Real-time Data Research Articles

Watermarking algorithm based on phase-only CGH in fractional Hartley domain for DICOM images

In this manuscript, we proposed a watermarking algorithm based on phase-only computer-generated holography (CGH) in the fractional Hartley domain for digital imaging and communications in medicine (DICOM) images. The proposed algorithm improves the security of the CGH-based algorithm. The cascaded use of fractional Hartley transform and attenuation factor β increases the keyspace of the proposed watermarking algorithm. The robustness and effectiveness of the proposed watermarking algorithm is validated using simulations on DICOM images. The effectiveness of the proposed watermarking algorithm is assessed using statistical tools in terms of mean-squared error, information entropy, correlation coefficient, histogram, and mesh plots. The robustness is evaluated by testing the proposed algorithm’s performance under real-time threats, including contamination and data loss attacks. Furthermore, the security of the proposed algorithm is also tested for existing cryptographic attacks, such as chosen-plaintext attacks, and known-plaintext attacks. The simulation results indicate that the proposed watermarking algorithm is robust and effective.

Real-time data visualization of welding robot data and preparation for future of digital twin system

The application of industrial technologies is undergoing significant changes. Finding the level at which to use efficient cyberphysical systems is perhaps one of the most important technical preparatory tasks in implementing digital manufacturing. Welding technology systems are investigated, and a framework for capturing the data sets required for data-driven manufacturing is developed. To make full autonomy in a manufacturing environment meaningful, formerly isolated groups of equipment need to be organized into a production information system. In our research, a test system is created that can implement a digital virtual interface and achieve new levels of efficiency with a future digital twin system. In the discourse of the study, the technological parameters of welding test pieces were investigated, namely the available measurement data sets of current, and voltage data. In the summary section, most of the tasks and research directions are presented, which can be envisaged as a continuation of the present study. Our study will be followed by further research, already testing a complete digital twin system, thus reaching another milestone on the way to autonomous manufacturing.

Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.

Epilepsy, the tendency to have recurrent seizures, can have various causes, including brain tumors, genetics, stroke, brain injury, infections, and developmental disorders. Epileptic seizures are usually transient events. They normally leave no trace after the postictal recovery period has passed. An electroencephalogram (EEG) can only detect brain activity during the recording. It will be detected if an epileptogenic focus or generalized abnormality is active during the recording. This work demonstrated a smart seizure detection system for Healthcare IoT, which is a challenging problem of EEG data analysis. The study suggested an integrated methodology in recognition of the drawbacks of manual identification and the significant negative effects of uncontrollable seizures on patients' lives. The research shows remarkable accuracy, up to 100% in some experiments, by combining classifier ensembles like Decision Trees, Logistic Regression, and Support Vector Machine with different signal processing techniques like Discrete Wavelet Transform, Hjorth Parameters, and statistical features. The results were compared using the kNN classifier, compared with other datasets and other state-of-the-art techniques. Healthcare IoT is further utilized by the methodology, which takes a comprehensive approach using classifier ensembles and signal processing approaches resulting in real-time data to help them make better decisions. This demonstrates how well the suggested method works for smart seizure detection, which is a crucial development for better patient outcomes.

Passive single-sphere multi-detector spectrometry system for profiling of neutron fields in the thermal to 20 MeV energy range

