Rapid Expansion Research Articles

Rapid seagrass meadow expansion in an Indian Ocean bright spot

The areal extent of seagrass meadows is in rapid global decline, yet they provide highly valuable societal benefits. However, their conservation is hindered by data gaps on current and historic spatial extents. Here, we outline an approach for national-scale seagrass mapping and monitoring using an open-source platform (Google Earth Engine) and freely available satellite data (Landsat, Sentinel-2) that can be readily applied in other countries globally. Specifically, we map contemporary (2021) and historical (2000–2021; n = 10 maps) shallow water seagrass extent across the Maldives. We found contemporary Maldivian seagrass extent was ~ 105 km2 (overall accuracy = 82.04%) and, notably, that seagrass area increased threefold between 2000 and 2021 (linear model, + 4.6 km2 year−1, r2 = 0.93, p < 0.001). There was a strongly significant association between seagrass and anthropogenic activity (p < 0.001) that we hypothesize to be driven by nutrient loading and/or altered sediment dynamics (from large scale land reclamation), which would represent a beneficial anthropogenic influence on Maldivian seagrass meadows. National-scale tropical seagrass expansion is unique against the backdrop of global seagrass decline and we therefore highlight the Maldives as a rare global seagrass ‘bright spot’ highly worthy of increased attention across scientific, commercial, and conservation policy contexts.

Comparative Analysis of Transposable Elements Dynamics in Fish with Different Sex Chromosome Systems.

Transposable elements (TEs) are widespread genomic components with substantial roles in genome evolution and sex chromosome differentiation. In this study, we compared the TE composition of three closely related fish with different sex chromosome systems: Megaleporinus elongatus (Z1Z1Z2Z2/Z1W1Z2W2), Megaleporinus macrocephalus (ZZ/ZW) (both with highly differentiated W sex chromosomes), and Leporinus friderici (without heteromorphic sex chromosomes). We created custom TE libraries for each species using clustering methods and manual annotation and prediction, and we predicted TE temporal dynamics through divergence-based analysis. The TE abundance ranged from 16 to 21% in the three mobilomes, with L. friderici having the lowest overall. Despite the recent amplification of TEs in all three species, we observed differing expansion activities, particularly between the two genera. Both Megaleporinus recently experienced high retrotransposon activity, with a reduction in DNA TEs, which could have implications in sex chromosome composition. In contrast, L. friderici showed the opposite pattern. Therefore, despite having similar TE compositions, Megaleporinus and Leporinus exhibit distinct TE histories that likely evolved after their separation, highlighting a rapid TE expansion over short evolutionary periods.

MANGLEE: A Tool for Mapping and Monitoring MANgrove Ecosystem on Google Earth Engine—A Case Study in Ecuador

Mangroves, integral to ecological balance and socioeconomic well-being, are facing a concerning decline worldwide. Remote sensing is essential for monitoring their evolution, yet its effectiveness is hindered in developing countries by economic and technical constraints. In addressing this issue, this paper introduces MANGLEE (Mangrove Mapping and Monitoring Tool in Google Earth Engine), an accessible, adaptable, and multipurpose tool designed to address the challenges associated with sustainable mangrove management. Leveraging remote sensing data, machine learning techniques (Random Forest), and change detection methods, MANGLEE consists of three independent modules. The first module acquires, processes, and calculates indices of optical and Synthetic Aperture Radar (SAR) data, enhancing tracking capabilities in the presence of atmospheric interferences. The second module employs Random Forest to classify mangrove and non-mangrove areas, providing accurate binary maps. The third module identifies changes between two-time mangrove maps, categorizing alterations as losses or gains. To validate MANGLEE’s effectiveness, we conducted a case study in the mangroves of Guayas, Ecuador, a region historically threatened by shrimp farming. Utilizing data from 2018 to 2022, our findings reveal a significant loss of over 2900 hectares, with 46% occurring in legally protected areas. This loss corresponds to the rapid expansion of Ecuador’s shrimp industry, confirming the tool’s efficacy in monitoring mangroves despite cloud cover challenges. MANGLEE demonstrates its potential as a valuable tool for mangrove monitoring, offering insights essential for conservation, management plans, and decision-making processes. Remarkably, it facilitates equal access and the optimal utilization of resources, contributing significantly to the preservation of coastal ecosystems.

