Search Results (636)

Pressure relief in high-pressure polyethylene processes involves dense, strongly non-ideal ethylene-based fluids. A transient real-fluid model was developed for the single-phase discharge of ethylene/vinyl acetate (VA) mixtures by coupling adiabatic vessel depressurization with quasi-steady isentropic nozzle flow. Thermodynamic properties were calculated using the Peng–Robinson equation of state in REFPROP. The admissible mass flux was maximized over single-phase throat states, and sonic choking was verified using u_t/a_t ≈ 1. For the validation case at 2000 bar and 25 °C, the calculated pressure history yielded an RMSE of 120 bar, an MAE of 97 bar, and R² = 0.93. Increasing the VA mass fraction from 0 to 40% increased the initial mass flux from 0.35 to 0.41 kg·mm⁻²·s⁻¹ and shortened the single-phase duration from 761 to 413 ms. Higher initial pressure increased the mass flux, whereas higher initial temperature reduced it and prolonged discharge. The discharge coefficient affected the time scale but not the vessel isentrope or terminal state. Sonic choking occurred during the high-pressure stage, while the flux maximum became phase-boundary-limited near termination. The results represent comparative single-phase trends because two-phase discharge is excluded and the ethylene/VA interaction parameter remains uncertain. Full article

Olive oil flavoring is gaining attention as a means of improving sensory characteristics, nutritional quality, and oxidative stability. However, current evidence remains fragmented across botanical ingredients, operating conditions, and production methods. This review critically compares conventional approaches, including maceration, infusion, co-processing, and direct addition, with emerging processing strategies. Particular attention is given to the transfer of phenolic and volatile compounds, extraction performance, oil quality, energy demand, scalability, economic viability, and regulatory considerations. Conventional methods are generally accessible and cost-effective, but extended contact times and the use of fresh plant materials may increase acidity and peroxide value. Emerging technologies can accelerate and improve the control of mass transfer while promoting the recovery or retention of bioactive and aroma compounds. Nevertheless, their performance varies with the plant matrix, olive cultivar, operational parameters, and production scale. Wider commercial application is also constrained by limited standardization, substantial equipment costs, insufficient long-term evidence, and uncertain regulatory frameworks. Future studies should develop comparable protocols, explore combined processing strategies, evaluate product quality and safety during storage, and conduct robust techno-economic assessments. Validation under pilot- and full-scale conditions in diverse geographical and regulatory settings will be essential to support the safe, consistent, and economically viable production of flavored olive oils. Full article

While the mesonephros persists in the adult mouse ovary and contributes to folliculogenesis, its inductive role in oogonial stem cell (OSC) differentiation remains uncertain. In this study, we investigated whether mesonephros-derived cells direct OSCs toward follicle-like structures in vitro. OSCs were co-cultured with mesonephros cells, and morphological assessments and factor expression analyses were conducted at multiple time points. On day 14 of co-culture, 10× Genomics single-cell and bulk RNA sequencing were performed to profile transcriptomic changes, while renal capsule transplantation was conducted to evaluate the functional potential of co-cultured clusters in recipient mice. Transcriptomic analyses revealed a robust expression of mesonephros-specific genes and a significant enrichment of PI3K/AKT pathway-associated genes in co-cultured cells. Notably, transplanted clusters exhibited signs of estrogen secretion with maintained graft integrity. Collectively, these findings demonstrate that the mesonephros promotes OSC differentiation into follicle-like structures, with the PI3K/AKT pathway identified as a candidate mediator of this process. Our study provides novel insights into the developmental regulation of ovarian folliculogenesis and offers a valuable experimental model for future investigations. Full article

