Search Results (21)

Obstetric critical care encompasses the management of pregnant and postpartum women with life-threatening conditions, requiring integration of intensive care principles with pregnancy-specific physiological, ethical, and organizational considerations. Although pregnancy is a physiological state, profound maternal adaptations may mask early signs of clinical deterioration, allowing rapid progression to a critical illness condition. This review provides a comprehensive overview of the foundations of obstetric intensive care, addressing maternal–fetal physiology, recognition of severity, organ support strategies, and contemporary models of care. Key aspects discussed include cardiovascular, respiratory, renal, and hematological adaptations of pregnancy; principles of airway management and mechanical ventilation; hemodynamic support; transfusion strategies guided by viscoelastic testing; renal replacement therapy; extracorporeal support, including extracorporeal membrane oxygenation and cardiopulmonary bypass; and the safe use of diagnostic imaging involving ionizing radiation. The role of point-of-care ultrasonography, structured early warning systems, and advanced monitoring in early detection and management of clinical deterioration is emphasized. Special attention is given to maternal–fetal interactions, fetal monitoring in the intensive care unit (ICU), and complex decision-making regarding timing and mode of delivery. The review also highlights the importance of multidisciplinary and multiprofessional collaboration, ethical challenges inherent to dual-patient care, and emerging strategies to expand access to specialized care, including tele–ICU models and artificial intelligence–assisted surveillance. Across all scenarios, maternal stabilization remains the primary determinant of fetal outcome. A structured approach grounded in maternal–fetal physiology and ethical principles is essential to reduce preventable maternal and perinatal morbidity and mortality in high-complexity settings. Full article

Postural orthostatic tachycardia syndrome (POTS), characterized by a rise in heart rate of at least 30 beats per minute from supine to standing position without accompanying orthostatic hypotension, is one of the most common autonomic disorders with disabling cardiovascular and neurologic manifestations. Hormonal influence has been long recognized by the disorder predominantly affecting women of reproductive age, with frequent onset around menarche, exacerbation of symptoms before or during menses, and pregnancy being one of POTS triggers. Hormone replacement therapy (HRT) and menopause in women with POTS have not been studied, but issues surrounding HRT are highly relevant as women with POTS transition from reproductive age to menopause. Given a rising prevalence of POTS due to post-COVID onset and the US Food and Drug Administration recently removing the black box warning on estrogen-containing HRT formulations, informed decisions and risk assessments regarding HRT use in women with POTS are warranted. In this narrative review, existing studies on hormones and POTS and its common comorbidities are reviewed, and key points in decision-making on the use of HRT in women with POTS are discussed. In summary, for women with significant menopausal symptoms and/or exacerbation of POTS during the peri- or postmenopausal period, using some forms of HRT for treatment of menopausal symptoms may be considered, accounting for comorbidities, cardiovascular risk and other factors. Vaginal estrogen appears to be safe for most women while transdermal estrogen and micronized progesterone can be utilized for significant menopausal symptoms, although outcomes of their long-term use are unknown. Full article

Attention mechanism-based deep learning has played an important role in vision-based smoke detection in forest fire early warning systems. However, the lack of consideration of the specific characteristics of smoke may render existing attention mechanisms ineffective, e.g., in detecting small areas of smoke, particularly in the complex forest smoke scenes involving multiple points of ignition or varying scales. To enhance the accuracy and the interpretability of smoke detection, we propose a Spatial and Efficient Channel Attention mechanism, termed SECA, and integrate SECA into deep models to incorporate the characteristics of smoke diffusion. Technically, multi-kernel 1-dimensional (1D) convolution is utilized for multi-scale smoke-capturing, to replace single-kernel 2D or 3D convolution in existing methods. In implementation, our SECA mechanism can also be used as a common module and easily plugged into a backbone network. To accelerate our model, a DSConv-Haar Wavelet Downsampling technique called DHWD is also provided. Extensive experiments were conducted on public datasets and self-collected datasets. Compared to existing methods, our method can achieve a better or at least a comparable performance in smoke detection in terms of smoke detection accuracy, computational efficiency, and ease of use. For example, it surpasses baseline methods, demonstrating average improvements of 4.2% in mAP₅₀ and of 3.7% in mAP_50-95, respectively. Full article

