Search Results (2,178)

Background/Objectives: In Japan, hospital births predominate, with home births comprising only 0.1% of deliveries. This study assessed how documented practices for planned home births attended by independent midwives align with national guidelines and WHO intrapartum care recommendations, and assess maternal and neonatal differences before and after the COVID-19 pandemic. Methods: Records of 430 low-risk pregnant women who received continuous care at a private midwifery home over 25 years were reviewed. After excluding 8 maternal and 22 neonatal transfers, 400 records were analyzed. Descriptive statistics were compared with WHO recommendations and between the pre-pandemic (1999–2019) and post-pandemic (2020–2024) periods. Results: All women experienced spontaneous singleton cephalic labors with intermittent fetal heart rate auscultation. The mean gestational age was 277.3 days and the median labor duration was 303.5 min. Labor onset was spontaneous in 83.5% of cases. Nearly half of the women had no perineal lacerations. Postpartum blood loss ≥500 mL occurred in 14.1% of cases. Family presence was nearly universal. Neonates had a mean birth weight of 3129.0 g and high Apgar scores. Skin-to-skin contact occurred in 52.9%; exclusive breastfeeding reached 93.8% at 1 month. Post-pandemic births showed higher maternal age and higher neonatal birth weight, although these differences should be interpreted cautiously due to the small post-pandemic sample. Conclusions: Independent midwives provided evidence-based, physiologically oriented care, partially aligning with selected WHO intrapartum recommendations during planned home births. Midwife-led home births may support positive childbirth experiences and favorable maternal/neonatal outcomes for low-risk women. Post-pandemic shifts underscore the need for continued monitoring and flexible, community-based perinatal support, while recognizing the limitations of retrospective, single-site data. Full article

With the continuous increase in the penetration rate of renewable energy sources, the uncertainty of new energy output has brought significant risks and challenges to the planning strategy of integrated energy systems. Meanwhile, power grid operators and heat network operators, belonging to different stakeholder entities, exhibit complex cooperative-competitive game relationships, making it difficult to balance the interests of all parties. To address this issue, this paper proposes a game theory and optimal planning strategy for electricity-heat multiple heterogeneous energy systems based on a deep temporal clustering method from the perspective of different stakeholders. Firstly, typical scenarios of renewable energy output are generated through the deep temporal clustering method. Simultaneously, the charging and discharging behaviors of energy storage devices are utilized to assist the distribution system in new energy consumption. This paper incorporates battery life degradation costs into the objective function on the power grid side to achieve accurate accounting of energy storage device dispatch expenses. Additionally, an optimal dispatch model is established on the heat network side, upon which a game framework for multiple heterogeneous energy systems is constructed. The construction capacity and installation location of each flexible device can be determined through planning decisions in typical multi-scenario situations. Considering the non-convex and nonlinear characteristics of the model, this paper employs an improved firefly algorithm to achieve optimal solution search and rapid convergence. Finally, the effectiveness and feasibility of the proposed method are demonstrated through a case study of an electricity-heat energy system. Full article

Background/Objectives: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood and it tends to remain during adulthood. It not only affects cognitive abilities and behavior but also often presents emotional disturbances and alterations in the perceived quality of life. These symptoms are primarily related to dysfunctions in the ventromedial and dorsolateral prefrontal network. The main objective was to evaluate the feasibility and explore the initial outcomes of an integrated protocol combining neuropsychological treatment and transcranial direct current stimulation (tDCS). Methods: This study presents a single-case experimental A-B design of a 21-year-old woman, diagnosed with predominantly inattentive ADHD, treated at the University Psychology Clinic of Loyola Andalucía University. The treatment was carried out twice a week for 5 weeks (10 sessions in total), with 20 min of anodal tDCS at F3 and cathodal tDCS at F4 (2 mA), while digital neurorehabilitation exercises and psychotherapeutic support were provided. Results: An overall significant improvement was observed in cognitive functions (p = 0.008), with clinically significant gains in cognitive flexibility, visual working memory, and planning. Mixed results were found in inhibition, with improvement in interference control but no change in response inhibition. No significant changes were observed in sustained attention, auditory working memory, or processing speed. In terms of emotional state, an overall improvement was noted (p = 0.046), particularly in depression symptoms and perceived quality of life related to physical and psychological health. However, no significant changes were observed in anxiety symptoms or in areas related to the environment and social relationships. These findings reflect pilot-level evidence of clinical change within a feasibility framework. Conclusions: The combined treatment was found to be safe and feasible, showing promising preliminary improvements in cognitive and emotional domains. As a single-case study, these results serve as hypothesis-generating evidence for future controlled trials. Full article

