Phenikaa University

Colorectal tumors are common, and magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced sequences can assist in distinguishing benign from malignant lesions. However, certain atypical cases exist in which malignancies do not exhibit restricted water molecule diffusion. We present the case of a 66-year-old male with moderately differentiated rectal adenocarcinoma showing heterogeneous diffusion restriction on MRI. The tumor surface demonstrated diffusion restriction (hyperintense on DWI, hypointense on ADC), whereas the base did not. Quantitative ADC analysis showed mean values of 0.82 × 10-3 mm2/s at the surface and 1.36 × 10-3 mm2/s at the base. This case highlights that regions with lower cellularity in moderately differentiated adenocarcinoma may lack diffusion restriction. Correlating imaging findings with histopathological results is essential to prevent diagnostic misinterpretation.

The emergence of antimicrobial resistance necessitates the exploration of novel therapeutic agents from natural sources. This study investigated the antimicrobial properties of Clausena anisata fruit ethanol extract (CAFE) and its isolated compounds against 11 bacterial and three fungal strains. The dried fruits were extracted with ethanol using a Soxhlet apparatus. The extract was partitioned and subjected to chromatography to isolate two compounds, which were characterized as stigmasteryl 3-palmitate (C-01) and phellopterin (C-02) using NMR spectroscopy and LC-MS analysis. CAFÉ demonstrated notable inhibition zones in agar well diffusion assays, with the strongest activity against Klebsiella pneumoniae (14.67 ± 2.08 mm) and Staphylococcus saprophyticus (13.67 ± 0.58 mm). Microbroth dilution assays revealed MIC values ranging from 0.0781 to 1.2500 mg/mL for CAFÉ and 0.0781 to 1.2500 mg/mL for stigmasteryl 3-palmitate and phellopterin, respectively. CAFE demonstrated bactericidal activity (MLC/MIC ≤ 4) against Pseudomonas aeruginosa, Proteus mirabilis, Salmonella typhimurium, K. pneumoniae, Streptococcus sanguis, S. saprophyticus, and Candida glabrata, while showing bacteriostatic activity (MLC/MIC > 4) against Candida albicans. Stigmasteryl 3-palmitate exhibited bactericidal activity against P. mirabilis, S. typhimurium, K. pneumoniae, and S. sanguis, with bacteriostatic effects against the other organisms. Phellopterin demonstrated primarily bacteriostatic activity except against S. saprophyticus. Both compounds showed potent fungicidal activity against Candida species. These findings highlight the therapeutic potential of C. anisata fruit and its constituents against typhoid fever, hospital-acquired pneumonia, and invasive candidiasis. This is the first report on the antibacterial and antifungal activities of C. anisata fruit, stigmasteryl 3-palmitate, and phellopterin.

Abstract Nondestructive Testing (NDT) is an essential methodology for ensuring the structural integrity and safety of critical engineering structures such as nuclear power plans, railways, and aerospaces. Leveraging advanced signal analysis and Artificial Intelligence (AI) is necessary to enhance detection capabilities, but the development of bespoke AI models presents a significant bottleneck, requiring deep, cross-disciplinary expertise, and extensive manual effort. To address this challenge, we introduce the AI Co-Scientist, a novel framework designed for human-AI cooperation throughout the entire model development lifecycle. Within this human-in-the-loop system, AI agents and human experts work in partnership to perform data analysis, iterative AI model development, and rigorous model evaluation.The effectiveness of this collaborative framework was rigorously verified by applying it to a real-world Gaussian-Pulsed Eddy Current Testing (GPECT) system for the task of detecting corrosion in an aluminum specimen. The framework autonomously performs signal analysis and converges on a high-performance model. The proposed approach achieved a recall of 99.6%, an F1-score of 95.1%, and an AUC-ROC of 99.9% on a highly imbalanced real-world GPECT dataset, demonstrating its suitability for safety-critical corrosion detection. This result validates our approach, demonstrating that the AI Co-Scientist framework can significantly accelerate research and empower domain experts to focus on high-level strategic oversight.