This study explores the development and application of a custom-designed single-sphere multi-detector spectrometer (SSMDSS) for characterizing neutron fields. Stray neutrons in reactor and accelerator facilities present complex, energy-diverse fields. Accurately assessing these fields is crucial for personnel safety. The SSMDSS system is designed to address this challenge, offering a compact and passive dosimetry solution for constrained workspaces. We studied the performance of SSMDSS in two environments: radio-nuclide based neutron sources and stray neutron fields at a research reactor hall. Our investigation demonstrates that SSMDSS provide reliable neutron energy distributions. The performance of the SSMDSS was compared with commercially available ROSPEC system. SSMDSS exhibits superior resolution due to its design, leading to a more accurate measurement of low-energy fluence when compared with ROSPEC. This distinction is reflected in various measured parameters, including average energy and fluence-to-dose conversion coefficients. Inside the reactor hall, SSMDSS measured a fluence-to-dose conversion coefficient of 121 pSv.cm2 compared to ROSPEC's 83 pSv.cm2. Additionally, SSMDSS measured a higher average energy (0.25 MeV) compared to ROSPEC (0.14 MeV). While ROSPEC remains a valuable tool for real-time data collection in dynamic fields with high gamma backgrounds, SSMDSS presents itself as a viable alternative for neutron spectrometry in neutron work fields due to its compact design and passive dosimetry capabilities. This makes it particularly suitable for deployment in constrained workspaces with potential stray neutron exposure.

Data-Monitoring Solution for Desalination Processes: Cooling Tower and Mechanical Vapor Compression Hybrid System.

The advancement of novel water treatment technologies requires the implementation of both accurate data measurement and recording processes. These procedures are essential for acquiring results and conducting thorough analyses to enhance operational efficiency. In addition, accurate sensor data facilitate precise control over chemical treatment dosages, ensuring optimal water quality and corrosion inhibition while minimizing chemical usage and associated costs. Under this framework, this paper describes the sensoring and monitoring solution for a hybrid system based on a cooling tower (CT) connected to mechanical vapor compression (MVC) equipment for desalination and brine concentration purposes. Sensors connected to the data commercial logger solution, Almemo 2890-9, are also discussed in detail such as temperature, relative humidity, pressure, flow rate, etc. The monitoring system allows remote control of the MVC based on a server, GateManager, and TightVNC. In this way, the proposed solution provides remote access to the hybrid system, being able to visualize gathered data in real time. A case study located in Cartagena (Spain) is used to assess the proposed solution. Collected data from temperature transmitters, pneumatic valves, level sensors, and power demand are included and discussed in the paper. These variables allow a subsequent forecasting process to estimate brine concentration values. Different sample times are included in this paper to minimize the collected data from the hybrid system within suitable operation conditions. This solution is suitable to be applied to other desalination processes and locations.

Realization of Urban Perception Art: Painting Expressions of Internet of Things Technologies in Urban Environments

INTRODUCTION: With the continuous progress of urbanization, people's perceptions and experiences of the urban environment are increasingly concerned. Traditional forms of artistic expression can no longer fully meet people's needs for urban perception. Therefore, it is especially important to explore new possibilities of urban perception art with the help of modern technology, especially intelligent technology.OBJECTIVES: The main purpose of this study is to explore the feasibility and effectiveness of utilizing advanced technology for urban perception art expression. Through an in-depth understanding of the urban environment and the perceptual needs of urban residents, as well as existing technological means, artistic expressions that can present urban perceptions more intuitively and vividly are developed.METHODS: This study adopts a combination of field research and art practice. Through urban observation and questionnaire surveys, the subjective experience and needs of urban residents for urban perception were collected. Then, using digital painting and video technology, combined with the principles of perception psychology, urban perception works with artistic and technological senses were designed.RESULTS: A series of urban perception artworks were designed in this study, covering all aspects of urban life, including architectural landscapes, transportation scenes, and humanistic customs. These works enable viewers to perceive the urban environment in a more intuitive and immersive way through digital painting and video technology, as well as real-time data and perceptual feedback.CONCLUSION: By exploring new ways of artistic expression of urban perception, this study provides urban residents with a richer and deeper experience of urban perception. The application of digital painting and video technology, as well as the interaction and feedback with urban residents, opens up new possibilities for the development of urban perceptual art. 

