Search Results (2,465)

Background: Caesarean section (CS) is a critical intervention, yet stark inequities in access persist across low- and middle-income countries (LMICs). Over the last decade, governments have introduced policies to eliminate or subsidize user fees; however, the collective impact of these initiatives on utilization, equity, and financial protection has not been fully synthesized. Methods: We conducted a systematic review in line with PRISMA 2020 guidelines. Searches were conducted in PubMed, Dimensions, Google Scholar, Scopus, Web of Science, and government portals for studies published between 1 January 2009 and 30 May 2025. Eligible studies evaluated government-initiated financing reforms, including full user-fee exemptions, partial subsidies, vouchers, insurance schemes, and provider-payment restructuring. Two reviewers independently applied the PICOS criteria, extracted data using a 15-item template, and assessed the study quality. Given heterogeneity, results were synthesized narratively. Results: Thirty-seven studies from 28 LMICs were included. Most (70%) evaluated fee exemptions. Mixed-methods and cross-sectional designs predominated, while only six studies employed interrupted time series designs. Twenty-two evaluations (59%) reported increased CS uptake, ranging from a 1.4-fold rise in Senegal to a threefold increase in Kano State, Nigeria. Similar surges were also observed in non-African contexts such as Iran and Georgia, where reforms included incentives for vaginal delivery or punitive tariffs to curb overuse. Fourteen of 26 fee-exemption studies documented pro-rich or pro-urban drift, while catastrophic expenditure persisted for 12–43% of households, despite the implementation of “free” policies. Median out-of-pocket costs ranged from USD 14 in Burkina Faso to nearly USD 300 in Dakar’s slums. Only one study linked reforms to a reduction in neonatal mortality (a 30% decrease in Mali/Benin), while none demonstrated an impact on maternal mortality. Qualitative evidence highlighted hidden costs, delayed reimbursements, and weak accountability. At the same time, China and Bangladesh demonstrated how demographic reforms or voucher schemes could inadvertently lead to CS overuse or expose gaps in service readiness. Conclusions: Government-led financing reforms consistently increased CS volumes but fell short of ensuring equity, financial protection, or sustained quality. Effective initiatives combined fee removal with investments in surgical capacity, timely reimbursement, and transparent accountability. Future CS policies must integrate real-time monitoring of equity and quality and adopt robust quasi-experimental designs to enable mid-course correction. Full article

Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m² quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency. Full article

After long-term water injection development in Block M of the Gudong Oilfield, the fluid seepage field in the formation has become increasingly complex, and the heterogeneity of the reservoir has intensified. The uneven injection-production and the differences in lithological permeability have further led to a series of problems such as uneven reserve utilization, a low injection-production correspondence rate, and a poor overall development effect. During the development process, severe water flooding at the bottom and rich remaining oil at the top within the layer also emerged. These issues not only affected the economic benefits of the oilfield but also put forward higher requirements for the subsequent development strategies. In order to solve the above problems, based on a detailed analysis of geological characteristics, reservoir engineering and monitoring data, this paper uses the numerical simulation method to systematically simulate the dynamic changes in the residual oil saturation and pressure field in the formation under different development stages. The simulation results show that the pressure and saturation in each layer are both on a downward trend, especially in the layers with large pressure changes, where the oil saturation changes are more significant. Therefore, combined with the results of numerical simulation, a series of profile control and water shutoff schemes were systematically designed. These schemes covered different types of profile control agents and technological parameters. Representative well groups were selected to predict and evaluate the profile control effect. In the technical and economic evaluation of the profile control effect, the input–output ratio method was adopted. Finally, an optimal scheme was selected and applied in the field. The results show that after the implementation of this scheme, the daily oil increment of the well group was remarkable. During the test period, the cumulative oil increment reached 100 t, and the total expected oil increment could reach 190 t. The input–output ratio reached 1:2.1. The profile control measures significantly improved the injection-production correspondence, slowed down the in-layer water flooding, and further enhanced the recovery rate of remaining oil. In conclusion, the methods and achievements of this study can provide important technical references and support for the efficient and long-term development of similar high-water-cut and heterogeneous complex oil reservoirs, and have guiding significance for the subsequent adjustment, potential tapping, stable production and efficiency improvement of oilfields. Full article

