Search Results (26,478)

The increasing deployment of photovoltaic-storage systems in distribution-level microgrids introduces a critical control conflict: traditional maximum power point tracking algorithms aim to maximize energy harvest, while grid-forming inverter control demands real-time power flexibility to deliver frequency and inertia support. This paper presents a novel two-layer co-optimization framework that resolves this tension by integrating adaptive traditional maximum power point tracking modulation and virtual synchronous control into a unified, grid-aware inverter strategy. The proposed approach consists of a distributionally robust predictive scheduling layer, formulated using Wasserstein ambiguity sets, and a real-time control layer that dynamically reallocates photovoltaic output and synthetic inertia response based on local frequency conditions. Unlike existing methods that treat traditional maximum power point tracking and grid-forming control in isolation, our architecture redefines traditional maximum power point tracking as a tunable component of system-level stability control, enabling intentional photovoltaic curtailment to create headroom for disturbance mitigation. The mathematical model includes multi-timescale inverter dynamics, frequency-coupled battery dispatch, state-of-charge-constrained response planning, and robust power flow feasibility. The framework is validated on a modified IEEE 33-bus low-voltage feeder with high photovoltaic penetration and battery energy storage system-equipped inverters operating under realistic solar and load variability. Results demonstrate that the proposed method reduces the frequency of lowest frequency point violations by over 30%, maintains battery state-of-charge within safe margins across all nodes, and achieves higher energy utilization than fixed-frequency-power adjustment or decoupled Model Predictive Control schemes. Additional analysis quantifies the trade-off between photovoltaic curtailment and rate of change of frequency resilience, revealing that modest dynamic curtailment yields disproportionately large stability benefits. This study provides a scalable and implementable paradigm for inverter-dominated grids, where resilience, efficiency, and uncertainty-aware decision making must be co-optimized in real time. Full article

Efficient nitrogen fertilization is critical for maximizing crop productivity while minimizing environmental and health risks. Red beet baby leaves are valued for their vibrant color, flavor, and antioxidant content, particularly betalains, but they are also prone to accumulating antinutritional compounds such as nitrate and oxalate. Excessive nitrogen supply can exacerbate this accumulation, highlighting the need to optimize nitrate input to balance yield, nutritional quality, and safety. This study examined how different nitrate concentrations (1 mM and 10 mM NO₃⁻) in hydroponic systems influence red beet baby leaf yield, quality, and levels of beneficial and harmful compounds. The plants were sampled at 10 and 17 days after planting (DAP), and the effects of the treatments in relation to plant age were assessed. Both sampling time and nitrate concentration significantly influenced red beet baby leaf growth and quality. Extending cultivation to 17 days improved yield and antioxidant levels (phenols, flavonoids, betalains) but also increased soluble oxalates. Low nitrate (1 mM) reduced both yield and antioxidant content, regardless of harvest time. However, after 17 days, low nitrate also lowered total oxalate levels, likely due to increased oxalate oxidase activity. Although 1 mM nitrate reduces fertilizer input, it compromises yield and quality. Therefore, intermediate nitrate levels should be explored to optimize both fertilizer use and product quality. Full article

This study evaluated the potential of utilizing Sesbania cannabina, produced during saline–alkali soil improvement, as a high-quality feed resource for ruminants. Mixed silages were prepared by combining S. cannabina and whole corn at ratios of 1:1 and 1:3, with or without a compound Lactobacillus (LAB) inoculant, and were assessed for fermentation quality, nutrient composition, ruminal degradation, intestinal digestibility, and energy value. Results: The addition of Lactobacillus (LAB) inoculants increased lactic acid content, crude protein effective degradability (CPED), gross energy (GE), and dry matter apparent digestibility (DMAD), while decreasing ammonia nitrogen (NH₃-N), acetic acid (AA), propionic acid (PA), neutral detergent fiber (NDF), acid detergent fiber (ADF), rumen undegradable protein (RUP), intestinal crude protein degradability (ICPD), and intestinal digestible crude protein (IDCP). Increasing the proportion of whole corn increased dry matter (DM) and gross energy (GE), while reducing crude protein (CP), NDF, ADF, Ash, rumen degradable protein (RDP), RUP, IDCP, and the effective ruminal degradability of NDF (NDFED) and ADF (ADFED). Overall, a 1:1 mixing ratio maximized S. cannabina utilization without compromising feeding value, and LAB inoculation ensured successful ensiling while enhancing nutrient utilization. Full article

