Real-time Laboratory Research Articles

Experimental investigation of adaptive multi-generalized integrator-based controller for electronically interfaced hybrid microgrid system

This research work explores the conceptualization and evaluation of a hybrid microgrid that taps into the potential of solar photovoltaic systems, wind energy conversion systems, and battery energy storage systems. In practice, nonlinear loads pose power quality issues that significantly impact the performance of hybrid microgrids. To tackle these challenges, an adaptive multi-generalized integrator (MGI) filter is proposed. This filter extracts the fundamental signal from the distorted utility grid voltages. It features a pre-filter with a DC-off set rejection loop, effectively minimizing the DC-offsets from the distorted grid voltages. This facilitates the distribution of active power generated by the hybrid microgrid's sources while simultaneously addressing various power quality problems. In addition, a dynamic power management control approach enhances grid resilience by operating the hybrid microgrid in grid-connected and islanding modes. It provides uninterrupted and high-quality power supply to consumers during grid outages. The performance of this filter is comprehensively assessed through numerical simulations in MATLAB®/Simulink® environment. A real-time laboratory prototype is developed with WAVECT® WUC300 FPGA controller, demonstrating the practical application of the proposed filter. It ensures the minimum DC-offsets of 0.02V, fast convergence, and minimum oscillations. The grid synchronization and power quality index of the grid currents simultaneously achieve a THD of 2.82 %, complying with the IEEE-1547 and IEEE-519 standards. Importantly, the proposed adaptive-MGI filter outperforms the conventional SOGI and LMF filters in terms of nonlinear load tracking capabilities, with a response speed of less than 200μs, thereby highlighting its practical relevance and importance.

Experimental validation of a novel hybrid Equilibrium Slime Mould Optimization for solar photovoltaic system

Maximizing Power Point Tracking (MPPT) is an essential technique in photovoltaic (PV) systems that guarantees the highest potential conversion of sunlight energy under any irradiance changes. Efficient and reliable MPPT technique is a challenge faced by researchers due to factors such as fluctuations in irradiance and the presence of partial shading. This paper introduced a novel hybrid Equilibrium Slime Mould Optimization (ESMO) MPPT-based algorithm combining the advantages of two recent algorithms, Slime Mould Optimization (SMO) and Equilibrium Optimizer (EO). The ESMO algorithm is compared with highly efficient MPPT-based techniques such as SMO, EO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA), both under a Simulink environment and a real-time experimental laboratory setup using a Dspace1104 controller and PV emulator. The comparison focuses on performance under several irradiance cases, including instant irradiance change, partial shading, complex partial shading, and dynamic partial shading. The key advantage of ESMO is the fact that it has a single tunable parameter, which makes implementation much easier and, at the same time, reduces the computational resources that are required by the control system. Extensive testing proves the superiority of ESMO over all other techniques, the average efficiency of which is 99.98% under all conditions. Additionally, ESMO provides fast average tracking times of 244 ms under simulation experiments and 200 ms for real-time experiments. These results show that ESMO can be very important for future implementation in large-scale solar PV systems.

Influence of flood events on the response of steep and coarse-grained channels to base-level lowering in an arid setting

The Dead Sea level has been undergoing a continuous and rapid lowering, presently ∼ 1.1 m/yr, providing a real time laboratory to examine the response of drainage systems to base level drop. There is a gap of knowledge concerning the impact of floods on the fluvial system in an environment of continuous base level lowering. Such knowledge is of pronounced importance due to the world-wide reduction of natural water recourses caused by human and climate impacts. We relate the effects of flash floods on morphological changes in small, steep and coarse-grained alluvial channels as a response to a rapid base level fall. We studied the steep (7–9 %) David and Qedem channels draining to the Dead Sea by (i) monitoring flow events using depth transducers and (ii) topographic changes by repeat drone-based photogrammetric surveys to quantify fluvio-morphological dynamics, and (iii) analyzed boulder-related bedforms and their distance from the thalweg. Altogether 15 monitored flow events of varying magnitude led to a wide range of fluvio-morphological changes. Considerable erosion occurred as expressed by bank collapse and channel bed incision. Small events displayed incision with morphological changes limited to the downstream reach, while larger events caused morphological changes throughout the entire channel. Substantial deposition at the outlet occurred together with local mechanisms of deposition upstream. The extents of erosion and net volumes of change highly correlated with event peak-discharge and event water volume. Frequent thalweg deflections occurred, some of which were related to the interaction with boulders. Boulder ribs displayed processes of formation/destruction in multiple phases, often occurring in larger events. The examined aspects illustrate the unique dynamics of a continuously unstable fluvial system under a flash-flood hydrological regime and its impact on fan delta incision and sediment transport processes.

