Search Results (1,300)

Wireless sensor networks (WSNs) are fundamental building blocks of modern ubiquitous sensing systems. In many practical WSN deployments, sensing devices are tightly constrained in buffer capacity, while device mobility leads to topology decentralization. These characteristics pose significant challenges for reliable and timely data delivery across WSNs. In this paper, we propose a general multi-probing opportunistic routing strategy tailored for buffer-constrained WSNs, aiming to enhance transmission opportunity utilization under realistic sensing device limitations. With the help of Queueing Theory and Markov Chain Theory, we capture the sophisticated queueing processes for the buffer space of sensors, which enables the limiting distribution of the buffer occupation state to be determined. On this basis, we develop a theoretical performance modeling framework to evaluate the fundamental performance metrics of the WSN with the multi-probing opportunistic routing, including the per-flow throughput and the expected end-to-end delay. The validity of the performance modeling framework is verified by network simulations. Moreover, extensive numerical results demonstrate the network performance behaviors comprehensively and reveal some insightful findings that can serve as important guidelines for the configuration and operation of WSNs. Full article

Background/Objectives: Epirubicin, Olaparib, and Ribociclib are widely used anticancer agents whose serum concentrations exhibit significant inter-individual variability, supporting the need for reliable and robust analytical methods suitable for pharmacokinetic evaluation and therapeutic exposure assessment. Variations in metabolism, drug–drug interactions, organ function, and treatment regimens may substantially influence systemic exposure, highlighting the importance of accurate quantification in clinical practice. This study describes the development and validation of a solid-phase extraction–liquid chromatography–mass spectrometry (SPE–LC–MS) method for the simultaneous quantification of these drugs in human serum. Methods: Sample preparation was performed using Oasis PRiME HLB^® cartridges to ensure efficient clean-up, optimal recovery, and reduced matrix effects. Chromatographic separation was achieved using gradient elution with 0.1% formic acid and acetonitrile on a reversed-phase column, followed by single-quadrupole mass spectrometric (QDa) detection in the selected ion recording mode. The total run time was 13 min, enabling high-throughput analysis. Results: The method demonstrated good linearity (r > 0.997) over the tested concentration ranges, along with adequate selectivity, precision, accuracy, recovery, and stability, fulfilling the ICH M10 guideline validation criteria. No significant carry-over or interference from endogenous compounds was observed. Conclusions: Application to patient samples confirmed reliable performance in real clinical matrices and consistent quantification across different concentration levels. The proposed approach provides a potentially more accessible alternative in laboratories already equipped with LC-MS systems compared to LC-MS/MS platforms and can be applied in pharmacokinetic studies, representing a proof-of-concept for exposure assessment in oncology. Full article

This study presents a structured narrative review integrating methodological and decision-oriented perspectives. Milk proteins, particularly β-casein, have attracted increasing scientific and commercial attention due to their genetic variability and role in dairy production and product differentiation. Among β-casein variants, the A1 and A2 alleles of the CSN2 gene are of particular relevance, as their single-nucleotide difference has influenced breeding strategies and the expansion of A2-oriented dairy markets. Although multiple validated molecular genotyping approaches are available for CSN2 A1/A2 discrimination, guidance on their context-appropriate deployment in agricultural systems remains largely technique-centric. The present framework integrates analytical performance, sample complexity, and operational constraints to support the selection of fit-for-purpose methods across breeding, diagnostic, and dairy authentication contexts. Classical and advanced approaches, including polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP), allele-specific polymerase chain reaction (AS-PCR) and amplification refractory mutation system PCR (ARMS-PCR), high-resolution melting (HRM) analysis, sequencing-based methods, single nucleotide polymorphism (SNP) arrays, and digital polymerase chain reaction (dPCR), are comparatively evaluated not only in terms of sensitivity and throughput but also with respect to scalability, reproducibility, and decision risk. This framework provides a practical decision-support tool for aligning genotyping strategies with application-specific risk profiles, thereby improving reliability, transparency, and regulatory compliance in modern dairy systems. Full article