Utilization of bis-MPA Dendrimers for the Calibration of Ion Mobility Collision Cross Section Calculations.

Ion mobility-mass spectrometry (IM-MS) has become increasingly popular with the rapid expansion of available techniques and instrumentation. To enable accuracy, standardization, and repeatability of IM-MS measurements, the community requires reliable and well-defined reference materials for calibration and tuning of the equipment. To address this need, synthetic dendrimers of high chemical and structural purity were tested on three ion mobility platforms as potential calibrants. First, synthesized dendrimers were characterized by drift tube ion mobility (DTIMS), using an Agilent 6560 IM-qTOF-MS to assess their drift tube collision cross section (DTCCS) values. Then, assessment of obtained CCS values on trapped ion mobility (TIMS) and traveling wave ion mobility (TWIMS) ion mobility platforms were compared to those found by DTIMS. Across all three systems, dendrimers were found to have high potential for m/z and ion mobility calibration in the CCS range of 160-1700 Å2. To further validate their use as calibrants, drift tube calculated CCS values for dendrimers were utilized to calibrate calculations of CCS for known standards including Agilent Tuning mix, the CCS Major mix from Waters, and SPLASH LIPIDOMIX. Additionally, structures of sodiated dendrimers were computated along with theoretical CCS values which showed good agreement with the experimental CCS values. On the basis of the results presented, we recommend the use of dendrimers as alternatives and/or complementary compounds to commonly used calibrants for ion mobility platforms.

Corporate social responsibility in hospitality and tourism: a systematic review

ABSTRACT Scholars have increasingly focused on the importance of corporate social responsibility (CSR) in the hospitality and tourism (H&T) industry due to its rapid growth and expansion. This study examined 192 CSR-focused empirical studies that were published in H&T journals. The report provides a comprehensive analysis by categorizing the data into several parts, such as publishing trends of CSR, segmentation by journals, theoretical frameworks, techniques, and CSR measurement scales. This study also develops a complex nomological network of CSR, examining its connections with the precursor factors, mediating variables, moderating impacts, and outcome effects. Key themes were identified using advanced thematic analysis techniques, providing crucial insights into the current status and future directions of CSR using Leximancer software. The results provide significant information for sustainable practices and influence prospective studies in the crucial field of corporate responsibility.

IoT Security Challenges and Solutions for Data at Rest: A Systematic Literature Review

The rapid expansion of the Internet of Things (IoT) has significantly transformed both consumer and industrial domains, driving the urgent need for robust security measures to protect data at rest. SLR investigates into the challenges associated with securing IoT devices and data, exploring the limitations of existing security frameworks and the intricate requirements imposed by global data protection regulations such as GDPR. The researcher review current approaches, including privacy-by-design principles and the deployment of symmetrical data protection frameworks, as highlighted in recent studies. Through a comprehensive analysis of literature and existing technologies, we identify critical gaps in the protection strategies and propose enhanced methods for ensuring data security and privacy in IoT systems. The findings emphasize the role of developers in integrating privacy considerations early in the development process and the impact of regulatory complexities on the practical implementation of data protection measures. Furthermore, the paper evaluates innovative security solutions, such as full stack security architectures and adversarial training models, assessing their effectiveness in real-world applications. This study aims to provide a deeper understanding of the IoT security landscape and to suggest actionable strategies for improving data protection practices across the IoT ecosystem

Mitigating greenhouse gas emissions from sheep production system in China: An integrated approach of data envelopment analysis and life cycle assessment

Over the past decades, the global population has significantly increased, resulting in high demand for food. China, the largest sheep-producing country, has experienced 20 rapid expansions in its sheep industry, which has caused environmental consequences. This study proposes an integrated approach that combines data envelopment analysis (DEA) and life cycle assessment (LCA) to assess the ecological consequences of the sheep production system. Therefore, this work aims to evaluate the energy consumption of the existing production system and identify optimization opportunities to reduce its environmental impacts. Our results demonstrate that sheep production can decrease total energy consumption by 9.82% using optimization. Food production was reported as a critical factor in environmental impact, responsible for 69%. However, the optimized approach led to a decrease of 4,677.64 MJ in energy consumption in sheep production compared to the initial state. The findings of this study showcase the possibility of decreasing energy usage and lessening greenhouse gas (GHG) discharges in sheep farming by implementing optimization strategies.