The safety of waterproof joints in concrete-faced rockfill dams (CFRDs) founded on deep overburden was determined during construction, impoundment, and sedimentation periods, employing the flexible FEM-NSBPFEM coupled method. Through eleven numerical scenarios, critical deformation zones are identified, and the effects of upper soil loads (upstream weighting and sedimentation) and cutoff wall design plans on the key joint between the connecting plate and the cutoff wall (J1) are systematically evaluated. The principal findings reveal that: (1) Joint deformation is dominated by vertical shear, primarily localized at J1, with the shear deformation at J1 reaching approximately 15 cm when the height of the upper soil load reaches 40 m. (2) Upper soil loads exert a greater influence on J1 shear deformation than hydrostatic pressure. (3) Increasing sedimentation loads cause J1 shear deformation to initially mirror impoundment trends before undergoing a sharp surge, and the effect is exacerbated by higher upstream weighting loads. (4) Shear deformation varies markedly between closed and suspended cutoff walls, whereas variations among different suspended wall designs are smaller. Based on these mechanical insights, two optimization schemes for the impermeable system are proposed, effectively constraining joint shear and opening displacements to within 4 cm. These findings provide critical guidance for the reliability analysis and design optimization of CFRD impermeable systems in deep overburden environments. Full article

The rapid growth of low-altitude air traffic demands airspace evaluation frameworks that are scalable, flexible, and efficient. However, existing airspace partitioning strategies, primarily designed for sparse, long-distance civil aviation, are ill-suited to the dense and complex low-altitude environment. Moreover, the heterogeneous nature of low-altitude conditions cannot be adequately captured. To address this challenge, we propose a novel low-altitude airspace evaluation framework centered on a hierarchical voxel-based partitioning strategy. This strategy explicitly accommodates the diverse operational requirements of drones across different airspace layers. We couple this with an efficient multi-resolution airspace unit encoding mechanism that dynamically aggregates and evaluates airspace availability. To demonstrate the practical utility of our framework, we further develop an energy-aware, multi-scale route-planning algorithm that operates seamlessly across the hierarchical representation. Simulation results show that our method significantly improves computational efficiency in airspace evaluation, while the proposed planner achieves higher energy efficiency compared to conventional approaches like A*. Full article

The large-scale grid integration of distributed renewable energy enhances the flexible regulation capacity of the power system. However, the inherent randomness and volatility of its output, coupled with weak coupling access characteristics, pose severe challenges to the safe and stable operation of the power system. To address these issues, this paper proposes a power system planning method suitable for urban power grids. To accurately characterize the uncertainty of renewable energy output, the method incorporates the concept of multi-scenario stochastic optimization and introduces a dynamic scenario generation method for wind and solar power based on nonparametric kernel density estimation and standard multivariate normal distribution sequence sampling. This method generates a set of typical daily dynamic output scenarios for wind and solar power that closely match actual output characteristics. Considering the spatiotemporal response characteristics of flexible resources, the Soft Open Point (SOP) DC link enables flexible cross-node power transmission and spatiotemporal coupling regulation of flexible resources. Therefore, this paper constructs a mathematical model for the grid integration of flexible resources based on the SOP DC link. By integrating operational constraints such as power flow constraints in the power grid and source-load uncertainty constraints, a power system planning model is established. However, traditional convex optimization methods require approximate simplifications of the model, which can easily lead to a loss of accuracy. Although the Particle Swarm Optimization (PSO) algorithm is suitable for nonlinear optimization, it is prone to getting trapped in local optima. Therefore, this paper introduces an improved PSO algorithm based on refraction opposite learning, which enhances the algorithm’s global optimization capability by expanding the particle search space and increasing population diversity. Finally, simulation verification is conducted based on an improved IEEE-39 bus test system, and the results show that the proposed scenario generation method achieves a sum of squared errors of only 4.82% and a silhouette coefficient of 0.94, significantly improving accuracy compared to traditional methods such as Monte Carlo sampling. Full article