Compared to antennas bearing a single port, MIMO antennas with several ports enable higher data throughput by exploiting spatial diversity. This capability is essential for next-generation implantable medical devices, where high channel capacity is a key requirement. A quad-element implantable MIMO antenna is designed and practically validated at 1420 MHz in this paper. It occupies a compact volume of $7\times 8\times 0.1 {\mathrm{mm}}^3$ ($5.6 {\mathrm{mm}}^3$). The compactness is realized by combining high-permittivity substrate (Rogers 3010 with relative permittivity of 10.2) with meandered radiator paths, which increase the effective current length while maintaining a small physical size. All antennas have very small mutual coupling with isolation of more than $31.78$ dB, which is mainly due to the spacing of 1 mm between the elements and the substrate, which is thin. The peak realized gain for each antenna element is $-27.3$ dBi. The simulation is performed within a capsule-like structure, which is embedded in the stomach tissue model. The experimental verification is carried out by embedding antenna within minced meat. The ECC, channel capacity, and link margin are also evaluated and found to be satisfactory. The proposed antenna ensures reliable communication performance, with the transmission range being as high as $2.5$ m, link margin being 15 dB, and the data rate being 120 Mb/s. The proposed antenna ensures a good level of ECC, which is less than $0.1$. The SAR is $52.3$ W/kg at 1420 MHz. This design is favorable for implants because of the small size, good impedance matching, high isolation, low correlation, good level of gain, and good link performance. Full article

Integrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding; however, studies integrating genomic and multi-source phenotypic data across multiple environments remain limited. This study addressed this gap by integrating genomic SNP data, UAV-based spectral data, and individual multidimensional phenotypic data of 61 indica rice varieties (field and greenhouse environments). As a proof-of-concept study, feature selection methods (LASSO, MI, RFE, SPA) were used to mitigate overfitting and the “p >> n” problem, with further validation needed in larger populations. The results showed that amylose content is genetically dominated, protein content is genetically determined and influenced by gene-environment interactions, and chalkiness traits are determined by three combined factors. For amylose content, SNP data under the Random Forest model at the population level (phenomics data from field UAV remote sensing of variety populations) achieved optimal performance (R² = 0.92; MAE = 1.1; RMSE = 1.5), while the Stacking Ensemble method enhanced accuracy at the individual level (phenomics data from greenhouse single-plant phenotyping per variety). Chalky grain rate and chalkiness degree showed SNP-comparable prediction accuracy, with Stacking significantly improving performance at the population level (R² = 0.89 and 0.85, respectively). Protein content prediction remained relatively low (optimal R² = 0.56) due to strong environmental sensitivity and complex interactions. This framework extends traditional single-environment/single-data-source approaches, providing an effective strategy for early, high-throughput, non-destructive rice quality screening. Further validation with larger datasets, more growing seasons, or independent populations is required for reliable application in breeding-related practices. Full article

Blockchain-based auctions often utilize smart contracts to automate auction rules, with much research focusing on enhancing privacy and fairness through cryptographic techniques. However, the authenticity of external data input into these systems is frequently overlooked. In particular, rational nodes may manipulate bidding data by submitting false types to maximize their utility, compromising market fairness and the reliability of auction outcomes. The aim of this study is to propose an alternative blockchain-based auction mechanism to incentivize nodes to report types honestly. We propose the Mixed-Node Advertising Auction (MNAA) mechanism for digital advertising auctions on blockchain systems. MNAA integrates quasi-linear and value maximization utility models to design allocation and pricing rules that eliminate nodes’ incentives to misreport their types, ensuring the authenticity of data submitted to the auction. To enhance efficiency, MNAA employs state channel technology and off-chain smart contracts, reducing main chain interactions. Theoretical analysis confirms that MNAA incentivizes truthful behavior and ensures security and correctness. Simulation results show that MNAA outperforms Generalized Second Price (GSP), Mixed Bidders with Private Classes (MPR), and Vickrey–Clarke–Grooves (VCG) auctions in terms of liquid social welfare (LSW), publisher revenue, and allocation efficiency, while also improving the transaction throughput and showing good performance in terms of transaction costs and latency. Full article