Rapid expansion and international spread of M1UK in the post-pandemic UK upsurge of Streptococcus pyogenes

The UK observed a marked increase in scarlet fever and invasive group A streptococcal infection in 2022 with severe outcomes in children and similar trends worldwide. Here we report lineage M1UK to be the dominant source of invasive infections in this upsurge. Compared with ancestral M1global strains, invasive M1UK strains exhibit reduced genomic diversity and fewer mutations in two-component regulator genes covRS. The emergence of M1UK is dated to 2008. Following a bottleneck coinciding with the COVID-19 pandemic, three emergent M1UK clades underwent rapid nationwide expansion, despite lack of detection in previous years. All M1UK isolates thus-far sequenced globally have a phylogenetic origin in the UK, with dispersal of the new clades in Europe. While waning immunity may promote streptococcal epidemics, the genetic features of M1UK point to a fitness advantage in pathogenicity, and a striking ability to persist through population bottlenecks.

Skeletal effects of posterior crossbite treatment with either quad helix or rapid maxillary expansion: a randomized controlled trial with 1-year follow-up

ABSTRACT Objectives To assess skeletal and dental effects and evaluate possible side effects of maxillary expansion with two different appliances, directly after expansion and 1 year postexpansion. Materials and Methods Forty-two patients with unilateral posterior crossbite (mean 9.5 ± 0.9 years) were randomized to either rapid maxillary expansion (RME) banded on the deciduous second molars and bonded to the primary canines or slow expansion with quad helix (QH) on the permanent first molars. Cone-beam computed tomography records were taken at baseline, directly after correction of the posterior crossbite and at follow-up 1 year after expansion. Results All patients were analyzed. RME opened the midpalatal suture more anteriorly and inferiorly (mean 4.1 mm) and less posteriorly and superiorly (mean 1.0 mm). No effect on midpalatal suture could be shown in the QH group after expansion, P &amp;lt; .001. Buccal bone width had significantly decreased (P &amp;lt; .001) in the QH group compared with the RME group. Buccal fenestrations and root resorption on the left first molar had a higher prevalence directly after expansion finished in the QH group (P = .0086, P = .013) but were not significant at 1-year follow-up (P = .11, P = .22). Conclusions Opening of the suture with RME was more anterior and inferior, and the QH did not open the midpalatal suture at all. More buccal bone loss and fenestrations were seen on the permanent first molar in patients treated with conventional QH than RME anchored to deciduous teeth.

Psychological factors enhanced heterogeneous learning interactive graph knowledge tracing for understanding the learning process

IntroductionWith the rapid expansion of online education, there is a burgeoning interest within the EdTech space to offer tailored learning experiences that cater to individual student's abilities and needs. Within this framework, knowledge tracing tasks have garnered considerable attention. The primary objective of knowledge tracing is to develop a model that assesses a student's proficiency in a particular skill based on their historical performance in exercises, enabling predictions regarding the likelihood of correct responses in future exercises. While existing knowledge tracing models often incorporate information such as students' exercise answering history and skill mastery level, they frequently overlook the students' mental states during the learning process.MethodsThis paper addresses this gap by introducing a novel psychological factors-enhanced heterogeneous learning interactive graph knowledge tracing model (Psy-KT). This model delineates the interactions among students, exercises, and skills through a heterogeneous graph, supplementing it with four psychological factors that capture students' mental states during the learning process: frustration level, confusion level, concentration level, and boredom level. In the modeling of students' learning processes, we incorporate the forgetting curve and construct relevant cognitive parameters from the features. Additionally, we employ the Item Response Theory (IRT) model to predict students' performance in answering exercises at the subsequent time step. This model not only delves into the psychological aspects of students during the learning process but also integrates the simulation of forgetting, a natural phenomenon in the learning journey. The inclusion of cognitive parameters enhances the description of changes in students' abilities throughout the learning process. This dual focus allows for a more comprehensive understanding of students' learning behaviors while providing a high level of interpretability for the model.Results and discussionEmpirical validation of the Psy-KT model is conducted using four publicly available datasets, demonstrating its superior performance in predicting students' future performance. Through rigorous experimentation, the integration of psychological and forgetting factors in the Psy-KT model not only improves predictive accuracy but also enables educators to offer more targeted tutoring and advice, enhancing the overall efficacy of the learning experience.

