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Phase transformations, morphology, and crystallographic texture evolution in hot and cold-rolled Ti-25.51 wt.% Nb alloys are investigated. The experimental procedure involves synthesis of the alloy by arc melting followed by cold or hot rolling with intermediate prior and postheat treatments. Composition and phase analysis of all alloys are conducted using x-ray diffraction techniques and microstructural observations are conducted using an optical microscope. These examinations reveal that the as-melted alloy possesses large millimeter size grains with no stored strain energy and a two phase β − α′ microstructure. Direct cold rolling followed by a short homogenization leads to a β − α′′ mixture with ω precipitates. Two hour annealing before cold rolling leads to an α′ − α′′ mixture with a characteristic triangular martensitic microstructure evidencing the act of shear on formation of the phase. Hot rolling followed by a water quench results in a β − α′′ mixture, while annealing prior to hot rolling transforms the arc-melted material to a α′ − α′′ mixture. The crystallographic textures of similar microstructure mixtures in hot and cold-rolled samples are distinctively different. The analysis shows that the microstructure serves as an identifying characteristic of the processing paths and is highly dependent on the mode of processing. [ABSTRACT FROM AUTHOR]

Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings' energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants' privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants' privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants' visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants' visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system.

Introduction: The prevalence of mild and major neurocognitive disorders (NCDs), also referred to as mild cognitive impairment and dementia, is rising globally. The prevention of NCDs is a major global public health interest. We sought to synthesize the literature on potentially modifiable risk factors for NCDs., Methods: We conducted an umbrella review using a systematic search across multiple databases to identify relevant systematic reviews and meta-analyses. Eligible reviews examined potentially modifiable risk factors for mild or major NCDs. We used a random-effects multi-level meta-analytic approach to synthesize risk ratios for each risk factor while accounting for overlap in the reviews. We further examined risk factors for major NCD due to two common etiologies: Alzheimer's disease and vascular dementia., Results: A total of 45 reviews with 212 meta-analyses were synthesized. We identified fourteen broadly defined modifiable risk factors that were significantly associated with these disorders: alcohol consumption, body weight, depression, diabetes mellitus, diet, hypertension, less education, physical inactivity, sensory loss, sleep disturbance, smoking, social isolation, traumatic brain injury, and vitamin D deficiency. All 14 factors were associated with the risk of major NCD, and five were associated with mild NCD. We found considerably less research for vascular dementia and mild NCD., Conclusion: Our review quantifies the risk associated with 14 potentially modifiable risk factors for mild and major NCDs, including several factors infrequently included in dementia action plans. Prevention strategies should consider approaches that reduce the incidence and severity of these risk factors through health promotion, identification, and early management., (© 2024 S. Karger AG, Basel.)

Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers., (Copyright © 2023. Published by Elsevier B.V.)

Purpose: To investigate the effect of technology-assisted Anterior Cruciate Ligament Reconstruction (ACLR) on post-operative clinical outcomes and tunnel placement compared to conventional arthroscopic ACLR., Methods: CENTRAL, MEDLINE, and Embase were searched from January 2000 to November 17, 2022. Articles were included if there was intraoperative use of computer-assisted navigation, robotics, diagnostic imaging, computer simulations, or 3D printing (3DP). Two reviewers searched, screened, and evaluated the included studies for data quality. Data were abstracted using descriptive statistics and pooled using relative risk ratios (RR) or mean differences (MD), both with 95% confidence intervals (CI), where appropriate., Results: Eleven studies were included with total 775 patients and majority male participants (70.7%). Ages ranged from 14 to 54 years (391 patients) and follow-up ranged from 12 to 60 months (775 patients). Subjective International Knee Documentation Committee (IKDC) scores increased in the technology-assisted surgery group (473 patients; P = 0.02; MD 1.97, 95% CI 0.27 to 3.66). There was no difference in objective IKDC scores (447 patients; RR 1.02, 95% CI 0.98 to 1.06), Lysholm scores (199 patients; MD 1.14, 95% CI - 1.03 to 3.30) or negative pivot-shift tests (278 patients; RR 1.07, 95% CI 0.97 to 1.18) between the two groups. When using technology-assisted surgery, 6 (351 patients) of 8 (451 patients) studies reported more accurate femoral tunnel placement and 6 (321 patients) of 10 (561 patients) studies reported more accurate tibial tunnel placement in at least one measure. One study (209 patients) demonstrated a significant increase in cost associated with use of computer-assisted navigation (mean 1158€) versus conventional surgery (mean 704€). Of the two studies using 3DP templates, production costs ranging from $10 to $42 USD were cited. There was no difference in adverse events between the two groups., Conclusion: Clinical outcomes do not differ between technology-assisted surgery and conventional surgery. Computer-assisted navigation is more expensive and time consuming while 3DP is inexpensive and does not lead to greater operating times. ACLR tunnels can be more accurately located in radiologically ideal places by using technology, but anatomic placement is still undetermined because of variability and inaccuracy of the evaluation systems utilized., Level of Evidence: Level III., (© 2023. The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).)