Since the implementation of the Belt and Road Initiative (BRI) in 2013, Chinese enterprises have expanded port and industrial investments along the Maritime Silk Road (MSR), forming a mutually reinforcing coupled system. Port investments reduce transportation costs and promote the relocation of industries to host countries. In turn, industrial agglomeration further promotes port investment. However, risks arising from political and economic uncertainties in host countries, as well as fluctuations in international relations, have become increasingly prominent. Due to the differences in the types and levels of risks faced by port and industrial investments, port investment decisions have become more complex and uncertain. To address this issue, this study constructs a bi-level optimization model. The upper model (UM) aims to maximize the total investment profit by optimizing the scale of multiple port investments. The lower model (LM) employs a User Equilibrium (UE) framework to determine the spatial distribution of industries under equilibrium conditions. Using 14 countries along the MSR as a case study, this paper estimates the number of newly constructed berths in each country and the corresponding investment returns. It also finds that local wages and land prices tend to rise after investment. The findings provide valuable references for Chinese enterprises in making overseas investment decisions. Full article

Maritime transport is vital to the global economy, yet the frequency of natural disasters at sea continues to rise, resulting in more persons falling overboard. Therefore, effective maritime search and rescue (SAR) hinges on accurately predicting the probable distribution of drifting victims and on rapidly devising an optimal search plan. Conventional SAR operations either rely on rigid, pre-defined patterns or employ reinforcement-learning techniques that yield non-unique solutions and incur excessive computational time. To overcome these shortcomings, we propose an adaptive SAR framework that integrates three modules: (i) the AP98 maritime-drift model, (ii) Monte Carlo particle simulation, and (iii) a mixed-integer linear programming (MILP) model. First, Monte Carlo particles are propagated through the AP98 model to generate a probability density map of the victim’s location. Subsequently, the MILP model maximizes the cumulative probability of rescue success while minimizing a composite cost index, producing optimal UAV search trajectories solved via Gurobi. Experimental results on a 10 km × 10 km scenario with five UAVs show that, compared with traditional parallel-line search, the proposed MILP approach increases cumulative success probability by 12.4% within the first twelve search steps, eliminates path overlap entirely, and converges in 9.5 s with an optimality gap of 0.79%, thereby demonstrating both efficiency and real-time viability. When MIPFocus (a solver setting in Gurobi that controls the emphasis of the Mixed Integer Programming solver) aims at the optimal solution and uses the parallel solution method at the same time, the best result is achieved. Full article

Accurate recognition of power grid operation behaviors is crucial for ensuring both safety and operational efficiency in smart grid systems. However, this task presents significant challenges due to dynamic environmental variations, limited labeled training data availability, and the necessity for continuous model adaptation. To overcome these limitations, we propose an Incremental Spatio-temporal Augmented Sampling (ISAS) method for power grid operation behavior recognition. Specifically, we design a spatio-temporal Feature-Enhancement Fusion Module (FEFM) which employs multi-scale spatio-temporal augmented fusion combined with a cross-scale aggregation mechanism, enabling robust feature learning that is resilient to environmental interference. Furthermore, we introduce a Selective Replay Mechanism (SRM) that implements a dual-criteria sample selection strategy based on error variability and feature-space divergence metrics, ensuring optimal memory bank updates that simultaneously maximize information gain while minimizing feature redundancy. Experimental results on the power grid behavior dataset demonstrate significant advantages of the proposed method in recognition robustness and knowledge retention compared to other methods. For example, it achieves an accuracy of 89.80% on sunny days and maintains exceptional continual learning stability with merely 2.74% forgetting rate on three meteorological scenarios. Full article

