Search Results (19,916)

Survival rates in pediatric cancer have increased substantially over recent decades. However, children and survivors frequently experience treatment-related alterations in physical function, body composition, bone health, and metabolic regulation. Chemotherapy, glucocorticoid exposure, physical inactivity, nutritional imbalance, and inflammatory and neuroendocrine disturbances may contribute to reduced lean mass, decreased bone mineral density, sarcopenic obesity, and long-term cardiometabolic risk. This narrative review critically summarizes current evidence on multimodal exercise and nutritional interventions in pediatric oncology, with particular attention to their effects on physical function, body composition, nutritional status, and metabolic health. Literature searches were conducted in PubMed, Scopus, and Web of Science up to April 2026, combining contextual evidence with studies evaluating combined exercise and nutritional strategies. Current evidence suggests that structured and supervised exercise, particularly resistance and combined aerobic–resistance training, is feasible and safe, and may improve cardiorespiratory fitness, muscle strength, functional capacity, and body composition. Nutritional care should be individualized, prioritizing adequate protein intake, micronutrient status, periodic reassessment of energy requirements, and body composition rather than relying on BMI alone. Nevertheless, available findings remain limited by small sample sizes, heterogeneous protocols, variable supervision, inconsistent outcome assessment, and limited long-term follow-up. Integrating exercise, nutrition, and regular monitoring into pediatric oncology care may help mitigate treatment-related functional and metabolic complications. Future studies should prioritize adequately powered randomized trials, standardized intervention protocols, objective monitoring of exercise intensity, harmonized body composition and functional outcomes, and longer follow-up to define clinically applicable multimodal care models. Full article

The explosive growth of electric vehicle (EV) charging infrastructure is increasingly straining power distribution networks, but the at-scale behavioral heterogeneity of charging stations remains poorly understood. In this study, we implement an unsupervised machine learning approach based on real data (encompassing 32,057 EV charging stations in the publicly available dataset of the Republic of Korea) to discover hidden load concentration patterns. We applied K-means clustering (k = 6) with the k-means++ initialization method to seven station-level features, which yielded six behavioral archetypes that were further evaluated using four supervised classifiers (Decision Tree, Logistic Regression, Random Forest, and XGBoost), all achieving an F1 macro ≥ 0.994 and ROC-AUC ≥ 0.999. The SHAP analysis revealed that geographic variables mainly explain the differentiation among low-use slow-charging sub-clusters, whereas operational variables such as session frequency, output capacity, charger type, and charging speed are decisive for the load-relevant C3 and C5 archetypes. We introduced three new grid load metrics: cluster load contribution, load imbalance coefficient of variation (CV = 1.1247), and the hidden load effect. Results indicate that the high-power fast cluster (C5) and high-use slow cluster (C3) combine to contribute 66.7% of the network station load score-based load while representing only 19.2% of stations. Under the station load score proxy assumption, C3 demonstrates 14.4% greater per-station utilization intensity than C5 (293.6 vs. 256.7), challenging the notion that fast chargers are the key source of infrastructure pressures. These insights provide actionable guidance for demand-side management approaches. Full article

Amid the ongoing acceleration of climate change over recent decades, extreme precipitation events have become more frequent and intense on a global scale, triggering severe natural hazards and considerable socioeconomic damage. Nevertheless, how extreme precipitation has evolved at the national level over long time spans, and what role atmosphere–ocean teleconnections play in driving regional differences, remains insufficiently explored. This study addresses that knowledge gap by conducting a comprehensive assessment of eight ETCCDI-based extreme precipitation indices (PRCPTOT, CWD, R20, R95p, R99p, RX1day, RX5day, and SDII) across six climatic sub-regions of China (Northeast, North, East, Central South, Northwest, and Southwest) over 1960–2020, drawing on daily records from 695 quality-controlled meteorological stations. Key atmospheric and oceanic circulation drivers were further diagnosed and their joint influence was quantified via multiple wavelet coherence (MWC). The analysis shows that five of the eight indices (CWD, R95p, R99p, RX1day, and RX5day) underwent statistically significant fluctuating changes (p < 0.05) throughout the 61-year record. Seven indices, all except CWD, demonstrated upward tendencies, with mutation points clustering after 2010, most notably between 2011 and 2016. Wavelet power spectra indicates elevated energy concentrations at multiple time scales, although only CWD exhibited a statistically significant periodicity of approximately 8–10 a (p < 0.05 against red noise). In terms of spatial patterns, index magnitudes generally increased along a northwest-to-southeast gradient. Stations registering significant upward shifts were concentrated in East and Central South China, whereas significant downward shifts appeared mainly in North China and the northern portion of East China. An altitude-dependent pattern was also detected: CWD rose with elevation, while the remaining indices declined sharply below 1288 m, fluctuated in the 1288–2090 m band, and dropped again above 2090 m. Wavelet coherence analysis uncovered significant resonance between extreme precipitation and four circulation indices—SCSMMI, WPSHI, PNA, and NAO. MWC further identified three driver combinations—ENSO-PNA, SCSMMI-WPSHI, and ENSO-NAO-EASMI—as the most influential, acting both individually and synergistically. These results furnish an empirical basis for forecasting, preventing, and managing precipitation-related disasters across China under future climate scenarios. Full article