Industrial hemp (Cannabis sativa L.) is versatile and rapidly developing, offering new prospects to producers as a multipurpose crop, yet limited water availability in the Mediterranean area due to climate change makes its sustainable management challenging. Although the plant’s water requirements have been studied, a significant gap remains regarding irrigation management based on the hydraulic properties that govern water movement. The present study elucidates the role of soil hydraulic parameters in water dynamics within the rhizosphere of industrial hemp (Cannabis sativa L., ‘Felina 32’). For this purpose, a pot experiment of three irrigation treatments (100% FC, 80% FC, 60% FC; FC is the field capacity) was set up using two different soil types (clay loam CL and sandy clay loam SCL). SCL soil showed a higher C_max of about 4 cm⁻¹ compared to the C_max of 0.11 cm⁻¹ of CL soil, but dropped drastically within a narrow frame of soil moisture. CL soil resulted in about 12-fold higher diffusivity (D_max ≈ 0.23 cm² min⁻¹) within a wider range of soil moisture compared to the SCL soil (D_max ≈ 0.02 cm² min⁻¹), which facilitated water redistribution at CL, allowing the plant to maximize its water uptake, even at the lowest water input. As a result, the CL soil allowed more flexible scheduling and in contrast, SCL soil necessitated a high frequency irrigation protocol. The integration of hydraulic properties into irrigation planning revealed the potential of CL to apply water to plants efficiently across full and deficit irrigation, showing the peak performance of the irrigation water use efficiency (IWUE) (0.929 g/mm) under the 60% FC regime. The findings provide a framework for linking soil physics–agricultural hydraulics with irrigation strategies in controlled environments. Full article

Uncertainty in travel and service times poses significant challenges for vehicle routing in logistics systems. This paper proposes a stochastic model predictive control (SMPC) strategy to manage a Vehicle Routing Problem with time windows (VRPTW) under stochastic service times with correlation across customers. The approach combines a dynamic optimization model with single and joint chance constraints and a forecasting tool for updating travel plans as new information becomes available. A deterministic reformulation of the stochastic constraints is developed so that the problem can be solved via mixed-integer programming. The aim of this paper is to demonstrate that the SMPC strategy can maintain a high level of time-window reliability (meeting customer time windows with high probability) at a reasonable cost by re-optimizing routes over a moving horizon. In numerical case studies, the SMPC approach achieves the desired reliability levels while incurring only modest increases in total cost, and it flexibly adjusts the cost–risk tradeoff by switching between single and joint chance constraints. These results illustrate the potential of the proposed method for real-time distribution routing under uncertainty and highlight the novel contribution of integrating chance-constrained optimization with Model Predictive Control in a VRPTW context. Full article

The design of sustainable power systems requires planning tools that jointly account for economic, environmental, and social dimensions. However, multi-objective energy system models typically prioritize economic–environmental trade-offs, while the social dimension is still rarely included as an explicit optimization objective. Furthermore, many formulations adopt a low temporal resolution (e.g., annual time steps) and assume fully flexible power plants, potentially overlooking temporal variability and operational constraints. This paper presents a three-objective optimization model for sustainable power system design that minimizes (i) costs, (ii) greenhouse gas (GHG) emissions, and (iii) social opposition (i.e., the public resistance to certain energy technologies). Temporal variability and operational detail are preserved using weighted representative periods with intra-period time steps and a clustered unit commitment (CUC) formulation. The Pareto frontier is generated using the normalized normal constraint (NNC) method, highlighting the space of efficient economic, environmental, and social solutions. A case study focused on the Italian electricity system exemplifies the model application by providing the cost-optimal, emissions-optimal, and social-optimal solutions, together with trade-off solutions. Among the trade-off solutions, the selected best balance solution achieves a significant reduction in emissions (−20%) compared to the cost-optimal solution, with a limited cost increase (+5%) and a marginal increase in social opposition (+0.7%). Overall, the proposed model enables transparent quantification of multi-dimensional trade-offs to support decision-making in sustainable power system design. Full article

Trajectory optimization of manipulators is crucial for achieving efficient, low-energy-consumption, and stable operation. The standard Chicken Swarm Optimization (CSO) algorithm and its variants tend to fall into local optima during optimization, making it difficult to obtain the optimal trajectory. Therefore, this paper employs multiple strategies to collaboratively improve the chicken swarm optimization algorithm: Tent chaotic mapping is used for population initialization, the position update strategies of the three populations are respectively reconstructed to expand the search scope of the population, and a genetic evolution strategy is introduced to escape from local optimal solutions. Simulation outcomes based on the CEC2022 benchmark functions show that the Multi-strategy Improved Chicken Swarm Optimization (MICSO) algorithm outperforms commonly used optimization algorithms at present in both convergence speed and solution accuracy. The MICSO is applied to the 3-5-3 polynomial interpolation trajectory planning of manipulators, and a comprehensive optimal objective function is constructed by combining time, energy consumption, and jerk. Simulation and experimental results demonstrate that the MICSO algorithm can flexibly adjust the optimization tendency according to practical requirements. The optimized trajectory effectively reduces running time, energy consumption, and joint impact. Full article