This paper investigates the quality of mobile network coverage along the Riga–Tukums railway corridor with a focus on the performance of 4G and 5G technologies. Ensuring reliable mobile connectivity along suburban railway corridors remains a significant technical challenge due to mixed forest–urban propagation conditions, macro-cell-dominated LTE infrastructure, mobility-induced channel variability, and fluctuating passenger density. Unlike high-speed railway environments that are extensively studied in dedicated 5G-R scenarios, suburban railway systems often rely on existing macro-cell deployments, where coverage continuity, signal quality stability, and capacity constraints must be addressed simultaneously. This study presents a measurement-based evaluation of 4G and 5G radio performance along the Riga–Tukums railway corridor under real operational conditions (50–90 km/h). Classical propagation models (Okumura–Hata and COST231-Hata) are quantitatively validated using MAE and RMSE metrics, followed by correlation analysis between RSSNR and QoS indicators. A theoretical Doppler sensitivity assessment (80–200 km/h) is conducted to evaluate mobility robustness across LTE and 5G frequency bands. Mobility transition regions and handover-related time windows are geometrically estimated, and passenger density-based capacity modeling is applied to assess throughput degradation under peak occupancy scenarios. Based on these results, a multi-layer network planning strategy integrating 700 MHz macro coverage, 1700 MHz capacity enhancement, and 3500 MHz 5G NR deployment is proposed. The optimization strategy resulted in an estimated 22–28% increase in stable service coverage in previously weak-signal zones and demonstrated that propagation model deviations remain within ranges comparable to recent railway studies (≈15–25 dB RMSE). These findings provide a structured framework for suburban railway communication optimization and support the gradual modernization of railway infrastructure toward FRMCS-ready architectures. The study illustrates the applicability of modern modelling tools for assessing and improving mobile communication systems and contributes to the broader development of digital infrastructure within Latvia’s transport sector. Full article

This paper presents the design and experimental validation of an innovative robotic test stand for measuring camshaft cam geometry, intended to support preventive quality control in high-volume production. The proposed solution integrates a collaborative robot with a dedicated measurement setup to enable repeatable positioning of the inspected camshaft and automated acquisition of geometric features critical for functional performance. A complete measurement methodology was developed, including the measurement sequence, data acquisition procedure, and processing of the recorded signals to determine key cam geometry parameters. To verify the reliability of the proposed approach, measurement results obtained using the robotic stand were compared with reference data acquired using conventional metrology tools and standard inspection procedures. Experimental studies confirmed that the developed stand provides repeatable measurement results, enabling the stable identification of the examined geometric features across repeated trials. Moreover, a high level of agreement was observed between the measurement data obtained using the proposed method and the reference measurements, demonstrating the suitability of the cobot-based test stand for preventive quality control applications in industrial environments. The concept presented offers a scalable and flexible alternative to manual inspection and dedicated special-purpose gauges, with potential benefits in terms of inspection throughput and standardization of quality control workflows. The novelty of the approach lies in the indirect ultrasonic measurement model combined with a quadrant-based sensor orientation strategy and repeatable 90° camshaft indexing, enabling full-profile acquisition within the robot workspace. Full article

Friction stir welding (FSW) is a critical solid-state joining process for lightweight and high-performance metallic structures, particularly in aerospace and automotive manufacturing, yet conventional implementations remain largely dependent on offline parameter optimization and open-loop control. The purpose of this review is to examine how Industry 4.0 technologies enable the transition of FSW from a parameter-driven process into an intelligent, adaptive, and increasingly autonomous manufacturing capability. A structured review methodology was employed, including systematic literature selection and synthesis of recent research on smart sensing, industrial internet of things (IIoT), data analytics, machine learning, digital twins, automation, robotics, and human–machine interaction in FSW. The review reveals that Industry 4.0 integration enables real-time process monitoring, predictive quality assurance, closed-loop control, and virtual process optimization, resulting in improved weld quality, reliability, productivity, and scalability. Significant benefits are observed for safety-critical aerospace components and high-throughput automotive production, where adaptability and consistency are essential. However, persistent challenges remain in data standardization, model generalization, real-time digital twin integration, interoperability, cybersecurity, and workforce readiness. This review concludes that addressing these challenges through interdisciplinary research, standardization efforts, and human-centered system design is essential for enabling adaptive and data-driven FSW systems. The findings position intelligent FSW as a foundational technology for smart, resilient, and sustainable metal manufacturing in the Industry 4.0 era. Full article

