Search Results (38)

This study presents the design of a high-efficiency pulse width modulation (PWM) power amplifier for marine biological sound reproduction. Due to the capacitive nature of underwater transducers and step-up transformers, output LC filter design is constrained, making it difficult to achieve a flat frequency response and low total harmonic distortion (THD). To address this, the electrical characteristics of these components were measured and modeled to construct equivalent circuits for the PSPICE simulator. Based on these models, an optimized LC filter was designed, and its performance was validated through simulation and experiments. The cause of THD occurring in specific frequency bands was analyzed, and two types of notch filters were applied to improve THD and switching signal attenuation. The proposed methodology offers a practical approach to improving PWM amplifier performance in underwater acoustic systems, supporting the development of compact, efficient, and reliable SONAR transmitters. Full article

Background: Ligusticum chuanxiong Hort. is a well-known traditional Chinese medicinal herb whose clinical application and international trade had been constrained by cadmium (Cd) contamination. However, the molecular mechanisms underlying its response to cadmium stress remained poorly understood. The ATP-binding cassette (ABC) transporter family plays crucial roles in various plant processes, including growth and development, hormone transduction, and stress responses. This study aimed to analyze the ABC transporter genes in L. chuanxiong to better understand their roles during cadmium stress responses. Methods: Genome-wide identification of ABC genes in L. chuanxiong was performed, and transcriptome sequencing of rhizomes under cadmium stress was conducted. Differentially expressed LcABC genes were screened using bioinformatic analysis. Results: A total of 368 LcABC genes were identified. Transcriptome analysis revealed 37 upregulated LcABC genes, which were classified into six subfamilies. Cis-element analysis indicated that their promoters contain hormone-, growth-, and stress-responsive elements. Notably, LcABCG8, LcABCG48, and LcABCG108 contain stress-responsive elements and show close evolutionary relationships with heavy metal-responsive genes such as AtABCC1/2/3 and AtABCG36/40, suggesting that they could be key candidates. qRT-PCR validation of nine LcABC genes confirmed their differential sensitivity to cadmium stress. Conclusions: This study conducted a comprehensive identification of the ABC gene family in L. chuanxiong. By integrating transcriptomic data with systematic bioinformatic analyses, we identified several LcABC transporters that may play important roles in cadmium stress responses. The results provide insights into the molecular mechanisms of ABC transporters in cadmium stress responses in L. chuanxiong and offer strategies for reducing cadmium accumulation. Full article

Conventional audio and video codecs are designed for human perception, often discarding subtle spectral cues that are essential for machine-based analysis. To overcome this limitation, we propose a machine-oriented compression framework that reinterprets spectrograms as visual objects and applies Feature Coding for Machines (FCM) to anomalous sound detection (ASD). In our approach, audio signals are transformed log-mel spectrograms, from which intermediate feature maps are extracted, compressed, and reconstructed through the FCM pipeline. For comparison, we implement AAC-LC (Advanced Audio Coding Low Complexity) as a representative perceptual audio codec and VVC (Versatile Video Coding) as spectrogram-based video codec. Experiments were conducted on the DCASE (Detection and Classification of Acoustic Scenes and Events) 2023 Task 2 dataset, covering four machine types (fan, valve, toycar, slider), with anomaly detection performed using the official Autoencoder baseline model released in DCASE 2024. Detection scores were computed from reconstruction error and Mahalanobis distance. The results show that the proposed FCM-based ACoM (Audio Coding for Machines) achieves comparable or superior performance to AAC at less than half the bitrate, reliably preserving critical features even under ultra-low bitrate conditions (1.3–6.3 kbps). While VVC retains competitive performance only at high bitrates, it degrades sharply at low bitrates. These findings demonstrate that feature-based compression offers a promising direction for next-generation ACoM standardization, enabling efficient and robust ASD in bandwidth-constrained industrial environments. Full article