Disruption detection for a cognitive digital supply chain twin using hybrid deep learning

Recent disruptive events, such as COVID-19 and Russia–Ukraine conflict, had a significant impact of global supply chains. Digital supply chain twins have been proposed in order to provide decision makers with an effective and efficient tool to mitigate disruption impact. This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience. The proposed disruption detection module utilises a deep autoencoder neural network combined with a one-class support vector machine algorithm. In addition, long-short term memory neural network models are developed to identify the disrupted echelon and predict time-to-recovery from the disruption effect. The obtained information from the proposed approach will help decision-makers and supply chain practitioners make appropriate decisions aiming at minimizing negative impact of disruptive events based on real-time disruption detection data. The results demonstrate the trade-off between disruption detection model sensitivity, encountered delay in disruption detection, and false alarms. This approach has seldom been used in recent literature addressing this issue.

Emergency Department Visits for Pedestrians Injured in Motor Vehicle Traffic Crashes - United States, January 2021-December 2023.

Traffic-related pedestrian deaths in the United States reached a 40-year high in 2021. Each year, pedestrians also suffer nonfatal traffic-related injuries requiring medical treatment. Near real-time emergency department visit data from CDC's National Syndromic Surveillance Program during January 2021-December 2023 indicated that among approximately 301 million visits identified, 137,325 involved a pedestrian injury (overall visit proportion=45.62 per 100,000 visits). The proportions of visits for pedestrian injury were 1.53-2.47 times as high among six racial and ethnic minority groups as that among non-Hispanic White persons. Compared with persons aged ≥65 years, proportions among those aged 15-24 and 25-34 years were 2.83 and 2.61 times as high, respectively. The visit proportion was 1.93 times as high among males as among females, and 1.21 times as high during September-November as during June-August. Timely pedestrian injury data can help collaborating federal, state, and local partners rapidly monitor trends, identify disparities, and implement strategies supporting the Safe System approach, a framework for preventing traffic injuries among all road users.

Optimizing Wireless Networks Performance Through Adaptive Machine Learning Strategies

This research aims to enhance wireless network performance through the development and implementation of a novel machine learning-based algorithm. The methodology involves comprehensive data collection in the Uttarakhand region, specifically in Dehradun, Haridwar, and Rishikesh, through LTE network drive tests. The collected data, over six months, serves as the foundation for the proposed algorithm. The research design focuses on parameterizing essential factors of path loss to optimize coverage. Various drive test tools, including NEMO, Agilent, TEMS, and XCAL-Mobile, are employed to measure and analyze field Received Signal Strength Indicators (RSSI) data. Statistical tools are utilized to collect network parameters, assess coverage, and analyze real-time mobile network data. The research objectives include the development and evaluation of the machine learning algorithm, comparing its performance across diverse network scenarios, and benchmarking against existing state- of-the-art algorithms. The proposed methodology provides a structured and relevant framework to achieve these objectives, fostering advancements in wireless network optimization through innovative machine-learning approaches.

Quantifying cigarette and e-cigarette marketing exposure among Chinese adolescents using ecological momentary assessment.

Addressing cigarette and e-cigarette use in China is key to reducing the global tobacco epidemic. Marketing exposure is one causal factor for adolescent smoking and e-cigarette use. Currently, China restricts cigarette and e-cigarette ads in public places and online; however, there may not be full policy compliance. We collected real-time data in the natural environment to estimate how much and where Chinese adolescents - a group susceptible to smoking and e-cigarette use - are exposed to cigarette and e-cigarette marketing to inform policy responses. In June 2022, we conducted a seven-day ecological momentary assessment (EMA) study with 15-16-year-olds (n=96) across eight Chinese cities. Participants completed up to 42 EMA surveys (6 per day), sent at random intervals outside of school hours. In each survey, participants reported whether they saw (1) displays and (2) ads in the past hour (none, cigarette, e-cigarette, both) in the past hour. We also captured the source of cigarette/e-cigarette ad exposure. Most participants were exposed to cigarette and/or e-cigarette displays (89.6%) or ads (79.2%) over the seven days. On average, participants reported past-hour exposure to displays 12.7 times and past-hour exposure to ads 10.8 times over the week. The most common sources of cigarette ads were public places (e.g., kiosks, supermarkets); the most common sources of e-cigarette ad exposure were social media/internet or e-cigarette stores. Findings highlight the need to enhance enforcement of restrictions on cigarette and e-cigarette ads in public places and online in China and extend restrictions to ban displays. Marketing exposure is a causal factor in youth smoking and e-cigarette use. We used ecological momentary assessments to estimate cigarette and e-cigarette display and ad exposure among Chinese adolescents. On average, participants reported past-hour exposure to cigarette and/or e-cigarette displays 13 times and past-hour exposure to cigarette and/or e-cigarette ads 11 times over one week. Most saw ads in public places and online. Results suggest strengthening implementation of China's ban on cigarette and e-cigarette ads in public places and online and banning product displays. These are policy responses that can contribute to reducing adolescent cigarette and e-cigarette uptake in China.

