Search Results (17,575)

This study investigates whether publicly listed cannabis shares provide enough risk-adjusted returns to warrant their incorporation into diversified portfolios. An equally weighted portfolio of cannabis companies is constructed using monthly data from January 2015 to December 2024. Risk-adjusted performance is assessed using the Sharpe, Sortino, and Omega ratios and compared to the Russell 3000 Index and the FTSE All-World ex-US Index. In addition, we estimate both unconditional and conditional Fama–French five-factor model enhanced by momentum. The findings indicate that cannabis stocks persistently underperform U.S. and global benchmarks in both absolute and risk-adjusted metrics. Downside risk is elevated because cannabis portfolios exhibit much higher value at risk (VaR) and conditional value at risk (CVaR) than broad indices, especially after COVID-19. The findings show that cannabis stocks are quite volatile and fail to generate significant returns on a risk-adjusted basis. The study highlights the sector’s structural vulnerabilities and cautions investors, portfolio managers, and regulators against treating cannabis shares as dependable long-term investments. Full article

Background: Patient satisfaction with diabetes technology is increasingly recognized as a key factor in therapeutic success. Patient-reported outcomes (PROs) are gaining importance in diabetes care and in the evaluation of advanced insulin delivery systems. Objectives: This study aimed to design and validate a new questionnaire, the Insulin Pump Infusion Sets Satisfaction Scale (IPISS), to assess satisfaction with insulin infusion sets among individuals with type 1 diabetes. Methods: The questionnaire was developed by our Diabetology Unit in two versions: one for patient self-reporting and one for caregivers when the patient is too young to complete it autonomously. Content validity was assessed by six healthcare professionals (three diabetologists and three nurses) based on Polit and Beck’s methodology. The Item Content Validity Index (I-CVI) was calculated for both relevance and comprehensibility and was considered satisfactory if expert agreement reached ≥83%. The Scale Content Validity Index (S-CVI) was computed as the average of I-CVIs, with a cut-off value > 90% deemed acceptable. Results: Almost all items achieved 100% positive agreement for both relevance and comprehensibility, except one item in the caregiver version, for which one rater did not provide a rating for comprehensibility (I-CVI = 83.3%). The S-CVI was 100% for relevance in both versions, 99.24% for comprehensibility in the caregiver version, and 100% in the patient version. Conclusions: The IPISS is a content-validated, self-reported tool, suitable for evaluating satisfaction with infusion sets in individuals using insulin pumps, with versions adapted for both patients and caregivers. Full article

Dioscorea polystachya (Chinese yam) is a crop valued for both medicinal and edible purposes, and exhibits rich genetic diversity. However, research into its germplasm resources remains understudied, and molecular breeding efforts lag behind. To bridge this gap, this study employed an integrated approach, combining the analysis of 23 phenotypic traits (17 qualitative and 6 quantitative) with genotyping using 19 polymorphic SSR markers. This combined strategy was applied to 53 accessions collected across 16 Chinese provinces to assess genetic diversity, population structure, and marker–trait associations. Phenotypic analysis revealed high diversity, with the Shannon diversity index (I) ranging from 0.09 to 1.15 for qualitative traits and from 1.45 to 1.79 for quantitative traits. Tuber traits exhibited the highest variability (with a CV up to 71.45%), indicating significant potential for yield improvement. Principal component analysis distilled phenotypic variation into eight principal components (accounting for 73.13% of the cumulative variance), and elite germplasm (e.g., DP24, DP52) was selected for breeding based on this analysis. Stepwise regression prioritized eight core evaluation traits (e.g., flowering rate, tuber length). SSR markers amplified 80 alleles (mean 4.211/locus), showing moderate genetic diversity (He = 0.529, PIC = 0.585). Population structure analysis divided accessions into two subpopulations, correlated with geographic origins: Group 1 (northern/southwestern China) and Group 2 (central/eastern China), reflecting adaptation to local climates and human selection. Association analysis identified 10 SSR loci significantly linked (p < 0.01) to key traits, including YM07_2 (flowering, R² = 13.94%), YM37_2 (leaf margin color, R² = 19.03%), and YM19_3 (leaf width, R² = 19.34%). This study establishes a comprehensive genetic framework for Chinese yam, offering molecular tools for marker-assisted breeding and strategies to conserve high-diversity germplasm, thereby enhancing the utilization of this orphan crop. Full article