Under extreme conditions in fields including aerospace exploration, deep earth excavation, and ocean engineering, mechanical components are subjected to severe environmental challenges, such as high temperature, heavy load, fatigue fracture and corrosion, which significantly limit their service life and operational reliability. Surface engineering has emerged as a critical strategy to address these problems by modifying surface characteristics while preserving basic properties of raw materials. Among various surface modification techniques, laser-based surface modification stands out due to its precise processing, high throughput, and outstanding surface performance. However, laser-based surface modification of metallic materials for industrial applications remains limited owing to inadequate systematic understanding regarding the fabrication mechanisms. Accordingly, a comprehensive and holistic review is essential to elucidate the effect of laser-based surface modification on process optimization, system development, microstructure evolution, and performance enhancement. This review systematically expounds two fundamental strategies in laser surface modification-based material modification (exemplified by laser cladding) and structural modification (exemplified by laser shock peening) in terms of mechanism, process, performance and application. In addition, the mechanism and potential of the synergistic integration of LC (laser cladding) and LSP (laser shock peening) are emphatically discussed. Finally, perspectives regarding process optimizations, material developments, and system improvements for laser surface engineering are presented. By establishing a clear “mechanism–process–performance–application” narrative, this review aims to provide both a scientific reference and a practical guideline for the severe demands of extreme operating conditions. Full article

This study provides a practical and replicable improvement model for productivity and inspection reliability improvement in resource-constrained food logistics environments. This study presents an engineering-based optimization of productivity and process reliability in an agro-industrial post-harvest processing system for canned young green jackfruit using an integrated Define–Measure–Analyze–Improve–Control (DMAIC) and Failure Mode and Effects Analysis (FMEA) framework. The case-study production system experienced high raw-material loss, prolonged blanching cycles, and low inter-operator inspection agreement, which reduced process yield and logistics throughput. Root causes were identified through process mapping and fishbone analysis and prioritized using FMEA Risk Priority Number (RPN) scoring. Key improvement actions included optimizing blanching time, standardizing supplier grading to reduce material variability, and strengthening inspection decisions through Attribute Gage Repeatability and Reproducibility (Gage R&R)-based training and criteria alignment. After implementation, productivity increased by 2.31%, raw-material loss decreased by 1.90%, and inter-operator inspection agreement improved by 16%, exceeding the benchmark. Blanching time was reduced from 3 to 1 min at ≥90 °C, shortening cycle time by 67% and generating an estimated annual cost saving of USD 7200 without major capital investment. The results demonstrate that structured, risk-based improvement combined with validated measurement systems can enhance workforce consistency, process stability, and logistics flow efficiency in agro-industrial food processing environments, providing a replicable improvement model for agro-industrial processing small and medium-sized enterprises (SMEs). Full article

Spiral inertial microfluidic devices provide a simple, high-throughput approach for size-based particle separation; however, translating PDMS-optimized designs into monolithic, fully enclosed 3D-printed channels is often limited by printability and post-print channel clearing. In our previous PDMS study, a $400\times 120\mu \mathrm{m}$ spiral achieved high separation performance after computational optimization and experimental validation. To translate this high-performing PDMS concept into a faster and more cost-effective manufacturing approach, the same separation principle is transferred to a fully 3D-printed, closed-channel spiral device, and the geometry is re-optimized around manufacturability constraints. Printing trials showed that enclosed channels at $400\times 120\mu \mathrm{m}$ and $600\times 180\mu \mathrm{m}$ could not be cleared reliably due to trapped resin and frequent blockage, most often near the inner-outlet region. In contrast, $800\times 240\mu \mathrm{m}$ and $1200\times 360\mu \mathrm{m}$ channels were printed and flushed successfully, and $800\times 240\mu \mathrm{m}$ was selected as the smallest reproducibly functional cross-section. Particle-tracking simulations were then used to re-optimize spiral development length, showing that a 4-turn device provides limited collection for $12\mu \mathrm{m}$ targets (10%), intermediate lengths (5–7 turns) improve collection to 50%, and an 8-turn spiral achieves complete large-particle collection (100%) across tested target sizes (12–$24\mu \mathrm{m}$) while reducing small-particle crossover. Experimental validation of the 8-turn $800\times 240\mu \mathrm{m}$ device at $Q=6\mathrm{m}\mathrm{L} {\min }^{-1}$ using fluorescent polystyrene particles ($18\mu \mathrm{m}$ target; $6\mu \mathrm{m}$ background) yielded an average collection efficiency of 84% and an inner-outlet purity of 92%. Overall, these results demonstrate that spiral inertial separation can be retained in a monolithic 3D-printed format when the design is re-optimized around the smallest reliably clearable enclosed cross-section and sufficient spiral development length. Full article