Abstract PO5-18-11: Use of DiviTum®TKa as a biomarker assay for CDK4/6 inhibitor medication compliance and drug-drug interaction assessment in ER/PR positive metastatic breast cancer

Abstract Background: CDK4/6 inhibitor (CDK4/6i) with endocrine therapy (ET) is standard first line (1L) treatment for metastatic hormone positive (ER/PR+) HER2- negative breast cancer (mBC). Some patients have progression free survival (PFS) of 2 years or more whereas some patients have very short PFS on these medications. Thymidine kinase is a biomarker that reflects cell proliferation. DiviTum®TKa is an assay that was cleared by the FDA in July 2022 based on data that showed that thymidine kinase activity (TKa) value of < 250 DiviTum units (DuA) is associated with the decreased likelihood of disease progression within 30 days or 60 days post testing. The purpose of this study is to assess the use of TKa as a biomarker for identifying potential medical compliance issues and drug-drug interactions (DDI) in patients on CDK4/6i and to evaluate whether counseling and optimization of concurrent medications will lead to lower TKa levels in subsequent treatment cycles and possibly longer PFS. Trial design: This is a pilot study to assess whether TKa can serve as a biomarker to identify patients with a suboptimal reduction in tumor cell proliferation caused by a potential CDK4/6i compliance or DDI issue. We will also assess whether correction of the issue can improve suppression of TKa levels in ER/PR+ mBC patients on 1L CDK4/6i and ET. For screening, we are utilizing a novel automated real-time data driven cohort identification system designed by Dr. Guannan Gong at Yale. Patients will be treated and monitored as per standard of care. An aliquot of serum obtained during routine blood draws will be sent to a CLIA-certified Biovica lab for TKa testing. TKa results will be returned in real time. Patients with a detectable TKa baseline level (above 145 DuA), followed by a sustained TKa level (>145 DuA) at cycle 1 D15 and D28 will be assessed for medication compliance and DDI. TKa levels will be repeated at subsequent standard of care blood draws to assess if the intervention affected TKa levels. Patients will be followed for 2 years. Eligibility criteria: Participants must have ER+ mBC and starting 1L CDK4/6i + ET and be previously CDK4/6i -naïve. Specific aims: Primary objective: to estimate the rate of improvement in CDK4/6i response (change in TKa levels) after counseling for medication compliance and adjustment of potential deleterious DDIs. Secondary objectives: 1) estimate the rate of sub-optimal CDK 4/6i response in cycle 1 of treatment. 2) Compare clinical benefit rate (CBR) in patients with sub-optimal and optimal CDK4/6i response after both cycles 1 and cycle 3. 3) Compare PFS in patients with sub-optimal and optimal CDK 4/6i response after both cycles 1 and cycle 3. 4) Assess CDK4/6i response via TKa levels upon CDK4/6i dose reductions or changes in CDK4/6i regimens. 5) Compare CDK4/6i response profiles across the three CDK 4/6i among different patients and within the same patients if CDK4/6i is changed throughout treatment course. 6) Correlate TKa levels with tumor marker levels. 7) Assess plasma concentrations of CDK4/6i in patients with suboptimal TKa levels. Statistical methods: For binary endpoints measured at a given time point, we will estimate the two-sided 95% exact CI using Clopper-Pearson method. With a sample size of 120 patients, assuming 20% drop out rate, we predict that about 15% of patients will have suboptimal CDK4/6i response, estimating a 95% CI of 0.086-0.235 in the first cycle. Comparisons between paired outcomes will be completed using the paired t-test or Wilcoxon signed-rank test for continuous parameters of interest, or McNemar’s Chi-square test for categorical parameters of interest. Time to event endpoints will be estimated using Kaplan-Meier method. Present accrual and target accrual: This trial has just been activated. Target accrual is 120 patients. Contact information for people with a specific interest in the trial: Mariya.Rozenblit@yale.edu Citation Format: Mariya Rozenblit, Adriana Kahn, Guannan Gong, Amy Williams, Lajos Pusztai. Use of DiviTum®TKa as a biomarker assay for CDK4/6 inhibitor medication compliance and drug-drug interaction assessment in ER/PR positive metastatic breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-18-11.

