Search Results (118)

With the rapid penetration of electric vehicles (EVs) and renewable energy generation in distribution networks, the coordinated scheduling of flexible EV loads and uncertain renewable resources has become a critical research focus in modern power systems. This study investigates the collaborative optimal dispatch of thermal units, EVs, and renewable power generation. Different from conventional closed-loop game-based bi-level optimization, this paper constructs a transmission–distribution integrated scheduling framework and proposes a sequential hierarchical progressive optimization strategy for EV charging and discharging dispatch to fully tap the cross-level coordination potential of power grids. The upper transmission layer optimizes the joint operation of thermal units, wind power, and photovoltaic units to minimize the overall power supply cost, where the inequality power balance constraint is reasonably adopted to reserve power regulation margin for renewable fluctuation and meet practical engineering operation requirements. To effectively address the severe uncertainty of renewable power output without relying on accurate probability distribution information, information gap decision theory (IGDT) is employed to realize robust scheduling with risk-averse and opportunity-seeking decision adaptability. In the lower distribution layer, a theoretically grounded nodal electricity price (NEP) model integrating node loss sensitivity (NLS) and node load rate (NLR) is applied to substitute iterative power flow calculation, which realizes the spatial optimal allocation of EV charging and discharging nodes while significantly improving computational efficiency. The proposed framework comprehensively minimizes network power loss and user charging cost. Finally, extensive simulations based on the IEEE 33-node distribution system verify the effectiveness, computational superiority, and robustness of the proposed sequential hierarchical coordinated scheduling strategy. Full article

Background/Objectives: Contralateral prophylactic mastectomy (CPM) is increasingly performed despite negative preoperative imaging. We evaluated the prevalence and MRI detectability of occult pathology in the contralateral breast. Methods: In this single-center retrospective cohort, 82 patients with unilateral invasive breast cancer underwent simultaneous CPM after preoperative contralateral MRI. Occult findings were classified as occult malignancy, atypical/high-risk lesions, or other lesions of uncertain malignant potential (B3 lesions); the primary outcome was clinically significant occult pathology (malignancy or an atypical/high-risk lesion). Proportions are reported with exact 95% confidence intervals and associations with exact odds ratios and Benjamini–Hochberg correction. Results: Occult malignancy occurred in 1/82 (1.2%; a single DCIS), clinically significant occult pathology in 14/82 (17.1%) and any occult pathology in 21/82 (25.6%). Among 59 patients with a negative MRI (BI-RADS 1–2), clinically significant occult pathology occurred in 18.6% and atypical/high-risk lesions in 16.9% (whole cohort, 15.9%). The single occult malignancy arose in an MRI-negative breast. No factor remained significant after correction for multiple comparisons. Conclusions: After preoperative MRI, occult malignancy is rare, whereas atypical and high-risk lesions frequently remain occult; a negative contralateral MRI excluded neither. These findings support individualized, shared decision-making rather than the yield of occult pathology as the basis for CPM. Full article

Background: Enhanced Recovery After Surgery (ERAS) pathways have become the standard of care in elective colorectal surgery. However, implementation remains heterogeneous across institutions, and the relative contribution of ERAS pathways and minimally invasive surgery to improved postoperative outcomes remains uncertain. This study evaluated the association between ERAS implementation and short-term outcomes in two university-affiliated colorectal units with different levels of ERAS adoption. Methods: A retrospective bi-centre observational study was conducted, and comprised 802 consecutive patients who underwent elective colorectal resection between January 2016 and December 2024. Patients managed according to a standardized ERAS pathway (Group 1, n = 406) were compared with patients who received conventional perioperative care (Group 2, n = 396). Primary endpoints included postoperative morbidity, anastomotic leakage, and length of hospital stay (LOS). Secondary endpoints included mortality, readmission, postoperative complications, and hospitalization-related costs. Multivariable logistic regression was performed, adjusting for age, sex, ASA score, tumour stage, and tumour location. Results: Baseline demographic characteristics were largely comparable between groups, although patients in the conventional care group had a higher proportion of ASA III–IV status. Overall postoperative morbidity was significantly lower in the ERAS cohort (8.6% vs. 20.9%, p < 0.001), together with a lower incidence of anastomotic leakage (1.2% vs. 4.5%, p < 0.001). Median LOS was reduced from 9 to 8 days overall and, among patients who underwent laparoscopic surgery, from 6 to 4 days (p = 0.010). After multivariable adjustment, conventional perioperative management remained independently associated with higher postoperative morbidity (OR 2.62, 95% CI 1.60–4.09; p < 0.001) and anastomotic leakage (OR 1.92, 95% CI 1.01–4.98; p = 0.048). Mortality, surgical site infections, intra-abdominal abscesses, and other postoperative complications were comparable between groups. Based on regional reimbursement tariffs, ERAS implementation was associated with an estimated annual reduction of 567 hospital bed-days. Conclusions: Implementation of a standardized ERAS pathway was associated with reduced postoperative morbidity, lower anastomotic leakage rates, and shorter hospital stay after elective colorectal surgery. These benefits persisted after adjustment for major clinical confounders, supporting the effectiveness of standardized perioperative care. The greatest reduction in hospital stay was observed when ERAS was combined with minimally invasive surgery, emphasizing the complementary role of these strategies in optimizing postoperative recovery. Full article

