Advancing Open Science

Poland, as a leading apple producer in the EU, must maintain high fruit quality during prolonged storage and distribution, which is crucial for exports to distant markets. Therefore, it is essential to clearly identify which factors most strongly affect quality and the magnitude of their effects in order to make informed choices about pre- and postharvest practices, storage technology, and logistics. The objective of this study was to assess the effect of selected factors on the quality of apples of the ‘Gala Schniga^® SchniCo Red(s)’ cultivar after long-term storage. The study analyzed the effects of harvest date (optimal and delayed), three variants of 1-methylcyclopropene application (control-0 µL·L⁻¹ 1-MCP, Harvista™, SmartFresh™, and Harvista™ + SmartFresh™), storage period (5, 7, and 9 months), simulated trading period (0 or 7 days at 20 °C) and storage technology (ULO: 1.2% CO₂: 1.2% O₂; DCA: 0.6% CO₂: 0.6% O₂) in two consecutive seasons (2022/2023 and 2023/2024). Five quality parameters were evaluated: flesh firmness (F), soluble solid content (SSC), titratable acidity (TA), SSC/TA ratio, and the concentration of 1-aminocyclopropane-1-carboxylic acid (ACC). Backward-elimination stepwise regression and partial eta squared (η²) calculations were used to analyze the data to determine the factors with the greatest impact. The post-harvest application of 1-MCP had the strongest effect in terms of maintaining firmness (η² = 70.4%) and acidity (η² = 38.0%) and reducing ACC content (η² = 21.3%). Harvista™ preparation had a weaker or negligible effect on ACC content, but reduced SSC (η² = 22.7%). Harvest date, storage duration, and shelf life significantly influenced all traits, with controlled-atmosphere regime further modulating outcomes. By integrating preharvest maturity with treatment timing and CA storage, we disentangled the relative contributions of harvest timing, treatment, and storage. The results provide actionable inputs for a decision-support tool to help producers maintain target quality—firmness, SSC, TA, SSC/TA, and ACC—through optimized practice, storage technology choice, and logistics.

The adoption of artificial intelligence (AI) is reshaping organizational practices, yet workers’ attitudes remain crucial for its successful integration. This study examines how perceived organizational ethical culture, organizational innovativeness, and job performance influence workers’ attitudes towards AI. A survey was administered to 356 workers across diverse sectors, with analyses focusing on 154 participants who reported prior AI use. Measures included the Attitudes Towards Artificial Intelligence at Work (AAAW), Corporate Ethical Virtues (CEV), Inventory of Organizational Innovativeness (IOI), and an adapted version of the In-Role Behaviour Scale. Hierarchical regression analyses revealed that ethical culture dimensions, particularly Clarity and Feasibility, significantly predicted attitudes towards AI, such as anxiety and job insecurity, with Feasibility also associated with the attribution of human-like traits to AI. Supportability, reflecting a cooperative work environment, was linked to lower perceptions of AI human-likeness and adaptability. Among innovation dimensions, only Raising Projects, the active encouragement of employees’ ideas, was positively related to perceptions of AI adaptability, highlighting the importance of participatory innovation practices over abstract signals. Most importantly, perceived job performance improvements through AI predicted more positive attitudes, including greater perceived quality, utility, and reduced anxiety. Overall, this study contributes to the growing literature on AI in organizations by offering an exploratory yet integrative framework that captures the multifaceted nature of AI acceptance in the workplace.