Review on Parking Demand Estimation Based on Crowdsourced Data

With the increase in urbanization and the growth of the economy, car ownership increases significantly, and at the same time demand for parking increases causing several issues like traffic congestion and resource wastage. There’s a critical significance of accurate parking demand estimation in the context of urban planning, addressing those issues. It emphasizes the emerging role of crowdsourced data as a novel solution for more efficient and cost-effective parking demand estimation. This review discusses the benefits of crowdsourcing, including real-time data and minimal infrastructure requirements, while acknowledging challenges like data accuracy, user privacy, and potential biases. Furthermore, it provides a comprehensive overview of the subject matter and it suggests future directions for improvement, proposing the integration of advanced technologies like Google APIs and IoT to augment parking demand estimation models and address the limitations associated with crowdsourced data.

Phylodynamics and Phylogenetic Analysis of Recent SARS CoV 2 Viral Strains from Pune, Maharashtra

In December 2019, medical practitioners from China identified a novel strain of severe acute respiratory syndrome-CoV (SARS-CoV). The disease was allotted a zoonotic origin and the spillover event is associated with Huanan Wholesale Seafood Market in Wuhan City, Hubei Province, China. On 30 January 2020, the World Health Organization (WHO) declared COVID- 19 as an international public-health emergency concern. In India, the index case was reported on 30th January in the district of Thrissur, state of Kerala and by 25th March 2020, the country was lockdown.  This research aims to analyze the viral phylogenetic and phylodynamics of strains from Pune, Maharashtra, followed by a comparison against real time data and an analysis of the potency of government prevention strategies. Publicly available SARS-CoV-2 sequences, specific to Pune, Maharashtra, were downloaded from GISAID during the time frame of the epidemics. Phylogenetic analysis of the sequences, including Maximum Likelihood trees and the nucleotide substitution model, were done using IQ-TREE software. Phylodynamic tools available as part of the BEAST2 software was used to estimate the evolution of R number through time. GTR+F+I was found to be the best-fit nucleotide substitution model for the data acquired. ML trees constructed reported a log likelihood of 44842.9931. Analysis of trace estimates reported R number with an average of 1, indicating transmission of virus. The data contains controversial elements which can only be clarified upon further sequencing of the SARS- CoV-2 strains, however, the results obtained are parallel to real time statistics.

A BIM-enabled digital twin framework for real-time structural health monitoring using IoT sensing, digital signal processing, and structural analysis