Background: Rare diseases are a new global health priority, requiring evidence-based estimates of the global prevalence of diseases to inform public policymakers and provide a serious challenge to the healthcare system that must not be ignored. The purpose of this study is to investigate Iranian future healthcare professionals' knowledge and opinions about rare diseases., Results: A total of 6838 students responded to the questionnaire. Nursing and medical students had the highest participation. Almost 85% of participants rated their knowledge about rare diseases as poor or insufficient. While nearly 70 percent of participants took courses about rare diseases at university. Finally, 72.7% of future healthcare professionals did not feel ready to take care of a patient with a rare disease., Conclusion: The present study has indicated a gap in Iranian medical students' knowledge of rare diseases. The researchers believe that health science policymakers should make a joint effort to improve knowledge about rare diseases. Including courses with regard to rare diseases would be of benefit to future healthcare professionals., (© 2022. The Author(s).)

In this study, a novel thorium metal organic framework was synthesized, characterized and used as a sorbent for very efficient pipette tip micro solid-phase extraction of bisphenol A in bottled drinking water samples using high-performance liquid chromatography as detecting instrument. Parameters which influence extraction efficiency such as pH, sample volume, amount of sorbent, type and volume of eluent, number of aspirating and dispensing cycles for extraction and elution, and volume of the sample solution were studied and optimized. A linear calibration curve was obtained in the range of 0.002-0.456 ng mL-1 (r2 = 0.996) with a detection limit of 0.0010 ng mL-1. Repeatability of batch-to-batch extraction was better than 5.0% and a reproducibility of 3.2% for real samples obtained., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Many individuals suffer from ailments such hypertension that require frequent health monitoring. Unfortunately, current technology does not possess the ability for unobtrusive, continuous monitoring. This paper proposes estimation of pulse pressure based on pulse transient time determined from one non-contact, and one contact sensor: Doppler radar for non-contact detection of heart beat, and piezoelectric finger pulse sensor. The time delay between heart beat and finger pulse was determined using MATLAB software, and pulse wave velocity (PWV) was calculated. Finally, subjects' pulse pressure estimated using PWV was found to be in good agreement with pulse pressure measured using an off the shelf sphygmomanometer by reading and taking difference of systolic and diastolic blood pressure.

Respiration monitoring using microwave Doppler radar has attracted significant interest over the last four decades due to its non-invasive and non-contact form of measurement. However, this technology is still not at the level of practical implementations in healthcare due to motion artifacts and interference from multiple subjects within the range of the Doppler radar sensor. Most reported results in literature focus only on single subject measurements because when multiple subjects are present there are interfering respiration signals which are difficult to separate as individual respiration signals. This paper investigates the feasibility of separating respiratory signatures from the multiple subjects. We employed a new approach using Independent Component Analysis (ICA) with the Joint Approximate Diagonalization of Eignematrices (JADE) algorithm to achieve this for closely spaced subjects, and the system is also capable of estimating Direction of Arrival (DOA) for well-spaced subjects. Experimental results demonstrated that the ICA-JADE method can separate respiratory signatures from two subjects one meter apart from each other at a distance from the radar of 2.89 meters. The separated respiratory pattern closely correlates with reference chest belt respiration patterns, and the mean square error is approximately 11.58%. Concisely, this paper clearly demonstrates that by integrating ICA with the JADE algorithm in a Doppler radar physiological monitoring system, multiple subjects can be monitored simultaneously.

Occupancy information and occupant counts can save significant amount of energy for occupant related building automation systems. Doppler radar occupancy detection sensor not only can detect the human presence but also has the potential to count the number of occupants. Continuous wave Doppler radar monitoring system is employed for occupant count estimation. The received signal strength (RSS) which is directly related to radar cross section is used as a measure for occupant counts. The preliminary results show RSS is a promising tool for occupant number estimation.

