Search Results (11,546)

The different Assisted Reproductive Technology techniques are offering hope to millions of couples struggling with infertility. However, the success of IVF/ICSI is related at least partially to the optimization of embryo culture conditions, which are influenced by myriad of physiological and environmental factors. This review reports the latest advancements in embryo culture techniques, with a particular focus on the roles of oxygen tension, pH regulation, temperature stability, air quality in enhancing embryo viability, competency and implantation rates. In addition, we explored the critical importance of quality assurance (QA) factors and key performance indicators (KPIs) to keep laboratory efficiency. We highlighted also some emerging technologies, such as dynamic culture systems, metabolomics, proteomics biomarkers potential, and artificial intelligence (AI) in embryo selection and monitoring, which hold promise for further improving embryo culture techniques. By providing a comprehensive overview of the current state of embryo culture optimization, this review aims to guide future research and clinical practices in the field of assisted reproductive technology (ART). Full article

Farmers rely on pesticides to keep their crops safe from pests, diseases, and weeds. However, if pesticides are not used properly, they can have serious consequences for human and environmental health. Many pesticides are not easily biodegradable and persist in the environment for a long time. Their residues, including toxic metabolites, pose risks to non-target organisms, contaminate surface- and groundwater sources, and may affect future crops. Among other soil remediation actions, it is important to highlight the impact of agricultural waste and agro-industrial byproducts on the behavior of pesticides as a strategy to eliminate or at least minimize soil pollution by their residues. Waste from various food industries and agriculture poses a severe threat to the ecosystem and is difficult to manage properly. Agriculture and food production waste accounts for over 30% of total global agricultural output. Therefore, managing agri-food waste from different sources is crucial to promoting sustainable development with minimal environmental impact. Key components of waste management interventions in the agricultural circular and bioeconomy include incorporating crop residues and food waste into soils. For these reasons, we present an updated review of the impact of agricultural waste and agro-industrial byproducts on the behavior of pesticides in soil. The goal of this review is to promote the sustainable use of these wastes within the context of a circular economy. Full article

A conventional resonant-inductive wireless charging system includes a power factor corrector (PFC) to maintain a high input power factor (PF) and low distortion of the input current (THDI). Although a conventional low-power wireless charging system with a PFC has relatively simple power electronic circuitry, its control stage is comparatively complex and expensive. This complexity arises because it relies on multiple feedback loops, as well as a radio communication link with complex communication protocols. As a result, the design complexity and development time are relatively high, and a highly qualified engineer with strong programming and communication expertise is needed. Some state-of-the-art solutions have eliminated the wireless communication link at the cost of increased size of the receiving side. To overcome these drawbacks, this paper proposes a simpler control and communication method that combines output voltage and current limiting with a low-latency wireless communication link transmitting 1-bit logic signals. This approach improves the cost-effectiveness of the control circuit, reduces system complexity, and keeps the receiving side compact, while maintaining performance comparable to conventional and state-of-the-art solutions. The proposed method is validated through simulations and experiments using a 60 W prototype. Results show that the power-factor-corrected wireless charging system with the proposed control and communication scheme achieves a THDI of 4.3%, a power factor of 0.99, high charging voltage accuracy (±0.5%), and satisfactory current accuracy (±9%). Full article

To address the stability issues of bridge piers on high expansive soil slopes in the Yangtze-Huaihe River Water Transfer Project and reveal the slope-bridge structure interaction mechanism, this study performed 100 g geotechnical centrifuge model tests. Slope failure modes under rainfall-bridge load coupling are investigated, with bridge pier deformation, earth pressure, and pile bending moment evolution analyzed. Results show that rainfall-induced failure causes shallow slope sliding with negligible pier displacement, keeping the structure safe. Conversely, under bridge working and ultimate loads, the slope will experience a mid-deep landslide with a sliding depth of 13–20 m, leading to slope instability and bridge overturning. The influence range of shallow landslides is 1–2 m, and the earth pressure at the pile cap is 132 kPa, which is a critical factor affecting bridge stability. In contrast, the bearing performance of pile foundations plays a dominant controlling role in deep-seated landslides. With the increase in landslide depth, the inflection point of the pile gradually moves downward. Numerical simulations further indicate that shallow landslides feature superficial slip–shear failure, and deep-seated landslides follow a progressive slip tensile cracking mechanism. Full article