Non-photochemical quenching (NPQ) is a key photoprotective mechanism in Symbiodiniaceae, enabling photosystem II (PSII) to dissipate excess excitation energy under stress. The balance between regulated (Φ_NPQ) and unregulated (Φ_NO) energy dissipation influences thermal tolerance, yet the temperature thresholds at which this balance shifts remain poorly defined. Here, we used the Phenoplate, a high-throughput fluorometric platform integrating rapid light curves with controlled temperature ramping, to examine short-term thermal responses in Breviolum minutum across 6–71 °C. We identified a sharp transition at 41 °C where Φ_NPQ collapsed and was replaced by Φ_NO, indicating loss of regulated photoprotection. This switch coincided with a pronounced drop in PSII effective quantum yield (Φ_II) and substantial reductions in cell density, marking a thermal Achilles’ heel in the photoprotective capacity of this species. Despite this regulatory breakdown, a fraction of cells persisted for at least three days post-exposure. These results demonstrate that B. minutum maintains regulated photoprotection up to a discrete threshold, beyond which unregulated becomes the dominant pathway and survival is compromised. Identifying such thermal inflection points in coral symbionts provides mechanistic insight into their vulnerability under acute heat stress and may inform early-warning indicators for coral bleaching susceptibility. Full article

Fiber-optic sensing systems based on a forward transmission interferometric structure can achieve high sensitivity and a wide frequency response over long distances. However, there are still shortcomings in its ability to position multi-point vibrations and detect low-frequency vibrations, which limits its usefulness. To address these challenges, we study the viability of merging long-range forward-transmission distributed vibration sensing (FTDVS) with high spatial resolution optical frequency-domain reflectometry (OFDR), forming the first reported hybrid distributed sensing method between these two methods. The probe light source is shared between the two sub-systems, which utilizes stable linear optical frequency sweeping facilitated by high-order sideband injection locking. As a result, this is a new approach for the FTDVS method, which conventionally uses fixed-frequency continuous light. The method of nearest neighbor signal replacement (NSR) is proposed to address the issue of discontinuity in phase demodulation under periodic external modulation. The experimental results demonstrate that the hybrid system can determine the position of vibration signals between 0 and 900 Hz within a sensing distance of 21 km. When the sensing distance is extended to 71 km, the FTDVS module can still function adequately for high-frequency vibration signals. This hybrid architecture offers a fresh approach to simultaneously achieving long-distance sensing and wide frequency response, making it suitable for the combined measurement of dynamic (e.g., gas leakage, pipeline excavation warning) and quasi-static (e.g., pipeline displacement) events in long-distance applications. Full article

Deep learning models have demonstrated remarkable success in recognising tipping points and providing early warning signals. However, there has been limited exploration of their application to dynamical systems governed by coloured noise, which characterizes many real-world systems. In this study, we show that it is possible to leverage the normal forms of three primary types of bifurcations (fold, transcritical, and Hopf) to construct a training set that enables deep learning architectures to perform effectively. Furthermore, we showed that this approach could accommodate coloured noise by replacing white noise with red noise during the training process. To evaluate the classifier trained on red noise compared to one trained on white noise, we tested their performance on mathematical models using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) scores. Our findings reveal that the deep learning architecture can be effectively trained on coloured noise inputs, as evidenced by high validation accuracy and minimal sensitivity to redness (ranging from 0.83 to 0.85). However, classifiers trained on white noise also demonstrate impressive performance in identifying tipping points in coloured time series. This is further supported by high AUC scores (ranging from 0.9 to 1) for both classifiers across different coloured stochastic time series. Full article

The detection of distant tiny objects in aerial imagery plays a pivotal role in early warning, localization, and recognition tasks. However, due to the scarcity of appearance information, minimal pixel representation, susceptibility to blending with the background, and the incompatibility of conventional metrics, the rapid and accurate detection of tiny objects poses significant challenges. To address these issues, a single-stage tiny object detector tailored for aerial imagery is proposed, comprising two primary components. Firstly, we introduce a light backbone-heavy neck architecture, named the Global Context Self-Attention and Dense Nested Connection Feature Extraction Network (GC-DN Network), which efficiently extracts and fuses multi-scale features of the target. Secondly, we propose a novel metric, MMPW, to replace the Intersection over Union (IoU) in label assignment strategies, Non-Maximum Suppression (NMS), and regression loss functions. Specifically, MMPW models bounding boxes as 2D Gaussian distributions and utilizes the Mixed Minimum Point-Wasserstein Distance to quantify the similarity between boxes. Experiments conducted on the latest aerial image tiny object datasets, AI-TOD and VisDrone-19, demonstrate that our method improves AP50 performance by 9.4% and 5%, respectively, and AP performance by 4.3% and 3.6%. This validates the efficacy of our approach for detecting tiny objects in aerial imagery. Full article