Background/Objectives: Postoperative nausea and vomiting (PONV) are common after general anesthesia (GA) and, in patients undergoing vitreoretinal surgery, may be triggered by the oculocardiac reflex (OCR) leading to the oculoemetic reflex (OER). Inadequate dosing of intravenous rescue opioid analgesics may further provoke OCR. Adequacy of Anesthesia (AoA) monitoring enables optimized titration of intravenous rescue opioid analgesics, while preemptive intravenous or peribulbar analgesia may reduce opioid use. This study evaluated the impact of preemptive paracetamol or peribulbar block (PBB) combined with AoA-guided GA on the incidence of PONV, OCR, and OER in patients undergoing vitreoretinal surgery. Methods: A total of 185 patients were randomized to four groups: GA with AoA-guided intraoperative rescue opioid analgesia plus a single intravenous dose of paracetamol 1 g, or PBB using 1% ropivacaine, 0.5% bupivacaine, or a 1:1 mixture of 0.5% bupivacaine/2% lidocaine. Data from 175 patients were analyzed. Results: AoA-guided GA yielded an OCR incidence of 11.4% and PONV incidence of 4%. PBB, regardless of anesthetic solution, did not significantly reduce intraoperative rescue opioid analgesia requirements or the incidence of PONV, OCR, or OER compared with intravenous paracetamol. Notably, no PONV occurred in patients with three Apfel risk factors (predicted risk ≈ 61%) who received PBB. Conclusions: No overall advantage of PBB over intravenous paracetamol was observed. It may, however, benefit patients at high PONV risk.

Bearing faults are the most common type of failure in induction motors, given their long operating times and mechanical loads. Because induction motors in industrial environments operate under various load conditions, effective methods for diagnosing bearing faults across these conditions have become increasingly important. Here, different load conditions were implemented with a powder clutch and a tension controller, and vibration data were acquired under both normal and faulty bearing conditions. To ensure diagnostic accuracy while improving time efficiency, a model bank-based fault diagnosis classifier is proposed, which utilizes independent classifiers trained for each load condition. For comparison, a single model-based classifier trained on all load conditions is also implemented. Both approaches are validated with three classifiers: support vector machine (SVM), multilayer neural network (MNN), and random forest (RF), with three input types: raw time-series signals, six statistical features, and three t-test–selected statistical features. Experimental results reveal that the model bank-based fault diagnosis classifier utilizing three statistical features selected by t-test maintained 98–100% accuracy while reducing operating time compared with Method 1 by 60.0, 71.2, and 60.0% for SVM, MNN, and RF, respectively. These results confirm that the proposed Method 2 utilizing time-domain analysis provides reliable and time-efficient performance for bearing fault diagnosis under variable load conditions.

Background: Preoperative evaluation in bariatric surgery aims to minimize perioperative risks and identify comorbid abdominal pathologies that may influence surgical planning. The role of routine abdominal ultrasonography (USG) remains debatable. Methods: This retrospective study included 1119 consecutive candidates for bariatric surgery who underwent routine preoperative ultrasonography (USG) between January 2022 and October 2024. Patients were stratified by BMI and categorized according to USG findings as normal, incidental, requiring follow-up/concomitant procedures, or necessitating cancellation. Baseline characteristics, USG findings, surgical outcomes, and predictors of cancellation were analyzed using univariate, multivariate, and Firth’s penalized logistic regression analyses. Ultrasonographic findings were further stratified as clinically significant (requiring intervention) or non-clinically significant (not requiring intervention) to standardize interpretation. Results: Abnormal USG findings were present in 77.5% of patients, with hepatic steatosis (60.8% [n = 680]), hepatomegaly (21.5%), and gallstones (13.9%) being the most frequent. Higher BMI was significantly associated with hepatomegaly, steatosis, and gallstones (all p < 0.05), but not with surgical cancellation. Bariatric surgery was cancelled in 11 patients (1.0%) due to critical findings exclusively identified on USG, including large ovarian/uterine masses, choledochal cysts, and suspected malignancies. In multivariate and Firth-adjusted regression, large ovarian/uterine masses (adjusted OR 12.9, 95% CI 3.0–55.2, p = 0.001; Firth OR 11.4, 95% CI 2.5–51.4, p = 0.002) and choledochal cysts (Firth OR 29.7, 95% CI 1.8–489.5, p = 0.048) emerged as independent predictors of cancellation. Conclusions: Although the overall cancellation rate was low, the detection of critical USG findings in 1.0% of patients had major clinical implications, preventing inappropriate or unsafe surgery and enabling timely referral for specialist management. Routine preoperative ultrasonography thus offers a clinically meaningful safeguard in bariatric surgery, supporting its inclusion in preoperative assessment algorithms.