Small fault offsets in thin coal seams can resemble responses caused by tuning, impedance variation, processing effects, or poor data quality. We developed the Scale- and Dependence-Aware Evidence-Gating workflow (SDEG) to separate candidate detection from fault attribution, treat products from the same seismic volume as dependent evidence, and retain uncertain and not-evaluable outcomes through non-compensatory gates. Evaluation combined one- and two-dimensional modeling, a descriptive three-dimensional spatial-response transfer experiment, a reference-masked five-reader crossover study using a separately assembled 93-case numerical cohort, controlled rule perturbations, and a masked field experiment. Under the prespecified reader–case analysis, the prespecified reader-comparison criterion was met: coverage-adjusted balanced accuracy increased from 0.692 to 0.792 (paired difference, 0.099; 95% confidence interval, 0.006–0.191), while active interpretation time increased by 13.7%; robustness to parent–model–family clustering could not be assessed. Normalized throw organized model-side responses, but the prespecified scale-stability criterion was not met, and observable-scale routing remained unconfirmed. Rule perturbations supported uncertainty preservation and non-compensatory integration. In the common-complete analysis of 31 of 36 field objects, Gwet’s AC1 increased from 0.144 to 0.588, indicating greater inter-reader reproducibility within one survey, not geological accuracy. SDEG provides a transparent framework for recording attribution decisions, evidence dependence, and unresolved outcomes. Full article

Background/Objectives: Trigger-based referral systems are widely used to identify intensive care unit (ICU) patients who may benefit from palliative care, but the extent to which trigger status corresponds to multidimensional patient and family need remains uncertain. This narrative review examines trigger-based referral, needs assessment, and integrated approaches to ICU palliative care. Methods: PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and reference lists were searched, and a purposive thematic synthesis was conducted. The original identification-stage record counts were unavailable; selection was reconstructed from an archived full-text library, and screening was primarily performed by one author. Results: Trigger-based systems improved recognition of clinically vulnerable patients and several care processes. Direct evidence comparing trigger status with measured need is limited, however, and derives principally from one multicenter study using a family-member-reported NEST score. The literature supports distinguishing clinical risk from measured need but does not establish the superiority of trigger-based, needs-based, or integrated models. Conclusions: Current evidence supports comparative testing rather than adoption of any integrated configuration. Candidate components, timing, thresholds, feasibility, and applicability across healthcare systems require prospective evaluation. Full article

This study investigates predictive maintenance for SCADA-controlled pump stations using multi-sensor data processing and hybrid machine-learning models. The conventional maintenance approaches adopted in practice remain reactive, with little provision for actual early warnings under real-world conditions such as noisy data, class imbalance, or varying sensor dynamics. A data-driven solution is proposed to predict pump tripping events using operational SCADA system data for early warning with useful lead times. The dataset, obtained from a water-utility SCADA system, contained missing values, heavy-tailed sensor distributions, and substantial class imbalance. The preprocessing strategy used time-aware imputation, winsorisation, and a sliding-window configuration informed by the characteristics of the SCADA data. Benchmark machine-learning models achieved PR-AUC values of approximately 0.55 or lower for trip-escalation prediction, highlighting the difficulty of predicting rare trip events directly from SCADA data. The proposed hierarchical GRU-based framework achieved PR-AUC values exceeding 0.80, demonstrating a substantial improvement in predictive performance while maintaining high precision and low false-alarm rates. In addition, a Remaining Useful Life (RUL) component was included to extend the system to support near-term risk forecasting. Even so, long-term forecasts remained uncertain, indicating that further model development is required. Full article

Governments increasingly use processing-time indicators to evaluate development assessment, although comparability across councils is uncertain. This study examines how application composition, determination pathways, recorded workflows and information practices affect council-level comparisons in Greater Sydney, Australia. The analysis links NSW Online Development Assessment Data API metadata to council tracking portal histories across 12 councils between January 2023 and April 2026. It compares application portfolios, processing durations and determination routing, maps events to five stages, and evaluates recording practices. It is found that median processing durations ranged from 53 to 153 days; higher application volumes were not consistently associated with longer processing. Portfolios differed by proposed use, limiting comparisons of aggregate timeframes. Recorded workflows varied, with referrals and internal review representing recurrent coordination pressures rather than a uniform bottleneck. These findings extend research on differentiated policy implementation; common statutory rules do not necessarily produce comparable administrative performance measures. Processing time is a composite governance outcome shaped by case mix, institutional routing, recorded workflows and administrative visibility. Aggregate benchmarks should therefore serve as screening signals. Although numerical findings are context-specific, this analytical argument is relevant to decentralised regulatory systems in which local organisations implement common higher-level rules. Full article