Biarticular actuators can enhance efficiency and stability in legged locomotion by transferring energy between joints. Their effectiveness depends strongly on the lever arm ratio—the ratio of the actuator’s moment arm at one joint to its moment arm at another—which governs how torque is distributed across joints during movement. Inspired by biomechanics, early robotic studies implemented biarticular actuators to improve energy efficiency, joint coordination, and positional control, primarily in planar or single-joint systems, leaving a gap in fully 3D robotic legs. Here, we present a geometry optimization framework for a robotic leg incorporating both biarticular and monoarticular actuators. Using human motion capture and joint torque data, we optimized the linkage mechanisms so that the system can maintain the required joint torques while keeping biarticular actuator moment arm ratios near their optimal values during walking and running. The optimized leg achieved a minimum achievable cost of transport of approximately $0.41 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for walking and $0.62 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for running. Full article

In recent decades, the reliability and safety of large engineering structures have become a critical issue due to failures caused by undetected micro-level deformations. Fiber-optic strain sensors, especially Fiber Bragg Grating (FBG) and interferometric systems, are widely used in structural health monitoring (SHM); however, their standard sensitivity is often insufficient for early detection of nano-strain level damage. This paper presents a comparative analysis and system-level optimization of the main sensitivity enhancement methods, including mechanical amplification, functional coatings and composite embedding, interferometric schemes, and advanced spectral signal processing. Analytical modeling and numerical simulations were performed. It is shown that flexure-beam amplifiers provide a stable sensitivity gain of 2.1–4.8, whereas lever-type mechanisms achieve higher amplification (5.6–9.3) at the cost of dynamic degradation. Functional coatings increase the strain transfer coefficient from 0.62 to 0.68 to 0.91–0.97, but introduce temperature-induced errors up to 1.5–2.0 µε. Interferometric systems can detect strains at the 10⁻⁸ level but exhibit high temperature cross-sensitivity. Advanced spectral processing reduces the Bragg wavelength estimation error by 8–15 times, improving the equivalent strain resolution to (2–5) × 10⁻⁸. Based on these results, an optimized integrated approach combining moderate mechanical amplification (2.5–3.5), improved strain transfer (η ≈ 0.85–0.92), and efficient spectral processing is proposed. This improves the equivalent strain resolution from 1 × 10⁻⁶ to (1.5–3.0) × 10⁻⁸ while keeping temperature-induced errors within 15–25% and limiting the computational load increase to 2–3 times. The proposed solution is suitable for long-term monitoring of large engineering structures. Full article

Steam turbines with controlled extraction require a flow control device to keep extraction pressure constant when the extraction mass flow rate is changed. An attractive option is an adaptive turbine stage with throttling nozzles. Flow measurements with a throttling nozzle are performed in a cascade wind tunnel. A linear cascade with seven blades is operated at an inlet flow angle of 90° and an exit Reynolds number of about 4 × 10⁵. Since the maximum exit Mach number is about 0.2, flow is essentially incompressible. A three-hole pressure probe is traversed at half span over one blade pitch 0.33 axial chord lengths downstream of the cascade. Degree of closing is gradually changed from zero (fully open) to 0.3 (partially closed). Two principal options, closing to the suction side as well as closing to the pressure side, are investigated. Local flow quantities as well as pitchwise mass averaged quantities are extracted from the measurement data. The major outcomes are as follows: If the throttling nozzle is closed, depth and width of the blade wake increase. With increasing degree of closing, pitchwise mass averaged flow angle decreases and total pressure losses increase. Concerning total pressure losses, closing to the pressure side is the preferred option. A semi-empirical flow model is presented to explain the influence of degree of closing on exit flow angle and total pressure loss. Full article

Deep time-series forecasting models can achieve strong average accuracy under normal conditions, yet they often struggle with rare, high-impact extremes, where severe class imbalance biases learning toward majority dynamics. Although infrequent, these extremes frequently correspond to critical events such as natural disasters or power outages. We address this challenge with a weak-label-guided mixture of experts (WL-MoE) that routes each input window to lightweight specialists designed to capture distinct temporal regimes. To prevent routing collapse during early optimisation, WL-MoE follows a two-stage training curriculum. In Stage I, cluster-derived weak labels encourage diverse expert utilisation and promote specialisation under imbalance. In Stage II, guidance is removed and training proceeds solely with the forecasting objective, ensuring that inferences remain fully data-driven. The expert-based structure also supports interpretable routing via expert-usage profiling, enabling regime-level auditing of model behaviour in high-stakes settings. Across seven benchmark datasets, WL-MoE reduces the average MSE by approximately $7.9\%$ and the extreme-case MSE by approximately $23.58\%$ relative to the best baseline. In a UK flood forecasting study, it reduces the all-water MSE by $31.6\%$ and the high-water MSE by approximately $35.0\%$. These results indicate that weak-label guidance can stabilise specialisation and improve reliability under rare extremes while keeping model behaviour auditable for real-world deployment. Full article