Background/Objectives: Mammography remains central to breast cancer diagnosis, yet clinically meaningful uncertainty persists in women with dense breasts and in patients with very small lesions or indeterminate imaging findings. We evaluated whether circulating thioredoxin 1 (Trx1) could provide biological information that complements imaging-based assessment. Methods: This multicenter clinical validation study evaluated circulating Trx1 in 1901 serum samples across four predefined cohorts. Diagnostic performance was assessed using receiver operating characteristic (ROC) and precision–recall analyses, together with evaluation of integration with mammographic assessment and decision curve analysis (DCA). A predefined Trx1 cutoff established in prior clinical investigations was applied. Results: Circulating Trx1 concentrations were significantly elevated in breast cancer compared with controls (p < 0.001). Trx1 showed excellent diagnostic discrimination (AUC 0.985; 95% CI, 0.974–0.996), with sensitivity and specificity of 96.4% and 97.3%, respectively. Diagnostic performance was consistent across disease stages, tumor-size categories, molecular subtypes, mammographic categories, and breast-density groups. Trx1 sensitivity remained high in dense breasts (98.9% in BI-RADS density grades C/D), whereas mammographic sensitivity was substantially lower (67.0%). Integration of Trx1 with mammographic assessment improved diagnostic discrimination (AUC 0.980) and provided greater net benefit in decision curve analysis, particularly among women with low-suspicion or indeterminate imaging findings. Conclusions: Circulating Trx1 demonstrated robust diagnostic performance across disease stages, tumor-size categories, and clinically relevant patient subgroups while providing biologically independent information that complements mammographic assessment. Although this retrospective multicenter study requires prospective validation in independent diagnostic populations, Trx1 may serve as a clinically useful adjunct to imaging-based breast cancer evaluation, particularly in diagnostically uncertain settings. Full article

Multi-source heterogeneous data in substation secondary systems are typically characterized by high entropy and disorder, which pose significant challenges for cross-modal information integration and efficient retrieval. Therefore, a method for multimodal information extraction and knowledge graph construction is proposed, enabling structured processing of heterogeneous data from multiple sources. For the image modality, positional and semantic information is extracted using YOLOv8n and Optical Character Recognition (OCR) techniques. To mitigate the effects of uncertain connection topology and noise interference, a Heuristic Circular Stepping Search Algorithm (HCSA) is designed to achieve deterministic path tracing of information flows. For the text modality, a RoFormer-BiLSTM-CRF model enhanced with Rotary Position Embedding (RoPE) is developed to alleviate information degradation in long-sequence texts, thereby enabling high-accuracy extraction of entities and relationships. Furthermore, by combining the domain ontology mapping rules and string similarity, the extracted device entities from the two modalities are aligned, thereby converting scattered data into a structured knowledge graph. Experiments conducted on the secondary-side data of a substation in China demonstrate that the proposed method effectively extracts multimodal information from substation secondary systems, providing valuable support for information management and decision-making assistance in complex industrial systems. Full article

