Search Results (200)

Cancers are diseases described by the irregular spread of cells that have developed invasive features, enabling them to invade adjacent tissues. The specific diagnosis and effective management of oncological treatments depend on the timely detection of circulating tumor cells (CTCs) in a patient’s bloodstream. One of the most promising approaches to CTC separation from blood fractions involves the dielectrophoresis (DEP) technique. This research presents a new DEP-based bionic system designed for MDA-MB-231 breast cancer cell isolation from white blood cell (WBC) subtypes with a viable approach to cell viability. This work leverages the principle that every cell type possesses a unique dielectric fingerprint. This dielectrophoresis microfluidic device is designed to act as a scanner, reading these fingerprints to achieve a continuous, label-free separation of cancer cells from blood components with a high efficiency. In the proposed system that consists of three different stages, the first stage allows for separating B-lymphocytes and Monocytes from Granulocytes and MDA-MB-231 cells. The separation of B-lymphocytes from Monocytes occurs in the second step, while the last step concerns the separation of Granulocytes and MDA-MB-231 cells. In the analysis, x-y graphs of the electric potentials, velocity fields, pressure distributions, and cellular DEP forces applied to the cells, as well as the resulting particle paths, are provided. The model predicts that the system operates with a separation efficiency of nearly 92%. This work focuses on an investigation of the impact of electrode potentials, the velocity of cells, the number of electrodes, the width of the channel, and the output angles on enhancing the separation efficiency of particles. Full article

Background: Cancer staging, guided by anatomical and clinicopathologic factors, is essential for determining treatment strategies and patient prognosis. The current gold standard for prostate cancer is the American Joint Committee on Cancer (AJCC) Tumor, Lymph Node, and Metastasis (TNM) Staging System 9th Version (2024). This system incorporates five prognostic variables: tumor (T), spread to lymph nodes (N), metastasis (M), prostate-specific antigen (PSA) levels (P), and Grade Group/Gleason score (G). While effective, further refinement of prognostic systems may improve prediction of patient outcomes and support more individualized treatment. Methods: We applied the Ensemble Algorithm for Clustering Cancer Data (EACCD), an unsupervised machine learning approach. EACCD involves three steps: calculating initial dissimilarities, performing ensemble learning, and conducting hierarchical clustering. We first developed an EACCD model using the five AJCC variables (T, N, M, P, G). The model was then expanded to include two additional factors, age (A) and race (R). Prostate cancer patient data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program from the National Cancer Institute. Results: The EACCD algorithm effectively stratified patients into distinct prognostic groups, each with well-separated survival curves. The five-variable model achieved a concordance index (C-index) of 0.8293 (95% CI: 0.8245–0.8341), while the seven-variable model, including age and race, improved performance to 0.8504 (95% CI: 0.8461–0.8547). Both outperformed the AJCC TNM system, which had a C-index of 0.7676 (95% CI: 0.7622–0.7731). Conclusions: EACCD provides a refined prognostic framework for primary localized prostate cancer, demonstrating superior accuracy over the AJCC staging system. With further validation in independent cohorts, EACCD could enhance risk stratification and support precision oncology. Full article