Air pollution has become a significant concern for human health, especially in developing countries. Among Primary Pollutants, particulate matter 2.5 (${\mathrm{PM}}_{2.5}$), refers to airborne particles which have a diameter of 2.5 micrometres or less, and has become a widely used measure for monitoring air quality globally. The standard go-to method usually uses Federal Reference Grade sensors to understand air quality. But, they are quite cost-prohibitive, so the popular alternative is low-cost (LC) air quality sensors. Even LC air quality monitors do not cover many areas, especially across the global south. On the other hand, the ubiquitous use of online social media OSM has led to its evolution in participatory sensing. While it does not function as a physical sensor, it can be a proxy indicator of public perception on the topic under study. OSM platforms such as Twitter/X and Reddit have already demonstrated their value in understanding human perception across various domains, including air quality monitoring. This study focuses on understanding air pollution in a resource-constrained setting by examining how the community perception on social media can complement traditional monitoring. We leverage metadata readily available from social media user data to find patterns with air quality fluctuations before and during the pandemic. We use the US Embassy ${\mathrm{PM}}_{2.5}$ data for baseline measurement. In the study, we empirically analyse the variations in quantitative & intent-based community perception in seasonal & pandemic outbreaks with varying air quality. We compare the baseline against temporal & user-specific attributes of Twitter/X relating to tweets like daily frequency of tweets, tweet lags 1–5, user followers, user verified, and user lists memberships across two timelines: pre-COVID-19 (20 March 2019– 29 February 2020) & COVID-19 (1 March 2020–20 September 2020). Our analysis examines both the quantitative and the intent-based community engagement, highlighting the significance of features like user authenticity, tweet recurrence rates, and intensity of participation. Furthermore, we show how behavioural patterns in the online discussions diverged across the two periods, which reflected the broader shifts in the air pollution levels and the public attention. This study empirically demonstrates the significance of X/Twitter metadata, beyond standard tweet content, and provides additional features for modelling and understanding air quality in developing countries. Full article

Exhaled breath (EB) contains numerous volatile organic compounds (VOCs) that can reflect pathological metabolic processes, making breath analysis a promising non-invasive diagnostic approach. In particular, volatile aldehydes and ketones have been identified as disease biomarkers in EB. Gas sensors are expected to play a crucial role in the diagnosis of numerous diseases at an early stage. Among the various available approaches, sensors stand out as especially attractive tools for diagnosing diseases such as lung cancer (LC) and diabetes, due to their affordability and operational simplicity. There is an urgent need in the field of disease detection for the development of affordable, non-invasive, and user-friendly sensors capable of detecting various biomarkers. Devices of the new generation should also demonstrate high repeatability of measurements and extended operational stability of the employed sensors. Due to these demands, the past few years have seen significant advancements in the development and implementation of electronic noses (ENs), which are composed of an array of sensors for the determination of VOCs present in EB. To meet these requirements, the development and integration of advanced receptor coatings on sensor transducers is essential. These coatings include nanostructured materials, molecularly imprinted polymers, and bioreceptors, which collectively enhance selectivity, sensitivity, and operational stability. However, reliable biomarker detection in point-of-care (PoC) mode remains a significant challenge, constrained by several factors. This review provides a comprehensive and critical evaluation of recent studies demonstrating that the detection of VOCs using gas sensor platforms enables disease detection and can be implemented in PoC mode. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article

The adverse propagation environment in underground coal mine tunnels caused by enclosed spaces, rough surfaces, and dense scatterers severely degrades reliable wireless signal transmission, which further impedes the deployment of IoT applications such as gas monitors and personnel positioning terminals. However, the conventional power enhancement solutions are infeasible for the underground coal mine scenario due to strict explosion-proof safety regulations and battery-powered IoT devices. To address this challenge, we propose singular value decomposition-based Lagrangian optimization (SVD-LOP) to minimize transmit power at the mining base station (MBS) for IRS-assisted coal mine wireless communication systems. In particular, we first establish a three-dimensional twin cluster geometry-based stochastic model (3D-TCGBSM) to accurately characterize the underground coal mine channel. On this basis, we formulate the MBS transmit power minimization problem constrained by user signal-to-noise ratio (SNR) target and IRS phase shifts. To solve this non-convex problem, we propose the SVD-LOP algorithm that performs SVD on the channel matrix to decouple the complex channel coupling and introduces the Lagrange multipliers. Furthermore, we develop a low-complexity successive convex approximation (LC-SCA) algorithm to reduce computational complexity, which constructs a convex approximation of the objective function based on a first-order Taylor expansion and enables suboptimal solutions. Simulation results demonstrate that the proposed SVD-LOP and LC-SCA algorithms achieve transmit power peaks of $20.8\mathrm{dBm}$ and $21.4\mathrm{dBm}$, respectively, which are slightly lower than the $21.8\mathrm{dBm}$ observed for the SDR algorithm. It is evident that these algorithms remain well below the explosion-proof safety threshold, which achieves significant power reduction. However, computational complexity analysis reveals that the proposed SVD-LOP and LC-SCA algorithms achieve $\mathcal{O}\left(N^3\right)$ and $\mathcal{O}\left(N^2\right)$ respectively, which offers substantial reductions compared to the SDR algorithm’s $\mathcal{O}\left(N^7\right)$. Moreover, both proposed algorithms exhibit robust convergence across varying user SNR targets while maintaining stable performance gains under different tunnel roughness scenarios. Full article