Coralline Algal Population Explosion in an Overgrazed Seagrass Meadow: Conditional Outcomes of Intraspecific and Interspecific Interactions

AbstractInteractions such as mutualism and facilitation are common in ecosystems established by foundation species; however, their outcomes vary and show conditionality. In a Mexican Caribbean Bay, a seagrass-coralline algae (rhodoliths) mutualism protects the seagrass Thalassia testudinum from green turtle overgrazing. We postulate that the state of the seagrass meadow in this bay depends on the strengths of the interactions among seagrasses, green turtles, and coralline algae. Spatio-temporal changes through satellite imagery showed rhodolith bed developed rapidly from 2009 (undetected) to 2016 (bed of 6934 m2). Typically, such rapid expansion of the rhodoliths does not occur in seagrass meadows. An in situ growth experiment of coralline algae showed that a combination of reduction in light and wave movement (usual in dense seagrass meadows) significantly reduced their growth rates. In the rhodolith beds, the growth rates of the coralline algae Neogoniolithon sp. and Amphiroa sp. were high at 9.5 mm and 15.5 mm per growth tip y−1, respectively. In a second experiment, we found lower mortality in coralline algae within a rhodolith bed compared to algae placed outside the bed, likely explained by the reduced resuspension that we found in a third experiment, and this positive feedback may explain the high population increase in the rhodoliths, once established when the turtles grazed down the seagrass canopy. Therefore, the grazing-protection mutualism between seagrasses and coralline algae is thus conditional and came into existence under a co-occurrence of intensive grazing pressure and rapid population growth of coralline algae facilitated by positive feedback from increased growth and reduced sediment resuspension by the dense rhodolith bed.

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

Evaluation index system of science and technology innovation think tank talents based on competency model

As China’s economy progresses towards high-quality development, the construction of science and technology think tanks, with talent construction as the key, has become an important supporting force and occupies a unique position in the national innovation system. In recent years, with the rapid expansion of the number of think tank talents, how to evaluate and manage think tank talents has become a key factor in the development of think tank influence and competitiveness. Based on the experience of talent team construction at home and abroad, Taking the typical science and technology innovation think tanks of Henan Province as an example, this paper analyzes the current situation and status quo of science and technology innovation think tank talents, and finds problems in the talents, Summarize the factors that affect the talent construction and development of science and technology innovation think tanks, From the perspective of six dimensions of competency model, four first-level indicators of talent quantity, talent quality, talent ability and talent contribution of science and technology innovation think tank and 16 subordinate second-level indicators are constructed for talent evaluation of think tank. Ahp is adopted to set questionnaires and collect data with Likert5-level scale, and yaahp software is used to verify the weight verification evaluation effect. Invite experts in the field to score the evaluation indicators. The research results show that the talent evaluation index of science and technology innovation think tank based on competency model pays more attention to the standardization, integration and systematization of the talent evaluation of the think tank, and the system is scientific and reasonable. It can provide reference and reference for promoting the construction, evaluation and development management of scientific and technological innovation think tanks.

Simulation modelling and analysis of linkage-controlled traffic scheme in Waterway Transport Key Nodes

As global shipping undergoes rapid expansion, pivotal waterway transport systems—including significant nodes like the Panama Canal, the Suez Canal, and the Three Gorges-Gezhouba dams—are increasingly emerging as system-wide bottlenecks that limit transportation capabilities. Recognizing the pressing need for efficient traffic organization at these critical junctures, we designed a hybrid simulation model, which integrates Cellular Automaton and Multi-Agent methods, to analyse traffic efficiency and evaluate different ship organization schemes at these key waterway nodes. The Three Gorges-Gezhouba dams serve as a case study, where we crafted and executed four simulation scenarios that accommodate a range of variables such as different traffic organization schemes, traffic flow volumes, and anchorage capacities. Key operational indicators such as the maximum average waiting time of ships at the anchorage, and the period when the anchorage along the waterway reaches saturation, provide insights into the system's operational condition. The simulation outcomes highlight the proposed model's capability to accurately quantify the impact of implementing a linkage-control scheme and underscore the utility of dynamic adjustment of water area ranges under linkage-control for managing various traffic scenarios. Consequently, our research not only enriches high-precision simulation methodologies but also bolsters decision-making processes concerning ship traffic organization at Waterway Transport Key Nodes.