Indian senna (Senna alexandrina Mill.), a perennial medicinal species belonging to the family Fabaceae, holds significant therapeutic and commercial importance owing to its rich content of sennosides and rhein derivatives, which confer well-established laxative properties. Its high market demand, however, renders the species vulnerable to deliberate or inadvertent adulteration. While previous investigations have utilized functional marker systems such as SCoT (Start Codon Targeted Polymorphism)- and CBDP (CAAT Box Derived Polymorphism)-derived SCAR (Sequence Characterised Amplified Region) markers for genetic characterization, the present study is the first to report the development of sequence-specific RAPD- and ISSR-based SCAR markers consolidated into a single-tube multiplex PCR assay. Genomic DNA isolated from young leaves of S. alexandrina and its commonly encountered adulterant species was amplified using RAPD primer OPI-02 and ISSR primer UBC-835. Polymorphic amplicons were cloned, sequenced, and employed for the design of SCAR primers, which were rigorously validated for specificity. Species-specific SCAR markers were successfully integrated into a single multiplex reaction, enabling precise and unequivocal identification of S. alexandrina, Cassia fistula and Senna sophera. The multiplex amplification profiles were entirely consistent with corresponding uniplex assays, endorsing the method’s robustness and reproducibility. This streamlined, one-tube multiplex SCAR-PCR system represents a significant advancement toward reliable, high-throughput molecular authentication of Indian senna and its closely related medicinal plant species (adulterants). Full article

Natural product research has experienced substantial growth over the past two decades, driven by a renewed appreciation for the structural complexity and biological relevance of compounds derived from nature. Technological advances in separation science, spectroscopic characterization, and high-sensitivity bioassays have collectively restored natural products to a position of prominence in modern drug discovery efforts. Nature remains the most prolific source of bioactive molecular diversity, drawing from microorganisms, plants, and marine life to offer a vast reservoir of structurally novel scaffolds whose pharmacological potential remains largely unexplored. Effective extraction and isolation remain foundational to natural product research, as the quality and purity of isolated compounds directly govern the reliability of downstream biological evaluation. Recent years have witnessed remarkable innovation in this space, spanning green and designer solvent systems, pressurized and ultrasound-assisted extraction platforms, supercritical fluid techniques, and integrated purification workflows that dramatically reduce processing time while improving compound recovery and analytical throughput. Particularly noteworthy is the growing application of artificial intelligence and machine learning tools for solvent selection, extraction optimization, and metabolite dereplication, which in combination with advanced phase-separation strategies and informatic platforms have substantially expanded the scope of detectable and characterizable metabolites within complex biological matrices. This review summarizes recent progress in extraction and isolation methodologies supporting natural product research, with particular emphasis on combinatorial extraction strategies, next-generation solvent systems, and AI-driven applications that have collectively improved operational efficiency, selectivity, and analytical output over the past five years. Full article

A major limitation in studying gastroesophageal adenocarcinoma (GEA) has been the lack of reliable models that represent the disease’s complexity. We present lessons learned from a comprehensive large-scale biobanking effort combining traditional sample collection with several in vitro models, including 3-dimensional patient-derived organoids (PDOs), 2-dimensional cancer-associated fibroblasts (CAFs), tumor-infiltrating lymphocytes (TILs), and/or in vivo xenografts. This initiative started in 2018, integrating multiple advanced ex vivo models such as PDOs, patient-derived xenografts (PDXs), and organoids (PDXOs). This unique resource now includes tumor avatars from over 380 consented patients, making it the world’s largest living GEA biobank. We achieved a >90% success rate in creating per-patient models, including 227 tumor-derived and 203 neighboring normal PDOs. These organoids accurately mirror key features of the original tumors, such as their histology (e.g., microsatellite instability), mutations, and drug response across treatment points. Notably, PDOs can predict individual patient responses to chemotherapy within five weeks, underscoring their clinical relevance. Furthermore, high-throughput drug screening on PDO subsets with known genetic landscapes generates personalized chemosensitivity profiles for 22 drugs. Through a process of continued refinement of culture techniques and tumor sampling approach, our large-scale comprehensive collection of GEA avatars represents a unique and valuable preclinical experimental resource for precision oncology. Full article