Search Results (103)

This research addresses the increasing demand for low-cost GNSS solutions in natural resources management and geodesy by comparing a dual-frequency RTK receiver and a single-frequency autonomous receiver under identical conditions. The novelty lies in the simultaneous testing of u-blox ZED-F9P and u-blox MAX-M10S receivers connected to a common antenna, eliminating different signal reception effects. The study also evaluates the horizontal accuracy and area determination accuracy and the influence of seasonal foliage. Experiments were conducted on three polygons with varying vegetation canopies during leaf-on and leaf-off periods. The ZED-F9P receiver demonstrated high accuracy and stability when using RTK corrections. Under canopy conditions, the average horizontal errors were 0.17–0.18 m during leaf-on and improved by 58% to approximately 0.07 m during leaf-off season. The average area determination errors remained below 2%, confirming its suitability for precise mapping. In contrast, the MAX-M10S receiver showed substantial variability under vegetation. Its average horizontal errors reached 1.5–3.0 m during leaf-on season, with the maximum errors exceeding 5 m. Its seasonal improvement ranged from 41 to 54%, while its area errors reached up to 14.7%. The study confirms that while vegetation cover and seasonal foliage are limiting factors for both types of devices, low-cost RTK receivers represent a viable alternative to expensive professional instruments, even in more challenging conditions. Full article

This paper presents a comprehensive and up-to-date description of a mature software framework for fuzzy relational calculus, developed and extended over more than a decade. The contribution of the paper lies in the unified presentation of theoretical foundations, solution algorithms, and their software implementation, which have not previously been documented in a single coherent form. The presented MATLAB software package is designed to model and solve a broad class of problems in fuzzy relational calculus, including inverse problems for fuzzy linear systems of equations and inequalities, behavior analysis, reduction and minimization of finite fuzzy machines, and optimization of linear objective functions under fuzzy linear system constraints. The implemented algorithms can be applied in areas such as data and software security, modeling and verification of access control policies, anomaly detection, and diagnostics. The software supports a variety of fuzzy algebras, including max–min, min–max, max–product, Goguen, Gödel, and Łukasiewicz algebras, providing tools for both direct and inverse problem resolution. The implementation is based on well-established theoretical results in fuzzy relational calculus, ensuring a robust foundation for exact and efficient computation. Several illustrative examples are provided to demonstrate the applicability of the software in different problem settings. Full article

Creating a digital 3D model of museum artefacts has been a common practice for many years. Such models can be used for archiving, research, and marketing purposes, as well as to counteract various types of exclusion. A digital copy created using professional 3D scanners using 3D structured-light scanning (3D SLS) or terrestrial laser scanning technology requires expensive equipment, specialised software for postprocessing, and a trained team. The introduction of mobile phones with Light Detection and Ranging (LiDAR) sensors and the development of appropriate open-access software have enabled the use of phones to generate digital 3D models. This study compares the quality of 3D models created with 3D SLS and mobile LiDAR technologies using three identical small museum artefacts from the Silk Road area of the Samarkand State University museum in Uzbekistan. They were digitised in 2017 and 2025. The results indicate that digital 3D models generated with an iPhone 16 PRO MAX device using Scaniverse LiDAR software are incomplete and thus less versatile. Therefore, they cannot serve as archival models. Their accuracy and quality (mesh density, size, and texture quality), as well as the speed of generating 3D models, make them ideal for marketing purposes and digital tourism. Full article

Brain cancer classification using deep learning has gained significant attention due to its potential to improve early diagnosis and treatment planning. In this work, we propose a reconfigurable and hardware-optimized CNN-2D architecture implemented on FPGA for multiclass classification of brain tumors from MRI images. The contribution of this study lies in the development of a lightweight CNN model and a modular hardware design, where three key IP coresConv2D, MaxPooling, and ReLUare architected with parameterizable kernels, efficient dataflow, and optimized memory reuse to support real-time processing on resource-constrained platforms. These IPs are iteratively reconfigured to process each CNN layer, enabling flexibility while maintaining low latency. To evaluate the proposed architecture, we first implement the model in software on a Dual-Core Cortex-A9 processor and then deploy the hardware-accelerated version on an XC7Z020 FPGA. Performance is assessed in terms of execution time, power consumption, and classification accuracy. The FPGA implementation achieves a 93.21% reduction in latency and a 67.5% reduction in power consumption, while maintaining a competitive accuracy of 96.09% compared with 98.43% for the software version. These results demonstrate that the proposed reconfigurable FPGA-based architecture offers a strong balance between accuracy, real-time performance, and energy efficiency, making it highly suitable for embedded brain tumor classification systems. Full article