Long-memory degradation processes in rotating machinery often exhibit nonlinear evolution, nonlocal temporal dependence, and hereditary characteristics, which are difficult to fully capture using conventional integer-order models or standard Markovian decision frameworks. To address this issue, this study proposes a hybrid fractional-dynamics and deep reinforcement learning framework for predictive maintenance of memory-dependent degradation systems. First, the Grünwald–Letnikov fractional difference operator is introduced to construct a fractional-memory representation of degradation trajectories, enabling the model to explicitly encode long-range dependence and accumulated historical degradation effects. Then, a bidirectional gated recurrent unit network is employed to learn sequential degradation representations from the fractional-memory state space, while a deep Q-network is designed to optimize maintenance decisions under uncertain degradation evolution. Experimental results on the IEEE PHM 2012 bearing dataset show that the proposed FM-BiGRU-DQN with safety-guided execution achieved a mean maintenance lead time of 33.9 ± 12.6 steps, an in-band rate of 0.85 ± 0.06, a failure rate of 0.00 ± 0.00, and a deployment reliability of 1.00 ± 0.00 over 10 independent random seeds. Compared with NM-BiGRU-DQN, the in-band rate increased from 0.55 ± 0.10 to 0.85 ± 0.06, with a paired-test p-value of 0.013. Cross-dataset validation on the XJTU-SY bearing dataset further achieved an in-band rate of 0.80 and a failure rate of 0.00. These results indicate that embedding fractional-memory dynamics into deep reinforcement learning improves maintenance timing accuracy, policy robustness, and deployment reliability for complex memory-dependent degradation systems. Full article

Multimodal transport has emerged as an effective solution for improving freight efficiency and promoting sustainable logistics, reducing environmental impacts; however, route choice remains challenging under uncertain demand and dynamic transshipment time. This study addresses this problem by developing a bi-objective route-choice model that minimises total transport cost and total transport time while explicitly capturing the correlation between freight demand and transshipment time. The model is transformed into a deterministic equivalent using chance-constrained programming, enabling rigorous optimisation under predefined confidence levels and solved by a simulated annealing-based genetic algorithm (SAGA), which combines the global exploration capability of genetic algorithms with the local search efficiency of simulated annealing to improve convergence and solution quality. By incorporating carbon emission costs into the objective functions, the model supports environmentally and economically sustainable transport strategies. A numerical case study is conducted to validate the proposed approach. The results show that when freight demand is significantly below the capacity threshold, the optimal solution tends to adopt a single-mode transport scheme with stable route structure, whereas higher demand necessitates multimodal strategies, with cost–time trade-offs clearly observed. Sensitivity analysis further reveals a clear trade-off between cost and time: a time-oriented strategy dominated by rail transport reduces total transport time by approximately 20%, whereas a cost-oriented strategy relying on waterway transport decreases total cost by about 73%. These findings demonstrate the effectiveness of the proposed model and provide decision support for efficient and sustainable multimodal transport planning under demand uncertainty. Full article

The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, as well as regulatory and operational constraints, and heterogeneous decision making behaviors. To address these challenges, this paper proposes an enhanced energy storage as a service (ESaaS) framework that enables distributed ESS owners to lease idle storage capacity to a centralized platform for coordinated participation in multiple grid support services. The proposed platform aggregates the distributed ESS capacity and allocates it across several value streams. Unlike conventional approaches that assume fully rational agents, this work incorporates behavioral decision making dynamics using prospect theory (PT), which captures loss aversion, asymmetric risk perception, and the subjective valuation of uncertain outcomes. The interaction between the ESaaS operator and ESS owners is formulated as a bi-level optimization problem, where the upper level determines leasing prices and operational strategies across multiple services while the lower-level models ESS owner participation decisions. Prospect theory is integrated at both decision layers to capture the behavioral preferences of the ESaaS operator and ESS owners under uncertainty. The resulting mixed-integer bi-level model is solved using a modified reformulation-and-decomposition approach that incorporates a nested column-and-constraint generation (NC&CG) method to ensure computational tractability. Numerical studies demonstrate that behavioral decision modeling significantly influences pricing strategies and the overall profitability of both the ESaaS platform and the participating energy storage system owners. Full article