Polarization-induced noise remains a primary source of bias drift, fundamentally limiting the performance of hollow-core photonic-crystal fiber optic gyroscopes (HC-RFOGs). To overcome this limitation, we propose and demonstrate a novel resonator design with an intrinsically high polarization extinction ratio (PER). The resonator’s core innovation is a four-port coupler architecture that strategically integrates a pair of polarization beam splitters (PBSs) with conventional beam splitters (BSs). This configuration functions as a high-fidelity polarization filter, suppressing undesired polarization states for both clockwise and counter-clockwise propagating light within the hollow-core fiber loop. Our theoretical model predicts that the effective in-resonator PER can exceed 48 dB, which is sufficient to mitigate polarization-related errors for tactical-grade applications. Experimental validation of a prototype HC-RFOG incorporating this resonator yields a bias instability of 1.34°/h and an angle random walk (ARW) of $0.078°/\sqrt{\mathrm{h}}$ (with a 200 s averaging time). These results confirm that engineering a high-polarization-extinction-ratio resonator (HPERR) is a potent and direct pathway to substantially reducing polarization noise and advancing the performance of HC-RFOGs.

Understanding groundwater quality and its controlling mechanisms is vital for the sustainable use of water resources in agriculturally intensive regions. This study evaluates the hydrochemical characteristics, controlling geochemical processes, and overall water quality of 226 groundwater samples collected from a typical agricultural reclamation area in the Sanjiang Plain, northeastern China. Major ion compositions indicate that groundwater is predominantly of the Ca–HCO₃ type, with bicarbonate, calcium, and magnesium as the dominant constituents. Spatial and statistical analyses reveal that rock weathering—particularly the dissolution of carbonates and silicates—is the primary natural process influencing groundwater chemistry, while cation exchange contributes moderately. Anthropogenic inputs, especially from fertilizers, livestock waste, and wastewater discharge, were found to elevate concentrations of NO₃⁻, Cl⁻, and SO₄²⁻ in localized zones. The entropy-weighted water quality index (EWQI) was applied to assess overall groundwater suitability. Results show that 89.8% of samples fall into “excellent” or “good” categories, though 6.6% of samples indicate poor to very poor water quality. This study identified the hydrochemical characteristics, sources of substances, and water quality of groundwater in the reclamation area, providing a basis for scientific prevention and control, rational utilization, and protection of groundwater resources.

Cardiovascular diseases (CVDs) are the leading cause of death globally. Electrocardiograms (ECGs) are crucial diagnostic tools; however, their traditional interpretations exhibit limited sensitivity and reproducibility. This systematic review discusses the recent advances in artificial intelligence (AI), including deep learning and machine learning, applied to ECG analysis for CVD detection. It examines over 100 studies from 2019 to 2025, classifying AI applications by disease type (heart failure, myocardial infarction, and atrial fibrillation), model architecture (convolutional neural networks, long short-term memory, and hybrid models), and methodological innovation (signal denoising, synthetic data generation, and explainable AI). Comparative tables and conceptual figures highlight performance metrics, dataset characteristics, and implementation challenges. Our findings indicated that AI models outperform traditional methods, especially in terms of detecting subclinical conditions and enabling real-time monitoring via wearable technologies. Nonetheless, issues such as demographic bias, lack of dataset diversity, and regulatory hurdles persist. The review concludes by offering actionable recommendations to enhance clinical translation, equity, and transparency in AI-ECG applications. These insights aim to guide interdisciplinary efforts toward the safe and effective adoption of AI in cardiovascular diagnostics.