Structural health monitoring (SHM) of civil infrastructure is critically important due to its direct influence on public safety and economic activities. Exiting Building Information Modeling (BIM)-based SHM systems often use offline data processing techniques to analyze and visualize structural health data. Despite some of them adopting Internet of Things (IoT) to enable real-time sensor data collection, sensor data quality still remains uncertain due to a lack of sensor signal preprocessing in those systems. Additionally, the IoT-based SHM systems often disregard structural analysis domain knowledge which is important for accurate and precise SHM. Therefore, there is still a need to improve existing practices by enabling more efficient and reliable data collection and processing as well as providing more representative SHM data visualization in BIM. As such, this paper proposes an intelligent BIM-enabled digital twin (DT) framework integrating wireless IoT communication, digital signal processing (DSP), and structural analysis domain knowledge. The proposed system (1) leverages IoT and wireless communication to enable autonomous and real-time SHM sensor data collection and transmission, (2) applies and compares multiple DSP techniques to preprocess the sensor data/signals, and (3) innovatively embraces structural analysis expertise into structural behavior visualization in BIM by performing sensor data interpolation for enabling the visualization of behaviors at different locations of a structural element/component. The proposed BIM-enabled DT framework was demonstrated and tested for monitoring and visualizing the structural deformations of critical components using a prototyped structural frame subject to bending forces. The developed framework could be used and extended for any structural elements (such as beams, columns, trusses, slabs, arches, bracings, walls, footings, foundations, and girders, among others) and could be applied to any kind of structure. Experimental results showed that the proposed framework could effectively monitor and intuitively visualize the structural deformations under different load configurations with a high DT updating frequency of 5 Hz. The innovation of this study is reflected by integrating structural analysis expertise with sensor data analytics in order to improve the representativeness of real-time structural behavior visualization in BIM and to advance the DT-based SHM systems in a faster and more adaptive direction. Ultimately, this paper contributes to the body of knowledge by developing a generic and easily extendable BIM-enabled DT framework for SHM with high sensor data quality and improved visualization to advance the existing practices of BIM-based SHM for civil infrastructure asset management.

Thermal efficiency and performance analysis of 50 MW concentrated solar power plant

This study evaluates the operational efficiency and performance of the Shagaya 50 MW Concentrated Solar Power (CSP) plant in Kuwait that has been operational since February 2019. Utilizing Parabolic Trough technology, the plant incorporates a large Solar Field (SF) comprising 8 platforms with total of 206 solar-collector loops. Thermal energy captured by the SF is utilized in a Low Temperature Rankine Cycle to generate electricity via water-steam cycle and heat exchangers, with a nominal turbine capacity of 50 MW. Supported by a thermal energy storage (TES) system capable of storing 1200 MWht energy in molten salt. Through a comprehensive assessment spanning 41 months, real-time operational data is analysed to evaluate the CSP-TES system's efficacy within Kuwait's climatic conditions. Various performance parameters are examined, providing insights into CSP technology's practical implications and operational strategies. Driven by the annual difference in downtime and TES availability, comparative analysis of the plant's annual electricity production over the three years reveals notable variations: 74.99 GWh in 2019, 160.62 GWh in 2020, and 140.04 GWh in 2021, corresponding to capacity factors of 19 %, 40.75 %, and 35.53 %, respectively. The findings underscore the strong correlation between the SF and TES system in determining overall plant efficiency. The study also highlights the impact of high wind speeds on plant operation, triggering shutdowns on 9 days during the assessment period. Additionally, soiling losses in the SF are assessed through daily reflectivity measurements, guiding cleaning efforts using semi-automated armed trucks. Seasonal peaks in cleanliness factor coincide with local spring season, underlining the detrimental effect of reflectivity losses on plant output due to higher dust mass densities. A design flaw causing frequent breakdowns of receiver tubes under high winds is identified and addressed.

Logging-While-Drilling Laterolog vs. Electromagnetic Propagation Measurements: Which Is Telling the True Resistivity?

