Sri Sri University

In this paper, we introduce and study the notions of statistical Cesàro summability and statistical deferred Cesàro summability of sequences of functions via the Laplace transform. First, we establish new inclusion theorems between these notions, supported by illustrative examples. We then apply these techniques to obtain the inverse Laplace transforms of sequences of functions. Using this framework, we derive significant results for solving higher-order ordinary differential equations and discuss applications to electrical circuit models, including the RL and LC circuits. Furthermore, we investigate the Laplace transforms of Fourier series by using Cesàro means of a sequence of positive kernel-based Laplace operators and establish some new results. Additionally, based on the proposed approach, we prove two new Korovkin-type theorems with the help of the trigonometric test functions 1, cos t, and sin t. An illustrative example involving a positive linear operator related to a new class of Fejér-type convolution operators is presented to demonstrate the applicability of the theoretical findings. Finally, the convergence behavior of the considered circuit models and operators is demonstrated graphically using MATLAB.

Cerium copper oxytelluride (CCOT) and lanthanum copper oxytelluride (LCOT) nanomaterials have been synthesized through a straightforward hydrothermal route to explore their potential applications in broadband photodetection and nonlinear optical (NLO) devices. Structural and morphological studies confirmed the successful synthesis of phase-pure, uniformly dispersed nanostructures. The indirect band gaps observed by UV-vis diffuse reflectance spectra (DRS) at 4.06 and 4.07 eV for CCOT and LCOT, respectively, in the visible range. Defect-assisted recombination was studied using photoluminescence (PL), and it influences charge-carrier dynamics. Measurements of photodetectors were found to have increased photoresponse to an illuminating condition, where LCOT had a higher photosensitivity (12.21) and detectivity (1.54 & times; 1010 Jones), and CCOT had a high responsivity (1.97 A/W). Temporal I-T experiments verified that switching behavior was stable, repeatable, with fast response and recovery times. Substantial third-order NLO responses with reverse saturable absorption (RSA) and self-focusing behavior were also observed in Z-scan studies, and CCOT exhibited some marginally higher nonlinear susceptibility (chi(3) approximate to 0.535 & times; 10-6 esu) than CCOT. NLO properties of these materials are supported through the two-photon absorption (2PA) and thermal effects. The synergistic photoresponse and NLO behavior of these Cu-based oxytelluride nanomaterials point to their potential for future optoelectronic, photonic, and optical limiting devices.

The intersection of open-source processor architectures and machine learning is driving the demand for customizable, efficient, and accessible hardware. This survey examines the state of the RISC-V ISA in machine learning applications, analyzing current capabilities, challenges, and future directions based on recent research. The analysis covers academic and commercial implementations, software frameworks, and real-world applications. The RISC-V machine learning ecosystem is evaluated, from instruction set extensions and core implementations to compiler optimizations and deployment strategies. Key contributions include a unified taxonomy of RISC-V ML implementations, a comparative analysis of performance and design trade-offs, an evaluation of software toolchain maturity, and the identification of emerging trends in instruction set extensions and specialized accelerators. Findings reveal progress in energy efficiency, specialized instruction development, and framework integration, while highlighting challenges in standardization, verification complexity, and ecosystem fragmentation. The analysis proposes four research directions to address current limitations: specialized neural processing extensions, adaptive and modular processor architectures, security frameworks, and energy-efficient multi-domain architectures. These directions provide a roadmap for advancing RISC-V as a foundational platform for next-generation machine learning systems.

While survival rates among breast cancer patients continue to improve, many women in India experience persistent physical, emotional, and social challenges during treatment. This study aimed to compare the quality of life (QOL) between reproductive-age women (≤ 49 years; youngest participant aged 35 years in the study sample) and older women (≥ 50 years) undergoing chemotherapy for breast cancer, and to identify factors associated with QOL in both groups to inform age-sensitive and supportive care practices. A cross-sectional study was conducted at a tertiary hospital in Bhubaneswar, India, involving 85 breast cancer patients who had completed at least three cycles of chemotherapy. QOL was assessed using the WHOQOL-BREF questionnaire. Association between independent variables and QOL were determined using an independent T-test and ANOVA. Multiple linear regression analysis was used to identify sociodemographic and clinical factors associated with overall QOL. The overall QOL score among participants was 60.8 ± 16.3. Age remained significantly associated with overall QOL after adjusting for sociodemographic and clinical variables. Reproductive-age women reported higher overall QOL (70 ± 10) compared to older women (47 ± 07). In the physical domain, younger women had the highest scores (83 ± 08), whereas older women had the lowest (24 ± 21). Poor overall QOL was significantly associated with older age (β = -28.174, p < 0.001), rural residence (β = -35.69 for environment domain), and advanced cancer stage (β = -38.62 for physical domain). Breast cancer affects QOL differently across age groups. Reproductive-age women demonstrated better physical health and stronger social support, while older women faced greater physical and psychological burden. These findings highlight the need for age-specific interventions, improved access to supportive services, and further longitudinal and mixed-methods research to enhance patient-centered oncology care.

This paper presents the design and numerical investigation of a microstrip patch antenna for microwave hyperthermia–based breast cancer treatment operating at 2.46 GHz in the industrial, scientific, and medical (ISM) band. The proposed antenna incorporates metamaterial-inspired features to enhance electromagnetic energy localization within the tumor region and is analyzed using CST Microwave Studio. Antenna performance is evaluated using a multilayer breast tissue phantom comprising skin, adipose tissue, and an embedded spherical tumor. Simulation results demonstrate a nearly linear relationship between input power and specific absorption rate (SAR), with peak SAR values increasing from 22.7 W/kg at 1 W to 89.9 W/kg at 5 W and a well-localized maximum at the tumor depth. Thermal analysis based on the bioheat transfer model indicates a controlled temperature rise in the tumor region from 37 °C to the therapeutic range of 42–43 °C within 10–15 min of exposure. With extended heating up to 20 min, peak tumor temperatures approach 45–46 °C while surrounding healthy tissue remains within safe thermal limits. These findings confirm the feasibility of the proposed antenna for localized, controllable, and safe microwave hyperthermia applications in breast cancer treatment.