Background/Objectives: Follicular lymphoma (FL) and marginal zone lymphoma (MZL) are low-grade B-cell lymphomas (LGBCLs) with indolent clinical courses but a lifelong risk of histologic transformation (HT) to aggressive lymphomas, particularly diffuse large B-cell lymphoma. Predicting HT can be challenging due to class imbalances and the inherent complexity of time-dependent events. While there are current prognostic indices for survival, they do not specifically address HT risk. This study aimed to develop and validate survival-based and traditional classification machine-learning models to predict HT in cohorts. Methods: Using a multicenter retrospective dataset (n = 1068), survival models (Cox proportional hazards, Lasso-Cox, Random Survival Forest, Gradient-boosted Cox [GBM-Cox], eXtreme Gradient Boosting [XGBoost]-Cox), and classification models (Logistic regression, Lasso logistic, Random Forest, Gradient Boosting, XGBoost) were compared. The best-performing survival models—XGBoost-Cox, Lasso-Cox, and GBM-Cox—were assessed on an independent test set (n = 92). Model sensitivity was maximized using optimal binary risk cutoff points based on Youden’s index. Results: Survival models showed superior predictive performance than classical classifiers, with XGBoost-Cox exhibiting the highest mean accuracy (85.3%), time-dependent area under the curve (0.795), sensitivity (98%), specificity (83.9%), and concordance index (0.836). Incorporating next-generation sequencing (NGS) data improved model accuracy and specificity, indicating that genetic factors improve HT prediction. Principal component analysis revealed distinct gene mutation patterns associated with HT risk, highlighting DNA-repair genes such as TP53, BLM, and RAD50. Conclusions: This study highlights the clinical value of survival-based machine-learning methods integrated with NGS data to personalize HT risk stratification for patients with FL and MZL. Full article

A complex network topology marked by co-competitive relationships between manufacturing enterprises can meaningfully influence low-carbon technology selection, thereby affecting the low-carbon technology diffusion process. This study develops a small-world network game model based on an optimal update strategy involving the government and manufacturers with co-competitive relationships, and then uses it to assess the evolutionary dynamics of low-carbon technology selection and diffusion among manufacturers. The results indicate that the government should identify the critical threshold for subsidies based on the carbon tax to optimize the regulatory and incentivizing effects of government subsidies. The topological structure of manufacturers’ small-world networks is the key to low-carbon technology selection and diffusion. In favorable conditions, when a small-world network approaches a regular network in terms of structure, the extent of low-carbon technology diffusion is maximized; in unfavorable conditions, diffusion is minimal. Thus, the government can tighten or relax market access restrictions on the manufacturing industry and encourage the development of manufacturing clusters to change the structure of market competition. Compared with the random selection, the optimal update strategy can increase the probability density of low-carbon technology diffusion among manufacturers and rapidly achieve a balanced, stable state. Full article

Adipose-derived mesenchymal stromal/stem cells (AT-MSCs) can be typically isolated from adipose tissue using a minimally invasive procedure. However, since AT-MSCs are usually obtained from subcutaneous tissue, there is a risk of contamination with fibroblasts (FBs), which can reduce the differentiation potential of AT-MSCs. To avoid this contamination, it is crucial to identify specific markers to effectively distinguish AT-MSCs from FBs. Analysis of microarray data obtained from three studies (GSE9451, GSE66084, GSE94667, and GSE38947) revealed 123 genes expressed at levels more than 1.5-fold higher in AT-MSCs compared to FBs. Using STRING, a protein–protein interaction (PPI) network consisting of 80 nodes and 197 edges was identified within the 123 genes. Further investigation using Molecular Complex Detection in Cytoscape identified a module of 12 genes: COL3A1, FBN1, COL4A1, COL5A2, POSTN, CTGF, SPARC, HSPG2, FSTL1, LAMA2, LAMC1, COL16A1. Gene Ontology analysis revealed that these genes were enriched in extracellular region (GO: 0005576). Additionally, these 12 genes corresponded to the top 12 of the 15 hub genes calculated using the Maximal Clique Centrality algorithm. The results of this study suggest that these 12 genes may serve as markers for distinguishing AT-MSCs from FBs, offering potential applications in regenerative medicine. Full article