End-stage kidney disease is preferably treated by kidney transplantation. The function of the allograft often determines kidney-controlled processes and requires long-term monitoring. Kidneys are organs with a very high metabolic rate, and, thus, a metabolomics approach is suitable to observe systemic metabolic changes that are related to graft adaptation. To understand these ongoing changes in post-transplant pediatric patients, we applied a targeted liquid chromatography/tandem mass spectrometry-based metabolomics approach. Time-dependent changes of 140 metabolites in plasma samples prospectively collected from 23 pediatric kidney graft recipients receiving tacrolimus-based immunosuppression were monitored over the first 4 years after transplantation and compared to levels prior to transplantation. Furthermore, by comparing the pre-transplant metabolite levels to those measured in healthy children, we were able to obtain insights into the pathways associated with kidney failure. Arginine biosynthesis, alanine, aspartate, glutamine, and glutamate metabolism, taurine and tryptophan metabolism were the most affected pathways that separate the pediatric patients with and without kidney failure. Accumulation of uremic toxins such as various tryptophan/kynurenine and tryptophan/indole metabolism pathway intermediates, and betaine and methionine cycle metabolites was evident in patients with restricted kidney function. Furthermore, reduced nicotinamide production, insufficient hydroxylation of phenylalanine to tyrosine, lowered cysteine, arginine, glutamine, taurine, and overall amino acid utilization, as well as diminished levels of protective antioxidants such as glutathione and vitamins B6 and C, were all the result of progressive kidney failure leading to transplantation. Importantly, following kidney transplantation and recovery of kidney function, the levels of most of the previously described metabolites normalized toward the levels observed in healthy participants. The here identified metabolic patterns could be used as markers to monitor the progression of pediatric chronic kidney disease patients towards kidney failure, and assuming their direct association with kidney function, they could serve as markers of successful graft adaptation. Full article

This article presents a comprehensive investigation of the dynamic coupling between a piezoelectric actuator (PZT) and its driving nonlinear stiffness mechanism (NSM) stage for precise positioning control. Particular emphasis is placed on the preload-induced effects on the force transmission and structural separation between the PZT and NSM. To ensure continuous mechanical contact between them, we propose a no-separation criterion based on acceleration matching, from which the minimum preload requirement is analytically derived. Additionally, static and dynamic simulations reveal that increasing the preload force from 0 N to 10 N can push the first natural frequency of the holistic system from $214.21$ Hz to $258.17$ Hz, respectively. This beneficially enhances the displacement consistency across different geometric configurations. Moreover, an appropriate preload force can prevent separation and increase system stiffness while reducing nonlinear deformation. Experimental results verifies that a preload of 10 N can prevent the separation between the PZT and NSM stage and maintain achievable output displacement of the stage within the range from $54.35\mathsf{\mu }$m to $129.42\mathsf{\mu }$m. This article offers the analytical results of preload setting to guarantee reliable actuation for nonlinear precision positioning stages. Full article

Increasing debt, natural disasters, and extreme weather events claim an ever-larger part of national budgets across the globe, undermining global stability and the capacity of our societies to transition to sustainability. The dominant crisis response policy paradigm treats the economy and the environment as separate domains and is based on a ‘fix-the-economy-first’ principle, i.e., fiscal consolidation and debt sustainability need to be achieved first before addressing other socio-environmental policy goals. This paper demonstrates that this approach entraps countries and the global economy in a vicious cycle. In the absence of an integrated policy framework for addressing these intersecting challenges, our responses to financial stress often exacerbate the environmental crisis and its consequences, adding further financial strain on an already fragile socio-environmental system. Breaking out from this conundrum requires a new crisis response policy paradigm. To this end, this study develops the Unsustainable Growth Vicious Cycle (UGVC) as an analytical framework that exemplifies the incentive structure that governs the dominant crisis response model, and the negative feedback loops that sustain it. Our analysis unfolds in four stages. We analyse how financial stress triggers multidimensional poverty traps and how these impact on the environment. We use the concept of poverty-environment trap 2.0 to capture the emergence of the environmental crisis as a global poverty and inequality trap in its own right. We explicate the limits of the dominant economic policy paradigm through the lens of unsustainable economic growth. We finally discuss the need of transforming ‘economic adjustment programmes’ into ‘sustainability adjustment programmes’, as part of a new global settlement for sustainability transition. Full article