Doppler radar can monitor vital sign wirelessly. Respiratory and heart rate have time-varying behavior. Capturing the rate variability provides crucial physiological information. However, the common time-frequency methods fail to detect key information. We investigate Synchrosqueezing method to extract oscillatory components of the signal with time varying spectrum. Simulation and experimental result shows the potential of the proposed method for analyzing signals with complex time-frequency behavior like physiological signals. Respiration and heart signals and their components are extracted with higher resolution and without any pre-filtering and signal conditioning.

In this paper, a bio-impedance analysis (BIA) based wrist-worn heartbeat monitoring system is proposed. The system is able to estimate heart rate from a subject's wrist with only four electrodes. The design is achieved with a standard BIA device and off-the-shelf components for signal conditioning. The measured heartbeat-related impedance signal is compared with a reference heart rate signal obtained from piezoelectric finger pulse transducer. The BIA results agree with the reference, which validates the feasibility of the proposed system. To the best of our knowledge, this is the first reported BIA heartbeat monitoring system in the wristband configuration.

In the present work, a simple and sensitive electrochemical sensor based on magnetic Fe3O4 nanoparticles modified carbon paste electrode (CPE) was introduced for detection of low level with 0.21μM of warfarin. Under the optimum experimental conditions the oxidation peak current of warfarin was used for its monitoring for the first time. The analytical curve was linear for warfarin concentrations from 0.5 to 1000μM with a limit of detection of 0.21μM by square wave anodic stripping voltammetry (SWASV). The proposed sensor showed excellent stability and was used for the determination of warfarin in tablet, human serum and urine with satisfactory results., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Packet Doppler radar is investigated for extracting physiological signals. System on Chip is employed as a signal source in packet mode, and it transmits signals intermittently at 2.405 GHz to save power. Reflected signals are demodulated directly by spectral analysis of received pulses in the baseband. Spectral subtraction, using data from an empty room, is applied to extract the periodic movement. It was experimentally demonstrated that frequency of the periodic motion can be accurately extracted using this technique. Proposed approach reduces the computation complexity of the signal processing part effectively.

A design for a physiological radar monitoring system (PRMS) that can be integrated with clinical sleep monitoring systems is presented. The PRMS uses two radar systems at 2.45 GHz and 24 GHz to achieve both high sensitivity and high resolution. The system can acquire data, perform digital processing and output appropriate conventional analog outputs with a latency of 130 ms, which can be recorded and displayed by a gold standard sleep monitoring system, along with other standard sensor measurements.

Human respiratory effort can be harvested to power wearable biosensors and mobile electronic devices. The very first step toward designing a harvester is to estimate available energy and power. This paper describes an estimation of the available power and energy due to the movements of the torso during breathing, using Doppler radar by detecting breathing rate, torso displacement, torso movement velocity and acceleration along the sagittal movement of the torso. The accuracy of the detected variables is verified by two reference methods. The experimental result obtained from a healthy female human subject shows that the available power from circumferential movement can be higher than the power from the sagittal movement.

Non-contact Doppler radar has been used extensively for detection of physiological motion. Most of the results published to date have been focused on estimation of the physiological rates, such as respiratory rate and heart rate, with CW and modulated waveforms in various settings. Accurate assessment of chest displacement may take this type of monitoring to the new level, by enabling the estimation of associated cardiopulmonary volumes, and possibly pulse pressure. To obtain absolute chest displacement with highest precision, full nonlinear phase demodulation of the quadrature radar outputs must be performed. The accuracy of this type of demodulation is limited by the drifting received RF power, varying dc offset, and channel quadrature imbalance. In this paper we demonstrate that if relatively large motion is used to calibrate the system, smaller motion displacement may be acquired with the accuracy on the order of 30 µm.

System-on-Chip (SoC) based Doppler radar occupancy sensor is developed through non contact detection of respiratory signals. The radio was developed using off the shelf low power RF CC2530 SoC chip by Texas Instruments. In order to save power, the transmitter sends signal intermittently at 2.405 GHz. Reflected pulses are demodulated, and the baseband signals are processed to recover periodic motion. The system was tested both with mechanical target and a human subject. In both cases Doppler radar detected periodic motion closely matched the actual motion, and it has been shown that an SoC based system can be used for subject detection.