Occupational asthma (OA) and rhinitis are health problems occurring in facilities employing animals for medical and scientific reasons. We have compared the UK trends (2006–2023) in these outcomes reported to the SWORD scheme with changes in routine and personal air monitoring for the major mouse (Mus m 1) and rat (Rat n 1) allergens. The exposure data contained 1540 and 688 mouse and rat results, respectively, expressed in ng.m⁻³. The median, 75th and 90th percentiles were used as exposure characteristics, and annually incrementing three-yearly rolling data slices compared exposure and health outcomes by linear regression. The median, P75 and P90 for Mus m 1 all showed annual declines of around 5–6% (p < 0.001), suggesting general improvements in controlling mouse allergen exposure, but without evidence of a decline in rat allergen levels (p > 0.05), although control measures for both species are largely identical. An annual mean decline in OA of 2.9% (p = 0.021) was identified, but without a significant decline in rhinitis (−1.4%; p = 0.21). Over 16 years, reductions in exposure to the predominant rodent species were accompanied by a concomitant but smaller reduction in OA. These data confirm the immediate value of controlling relevant allergen exposure in reducing the incidence of IgE-E mediated OA. Full article

Deep-well radar telemetry over ultra-long cables suffers from strong frequency-selective attenuation and impedance drift under high temperature and pressure. We have proposed a channel-adaptive “communication + acquisition” architecture for a 7500 m borehole radar system. The scheme integrates spread-spectrum time domain reflectometry (SSTDR; m-sequence with BPSK) to monitor the cable in situ, identify termination/cable impedance, and adaptively match the load, thereby reducing reflection-induced loss. On the receiving side, we combine time domain adaptive equalization—implemented as an LMS-driven FIR filter—with frequency domain OFDM equalization based on least-squares (LS) channel estimation, enabling constellation recovery and robust demodulation over the distorted channel. The full processing chain is realized in real time on a Xilinx Artix-7 (XC7A100T) FPGA with module-level reuse and pre-stored training sequences for efficient hardware scheduling. In a field deployment in the Shunbei area at 7500 m depth, radar results show high agreement with third-party geological logs: the GR-curve correlation reaches 0.92, the casing reflector at ~7250 m is clearly reproduced, and the key bottom depth error is 0.013%. These results verify that the proposed system maintains stable communication and accurate imaging in harsh deep-well environments while remaining compact and implementable on cost-effective hardware. Full article

The research work presents an artificial intelligence-driven, energy-aware data aggregation and routing protocol for wireless sensor networks (WSNs) with the primary objective of extending overall network lifetime. The proposed scheme leverages reinforcement learning in conjunction with deep Q-networks (DQNs) to adaptively optimize both Cluster Head (CH) selection and routing decisions. An adaptive clustering mechanism is introduced wherein factors such as residual node energy, spatial proximity, and traffic load are jointly considered to elect suitable CHs. This approach mitigates premature energy depletion at individual nodes and promotes balanced energy consumption across the network, thereby enhancing node sustainability. For data forwarding, the routing component employs a DQN-based strategy to dynamically identify energy-efficient transmission paths, ensuring reduced communication overhead and reliable sink connectivity. Performance evaluation, conducted through extensive simulations, utilizes key metrics including network lifetime, total energy consumption, packet delivery ratio (PDR), latency, and load distribution. Comparative analysis with baseline protocols such as LEACH, PEGASIS, and HEED demonstrates that the proposed protocol achieves superior energy efficiency, higher packet delivery reliability, and lower packet losses, while adapting effectively to varying network dynamics. The experimental outcomes highlight the scalability and robustness of the protocol, underscoring its suitability for diverse WSN applications including environmental monitoring, surveillance, and Internet of Things (IoT)-oriented deployments. Full article

Classification of island coastal landscapes is a challenge to incorporate both the terrestrial and the aquatic environment characteristics, and place biological diversity in a regional and insular context. The Coastal and Marine Ecological Classification Standard (CMECS) was developed for use in the United States and incorporates geomorphic data, substrate data, biological information, as well as water column characteristics. The CMECS framework was applied to the island of Great Exuma, The Bahamas. The classification used data from existing studies to include oceanographic data, seawater temperature, salinity, benthic invertebrate surveys, sediment analysis, marine plant surveys, and coastal geomorphology. The information generated is a multi-dimensional description of benthic and shoreline biotopes characterized by dominant species. Biotopes were both mapped and described in hierarchical classification schemes that captured unique components of diversity in the mosaic of coastal natural communities. Natural community classification into biotopes is a useful tool to quantify ecological landscapes as a basis to develop monitoring over time for biotic community response to climate change and human alteration of the coastal zone. Full article