Supply chain management (SCM) involves complex coordination among multiple actors under demand uncertainty. However, most existing studies focus on simplified network structures that fail to capture all relevant dimensions of real-world supply chains or assume deterministic demand. This study proposes a comprehensive stochastic bi-level optimization framework for a multi-factory, multi-retailer, multi-customer, and multi-product supply chain network. The model captures the hierarchical interaction between decision-makers, where the production facility owner acts as the leader and the retailer as the follower, and jointly optimizes profit across both levels. To efficiently solve the resulting bi-level problem, two tailored metaheuristic solution approaches—a two-tier genetic algorithm (TT-GA) and a two-tier particle swarm optimization (TT-PSO)—are developed. Computational experiments across multiple scenarios demonstrate that TT-PSO outperforms TT-GA in Scenarios 1 and 2, achieving overall profit improvements of 6.46% and 0.76%, respectively, while TT-GA yields superior performance in Scenario 3 with a 2.80% profit improvement. The proposed framework provides decision-makers with a robust and practical tool for improving profitability and operational efficiency in complex, uncertain supply chain environments. Full article

The ramping requirement in new power systems primarily stems from net load variations and forecast errors of renewable energy and load. Designing an equitable cost allocation mechanism for ramping services based on these factors facilitates incentives for generation and load to actively reduce ramping demands, thereby alleviating system ramping pressure. Accordingly, this paper proposes a fair ramping cost allocation mechanism based on the ramping responsibility coefficients of market participants. Under this mechanism, a market-oriented operation model for wind–hydro-storage joint operation is established to verify its effectiveness in market applications. First, a ramping cost allocation mechanism is constructed based on ramping responsibility coefficients. According to the responsibility coefficients of market participants for deterministic and uncertain ramping requirements, ramping costs are allocated to the corresponding contributors in proportion to the ramping demands caused by net load variations, load forecast deviations, and renewable energy forecast deviations. Specifically, for costs arising from renewable energy forecast errors, an allocation mechanism is designed based on the difference between the declared error range and the actual error. Second, within this allocation framework, hydropower and storage (including cascade hydropower and hybrid pumped storage) are utilized as flexible resources to mitigate wind power uncertainty and reduce its ramping costs. A two-stage day-ahead and real-time bi-level game model for wind–hydro-storage cooperative decision-making is developed. The upper level optimizes bilateral trading and market bidding strategies for wind–hydro-storage, while the lower level simulates the market clearing process. Through Stackelberg game modeling, joint optimal operation of wind–hydro-storage is achieved, ensuring mutual benefits. Finally, simulation results validate that the proposed ramping cost allocation mechanism can guide renewable energy to improve output controllability through economic signals. Furthermore, the bilateral trading and coordinated market participation of wind–hydro-storage realize win–win outcomes, reduce the ramping cost allocation for wind power by 23.10%, effectively narrow peak-valley price differences, and enhance market operational efficiency. Full article

The increasing depth and complexity of underground metal mining has raised ventilation energy demands and safety risks, driving the need for intelligent and more adaptive ventilation systems. Ventilation on Demand (VOD) systems dynamically adjust airflow using real-time operational and environmental data to improve energy efficiency while maintaining safety. Although VOD has been applied for over a decade, deeper and more extreme mining environments associated with critical minerals extraction introduce new challenges and opportunities. VOD systems rely on the tight integration of hardware, sensing, optimization-based control, and flexible infrastructure as mining operations evolve. The application of Artificial Intelligence (AI) introduces significant opportunities to further enhance and adapt VOD systems to these emerging challenges. This work presents a comprehensive review of the state of the art in AI integration within VOD technologies, covering sensing and prediction models, control strategies, and optimization frameworks aimed at improving energy efficiency, safety, and overall system performance. Findings show an increasing use of hybrid deep learning architectures, such as CNN-LSTM and Bi-LSTM, for forecasting, as well as AI-enabled optimization methods for sensor and actuator placement. Key research gaps include a reliance on narrow AI models, limited long-term predictive capabilities for maintenance and strategic planning, and a predominance of simulation-based validation over real-world field deployment. Future research directions include the integration of generative and generalized AI approaches, along with human–cyber–physical system (Human-CPS) designs, to enhance robustness and reliability under the uncertain and dynamic conditions characteristic of deep underground mining environments. Full article