Multi‐response optimization of characteristics for graphite reinforced biodegradable PVA‐fumaric acid cross‐linked composite: A gray‐fuzzy logic‐based hybrid approach

AbstractIn the current investigation, enhancement in the mechanical properties, thermal stability, and decrement in the moisture absorption capabilities of polyvinyl alcohol was observed by cross‐linking it with different weight percentages (wt. %) of fumaric acid and reinforcing them along with sulfuric acid‐modified graphite particles. The effects of altering the cross‐linking duration and fumaric acid wt. % were investigated in the resulting composite using the Taguchi L16 experimental design method. As in this contemporary era of rapid industrial expansion and fierce competition, the primary goals of manufacturers revolve around the production of high‐quality goods at optimal costs within a specified timeframe. Therefore, to save both cost and time, the authors have utilized a technique known as “gray‐fuzzy optimization” to balance competing goals between mechanical (toughness, elongation and strength) and physical (water intake capacity) attributes. The present approach aimed to reduce the experimentation expenses and increase the effectiveness of composite manufacturing by considerably reducing the intricacy of practical multi‐response optimization issues. Response graphs and analysis of variance tests were also utilized to calculate the percentage contribution of the input variables and provide model significance.Highlights Polyvinyl alcohol is cross‐linked with different weight percentages (wt. %) of fumaric acid, and reinforced them with sulfuric acid‐modified graphite particles. Enhancement in mechanical properties, thermal stability, and decrease in moisture absorption capabilities were perceived for the fabricated composite. Effects of altering the cross‐linking duration and fumaric acid wt. % were investigated in the resulting composite using the Taguchi L16 experimental design. Gray‐fuzzy optimization technique was utilized to balance competing goals between water intake and mechanical attributes like toughness, elongation and strength. Response graphs and analysis of variance (ANOVA) tests were also utilized to evaluate the percentage contribution of input variables and provide model significance.

IoTvulCode: AI-enabled vulnerability detection in software products designed for IoT applications

AbstractThe proliferation of the Internet of Things (IoT) paradigm has ushered in a new era of connectivity and convenience. Consequently, rapid IoT expansion has introduced unprecedented security challenges , among which source code vulnerabilities present a significant risk. Recently, machine learning (ML) has been increasingly used to detect source code vulnerabilities. However, there has been a lack of attention to IoT-specific frameworks regarding both tools and datasets. This paper addresses potential source code vulnerabilities in some of the most commonly used IoT frameworks. Hence, we introduce IoTvulCode - a novel framework consisting of a dataset-generating tool and ML-enabled methods for detecting source code vulnerabilities and weaknesses as well as the initial release of an IoT vulnerability dataset. Our framework contributes to improving the existing coding practices, leading to a more secure IoT infrastructure. Additionally, IoTvulCode provides a solid basis for the IoT research community to further explore the topic.

Experimental study on diesel spray flame and wall heat transfer of two-dimensional combustion chamber: effect of common rail pressure

Reducing the heat transfer occurring during the spray combustion of diesel engines remains a challenging task. This study investigated the spray flame behaviour, wall heat flux, and heat transfer phenomena of diesel engines at varying common rail pressures. A rapid compression and expansion machine (RCEM) was employed to simulate a single cycle of diesel spray combustion using split injection sprays. The two-dimensional (2D) stepped piston cavity is a uniquely shaped model used to describe the spray flame behaviour in the combustion chamber. To investigate the heat transfer phenomena, we installed wall heat flux sensors in the cylinder and cavity side. The results indicated that higher common rail pressures increased the peak value of the wall heat flux and reduced the combustion duration and the luminous flame. The higher common rail pressure led to an increase in spray velocity, thereby enhancing the turbulence level. This increase in turbulence to higher peak value of the wall heat flux. The highest peak value of the wall heat flux was reached by the cylinder head owing to the intense combustion region, followed by the cavity bottom owing to the highest flame temperature occurring around this location. Additionally, the heat transfer phenomena in diesel engines were successfully represented by local Nu-Re correlations. Our results contribute to the discussion on heat transfer phenomena that influence the thermal efficiency of diesel combustion engines.

