Search Results (97,615)

Profound global transformations in the Anthropocene epoch are hastening shifts in species ranges, with natural mountain treeline migration playing a crucial role in this overarching species movement. The varied reactions of mountain treelines to climatic conditions across diverse climatic zones, when compounded by local disturbances, result in distinct migration patterns. Usually, warming encourages mountain treelines to migrate to higher elevations. Nevertheless, in a period of rapid warming, it remains unclear whether the natural mountain treeline in global thermal climatic zones and subclimatic zones has expedited its upward movement. Here, we employed remote sensing observations and the random forest algorithm to investigate the natural treeline dynamics across 24 major mountain ranges worldwide amidst a period of rapid warming (1990–2020). Our research shows substantial disparities in the migration patterns of natural mountain treelines across the global thermal zone. The natural mountain treeline in tropical and subtropical zones descends by an average of 1.1 and 0.8 m per year, respectively. Only 18.8 and 35.5% of the natural mountain treelines in these regions had undergone upward migration, respectively. The average migration rates of natural mountain treelines in temperate and boreal zones were 0.7 m per year. Correspondingly, 47 and 33.2% of the natural mountain treelines in these zones had already shifted to higher elevations. The highest average migration rate of natural mountain treelines occurs in temperate continental climates (1.7 m per year). The loss or degradation of alpine species habitats, a direct consequence of the upward movement of the treeline, highlights the necessity for increased monitoring and protection of alpine species in temperate and boreal zones in the future. Full article

Benzodiazepines, a significant group of newly recognised water contaminants, are psychotropic medications prescribed for common anxiety symptoms and sleep disorders. They resist efficient degradation during sewage treatment and endure in aquatic environments. Their presence in aquatic matrices is increasing, particularly after the recent pandemic period, which has led many people to systematically use benzodiazepines to manage anxiety. In previous studies, an important interference of this class of drugs on both the larval and adult stages of some aquatic species has been demonstrated, with effects on behaviour and embryonic development. This study examined the influence of delorazepam, a diazepam metabolite, on Artemia salina development to gain insight into responses in naupliar larvae. Results demonstrated that treatments (1, 5, and 10 µg/L) increase the hatching percentage and induce a desynchronisation in growth. Mortality was only slightly increased (close to 10% at six days post-hatching), but lipid reserve consumption was modified, with the persistence of lipid globules at the advanced naupliar stages. Locomotory activity significantly decreased only at 10 µg/L treatment. No teratogenic effects were observed, though modest damages were noticed in the posterior trunk and eyes, two targets of environmental toxicity. The negative impact of delorazepam on Artemia salina adds to those already reported in other species of invertebrates and vertebrates, which are not yet considered targets of these drugs. This study underscores the need for further research and immediate attention to this class of contaminants and the importance of monitoring their presence during environmental risk assessments. Full article

This article discusses the development of an enhanced monitoring and control system for helicopter turboshaft engines during flight operations, leveraging advanced neural network techniques. The research involves a comprehensive mathematical model that effectively simulates various failure scenarios, including single and cascading failure, such as disconnections of gas-generator rotor sensors. The model employs differential equations to incorporate time-varying coefficients and account for external disturbances, ensuring accurate representation of engine behavior under different operational conditions. This study validates the NARX neural network architecture with a backpropagation training algorithm, achieving 99.3% accuracy in fault detection. A comparative analysis of the genetic algorithms indicates that the proposed algorithm outperforms others by 4.19% in accuracy and exhibits superior performance metrics, including a lower loss. Hardware-in-the-loop simulations in Matlab Simulink confirm the effectiveness of the model, showing average errors of 1.04% and 2.58% at 15 °C and 24 °C, respectively, with high precision (0.987), recall (1.0), F1-score (0.993), and an AUC of 0.874. However, the model’s accuracy is sensitive to environmental conditions, and further optimization is needed to improve computational efficiency and generalizability. Future research should focus on enhancing model adaptability and validating performance in real-world scenarios. Full article

Dye-sensitized solar cells (DSSCs) are a type of thin-film solar cell that has been extensively studied for more than two decades due to their low manufacturing cost, flexibility and ability to operate under low-light conditions. However, there are some challenges that need to be addressed, such as energy losses, material integration, weak photocurrent generation and stability, to enhance the performance of DSSCs. One of the approaches to enhance the performance of DSSCs is the use of luminescent materials. These are materials that can absorb light and re-emit at different wavelengths, allowing the conversion of ultraviolet (UV) and near-infrared (NIR) light, which DSSCs do not efficiently utilize, into visible light that can be absorbed. The main objective of this article is to provide an in-depth review of the impact of luminescent materials in DSSCs. Research interest on luminescent materials, particularly down conversion, up-conversion and quantum dots, was analyzed using data from the “Web of Science”. It revealed a remarkable number of over 200,000 publications in the past decade. Therefore, the state of the art of luminescent materials for enhancing the performance of the solar cells was reviewed, which showed significant potential in enhancing the performance of DSSCs. Full article

