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Vision-based adaptive path following controller for 4W mobile robots on varied terrains
Investigation of crack propagation patterns on the borehole wall during single-borehole hydraulic fracturing under different in-situ stress ratios
Assessing SRTM one Arc second DEM accuracy for small dam volume-elevation curves using terrain metrics
Abstract Accurate reservoir storage estimation is fundamental to sustainable water resources management; however, small dam projects are often hindered by the prohibitive costs and time required for high-precision topographic surveys. In this study, a rigorous validation of freely available one-arc-second SRTM DEMs was conducted as an alternative approach for estimating volume-elevation relationships at ten small dams in Iraq. High-precision surveys served as benchmarks, enabling statistical validation of DEM-derived estimates using absolute relative error (ARE), root mean square error (RMSE), mean absolute error, and the coefficient of determination (R²). Reservoir basin morphology was further characterised through planimetric indices, including area-to-volume ratio (AVR), shape factor, and solidity. In parallel, terrain complexity within a 5 km buffer zone was quantified using slope variability, curvature, vector ruggedness measure (VRM), and terrain ruggedness index (TRI). A strong structural agreement was demonstrated (R² > 0.98), although substantial variation in volumetric precision was observed. A global sensitivity analysis using the Morris Method identified the standard deviation of the Terrain Ruggedness Index (TRI) as the dominant predictor of accuracy, with µ* values of 83–86, while all other metrics showed minimal influence (µ* ≈ 0–24). These results establish a clear accuracy threshold for one-arc-second SRTM DEMs: they are sufficiently reliable for preliminary planning (< 20% error) in low-ruggedness terrain (TRI SD < 0.1) but become highly unreliable in rugged landscapes, where errors exceed 150% (TRI SD > 0.1). These findings provide a predictive framework for assessing DEM suitability, supporting the integration of satellite topography into small-scale reservoir planning.
IPP-DMS: A scalable privacy-preserving data management system for secure and efficient handling of large-scale datasets
Abstract The rapid growth of data in various industries has led to a pressing need for innovative solutions that ensure both the security and efficiency of data management systems. As organizations increasingly rely on large-scale datasets for decision-making, ensuring the privacy and integrity of this data becomes a critical challenge. The study introduces the Integrated Privacy-Preserving Data Management System (IPP-DMS), designed to enhance data security, computational efficiency, and user privacy. Unlike conventional systems, IPP-DMS focuses on processing large-scale datasets, such as financial records, customer interactions, and sensor data, employing advanced feature extraction techniques like Gray-Level Co-occurrence Matrix (GLCM) and Principal Component Analysis (PCA) to uncover critical patterns and optimize data handling. The system uses Advanced Encryption Standard (AES) for robust data encryption, differential privacy for data anonymization, and Role-Based Access Control (RBAC) for secure user management. Additionally, an AI-driven anomaly detection module monitors real-time access patterns to ensure compliance with data protection regulations. With an AUC score of 0.91 for anomaly detection, a 0.85-second authentication time, and a 0.45-second access delay, experimental data demonstrate that IPP-DMS works better than conventional systems. In addition, the system provides better scalability and efficiency than PACS, SEISS, and DEIS, processing up to 2000 requests per minute. IPP-DMS provides a scalable, secure, and efficient solution for privacy-preserving data management, setting a new benchmark in the field by integrating advanced methodologies with standard security measures.
Numerical simulation of a nonlinear hepatitis B virus mathematical model using the Dickson collocation technique
Abstract This work introduces an innovative nonlinear model of hepatitis B virus (HBV) dynamics, emphasizing the utilization of the Dickson collocation method for numerical simulation. Our method, in contrast to conventional techniques, adeptly tackles the complex interactions between the virus and the host’s immune response using a system of ordinary differential equations (ODEs). We present a transformation of the ordinary differential equations into a nonlinear system of algebraic equations, facilitating the determination of unknown coefficients in a truncated Dickson polynomial series. The novel implementation of the Newton algorithm for addressing this nonlinear system improves computing efficiency and convergence rate. Our findings indicate that the Dickson collocation method provides highly precise approximations and surpasses traditional artificial neural network models regarding convergence rate and computational efficiency. This study highlights the Dickson collocation approach as an effective instrument for simulating intricate biological systems, offering valuable insights into HBV dynamics and laying the groundwork for future research.