This study develops a Fuzzy C-Means-based mathematical framework for the storage-oriented evaluation and classification of hybrid energy system alternatives. The analysis considers fifteen hybrid configurations generated through pairwise combinations of solar, wind, biomass, geothermal, hydropower, and fossil-based energy sources. These alternatives are evaluated with respect to fourteen storage-related criteria, namely energy efficiency, exergy efficiency, entropy, lifetime, cost, CO₂ emissions, recyclability, decarbonization potential, discharge duration, charge duration, power capacity, energy capacity, sustainability, and environmental impact. After constructing and normalizing the decision matrix, the Fuzzy C-Means algorithm is employed to identify latent similarity structures and to determine the degree of membership of each hybrid alternative to multiple clusters. To support the selection of an analytically meaningful partition, alternative cluster structures are compared in terms of partition quality and interpretability. The results indicate that the considered hybrid configurations can be grouped into distinct yet partially overlapping storage-oriented profiles, reflecting differences in technical performance, environmental burden, and sustainability characteristics. In particular, hydropower-supported systems are associated with more stable and infrastructure-compatible profiles, while biomass- and geothermal-related combinations occupy more balanced transitional positions. By extending fuzzy clustering to the storage-oriented analysis of hybrid energy systems, the study provides a mathematically transparent basis for comparative assessment, exploratory classification, and preliminary decision support. Full article

Salt cavern Compressed Air Energy Storage (CAES) is one of the critical technologies for energy storage and an important infrastructure supporting the construction of new power systems and facilitating the achievement of the dual carbon goals. The salt rock resources in China are primarily composed of continental strata salt rocks, characterized by high heterogeneity, well-developed thin-layer interbedding, dissolution resistance among different lithologies, and significant creep variations. These features, to some extent, limit the improvement of wellbore construction accuracy, the reliability of abandoned well sealing, the safety of natural gas storage operations, and enhancements in gas injection–brine displacement efficiency. This study takes the continental bedded salt rock in the Dawenkou Basin as the research object and adopts a method combining theoretical analysis and field engineering verification to improve the systematic construction technology system, covering the whole process of drilling engineering, abandoned well plugging, the design of an injection and brine extraction device, and gas injection and brine drainage. The research results optimize four key technologies, including precise wellbore trajectory control, dual-section milling, and multi-stage redundant plugging of abandoned wells and long-term anti-corrosion completion with laser cladding, and dual-mode adaptive gas injection and brine drainage, and improve the technical system from wellbore construction to salt cavity formation. This study can provide valuable theoretical references and engineering demonstration guidance for underground space development projects in similar salt basins in China. Full article

Given the complex operating conditions and latent faults exhibited by electric vehicle charging infrastructure amid massive order volumes, traditional monitoring methods based on thresholds or single statistical metrics struggle to detect dynamic, time-varying anomalies. This paper proposes a method for identifying and tracing the operational status of charging facilities based on the k-shape time-series clustering algorithm. This method directly uses charging current time series as the research object, eliminating the cumbersome manual feature extraction process. By utilizing a shape-based distance (SBD) metric strategy, it overcomes common time-series data issues such as phase shifts and amplitude scaling while preserving the integrity of the time dimension. Through iterative calculation of cluster centroids, the algorithm successfully and adaptively classifies massive amounts of data into typical clusters such as “standard charging,” “deep oscillation,” and “power-limited.” Based on the clustering results, this paper further constructs a “shape-operating condition” mapping mechanism. Combined with a Bayesian posterior probability model, this enables the localization of high-risk “vehicle-charger” combinations statistically associated with abnormal waveforms. Empirical studies demonstrate that this method can effectively identify equipment performance degradation at the micro-level of waveforms and provide prioritized inspection clues for the intelligent operation and maintenance of charging networks. Full article