With the increasing popularity of photovoltaic (PV) equipment in residential and commercial buildings, there is a pressing need for systems that maximize energy efficiency and self-consumption. This paper introduces an integrated management framework for retrofitting existing infrastructures, enabling high photovoltaic (PV) self-consumption in residential buildings through a rule-based control strategy. The framework supports three service classes—defined by user-level Quality of Service (QoS) parameters—and monitors battery voltage along with grid power exchange to coordinate heat pumps, batteries, and hot water cylinders. Experimental deployment in a residential testbed achieved up to 89% PV self-consumption while keeping daily grid usage below 0.5 kWh. Ablation experiments on battery size further demonstrated the approach’s robustness to reduced storage capacities. The use of Commercial-Off-The-Shelf (COTS) components underscores the minimal intrusiveness of this solution, highlighting its potential for seamlessly integrating diverse, vendor-specific equipment into a coordinated control system. Full article

Quadruped robots possess strong adaptability to rugged terrain, soft ground, and multi-obstacle environments, offering broad application prospects in extraterrestrial planetary exploration. However, large diurnal temperature variations on extraterrestrial bodies exacerbate joint friction nonlinearity, degrading motion control accuracy and stability. To address this, a quadruped robot prototype with hybrid serial–parallel legs is designed for lunar exploration, and an 18-DOF dynamic model is derived using d’Alembert’s principle. Based on the PPO (Proximal Policy Optimization) reinforcement learning algorithm, joint friction parameters are identified using joint velocity and foot–ground contact force. By introducing friction compensation and contact force, an accurate dynamics-based feedback linearization control model is constructed, and a motion impedance control law is designed. Finally, joint friction parameters are identified and validated through both virtual and experimental prototypes, and the proposed control method is tested on flat and sloped terrain. Results show that the method can precisely regulate contact force and foot position, keeping RMSE (Root Mean Square Error) of position within 21.04 mm while preventing slipping and false contact. Full article

This study traces the metamorphoses of concealment from Hesiod’s observation that “the gods keep the means of life concealed from human beings” to contemporary attention economies. In Hesiod’s pronouncement the event of concealment generates the dialectic of need and surplus, from which anthropological difference emerges, distinguishing the human from the animal and the divine. Divine concealment simultaneously creates humanity as seeker, sustenance as sought, and technics as necessity. Bataille’s “general economy” expands this framework from its theological to secular dimensions and from human labor to terrestrial life through solar energy, showing how technique generates discrete perception. Platonov’s revolutionary writings attempt to overcome nature’s dialectics through a quasi-theology of labor, yet the resulting socialist tragedy reveals a marked disproportion between technical development and subjective formation. Contemporary digital technologies transform concealment fundamentally: attention becomes liberated from searching for the hidden, only to be captured and commodified. Semiotic surplus manifests everywhere while material access remains restricted. The ancient matrix of concealment persists through digital transformations, assuming new forms while preserving its essential structure across radically different economic and technological conditions. Full article