Our study aimed to examine and compare the effects of 12-week repeated sprint intervals with change of direction and linear sprint intervals on physical performance in young soccer players. In this randomized, parallel three-group study, we included 60 male soccer players assigned to (i) a sprint interval with change of direction group (RS–CoD; n = 20); (ii) a linear sprint interval group (RS–LiN; n = 20); and (iii) a soccer group (SOC; n = 20). Physical performance included explosive power (countermovement jump [CMJ] and squat jump [SJ]), agility (T505, 93,639, 20Y), speed (sprints over 5 m, 10 m and 20 m), anaerobic capacity (the Running-Based Anaerobic Sprint Test [RAST]) and maximal oxygen uptake (VO2max). Over the 12 weeks, the RS–CoD group displayed significantly beneficial effects in the 93639 test (effect size [ES] = 0.42), compared to the RS–LiN (ES = 0.18) and SOC (ES = 0.12) groups. The RS–CoD group also had larger improvements in their SJ (ES = 0.87; RS–LiN 0.37; SOC 0.18), CMJ (ES = 0.56; RS–LiN 0.39; SOC 0.43), 20Y test (ES = 1.05; RS–LiN 0.67; SOC 0.56) and sprints at 5 m (ES = 1.18; RS–LiN 0.50; SOC 0.21) and 20 m (ES = 1.43; RS–LiN 0.71; SOC 0.25). The RS–CoD group displayed more beneficial improvements, making the CoD interval sprints effective training stimuli. Full article

Traditional robust model predictive control (RMPC) strategies often face challenges of excessive conservatism and limited dynamic performance, which can hinder their effectiveness in trajectory tracking applications. To overcome these issues, this paper proposes a novel RMPC strategy based on crisscross optimization (CSO). The core innovation lies in a composite control framework that jointly designs a nominal controller and an additional optimized term. The nominal controller, derived from a min-max optimization problem, guarantees the closed-loop stability of the system. Building upon this stable foundation, the CSO algorithm is innovatively employed to search for a more effective control input within the feasible region, thereby actively enhancing the transient performance. The proposed method is validated through two trajectory tracking simulation cases on an angular positioning system in comparison with conventional RMPC. Results demonstrate that the new strategy not only maintains system stability but also significantly reduces the dynamic response time and improves overall control performance, confirming its superiority in mitigating conservatism while achieving better tracking responsiveness. Full article

Excavation of ultra-shallow pilot tunnels triggers surface settlement and endangers surrounding pipelines. The discontinuous settlement curve from traditional stochastic medium theory cannot be directly integrated into the foundation beam model, limiting pipeline deformation prediction accuracy. The key novelty of this study lies in proposing an improved coupled method tailored to ultra-shallow burial conditions: converting the discontinuous settlement solution into a continuous analytical one via polynomial fitting, embedding it into the Winkler elastic foundation beam model, and realizing pipeline settlement prediction by solving the deflection curve differential equation with the initial parameter method and boundary conditions. Four core factors affecting pipeline deformation are identified, with pilot tunnel size as the key. Shallower depth (especially 5.5 m) intensifies stratum disturbance; pipeline parameters (diameter, wall thickness, elastic modulus) significantly impact bending moment, while stratum elastic modulus has little effect on settlement. Verified by the Xueyuannanlu Station project of Beijing Rail Transit Line 13, theoretical and measured settlement trends are highly consistent, with core indicators meeting safety requirements (max theoretical/measured settlement: −10.9 mm/−8.6 mm < 30 mm; max rotation angle: −0.066° < 0.340°). Errors (max 5.1 mm) concentrate at the pipeline edge, and conservative theoretical values satisfy engineering safety evaluation demands. Full article