Investigating the influence of measurement uncertainty on chlorophyll-a predictions as an indicator of harmful algal blooms in machine learning models

Advancements in data availability, including high frequency, near real-time multiparameter sensors, laboratory analysis, and in-situ and remote observations, have driven the development of machine learning (ML) models for applications such as toxic Harmful Algal Bloom (HABs) monitoring. However, the performance of ML predictions is influenced by both model uncertainties due to inherent model structures and errors associated with input dataset measurements. For example, measurement uncertainty arises from sample collection, sensor drift and laboratory analysis and sample handling errors. While impacts of model uncertainty are commonly addressed using probabilistic approaches, the effect of measurement uncertainty is less studied due to the limited availability of detailed measurement information. This study focuses on assessing the impact of measurement uncertainty on the ML prediction of chlorophyll-a concentration as an index of HABs in a mesotrophic lake. Using randomized subsets of input measured datasets that mimic possible chlorophyll-a concentration distributions, the study built 1000 Random Forest (RF) and Support Vector Regression (SVR) models. An independent measured dataset was used to validate the ensemble models, allowing for model performance evaluation and the creation of prediction intervals to measure the propagated uncertainty. Our findings showed that the model predictions have MAE that ranged between 0.16 μg/l and 5.19 μg/l, and RMSE ranging between 0.20 μg/l and 7.39 μg/l. The highest uncertainty coverage of 0.71 was observed in the RF model without chlorophyll-a sensor values as a predictor. The study found that the training dataset sizes due to the high frequency and manually sampled nature influence how much measurement uncertainty is covered. The results of this study demonstrate how well ML models can capture various HABs patterns when given diverse measurement variables. Our findings will give researchers insightful information on how to lessen the impact of measurement uncertainty when using ML models as decision-support tools for HABs management.

Zinc tungstate integrated onto zinc oxide h-MDs-based flexible piezoelectric power device: A new energy technology for real-time indoor laboratory safety warning system

With the rapid development of research world, the number of researchers and research laboratories is increasing rapidly. Simultaneously, accidents in laboratories are on the rise, drawing significant attention from everyone. To address this issue, the current study demonstrates a real-time safety early warning alarm system aimed at preventing accidents and enhancing safety management in laboratories. We developed a zinc tungstate nanoparticles (NPs) integrated on zinc oxide hexagonal microdiscks (h-MDs) (ZnO–ZnWO4@PDMS) composite based on a novel flexible piezoelectric nanogenerator (FPENG). The observed piezoelectric response was 1.32 V, which is ∼4 times better than that of pure ZnWO4, owing to the synergistic effect of ZnO–ZnWO4@PDMS. When subjected to finger-tapping motion, the FPENG can charge a 1 μF capacitor, and it reached 81 mV within 56 s. Subsequently, the light-emitting diodes (LEDs) are powered with amplification. The FPENG system was demonstrated and validated through real-time application in the laboratory setting, marked as a “caution/restricted” line. The application of the system demonstrated its capability to simultaneously illuminate LEDs and sound alarms when people touch or cross the caution line. Furthermore, the proposed FPENG exhibited ∼660 mV even ∼90 % humidity, with no noticeable electrical changes under different solvents, confirming that the proposed FPENG is not affected by varying humidity and various indoor conditions. These outstanding results and real-time demonstrations unequivocally establish the feasibility of this system in contexts such as bank lockers, metro systems, hilly areas, and more.

