activities were determined using 2,2-diphenyl-1-picrylhydrazyl, ferric reducing antioxidant power, and total antioxidant capacity assays (in vitro antioxidant); microdilution (antimicrobial); prothrombin time and activated partial thromboplastin time (anticoagulant); and α-amylase/α-glucosidase enzymatic inhibition and oral glucose tolerance tests on normoglycemic rats. Also, acute toxicity was evaluated, and molecular interactions between these proteins and ligands (docking, molecular dynamics, and MM-PBSA) were analyzed. Results: Physicochemical analyses showed an acidic pH (3.06) and high ash content (15.21%), with the concentration of regulated elements remaining within FAO/WHO limits. The extractive content was between 6.90% E0 and 19.18% E2. The E1 extract had the maximum amount of total polyphenols (178.95 mg GAE/g); on the other hand, E2 was the richest in flavonoids by 121.43 mg QE/g. The HPLC/ESI-MS analysis of E0 revealed 20 compounds, among which flavonoids (84.93%) were predominant, with isorhamnetin (30.26%), followed by quercetin and its glycosylated forms. E1 showed the most potent antioxidant effects (IC₅₀ DPPH, 22.38 µg/mL, as that of ascorbic acid). The antibacterial activity of E0 was especially potent towards Enterobacter cloacae and Pseudomonas aeruginosa (MIC 75 µg/mL). A mild dose-dependent anticoagulant effect was seen. Antidiabetic activity was found to be outstanding: α-amylase (IC₅₀ 62.75 µg/mL) and α-glucosidase (IC₅₀ 8.49 µg/mL, stronger than acarbose) inhibitions were corroborated in vivo by a considerable decrease in the glycemic area under the curve. The molecular docking study in silico demonstrated strong molecular interactions, especially for quercetin 4′-O-glucoside with good binding energies. Conclusions: A. cepa peels from Morocco can be considered a safe plant matrix containing bioactive flavonoids with strong antioxidant and selective antimicrobial activities and promising antidiabetic effects, supported by molecular modeling. Full article