Background and Objectives: Systemic inflammatory markers from an ordinary complete blood count (CBC) may foreshadow where non-small-cell lung cancer (NSCLC) will first spread, but organ-specific signatures remain poorly defined. Materials and Methods: We retrospectively reviewed 302 adults (mean age 60.7 ± 13.4 years; 80.8% men) with stage IV NSCLC managed at OncoHelp Medical Center, Timișoara, between January 2022 and December 2024. Eligibility demanded a single radiologically confirmed distant site at diagnosis and pre-treatment CBC. Neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), and lymphocyte-to-monocyte (LMR) ratios were compared across pleural (n = 52), bone (n = 86), liver (n = 66), and brain (n = 98) metastases using Kruskal–Wallis tests with Bonferroni adjustment; z-standardized logistic models identified independent predictors. Results: Metastases clustered most often in brain (32.5%), followed by bone (28.5%), liver (21.9%), and pleura (17.2%). Median PLR rose selectively in pleural disease (274 vs. 217–253 in other sites; p = 0.006). LMR fell to 2.0 in bone but climbed to 2.8 in brain lesions (p = 0.032 and 0.008, respectively). NLR was globally elevated (6.7–7.6), yet differed significantly only for bone and liver deposits. Logistic modeling showed that each standard-deviation rise in absolute neutrophil count quadrupled the odds of hepatic involvement (Odd Ratio (OR) 4.26; 99% Confidence inerval (CI) 2.20–6.25), monocytosis nearly doubled bone risk (OR 1.83; 1.01–3.33), while higher erythrocytes, eosinophils, and lymphocytes independently protected against pleural seeding (all p < 0.01). Age-stratified analysis revealed that osseous and cerebral metastases predominated in patients ≤ 50 years, whereas inflammatory indices were age-invariant. Conclusions: Routine CBC ratios encode distinct “inflammatory fingerprints” that mirror the first metastatic destination in NSCLC: platelets herald pleural spread, neutrophils favor liver and bone, and divergent lymphocyte–monocyte balances separate bone from brain. Although no substitute for cross-sectional imaging, these low-cost markers could refine clinical suspicion, guide targeted work-up, and illuminate the biology of organ-selective dissemination, particularly in resource-limited settings. Full article

A two-level model-based control system for energy-optimal operation of a two-stage reverse osmosis (RO) membrane desalination system was developed and field demonstrated. The control scheme was based on the specific energy consumption (SEC) framework accounting for pump efficiencies, physical system constraints, and temporal variability of feed salinity. The SEC framework consisted of a higher-level (supervisory) control system that guided a lower-level controller for real-time SEC optimization. The supervisory controller combined real-time plant data and the SEC model to determine the energy-optimal first-stage water recovery and the overall permeate water recovery (unless specified), and membrane permeability for a target permeate production. The derived operating state was then applied to control the RO plant operation through the lower-level control system, consisting of three separate feedback loops regulating the RO feed flow rate, first-stage RO pressure, and the second-stage RO pressure via control of the first-stage and second-stage RO feed pumps, and the RO concentrate valve. The two-level control system was demonstrated for a mobile brackish water desalination plant capable of permeate productivity up to 98 m³/day. Field testing demonstrated robust simultaneous control of the dynamically coupled control variables and effective energy-optimal operation. Full article

Accurate and uncertainty-aware wind power forecasting is essential for reliable and cost-effective power system operations. This paper presents a novel probabilistic forecasting framework based on diffusion probabilistic models. We adopted a two-stage modeling strategy—a deterministic predictor first generates baseline forecasts, and a conditional diffusion model then learns the distribution of residual errors. Such a two-stage decoupling strategy improves learning efficiency and sharpens uncertainty estimation. We employed the elucidated diffusion model (EDM) to enable flexible noise control and enhance calibration, stability, and expressiveness. For the generative backbone, we introduced a time-series-specific diffusion Transformer (TimeDiT) that incorporates modular conditioning to separately fuse numerical weather prediction (NWP) inputs, noise, and temporal features. The proposed method was evaluated using the public database from ten wind farms in the Global Energy Forecasting Competition 2014 (GEFCom2014). We further compared our approach with two popular baseline models, i.e., a distribution parameter regression model and a generative adversarial network (GAN)-based model. Results showed that our method consistently achieves superior performance in both deterministic metrics and probabilistic accuracy, offering better forecast calibration and sharper distributions. Full article