Low-frequency oscillation modes are studied in small-signal angular stability because, if not adequately damped, they can cause power system instability in the event of a contingency. The interconnection and expansion of large power systems has led to the emergence of multiple local and inter-area modes and required new damping control strategies for these modes. The expansion of the use of Phasor Measurement Units in power systems has led to the development of new control strategies such as Wide-Area Damping Controllers (WADCs) that use data from PMUs to dampen low-frequency oscillations. Although the benefits of WADCs are promising, there are challenges in designing a WADC. This paper proposes a two-stage algorithm for the robust design of a WADC for modern power systems. The first stage consists of solving an optimization model and finding the WADC parameters that maximize the damping ratios of all modes of the linearized system model for a set of operating points. The second stage consists of refining the WADC parameters through an iterative algorithm. Cases were studied for a set of IEEE 68-bus operating points through modal analysis and time-domain simulations. The results obtained demonstrated the good performance of the proposed two-stage algorithm compared with an existing WADC design method based on a Linear Quadratic Regulator. Full article

Morocco, located in an arid region and increasingly affected by climate change, faces chronic water stress. This structural vulnerability places mounting pressure on the country’s water resources. International trade contributes significantly to this pressure, particularly through the export of water-intensive agricultural products. This study investigates the virtual water trade flows of the 32 most-traded agricultural products between Morocco and its primary trading partner, the European Union, over the period of 2000–2020. This study adopts a bottom-up approach, employing the FAO’s CROPWAT 8.0 software based on the Penman–Monteith climatic model to estimate crop water requirements. The results indicate that Morocco was a net importer of virtual water in its agricultural trade with EU countries, with a cumulative net virtual water of 51,839.171 million cubic meters (Mm³). During the study period, Morocco exported a total of 3393.791 Mm³ of virtual water to the EU, primarily through fruits (2903.028 Mm³; 85.539%) and vegetables (467.928 Mm³; 13.788%), notably those with high water footprints. The top three EU importers of Moroccan virtual water were France (1138.785 Mm³), the Netherlands (874.323 Mm³), and the United Kingdom (430.872 Mm³). Conversely, virtual water imports by Morocco amounted to 55,232.963 Mm³, overwhelmingly dominated by cereals, which accounted for 99.697% of the total. These imports originated mainly from France (37,154.090 Mm³), Germany (4980.296 Mm³), and Poland (2330.039 Mm³). The analysis of Morocco’s virtual water balance with EU countries revealed that Morocco was a net virtual importer in trade with most of them. Furthermore, the crop-level virtual water trade balance revealed a tendency to export water-intensive crops that offer relatively low economic water productivity. However, four agricultural products recorded a high economic return per unit of Virtual Water Exported: tomatoes returned 19.80 USD/m³, strawberries 16.02 USD/m³, carrots 13.06 USD/m³, and watermelons 8.11 USD/m³. These findings underscore the importance of integrating water footprint analysis into national agricultural policy to maximize the economic productivity of water and ensure the sustainability of resources in a water-stressed country. Full article