Single drop cytometry onboard the International Space Station

Real-time lab analysis is needed to support clinical decision making and research on human missions to the Moon and Mars. Powerful laboratory instruments, such as flow cytometers, are generally too cumbersome for spaceflight. Here, we show that scant test samples can be measured in microgravity, by a trained astronaut, using a miniature cytometry-based analyzer, the rHEALTH ONE, modified specifically for spaceflight. The base device addresses critical spaceflight requirements including minimal resource utilization and alignment-free optics for surviving rocket launch. To fully enable reduced gravity operation onboard the space station, we incorporated bubble-free fluidics, electromagnetic shielding, and gravity-independent sample introduction. We show microvolume flow cytometry from 10 μL sample drops, with data from five simultaneous channels using 10 μs bin intervals during each sample run, yielding an average of 72 million raw data points in approximately 2 min. We demonstrate the device measures each test sample repeatably, including correct identification of a sample that degraded in transit to the International Space Station. This approach can be utilized to further our understanding of spaceflight biology and provide immediate, actionable diagnostic information for management of astronaut health without the need for Earth-dependent analysis.

Power quality investigation with multilevel inverter by photovoltaic-fed dynamic voltage restorer

ABSTRACT In this research endeavor, a novel approach has been presented to enhance the power quality through a Photovoltaic (PV)-based Dynamic Voltage Restorer (PV-DVR). The proposed work is designed to address voltage fluctuations, flicker, and harmonics within medium or low voltage micro-grid systems. Unlike existing DVR systems that often neglect injected harmonics and converter losses, in the proposed work there is an integration of a PV-fed multilevel inverter system employing Selected Harmonic Elimination and Moderation-based Pulse Width Modulation (SHEAM-PWM). This innovative configuration, coupled with a DC – DC boost converter and a series injection transformer, not only aims to alleviate voltage stress on devices but also optimizes the number of switches to minimize switching losses. The proposed setup successfully reduces the overall voltage Total Harmonic Distortion (THD) to a significant extent, delivering superior dynamic performance. To determine optimal switching angles for the SHEAM-PWM technique, ensuring the elimination or mitigation of the maximum number of harmonics from the output voltage waveform, we employ the Biogeography-Based Optimization (BBO) approach. The computation of these angles is performed offline to eliminate lower-order harmonics, and the results are stored in the microcontroller’s memory using mixed model equations for online applications. The efficacy of our proposed method is demonstrated through both simulation and experimental validation in a real-time laboratory system. The simulation and experimental results obtained underscore the potential utility of our technique for Dynamic Voltage Restorer (DVR) applications, particularly those requiring low harmonic injection. The proposed method introduces a comprehensive and innovative solution for power quality enhancement, combining PV technology, multilevel inverter systems, and advanced optimization techniques. The proposed PV-DVR system exhibits promising results, showcasing its potential for practical implementation in residential micro-grid systems with a focus on minimizing harmonics and ensuring efficient grid compensation.

Multisite spontaneous hematomas and bleeding in critically ill Chinese patients with COVID-19: two case reports and a literature review

BackgroundAnticoagulation is recommended as a standardized therapy for COVID-19 patients according to the WHO guidelines. However, bleeding events have also been reported. Hemorrhage or hematoma was observed in sites including the retroperitoneum, brain, alimentary tract, muscles, and soft tissues. Reduction or suspension of anticoagulants is a common intervention. Transfusion, endoscopic hemostasis, and vascular interventional therapy have been used to improve the condition.Case presentationIn this article, we present two cases of concurrent multisite hematomas and bleeding at other sites in patients with SARS-CoV-2 infection. Both patients were treated with heparins and experienced bleeding after the anticoagulation therapy. Both patients were older with more than two comorbidities, and critical COVID-19. Laboratory tests revealed a considerable decrease in hemoglobin levels and alterations in the coagulation system. In the first patient, the main intervention was embolization using angiography. However, we only adjusted the anticoagulation strategy in the second case. The first patient recovered and was discharged; however, the second died of other causes. This study provides a retrospective review of typical hemorrhagic cases during anticoagulation in COVID-19 patients over the course of four years. A relatively comprehensive search was performed in Pubmed by constructing MeSH subject terms on limiting the search period and specific contents. It summarizes and synthesizes the research related to heparins and other novel anticoagulants in the context of COVID-19 from the onset of the pandemic to the present disseminated phase. This study aimed to offer valuable insights and reference points for developing anticoagulation treatment strategies for patients with COVID-19.ConclusionsAnticoagulation is a crucial treatment option for patients with COVID-19. The difference in anticoagulant effects is related to the severity of COVID-19. Nafamostat can reduce thrombosis in the extracorporeal circuits in critically ill patients with COVID-19. The efficacy and safety of novel anticoagulants require further clinical data. Routine bedside assessments and real-time laboratory monitoring are essential for early identification of bleeding events during anticoagulant therapy and administering intervention.