Wildfires, which are distinguished by their destructive nature and challenging suppression, present a significant threat to ecological environments and socioeconomic systems. In order to address this issue, the development of efficient and accurate fire detection technologies for early warning and timely response is essential. This paper addresses the complexity of forest and mountain fire detection by proposing YOLO-CSQ, a drone-based fire detection method built upon an improved YOLOv8 algorithm. Firstly, we introduce the CBAM attention mechanism, which enhances the model’s multi-scale fire feature extraction capabilities by adaptively adjusting weights in both the channel and spatial dimensions of feature maps, thereby improving detection accuracy. Secondly, we propose an improved ShuffleNetV2 backbone network structure, which significantly reduces the model’s parameter count and computational complexity while maintaining feature extraction capabilities. This results in a more lightweight and efficient model. Thirdly, to address the challenges of varying fire scales and numerous weak emission targets in mountain fires, we propose a Quadrupled-ASFF detection head for weighted feature fusion. This enhances the model’s robustness in detecting targets of different scales. Finally, we introduce the WIoU loss function to replace the traditional CIoU object detection loss function, thereby enhancing the model’s localization accuracy. The experimental results demonstrate that the improved model achieves an mAP@50 of 96.87%, which is superior to the original YOLOV8, YOLOV9, and YOLOV10 by 10.9, 11.66, and 13.33 percentage points, respectively. Moreover, it exhibits significant advantages over other classic algorithms in key evaluation metrics such as precision, recall, and F1 score. These findings validate the effectiveness of the improved model in mountain fire detection scenarios, offering a novel solution for early warning and intelligent monitoring of mountain wildfires. Full article

The Industrial Metaverse paradigm can be broadly described as a virtual environment that integrates various technologies such as augmented reality and mixed reality to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the Industrial Metaverse to drive sustainability and resource efficiency is compelling. Using advanced technologies such as the Industrial Metaverse is vital in an endeavor to have a competitive edge in a rapidly evolving business environment. However, the environmental impact of the technologies underpinning the Industrial Metaverse, like data centers and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that, by 2025, without sustainable artificial intelligence (AI) practices, AI will consume more energy than the human workforce, significantly offsetting zero carbon gains. As the Metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. Moreover, the specific extent to which this substitution can alleviate environmental concerns remains an open issue, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the Industrial Metaverse, as well as explore the environmental impact of the Industrial Metaverse. The integrative literature review design and methodological approach involved multiple sources from the Web of Science and databases such as the ACM library, IEEE Library, and Google Scholar, which were analyzed to provide a comprehensive understanding of the developments in the Industrial Metaverse. Firstly, by considering the Industrial Metaverse’s architecture, we elucidate the Industrial Metaverse concept and the associated enabling technologies. Secondly, we performed an exploration through a discussion of the prevalent use cases and the deployment of the emerging Industrial Metaverse. Thirdly, we explored the impact of the Industrial Metaverse on the environment. Lastly, we address novel security and privacy risks, as well as upcoming research challenges, keeping in mind that the Industrial Metaverse is based on a strong data fabric. The results point to the Industrial Metaverse as having both positive and negative environmental effects in terms of energy consumption, e-waste, and pollution. Research, however, indicates that most Industrial Metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Finally, for sustainability in the Industrial Metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, we examined the effects of products on the environment, as well as in the creation of a circular economy. Full article

Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems. Full article

As a globally invasive quarantine pest, the cotton mealybug, Phenacoccus solenopsis, is spreading rapidly, posing serious threats against agricultural and forestry production and biosecurity. In recent years, the niche conservatism hypothesis has been widely debated, which is particularly evident in invasive biology research. Identifying the niche dynamics of P. solenopsis, as well as assessing its global invasion risk, is of both theoretical and practical importance. Based on 462 occurrence points and 19 bioclimatic variables, we used n-dimensional hypervolume analysis to quantify the multidimensional climatic niche of this pest in both its native and invasive ranges. We examined niche conservatism and further optimized the MaxEnt model parameters to predict the global invasion risk of P. solenopsis under both current and future climate conditions. Our findings indicated that the niche hypervolume of this pest in invasive ranges was significantly larger than that in its native ranges, with 99.45% of the niche differentiation contributed by niche expansion, with the remaining less than 1% explained by space replacement. Niche expansion was most evident in Oceania and Eurasia. The area under the receiver operating characteristic curve (0.83) and true skill statistic (0.62) indicated the model’s robust performance. The areas of suitable habitats for P. solenopsis are increasing significantly and the northward spread is obvious in future climate change scenarios. North Africa, northern China, Mediterranean regions, and northern Europe had an increased invasion risk of P. solenopsis. This study provided scientific support for the early warning and control of P. solenopsis. Full article

Underwater acoustic communication (UWAC) has a wide range of applications, including marine environment monitoring, disaster warning, seabed terrain exploration, and oil extraction. It plays an indispensable and increasingly important role in marine resource exploration and marine economic development. In current UWAC systems, the terminal nodes are usually powered by energy-limited batteries. Due to the harshness of the underwater environment, especially in the ocean environment, it is very costly and difficult, even impossible, to replace the batteries for the terminal nodes in UWACs, which results in the short lifetime and unreliability of the terminal nodes and the systems. In this paper, we present the application of a wireless powered communication network (WPCN) to the UWAC systems to provide an auxiliary and convenient energy supplement for solving the energy-limited problem of the terminal nodes, where the hybrid access point (H-AP) transfers energy to the terminal nodes in the downlink. In contrast, the terminal nodes use the harvested energy to transmit the information to the H-AP in the uplink. To evaluate the proposed WPCN-based UWAC systems, we investigate the performance of the average bit error rate (BER), outage probability, and achievable information rate for the systems in a frequency-selective sparse channel and non-white noise environment. We derive the closed-form expression for the probability density function (PDF) of the received signal-to-noise ratio (SNR). Based on this, we further derive novel closed-form expressions for the average BER and the outage probability of the systems. Numerical results confirm the validity of the proposed analytical results. It is shown that there exists an optimal signal frequency and time allocation factor for the systems to achieve optimal performance, and a larger optimal time allocation factor is preferred for a smaller hybrid access point (H-AP) transmit power or a larger transmission distance, while a smaller optimal signal frequency is required for a larger transmission distance. Full article

Over the past four decades, people around the globe have experienced unprecedented escalations in the frequency, intensity, magnitude, and location of anomalous hydrometeorological (hydromet) hazards attributed in large measure to the direct and indirect effects of global climate-change-related variability and extremes. The WMO, impelled by an unabated warming of the global climate system and its related extremely anomalous hydromet impacts, chose in March 2022 “Early Warning, Early Action” (EWEA) as the theme for its World Meteorology Day. The theme was praised in a press release by UN Secretary-General Antonio Guterres, who called for the development of a new EWEA initiative to ensure that “every person on Earth is protected by early warning systems within five years”. By mid-2022, several meetings and workshops had already been held by the WMO to forge the new initiative on its road to the UN Climate Conference of Parties (COP27) in November in Sharm El Sheikh, Egypt. COP27 provided a suitably prominent venue for launching the new USD 3.1 billion, 5-year EWEA initiative; there, Secretary-General Guterres formally tasked the WMO, in partnership with the UNDRR, to lead it. But COP27 proved to be interesting as well as illuminating in other, less publicized ways having to do with EWEA. There, what had been the working title of the new initiative was officially changed to EW4A, “Early Warning for All”. Despite the seemingly perfunctory nature of this change, the reality is that it will almost certainly have outsized impacts on the strengths, weaknesses, opportunities, and constraints (SWOC) met specifically in planning and implementing the new initiative’s “early action” strategies and tactics. It is particularly important to bear in mind that, as things now stand, various unanticipated challenges having to do with the lack of organizational experience and capacity with regard to “early action” are likely to arise with the WMO-led implementation of the new initiative. Considering the new EW4A acronym as if it was a commercial brand can, like this, be instructive in thinking about how the seemingly perfunctory name change—from EWEA to EW4A—will impact the initiative’s implementation of “early action”. Doing so can be instructive because, just as the logos of companies like Apple, Nike, or Starbucks eventually became the face of their respective products, so too have branded acronyms like NASA, IOC, WHO, and INTERPOL become the face of their governmental institutions’ or global initiatives’ respective commissions and commitments. It follows then that if “consumer” interest is to be taken seriously and is (hopefully) long-lasting, then the branding of a new product or initiative must be undertaken with great consideration before a final identifier—be it a logo, a catchphrase, or an acronym—is selected. The question in the case of the new WMO-led initiative, then, is the following: Was this issue seriously taken into consideration before EWEA was so abruptly replaced by EW4A at COP27 in Egypt in November 2022? This pointed question is especially meant to highlight how the continued use of the original EWEA acronym by way of developing regional EWEA centers under the “Early Warning for All” umbrella has the possibility of turning regional potential energy into kinetic energy which will be essential if the theoretical gains of future “early warning” (EW) forecasting science are to be effectively translated into “early action” (EA) strategies and tactics that actually, finally, protect people and property across the entirety of the earth from the impending severe impacts of our changing climate future. Thus does this paper raise valid concerns about the balance between support and funding for EW and EA. Full article

