Short Run Time Research Articles

An accelerated computational platform for optimal field developments with reduced footprint

Reducing the environmental footprint and increasing efficiency of hydrocarbon production as well as geological sequestration of carbon dioxide (CO2) through optimally selected and operated fewer wells is crucial for cleaner and more sustainable energy transition. We describe a parallel subsurface field-development optimization (SFDO) platform with novel components. SFDO enables the optimization of well-locations/-trajectories (WLO), controls (WCO), and both jointly (WLO–WCO). All objective function computations are performed in parallel at a given iteration in SFDO.The novel distributed quasi-Newton (DQN) optimizer is an important new element in accelerating SFDO. DQN is developed using a multi-threaded optimization paradigm that utilizes high-performance computing (HPC) to distribute an ensemble of optimization threads. By operating its threads in parallel within a single optimization run, DQN efficiently identifies multiple distinct local optima of the objective function. DQN tracks and updates a common input-output (training) data set by recording the responses from successful simulation runs. The support-vector regression (SVR) method is used to approximate the Hessian matrix while objective function gradients are estimated analytically using SVR-computed sensitivities.The DQN implementation inside SFDO is extensively tested and validated in this work. We first validate DQN using a synthetic test case with a pre-computed solution. DQN incurs fewest number of simulations, and thus, the shortest run time while successfully finding the global optimum. For the first time in the literature, we show that DQN is more effective than alternative methods in problems involving WCO and WLO–WCO. Then, we report on DQN's performance against alternative techniques in four realistic applications, namely on a WLO problem for environmental footprint reduction and a novel WLO–WCO application for the geological sequestration of CO2 involving a novel take on WLO with deforming well trajectories. Finally, we describe two realistic WLO problems in which the well count is also optimized in addition to the well locations. One of these applications is for heater-well pattern optimization for an innovative unconventional thermal recovery method. DQN finds local optima by incurring a significantly lower computational cost in comparison to alternative methods. Results of the field tests support the advantageous qualities of DQN previously observed in synthetic tests. We conclude that SFDO accelerated with DQN helps increase the efficiency and reduce the footprint of realistic field-scale developments.

An effective approach for bi-objective multi-period touristic itinerary planning

Planning effective itineraries for tourists is a major problem that has been gaining attention over the last years. This paper proposes a new bi-objective integer linear programming model for this problem. Decisions include the choice of the best itinerary to be performed considering multi-period routing, time windows for the visited attractions and the choice of restaurants and hotels. The conflicting objectives considered are: (i) maximizing the level of service offered by the itinerary, and (ii) minimizing the total distance traveled. The problem resolution, even for small instances by exact methods, is limited. This motivated the proposition of a new customized MIP-heuristic based on decomposition, fix-and-optimize and MIP-start, to produce good-quality solutions with moderate computational effort. Tchebycheff’s scalarization method was coupled to this heuristic and multiple compromise solutions were obtained. Extensive results with problem instances of different sizes and characteristics showed a good performance of this approach, capable of producing effective solutions within short runtimes. The analysis of the solutions indicated a strong conflict between the objectives, allowing the user to quantify the losses and gains when one criterion is prioritized over the other. A brief sensitivity analysis of some model parameters revealed interesting managerial insights. Some examples include quantifying the negative impacts in terms of the level of service offered by concentrating hotels and restaurants in the center of tourist attractions, increasing visit and transfer times between attractions and reducing the planning horizon for the entire itinerary. These aspects validate the potential of using this MIP model and applying this MIP-heuristic in real situations.

A column screening study: Validation of a RP-LC method for determination of teriflunomide with pentafluorophenyl stationary phase

The research focuses on column screening in order to develop a novel validation method to separate Teriflunomide significantly using comparative and equivalent column selection studies. This gives stationary phase efficiency relative to height equivalent to a theoretical plate (HETP) (>16000) and symmetry factor (<2.0%). After extensive column chemistry and technique development, the advanced Pentafluorophenyl (PFP) column separates halogenated drugs better. Teriflunomide is fluorinated drug, hence PFP is optimal for analysis. For statistical data comparison, the same chromatographic method was developed and transferred to another YMC Pack Pro C18 column that gives almost the same results as PFP column in terms of carbon load, pore size, and surface area. The refined and validated method yielded equivalent results with two stationary phases using a single analysis procedure. Column screening, technique creation, transfer, and validation with comparison studies with correlation coefficients over 0.999 are unique. The statistical data from both columns meets ICH requirements. The sensitive, reliable, robust, and reproducible approach is easy to apply in regular laboratory analysis. Data analysis showed that the validation study with PFP is better (LoD - 0.0015 ppm) for column ageing due to smaller injection volume and amount loaded in the column, shorter run time, and cheaper analysis cost. This research helps with column selection and method transfer with diverse column specifications. With less than 2.0% of RSD, the column screening analytical method validation is linear, precise, accurate, rugged, and efficient.