The diameter of basalt fiber influences the reinforcement of basalt fiber asphalt mixtures. However, the performance evaluation and mechanistic analysis of asphalt mixtures reinforced with varying fiber diameters have been insufficiently studied. AC-13 asphalt mixtures were designed and prepared with four different fiber diameters 7 μm, 16 μm, 25 μm, and an equal-mass mixture of these. The reinforcement mechanisms were analyzed using the equal cross-section theory. Results indicate that the incorporation of 7 μm and mixed-diameter basalt fibers significantly enhances the pavement performance of the asphalt mixtures compared to the control group without fibers. Additionally, it is shown by triaxial shear tests that the cohesion of the asphalt mixtures with the aforementioned two diameters of basalt fibers is strengthened by 61.5% and 55.5%, respectively. The dynamic modulus values in the high-frequency range are found to be positively correlated with fiber diameters. Since the fiber mass content and modulus were held constant, a decrease in diameter was observed to lead to an increase in fiber quantity. This is manifested by a multiple-fold increase in the total transformed cross-section (TTC_R) index for 7 μm fiber asphalt mixtures, as described by the equal cross-section theory. It is concluded that the performance improvement of the asphalt mixtures can be further enhanced under the same fiber content and cost conditions by optimizing diameter parameters. Full article

Objectives: Neurocognitive disorders (NCDs) refer to a broad spectrum of conditions characterized by declining cognitive functions, such as memory, attention, language, and executive abilities. It is estimated that up to half of patients affected by NCDs remain undiagnosed or are diagnosed at an advanced stage of the disease. This study aimed to analyze the utility of subclinical organ damage markers, which could be used in primary care for the detection and prevention of NCD. Methods: The study participants (n = 137) completed neuropsychological tests (Addenbrooke’s Cognitive Examination/ACE and Mini-Mental State Examination/MMSE), a sociodemographic survey, an interview on past illnesses, and had their ankle-brachial index (ABI) and pulse wave velocity (PWV) values measured. Results: Based on the MMSE test, 26 participants (19.0%) were diagnosed with mild cognitive impairment (MCI) and 8 participants (5.8%) with NCDs. The study found that lower ABI values were associated with worse cognitive performance, suggesting that the ABI may be a useful tool for identifying individuals at increased risk of NCDs, while PWV cannot be used as a predictor for this group of diseases. Conclusions: Lower ABI values were associated with reduced cognitive performance, whereas PWV showed no significant relationship. The secondary findings suggest that physical activity, regular computer use, and better mental well-being were linked to improved cognitive outcomes. A low ABI value could potentially serve as a predictor of cognitive disorders, and as a diagnostic tool that is easily accessible and quick, it may improve diagnostics and the overall health of primary care patients. Health education regarding modifiable risk factors for dementia is also of crucial importance. Full article