Nowadays, the online comment process of product after-sales has become a key part of product development. Quality problems, such as the failure of products or services, are more likely to exist or hide in negative comments. Therefore, this paper focuses on detecting abnormal changes in both the time between review T and the emotional score S of negative comments. Due to the complexity of the online review process, the distribution assumption of S and T may be invalid. To solve this problem, this study propose a distribution-free monitoring scheme that combines the exponentially weighted moving average-based Lepage statistics of S and T using a max-type combining function. This scheme is designed for joint monitoring of location and scale parameters in Phase II of an unknown but continuous process. The scheme’s performance is evaluated via Monte Carlo simulation under in-control and out-of-control conditions, using statistical measures such as the mean, standard deviation, median, and selected percentiles of the run length distribution. Simulation results indicate that the scheme is effective in detecting shifts in both location and scale parameters. Furthermore, an application of the proposed scheme for monitoring online reviews is discussed to illustrate its implementation design. Full article

This paper presents a Class E zero-voltage soft-switching (ZVS) resonant inverter integrated with a CLC filter and a digital resonant frequency tracking technique for driving a piezoelectric ceramic transducer (PZT) in ultrasonic cleaning applications. A digital signal processor (DSP) is used to dynamically monitor and adjust the operating frequency in response to slight variations in the cleaning load, employing a phase-locked loop (PLL) control scheme. The proposed method ensures that the inverter maintains ZVS operation across a frequency range from 30.0 kHz to 34.0 kHz, thereby improving energy efficiency and reducing switching losses. The system is capable of delivering a stable power output of 100 W. Both the simulation and experimental results validate the effectiveness of the proposed technique, demonstrating improved performance under varying load conditions. The combination of CLC filtering and frequency tracking offers a compact and robust solution suitable for ultrasonic cleaner systems and similar resonant-load applications. Full article

This paper proposes two process monitoring schemes, namely the Shiryaev–Roberts (SR) procedure and the cumulative sum (CUSUM) procedure, to detect shifts in the shape and scale parameters of Type I right-censored Gamma-distributed lifetime data. The performance of the proposed schemes is compared with that of an exponentially weighted moving average (EWMA) control chart based on deep learning networks. The performance of the proposed schemes is evaluated under various censoring rates using Monte Carlo simulations, with the average run length (ARL) as the primary metric. Furthermore, the SR and CUSUM schemes are compared for both zero-state and steady-state shifts. Simulation results indicate that the SR and CUSUM procedures exhibit superior performance, with the SR scheme showing particular advantages when the actual shift is small, while the CUSUM chart proves more effective for identifying larger shifts. The shape parameter has a significant effect on the performance of the control charts such that a reduction in the shape parameter effectively improves the ability to capture early offsets. Increased censoring rates reduce detection sensitivity. To maintain ${\mathrm{ARL}}_0$= 370, control limits h adapt differentially. The SR and CUSUM charts with different censoring rates need to recalibrate the parameter to mitigate performance losses under higher censoring conditions. The monitoring performance of the SR and CUSUM chart is enhanced by an increase in sample size. Finally, a practical example is provided to illustrate the application of the proposed monitoring schemes. Full article

As the main load-bearing components of engineering structures, regular health assessment of reinforced concrete (RC) columns is crucial for improving the service life and overall performance of the structures. This study focuses on the health detection problem of in-service RC columns. By combining deep learning algorithms and acoustic emission (AE) technology, the AE sources of in-service RC columns are located, and the optimal sensor layout form for the health monitoring of in-service RC columns is determined. The results show that the data cleaning method based on the k-means clustering algorithm and the voting selection concept can significantly improve the data quality. By comparing the localization performance of the Back Propagation (BP), Radial Basis Function (RBF) and Support Vector Regression (SVR) models, it is found that compared with the RBF and SVR models, the MAE of the BP model is reduced by 7.513 mm and 6.326 mm, the RMSE is reduced by 9.225 mm and 8.781 mm, and the R² is increased by 0.059 and 0.056, respectively. The BP model has achieved good results in AE source localization of in-service RC columns. By comparing different sensor layout schemes, it is found that the linear arrangement scheme is more effective for the damage location of shallow concrete matrix, while the hybrid linear-volumetric arrangement scheme is better for the damage location of deep concrete matrix. The hybrid linear-volumetric arrangement scheme can simultaneously detect damage signals from both shallow and deep concrete matrix, which has certain application value for the health monitoring of in-service RC columns. Full article

