Systems Technology (United States)

A female adult patient presented with a constellation of gastrointestinal and systemic symptoms including bloating, altered bowel function, fatigue, cognitive complaints, and progressive musculoskeletal discomfort. Given the patient’s history of prior unresolved gastrointestinal issues and the absence of a unifying conventional diagnosis, a functional medicine-informed evaluation was pursued. Baseline symptom burden was assessed using the Medical Symptom Questionnaire (MSQ) and the Gastrointestinal Symptom Rating Scale (GSRS), the latter used with formal permission granted by AstraZeneca. A comprehensive stool analysis (GI Spotlight™ stool test) was obtained and used to help inform dietary and nutraceutical intervention strategies targeting digestive function, microbial balance, and intestinal barrier support. The patient underwent a personalized gut-focused intervention consisting of dietary modification and a targeted nutraceutical protocol designed to support digestive function, microbial balance, intestinal barrier integrity, and inflammation modulation. Symptom outcomes were tracked over approximately ten weeks with follow up assessments at five and ten weeks. Compared with baseline, total MSQ scores improved by 58.7% at the first follow-up and 82.7% by the final assessment. Improvements were observed across multiple domains, including digestive, musculoskeletal, energy, cognitive, and weight-related symptom categories. GSRS domain scores demonstrated parallel reductions in gastrointestinal symptom severity over the same period. No adverse effects were reported. This case report highlights the potential for symptom improvement across gastrointestinal and extraintestinal domains following a comprehensive, gut-focused intervention. While causality cannot be established, the findings underscore the interconnected nature of gastrointestinal and systemic symptoms and support further investigation into individualized, microbiome-informed approaches.

Fine-grained power estimation is fundamental to modern MPSoC design and operation, enabling early power and thermal budgeting before fabrication, predictive thermal control at runtime, post-silicon microarchitectural energy debugging, and hardware security analysis. Existing System-on-Chip (SoC) designs often rely solely on total power consumption measurements because of the cost, size, and complexity of power sensors. However, the lack of precision and granularity in power measurements leads to inefficient power and thermal management and limits the ability to detect failed or compromised sensors. To tackle these challenges, we propose the Improved Clustering Blind Power Identification (ICBPI) approach, an innovative approach for fine-grained power estimation in SoCs. ICBPI leverages total power measurements and per-unit temperature data, even with potentially unreliable sensors, to deliver accurate per-unit power estimates. This dual focus not only enhances power estimation, leading to better power management decisions, but also improves the detection of malicious sensors—such as those compromised by hardware Trojan attacks—that manipulate sensor data to induce potential system failures. Extensive evaluations across four SoC architectures demonstrate ICBPI’s superior performance in reducing per-core power estimation errors by \({77.56\%} \) compared to the baseline Blind Power Identification (BPI) and by \({68.44\%} \) compared to the state-of-the-art approaches. Furthermore, ICBPI achieves \({100\%} \) detection of malicious thermal sensor attacks and enhances the localization process compared to the Blind Identification Countermeasure (BIC) approach. In the comparative analysis between ICBPI and the most recent model, Alternating Blind Power Identification (ABPI), ICBPI shows error rates as low as \({0.5\%} \) compared to \({2.8\%} \) in a 2 × 2 mesh benchmark and outperforms ABPI on heterogeneous architectures. The NVIDIA Jetson Xavier AGX development board serves as a platform for validating ICBPI. Consequently, ICBPI demonstrates an approximate reduction in average error of \(79.71\% \) on the CPU and \(79.59\% \) on the GPU relative to three existing state-of-the-art approaches.

Split-thickness skin grafts are an effective therapy for treating large skin wounds; however, patients may experience chronic pain and dysesthesia from poor regeneration of sensory nerves into the harvest site. Endogenous and exogenous electrical fields are known to direct axon regeneration and support epithelial wound healing. This study aimed to demonstrate the efficacy of a novel therapeutic electrical stimulation protocol, asymmetric charge-balanced (ACB) waveforms, to aid in peripheral nerve regeneration and wound healing. Split-thickness skin harvests were collected from 4 Thy1-GFP rats, with each rat receiving 2 harvests. After this, rats received electrical stimulation on 1 of their wounds for 5 days using ACB waveforms. After the animals were killed, the harvest sites were collected, and fluorescence levels were quantified using ImageJ software. Split-thickness skin harvests were collected from 4 Long-Evans rats that were stimulated for 5 days using ACB waveforms. Equivalent wound resistance was characterized to quantify the rate of wound healing. Increased peripheral nerve regeneration was observed in the split-thickness skin harvest sites of the stimulated group compared with the control group. Impedance measurements showed significantly increased impedance in wounds that underwent stimulation compared with sham controls, suggesting accelerated wound healing in the stimulated group. The authors' work suggests a potential noninvasive strategy, translatable to humans, for enhancing healing of split-thickness skin harvest sites that could minimize associated morbidity. This study shows that pulsed asymmetric biphasic electrical fields regenerate peripheral nerves and accelerate wound healing in split-thickness skin graft donor sites, offering a promising approach to reduce chronic pain, hyperesthesia, and pruritus, while restoring normal sensation to patients.