Background: Cervical cancer remains a major global health concern, with high recurrence rates in advanced stages. [¹⁸F]FDG PET/CT provides prognostic biomarkers such as SUV, MTV, and TLG, though these are not routinely integrated into clinical protocols. Radiomics offers quantitative analysis of tumor heterogeneity, supporting risk stratification. Purpose: To evaluate the prognostic value of clinical and radiomic features for disease-free survival (DFS) in locoregionally advanced cervical cancer using machine learning (ML). Methods: Sixty-three patients (mean age 47.9 ± 14.5 years) were diagnosed between 2015 and 2020. Radiomic features were extracted from pre-treatment PET/CT (IBSI-compliant PyRadiomics). Clinical variables included age, T-stage, Dmax, lymph node involvement, SUVmax, and TMTV. Forty-two models were built by combining six feature-selection techniques (UCI, MD, MI, VH, VH.VIMP, IBMA) with seven ML algorithms (CoxPH, CB, GLMN, GLMB, RSF, ST, EV) using nested 3-fold cross-validation with bootstrap resampling. External validation was performed on 95 patients (mean age 50.6 years, FIGO IIB–IIIB) from an independent cohort with different preprocessing protocols. Results: Recurrence occurred in 31.7% (n = 20). SUVmax of lymph nodes, lymph node involvement, and TMTV were the most predictive individual features (C-index ≤ 0.77). The highest performance was achieved by UCI + EV/GLMB on combined clinical + radiomic features (C-index = 0.80, p < 0.05). For single feature sets, IBMA + RSF performed best for clinical (C-index = 0.72), and VH.VIMP + GLMN for radiomics (C-index = 0.71). External validation confirmed moderate generalizability (best C-index = 0.64). Conclusions: UCI-based feature selection with GLMB or EV yielded the best predictive accuracy, while VH.VIMP + GLMN offered superior external generalizability for radiomics-only models. These findings support the feasibility of integrating radiomics and ML for individualized DFS risk stratification in cervical cancer. Full article

Objective: This study evaluates the efficacy of a novel approach to oral appliance therapy (“OAT”) for the treatment of obstructive sleep apnea (“OSA”). This novel approach utilizes a systemized, oximetry-informed, treatment protocol and a precision-custom oral appliance. Methods: Sixty consecutive patients diagnosed with OSA were treated at Sleep Better Austin (“SBA”) using a structured, multi-step protocol and a precision-custom oral appliance (ProSomnus EVO). Baseline and post-treatment apnea–hypopnea index (“AHI”) values were compared using a matched-pair design. The primary outcome was the percentage of patients achieving a residual AHI of <10 events/h. Secondary outcomes included severity classification improvement. Results: In total, 90% of patients achieved the primary endpoint, and 87% improved by at least one severity classification. The mean AHI improved by 63% from baseline with the precision-custom OAT in situ (p < 0.001). In the moderate-to-severe subgroup, AHI improved by 70%, with 100% of severe patients achieving a residual AHI of <20 and a ≥50% improvement, without patient preselection. No serious adverse events were reported, and all patients continued therapy at follow-up. Conclusions: Precision-custom OAT, when delivered through a standardized clinical protocol informed by oximetry, can be a highly effective and well-tolerated treatment for OSA. These findings support its broader adoption as a non-invasive alternative to continuous positive airway pressure (“CPAP”) and surgical interventions, particularly for patients seeking personalized, high-compliance solutions. Full article

To address the limitations of traditional cotton leaf nitrogen content estimation methods, which include low efficiency, high cost, poor portability, and challenges in vegetation index acquisition owing to environmental interference, this study focused on emerging non-destructive nutrient estimation technologies. This study proposed an innovative method that integrates multi-color space fusion with deep and machine learning to estimate cotton leaf nitrogen content using smartphone-captured digital images. A dataset comprising smartphone-acquired cotton leaf images was processed through threshold segmentation and preprocessing, then converted into RGB, HSV, and Lab color spaces. The models were developed using deep-learning architectures including AlexNet, VGGNet-11, and ResNet-50. The conclusions of this study are as follows: (1) The optimal single-color-space nitrogen estimation model achieved a validation set R² of 0.776. (2) Feature-level fusion by concatenation of multidimensional feature vectors extracted from three color spaces using the optimal model, combined with an attention learning mechanism, improved the validation R² to 0.827. (3) Decision-level fusion by concatenating nitrogen estimation values from optimal models of different color spaces into a multi-source decision dataset, followed by machine learning regression modeling, increased the final validation R² to 0.830. The dual fusion method effectively enabled rapid and accurate nitrogen estimation in cotton crops using smartphone images, achieving an accuracy 5–7% higher than that of single-color-space models. The proposed method provides scientific support for efficient cotton production and promotes sustainable development in the cotton industry. Full article