Summary The electromagnetic propagation (EMP) measurement frequently acquired with logging-while-drilling (LWD) tools in high-angle wells is sensitive to geometrical effects that can mask the true formation resistivity. Less commonly used, the LWD laterolog measurement is sometimes perceived as providing data too shallow to give a true formation resistivity (Rt). In this paper, we presents modeling and actual examples to demonstrate that the laterolog can often provide a superior resistivity measurement for formation evaluation to that of the LWD EMP tool. We examine the laterolog and EMP resistivities in several high-angle wells crossing carbonate formations in 8.5-in. and 6.125-in. hole sizes. In the 8.5-in. sections, producers and water injectors (high- and low-resistivity ranges) were evaluated. In the 6.125-in. sections, one reservoir sandwiched between two very high-resistivity layers and another borehole in a highly fractured reservoir were examined. The laterolog data were corrected for invasion using a 1D inversion of the memory data. Structure-based forward modeling was used to examine and explain the differences between the laterolog and EMP resistivity measurements. In the first example in a thick low-resistivity water reservoir, laterolog resistivity and EMP resistivity agree, showing that the two tools provide the same measurement when no geometrical effects are present. In the first part of the second example, a reservoir zone was initially drilled only with the LWD EMP resistivity measurement. The LWD laterolog was run several days later, and the resistivity data read much lower in the relogged section compared with the EMP resistivity. The laterolog 1D inversion was unable to resolve Rt because of the excessively deep invasion that occurred over the course of several days. In the second part of the second example, the laterolog resistivity showed a clear conductive invasion profile. While the deepest laterolog real-time resistivity data indicated lower resistivity than the EMP resistivity, the true resistivity, Rt (invasion-corrected 1D-inverted laterolog resistivity), matched the EMP Rt resistivity. This result validated both measurements and emphasized that the differences were due to invasion. The first two examples demonstrated that when acquired in normal drilling conditions (within 1–2 hours of drilling the section), the laterolog measurements can provide uninvaded formation resistivity even in the presence of invasion. A reservoir in another example was sandwiched between resistive layers that caused difficult-to-explain elevated EMP resistivity readings. Structural modeling reproduced the elevated behavior of the EMP data and explained the differences between resistivity measurements. This result showed that the laterolog is better suited to evaluate resistivity in thin reservoirs where there is a high-resistivity contrast to the adjacent layer. Finally, fractured reservoir examples are presented, which show that both the laterolog and EMP can be affected by the presence of fracture swarms. The examples presented in this paper demonstrate that in high-angle wells, when acquired under normal drilling conditions, invasion-corrected laterolog resistivity is often nearer to Rt than EMP resistivity. In those cases, the laterolog measurement provides data that are better inputs to water saturation calculations. Multiple examples from high-angle wells illustrate the findings.

Enhanced Recovery after Surgery Day Surgery for MAKO® Robotic-arm Assisted TKA; Better Outcome for Patients, Improved Efficiency

IntroductionRobotic-assisted Total Knee Arthroplasty (TKA) was designed to improve implant position accuracy by providing surgeons with real-time intra-operative data to tailor the operation to the patient. Proponents of robotic assisted TKA believe that this translates into meaningful improvements in outcomes. However, there are concerns that the longer surgical duration associated with robotic assisted TKA leads to longer length of stay (LOS). In this study, the authors investigated the outcome of MAKO® Robotic-arm Assisted TKA combined with ERAS protocol to assess its effect on LOS and short-term outcomes. MethodsAll patients who had undergone unilateral ERAS Day Surgery MAKO® TKA from August 2020 to July 2021 were prospectively followed up and matched to patients who underwent conventional ERAS Day Surgery TKA in the same time period. Factors such as surgical duration, LOS, immediate reduction in pain, 30-days complications, and 6-month PROMs and knee ROM were compared between the two groups. Results42 patients underwent MAKO® ERAS Day surgery TKA and were matched to 42 patients who underwent conventional ERAS Day surgery TKA. The study found that despite the longer surgical duration, LOS was comparable between both groups (1.1±0.9days in the MAKO® group vs 1.0±0.3days in the conventional group, p=0.755) with successful 24-hour discharge in 88.1% of patients in the MAKO® group. The MAKO® group achieved significantly better ROM compared to the conventional group 6-months post operatively. Post-operative PROMs were comparable between both groups. ConclusionERAS Day Surgery protocol can significantly reduce the LOS of patient undergoing MAKO® Robotic-arm Assisted TKA, conferring cost savings and making it a valid option for patients.