Background and objectives: In older and frail patients, in-hospital cardiac arrest (IHCA) is associated with high mortality. Early warning scores such as the National Early Warning Score 2 (NEWS2) are widely used to detect clinical deterioration, but their predictive accuracy in frail populations remains uncertain. This study aimed to assess whether integrating frailty measures with NEWS2 could better describe elderly IHCA patients. Materials and Methods: We conducted a single-center, retrospective observational study in adult and frail patients (≥18 years) admitted to medical and surgical wards of the University Hospital of Siena who experienced IHCA between January 2022 and January 2024. Data on demographics, such as last NEWS2 before IHCA, Clinical Frailty Scale (CFS), Barthel Index (BI), and Charlson Comorbidity Index (CCI) were retrospectively collected and analyzed. Patients were stratified into three categories, according to NEWS2: Stable (A), Potentially Unstable or Unstable (B), and Critical (C). Results: Seventy patients were analyzed (mean age 76.9 ± 11.0 years; 56% male). The mean pre-IHCA NEWS2 score was 6.0 ± 3.5, with 41% of patients classified as NEWS2-C, 48% classified as NEWS2-B, and 11% classified as NEWS2-A. The NEWS2-A category showed higher BI and lower CFS than NEWS2-B and NEWS2-C (p < 0.01), while CCI and age did not significantly differ. Conclusions: The association of NEWS2 with frailty scores could identify some elderly patients with limited pre-arrest physiological derangements but high frailty who suffered from IHCA. These findings provide descriptive insights that may inform monitoring strategies for “at-risk” elderly patients to help prevent IHCA. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

Cardiogenic pulmonary edema (CPE) is a life-threatening manifestation of acute heart failure characterized by rapid accumulation of fluid in the interstitial and alveolar spaces, leading to severe dyspnea, hypoxemia, and respiratory failure. The condition arises from elevated left-sided filling pressures that increase pulmonary capillary hydrostatic pressure, disrupt alveolo-capillary barrier integrity, and impair gas exchange. Neurohormonal activation further perpetuates congestion and increases myocardial workload, creating a vicious cycle of hemodynamic overload and respiratory compromise. Respiratory support is a cornerstone of management in CPE, aimed at stabilizing oxygenation, reducing the work of breathing, and facilitating ventricular unloading while definitive therapies, such as diuretics, vasodilators, inotropes, or mechanical circulatory support (MCS), address the underlying cause. Among available modalities, non-invasive ventilation (NIV) with continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP) has the strongest evidence base in moderate-to-severe CPE, consistently reducing the need for intubation and providing rapid relief of dyspnea. High-flow nasal cannula (HFNC) represents an emerging alternative in patients with moderate hypoxemia or intolerance to mask ventilation, and should be considered an adjunctive option in selected patients with less severe disease or NIV intolerance, although its efficacy in severe presentations remains uncertain. Invasive mechanical ventilation is reserved for refractory cases, while extracorporeal membrane oxygenation (ECMO) and other advanced circulatory support modalities may be necessary in cardiogenic shock. Integration of respiratory strategies with hemodynamic optimization is essential, as positive pressure ventilation favorably modulates preload and afterload, synergizing with pharmacological unloading. Future directions include personalization of ventilatory strategies using advanced monitoring, novel interfaces to improve tolerability, and earlier integration of MCS. In summary, respiratory support in CPE is both a bridge and a decisive therapeutic intervention, interrupting the cycle of hypoxemia and hemodynamic deterioration. A multidisciplinary, individualized approach remains central to improving outcomes in this high-risk population. Full article

The bipolar fuzzy set and bipolar soft set have inspired the development of a new framework called double-framed bipolar fuzzy soft sets (DFBFSSs). This structure represents positive and negative membership information through ordered pairs, enabling a balanced treatment of uncertainty, imprecision, and bi-directional information in complex decision-making scenarios. The fundamental concepts and operations of DFBFSSs are rigorously defined and analyzed. The double-framed formulation is symmetric: exchanging the frames preserves the structure of DFBFSSs. This symmetry enables balanced handling of opposing or complementary information. The key properties of the proposed set show improved handling of uncertainty over existing fuzzy and soft set models. In addition, a decision-making algorithm based on DFBFSSs is developed and applied to a real-world problem to validate the framework’s feasibility. Comparative analysis confirms the method’s robustness and advantages in uncertain, dual-information settings. Full article