Loop closure detection is essential for improving the long-term consistency and robustness of simultaneous localization and mapping (SLAM) systems. Existing LiDAR-based loop closure approaches often rely on limited or partial geometric features, restricting their performance in complex environments. To address these limitations, this paper introduces a Density Triangle Descriptor (DTD). The proposed method first extracts keypoints from density images generated from LiDAR point clouds, and then constructs a triangle-based global descriptor that is invariant to rotation and translation, enabling robust structural representation. Furthermore, to enhance local discriminative ability, the neighborhood around each keypoint is modeled as a Gaussian distribution, and a local descriptor is derived from the entropy of its probability distribution. During loop closure detection, candidate matches are first retrieved via hash indexing of triangle edge lengths, followed by entropy-based local verification, and are finally refined by singular value decomposition for accurate pose estimation. Extensive experiments on multiple public datasets demonstrate that compared to STD, the proposed DTD improves the average F1 max score and EP by 18.30% and 20.08%, respectively, while achieving a 50.57% improvement in computational efficiency. Moreover, DTD generalizes well to solid-state LiDAR with non-repetitive scanning patterns, validating its robustness and applicability in complex environments. Full article

Accurate and efficient forest inventory methods are crucial for monitoring forest ecosystems, assessing carbon stocks, and supporting sustainable forest management. Traditional field-based techniques, which rely on manual measurements such as diameter at breast height (DBH) and tree height (TH), remain labour-intensive and time-consuming. In this study, we introduce and validate a fully open-source, low-cost terrestrial laser scanning system (LCA-TLS) built from commercially available components and based on the Livox Avia sensor. With a total cost of €2050, the system responds to recent technological developments that have significantly reduced hardware expenses while retaining high data quality. This trend has created new opportunities for broadening access to high-resolution 3D data in ecological research. The performance of the LCA-TLS was assessed under controlled and field conditions and benchmarked against three reference devices: the RIEGL VZ-1000 terrestrial laser scanner, the Stonex X120GO handheld mobile laser scanner, and the iPhone 15 Pro Max structured-light device. The LCA-TLS achieved high accuracy for estimating DBH (RMSE: 1.50 cm) and TH (RMSE: 0.99 m), outperforming the iPhone and yielding results statistically comparable to the Stonex X120GO (DBH RMSE: 1.32 cm; p > 0.05), despite the latter being roughly ten times more expensive. While the RIEGL system produced the most accurate measurements, its cost exceeded that of the LCA-TLS by a factor of about 30. The hardware design, control software, and processing workflow of the LCA-TLS are fully open-source, allowing users worldwide to build, modify, and apply the system with minimal resources. The proposed solution thus represents a practical, cost-effective, and accessible alternative for 3D forest inventory and LiDAR-based ecosystem monitoring. Full article

Background/Objectives: Sepsis-associated liver injury (SALI) is a critical and often early complication of sepsis, defined by distinct hyper-inflammatory and immunosuppressive phases that shape patient phenotypes. Methods: Characterizing these phases establishes a foundation for immunomodulation strategies tailored to individual immune responses, as discussed subsequently. Results: The initial inflammatory response activates pathways such as NF-κB and the NLRP3 inflammasome, leading to a cytokine storm that damages hepatocytes and is frequently associated with higher SOFA scores and a higher risk of 28-day mortality. Kupffer cells and infiltrating neutrophils exacerbate hepatic injury by releasing proinflammatory cytokines and reactive oxygen species, thereby causing cellular damage and prolonging ICU stays. During the subsequent immunosuppressive phase, impaired infection control and tissue repair can result in recurrent hospital-acquired infections and a poorer prognosis. Concurrently, hepatocytes undergo significant metabolic disturbances, notably impaired fatty acid oxidation due to downregulation of transcription factors such as PPARα and HNF4α. This metabolic alteration corresponds with worsening liver function tests, which may reflect the severity of liver failure in clinical practice. Mitochondrial dysfunction, driven by oxidative stress and defective autophagic quality control, impairs cellular energy production and induces hepatocyte death, which is closely linked to declining liver function and increased mortality. The gut-liver axis plays a central role in SALI pathogenesis, as sepsis-induced gut dysbiosis and increased intestinal permeability allow bacterial products, including lipopolysaccharides, to enter the portal circulation and further inflame the liver. This process is associated with sepsis-related liver failure and greater reliance on vasopressor support. Protective microbial metabolites, such as indole-3-propionic acid (IPA), decrease significantly during sepsis, removing key anti-inflammatory signals and potentially prolonging recovery. Clinically, SALI most commonly presents as septic cholestasis with elevated bilirubin and mild transaminase changes, although conventional liver function tests are insufficiently sensitive for early detection. Novel biomarkers, including protein panels and non-coding RNAs, as well as dynamic liver function tests such as LiMAx (currently in phase II diagnostics) and ICG-PDR, offer promise for improved diagnosis and prognostication. Specifying the developmental stage of these biomarkers, such as identifying LiMAx as phase II, informs investment priorities and translational readiness. Current management is primarily supportive, emphasizing infection control and organ support. Investigational therapies include immunomodulation tailored to immune phenotypes, metabolic and mitochondrial-targeted agents such as pemafibrate and dichloroacetate, and interventions to restore gut microbiota balance, including probiotics and fecal microbiota transplantation. However, translational challenges remain due to limitations of animal models and patient heterogeneity. Conclusion: Future research should focus on developing representative models, validating biomarkers, and conducting clinical trials to enable personalized therapies that modulate inflammation, restore metabolism, and repair the gut-liver axis, with the goal of improving outcomes in SALI. Full article