Unmanned Aerial Vehicle (UAV) clusters have broad applications in agricultural detection, traffic control, and disaster rescue, where navigation and positioning serve as the core technology. However, satellite navigation fails to meet the requirements of region-wide navigation due to the urban canyon effect. Although the China Satellite Network (CSN) boasts advantages such as high landing power and low latency, it can only achieve single-link communication. Consequently, exploring how to realize cooperative positioning via UAV clusters has become an urgent research need. In this paper, an Unmanned Aerial Vehicle Cluster Communication–Navigation Integrated Cooperative Positioning (UCNCP) algorithm is proposed. This algorithm combines the communication and navigation characteristics of the CSN, establishes a single pseudorange measurement model and cluster geometric topology, and constructs an architecture for cooperative positioning based on UAV cluster pseudorange measurements and inter-UAV ranging data, thereby achieving reliable navigation and positioning of UAV clusters. Comparative experiments between the proposed method and other low-orbit satellite positioning methods demonstrate that the UCNCP algorithm exhibits higher positioning stability. When abrupt changes occur in navigation information, it can effectively mitigate the impact of abrupt change errors on positioning accuracy, improving the positioning stability of UAV clusters by more than 30%. Full article

In modern reliability engineering, modeling bounded lifetime data under realistic experimental conditions is still challenging, especially when censoring schemes and unit removals are random. This study proposes a new and unified reliability framework by combining the flexible powering new power (PNP) distribution with a grouping-based progressive first-failure mechanism using a beta-binomial random design. The proposed approach explicitly accounts for the randomness in group removals, providing a more realistic description of practical life-testing experiments. Classical estimation is carried out using maximum likelihood methods with the Newton-Raphson algorithm, along with confidence intervals constructed under both standard and log-transformed parameterizations. To increase flexibility in inference, a Bayesian approach is developed based on a joint gamma and shifted log-normal prior, which respects parameter constraints and incorporates prior uncertainty. Since the posterior distributions cannot be obtained in closed form, a Metropolis-Hastings Markov chain Monte Carlo algorithm is used to generate reliable posterior estimates and credible intervals. Additionally, beyond sensitivity analysis, multiple prior robustness diagnostics are incorporated to ensure reliable hyperparameter calibration and to safeguard against prior misspecification. The performance of the proposed estimators is carefully examined through extensive Monte Carlo simulations under different censoring schemes and parameter settings. The simulation results indicate that the proposed Bayesian procedures often provide more stable estimation and shorter interval estimates with competitive coverage probabilities compared with the corresponding classical methods, particularly under moderate-to-heavy censoring settings. To demonstrate its practical usefulness, the proposed model is applied to two real datasets on tensile strength of carbon and polyester fibers, where it provides a good fit and useful insights into material reliability and failure behavior. In the same applications, the practical relevance and superior performance of the proposed distribution are demonstrated, where it outperforms existing bounded versions of several well-known models, including the gamma, Weibull, and Birnbaum-Saunders distributions. Overall, this work contributes to reliability analysis by offering a flexible and computationally efficient framework that accounts for both random censoring and complex lifetime patterns, with potential applications in engineering, materials science, and applied reliability studies. Full article

This paper presents a practical and communication-independent energy management system (EMS) for a DC microgrid supply within the firezone of a cruise ship. The proposed approach prioritizes operational reliability and fault tolerance under emergency conditions, where communication availability and control complexity should be minimized. The proposed DC microgrid integrates photovoltaic systems (PVs), fuel cell systems (FCs), and lithium-iron-phosphate (LFP) battery energy storage systems (BESSs), coordinated through a rule-based EMS combined with droop-controlled converters. The electrical topology considered in this study is a collaborative development of the project consortium of the publicly funded project Sustainable DC Systems (SuSy), featuring a novel configuration with two independent horizontal busbars for the Cabin Area Distribution (CAD) and Technical Area Distribution (TAD). The EMS can manage two operational scenarios: (i) regular operation, with two decentralized droop controls where power generation is distributed among all generators based on their respective capacities, and a power curtailment strategy is applied to prevent overcharging of BESSs; and (ii) irregular operation, where a fault on one of the vertical busbars triggers the use of reserved battery storage capacity on both sides of the ship and activates load-shedding to ensure continued operation of critical loads and sustain grid functionality. The effectiveness of the proposed architecture is validated through detailed MATLAB/Simulink simulations. Under regular conditions, the EMS achieves stable voltage regulation, balanced power sharing, and efficient energy curtailment. During fault conditions, the battery storage on both sides successfully supports the critical loads. The fuel cells are operated in power-controlled mode effectively up to their full rated 6$\mathrm{k}$$\mathrm{W}$ capacity while the DC bus voltage stabilization is ensured by the battery energy storage systems. These results validate the proposed EMS as a robust and low-complexity solution for maritime DC microgrids, offering stable voltage regulation, effective load prioritization, and resilient operation of critical loads. Full article