Background: Automated surgical navigation can be separated into three stages: (1) organ identification and localization, (2) identification of the organs requiring further surgery, and (3) automated planning of the operation path and steps. With its ideal visual and operating system, the da Vinci surgical system provides a promising platform for automated surgical navigation. This study focuses on the first step in automated surgical navigation by identifying organs in gynecological surgery. Methods: Due to the difficulty of collecting da Vinci gynecological endoscopy data, we propose DeepVinci, a novel end-to-end high-performance encoder–decoder network based on convolutional neural networks (CNNs) for pixel-level organ semantic segmentation. Specifically, to overcome the drawback of a limited field of view, we incorporate a densely multi-scale pyramid module and feature fusion module, which can also enhance the global context information. In addition, the system integrates an edge supervision network to refine the segmented results on the decoding side. Results: Experimental results show that DeepVinci can achieve state-of-the-art accuracy, obtaining dice similarity coefficient and mean pixel accuracy values of 0.684 and 0.700, respectively. Conclusions: The proposed DeepVinci network presents a practical and competitive semantic segmentation solution for da Vinci gynecological surgery. Full article

A novel modified oyster shell (MOS-800) was developed to enhance phosphorus sequestration and recovery from wastewater. Approximately 33.3% of phosphate was eliminated by the MOS-800, which also exhibited excellent pH regulation capabilities. In semicontinuous tests, a synergistic phosphorus separation was achieved through the coupling process of CaCl₂/MOS-800 and a circulating fluidized bed (CFB), resulting in an 86.5% phosphate separation. In continuous flow experiments, phosphorus elimination reached 98.2%. Material characterization revealed that hydroxyapatite (HAP) was the primary component of the crystallized products. Additionally, MOS-800 released 506.5–572.2 mg/g Ca²⁺ and 98.1 mg/g OH⁻. A four-stage heterogeneous crystallization mechanism was proposed for the coupling process. In the first stage, Ca²⁺ quickly reacted with phosphate to form Ca-P ion clusters, etc. In the second stage, these clusters packed randomly to form spherical amorphous calcium phosphate (ACP). In the third stage, the ACP spheres were transformed and rearranged into sheet-like HAP crystallites, Finally, in the fourth stage, the HAP crystallites aggregated on the surface of crystal seeds, also with the addition of crystal seeds and undissolved MOS-800, potentially catalyzing the heterogeneous crystallization. These findings suggest that the CaCl₂/MOS-800/CFB system is a promising technique for phosphate recovery from wastewater. Full article

Several pressing issues have been revealed by deep learning-based tomato ripening detection technology in intricate environmental applications: The ripening transition stage distinction is not accurate enough, small target tomato detection is likely to miss, and the detection technology is more susceptible to variations in light. Based on the YOLOv11 model, a YOLOv11-SLBA tomato ripeness detection model was presented in this study. First, SPPF-LSKA is used in place of SPPF in the backbone section, greatly improving the model’s feature discrimination performance in challenging scenarios including dense occlusion and uneven illumination. Second, a new BiAttFPN hierarchical progressive fusion is added in the neck area to increase the feature retention of small targets during occlusion. Lastly, the feature separability of comparable categories is significantly enhanced by the addition of the auxiliary detection head DetectAux. In this study, comparative experiments are carried out to confirm the model performance. Under identical settings, the YOLOv11-SLBA model is compared to other target detection networks, including Faster R-CNN, SSD, RT-DETR, YOLOv7, YOLOv8, and YOLOv11. With 2.7 million parameters and 10.9 MB of model memory, the YOLOv11-SLBA model achieves 92% P, 83.5% R, 91.3% mAP50, 64.6% mAP50-95, and 87.5% F1-score. This is a 3.4% improvement in accuracy, a 1.5% improvement in average precision, and a 1.6% improvement in F1-score when compared to the baseline model YOLOv11. It outperformed the other comparison models in every indication and saw a 1.6% improvement in score. Furthermore, the tomato-ripeness1public dataset was used to test the YOLOv11-SLBA model, yielding model p values of 78.6%, R values of 91.5%, mAP50 values of 93.7%, and F1-scores of 84.6%. This demonstrates that the model can perform well across a variety of datasets, greatly enhances the detection generalization capability in intricate settings, and serves as a guide for the algorithm design of the picking robot vision system. Full article