Study on the smoke flow characteristics in inclined tunnel with an upstream shaft under natural ventilation

The rapid expansion of urban highway tunnels has led to a significant increase in the risk of fire incidents. Moreover, the urban highway tunnels often have inclinations, which further exacerbates the complexity of the smoke flow. In contrast to the extensive research on natural ventilation in horizontally tunnels, there is still a lack of studies on the natural ventilation of shaft in inclined tunnels. Therefore, this study employed numerical simulations and theoretical analysis to investigate the length of smoke back-layering and flow patterns in inclined tunnel fires with an upstream shaft. By comparing the induced air inflow velocity and smoke back-layering length in inclined tunnels, both with and without an upstream shaft, three types of smoke flow states were identified. State 1: smoke spreading to the upstream shaft, State 2: smoke being limited between the shaft and the fire source, State 3: no smoke backflow in the inclined tunnel. In State 1, the shaft discharges smoke outward. The downstream length and the distance between the shaft and the fire source determined smoke back-layering length. In States 2 and 3, the shaft fills the air inward. The shaft has no obvious effect on the smoke back-layering length. Furthermore, the effects of HRR, fire source position and shaft position on smoke flow characteristics are analyzed. As a result of the analysis, a segmented prediction model for smoke back-layering length and a quantitative method for predicting smoke flow state are proposed. By fine-tuning the tunnel slope, two critical values between the three smoke flow states are obtained. The research results can provide a useful reference for ventilation and evacuation design of inclined tunnels.

Rediscovery of the implication of albuminuria in heart failure: emerging classic index for cardiorenal interaction.

The development of new drugs and device therapies has led to remarkable advancements in heart failure (HF) treatment in the past couple of decades. However, it becomes increasingly evident that guideline-directed medical therapy cannot be one-size-fits-all across a wide range of ejection fractions (EFs) and various aetiologies. Therefore, classifications solely relying on EF and natriuretic peptide make optimization of treatment challenging, and there is a growing exploration of new indicators that enable efficient risk stratification of HF patients. Particularly when considering HF as a multi-organ interaction syndrome, the cardiorenal interaction plays a central role in its pathophysiology, and albuminuria has gained great prominence as its biomarker, independent from glomerular filtration rate. Albuminuria has been shown to exhibit a linear correlation with cardiovascular disease and HF prognosis in multiple epidemiological studies, ranging from normal (<30mg/g) to high levels (>300mg/g). However, on the other hand, it is only recently that the details of the pathological mechanisms that give rise to albuminuria have begun to be elucidated, including the efficient compaction/tightening of the glomerular basement membrane by podocytes and mesangial cells. Interestingly, renal disease, diabetes, and HF damage these components associated with albuminuria, and experimental models have demonstrated that recently developed HF drugs reduce albuminuria by ameliorating these pathological phenotypes. In this review, facing the rapid expansion of horizons in HF treatment, we aim to clarify the current understanding of the pathophysiology of albuminuria and explore the comprehensive understanding of albuminuria by examining the clinically established evidence to date, the pathophysiological mechanisms leading to its occurrence, and the outcomes of clinical studies utilizing various drug classes committed to specific pathological mechanisms to put albuminuria as a novel axis to depict the pathophysiology of HF.

Crack detection and damage evaluation of asymmetrical steel-reinforced concrete frame nodes using acoustic emission technology

ABSTRACT Detecting cracks in steel-reinforced concrete (SRC) frame nodes is crucial for ensuring structural safety. This study employs acoustic emission (AE) technology to monitor the growth of concrete structural defects in real time. The aim is to establish a sensitive assessment method based on AE parameters to accurately assess the damage stages of SRC frame nodes by considering the complex crack extension mechanism in SRC structures. An experimental study of the AE characteristics of SRC frame nodes under low-cycle loading was performed. The crack evolution process of SRC frame nodes was monitored and analysed using the AE parameters. The results showed that ringing count is the AE parameter more sensitive to the damage of the SRC frame node. The damage evaluation method was established based on the variation coefficient analysis of the AE parameters. Considering the sudden change in the growth rate of the damage index, the damage evolution process of the SRC frame nodes can be accurately divided into three stages: initial crack compaction, rapid crack expansion, and crack penetration. These stages are consistent with the mechanical properties of SRC frame nodes.