Advancing Open Science

The supply–demand relationship of flood regulation services (FRS) plays a vital role in mitigating urban flooding. Yet, existing studies still fall short in the comprehensiveness of FRS indicators, the accuracy of assessment scope, and the fine-scale analysis needed to delineate spatial supply–demand features and precisely identify critical areas. Using Xiamen Island as a case study, we first quantify ecosystem-based FRS supply with the InVEST model and assess socioeconomic FRS demand under the H-E-V framework; second, we perform parcel-level supply–demand matching to identify spatial patterns and typologies; then, we diagnose FRS status via the coupling–coordination degree model (CCDM); and finally, we delineate flood-risk hotspots through priority-intervention grading. The results indicate that (1) higher FRS supply clusters in the south, southwest, and northeast, whereas demand is markedly higher in the central–northern sector, yielding an overall pattern of “pronounced mismatch in the central and north, with relatively sufficient supply along the periphery.” (2) Low supply–high demand zones exhibit the highest flood risk and contain higher proportions of industrial, transportation, and residential land. (3) These low supply–high demand zones are further subdivided into five priority-intervention levels, for which we propose tiered, differentiated risk-management strategies. Collectively, the findings clarify supply–demand mechanisms and mismatch characteristics, providing decision support for urban flood safety and sustainable development.

This study aims to identify the effect of using a problem-solving-based sustainable learning model in the Educational Guidance and Counseling course, aiming to promote moral values and enhance the academic achievement of Education Sciences students at the University of El Oued in Algeria. A quasi-experimental design was used to measure the effect on (38 students) divided into two groups: an experimental group (19 students) and a control group (19 students). Data were collected through a pre-test and a post-test of the moral values scale, in addition to an achievement test administered to both groups. The results reveal that the use of the problem-based sustainable learning method has a positive impact on students of Education Sciences in terms of promoting their moral values and improving their academic achievement. Finally, this study recommends the necessity of applying sustainable learning models in university teaching as a way to contribute to the process of improving the outcomes of the higher education system.

This study investigates how artificial intelligence (AI) capabilities shape sustainable entrepreneurship (SE) among small and medium-sized enterprises (SMEs) in emerging economies. Focusing on knowledge management (KM) as a mediator, entrepreneurial orientation (EO) as a moderator, and government policy support (GPS) as an enabler, the research draws upon the Knowledge-Based View, Dynamic Capabilities Theory, and Institutional Theory. Using data from Saudi Arabian SMEs operating within the Vision 2030 agenda, the structural model demonstrates that AI primarily influences sustainability when firms possess robust KM systems capable of translating digital insights into actionable practices. Both EO and GPS strengthen the conversion of knowledge into sustainable outcomes, where EO fosters innovation and proactivity, and GPS provides essential resources and legitimacy. Nevertheless, excessive reliance on policy incentives may divert firms toward compliance rather than substantive transformation. Conceptually, this paper situates KM at the core of sustainability transformation, with policy support shaping the institutional context. The findings offer actionable guidance for SME managers and policymakers seeking to advance the United Nations Sustainable Development Goals (SDGs) through strategic engagement with AI and KM.

The mechanized processing of waste agricultural film is a crucial technical pathway for addressing agricultural-film pollution. Achieving resource recovery through mechanized waste-film processing—and thereby promoting the sustainable management of agricultural-film pollution—remains a major challenge for green agricultural development. This study systematically reviews the progress and limitations of shredding and film–impurity separation technologies deployed in China’s mechanized waste-film treatment. Based on multi-database searches and citation tracking of the literature published between 2000 and 2025, it comparatively evaluates key unit operations, including cutterhead/blade kinematics, specific energy-consumption (SEC) control, and airflow (air-classification) separation, complemented by engineering analyses of representative machinery. The findings indicate that integrated mechanized recovery lines have become the mainstream approach, although the recovered fraction still contains a high impurity load. Drum-type and shear-type shredding exhibit trade-offs between energy efficiency and mitigation of film wrapping/entanglement. Airflow separation and drum-screen or vibrating-screen modules show reduced separation efficiency and process stability at high moisture contents or when impurities have particle sizes comparable to the film; system complexity and maintenance burdens also warrant consideration. To address these issues, a process framework is proposed that integrates drum pre-crushing, shear fine shredding, air classification, and multi-stage screening, together with variable-frequency drive (VFD) speed control, torque monitoring, and modular design, providing a sustainable pathway for the clean separation and resource recovery of agricultural plastic film waste.