Use of online sequential segmented heart cutting modulation and orthogonality approach for determination of isomeric impurity profile of a drug molecule using parallel 2-dimensional mass spectroscopy

Determination of impurity profile including chiral and achiral impurities is challenging as well as time consuming while analysis of the active moiety. With the recent evolution of 2-Dimensional separation modes, significant improvement can be made for simultaneous determination of both achiral and chiral impurities in a single set-up. Herein, the present work represents example of how both chiral and achiral impurities can be determined simultaneously. To represent the work, galantamine HBr was selected as the active moiety since it has achiral as well as many chiral impurities. Further, a 1D UPLC method with shorter runtime was developed to determine the achiral impurities and simultaneous heart cutting approach was used to cut the impurities and active compound and transfer to 2D chiral setup to separate out chiral impurities and perform its quantification. Both methods were proven to be orthogonal to each other with orthogonality factor of 0.241.

Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis.

Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time series sensor signals are converted into two-dimensional images using the Gramian angular field method with sliding window augmentation. Next, weighted fusion methods are employed to combine Gramian angular summation field images and Gramian angular difference field images, allowing for the full utilization of image information. Subsequently, a lightweight AlexNet is developed to extract features and classify fused images for fault diagnosis with fewer parameters and a shorter running time. To improve diagnosis accuracy, the particle swarm optimization algorithm is used to optimize the weighted fusion coefficient. The results indicate that the proposed method achieves a fault diagnosis accuracy of 99.72% when the weighted fusion coefficient is 0.276. These findings demonstrate the effectiveness of the proposed method for diagnosing depth sensor faults in robotic fish.

Cut‐set matrix methods for frequency response analysis curves of lumped parameter equivalent circuit model of three‐winding power transformer

SummaryTo accurately and efficiently calculate the frequency response of the lumped parameter equivalent circuit model (LPECM) of a transformer winding is the basis for studying the simulation method of the frequency response analysis (FRA). In this paper, two different cut‐set matrix (CSM) methods, including fundamental cut‐set (FCS) and independent cut‐set (ICS) matrix methods, are proposed to calculate the FRA curves of the LPECM of a three‐winding power transformer. First, based on graph theory and electrical circuit laws, the basic CSM equation is deduced. Second, four different FCS matrix selection methods of the LPECM are introduced and the best FCS matrix is determined by deeply analyzing their characteristics. Then, an ICS matrix method is proposed to obtain the ICS of the LPECM. Furthermore, FCS and ICS are taken into the CSM equation to calculate the FRA results of the LPECM, respectively. Finally, the accuracy of the proposed methods is validated by Multisim software and the performance of these two methods is compared with node voltage matrix (NVM) method by analyzing the calculation time. The results show that FCS and ICS matrix methods both can be used to accurately obtain the FRA results of the LPECM of the three‐winding power transformer, but ICS is more simple than FCS matrix method due to avoiding the procedure for the selection of the tree. In addition, since ICS matrix method has the shortest running time among these methods, it can be used to improve the calculation efficiency of the FRA curves of the LPECM of a three‐winding power transformer.