This article presents a hybrid methodology for the intelligent control of the reactor-regenerator unit of a residual fluid catalytic cracking (RFCC) plant under conditions of uncertain input information. The main challenge in catalytic cracking process control is the instability of feedstock composition and the delay in laboratory data on product quality, which complicates the operational management of the RFCC process while maintaining the required gasoline quality. The objective of this study is to support effective management of the gasoline production process by maximizing the yield of high-quality gasoline while keeping its density within specified limits. The proposed architecture combines a regression model built on six dominant parameters selected through Pearson’s correlation analysis, a Mamdani-type fuzzy inference system with a database of 26 expert rules, and a machine-learning module (Random Forest and gradient boosting) that corrects the residual error of the combined regression-fuzzy model, allowing the control system to adapt to changing process conditions. Testing the developed models on real data from the Shymkent Oil Refinery shows that the hybrid model reduces the root-mean-square error from 0.1973 (regression model alone) to 0.0161 and the mean absolute percentage error from 0.328% to 0.028%, while the coefficient of determination increases from 0.9895 to 0.9999; this improved accuracy is estimated to correspond to an approximate 3.2% increase in achievable gasoline yield and a 0.4% improvement in density stability. The developed decision support system for catalytic cracking process control, based on the proposed methodology, acts as a “virtual analyzer”, ensuring effective control in real time without the use of expensive in-line quality control devices. Full article

Reliable classification of brain sequence cases is a challenging problem due to signal ambiguity and noise. Personal concentration is primarily determined by the base frequency of electroencephalogram (EEG) waves, i.e., the task rests on appropriate modelling and recognition of patterns in the corresponding human (in)activity (e.g., reading, relaxation, solving maths problems, etc.). Five underlying types of frequency (alpha, beta, gamma, delta, and theta) were considered as secondary input wave parameters in complex-valued node extensions to the prime amplitude in processing signals. Self-optimisable Artificial Intelligence (AI) methods can process, statistically analyse, and model the series-specific character and time behaviour to recognise untrained session assigned labels. This procedure involves signal pre-processing (transformation) and feature extraction to enhance the representation in time variability, eliminate uncertain cases, and reduce the unacceptable large raw format of data in detailed frequency band recording. This study focuses on improving AI modelling through brain-inspired doubled amplitude/frequency signal processing. This extended concept is based on an analogy with neural activity that generates dynamic frequency pulses as the main information holder in response to time excitations. The model is obtained in partial differential equation (PDE) solutions of evolutionary tree structure nodes—self-computational terms, using the optimal sine/cosine or rational expression. It enables the representation of periodic patterns in their intrinsic form related to primary wave characteristics. Two different machine learning methodologies were compared: the first evolutionary PDE transform and deep learning-based recurrent processing applied to all session records in bloc, assessing only one final class assessment, achieving predictive accuracy above 90% on untrained 1/3 data. The second group of regular modelling techniques evaluates each data row separately to compute its bound-label output in a time-lagged frame, reaching accuracy above 70%. An executable parametric software with a link to the public EEG data repository is available. Full article

Type 2 diabetes mellitus (T2DM) is associated with heterogeneous cognitive and functional trajectories rather than a single diabetes-specific encephalopathy. This structured narrative review proposes a conceptual, hypothesis-generating exposure–injury–structure–function framework linking metabolic, vascular, and frailty-related exposures with molecular and neurovascular injury, structural change, cognitive decline, and loss of independence. Its contribution is the temporal assignment of measurements, lagged testing between adjacent levels, and comparison with clinical-risk and unordered biomarker models. For example, a study-specific threshold of at least 10% improvement in held-out root-mean-square error for 24–36-month executive/processing-speed decline could indicate incremental value. Repeated failure of a specified temporal link challenges that link; failure to improve prediction rejects incremental predictive value, not biological plausibility. Molecular hypotheses focus on AGE–RAGE signalling, NLRP3–IL-1β activation, mitochondrial quality control, endothelial dysfunction, and neuroglial injury, although human timing is uncertain. Sodium–glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are considered complementary therapeutic probes. Both offer systemic benefits, but neither has proven efficacy in preventing cognitive decline or direct target engagement in the human brain. Visceral adiposity, skeletal-muscle health, sarcopenia, sex/gender, kidney function, co-pathology, and reserve are treated as exposures or modifiers. This framework is intended for longitudinal research, not clinical staging or treatment selection. Full article