Cropland abandonment, a global challenge, necessitates comprehensive monitoring to achieve the zero hunger goal. Prior monitoring approaches to cropland abandonment often face constraints in resolution, time series, drivers, prediction, or a combination of these. Here, we proposed an artificial intelligence framework to comprehensively monitor cropland abandonment and tested the framework in Hengyang City, China. Specifically, we first mapped land cover at 30 m resolution from 1985 to 2023 using Landsat, stable sample points, and a machine learning model. Subsequently, we constructed the extent, time, and frequency of cropland abandonment from 1986 to 2022 by analyzing pixel-level land-use trajectories. Finally, we quantified the drivers of cropland abandonment using machine learning models and predicted the spatial distribution of cropland abandonment risk from 2032 to 2062. Our results indicated that the abandonment maps achieved overall accuracies of 0.88 and 0.78 for identifying abandonment locations and timing, respectively. From 1986 to 2022, the proportion of cropland abandonment ranged between 0.15% and 4.06%, with an annual average abandonment rate of 1.32%. Additionally, the duration of abandonment varied from 2 to 38 years, averaging approximately 14 years, indicating widespread cropland abandonment in the study area. Furthermore, 62.99% of the abandoned cropland experienced abandonment once, 27.17% experienced it twice, and only 0.23% experienced it five times or more. Over 50% of cropland abandonment remained unreclaimed or reused. During the study period, tree cover, soil pH, soil total phosphorus, potential crop yield, and the multiresolution index of valley bottom flatness emerged as the five most important environmental covariates, with relative importances of 0.087, 0.074, 0.068, 0.050, and 0.043, respectively. Temporally, cropland abandonment in 1992 was influenced by transportation inaccessibility and low agricultural productivity, soil quality degradation became an additional factor by 2010, and synergistic effects of all three drivers were observed from 2012 to 2022. Notably, most cropland had a low abandonment risk (mean: 0.36), with only 0.37% exceeding 0.7, primarily distributed in transitional zones between cropland and non-cropland. Future risk predictions suggested a gradual decline in both risk values and the spatial extent of cropland abandonment from 2032 to 2062. In summary, we developed a comprehensive framework for monitoring cropland abandonment using artificial intelligence technology, which can be used in national or regional land-use policies, warning systems, and food security planning. Full article

Road and pavement construction require huge volumes of borrowed soils in addition to the foundation soils. Unfortunately, not all soils are suitable for construction purposes. Soil stabilization is a fundamental technique used to enhance the engineering properties of weak ground/soil to meet the demands of large infrastructure projects, such as roads. It is in this regard that this study investigates the strength development, durability, and effectiveness of cement and quarry dust as stabilizers to enhance the geotechnical properties of a weak tropical clay soil. Cement was added in the range of 0% to 10% while quarry dust was used to partially replace soil in the range of 0% to 50%. The results show significant improvements in the Atterberg limits and strength properties of the tropical clay. The liquid limit reduced from 43.2% to 25.1% while the plasticity index reduced from 17.6% to 10.2% at 50% quarry dust and 10% cement content. Similarly, the maximum dry unit weight increased from 17.4 kN/m³ to 21.3 kN/m³ while the optimum moisture content decreased from 17.1% to 12.9%. The maximum soaked CBR value was 172%, representing a 1497% enhancement over untreated soil. Also, the maximum unconfined compressive strength (UCS) reached 2566 kN/m² at 28 days of curing, representing a 1793.73% increase when compared to the untreated soil. Cement content was found to be the predominant factor influencing strength development. The study shows that cement–quarry dust blends compacted at high energy can be adopted in sustainable road construction. Full article