Borderless Lab 365: A Real-time Web-based Remote Laboratory Platform for Hong Kong High School Students in Response to Pandemic

Borderless Lab 365: A Real-time Web-based Remote Laboratory Platform for Hong Kong High School Students in Response to Pandemic

Machine Learning Aided Numerical and Experimental Investigation of Hydrodynamic Performance in the Circulating Fluidized Bed Boiler

Abstract This study attempts to illustrate the benefits of integrating the concepts of machine learning algorithms with the field of thermo-fluidic applications. The current work is aimed at identifying effective or significant hydrodynamic input parameters, which are capable of deriving full benefit of fluidization that could yield a better circulating fluidized bed (CFB) furnace design using the Apriori algorithm. For this, historical datasets from the literature are collected and pretreated based on the design under observation. Association analysis performed by this Apriori algorithm measures the comparative strength of parameters under consideration. Also, this algorithm is capable of identifying the right combinations of parameters that can produce maximum fluidization performance. The end results suggested by this Apriori algorithm are validated using the computational fluid dynamics package. For this, the transient behavior of a scaled-down (1:20) reactor model of a real-time industrial CFB boiler is simulated using ansys fluent 18.0. In specific, the effects of fluidizing velocities, inventory heights of the bed, and particle sizes recommended by the Apriori algorithm are investigated. Here, the effects are assessed in terms of volume fraction distribution and axial velocity profile distribution profiles. From the results of simulations, it was clearly found that 2 m/s inlet velocity produced good circulating fluidized bed patterns on a bed inventory height of 0.5 m for a mean particle size of 200 µm. The results obtained from the simulations are once again validated visually against snapshots obtained during real-time laboratory fluidization experimental runs. Among all the cases of comparisons, the best agreement is demonstrated by Apriori algorithm compared to the numerical and the experimentally obtained results. Also, it is found that the manual time taken to identify the right combinations of parameters is drastically reduced by this method compared to conventional optimization algorithm and trial error methods.

Improved multistep model predictive control method with reduced finite set for MMC-UPQC

Multistep model predictive control (M-MPC) can improve control accuracy but leads to an increased computational burden. To reduce the computational burden and restore power quality, an improved M-MPC method with reduced finite set for unified power quality conditioner (UPQC) based on modular multilevel converter (MMC) is presented in this paper. First, a finite set of M-MPC is formed according to the inserted sub-modules (SMs) number calculated from cost functions. Second, we investigate the relationship between the inserted SMs number and the bridge arm output voltages and find that the inserted SMs number will increase as voltages rise. As such, by judging the change rate of the output voltages, switching states that cannot be realized within the finite set can be eliminated, thus the finite set can be reduced. In addition, circulating currents and capacitance voltage control methods are designed to ensure the internal stability of MMC. Finally, the MATLAB/Simulink simulation and the real time laboratory (RT-LAB) based hardware-in-the-loop (HIL) experimental results show effectiveness of the proposed M-MPC. The results show the proposed M-MPC exhibits better dynamic characteristics and fewer control options as compared to the single-step MPC.