As aquaculture expands, there is a growing demand for sustainable and environmentally friendly feed ingredients that can replace conventional fish meal while maintaining high biological value and digestibility. The use of fishmeal has contributed to overfishing, making it an increasingly limited and unsustainable resource. Tenebrio molitor (TM) is emerging as a sustainable alternative to fishmeal (FM) in aquaculture diets, gaining attention due to its balanced protein composition profile and low environmental footprint. This review critically analyses data from the literature on the use of TM meal as a substitute for fish feed ingredient, focusing on its effects on growth performance, physiological status, and histological changes in the digestive and muscular systems. The influence on the physicochemical and sensory quality of fish meat is also evaluated. The discussion highlights both the benefits and possible adverse effects, such as intestinal inflammation or changes that may occur, depending on the replacement level. The paper presents recommendations and strategies to mitigate these effects, including the use of dietary supplements or partial replacement schemes. Overall, this paper emphasises the promising potential of TM as a sustainable alternative to FM in aquaculture feed, while highlighting the need for further research into the long-term effects, involved metabolic pathways, and standardisation of insect meal production. This review provides valuable insight into the physiological changes that may occur, particularly at high inclusion levels. As TM is utilized in both human nutrition and aquaculture diets, monitoring its physiological effects in fish is essential, since any alterations may have implications for human food safety. Full article

The integrated assessment of watershed ecosystems is increasingly critical for sustainable water resource management amid global environmental change. Multi-source data integration—encompassing in situ monitoring, remote sensing, and model-based observations—has significantly expanded the spatial and temporal scales at which watershed processes can be analyzed. Concurrently, advances in model coupling strategies, ranging from loose to embedded architectures, have enabled more dynamic and holistic representations of interactions among hydrology, water quality, and ecological systems. However, a unifying operational framework that links multi-source data, cross-scale coupling, and rigorous uncertainty propagation to actionable, real-time decision support is still missing, largely due to gaps in interoperability and stakeholder engagement. Addressing these limitations demands the development of intelligent, adaptive modeling frameworks that leverage hybrid physics-informed machine learning, cross-scale process integration, and continuous real-time data assimilation. Open science practices and transparent model governance are essential for ensuring reproducibility, stakeholder trust, and policy relevance. The recent literature indicates that loose coupling predominates, physics-informed ML tends to generalize better in data-sparse settings, and uncertainty communication remains uneven. Building on these insights, this review synthesizes methods for data harmonization and cross-scale integration, compares coupling architectures and data assimilation schemes, evaluates uncertainty and interoperability practices, and introduces the Smart Integrated Watershed Eco-Assessment Framework (SIWEAF) to support adaptive, real-time, stakeholder-centered decision-making. Full article

Advancements in communication technologies and the proliferation of smart devices have significantly increased the demand for wireless sensor networks (WSNs). These networks play an important role in the IoT environment. The wireless sensor network has many sensor nodes that are used to monitor the surrounding environment. Energy consumption is the main issue in WSN due to the difficulty in recharging or replacing batteries in the sensor nodes. Cluster head selection is one of the most effective approaches to reduce overall network energy consumption. In recent years, quantum technology has become a growing research area. Various quantum-based algorithms have been developed by researchers for clustering. This article introduces a novel, energy-efficient clustering scheme called the quantum-inspired clustering scheme (QICS), which is based on the Quantum Grover algorithm. It is mainly used to improve the performance of cluster head selection in a wireless sensor network. The research conducted simulations that compared the proposed cluster selection method against established algorithms, LEACH, GSACP, and EDS-KHO. The simulation environment used 100 nodes connected via specific energy and communication settings. QICS stands out as the superior clustering method since it extends the lifetime of the network by 30.5%, decreases energy usage by 22.4%, and increases the packet delivery ratios by 19.8%. The quantum method achieved an increase in speed with its clustering procedure. This study proves how quantum-inspired techniques have become an emerging approach to handling WSN energy restrictions, thus indicating future potential for IoT systems with energy awareness and scalability. Full article

Directly measuring the mooring cable load while a ship is moored at a wharf poses significant practical challenges. This paper proposes an indirect load measurement method to identify mooring cable static loads based on the Influence Coefficient Matrix (ICM) method. First, a finite element analysis of the bollard is conducted to obtain the full-field strains under each unit load. A solution procedure based on the genetic algorithm (GA) is then implemented to determine the optimal placement and orientation of strain gauges, aiming to improve load identification accuracy. An optimal load coefficient matrix is derived to establish the correlation between cable loads and bollard strains. Subsequently, following the established measured point placement scheme, strain gauges are installed on the bollard surface to capture the strains, enabling inverse identification of mooring cable loads through the measured strains and the pre-established load–strain relationship. A numerical case study validated the feasibility of this method, demonstrating high identification accuracy. Furthermore, experimental verification was conducted to assess its reliability under different conditions. Results confirmed the effectiveness of this indirect approach for mooring cable static loads measurement. The research findings provide a technical framework for real-time monitoring of mooring cable loads. Full article