This paper establishes Cyclograms as a novel rainflow-counting-based method for analyzing aircraft data for the purpose of handling qualities evaluations. Conventional metrics such as Adequate and Desired performance percentages provide useful compliance measures but fail to capture the dynamic structure of tracking errors. The proposed approach leverages rainflow algorithms to transform time-domain tracking errors into structured cycle-based representations, enabling visualization of the underlying tracking error structure. Using archival flight test data from a variable-stability aircraft, sum-of-sines tracking tasks were performed under baseline, failure, and adaptive cueing configurations. Using the data set consisting of over 90 rated runs, Cyclogram-derived metrics were evaluated for statistical significance. Principal Component Analysis and correlation studies reveal strong alignment between Cyclogram metrics and traditional measures, with Cyclogram based metrics exhibiting the highest correlation to awarded Handling Qualities Ratings (HQRs). Results demonstrate that Cyclograms provide scalable visualization, capture dynamic performance trends, and offer robust quantitative indicators consistent with Cooper-Harper ratings. These findings support Cyclograms as a practical addition to the handling qualities evaluation toolkit.

This review paper provides a comprehensive overview of logistics behavior analysis and predictive modeling methods based on multimodal data fusion. The increasing complexity of modern logistics necessitates advanced analytical techniques to optimize operations, enhance efficiency, and improve decision-making. Multimodal data fusion, which integrates diverse data sources such as GPS tracking, sensor data, transaction records, and weather information, offers a powerful approach to understanding and predicting logistics behavior. This paper explores the historical evolution of logistics analytics, focusing on the shift from traditional statistical methods to contemporary machine learning techniques. Core themes examined include data acquisition and preprocessing, feature engineering for logistics applications, and the application of various predictive models such as regression models, time series analysis, and deep learning algorithms. A comparative analysis highlights the strengths and weaknesses of different modeling approaches, addressing challenges related to data heterogeneity, scalability, and real-time processing. Furthermore, the review identifies emerging trends and future research directions in multimodal logistics data analysis, including the integration of blockchain technology, the utilization of edge computing, and the development of explainable AI models. This paper aims to serve as a valuable resource for researchers and practitioners seeking to leverage multimodal data fusion for improved logistics management, offering insights into the state-of-the-art techniques and future opportunities in this rapidly evolving field.

Biometric authentication is widely regarded as more secure and reliable than conventional approaches like passwords and PINs. Nonetheless, many current systems rely on active user participation, such as fingerprint scanning or facial recognition, which can disrupt tasks, increase the likelihood of errors, and raise privacy concerns. To address these challenges, this study introduces a continuous, seamless authentication framework that utilizes vital signs for passive identity verification across various activities, including resting, walking, and running. The framework analyzes physiological indicators such as Heart Rate (HR), Heart Rate Variability (HRV), Skin Temperature, Peripheral Oxygen Saturation (SpO2), and Breathing Rate to provide zero-effort authentication without requiring user intervention. Multiple machine learning algorithms, including Decision Tree, Random Forest, XGBoost, Gradient Boosting, and K-Nearest Neighbors, were implemented and compared to identify the most effective predictive model. The methodology involved data collection, preprocessing, model construction, evaluation, and comparison. Experimental results revealed that the XGBoost Classifier achieved the highest accuracy at 96%. Overall, the proposed framework demonstrates strong reliability, scalability, adaptability, and flexibility, making it suitable for practical deployment. By continuously verifying identity without interrupting user activity, it improves both security and usability, offering a modern and convenient alternative to traditional authentication methods.

In the mining industry, the reactive maintenance of mining equipment significantly affects miners, original equipment manufacturers (OEMs), and aftermarket parts suppliers financially. This paper aims to design a conceptual architecture for a proposed digital twin-based condition monitoring system for predictive maintenance, grounded in a case study. The design is organised into three groups of technologies: operational technologies, data transmission and storage technologies, and digital twin technology. These technologies are integrated to provide a real-time or near realtime condition assessment of mining equipment, enabling advanced planning for maintenance. The implementation of this system will yield financial benefits for all stakeholders, including miners, OEMs, and parts suppliers.

PBF-LB/M offers efficient production of complex and functional metal components. Research shows that the mechanical performance of components can be enhanced by incorporating biomimetic shapes, such as banana pseudo stem-inspired biomimetic beams. This work addresses three mechanical topology optimization problems, using AlSi10Mg. Leveraging these models, novel component designs featuring biomimetic beams and novel node designs were developed based on the input topology optimization. The new biomimetic component designs were compared to their topology optimization counterparts that were used as references. Numerical analyses of mass and structural integrity were performed to evaluate improvements. Lightweight biomimetic component designs for additively manufactured components were developed using the developed methods. Weight savings of 12.5-30.3 % were achieved compared to the input topology optimization results. However, further research on the design methodologies is needed to ensure that the mechanical stress criterion is also met within the nodes of the generated biomimetic designs.