AC microgrids with high penetration of inverter-based distributed energy resources (IBDERs) introduce major protection challenges due to reduced fault current levels, bidirectional power flows, and control-dependent fault behavior. Under these conditions, short-circuit current calculation and relay protection coordination become tightly coupled, since inaccurate fault modeling directly degrades relay sensitivity and selectivity. This review presents a protection-oriented assessment of state-of-the-art short-circuit calculation and relay protection strategies for AC microgrids. The analysis shows that conventional IEC-based fault models and static overcurrent protection schemes are insufficient for inverter-dominated networks. Generalized Δ-circuit–based modeling framework is identified as the most suitable foundation for microgrid fault analysis, as they enable inverter-aware phasor-domain representation and support both grid-connected and islanded operation. In addition, adaptive relay coordination approaches that incorporate time-varying IBDER participation and fault ride-through behavior demonstrate improved coordination robustness compared to conventional fixed settings, although their practical deployment remains constrained by network topology and communication requirements. Simulation results obtained on a representative microgrid case study confirm that the combined application of protection-oriented short-circuit modeling and adaptive relay coordination significantly improves fault detection reliability and coordination performance. The findings highlight the necessity of jointly addressing fault modeling and protection design to ensure reliable operation of inverter-dominated AC microgrids. Full article

Thalassemia is one of the most common inherited diseases in Guangxi, China. Early identification of thalassemia by neonatal screening is beneficial for effective clinical management and treatment. A total of 3671 newborns from multiple centers of Guangxi were prospectively recruited and screened for thalassemia using single molecule real-time (SMRT) sequencing technology. A total of 36 types of variants of globin genes were identified, including 16 common variants and 20 rare variants in the Chinese population. In total, 956 (26.04%) newborns were identified to carry thalassemia variants, including 672 (18.31%) α-thalassemia, 228 (6.21%) β-thalassemia, 55 (1.50%) combined α/β-thalassemia and 1 (0.03%) δ-thalassemia. In addition, this study showed that the carrier rates of structural variants of α-globin genes and abnormal hemoglobin variants were 1.28% and 0.93% respectively. Phenotypically, 12 newborns with hemoglobin H disease and 2 cases with intermedia β-thalassemia were found, two of whom would be misdiagnosed by conventional genetic analysis methods. Collectively, this study characterized the complexity and diversity of thalassemia gene variants in newborns of Guangxi, and further achieved early identification of newborns with intermedia thalassemia, which facilitated precision prevention of thalassemia in this region. Also, SMRT provided a powerful tool for neonatal thalassemia screening, especially in prevalent regions. Full article

With the high penetration of distributed photovoltaic and storage systems, active distribution grids are prone to experiencing “active power surplus and reactive power shortage” during the evening peak, leading to voltage sags at the network end. Although electric vehicle (EV) grid-connected inverters possess four-quadrant reactive power regulation capabilities without causing the additional chemical cyclic aging of the battery cells, existing dispatch systems often treat them as unconditional response resources, overlooking users’ actual willingness to cede control and the associated strategic interactions. To address this, this paper proposes a “grid-load” coordinated reactive power optimization strategy that accounts for EV users’ willingness to respond: a Logit model incorporating price incentives, initial energy consumption, and parking duration is constructed based on discrete choice theory. By combining a truncated normal distribution with the Monte Carlo method to eliminate micro-sampling errors, a model of the expected reactive power capacity of charging stations under dynamic incentives is established; considering the physical constraints of SVCs and EVs, a scalarized single-objective optimization model is constructed with grid loss-equivalent costs, ancillary service costs, and voltage deviation as objectives, and solved using an improved particle swarm optimization algorithm with linearly decreasing weights. Simulations on a modified 33-node IEEE system incorporating storage indicate that this strategy can assign optimal compensation prices to each node based on the spatial value of reactive power. Compared to traditional single-voltage regulation and fixed subsidies, it not only stabilizes the grid-wide voltage within a safe range but also avoids overcompensation, achieving global optimization of both power quality and economic efficiency. Full article