Polders are typical semi-artificial and human-dominated ecosystems widely distributed in the middle and lower reaches of the Yangtze River Basin. They serve as important sinks and sources of nitrogen (N) and phosphorus (P) in agricultural watersheds. Long-term intensive human intervention substantially alters the hydrological and biogeochemical processes of polder ecosystems, resulting in complex and uncertain effects on water quality that remain insufficiently understood. This study conducts a systematic literature review and narrative synthesis of evidence on N and P transport in polders across the middle–lower Yangtze River plain. The evidence is derived from field monitoring, plot experiments, and numerical simulations. The review focuses on the spatiotemporal patterns of nutrient variation, sink–source conversion functions of ditches and small ponds, drivers of nutrient loss, and current research bottlenecks under artificial sluice-pump regulation. The synthesized results indicate that polders exhibit a distinctive nutrient transport pattern characterized by dispersed in situ retention under conventional water management and concentrated pulse export during drainage events. Artificial sluice-pump operation drives episodic nutrient export throughout the crop growth period, imposing persistent pressure on the water quality of downstream rivers and lakes. N and P transformations are jointly controlled by natural hydrological fluctuations and human activities. Within agricultural lands of polders, fertilizers account for 73.3% of total nitrogen inputs and 87.9–93.5% of total phosphorus inputs. Crop harvesting and regulated drainage constitute the two dominant pathways for nutrient export. Hydraulic regulation prolongs water residence time in polder ditches and ponds, resulting in retention efficiencies of 52–65% for allochthonous N and P. However, seasonal flooding and waterlogging can induce sediment hypoxia and endogenous nutrient release, thereby causing secondary internal pollution and increasing the eutrophication risk of adjacent receiving water bodies. Three major research gaps are identified: insufficient long-term continuous multi-indicator monitoring data, limited model applicability for simulating human-regulated hydrology–nutrient coupling, and poorly defined critical thresholds for polder sink–source functional reversal. This regional systematic review advances the understanding of human–hydrology–nutrient coupling mechanisms in Yangtze River polder systems. It also provides targeted theoretical support for agricultural non-point source pollution mitigation and water environment management in floodplain agricultural areas. Full article

The rapidly changing digitalization of the energy sector, fueled by smart grids, the Industrial Internet of Things (IIoT), Advanced Metering Infrastructure (AMI), and Supervisory Control and Data Acquisition (SCADA) systems, is leading to a great and symmetrical improvement in workflow and the ability to have real-time insights. In parallel, the massive adoption of low-power and resource-constrained devices that are still connected makes cybersecurity threats more serious and thus necessitates lightweight authentication mechanisms to have safe, secure, and symmetrical communicative. Selecting the right authentication method involves a challenging multi-criteria decision-making (MCDM) process where various factors such as security aspects, computation power needed, communication capabilities that can be delivered, and deployment-related aspects are considered together, along with inherent uncertainties in expert evaluations. This paper proposes a Hesitant Fuzzy (HF)-based hybrid method that combines the Analytic Network Process (ANP) with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for the evaluation of lightweight authentication systems in the energy domain. This symmetrical HF-ANP is used for the modeling of the interrelations between major evaluation criteria such as security strength, computation efficiency, communication effectiveness, and deployment scalability. It can also handle the experts’ hesitant and uncertain preferences. The calculated weighting factors are then input to the HF-TOPSIS method to rank five different lightweight authentication protocols: Hash-Based Authentication, Elliptic Curve Cryptography (ECC)-Based Lightweight Authentication, Physical Unclonable Function (PUF)-Based Authentication, Blockchain-Assisted Lightweight Authentication, and Certificate-less Lightweight Authentication. Furthermore, sensitivity analysis and comparison analysis are conducted to verify the strength, symmetry, consistency, and reliability of the proposed framework. Our results indicate that the integrated HF-ANP and TOPSIS procedure provides a symmetrical, comprehensive, and systematic decision-making tool to appraise lightweight authentication techniques amid uncertainties. The proposed method will be very beneficial for different types of energy industrial players, system designers, and security experts to pick secure, efficient, and scalable methods of authentication to protect the critical energy facilities against the new generation of cyber threats. Full article