The aim of the study was to compare grain yield, grain quality, and morphophysiological parameters of three winter wheat cultivars: Kilimanjaro, Hymalaya, and Ostroga. The cultivars were grown in crop rotation after grain maize harvest, using three tillage systems: conventional (C), reduced (R), and no-tillage (N). A three-year field experiment was conducted in southeastern Poland. Compared to no-tillage, the use of conventional and reduced systems resulted in higher grain yield, increased leaf area index and relative chlorophyll content, and higher gas exchange parameters. In the conventional system, the highest grain yield was achieved by cvs. Hymalaya and Ostroga, while in no-tillage and reduced, it was cv. Hymalaya. Compared to no-tillage, the conventional system resulted in higher values of grain quality parameters, while simultaneously reducing ash content, and the reduced system promoted a better gluten index. Interactions between cultivar and tillage system demonstrated good grain quality in terms of protein, falling number, and gluten index. Gluten content above 25.0% was found in grains of cvs. Kilimanjaro and Hymalaya in the reduced and conventional systems, and cv. Ostroga in the conventional system. The dry and semi-drought periods in the 2018/2019 season were conducive to more favorable grain quality parameter values: protein, gluten, falling number, and ash. However, the resulting grain was characterized by a lower gluten index and lower physical parameters. Cvs. Hymalaya and Ostroga are recommended for cultivation in conventional and reduced tillage systems, and cv. additionally for no-tillage systems. Growing the cv. Kilimanjaro in no-tillage and reduced tillage systems, and the cv. Ostroga in a no-tillage system, will result in lower grain yields. Full article

Background: Coronary collateral circulation (CCC) plays a crucial protective role in patients with chronic total occlusion (CTO), mitigating ischemia and improving long-term outcomes. However, the degree of collateral vessel development varies substantially among individuals. Systemic inflammatory and nutritional status may influence this variability. The Naples Prognostic Score (NPS) is a composite index reflecting these parameters, yet its relationship with CCC remains incompletely defined. Methods: We retrospectively analyzed 324 patients with angiographically confirmed CTO at Selçuk University Faculty of Medicine between 2014 and 2025. Coronary collaterals were graded using the Rentrop classification, and patients were categorized as having poor (grades 0–1) or good (grades 2–3) collaterals. The NPS was calculated using serum albumin, cholesterol, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. Baseline clinical and laboratory data were compared between groups. Univariate and multiple binary logistic regression analyses were performed to identify independent predictors of collateral development. Results: Of the 324 patients, 208 (64.2%) had poor and 116 (35.8%) had good collateral circulation. Patients with good collaterals had higher body mass index, HDL Cholesterol (HDL-C), and triglyceride levels, and significantly lower NPS values compared with those with poor collaterals (p < 0.05 for all). In multiple binary logistic regression analysis, HDL-C (OR 1.035; 95% CI 1.008–1.063; p = 0.011) and NPS (OR 0.226; 95% CI 0.130–0.393; p < 0.001) emerged as independent predictors of well-developed collaterals. Conclusions: Both NPS and HDL-C are independently associated with the degree of coronary collateral circulation in CTO patients. These findings highlight the interplay between systemic inflammation, nutritional status, lipid metabolism, and vascular adaptation. As simple and routinely available measures, NPS and HDL-C may serve as practical tools for risk stratification and identifying patients at risk of inadequate collateral formation. Prospective studies with functional assessments of collateral flow are warranted to confirm these associations and explore potential therapeutic interventions. Full article

The aim of this study is to determine the spatial distribution of geochemical anomalies of selected potential toxic elements in the soils of the river basins in the Northeastern Caucasus—specifically the Ulluchay, Sulak, and Sunzha Rivers. A concentration of 25 chemical elements was measured using inductively coupled plasma mass spectrometry (ICP-MS). Petrogenic elements commonly found in the Earth’s crust (Al, Na, Ca, Fe, Mg) showed high concentrations (Na up to 306,600.70 mg/kg). Conversely, concentrations of Ag, Cd, Sn, Sb, and Te at many sampling sites were extremely low, falling below the detection limits of analytical instruments. The geochemical indicators Cf (contamination factor) and Igeo (geoaccumulation index) indicate that the regional characteristics of the territory, such as lithological conditions, hydrochemical schedules, and the history of geological development of the territory, affect the concentration of elements. Anomalous concentrations were found for seven elements (Ba, Na, Zn, Ag, Li, Sc, As), whereas no anomalies were identified for Be, Mg, Al, Mn, Fe, Co, Ni, Cu, Pb, Te, and Cs. For the most part (8 of 10), the sampling sites with anomalous chemical element content are located in the basin of the Sunzha River. Two sites with anomalous chemical element content have been identified in the Sulak River Basin. Anomalous values in the Sulak River Basin are noted for two chemical elements—Ba and Na. Natural features such as geological structure, parent rock composition, vertical climatic zonation, and landscape diversity play a major role in forming geochemical anomalies. The role of anthropogenic factors increases in localized areas near settlements, industrial facilities, and roads. The spatial distribution of geochemical anomalies must be considered in agricultural management, the use of water sources for drinking supply, the development of tourist routes, and comprehensive spatial planning. Full article