An Energy Flow Control Algorithm of Regenerative Braking for Trams Based on Pontryagin’s Minimum Principle

Energy savings in electric rail transport are important in order to increase energy efficiency and reduce its carbon footprint. This can be achieved by storing and using the energy generated during regenerative braking. The system described in this paper consists of a supercapacitor energy storage system (SC ESS), a bidirectional DC/DC converter, and an algorithm to control the energy flow. The proper design of the algorithm is critical for maximizing energy savings and stabilizing the power grid, and it affects the lifetime of the SC ESS. This paper presents an energy flow control algorithm based on Pontryagin’s minimum principle that balances maximum energy savings with maximum SC ESS lifetime. The algorithm also performs SC ESS recharging while the rail vehicle stops on inclines to reduce the impact of its next acceleration on the power grid. To validate the algorithm, offline simulations are performed using real tram speed measurements. The results are then verified with a real-time laboratory emulation setup with HIL simulation. The tram and power grid are emulated with LiFePO4 batteries, while the SC ESS is emulated with a supercapacitor. The proposed algorithm controls a three-phase converter that enables energy exchange between the batteries and the supercapacitor. The results show that the proposed algorithm is feasible in real time and that it can be used under real operating conditions.

B-137 Development of a Novel Software System for Providing Real-Time Laboratory Test Information to Clinicians at Point of Order Entry

Abstract Background Clinicians depend on an increasingly complex set of laboratory tests to aid in the diagnosis and monitoring of patients. Staying informed about the proper usage of these tests requires active effort, especially as test offerings continually evolve. To aid in test decision making, institutions often implement some form of decision support, including: grouping of common “order sets”, “best practice alert” popups, and web-based “lab catalogs”. For many institutions, lab catalogs provide the most comprehensive information on available tests, but typically exist separate from the interface used to place test orders, and thus suffer from underutilization in actual clinical practice. Methods We developed a novel, open-source software to closely integrate our institution’s lab catalog at the point of order entry within our electronic medical record (EMR) system. The software provides an immediate pop-up window anytime a clinician needs additional information while placing orders. The software also provides an independent, customizable search function that helps steer clinicians away from commonly mis-ordered tests. Both functions are immediately available at the point of order entry, without requiring the user to leave the EMR or otherwise interrupt their normal workflow. We conducted a small usability pilot with a focus group of clinicians, using the System Usability Scale (SUS) (1). Results Our software reduced the number of clicks and keystrokes needed to access detailed lab information from 27 to 2. The focus group (n = 6) rated the product favorably on the SUS, with average rating 89 ± 9 out of 100. Conclusion This study demonstrates proof-of-concept for a novel software decision support tool that provides instant access to lab information at the point of order entry. Future work will focus on evaluating utility in a real-world clinical pilot.

Modifying Escherichia coli to mimic Shigella for medical microbiology laboratory teaching: a new strategy to improve biosafety in class.

Laboratory teaching of medical microbiology involves highly pathogenic microorganisms, thus posing potential biosafety risks to the students and the teacher. To address these risks, non/low-pathogenic microorganisms were modified to mimic highly pathogenic ones or highly pathogenic microorganisms were attenuated directly using the CRISPR/Cas9 technology. This study describes the modification of Escherichia coli DH5α to mimic Shigella and its evaluation as a safe alternative for medical laboratory teaching. To generate E. coli DH5α△FliC△tnaA2a, the tnaA and FliC genes in E. coli DH5α were knocked out using CRISPR/Cas9 technology; a plasmid bearing the O-antigen determinant of S. flexneri 2a was then constructed and transformed. Acid tolerance assays and guinea pig eye tests were used to assess the viability and pathogenicity, respectively. Questionnaires were used to analyze teaching effectiveness and the opinions of teachers and students. The survey revealed that most teachers and students were inclined towards real-time laboratory classes than virtual classes or observation of plastic specimens. However, many students did not abide by the safety regulations, and most encountered potential biosafety hazards in the laboratory. E. coli DH5α△FliC△tnaA2a was biochemically and antigenically analogous to S. flexneri 2a and had lower resistance to acid than E. coli. There was no toxicity observed in guinea pigs. Most of teachers and students were unable to distinguish E. coli DH5α△FliC△tnaA2a from pure S. flexneri 2a in class. Students who used E. coli DH5α△FliC△tnaA2a in their practice had similar performance in simulated examinations compared to students who used real S. flexneri 2a, but significantly higher than the virtual experimental group. This approach can be applied to other high-risk pathogenic microorganisms to reduce the potential biosafety risks in medical laboratory-based teaching and provide a new strategy for the development of experimental materials.