Hyperspectral image change detection (HSI-CD) is becoming increasingly important in understanding how the Earth’s surface evolves over time, from monitoring ecosystems to tracking urban expansion. Unlike traditional pixel-based or hand-crafted approaches, deep learning models can automatically learn powerful spectral–spatial features, making them especially effective for this task. In this review, we bring together recent advances in deep learning for HSI-CD, combining a meta-analysis of the literature with an overview of the main model families and training strategies. We cover supervised, semi-supervised, and unsupervised methods, as well as newer directions such as transfer learning, self-supervised frameworks, and hybrid designs that blend CNNs, transformers, and graph neural networks. We also discuss benchmark datasets, evaluation protocols, and case studies that show how these methods perform in practice. Beyond summarizing the current progress, the review highlights ongoing gaps, such as limited labeled data, generalization across sensors, computational efficiency, and the need for interpretability, and points to emerging opportunities for future work. Our goal is to provide both a snapshot of the current state of the field and a road map for advancing deep learning-based HSI-CD. Full article

Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has become an effective architecture for supporting computation-intensive and latency-sensitive applications by enabling flexible deployment and enhanced wireless coverage. However, when non-orthogonal multiple access (NOMA) is incorporated, the joint optimization of computation offloading, wireless resource allocation, RIS phase configuration, and UAV trajectory design becomes highly challenging owing to the strong coupling among decision variables, problem non-convexity, and time-varying system dynamics. To address these challenges, this paper investigates the energy consumption minimization problem in a NOMA-based RIS-assisted UAV-MEC system by jointly optimizing user offloading ratios, transmit power, UAV computing resource allocation, and flight trajectory. A long short-term memory (LSTM)-embedded proximal policy optimization (PPO) algorithm is developed to capture the temporal dependencies of system states and enable adaptive decision-making in dynamic environments. In addition, a closed-form phase conjugation-based optimal RIS configuration is derived and incorporated into the environment model to reduce the action space and improve training efficiency. The simulation results show that the proposed LSTM-PPO method converges faster and achieves lower energy consumption than conventional PPO, deep deterministic policy gradient (DDPG), and fixed offloading schemes, while exhibiting improved stability and scalability in the tested multi-user scenarios. These results demonstrate the effectiveness of combining temporal learning and model-assisted RIS optimization for energy efficient resource management in RIS-assisted UAV-MEC systems. Full article

With the large-scale deployment of distributed photovoltaics (PVs) on the user side, integrated PV-storage systems have become a critical means to reduce electricity costs and enhance energy flexibility. However, the volatility of PV output and the dynamic nature of time-of-use (TOU) pricing render the economic viability of such systems highly dependent on the coordinated optimization of capacity configuration and operational strategies. To address this, a bi-level optimization model is developed. The upper level maximizes the equivalent annual economic benefit by determining the installed capacities of PV and storage, explicitly incorporating power-sensitive operation and maintenance costs. The lower level, formulated as a mixed-integer programming problem, minimizes the daily net electricity cost by optimizing charging/discharging schedules and grid interaction. The model is solved through an iterative hierarchical approach combining the chaotic sparrow search algorithm (CSSA) and the CPLEX solver. A case study using actual data from an industrial park demonstrates that, compared with scenarios without PV-storage and with PV only, the joint PV-storage configuration reduces total electricity costs by 17.3% and 4.5%, respectively. Furthermore, the asymmetric impacts of PV forecast errors on operational economics are quantitatively analyzed: when PV output is underestimated, the failure to pre-reserve accommodation capacity leads to an increase in electricity procurement costs of RMB 1927.84 compared with the ideal scenario. To mitigate this, a risk-aware fault-tolerant scheduling strategy is proposed, which reserves a 5% accommodation margin through conservative biasing, reducing the additional cost caused by forecast errors by 20.14% and significantly enhancing the system’s economic robustness under forecast uncertainty. Full article