ABSTRACT The well‐known offline robust model predictive control (RMPC) effectively addresses uncertain parameters for autonomous vehicle systems. However, it requires memory to save the variable and more computation time to search the lookup table to find the best values of the weighting matrices. To address these issues, this article introduces a new offline RMPC approach for enhancing the steering control of autonomous vehicles (AVs) using linear matrix inequality (LMI) optimization that can eliminate storing variables and lookup tables. In order to effectively handle the potential uncertainties associated with the vehicle system parameters, such as total vehicle mass, front and rear cornering stiffness, and varying vehicle speeds by confining them within a defined polytope. By utilizing the polytopic uncertainty method, the offline RMPC is designed to handle a wide range of parameter variations within the defined polytope. Specifically, by defining the upper bound of the cost function as a quadratic form, an offline RMPC method is directly designed, resulting in a significant reduction in the computation burden. Moreover, optimal control is designed by solving an optimization problem based on LMI, allowing the use of efficient convex optimization algorithms. Input and output constraints are enforced to guarantee the safe operation of the AV system in complex and dynamic environments. Finally, the simulation results are presented and compared with the existing studies to demonstrate the effectiveness of our approach. Additionally, co‐simulation based on Matlab and Simulink and CarSim software were also conducted to further verify the proposed method. The results show that all state variables satisfy the enforced constraints, and both the path‐tracking trajectory performance and execution time are better than those of recent research.

Tungsten plays a crucial role in the development of near-field thermophotovoltaic devices. However, available information about tungsten is insufficient to model radiative heat transfer at elevated temperatures. In this study, we develop a statistical theory to evaluate finite-temperature effects on the physical properties of tungsten up to 2000 K, corresponding to extreme conditions in practical emitters. First, the moment expansion technique is applied to calculate the atomic volume, the mechanical modulus, and the Debye temperature. Quasi-harmonic and anharmonic contributions are clarified via simple analytical formulas for free energies. Then, we utilize these thermodynamic quantities to deduce the electrical resistivity from the Bloch–Grüneisen law and the dielectric function from the Drude–Lorentz model. Our calculations are in good agreement with previous experiments. Finally, based on fluctuational electrodynamics, we reconsider energy-conversion processes in a representative system made of tungsten and GaSb. Both radiative and electric power densities will be severely underestimated if the thermal variation of input parameters is ignored. This result suggests that experimental and computational databases need to be expanded from low to high temperatures before actualizing the potential applications of near-field thermophotovoltaics.

Chairside bonding of auxiliaries directly to aligners can avoid remanufacturing trays, but optimal protocols may be substrate-specific across modern thermoformed and 3D-printed materials. This study aimed to compare bond strength and failure mode across six representative aligner materials using a universal primer-orthodontic adhesive combination and a one-step aligner adhesive, with and without sandblasting. Polyethylene terephthalate glycol-modified (PETG), thermoplastic polyurethane (TPU), and glycol-modified polycyclohexylenedimethylene terephthalate (PCTG), together with three 3D-printed resins (TA-28, TC-85DAC, DCA), were prepared as 0.76-mm plates (n = 64). Specimens received alumina sandblasting or no treatment, then were bonded with either of two bonding strategies (n = 16). After thermocycling, bond strength was tested, and failures were scored by ARI. Two- and three-way ANOVA and proportional-odds modeling assessed effects (α = 0.05). Bond strength showed significant main effects of material and sandblasting, with significant material-sandblasting and material-primer interactions. The primer main effect was not significant. Post hoc tests confirmed substrate-specific rankings. PETG with Bond Aligner (non-sandblasted) reached 26.71 MPa, while DCA with universal primer (sandblasted) reached 22.36 MPa. Sandblasting generally increased bond strength, with some exceptions. Failure mode was material-dependent and not completely parallel with bond strength. Bonding efficacy depends on the aligner substrate. For thermoformed trays, a one-step aligner adhesive is preferable, with sandblasting contraindicated for PETG but advantageous for more elastic TPU and PCTG. For 3D-printed trays, a universal primer-orthodontic adhesive combination performs more consistently, with sandblasting benefiting DCA and TA-28, whereas TC-85DAC performs slightly better without it.

The demand for high-resolution ultrasound imaging is increasing, particularly in medical diagnostics, where the number of measurements is often limited due to physical and technical constraints. Ultrasonic tomography, based on linearizing the inverse acoustic scattering problem, enables the detection of small variations in a reference medium. To address these challenges, this study proposes a novel combination of resolution-jumping and beamforming techniques. The resolution-jumping approach progressively refines reconstruction accuracy by leveraging coarse-to-fine information, improving performance under limited measurement conditions. Simultaneously, beamforming employs multiple transmitting elements to generate a narrow beam, effectively reducing noise and enhancing structural detail recovery. Numerical simulations demonstrate that the proposed method significantly improves reconstruction quality while considerably reducing processing time, making it well-suited for real-time, high-resolution ultrasound imaging applications.