Intelligent vision for the detection of chemistry glassware toward AI robotic chemists

One of the key steps to make an artificially intelligent (AI) and robotic chemist is the introduction of machine vision for guiding the experiment operation in the AI-redefined laboratory. In order to realize the targets, the prerequisites are to innovate/implement the intelligent vision for the detection of chemistry glassware. Here, we reported a computer vision method based on You only look once (YOLO) with a self-developed Chemical Vessel Identification Dataset (CViG) for the improvement of classification and recognition performance. The training dataset has been collected that includes 4072 images in real-time chemical laboratory. Three models, YOLOv5s, Slim-YOLOv5s and YOLOv7, have been exploited for the recognition of seven types of glassware in the condition of different scenarios (recognition distance, light and dark, stationary and moving). The improved Slim-YOLOv5s exhibited better recognition ability in various scenes, and the recognition accuracy of chemical vessels is improved by 1.51 % compared with YOLOv5s, and the size of the model is reduced from 14.4 MB to 11.0 MB. Slim-YOLOv5s's mAP is similar to YOLOv7's ability with a disadvantage of large volume, suggested that the improved Slim-YOLOv5s clearly has more advantages in terms of embedded requirements. This vision-assisted system capable of classifying chemical containers accurately in the scenarios of real-time chemical experiments will provide a good vision solution in the frontier fields of automated machine chemistry.

AC-Heating and Fast-Charging Power Requirements of EV Battery Packs in Subzero Temperature

An emerging problem in power engineering pertains to ascertaining the impacts of fast-charging processes of all-electric vehicles (EVs) on electric grids. This problem exacerbates in subzero climates because it is necessary to pre-heat battery cells within EVs before fast charging to mitigate lithium plating. To study such processes, we set forth detailed and reduced-order simulation models as to determine grid power requirements for ac-heating and fast-charging of EVs. The detailed representation considers ac and dc control loops, a four-quadrant power converter, and an estimator of the state of charge of the EV battery pack. The reduced-order model has close agreement with detailed real-time simulation and laboratory experimentation.

Asynchronous Remote Usability Tests Using Web-Based Tools Versus Laboratory Usability Tests: An Experimental Study

Remote usability testing is performed by evaluators who are in different physical locations from the participants (synchronous remote testing) and possibly operating at different times (asynchronous remote testing). The tools developed in recent years to support remote tests exploit web technology based on HTML5 and JavaScript ES6 and thus enable previously unexplored scenarios. However, studies providing evidence on the benefits or drawbacks of utilizing recent web-based tools have not yet been reported in the literature. This article sheds some light on the impact of such tools on asynchronous remote usability testing of websites by reporting an experimental study with 100 participants and 15 evaluators to compare real-time laboratory tests with asynchronous remote tests. The study investigates: 1) how the metrics results of asynchronous remote usability tests performed through a web-based tool differ from those of usability tests conducted in real-time laboratory settings; and 2) how the experience of participants differs in the two types of tests. The lessons learned in the study are instrumental in informing the design of future tools. Some results of particular interest indicate that the web technology used by the tool for asynchronous remote testing affects task execution times and participants’ satisfaction. Another indication is that slow internet connections must be managed in asynchronous remote testing; slow connections introduce delays when transferring large amounts of collected data, which, together with the lack of human support, make participants of asynchronous remote tests more prone to feel negative emotions.