Background/Objectives: Paspalum nicorae Parodi is a native subtropical grass species with promising agronomic attributes, such as persistence, drought and cold tolerance, and rapid establishment. However, the species remains underutilized in breeding programs due to the absence of well-characterized germplasm and limited studies on its genetic variability and agronomic potential. This study aimed to estimate genetic parameters, predict genotypic values, and identify superior ecotypes with desirable forage traits, integrating stability and adaptability analyses. Methods: A total of 84 ecotypes were evaluated over three consecutive years for twelve morphological and forage-related traits. Genetic parameters, genotypic values, and selection gains were estimated using mixed models (REML/BLUP). Stability was assessed through harmonic means of genotypic performance, and the multi-trait genotype–ideotype distance index (MGIDI) was applied to identify ecotypes with balanced performance across traits. Results: Substantial genetic variability was detected for most traits, particularly those related to biomass accumulation, such as total dry matter, the number of tillers, fresh matter, and leaf dry matter. These traits exhibited medium to high heritability and strong potential for selection. Ecotype N3.10 consistently showed superior performance across productivity traits while other ecotypes, such as N4.14 and N1.09, stood out for quality-related attributes and cold tolerance, respectively. The application of the MGIDI index enabled the identification of 17 ecotypes with balanced multi-trait performance, supporting the simultaneous selection for productivity, quality, and adaptability. Comparisons with P. notatum suggest that P. nicorae harbors competitive genetic potential, despite its lower level of domestication. Conclusions: The integration of REML/BLUP analyses, stability parameters, and ideotype-based multi-trait selection provided a robust framework for identifying elite P. nicorae ecotypes. These findings reinforce the strategic importance of this species as a valuable genetic resource for the development of adapted and productive forage cultivars in subtropical environments. Full article

Imaging in controlled agriculture helps maximize plant growth by saving labor and optimizing resources. By monitoring specific plant traits, growers can prevent crop losses by correcting environmental conditions that lead to physiological disorders like leaf tipburn. This study aimed to identify morphometric and spectral markers for the early detection of tipburn in two Romaine lettuce (Lactuca sativa) cultivars (‘Chicarita’ and ‘Dragoon’) using an image-based system with color and multispectral cameras. By monitoring tipburn in treatments using melatonin, lettuce cultivars, and with and without supplemental lighting, we enhanced our system’s accuracy for high-resolution tipburn symptom identification. Canopy geometrical features varied between cultivars, with the more susceptible cultivar exhibiting higher compactness and extent values across time, regardless of lighting conditions. These traits were further used to compare simple linear, logistic, least absolute shrinkage and selection operator (LASSO) regression, and random forest models for predicting leaf fresh and dry weight. Random forest regression outperformed simpler models, reducing the percentage error for leaf fresh weight from ~34% (LASSO) to ~13% (RMSE: 34.14 g to 17.32 g). For leaf dry weight, the percentage error decreased from ~20% to ~12%, with an explained variance increase to 94%. Vegetation indices exhibited cultivar-specific responses to supplemental lighting. ‘Dragoon’ consistently had higher red-edge chlorophyll index (CIrededge), enhanced vegetation index, and normalized difference vegetation index values than ‘Chicarita’. Additionally, ‘Dragoon’ showed a distinct temporal trend in the photochemical reflectance index, which increased under supplemental lighting. This study highlights the potential of morphometric and spectral traits for early detection of tipburn susceptibility, optimizing cultivar-specific environmental management, and improving the accuracy of predictive modeling strategies. Full article