In response to the problems of low control accuracy, single detection of operating parameters, and insufficient collaborative control of unmanned fertilization in field fertilization operations, this paper proposes an adaptive control strategy for fertilizer discharging shaft speed based on segmented linear interpolation method. By constructing a relationship model between fertilizer discharging shaft speed and motor control signals in different speed ranges, the on-site self-calibration of fertilizer discharging shaft speed and the precise control of the fertilization rate is achieved. At the same time, real-time detection and warning technology for multiple working condition parameters were integrated, and a bus communication protocol between unmanned tractors and variable-rate fertilizer applicators was developed. A variable-ratefertilization monitoring system based on unmanned tractors was developed, and actual experimental tests were conducted to test the system’s performance. Among them, the calibration test results of fertilization rate showed that the discharging rate of the fertilizer apparatuses (p) was negatively correlated with the rotation speed of the fertilizer discharging shaft, and that the installation position of the fertilizer apparatuses affected the uniformity of fertilization between the rows of the fertilizer applicator. The speed response test of the fertilizer discharging shaft showed that the average response time (T_s) of the fertilizer discharging shaft speed controlled by the self-calibration model was 0.40 s, the average steady-state error (e_ss) was 0.13 r/min, and the average overshoot (σ) was 7.33%. Compared with the original linear model, the e_ss was reduced by 0.23 r/min, and the σ was reduced by 1.54 percentage points. The results of the fertilization status detection tests showed that the system can achieve real-time detection of different operating parameters and states, as well as collaborative control of tractors and fertilizer applicators. The results of the fertilization rate control accuracy test showed that the average fertilization control error of the system was 1.91% under different target fertilization rate, which meets the requirements of variable-rate fertilization field operations. This study can serve as a technical reference for the design and development of fertilization robots in the context of unmanned farm development. Full article

The modern healthcare system strives to provide patients with more comfortable and less invasive experiences, focusing on noninvasive and painless diagnostic and treatment methods. A key priority is the early diagnosis of life-threatening diseases, which can significantly improve patient outcomes by enabling treatment at earlier stages. While most patients must undergo diagnostic procedures before beginning treatment, many existing methods are invasive, time-consuming, and inconvenient. To address these challenges, electrochemical-based wearable and point-of-care (PoC) sensing devices have emerged, playing a crucial role in the noninvasive, continuous, periodic, and remote monitoring of key biomarkers. Due to their numerous advantages, several wearable and PoC devices have been developed. In this focused review, we explore the advancements in metal–organic frameworks (MOFs)-based wearable and PoC devices. MOFs are porous crystalline materials that are cost-effective, biocompatible, and can be synthesized sustainably on a large scale, making them promising candidates for sensor development. However, research on MOF-based wearable and PoC sensors remains limited, and no comprehensive review has yet to synthesize the existing knowledge in this area. This review aims to fill that gap by emphasizing the design of materials, fabrication methodologies, sensing mechanisms, device construction, and real-world applicability of these sensors. Additionally, we underscore the importance and potential of MOF-based wearable and PoC sensors for advancing healthcare technologies. In conclusion, this review sheds light on the current state of the art, the challenges faced, and the opportunities ahead in MOF-based wearable and PoC sensing technologies. Full article

The plant vascular system is not only a transportation system for delivering nutrients but also a highway transport network for spreading viruses. Tomato spotted wilt orthotospovirus (TSWV) is among the most destructive viruses that cause serious losses in economically important crops worldwide. However, there is minimal information about the long-distance movements of TSWV in the host plant vascular system. In this this study, we confirm that TSWV virions are present in the xylem as observed by transmission electron microscopy (TEM). Further, a quantitative proteomic analysis based on label-free methods was conducted to reveal the uniqueness of protein expression in xylem sap during TSWV infection. Thus, this study identified and quantified 3305 proteins in two groups. Furthermore, TSWV infection induced three viral structural proteins, N, Gn and Gc, and 315 host proteins differentially expressed in xylem (163 up-regulated and 152 down-regulated). GO enrichment analysis showed up-regulated proteins significantly enriched in homeostasis, wounding, defense response, and DNA integration terms, while down-regulated proteins significantly enriched in cell wall biogenesis/xyloglucan metabolic process-related terms. KEGG enrichment analysis showed that the differentially expressed proteins (DEPs) were most strongly associated with plant-pathogen interaction, MAPK signaling pathway, and plant hormone signal transduction. Cluster analysis of DEPs function showed the DEPs can be categorized into cell wall metabolism-related proteins, antioxidant proteins, PCD-related proteins, host defense proteins such as receptor-like kinases (RLKs), salicylic acid binding protein (SABP), pathogenesis related proteins (PR), DNA methylation, and proteinase inhibitor (PI). Finally, parallel reaction monitoring (PRM) validated 20 DEPs, demonstrating that the protein abundances were consistent between label-free and PRM data. Finally, 11 genes were selected for RT-qPCR validation of the DEPs and label-free-based proteomic analysis concordant results. Our results contribute to existing knowledge on the complexity of host plant xylem system response to virus infection and provide a basis for further study of the mechanism underlying TSWV long-distance movement in host plant vascular system. Full article