This research presents an advanced methodology for smart management of energy losses in electrical distribution networks by leveraging deep neural network architectures. The primary objective is to enhance the accuracy of short-term forecasting for nodal loads and corresponding energy losses, enabling more efficient and intelligent grid operation. Two predictive approaches were explored: the first involves separate forecasting of nodal loads followed by loss calculations, while the second directly estimates network-wide energy losses. For model implementation, Long Short-Term Memory (LSTM) networks and the enhanced Residual Network (eResNet) architecture, developed at the Institute of Electrodynamics of the National Academy of Sciences of Ukraine, were utilized. The models were validated using retrospective data from a Ukrainian Distribution System Operator (DSO) covering the period from 2017 to 2019 with 30 min sampling intervals. An adapted CIGRE benchmark medium-voltage network was employed to simulate real-world conditions. Given the presence of anomalies and missing values in the operational data, a two-stage preprocessing algorithm incorporating DBSCAN clustering was applied for data cleansing and imputation. The results indicate a Mean Absolute Percentage Error (MAPE) of just 3.29% for nodal load forecasts, which significantly outperforms conventional methods. These findings affirm the feasibility of integrating such models into Smart Grid infrastructures to improve decision-making, minimize operational losses, and reduce the costs associated with energy loss compensation. This study provides a practical framework for data-driven energy loss management, emphasizing the growing role of artificial intelligence in modern power systems. Full article

Since the beginning of the 21st century, the supercritical carbon dioxide (sCO₂) Brayton cycle has emerged as a hot topic of research in the energy field. Among its key components, the sCO₂ compressor has received significant attention. In particular, axial-flow sCO₂ compressors are increasingly being investigated as power systems advance toward high power scaling. This paper reviews global research progress in this field. As for performance characteristics, currently, sCO₂ axial-flow compressors are mostly designed with large mass flow rates (>100 kg/s), near-critical inlet conditions, multistage configurations with relatively low stage pressure ratios (1.1–1.2), and high isentropic efficiencies (87–93%). As for internal flow characteristics, although similarity laws remain applicable to sCO₂ turbomachinery, the flow dynamics are strongly influenced by abrupt variations in thermophysical properties (e.g., viscosities, sound speeds, and isentropic exponents). High Reynolds numbers reduce frictional losses and enhance flow stability against separation but increase sensitivity to wall roughness. The locally reduced sound speed may induce shock waves and choke, while drastic variation in the isentropic exponent makes the multistage matching difficult and disperses normalized performance curves. Additionally, the quantitative impact of a near-critical phase change remains insufficiently understood. As for the experimental investigation, so far, it has been publicly shown that only the University of Notre Dame has conducted an axial-flow compressor experimental test, for the first stage of a 10 MW sCO₂ multistage axial-flow compressor. Although the measured efficiency is higher than that of all known sCO₂ centrifugal compressors, the inlet conditions evidently deviate from the critical point, limiting the applicability of the results to sCO₂ power cycles. As for design and optimization, conventional design methodologies for axial-flow compressors require adaptations to incorporate real-gas property correction models, re-evaluations of maximum diffusion (e.g., the DF parameter) for sCO₂ applications, and the intensification of structural constraints due to the high pressure and density of sCO₂. In conclusion, further research should focus on two aspects. The first is to carry out more fundamental cascade experiments and numerical simulations to reveal the complex mechanisms for the near-critical, transonic, and two-phase flow within the sCO₂ axial-flow compressor. The second is to develop loss models and design a space suitable for sCO₂ multistage axial-flow compressors, thus improving the design tools for high-efficiency and wide-margin sCO₂ axial-flow compressors. Full article