Biological recognition element anchored 2D graphene materials for the electrochemical detection of hazardous pollutants

The detection of hazardous pollutants in environmental and industrial settings is of critical importance for safeguarding human health and the ecosystem. Recent years witnessed significant progress and development in novel sensing platforms. The combination of biological recognition elements with two-dimensional (2D) materials enhanced the selectivity and sensitivity of pollutant detection. This review focuses on the integration of biological recognition elements, such as antibodies, nucleic acids, enzymes, aptamers, and proteins with 2D graphene for electrochemical detection of hazardous pollutants. The unique properties of graphene include high electrocatalytic activity, high surface area, and excellent conductivity, combined with the specificity and affinity of biological recognition elements, enabling selective and sensitive detection of hazardous pollutants. The synergistic coupling between 2D graphene and biological recognition elements offers several advantages, including improved sensor performance, rapid response, and potential for multi-analyte detection. Furthermore, the immobilization strategies, surface modifications, and fabrication techniques employed to anchor biological recognition elements onto graphene are discussed. The application of these bio-functionalized 2D material-based sensors for the detection of various hazardous pollutants, such as heavy metals, organic compounds, and environmental toxins, is highlighted. Moreover, the challenges and future perspectives in this field are addressed, including sensor stability, scalability, and the integration of advanced technologies, such as nanotechnology and artificial intelligence, for real-time monitoring and data analysis.

Smart Capsule for Targeted Detection of Inflammation Levels Inside the GI Tract.

Effective management of Inflammatory Bowel Disease (IBD) is contingent upon frequent monitoring of inflammation levels at targeted locations within the gastrointestinal (GI) tract. This is crucial for assessing disease progression and detecting potential relapses. To address this need, a novel single-use capsule technology has been devised that enables region-specific inflammation measurement, thereby facilitating repeatable monitoring within the GI tract. The capsule integrates a pH-responsive coating for location-specific activation, a chemiluminescent paper-based myeloperoxidase (MPO) sensor for inflammation detection, and a miniaturized photodetector, complemented by embedded electronics for real-time wireless data transmission. Demonstrating linear sensitivity within the physiological MPO concentration range, the sensor is capable of effectively identifying inflammation risk in the GI fluid. Luminescence emitted by the sensor, proportional to MPO concentration, is converted into an electrical signal by the photodetector, generating a quantifiable energy output with a sensitivity of 6.14 µJ/U.ml-1. The capsule was also tested with GI fluids collected from pig models simulating various inflammation states. Despite the physiological complexities, the capsule consistently activated in the intended region and accurately detected MPO levels with less than a 5% variation between readings in GI fluid and a PBS solution. This study heralds a significant step towards minimally invasive, in situ GI inflammation monitoring, potentially revolutionizing personalized IBD management and patient-specific therapeutic strategies.

Advancing Autologous CAR T-cell Therapy through Real-Time Patient Health Data Integration: A Simulation-Based Approach

Autologous chimeric antigen receptor (CAR) T-cell therapy presents promising treatment outcomes for various cancers. However, its potential is restrained by unique supply chain challenges, including dynamic patient health conditions and extended turnaround time. These challenges often lead to missed optimal treatment windows, impeding the effective delivery of life-saving treatments. This paper presents SimPAC (Simulation-based decision support for Patient-centric manufacturing of Autologous Cell therapies). SimPAC is designed to model and incorporate real-time patient health conditions into the supply chain decisions of autologous CAR T-cell therapy. SimPAC integrates system dynamics and agent-based simulation techniques, facilitating the adaptation of manufacturing processes and production schedules based on real-time patient health conditions. SimPAC can model various patient disease progressions using parametric functions, non-parametric functions, or tabular data. Additionally, SimPAC offers easy configuration options to model various cell therapy supply chains. We provide two case studies to demonstrate the capabilities of SimPAC and highlight the benefits of patient-centric manufacturing, including improved survival rates and potential economic advantages. However, while the benefits are significant, our study also emphasizes the importance of balancing improved patient outcomes, economic viability, and ethical considerations in the context of personalized medicine. SimPAC can be used to explore applications of this approach to diverse therapeutic contexts and supply chain configurations.