Traffic flow prediction (TFP) is a fundamental component of intelligent transportation systems (ITS) that supports traffic management, congestion mitigation, and route planning. Although recent advances in deep learning have demonstrated strong capability in modeling non-linear spatio-temporal correlations, most existing approaches rely on centralized training paradigms, which incur substantial communication costs, high computational overhead, and significant data privacy risks. Federated Learning (FL) has emerged as a promising alternative by enabling decentralized model training across distributed clients while reducing privacy risks and communication overhead. However, existing FL-based TFP frameworks often employ local models with limited capacity to capture complex spatio-temporal dependencies, and their reliance on the conventional FedAvg aggregation approach restricts robustness under heterogeneous traffic data distributions. To address these challenges, this study proposes the FedASTAM framework, which integrates FL with the Adaptive Spatio-Temporal Attention-based Multi-Model (ASTAM) to effectively model complex and non-linear spatio-temporal traffic correlations in a data-local FL setting. Within FedASTAM, the road network is divided into sub-regions using spectral clustering, allowing each sub-region to train a local ASTAM model tailored to localized and heterogeneous traffic patterns. At the central server, locally trained models are aggregated using seven aggregation schemes, including the classical FedAvg, to optimize global model updates while preserving data locality. Extensive experiments conducted on two real-world benchmark datasets, PeMS04 and PeMS08, demonstrate that FedASTAM achieved strong and stable predictive performance while keeping raw data localized throughout the federated training process. The results further indicate that the aggregation approaches used in the proposed FedASTAM framework generally outperform classical FedAvg under heterogeneous traffic conditions, highlighting FedASTAM as an effective approach for traffic flow prediction in complex, distributed ITS environments. Full article

Seismic signal denoising stands as a vital process that enables precise seismic data analysis because noise interference blocks the detection of weak but valuable seismic signals. The current traditional denoising methods together with deep learning-based data-driven approaches encounter difficulties when they need to remove noise from seismic signals while keeping their fundamental structural elements, especially under conditions of low signal-to-noise ratios. In this study, we propose a novel denoising framework that integrates a physics-guided neural network with adaptive time–frequency decomposition, referred to as TF-PhysNet. The system breaks down broadband seismic data into separate frequency bands. Scientists can use these to study specific noise patterns that appear at various frequency points. The system uses a shared convolutional neural network-long short-term memory architecture to remove noise from each sub-band, which helps it learn both short-term waveform patterns and extended temporal relationships. The system uses physics-guided restrictions to eliminate false signal variations, which appear during the signal recovery process. The experimental findings from synthetic and real seismic data sets show that TF-PhysNet delivers better results than standard denoising techniques and deep learning-based methods for signal-to-noise ratio improvement and correlation coefficient enhancement. Full article

Reliable fault detection along transmission corridors is essential for preventing small defects from developing into long outages and costly emergency operations. This study aims to improve the field reliability of an open vocabulary vision language backbone without retraining the large model in an end-to-end manner. The work focuses on four operational fault classes in multi-region corridor imagery collected during routine inspections and uses a Florence-2 vision language model as the base recognizer. On top of this backbone, three domain-specific components are introduced. A subclass-aware fusion scheme keeps probability mass within the active parent concept so that insulator icing and conductor icing produce stable, action-oriented decisions. A Power-Line Focus Then Crop normalization uses an attention-guided corridor window together with isotropic resizing so that thin conductors and small fittings remain visible in the processed image. A corridor geo prior reduces scores as the distance from the mapped centerline increases and in this way suppresses detections that lie outside the corridor. All methods are evaluated under a shared preprocessing and scoring pipeline in training-free and parameter-efficient tuning modes. Experiments on unseen regions show higher accuracy for skinny and low-contrast faults, fewer false alarms outside the right-of-way, and improved score calibration in the confidence range used for triage, while keeping throughput and memory usage suitable for unmanned aerial vehicles and substation edge devices. Full article

Dynamic positioning (DP) systems are essential to maritime operations, as they ensure precise station keeping. Yet human error remains a major contributor to DP incidents, often interacting with technical failures and environmental conditions. This study proposes an adaptive probabilistic framework to characterise human-error contributions to DP risk and support targeted mitigation. We compare integrated Bayesian network (BN)/fuzzy analytic hierarchy process (AHP) and Bayesian network (BN)/Dempster–Shafer (D-S) theory to model causal relationships, aggregate uncertain expert judgements, and prioritise risk factors. Historical incident narratives, accident reports, and expert elicitation inform the model to analyse failure propagation and quantify factor contributions. In a representative DP case application, insufficient training, operator fatigue, and reduced situational awareness—together with software anomalies and adverse environmental loads—emerge as dominant contributors; BN backward analysis corroborates their diagnostic relevance. The approach yields actionable insights for risk reduction, including tailored training programmes, strengthened safety protocols, and integration of real-time monitoring. It provides an auditable, updateable basis for scenario-based training, software/maintenance assurance, and environment-aware operating envelopes, and is readily extendable as new evidence becomes available. Overall, the framework offers practical value for improving safety, operational continuity, and system resilience in DP operations. Full article