Vanguard University

This paper presents a novel temperature compensation approach for dual-axis Micro–Electro–Mechanical System (MEMS) accelerometers, integrating Adaptive Mode Decomposition (AMD) with Grey Wolf Optimization (GWO) and Hybrid Convolutional–Recurrent Temporal Network (HCR-TN). The proposed method aims to address temperature-induced bias drift, which significantly affects accelerometer performance. Experiments were conducted across a temperature range from −40 °C to +60 °C to evaluate the effectiveness of the compensation algorithm. The results show considerable improvements in bias stability, with the compensation method successfully reducing temperature-induced drift across both axes. Additionally, the algorithm was tested under realistic conditions, including noise and mechanical disturbances, demonstrating its robustness in practical applications. These findings highlight the potential of the proposed method for enhancing the reliability and accuracy of MEMS accelerometers in real-world sensing environments.

Abstract This paper investigates a nested biaxial micro-electro-mechanical systems (MEMS) accelerometer with low coupling error, derives the kinetic model equations of the accelerometer, and performs finite element simulations of the operating modes and mechanical coupling of the structure. The accelerometer uses an inner and outer nested structure with non-rigid links to reduce cross-coupling between orthogonal sensitive axes. Closed-loop control is employed to further improve the device stability and reduce the coupling coefficient. The preparation and testing of the structure was finally completed. Without compensation, tests show that the accelerometer works in closed-loop mode with low cross-coupling, and the inter-axis cross-coupling coefficients are less than 1.31 % and 2.4 %, respectively. The accelerometer exhibits an X-axis output drift of 1.26 mg/°C and a Y-axis output drift of 0.91 mg/°C in its natural state over the full temperature range of −40 °C to 60 °C. After compensation by the temperature compensation system, the X-axis output bias is optimized to 0.02008 mg/°C and the Y-axis output bias to 0.02041 mg/°C.

Abstract Disclosure: R. Elangovan: None. K. Elangovan: None. J.R. Sethuraj: None. E. Krishnan: None. G. Palaniswamy: None. S. Nanduri: None. K. Sakalabaktula: None. U. Qureshi: None. F. Rahman: None. Z. Baloch: None. Z. Ahmed: None. S. Biswas: None. R. Muralidhar: None. C. Robert: None. K.J. Patel: None. G. Rajamani: None. N. Vora: None. S. Malapati: None. K. Noor: None. M. Kaur: None. A.S. Agrawal: None. A. Franklin Johnson: None. P. Uvaraj: None. T. Dontulwar: None. F. Madhumithaa Jagannathan: None. P.C. Sirait: None. M. Amro Alrouh: None. S. Tamma: None. S.S. Bhurchandi: None. S.K. Bhurchandi: None. Introduction: Endocrine cancers present critical challenges due to their systemic hormonal effects and intricate diagnostic profiles. With nearly 10 million cancer-related deaths annually, innovative solutions are imperative. Artificial intelligence (AI) is revolutionizing endocrinology by achieving unparalleled diagnostic precision in endocrine malignancies, offering scalable tools to reduce diagnostic errors and improve outcomes. AIM: This study aimed to develop and validate cutting-edge AI models for endocrine tumor evaluation, using multimodal data, ensuring their global generalizability, and deployment feasibility through a privacy-preserving, universally accessible application to promote equitable cancer care. Methods: Advanced deep learning architectures (EfficientNets and ResNets) were rigorously tested on anonymized multimodal datasets (CT, MRI, ultrasonography, cytopathology, and histopathology) from endocrine tumors in the thyroid, pancreas, pituitary gland, adrenal glands, and ovaries. Internal evaluations ensured model reliability, while external testing across diverse datasets from six continents validated generalizability. An online platform was developed, enabling users to test the models with their own data locally. Real-world applicability was confirmed through independent assessments by healthcare professionals worldwide. Results:EfficientNets and ResNets achieved exceptional diagnostic performance across multiple endocrine tumors (AUROC >99%) and robust generalizability. EfficientNetB0 excelled in treatment response monitoring (>97%). When deployed in an online application, EfficientNetB0 exhibited superior computational efficiency, while maintaining rapid processing speeds (<1 second/image) and diagnostic accuracy. Multi-national healthcare professionals validated the online platform’s reliability and usability, underscoring its global applicability. Conclusion: This study demonstrates the transformative impact of AI in endocrine oncology. Our findings illustrate a paradigm shift in biomedical AI, where minimal computational resources yield maximal diagnostic performance. This highlights the potential of AI-driven approaches to optimize diagnostic workflows, even with minimal computational resources, revolutionizing biomedical AI applications in cancer care. APPLICATION: The AI-powered application provides a privacy-preserving, scalable solution for endocrine tumor detection, classification, treatment monitoring, and advanced clinical decision-making. Validated multi-nationally by healthcare professionals, it ensures robust real-world applicability, fostering equitable cancer care globally. Its adaptability across platforms positions it as a universal tool for cancer care worldwide. Presentation: Sunday, July 13, 2025