Synergistic potential of clindamycin hydrochloride loaded on zinc oxide nanoparticles: A novel approach to combat multidrug-resistant infections
Molecular epidemiology and genetic relatedness of zoonotic Campylobacter species from human and food-producing animal sources in Enugu State, Nigeria
Structural analysis and antimicrobial assessment of bioinspired silver nanoparticles from Ferula communis
Securing IoT networks: a machine learning approach for detecting unusual traffic patterns
Plasma treatment of ZnO tetrapod–BiOBr heterojunction supported on PET waste for photocatalytic degradation of oil in water
Abstract In this study, a ZnO tetrapod–BiOBr heterojunction was ultrasonically immobilized onto floating recycled polyethylene terephthalate (PET) at different weight ratios (5, 10, and 15%) and evaluated for the photocatalytic degradation of hexane as a refractory oily pollutant under simulated solar light. The ZnOT(5)-B/P composite exhibited outstanding performance, achieving 96.6% degradation within 40 min due to its high surface area, strong ZnO–BiOBr interfacial contact, and efficient visible-light utilization. Comprehensive characterization (XRD, UV–Vis DRS, FESEM, BET/BJH, PL, AFM, CV, FTIR, Mott Schottky, ICP, and WCA) confirmed the formation of highly crystalline ZnO tetrapods and BiOBr microspheres with robust adhesion to the PET substrate and favorable bandgap reduction (3.1 → 1.8 eV), promoting enhanced charge separation. In the subsequent phase, the ZnOT(15)-B/P sample, which initially exhibited the lowest activity, was subjected to air-plasma surface engineering to address its weaker interfacial and textural features. Post-treatment structural and surface analyses (FESEM, FTIR, CV, and WCA) revealed that plasma exposure generated nanoscale surface etching, increased roughness and interparticle connectivity, and improved hydrophobicity, thereby facilitating stronger hexane adsorption and accelerated interfacial charge transfer. Furthermore, CV and XPS results indicated enhanced redox kinetics and increased density of surface oxygen vacancies, while AFM showed a ≈ 2.3-fold increase in surface roughness. As a result, the photocatalytic performance of ZnOT(15)-B/P improved substantially, rising from 67.5% to 84.8%, demonstrating the effectiveness of plasma post-activation in restoring and boosting the activity of an initially underperforming photocatalyst.
Development of a compliant spine mechanism for enhanced humanoid robotics locomotion
Abstract Humanoid robots often employ rigid-trunk designs which limit locomotion capabilities and payload capacity. This paper presents a novel bio-inspired, tensegrity-based flexible spine mechanism designed to address these limitations. The design integrates a modular, multi-segment structure combining rigid struts and flexible TPU cables, creating a compliant, stable, and adaptable spine. We developed a novel dynamic model of this tensegrity-based spine to analyze its motion characteristics, providing detailed insights into its load-bearing capabilities and range of motion. Experimental results, obtained using a novel humanoid robot platform (“Flexinoid”), demonstrate improvements in locomotion performance. Furthermore, the design mitigates non-linear movement challenges, allowing for an enhanced range of flexion to -30°:65° and lateral bending by ± 30°. The experimental results confirm an increase in sensitivity and a decrease in the minimum detectable payload following the onset of motor back-driving, validating the effectiveness of the passive energy storage mechanism during initial loading. This enhancement in performance underscores the potential of this bio-inspired design for applications requiring precise control and high payload capacity. This research presents a novel approach to humanoid robot design, paving the way for more versatile and capable robots in various applications.
Low-SNR and BER reduction in UWOC systems using DESN and CNN-TCN deep learning models
Abstract Both commercial and scientific underwater wireless optical communication (UWOC) systems are essential and significant for several applications with the ability to provide high data transmission rates over distances up to tens of meters. There are several research gaps for UWOC like the limitation of the UWOC performance which is impacted by the intrinsic characteristics of ocean water which includes losses that reduce the signal-to-noise ratio (SNR) and impede communication quality. The main objective of this study is to enhance the performance of the UWOC system by utilizing recent artificial intelligence (AI) technologies to reduce the bit error rate (BER) with different neural network (NN) models. We apply both pulse amplitude modulation (PAM) and quadrature phase shift keying-orthogonal frequency division multiplexing (QPSK-OFDM) with several NN models across a range of underwater transmission ranges with PAM of 100, 125, 167 m and QPSK-OFDM of 100, 110, 120, 130 m. Deep echo state network (DESN) and recursive least square (RLS) are the two channel estimation techniques used. A variety of deep learning (DL) models and convolutional neural network (CNN) are assessed to improve system robustness and efficiency. CNN-RNN-AM shows performance gains of 18.9, 23.5, 33.3, and 29.4% at 100, 110, 120, and 130 m, respectively. CNN-LSTM-AM shows gains ranging from 10 to 17.6%; and TCN-LSTM-AM shows the largest gains, 29.4, 41.17, 53.3, and 71.4%, at the same distances. As implications of these results that, by comparison, DESN improves by up to 25% at 167 m, while PAM with TCN-LSTM-AM only improves by 17.3% at 100 m utilizing RLS. The most notable improvements are seen in CNN-LSTM-AM and TCN-LSTM-AM, which achieve accuracy gains of + 10.41 and + 11.80%, respectively, along with notable decreases in error metrics like mean squared error (MSE) of 58.46 and 66.15%, root mean square error (RMSE) of 30.48 and 41.90%, and mean absolute error (MAE) of 34.94 and 40.96%. Remarkably, TCN-RNN-AM exhibits a significant improvement in accuracy of 7.25% and RMSE of 32.38%, but it also experiences a minor rise in MAE of −4.82%, suggesting less consistent absolute error handling. When AM is added to CNN-LSTM, the accuracy increases from 94.33 to 96.77%, and the MSE, RMSE, and MAE decrease to 0.0054, 0.073, and 0.054, respectively. The TCN-LSTM model with AM also attains the maximum accuracy of 97.99%, indicating a 3.65% improvement. These findings demonstrate how well DL works in conjunction with sophisticated modulation and channel estimation methods to significantly enhance UWOC system performance in demanding underwater conditions.