Background: Rapid multiplex PCR assays promise faster and broader detection of uropathogens and resistance markers than conventional quantitative urine culture and susceptibility testing (C&S), but trial evidence linking PCR-guided management to patient-centered outcomes and the mechanisms of any benefit is limited. We performed an ad hoc analysis of the randomized, multicenter NCT06996301 trial to evaluate whether PCR-guided diagnostic management improves clinical symptom resolution in complicated urinary tract infection (cUTI) and to quantify mediation by time-to-antibiotic start and antibiotic appropriateness. Methods: Paired PCR and C&S were collected for all participants; treating investigators received and acted on randomized results from one diagnostic modality and remained blinded to the comparator. The modified intention-to-treat (Mod-ITT) cohort at end-of-study (EOS) included 362 participants (PCR n = 193; C&S n = 169). The primary outcome was complete clinical cure at EOS (absence of all baseline symptoms). Secondary outcomes included partial cure (≥50% symptom reduction) and per-symptom changes. We used mixed-effects logistic regression (site random intercept) to estimate associations, and causal mediation analysis with nonparametric bootstrap (B = 2000) to decompose PCR’s total effect into indirect effects via time-to-antibiotic (log-transformed) and antibiotic appropriateness (binary, adjudicated at EOS) for complete clinical cure and partial cure. Results: Median time-to-first antibiotic was substantially shorter in the PCR arm (20 h; IQR 12–36) than in the C&S arm (52 h; IQR 30–66; p < 0.001). Antibiotic appropriateness was higher after PCR-guided care (161/193; 83.4%) versus C&S (105/169; 62.1%; p < 0.001). Complete clinical cure occurred in 143/193 (74.1%) PCR versus 106/169 (62.7%) C&S (p = 0.020); partial cure in 161/193 (83.4%) versus 121/169 (71.6%; p = 0.014). In a total-effect mixed model (no mediators), PCR assignment was associated with higher odds of cure (adjusted OR 1.95; 95% CI 1.12–3.39; p = 0.018). In the mechanistic model including mediators, antibiotic appropriateness (OR 2.48; 95% CI 1.45–4.24; p = 0.001), and time-to-antibiotic (per 1 h, OR 0.95; 95% CI 0.926–0.975; p < 0.001) were independently predictive, while the direct arm effect was attenuated (OR 1.10; 95% CI 0.33–3.71). Mediation analysis estimated a statistically significant combined indirect effect (ACME) of 0.0648 (95% CI 0.0343–0.0977), ADE 0.0207 (95% CI −0.0282–0.0784), total effect 0.0796 (95% CI 0.0419–0.1225), and proportion mediated ≈ 74%. Both time-to-antibiotic and appropriateness contributed, with ACME_time ≈ 0.046 and ACME_appropriateness ≈ 0.019. Exploratory analysis using partial cure as the outcome confirmed the robustness and internal validity of the complete-cure findings. Conclusions: In this ad hoc analysis of a randomized trial, PCR-guided management of cUTI improved patient-centered symptom outcomes compared with culture-guided care. Most of the benefit was mediated through faster initiation of antibiotics and, to a lesser extent, increased probability of an appropriate initial antibiotic. These results support stewardship-integrated, rapid molecular diagnostics (used alongside culture) to shorten time-to-effective therapy and improve clinical outcomes in cUTI.