Background: Undifferentiated pleomorphic sarcoma (UPS) of the bone is a rare, high-grade malignancy of uncertain origin, most commonly affecting the extremities of adults. Its diagnosis may be challenging, particularly when clinical and radiological findings are confounded by trauma-related conditions. Case presentation: In this paper, the case of a 51-year-old man who sustained a high-energy trauma resulting in a diaphyseal fracture of the left tibia and fibula, initially treated with intramedullary nailing. Preoperative radiographs raised suspicion of an osteolytic lesion in the distal tibia; however, second-level imaging was performed postoperatively, suggesting a benign process. At initial evaluation at a primary Musculoskeletal Oncology Centre, strict clinical and radiological follow-up was recommended, but the patient failed to adhere. Over the following years, persistent symptoms were attributed to post-traumatic and inflammatory conditions, including suspected complex regional pain syndrome. Progressive clinical deterioration led to repeat imaging, revealing an aggressive lesion. A biopsy (March 2025) ultimately confirmed a primary bone high-grade UPS with no metastasis. Following multidisciplinary discussion, below-knee amputation was performed, achieving clear surgical margins. At one-year CT imaging follow-up, the patient remained disease-free and was able to ambulate with a prosthesis. Conclusions: High-energy trauma and later attribution of symptoms to post-traumatic conditions may complicate the diagnostic process, contributing to diagnostic delay. Early referral to specialised Musculoskeletal Oncology Centres, strict adherence to follow-up, and timely histological confirmation are essential to optimise outcomes. Suspicious bone lesions should always be fully characterised prior to surgical intervention. Full article

In increasingly complex and uncertain educational contexts, school leadership is expected to foster improvement while responding to both external pressures and internal organizational challenges. This study examines how principal leadership promoted changes in instructional practice and enabled the emergence of teacher leadership and professional engagement in early childhood education. An instrumental qualitative case study was conducted at an early childhood level of a school in Spain, drawing on semi-structured interviews, a focus group, non-participant observations, and document analysis. The findings show that principal leadership played a catalytic role not through prescriptive actions, but by creating organizational and relational conditions, such as trust, protected time and space, professional learning opportunities, and curricular prioritization, that enabled teachers to redesign practice collaboratively. The establishment of expert groups fostered pedagogical experimentation, professional learning, and knowledge sharing, thereby expanding teachers’ agency and leading to the emergence of informal teacher leadership. These processes strengthened professional confidence, ownership, and alignment with the school’s educational project. The study concludes that sustainable improvement in complex times depends on integrating principal and teacher leadership by creating a supportive school climate as interdependent, mutually reinforcing forms of influence that build organizational capacity for continuous learning and change. Full article

Robot mobile fulfillment systems (RMFSs), as human–robot collaborative smart warehouses, transform the traditional person-to-goods order picking mode into a goods-to-person mode. Order picking optimization is a core decision-making challenge in RMFSs to improve the efficiency of the system, which needs to jointly optimize pod selection, robot scheduling, station assignment, and manual picking. Although recent studies have widely investigated integrated operational optimization in RMFSs, most of them rely on deterministic transportation and processing time and ignore uncertainties in practical human–robot collaborative operations. It remains challenging to jointly optimize these coupled decisions under uncertain operation times. To address this challenge, we model the concerned problem with the objective of minimizing fuzzy makespan and design an adaptive large-neighborhood-based variable neighborhood descent algorithm to efficiently solve it. The algorithm adopts three-dimensional coupling encoding and multi-stage heuristic decoding mechanisms. It further integrates a learning-based adaptive destroy operator selection method and a variable neighborhood descent search strategy to enhance its exploration and exploitation abilities. In a large number of systematic experiments, ALVND achieved great performance in solving the concerned problem. The objective function value obtained by it was 5.6–25.1% lower than its competitors, demonstrating its effectiveness in uncertain human–robot collaborative warehouse scenarios. Full article