Association of ABCB1 gene polymorphisms rs1128503, rs2032582, rs4148738 with anemia in patients receiving dabigatran after total knee arthroplasty

PurposeDabigatran is usually prescribed in recommended doses without monitoring of the blood coagulation for the prevention of venous thromboembolism after joint arthroplasty. ABCB1 is a key gene in the metabolism of dabigatran etexilate. Its allele variants are likely to play a pivotal role in the occurrence of hemorrhagic complications. MethodsThe prospective study included 127 patients with primary knee osteoarthritis undergoing total knee arthroplasty. Patients with anemia and coagulation disorders, elevated transaminase and creatinine levels as well as already receiving anticoagulant and antiplatelet therapy were excluded from the study. The association of ABCB1 gene polymorphisms rs1128503, rs2032582, rs4148738 with anemia as the outcome of dabigatran therapy was evaluated by single-nucleotide polymorphism analysis with a real-time polymerase chain reaction assay and laboratory blood tests. The beta regression model was used to predict the effect of polymorphisms on the studied laboratory markers. The probability of the type 1 error (p) was less than 0.05 was considered statistically significant. BenjaminiHochberg was used to correct for significance levels in multiple hypothesis tests. All calculations were performed using Rprogramming language v3.6.3. ResultsFor all polymorphisms there was no association with the level of platelets, protein, creatinine, alanine transaminase, prothrombin, international normalized ratio, activated partial thromboplastin time and fibrinogen. Carriers of rs1128503 (TT) had a significant decrease of hematocrit (p = 0.001), red blood count and hemoglobin (p = 0.015) while receiving dabigatran therapy during the postoperative period compared to the CC, CT. Carriers of rs2032582 (TT) had a significant decrease of hematocrit (p = 0.001), red blood count and hemoglobin (p = 0.006) while receiving dabigatran therapy during the postoperative period compared to the GG, GT phenotypes. These differences were not observed in carriers of rs4148738. ConclusionIt might be necessary to reconsider thromboprophylaxis with dabigatran in carriers of rs1128503 (TT) or rs2032582 (TT) polymorphisms in favor of other new oral anticoagulants. The long-term implication of these findings would be the reduction of bleeding complications after total joint arthroplasty.

Control of time-delay systems through modified Smith predictor using sliding mode controller

Smith-Predictor (SP) is used as a dead-time compensating tool for chemical processes as they are Integrating-Plus-Dead-Time (IPDT), pure integrating-type process, and higher-order-integrating-process. The performances of these processes can be improved by modifying SP. The present work deals with the control of dead-time by integrating the first-order-plus-dead-time (IFOPDT) or first-order-plus-dead-time process (FOPDT), and a modified sliding-mode-controller (SMC) in the proposed modified-Smith-predictor (MSP) structure. Here, a modified novel SMC discontinuous tuning-parameter is derived analytically to satisfy performance criteria such as speed adjustment, reducing overshoot and chattering effects. Furthermore, the MSP-SMC structure is implemented in six different processes. This is done to counteract time-delay and constant load-disturbance for integrating processes and a nonlinear system separately. Also, the proposed work is implemented in the real-time lab setup of a neutralisation process. Furthermore, the robustness and invariance properties of the SMC have been analysed against parametric uncertainty. The optimality of the proposed controller parameters is tested by giving uncertainty in the dead time of the process parameters. The proposed design is also implemented in the non-linear and Multi-Input-Multi-Output processes. The performance metrics, IAEs, of 6 case studies have been obtained using MATLAB and the peak overshoots are compared with similar kinds of works.

Lab Medicine in Space.

As humanity continues to push towards interplanetary travel and beyond, short- and long-term adaptation of the human body to extreme environmental conditions in space remains a fundamental concern. An astronaut is subjected to physically and mentally enduring situations like microgravity, extreme heat or cold, radiation, isolation, and other unforeseen technical challenges. To manage the physiological impact of these situations, the use of real-time diagnostic laboratory medicine will be necessary for the health of the astronaut. With advancing technology, the recent implementation of point-of-care testing (POCT) on the International Space Station (ISS) provides a means to reduce the need to freeze and send specimens back to earth for analysis. However, the quality of these lab tests in a microgravity environment continues to be investigated. Similar to traditional laboratory medicine, there are laboratory analysis concerns during space travel such as pre-analytical errors, competency, interface issues, specimen type differences, reference intervals, and consumable storage. As the frequency and duration of human spaceflight increases, research in these areas of laboratory testing will be key for space travel and astronaut health. For this Q&A, we invited a diverse group of experts to explore the current and desired future state of laboratory medicine in space as well as innovations that will expand our current horizon within this area of interest.