This study addresses the critical challenge of optimizing water resource allocation in fragmented citrus cultivation zones, particularly in Anfusi Town, a key citrus production area in China’s middle-lower Yangtze River region. To overcome the limitations of traditional deterministic models and spatially heterogeneous water supply–demand dynamics, an innovative framework integrating interval two-stage stochastic programming (ITSP) with long short-term memory (LSTM) neural networks is proposed. The LSTM component forecasts irrigation demand and supply under climate variability, while ITSP optimizes dynamic allocation strategies by quantifying uncertainties through interval analysis and balancing economic returns with hydrological risks. Key results demonstrate an 8.67% increase in system-wide benefits compared to baseline practices in the current year scenario. For the planning year (2025), the model identifies optimal water distribution thresholds: an upper limit of 3.85 × 10⁶ m³ for high-availability zone A and lower limits of 1.62 × 10⁶ m³ for moderate-to-low-availability zones B and C. These allocations minimize water scarcity penalties while maximizing net benefits, prioritizing local over external water sources to reduce costs. The study innovates by integrating stochastic-economic analysis with spatial prioritization of high-marginal-benefit zones and uncertainty robustness via interval analysis and two-stage decision making. By bridging a research gap in citrus irrigation optimization, this approach advances sustainable water management in complex agricultural systems, offering a scalable solution for regions facing fragmented landscapes and climate-driven water scarcity. Full article

This paper compares two multi-objective optimization strategies for spiral-grooved dynamic gas thrust bearings. The first optimization is carried out using an analytical model, which is valid under the assumption of a high number of grooves. The second one is carried out by using a numerical model based on a finite difference (FD) technique, which is valid also in case of a limited number of grooves. The FD model was validated with data from the literature, then it was compared with the analytical model. The multi-objective optimization is based on a genetic algorithm and it is aimed at maximizing the load-carrying capacity (LCC) of the thrust bearing while minimizing its friction torque. It was found that the analytical model overestimates both the friction torque and the load capacity compared to the FD model, and that the Pareto front optimizations reveal almost identical trends in the optimized parameters. Full article

Increased basal protein synthesis activity is a hallmark feature that distinguishes many types of malignant cells from their normal counterparts. The survival and proliferation of cancer cells are tightly linked to functional unfolded protein response (UPR) and endoplasmic reticulum (ER)-associated degradation (ERAD) pathways due to their high rates of protein synthesis. The evolutionarily conserved AAA+ ATPase valosin-containing protein (VCP)/p97 facilitates the extraction of proteins from organelles, chromatin, and protein complexes to target them for ubiquitin–proteasome system (UPS)-mediated degradation. p97 plays a key role in protein quality control and in the maintenance of protein homeostasis through its regulation of ERAD. The disruption of p97 activity leads to an accumulation of undegraded proteins, triggers the UPR, and can culminate in proteotoxic cell death. Given this, p97 inhibition offers an opportunity to selectively kill cancer cells that exhibit high basal protein synthesis rates. This review explores p97’s molecular structure, diverse cellular roles, and clinical potential with a particular focus on CB-5083 and CB-5339, the only p97 inhibitors to date that have advanced into clinical trials. We discuss their mechanisms of action, clinical trial outcomes, and the transformative potential of rational combination strategies to maximize their therapeutic potential. By integrating foundational biological insights with translational perspectives, we highlight p97 as a precision target for cancer treatment. Full article

This study presents a geomechanics-driven marine approach for optimizing well trajectories in fractured offshore reservoirs, with a focus on the X Block of the Zhujiangkou Basin. The method integrates in situ stress analysis and fracture prediction within a three-dimensional geomechanical modeling framework, tailored to the complex tectonic and sedimentary characteristics of offshore environments. Multi-source geological and engineering data—including core observations, borehole imaging, well logs, and marine seismic interpretation—are synthesized to reconstruct the subsurface stress field and assess fracture development along potential well paths. Key geomechanical parameters, such as principal stress magnitudes and orientations, rock mechanical properties, and fracture propagation tendencies, are quantitatively evaluated to identify fracture-prone zones and mitigate drilling risks. This methodology enables dynamic adjustment of well trajectories to avoid high-stress zones while maximizing contact with productive fracture networks. A case study from a structurally complex marine fractured reservoir demonstrates the practical applicability of this approach, offering valuable guidance for safe and efficient offshore drilling design in geomechanically sensitive environments. The results highlight the significance of incorporating marine geomechanical insights into trajectory planning for enhanced reservoir development. Full article