In this paper, we study a class of nonlinear problems driven by mixed local–nonlocal operators and involving general nonlinearities. Using variational methods, we prove the existence of a least energy solution and of infinitely many radially symmetric solutions, in the spirit of the seminal works by Berestycki–Lions [16], [17] and Berestycki–Gallouët–Kavian [15]. We further investigate qualitative properties of these solutions, including regularity, decay, sign, and symmetry.
In the realm of homecare services, there is still a lack of a comprehensive conceptual framework and appropriate identification of performance dimensions to guide public managers in creating public value, guiding efficient resource allocation and effectiveness of homecare services. Addressing this gap requires the integration of financial variables with technical variables, strategic and operational needs, and both internal and external organizational perspectives. Only a few studies have explored how value is co-created during service delivery, particularly when considering the service user’s life context and the importance of cross-sector relationships in public service provision. Despite growing scholarly interest in cross-sector collaboration, practical evaluation of their overall performance remains limited, and there is insufficient attention to the internal dynamics that drive performance within collaborative processes. The aim of this study is to identify the dimensions of a performance measurement system, able to reach efficient resource allocation and effectiveness of homecare services. The study is based on field research using semi-structured interview as a data collection method, involving thirty-four healthcare providers and nine members of health organizations and regional government. The results of the empirical analysis are presented using the Public Service Environment framework, articulated at macro-, meso, micro-, and submicro- level.
This study examines the dynamic and asymmetric connectedness among global sustainability-focused financial markets, including clean energy, the hydrogen market, ESG, carbon markets, and green bonds. The study uses an integrated methodological framework—Quantile-TVP-VAR, connectedness network, and Wavelet coherence—examining spillovers across market regimes and investment horizons. The results reveal that spillover among sustainability markets largely depends on markets’ regimes and frequency horizons. Specifically, the study found that at lower and higher quantiles, clean energy, ESG, and hydrogen markets act as net transmitters. Whilst, at higher quantiles, green bonds and carbon markets absorb systemic shocks and stabilise sustainability markets. The frequency distribution highlights a prominent connectedness in the short-term frequency horizons, while moderate at medium and long-run. The study recommends that diversification benefits are temporary and regime-dependent, while portfolio risk management must be horizon-specific. The hydrogen and clean energy markets can be used for resilience enhancement in sustainability-focused portfolios. Moreover, carbon and green bonds can improve market liquidity and stabilise the sustainable finance framework.
Abstract Background The quadriceps tendon (QT) has emerged as a reliable autograft for anterior cruciate ligament reconstruction (ACLR), but uncertainty remains regarding several key comparative aspects—particularly donor-site morbidity, long-term graft survival, knee stability, and complication rates—when evaluated against hamstring tendon (HT) and bone–patellar tendon–bone (BPTB) autografts. High-level evidence restricted to randomized controlled trials directly comparing QT with HT or BPTB remains limited. To compare clinical outcomes, graft failure, donor-site morbidity, and knee stability among QT, HT, and BPTB autografts for primary ACLR using level-I and level-II randomized controlled trials (RCTs). Methods The MEDLINE (PubMed), Embase (Elsevier), and Cochrane Library databases were searched on 1 September 2025, and repeated 2 weeks later. Only level-I or -II RCTs comparing QT to HT or BPTB in primary ACLR were included. Random-effects meta-analyses were performed for International Knee Documentation Committee (IKDC) and Lysholm scores, instrumented laxity, graft failure, donor-site morbidity, and reoperation. Risk of bias was assessed with RoB 2.0, and small-study effects with funnel and doi plots. Results Eleven RCTs (mean follow-up, 2–10 years) were included. Pooled IKDC scores averaged 84.8 (95% CI 81.9–87.9) and Lysholm scores averaged 93.1 (95% CI 91.6–94.6), with no significant differences between QT and either comparator (P > 0.05). Side-to-side anterior tibial translation averaged 1.2 mm (95% CI 0.99–1.54 mm) across all grafts, also without significant differences (P > 0.05). Pooled graft failure and ipsilateral reoperation rates were 0.7% (95% CI 0.0–1.9%) and 2.3% (95% CI 0.6–4.7%), respectively, again with no between-graft differences (P > 0.05). Donor-site morbidity did not differ significantly between QT and HT (mean 13.83 [95% CI 9.6–19.83]; P > 0.05). Conclusion This meta-analysis of level-I/II randomized controlled trials found no statistically significant differences among quadriceps tendon, hamstring tendon, and bone–patellar tendon–bone autografts in patient-reported outcomes, knee stability, graft re-rupture, or additional knee surgery. Donor-site morbidity comparisons were limited by incomplete reporting, particularly for BPTB. These findings suggest that contemporary surgical techniques and rehabilitation protocols may minimize graft-specific differences in mid-term outcomes, although interpretation should consider the limited number of direct comparative trials across all three graft types. Level of evidence Systematic review and meta-analysis; level of evidence, 1 and 2.
Sustainable grazing management requires precise knowledge of daily nutritional requirements and the quantity and quality of pasture dry matter. Combining multiple data sources with machine learning models can create accurate predictive systems to optimize feeding, cut expenses, and maintain pasture productivity, helping farms stay economically viable long-term. This study evaluated 69 machine learning models—combinations of three algorithms and 23 datasets including thermo-pluviometric, pasture classification, Sentinel 1 2, and soil data—from a one-year study on two Mediterranean wood-pasture fields located in Sardinia (Italy). The models were compared for accuracy, scalability, and application cost to identify the most effective framework for predicting pasture and grazing conditions. The most accurate framework used the Ensamble Learner (EL) algorithm with thermo-pluviometric, Sentinel-2 and pasture classification data, achieving RMSE 469.92 kg·ha⁻¹ DW, MAE 402.61 kg·ha⁻¹ DW and R² 0.98, but is impractical at large scale because pasture classification inputs require highly qualified staff and time-consuming on-site surveys. A scalable, zero-cost alternative uses EL with thermo-pluviometric and Sentinel-2, with comparable error metrics. Research should focus on the whole Machine Learning workflow, from problem definition and covariate selection to preprocessing and evaluation rather than algorithms alone. Reliable pasture-yield modeling should include at least the thermo-pluviometric and Sentinel-2 multispectral data. Future work will apply models to estimate yield, map management zones, and generate grazing-rotation prescription maps using measured pasture utilization.