This paper presents a new quantitative model to study the interplay across CIA (Confidentiality, Integrity, Availability) Triad and performance in blockchain-based crypto space with specific reference to Ethereum or Ethereum-equivalent chains. The model introduces and incorporates three new random variables on top of the baseline chain model [16], C: the likelihood to secure confidentiality; $\boldsymbol{I}$: to secure integrity; and $\boldsymbol{A}$: to secure availability. Thus, the model will orchestrate an extensive set of key random variables such as λ: transaction slot arrival rate; μ: block posting rate; $\boldsymbol{i}$: number of transaction slots pending on the current block along with $\boldsymbol{C}, \boldsymbol{I}$ and $\boldsymbol{A}$. The underlying mathematical method employed is an embedded Markovian queueing model as the model traces the stochastic flow of the transactions as well as the Markovian flow of them with respect to $\boldsymbol{C}, \boldsymbol{I}$ and $\boldsymbol{A}$. The state in the model is defined by $P_{i_{C / I / A}}$, i.e., the likelihood to have i number of transaction slots pending on the current block and the stochastic CIA status of the crypto space thus far is in C or $\bar{C}, I$ or $\overline{\boldsymbol{I}}$, and $\boldsymbol{A}$ or $\overline{\boldsymbol{A}}$. The solutions to the model will be provided to assess a few basic performance metrics such as W: the average transaction waiting time, $L:$ the average block capacity required; and G: the throughput of transactions per block. And further and primarily, a unique and extensive simulation and analysis will be conducted to evaluate the impact of base random variables such as i, λ, μ, and various combinations of $\mathcal{C}, I$ and A on the overall $P_{i_{C / I / A}}$ in steady state. The results of the simulation reveal tradeoffs between the CIA Triad and performance that is uniquely identifiable by the proposed model.

In large-scale distributed systems, log data grows rapidly and poses significant challenges to storage and transmission. Traditional general-purpose compression methods often fail to exploit the structural characteristics of logs, leading to suboptimal compression performance. Existing log parsingbased compression methods exhibit unstable performance in parameter mapping dictionaries and fail to account for nonnumerical data compression. To address the two problems, we proposes LogTRV, a log compression framework that integrates log parsing with multi-level encoding strategies. The framework first utilizes the TCMS parser to separate log templates and parameters. It then applies a series of data processing models, including timestamp delta encoding, token run-length encoding, and numeric variable-length encoding, to reduce redundancy. Finally, the processed data is compressed using a general-purpose algorithm (e.g., bzip2). Experiments on large-scale real-world log datasets demonstrate that LogTRV achieves significantly higher compression ratios than baseline methods such as gzip, bzip2, and Logzip, while maintaining competitive compression efficiency. The results also highlight the effectiveness of different parsing and encoding modules within LogTRV.

Alzheimer’s disease (AD) is a disorder that causes degeneration of brain cells, cognitive decline, and memory loss. AD is characterized by both the accumulation of tau proteins and the amyloid plaques, which inhibit cell function. Cognitive symptoms are considered manifestations of late-stage AD, though other manifestations such as sleep alterations occur long before these symptoms. Moreover, specific subcortical areas of the brain are affected very early on in AD progression, even before cognitive structures such as the hippocampus. One notable region is the hypothalamus, which regulates circadian rhythms, sleep-wake structure, and other metabolic signals. The hypothalamus is significant in AD progression due to its protective qualities and influence on disease development. This paper investigates the regulation of circadian and sleep-wake cycles within the hypothalamus and how desynchronization may contribute to AD pathogenesis in preclinical and early stages of the disease. Because the hypothalamus is a complex and varied structure, other processes of the hypothalamus are also discussed due to their influence on the sleep-wake cycle. Circadian rhythms also vary among different populations, potentially affecting AD onset. Understanding the influence of circadian and sleep-wake cycles on early AD pathology can provide insight into new interventions and therapeutics preceding later stages of the disease.

O presente trabalho propõe analisar a construção da produção artística e estabelecer um paralelo comparativo entre Pablo Picasso e Candido Portinari, observando seus processos criativos no contexto dos movimentos de Vanguarda, no sentido da construção de expressões próprias e, no caso do pintor brasileiro, de identidade local. Propõe-se apontar, nas obras de Portinari, as relações de negação e ao mesmo tempo de incorporação da estética europeia. Com suporte em autores como Mendonça Teles e Müller-Bergh (2007 ), Giunta (2011 ) e Jozef (2005), busca-se identificar aqui as influências do cubismo picassiano nas obras de Portinari no contexto da arte brasileira.

Inflation-sensitive investors who have an explicit goal of hedging inflation in the short to medium term cannot rely on traditional portfolio optimization to achieve their desired inflation hedge because common frameworks focus on long-term wealth maximization and total return efficiency rather than an inflation-hedging objective. When it comes to measuring inflation, an investor’s personalized consumption basket can also differ by archetype and result in anticipated spending needs that materially differ from broad measures of inflation such as the Consumer Price Index. In this article, the authors introduce a methodology to systematically construct portfolios through an expected utility of wealth maximization approach that is also tailored to an investor’s personalized inflation rate (PIR) over the short to medium term. This framework can balance the likelihood that the portfolio will outperform an investor’s PIR in the long run with the likelihood that it will achieve a specific beta to PIR in the near term. A novel framework is presented for how to calculate an investor’s PIR and allocate to assets with greater inflation-hedging properties such as commodities and, to a lesser extent, Treasury Inflation-Protected Securities to hedge inflation volatility in the short to medium term or equities and other risky assets to grow the portfolio in excess of inflation over the long term.