To calyx persistence in Korla fragrant pear (Pyrus sinkiangensis) significantly impacts fruit marketability, with persistent calyx causing up to 40% reduction in premium-grade fruit yield. Investigating the hormonal mechanisms underlying calyx abscission and persistent in Korla Fragrant Pear, we performed comprehensive phytohormone profiling using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS; EXIONLC system coupled with SCIEX 6500 QTRAP+). Flowers from first-position (persistent-calyx) and fourth-position (deciduous-calyx) inflorescences were collected at six developmental stages (0–10 days after flowering). Fourteen endogenous hormones—ACC, ME-IAA, IPA, TZR, SA, IAA, ICA, IP, tZ, DHJA, ABA, JA-ile, cZ, and JA—were identified in the calyx during the flowering stage. The calyx abscission rate was significantly higher in the fourth position (79%) compared to the first position (32%). ACC and ABA are closely linked to abscission, with increased ACC at 0 DAF signaling early abscission and ABA accumulation accelerating late abscission at 8 DAF. Auxin exhibited spatiotemporal specificity, peaking in first-order flowers at 4–6 DAF, potentially inhibiting abscission by maintaining cell activity. Cytokinins generally decreased, while jasmonates significantly increased during the fourth-position anthesis stage 8–10 DAF, suggesting a role in stress-related senescence. By systematic analysis of the flowers at the first order (persistent calyx) and the fourth order (deciduous calyx) from 0 to 10 days after anthesis, we found three key stages of hormone regulation: early prediction stage (0–2 DAF), ACC accumulation at the fourth order was significantly higher than that at the first order at 0 days after anthesis, ACC accumulation at the early stage predicted abscission; During the middle maintenance stage (4–6 DAF), the accumulation of cytokinin decreased significantly, while the accumulation of IAA increased significantly in the first position (persistent calyx); Execution Phase (8–10 DAF), ABA reached its peak at 8 DAF, coinciding with the final separation time. JA played an important role in the late stage. Gibberellin was undetected, implying a weak association with calyx abscission. Venn diagram identified N6-(delta 2-Isopentenyl)-adenine (IP) in first-position flowers, which may influence calyx persistence or abscission. These findings elucidate hormone interactions in calyx abscission, offering a theoretical basis for optimizing exogenous hormone application to enhance fruit quality. Full article

The integration of in vitro biological neural networks (BNNs) with robotic systems to explore their information processing and adaptive learning in practical tasks has gained significant attention in the fields of neuroscience and robotics. However, existing BNN-based robotic systems cannot perceive the visual environment due to the inefficiency of sensory information encoding methods. In this study, we propose a biomimetic visual information spatiotemporal encoding method based on improved delayed phase encoding. This method transforms high-dimensional images into a series of pulse sequences through convolution, temporal delay, alignment, and compression for BNN stimuli. We conduct three stages of unsupervised training on in vitro BNNs using high-density microelectrode arrays (HD-MEAs) to validate the potential of the proposed encoding method for image recognition tasks. The neural activity is decoded via a logistic regression model. The experimental results show that the firing patterns of BNNs with different spatiotemporal stimuli are highly separable in the feature space. After the third training stage, the image recognition accuracy reaches 80.33% ± 7.94%, which is 13.64% higher than that of the first training stage. Meanwhile, the BNNs exhibit significant increases in the connection number, connection strength, and inter-module participation coefficient after unsupervised training. These results demonstrate that the proposed method significantly enhances the functional connectivity and cross-module information exchange in BNNs. Full article