Atorvastatin suppresses high-risk colorectal adenomas via reprogramming of lipid metabolism and Inhibition of stemness
Transcriptional regulation of telomeric repeat-containing RNA by the G-quadruplex-binding Ewing sarcoma protein
Development of machine learning models to identify potentially active compounds against tuberculosis
Rare-earth doped strontium hexaferrite nanocomposites for enhanced electromagnetic shielding and heavy metal remediation
Abstract The textile and dyeing industries contribute significantly to environmental pollution through wastewater containing hazardous heavy metals, resulting in around 1.7 million deaths annually. Effective treatment methods are urgently needed. This study explores samarium (Sm) and dysprosium (Dy) doped M-type strontium hexaferrites (M-SrHFs), which improve lead adsorption due to their high surface reactivity and ion-exchange capacity. Additionally, these materials exhibit tunable electromagnetic properties, making them suitable for low-frequency attenuation or signal-conditioning applications. Dy-doped samples show optimal EMI performance at 20 MHz (RL ≈ − 0.15 dB), while co-doped variants exhibit strong absorption at 3–4 MHz with large skin depths, ideal for low-frequency industrial applications. The dual functionality of these doped M-SrHFs—effective heavy metal removal and low-frequency attenuation—highlights their potential as sustainable, multifunctional materials for environmental and electronic industry applications. This research bridges materials science and environmental engineering, offering an integrated solution for pollution control and electromagnetic compatibility.
Identifying key genes for European canker resistance in apple: machine learning and gene expression profiling of quantitative disease resistance
Abstract European canker, caused by Neonectria ditissima , is a major disease of apple ( Malus × domestica ) with limited control options, making host resistance a key management strategy. Although quantitative disease resistance (QDR) has been identified, the underlying molecular basis remains poorly understood. We investigated candidate genes associated with resistance using transcriptomic profiling of a bi-parental population segregating for six QTLs linked to canker resistance. RNA sequencing combined with machine learning enabled the identification of key biomarkers predictive of disease resistance. Integration of expression and QTL data highlighted genes involved in phenylpropanoid biosynthesis, immune receptors (NLRs, RLKs, WAKs), and epigenetic regulators, implicating their roles in host defense. Expression patterns were further resolved into cis- and trans-regulatory effects, providing insight into allele-dependent regulation. Independent validation in a separate dataset confirmed the robustness of key expression patterns. These findings advance understanding of the genetic architecture underlying QDR in apple and provide a basis for marker development to support breeding of cultivars with durable resistance to European canker.
Tensile and thermal properties of ceramic particulate and natural seaweed reinforced hybrid particulate polymer composites using the Taguchi approach
Abstract Mechanical and thermal properties are critical when selecting materials for structural and semi structural applications. Mechanical strength ensures durability, while thermal conductivity is essential for performance under heat or thermal stress. Together, these properties help to develop composites suited for demanding fields like electronics, automotive and aerospace. This study investigates the effect of various weight percentages of natural and ceramic fillers on the mechanical and thermal behaviours of polymer composites (PCs). Epoxy composite laminates are fabricated using the hand layup method, incorporating seaweed (SW), silicon carbide (SiC) and aluminum oxide (Al₂O₃) as reinforcements. The design followed an L9 Taguchi orthogonal array, allowing for combinations of filler contents ranging from 2 to 6 wt.%. After fabrication, the laminates are subjected to waterjet cutting to prepare specimens for tensile and thermal conductivity testings. To identify the optimal parameter combinations, signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) are employed. The results demonstrate that ceramic fillers significantly enhance both tensile strength (TS) and thermal conductivity (TC). The PCs of SWSCAO, containing 6 wt% Al₂O₃, exhibits maximum TS (87 MPa). TS of the SWSCAO3 composite is 22.84% greater than that of the SWSCAO1 composite with minimal ceramic filler (2 wt.% of SiC and Al₂O₃). This elucidates that composites with minimal amount of seaweed and maximum SiC and Al₂O₃ content are the most tensile effective. Specifically, the SWSCAO3 composite exhibited a 22.84% increase in TC compared to the SWSCAO1 composite, which contained the higher concentration of ceramic filler content (SiC and Al₂O₃). Thermal conductivity tests are performed at varying heat inputs (5W, 10W, 15W and 20W), with results showing conductivity values ranging from 1.91 to 2.71 W/m·K. Composites with higher Al₂O₃ content show improved thermal conductivity, whereas an increased proportion of seaweed filler tends to reduce it.