The construction sector significantly contributes to global environmental degradation through intensive resource extraction, high energy consumption, and substantial waste generation. Addressing this unsustainable trajectory requires integrated approaches that simultaneously improve operational efficiency and material circularity. Lean Construction (LC) and Circular Economy (CE) offer complementary frameworks for enhancing process performance and reducing environmental impacts. However, their combined implementation remains underdeveloped and fragmented. This study conducts a systematic literature review (SLR) of 18 peer-reviewed articles published between 2010 and 2025, selected using PRISMA 2020 guidelines and sourced from Scopus and Web of Science databases. A mixed-method approach combines bibliometric mapping and qualitative content analysis to investigate how LC and CE are jointly operationalized in construction contexts. The findings reveal that LC improves cost, time, and workflow reliability, while CE enables reuse, modularity, and lifecycle extension. Integration is further supported by digital tools—such as Building Information Modelling (BIM), Design for Manufacture and Assembly (DfMA), and digital twins—which enhance traceability and flow optimization. Nonetheless, persistent barriers—including supply chain fragmentation, lack of standards, and regulatory gaps—continue to constrain widespread adoption. This review identifies six strategic enablers for LC-CE integration: crossdisciplinary competencies, collaborative governance, interoperable digital systems, standardized indicators, incentive-based regulation, and pilot demonstrator projects. By consolidating fragmented evidence, the study provides a structured research agenda and practical insights to guide the transition toward more circular, efficient, and sustainable construction practices. Full article

The laser cladding (LC) of titanium matrix composite coatings (TMCCs) on titanium components not only effectively enhances the wear resistance, fatigue resistance, corrosion resistance, and biocompatibility of titanium and its alloys, but also circumvents the incompatibility and low bonding strength issues associated with other metallic composite coatings. While the incorporation of ceramic particles is a critical strategy for improving the coating performance, the limited interfacial bonding strength between ceramic particles and the matrix has historically constrained its advancement. To further elevate its performance and meet the demands of components operating in harsh environments, researchers worldwide have employed LC to synthesize in situ hard ceramic reinforcements such as TiC, TiB, TiN, and others within TMCCs on titanium substrates. This approach successfully addresses the aforementioned challenges, achieving coatings that combine a high interfacial bonding strength with superior mechanical properties. This paper provides a comprehensive review of the processing techniques, phase composition, microstructure, and mechanical properties of in situ-synthesized ceramic-reinforced TMCCs via LC on titanium components, with a focused summary of their strengthening mechanisms. Furthermore, it critically discusses the challenges and future prospects for advancing this technology. Full article

Objectives: The anticancer effects of PI3Kα inhibitors (PI3Ki) are constrained by their hyperglycemic side effects, while the efficacy of conventional hypoglycemic agents, such as insulin, metformin, and SGLT-2 inhibitors, in mitigating PI3Ki-induced hyperglycemia remains suboptimal. Dorzagliatin, a novel glucokinase activator, has been approved in China for the management of hyperglycemia, offering a promising alternative. This study aims to investigate the pharmacokinetic properties and potential mechanisms of drug interactions of dorzagliatin in the regulation of PI3K-induced hyperglycemia. Methods: Plasma concentrations of WX390, BYL719, and Dorz in mice were measured using high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Pharmacokinetic (PK) parameters and PK/PD models were derived by using Phoenix WinNonlin 8.3.5 software. Blood glucose levels at various time points and tumor volume changes over a four-week period were assessed to explore the interactions when PI3Ki were combined with dorzagliatin. Results: The results indicated that, compared to the Dorz group, the combination groups (Dorz + BYL719, Dorz + WX390) exhibited increases in ${\mathrm{A}\mathrm{U}\mathrm{C}}_{0\to t}$ of dorzagliatin by 41.65% and 20.25%, and in C_max by 33.48% and 13.32%, respectively. In contrast, co-administration of these PI3Ki with dorzagliatin resulted in minimal increase in their plasma exposure. The combination therapy group (Dorz+BYL719) exhibited superior antitumor efficacy compared to the BYL719 group. Conclusions: Our findings indicate that the drug–drug interactions (DDIs) between dorzagliatin and multiple PI3Ki (including WX390 and BYL719) may partially account for the enhanced antitumor efficacy observed in the combination therapy group compared to PI3Ki monotherapy. This interaction may be explained by the inhibition of P-glycoprotein (P-gp) and the pharmacological mechanism of dorzagliatin regarding the activation of insulin regulation. Full article