The energy sector is currently under enormous transition, moving from fossil fuels to renewable energies and integrating energy efficiency measures. This transition can hold opportunities for new and innovative energy systems. This study presents an energetic and economic assessment of an innovative tri-generation unit working with a two-phase thermodynamic cycle. The tri-generation unit is driven by heat and is capable of providing heat at lower level, cold, and electricity to end users. The use cases—residential, day-use offices, commercial retail, and manufacturing industry—are integrated in a dynamic simulation model, indicating the operation mode of the unit. The results show that the tri-generation unit is able to provide heat and cold with an Energy Utilization Factor of 35% to 68%, depending on the use case. Solar thermal has a limited to potential to supply the unit with heat, due to the high temperature of 180 °C and the required unit operation at nighttime. The economic comparison indicates that the driving heat must be as low as possible and that savings through self-consumption is most relevant.

Background/Objectives: Large language models (LLMs) show promise for patient education, yet their safety and efficacy for chronic diseases requiring lifelong management remain unclear. This study presents the first comprehensive comparative evaluation of three leading LLMs for celiac disease patient education. Methods: We conducted a cross-sectional evaluation comparing ChatGPT-4, Claude 3.7, and Gemini 2.0 using six blinded clinical specialists (four gastroenterologists and two dietitians). Twenty questions spanning four domains (general understanding, symptoms/diagnosis, diet/nutrition, lifestyle management) were evaluated for scientific accuracy, clarity (5-point Likert scales), misinformation presence, and readability using validated computational metrics (Flesch Reading Ease, Flesch-Kincaid Grade Level, SMOG index). Results: Gemini 2.0 demonstrated superior performance across multiple dimensions. Gemini 2.0 achieved the highest scientific accuracy ratings (median 4.5 [IQR: 4.5–5.0] vs. 4.0 [IQR: 4.0–4.5] for both competitors, p = 0.015) and clarity scores (median 5.0 [IQR: 4.5–5.0] vs. 4.0 [IQR: 4.0–4.5], p = 0.011). While Gemini 2.0 showed numerically lower misinformation rates (13.3% vs. 23.3% for ChatGPT–4 and 24.2% for Claude 3.7), differences were not statistically significant (p = 0.778). Gemini 2.0 achieved significantly superior readability, requiring approximately 2–3 fewer years of education for comprehension (median Flesch-Kincaid Grade Level 9.8 [IQR: 8.8–10.3] vs. 12.5 for both competitors, p < 0.001). However, all models exceeded recommended 6th–8th grade health literacy targets. Conclusions: While Gemini 2.0 demonstrated statistically significant advantages in accuracy, clarity, and readability, misinformation rates of 13.3–24.2% across all models represent concerning risk levels for direct patient applications. AI offers valuable educational support but requires healthcare provider supervision until misinformation rates improve.

Soil Organic Carbon (SOC) stocks in croplands play a key role for climate change mitigation and soil sustainability, with proper management techniques enhancing carbon storage to support these goals. This study focuses on the development of a hybrid carbon modeling approach for the simulation of topsoil SOC stocks across the entire agricultural area of Lithuania. In essence, the proposed hybrid approach combines a custom cloud-based Soil Data Cube (SDC) and the RothC process-based model. High-resolution annual soil layers produced via the SDC (developed using Earth Observation and Copernicus datasets processed through AI-based methodologies) were incorporated into the RothC model to achieve reliable and detailed spatial estimations of SOC stocks. Moreover, 20-year projections into the future were conducted for (i) the business as usual scenario, and (ii) two different IPCC climate change scenarios (RCP 4.5 and 8.5) for the estimation of the SOC stock changes. The initial SOC stock varies from 15 to over 80 while the projections present an average SOC loss of $-0.14$ or the business-as-usual scenario and an average SOC sequestration of $-0.24$ and $-0.34$ under RCP 4.5 and RCP 8.5, respectively. The framework aims to provide a robust and cost-effective solution for estimating SOC stocks under climate pressures, supporting EU policies such as the Common Agricultural Policy.