Background/Objectives: The ¹³C-Methacetin Breath Test (MBT) is a non-invasive tool to evaluate hepatic microsomal function via exhaled ¹³CO₂, reflecting cytochrome P450 1A2 (CYP1A2)-mediated metabolism. Despite decades of evidence demonstrating its utility in diagnosing cirrhosis, stratifying liver disease severity, and predicting outcomes, MBT adoption remains limited due to methodological inconsistencies and variable diagnostic thresholds. This review aimed to summarize MBT data in adults and assess its diagnostic and prognostic performance. Methods: A literature review was conducted using PubMed, Web of Science, and Scopus. Eligible studies included those applying oral or intravenous methacetin with defined reference values or diagnostic cutoffs. Outcomes of interest were percent dose recovery (PDR), cumulative PDR (cPDR), and LiMAx^® values. Due to heterogeneity in protocols, units, and endpoints, results were synthesized narratively. Results: Healthy individuals typically demonstrated rapid metabolism (e.g., cPDR30 10–15%), whereas cirrhotic patients showed significantly reduced values (e.g., cPDR30 ≈ 1%). Diagnostic cutoffs varied widely (<0.35% to <8%), reflecting methodological and population differences. MBT reliably identified advanced liver disease but showed inconsistent sensitivity for early-stage fibrosis and metabolic dysfunction-associated steatotic liver disease. Variability in dosing, timing, measurement duration, and analytic technique limited cross-study comparability. Conclusions: MBT is a validated, dynamic marker of liver function with both diagnostic and prognostic relevance. However, inconsistent protocols and thresholds hinder its clinical implementation. Standardization of MBT procedures, reference ranges, and reporting metrics is essential. A harmonized protocol (“MBT-60”), supported by multicenter validation, demographic stratification, and direct comparison with structural and serologic liver tests, is recommended to facilitate MBT integration into routine hepatology practice. Full article

In graph theory and network design, the minimum cut is a fundamental measure of system connectivity and communication capacity. While prior research has largely focused on computing the minimum cut for a fixed source–sink pair, practical scenarios such as data center communication often demand a different objective: identifying the source node whose minimum cut to a designated sink is maximized. This task, which we term the Global Maximum Minimum Cut with Fixed Sink (GMMC-FS) problem, captures the goal of locating a high-capacity source relative to a shared sink node that aggregates multiple servers. The problem is of significant engineering importance, yet it is computationally challenging as it involves a nested max–min optimization. In this paper, we present a recursive reduction (RR) algorithm for solving the GMMC-FS problem. The key idea is to iteratively select pivot nodes, compute their minimum cuts with respect to the sink, and prune dominated candidates whose cut values cannot exceed that of the pivot. By recursively applying this elimination process, RR dramatically reduces the number of max-flow computations required while preserving exact correctness. Compared with classical contraction-based and Gomory–Hu tree approaches that rely on global cut enumeration, the proposed RR framework offers a more direct and scalable mechanism for identifying the source that maximizes the minimum cut to a fixed sink. Its novelty lies in exploiting the structural properties of the sink side of suboptimal cuts, which leads to both theoretical efficiency and empirical robustness across large-scale networks. We provide a rigorous theoretical analysis establishing both correctness and complexity bounds, and we validate the approach through extensive experiments. Results demonstrate that RR consistently achieves optimal solutions while significantly outperforming baseline methods in runtime, particularly on large and dense networks. Full article