Background/Objectives: Dendritic-cell-derived exosomes (DEXs) have demonstrated immunostimulatory potential in cancer immunotherapy, yet their clinical application remains constrained by their cryodependence, compositional heterogeneity, and limited scalability. To address these limitations, we developed an ultrapurified phospholipoproteic complex (PLPC), a dendritic-secretome-derived formulation stabilized through ultracentrifugation and lyophilization that has been engineered to preserve its immunological function and structural integrity. Methods: Secretomes were processed under four conditions (fresh, concentrated, cryopreserved, and lyophilized PLPC) and compared through proteomic and functional profiling. Mass spectrometry (LC-MS/MS) analysis revealed that the PLPC retained a significantly enriched set of immunoregulatory proteins—including QSOX1, CCL22, and SDCBP—and exhibited superior preservation of post-translational modifications. Results: Ex vivo co-culture assays with human peripheral blood mononuclear cells (PBMCs) demonstrated that the PLPC induced robust secretion of IFN-γ, TNF-α, and IL-6 while concurrently suppressing IL-10, achieving an IFN-γ/IL-10 ratio exceeding 3.5. Flow cytometry confirmed the substantial activation of both CD4⁺ and CD8⁺ T cells, while apoptosis assays showed selective tumor cytotoxicity (>55% tumor apoptosis) with minimal impact on non-malignant cells (>92% viability). Conclusions: These findings establish the PLPC as a reproducible, Th1-polarizing immunomodulator with selective antitumor activity, ambient-temperature stability, and compatibility with non-invasive administration. Overall, the PLPC emerges as a scalable, cell-free immunotherapeutic platform with translational potential to reprogram immune suppression in metastatic therapy-resistant cancer settings. Full article

The rolling system for stainless steel, particularly in the production of diamond plates, represents a complex industrial control scenario. The process requires precise load distribution to effectively manage pattern height, due to the high strength, hardness, and required dimensional accuracy of the material. This paper addresses the limitations of offline methods, which include heavy reliance on initial conditions, intricate parameter settings, susceptibility to local optima, and suboptimal performance under stringent constraints. A Multi-Objective Adaptive Rolling Iteration method that incorporates local constraints (MOARI-LC) is proposed. The MOARI-LC method simplifies the complex multi-dimensional nonlinear constrained optimization problem of load distribution, into a one-dimensional multi-stage optimization problem without explicit constraints. This simplification is achieved through a single variable cycle iteration involving reduction rate and rolling equipment selection. The rolling results of HBD-SUS304 show that the pattern height to thickness ratio obtained by MOARI-LC is 0.20–0.22, which is within a specific range of dimensional accuracy. It outperforms the other two existing methods, FCRA-NC and DCRA-GC, with results of 0.19~0.24 and 0.15~0.25, respectively. MOARI-LC has increased the qualification rate of test products by more than 25%, and it has also been applied to the other six industrial production experiments. The results show that MOARI-LC can control the absolute value of the rolling force prediction error of the downstream stands of the hot strip finishing rolls within 5%, and the absolute value of the finished stand within 3%. These results validate the scalability and accuracy of MOARI-LC. Full article

This article presents the experimental validation of a model-based predictive control (MPC) strategy for the safe interconnection of voltage source inverters (VSI) with output LC filters for the grid connection of DC energy resources. The MPC is formulated as a quadratic programming (QP) problem and solved using the operator splitting quadratic programs (OSQP). The proposed approach incorporates integral action to achieve precise voltage magnitude reference tracking while accounting for modulated voltage limits and nominal current constraints within the control design. The effectiveness of the proposed strategy is validated through simulations conducted in MATLAB, demonstrating superior dynamic performance compared to the traditional hierarchical PI control. The implementation of the proposed MPC is experimentally verified on a VSI setup using the dSPACE MicroLabBox. The results confirm that the computational requirements are satisfied, establishing this approach as a practical alternative for modern power electronic systems. The proposed MPC for VSIs offers an effective approach to enforcing operational constraints, improving dynamic performance, and facilitating the robust integration of renewable energy sources in microgrids. Full article

The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems. Full article