While the macronutrient content of a ketogenic diet specifically utilized for childhood epilepsy is clearly defined in the literature, variations among other ketogenic diets exhibit substantial heterogeneity. Furthermore, studies utilizing ketogenic diets contain several confounders with notable impacts on outcomes, thereby rendering both their findings and those of the meta-analyses less reliable. The objective of this meta-epidemiological assessment was to scrutinize existing clinical trials that investigated the effects of ketogenic diets on patients with obesity and diabetes, thereby determining the feasibility of conducting a meta-analysis. The Ovid Medline, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), and Embase databases were searched from 1946 to 24 September 2024. Of the studies reviewed, none met the predefined inclusion criteria. However, seven articles met these criteria very closely. In the future, studies investigating the effects of ketogenic diets containing significant confounding factors should adopt a single definition of a ketogenic diet. Additionally, accurate measurement of actual macronutrient and caloric intake, along with regularly monitored nutritional ketosis, will be essential to highlight the true effects of a ketogenic diet. Full article

The isolation of circulating tumoral DNA (ctDNA) present in the bloodstream brings about the opportunity to detect genomic aberrations from the tumor of origin. However, the low amounts of ctDNA present in liquid biopsy samples makes the development of highly sensitive techniques necessary to detect targetable mutations for the diagnosis, prognosis, and monitoring of cancer patients. Here, we employ standard genomic DNA (gDNA) and eight liquid biopsy samples from different cancer patients to examine the newly described CRISPR-Cas13a-based technology in the detection of the BRAF p.V600E actionable point mutation and appraise its diagnostic capacity with two PCR-based techniques: quantitative Real-Time PCR (qPCR) and droplet digital PCR (ddPCR). Regardless of its lower specificity compared to the qPCR and ddPCR techniques, the CRISPR-Cas13a-guided complex was able to detect inputs as low as 10 pM. Even though the PCR-based techniques have similar target limits of detection (LoDs), only the ddPCR achieved a 0.1% variant allele frequency (VAF) detection with elevated reproducibility, thus standing out as the most powerful and suitable tool for clinical diagnosis purposes. Our results also demonstrate how the CRISPR-Cas13a can detect low amounts of the target of interest, but its base-pair specificity failed in the detection of actionable point mutations at a low VAF; therefore, the ddPCR is still the most powerful and suitable technique for these purposes. Full article

This study focuses on the development and evaluation of an AI-based digital therapeutic prototype for adolescent mental health management and disaster response. The system integrates real-time monitoring, AI-driven conversation analysis, personalized psychological treatment programs, and multimodal data analysis. An algorithm was developed to detect gaslighting and verbal abuse using a BERT-based classification model, achieving 85% accuracy in gaslighting detection and 87% accuracy in verbal abuse detection. Additionally, a psychological disaster-recovery support module was included, which demonstrated a 30% improvement in users’ stress reduction rates during simulated disaster scenarios. This study demonstrates that digital therapeutic approaches can significantly contribute to early intervention in adolescent mental health issues. Additionally, these approaches provide effective support during disasters. The developed prototype demonstrates the potential of AI and digital technology to innovate mental health management and disaster response strategies. Full article