With the growing penetration of photovoltaic (PV) generation, robust uncertainty characterization is essential for secure operation, economic dispatch, and flexibility planning. This review surveys PV scenario generation from three perspectives: (i) explicit probabilistic approaches (distribution fitting, Copula-based dependence modeling, autoregressive moving average (ARMA)-type time-series methods, and clustering/dimensionality reduction), (ii) deep generative models (GANs, VAEs, and diffusion models), and (iii) hybrid Statistical Relational AI (SRAI) frameworks. We discuss the strengths of explicit models in interpretability and tractability, and their limitations in representing high-dimensional nonlinear, multimodal, and multiscale spatiotemporal dependencies. We also examine the ability of deep generative methods to synthesize diverse scenarios across meteorological regimes and multiple sites, while noting persistent challenges in interpretability, physical consistency, and deployment. To bridge these gaps, we outline an SRAI-oriented integration pathway that embeds statistical structure, meteorology–power relations, spatiotemporal coupling, and operational constraints into generative architectures. Finally, we highlight directions for future research, including unified evaluation protocols, cross-regional data collaboration, controllable extreme-scenario generation, and computationally efficient generative designs. Full article

With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article

In structured orchard environments, harvesting robots operate where rigid bodies (e.g., trunks, poles, and wires) coexist with flexible foliage. Strict avoidance of all obstacles significantly compromises operational efficiency. To address this, this study proposes an end-to-end autonomous harvesting framework characterized by an “avoid-rigid, push-through-soft” strategy. This framework explicitly propagates uncertainties from sensor data and reconstruction processes into the planning and policy phases. First, a multi-task perception network acquires 2D semantic masks of fruits and branches. Class probabilities and instance IDs are back-projected onto a 3D Gaussian Splatting (3DGS) representation to construct a decision-oriented, semantically enhanced 3D scene model. The policy network accepts multi-channel 3DGS rendered observations and proprioceptive states as inputs, outputting a continuous preference vector over eight predefined motion primitives. This approach unifies path planning and action decision-making within a single closed loop. Additionally, a dynamic action shielding module was designed to perform look-ahead collision risk assessments on candidate discrete actions. By employing an action mask to block actions potentially colliding with rigid obstacles, high-risk behaviors are effectively suppressed during both training and execution, thereby enhancing the robustness and reliability of robotic manipulation. The proposed method was validated in both simulation and real-world scenarios. In complex orchard scenarios, the proposed AE-TD3 algorithm achieves a harvesting success rate of 77.1%, outperforming existing RRT (53.3%), DQN (60.9%), and TD3 (63.8%) methods. Furthermore, the method demonstrates superior safety and real-time performance, with a collision rate reduced to 16.2% and an average operation time of only 12.4 s. Results indicate that the framework effectively supports efficient harvesting operations while ensuring safety. Full article

Vibrations of thin sheet-metal parts during robotic manipulation on a production line create a number of serious challenges for production process planning. Modeling the behavior of an elastic plate or shell as a function of the robot manipulator trajectory is typically performed using the finite element method (FEM) and requires significant computational effort. The time factor remains a key limitation for integrating operations involving flexible parts into the virtual commissioning process. In this work, a methodology is proposed that enables accurate real-time reproduction of the behavior of an elastic part during linear robotic manipulation. The approach is based on modeling the response of an elastic part to a prescribed base excitation using the FEM and on the development of a reduced model compliant with the FMI/FMU standard. This reduced model computes, in real time, the convolution of the precomputed base response with the acceleration profile corresponding to the robot TCP trajectory. This makes it possible to determine the total cycle duration, which consists of the part transfer time and the time required for vibration decay at the end of the trajectory down to an acceptable threshold, as well as to perform collision checking while accounting for the deformation of the flexible part. As a result, operations involving elastic parts can be integrated into the virtual commissioning process. Full article

With rising renewable energy penetration, wind–hydrogen coupling systems are key to large-scale green hydrogen production and wind power integration. This paper proposes a multi-timescale power allocation measure and evaluation framework that executes scheduling planning, rolling updates and real-time control sequentially. First, an intelligent power allocation strategy based on model predictive control (MPC) and State of Health (SOH) prediction is designed, which pursues short-term operational efficiency while actively avoiding electrolyzer-damaging conditions. Second, a comprehensive evaluation model integrating dynamic hydrogen value and flexibility value is built, overcoming the limitations of traditional fixed-hydrogen-value and single-system-value evaluations to quantify operational strategy viability more accurately. Simulation results show that the proposed strategy boosts the system’s lifecycle Net Present Value (NPV) by ~12.7% versus conventional strategies, verifying the framework’s effectiveness and superiority in improving wind–hydrogen coupling system performance. Full article