This study used density functional theory (DFT)-based first-principles calculations to investigate the desolvation effect of single-walled carbon nanotubes (SWCNTs) modified with hydroxyl (-OH), carbonyl (-C=O), and carboxyl (-COOH) groups. SWCNTs have great potential as supercapacitor electrode materials due to their unique structural and electronic properties, but their practical application is limited by poor solvation-induced dispersibility and low ion transport efficiency. To solve this, this study constructed functionalized SWCNT models, simulated their interaction with lithium ion (Li⁺) complexes in acetonitrile (AN) solvent, and analyzed Li⁺ desolvation behavior, relative capacitance, and post-desolvation density of states (DOSs). The key research results are as follows: [Li(AN)]⁺ complete desolvation sizes differed: 5.91 Å (pristine SWCNTs), 6.26 Å (hydroxylated SWCNTs, HCNT), 6.11 Å (carbonylated SWCNTs, CNCNT; carboxylated SWCNTs, CXCNT). HCNT showed the largest relative capacitance enhancement (max 1.4× pristine), while CNCNT and CXCNT both had a max 1.3× improvement. Post-desolvation DOS analysis revealed distinct electronic property changes: HCNT-Li⁺ enhanced metallicity and conductivity; CNCNT-Li⁺ increased metallicity but reduced conductivity; and CXCNT-Li⁺ decreased metallicity with nearly unchanged conductivity. This study provides an atomic-scale theoretical basis for optimizing the properties of SWCNT solutions, supporting their application in high-performance supercapacitors, particularly in enhancing device energy density and cycle stability. Full article

Lithium–sulfur (Li-S) batteries are promising energy storage solutions due to their high density and cost-effectiveness. However, the risk of thermal failure limits their widespread use. Understanding thermal failure characteristics and developing accurate prediction methods are crucial for ensuring battery safety and reliability. This study aims to analyze the thermal failure characteristics of Li-S batteries and offer machine learning-based prediction methods for the early detection of potential thermal failures. The research begins with collecting temperature data from sensors deployed over numerous planes of a Li-S battery module under varied operating conditions. The data are created using proven numerical models that simulate various failure conditions. To improve model stability and learning efficiency, temperature data are preprocessed using min–max normalization to scale them to a consistent range. We suggest using a machine learning algorithm, such as the Energy Valley Optimizer Muted Multilayer Perceptrons with Mutual Information (EneVO-MPMI) algorithm. These models are trained on temperature data which are combined with Multilayer Perceptrons (MPs) to capture complicated, nonlinear correlations in thermal failure predictions, whereas the Energy Valley Optimizer (EneVO) optimizes the model’s structure and hyperparameters to avoid overfitting. Mutual Information (MI) assists in the selection of relevant features, resulting in accurate prediction from sensor data. To assess the models’ generalizability, five-fold cross-validation is used and achieves an average F1-score of 97.2%, a recall of 97.6%, an accuracy of 97.3%, and a precision of 96.9%. The EneVO-MPMI method emerges as the most effective, delivering a higher accuracy in forecasting thermal failure while requiring less training and prediction time. It shows that the EneVO-MPMI method is the most accurate and efficient at forecasting thermal breakdown in Li-S batteries. The technique can be used to identify Li-S battery defects early on, reducing the possibility of thermal instability and improving battery safety in a variety of applications. Full article

Multifunctional phosphors that integrate optical temperature measurement and counterfeit detection capabilities have garnered considerable interest owing to their diverse application potential. In this study, novel CaLaLiTeO₆:Mn⁴⁺/Er³⁺ phosphors were prepared via the high-temperature solid-phase method. The phase structure and morphology characterization confirmed the successful synthesis of CaLaLiTeO₆ material with effective doping of Mn⁴⁺ and Er³⁺ into the host lattice. Upon excitation at 379 nm, the CaLaLiTeO₆:Mn⁴⁺/Er³⁺ material exhibits far-red emission at 716 nm (Mn⁴⁺:²E_g → ⁴A_2g) and green emission at 525/548 nm (Er³⁺:²H_11/2/⁴S_3/2 → ⁴I_15/2). The emission peak intensities of Er³⁺ and Mn⁴⁺ ions in the CaLaLiTeO₆:0.015Mn⁴⁺/0.01Er³⁺ sample displayed distinct variations with temperature under different excitation wavelengths (325 nm, 379 nm, and 980 nm). Subsequently, a dual-mode optical temperature sensing system was developed based on the fluorescence intensity ratio and the dual excitation single-band ratiometric method, which achieved a maxed relative sensitivity of 1.12% K⁻¹ at 343 K. Moreover, the excitation-dependent luminescence color changes of CaLaLiTeO₆:Mn⁴⁺/Er³⁺ make it particularly suitable for anti-counterfeiting applications. The present study underscores the dual-functional capabilities in sophisticated non-contact optical temperature measurement and anti-counterfeiting applications. Full article