Achieving precise and swift monitoring of aquaculture ponds in coastal regions is essential for the scientific planning of spatial layouts in aquaculture zones and the advancement of ecological sustainability in coastal areas. However, because the distribution of many land types in coastal areas and the complex spectral features of remote sensing images are prone to the phenomenon of ‘same spectrum heterogeneous objects’, the current deep learning model is susceptible to background noise interference in the face of complex backgrounds, resulting in poor model generalization ability. Moreover, with the image features of aquaculture ponds of different scales, the model has limited multi-scale feature extraction ability, making it difficult to extract effective edge features. To address these issues, this work suggests a novel semantic segmentation model for aquaculture ponds called MPG-Net, which is based on an enhanced version of the U-Net model and primarily comprises two structures: MS and PGC. The MS structure integrates the Inception module and the Dilated residual module in order to enhance the model’s ability to extract the features of aquaculture ponds and effectively solve the problem of gradient disappearance in the training of the model; the PGC structure integrates the Global Context module and the Polarized Self-Attention in order to enhance the model’s ability to understand the contextual semantic information and reduce the interference of redundant information. Using Sentinel-2 and Planet images as data sources, the effectiveness of the refined method is confirmed through ablation experiments conducted on the two structures. The experimental comparison using the FCN8S, SegNet, U-Net, and DeepLabV3 classical semantic segmentation models shows that the MPG-Net model outperforms the other four models in all four precision evaluation indicators; the average values of precision, recall, IoU, and F1-Score of the two image datasets with different resolutions are 94.95%, 92.95%, 88.57%, and 93.94%, respectively. These values prove that the MPG-Net model has better robustness and generalization ability, which can reduce the interference of irrelevant information, effectively improve the extraction precision of individual aquaculture ponds, and significantly reduce the edge adhesion of aquaculture ponds in the extraction results, thereby offering new technical support for the automatic extraction of aquaculture ponds in coastal areas. Full article

Cardiovascular disease (CVD) represents the leading cause of death worldwide. For individuals at elevated risk for cardiovascular disease, early detection and monitoring of lipid status is imperative. The majority of lipid measurements conducted in hospital settings employ optical detection, which necessitates the use of relatively large-sized detection machines. It is, therefore, necessary to develop point-of-care testing (POCT) for lipoprotein in order to monitor CVD. To enhance the management and surveillance of CVD, this study sought to develop a POCT approach for apolipoprotein B (ApoB) utilizing a shear horizontal surface acoustic wave (SH-SAW) platform to assess the risk of heart disease. The platform employs a reflective SH-SAW sensor to reduce the sensor size and enhance the phase-shifted signals. In this study, the platform was utilized to monitor the impact of a weekly almond and oat milk or statins intervention on alterations in CVD risk. The SH-SAW ApoB test exhibited a linear range of 0 to 212 mg/dL, and a coefficient correlation (R) of 0.9912. Following a four-week intervention period, both the almond and oat milk intervention (−23.3%, p < 0.05) and statin treatment (−53.1%, p < 0.01) were observed to significantly reduce ApoB levels. These findings suggest that the SH-SAW POCT device may prove a valuable tool for monitoring CVD risk, particularly during routine daily or weekly follow-up visits. Full article

Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area. Full article

Controlling the ground settlement and building deformation triggered by shield tunnelling, particularly within water-rich strata, poses a significant engineering challenge. This study conducts a finite element (FE) analysis focusing on the ground settlement and deformation of adjacent structures (with a minimum distance of 2.6 m to the tunnel) due to earth pressure balance (EPB) shield tunnelling. The analysis incorporates the influence of groundwater through a 3D fluid–solid coupling model. This study assesses the effects of tunnelling on the behaviour of nearby buildings and introduces two mitigation strategies: the vertical partition method and the portal partition method. Their effectiveness is compared and evaluated. Our findings reveal that the deformation curves of the stratum and the building are influenced by the accumulation and dissipation of pore pressure. The vertical partition method reduced surface settlement by approximately 70%, while the portal partition method further minimized building deformation but required careful application to avoid issues like uplift. Both methods effectively mitigate the impacts of tunnel construction, with the portal partition method offering superior performance in terms of material use and cost efficiency. This research provides a scientific foundation and technical guidance for similar engineering endeavours, which is vital for ensuring the safety of metro tunnel construction and the stability of adjacent buildings. Full article

This study presents the design and implementation of a piezoelectric power harvesting device to capture vibrational energy from pipelines to self-powered IoT devices. The device utilizes key components along with the PPA-1001 piezoelectric sensor, the STM32F103C8T6 microcontroller, and LTC-3588 energy harvesting power supply. Experimental results verified the system’s performance in harvesting power within a specific frequency range of 10 Hz to 50 Hz, with the foremost overall performance at 30 Hz. The device generated the highest voltage of 3.3 V, delivering a power output of 2.18 mW, which is sufficient to power low-power electronic devices. The device maintained solid performance across a temperature range of 40 °C to 50 °C, underscoring its robustness in various environmental situations. The findings highlight the capacity of this form of generation to offer a sustainable power source for remote pipeline tracking, contributing to stronger protection and operational efficiency. Full article