Deformation can effectively reflect the structural state of concrete dams and, thus, establishing na accurate concrete dam deformation prediction model is important for dam health monitoring and early warning strategies. To address the problem that the spatial coordinates introduced in the traditional multi-point deformation prediction model of dams not being able to accurately and efficiently reflect the spatial correlation of multiple-measuring points, a 2D-1D-CNN model is proposed which expresses the spatial correlation between each measuring point through spatial feature vectors, replacing the spatial coordinates in the traditional multi-point model. First, the spatial feature vector is extracted from the historical spatio-temporal panel series of deformation values of measuring points via a Two-Dimensional Convolutional Neural Network (2D-CNN); second, the vector is combined with the environmental impact factor of dam deformation to form the final input factor of fused spatial features; and, thirdly, this vector is combined with the environmental impact factors of dam deformation to form the final input factor of fused spatial features, and the non-linear linkage between the factors and the measured displacement values is constructed by the efficient feature processing capability of a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the prediction results. Finally, the actual monitoring data of a concrete dam in China are used as an example to verify the validity of the model. The results show that the proposed model outperforms the other models in most cases, respectively, which verifies the effectiveness of the proposed model in this paper. Full article

Underwater target detection plays a crucial role in marine environmental monitoring and early warning systems. It involves utilizing optical images acquired from underwater imaging devices to locate and identify aquatic organisms in challenging environments. However, the color deviation and low illumination in these images, caused by harsh working conditions, pose significant challenges to an effective target detection. Moreover, the detection of numerous small or tiny aquatic targets becomes even more demanding, considering the limited storage and computing power of detection devices. To address these problems, we propose the YOLOv7-CHS model for underwater target detection, which introduces several innovative approaches. Firstly, we replace efficient layer aggregation networks (ELAN) with the high-order spatial interaction (HOSI) module as the backbone of the model. This change reduces the model size while preserving accuracy. Secondly, we integrate the contextual transformer (CT) module into the head of the model, which combines static and dynamic contextual representations to effectively improve the model’s ability to detect small targets. Lastly, we incorporate the simple parameter-free attention (SPFA) module at the head of the detection network, implementing a combined channel-domain and spatial-domain attention mechanism. This integration significantly improves the representation capabilities of the network. To validate the implications of our model, we conduct a series of experiments. The results demonstrate that our proposed model achieves higher mean average precision (mAP) values on the Starfish and DUO datasets compared to the original YOLOv7, with improvements of 4.5% and 4.2%, respectively. Additionally, our model achieves a real-time detection speed of 32 frames per second (FPS). Furthermore, the floating point operations (FLOPs) of our model are 62.9 G smaller than those of YOLOv7, facilitating the deployment of the model. Its innovative design and experimental results highlight its effectiveness in addressing the challenges associated with underwater object detection. Full article