Reconstruction of incomplete flow fields based on unsupervised learning

The loss of flow field data is a common challenge in experimental techniques and data transmission processes. Traditional methods often rely on another complete or high-fidelity dataset to compensate for missing information regarding fluid characteristics. However, obtaining such complete data is not always feasible. In this study, we propose an unsupervised learning approach that efficiently, swiftly, and cost-effectively reconstructs the complete flow field by leveraging the inherent properties of the flow field itself, without the need for high-fidelity data integration. Our proposed methods primarily encompass three techniques: Fourier basis function interpolation, the Autoregressive Integrated Moving Average model (ARIMA), and AMF (ARIMA-Median Filter). The first two methods are founded on the temporal evolution characteristics of the flow field and reconstruct the missing fluid information by extrapolating from the available data. The AMF method, building upon the ARIMA reconstruction outcomes, employs a median filtering approach to eliminate noise and outliers introduced by the lack of spatial correlation. All three methods perform well, with the AMF method exhibiting superior performance, particularly in cases involving scatter missing data with higher absence rates. While ARIMA and AMF entail longer computational times, they offer stability and are less prone to overfitting during the reconstruction process, rendering them more suitable for reconstructing flow fields with varying degrees of missing data. The Fourier basis function interpolation method, with its shorter runtime, is better suited for swiftly reconstructing flow fields with lower rates of missing data. However, it is worth noting that improper parameter selection for this method can lead to overfitting. Additionally, the non-stationarity of the flow field has an impact on reconstruction accuracy, with regions exhibiting stronger non-stationarity resulting in more noticeable reconstruction errors. For the ARIMA method, which relies on temporal correlation for reconstruction, the missing structures have a relatively minor effect. However, in the case of flow fields with block missing data, the improvement in reconstruction accuracy when using median filtering compared to ARIMA is not substantial.

Research on steel structure damage detection based on TCD-CNN method

With the development of social economy, steel structures are widely adopted in building structures. However, the structure is inevitably damaged during service due to environmental erosion, load action, and other reasons. Therefore, it is significant to detect the structural damage. This study propose a damage detection method of steel structure based on TCD-CNN. The method uses transmissibility change data(TCD) as the damage indicator and the convolutional neural network(CNN) model for damage detection of the structure. The benefit of this method is no need to measure the load response of the structure, which is more conducive to practical engineering applications. In this paper, the vibration experiments of the cantilever beam steel structure are performed, and three damage indicators, namely, time domain data(TDD), frequency domain data(FDD) and TCD, are proposed. The comparison of detection results of these three damage indicators shows that the use of TCD has higher detection accuracy and more stable detection results. Furthermore, 1D-TCD-CNN and 2D-TCD-CNN are used to detect the damage degree of structure respectively. The results illustrate that test accuracy with 2D-TCD-CNN is higher, even up to 100%, and with a shorter running time.

B-339 A Clinical Research LC-MS/MS Method for the Analysis of Antidepressants in Plasma

Abstract Background Depression is common globally, with an estimated 3.8% of the population affected. The condition can impact individuals’ ability to function in work, social and family settings. Many antidepressant drugs are currently prescribed, encompassing selective serotonin reuptake inhibitors (SSRIs), serotonin-nonresponsive reuptake inhibitors (SNRIs) and tetracyclic antidepressants (TeCAs). However pharmacokinetic and drug interactions are known, therefore a reliable quantitative clinical research method may play a role in researching the effects of their administration. Waters has developed a clinical research method for the following antidepressants in plasma; citalopram, desmethylfluoxetine, duloxetine, fluoxetine, fluvoxamine, O-desmethylvenlafaxine, sertraline and venlafaxine (10–1000 ng/mL); mirtazapine (5–500 ng/mL) and trazadone (30–3000 ng/mL). Methods Matrix matched calibrators and QCs were prepared using in-house stocks and pooled plasma. Samples (50 µL) were treated with internal standard in acetonitrile. A water/methanol/ammonium acetate gradient was used with a Waters™ XSelect™ Premier HSS T3 column on a Waters ACQUITY™ UPLC™ I-Class followed by detection on a Xevo® TQD mass spectrometer in a 5 min run. Results No system carryover was observed following analysis of plasma samples containing the highest concentration calibrators. Analytical sensitivity investigations indicated precise quantification (≤20% CV, ≤17.6% bias) at concentrations equal to or lower than the lowest concentration calibrator. Total precision and repeatability were assessed (3 pools, 5 replicates, 5 days; n = 25) and determined to be ≤10.0% RSD. Linearity experiments determined the method provided first or second order fits over the ranges analyzed; additionally, each run met acceptance criteria (coefficient of correlation ≥ 0.995, determined concentrations of calibrators ±15% of nominal, ±20% in the case of the lowest calibrator). Post-column infusion experiments demonstrated analytes eluted in regions free of major ion suppression or enhancement. Evaluation of matrix effects at low and high concentrations indicated compensation by the internal standard. Addition of high concentrations of several endogenous and exogenous materials did not affect quantification. Conclusion This quantitative method for clinical research demonstrates very good precision with minimal matrix effects and allows for the multiplexing of a panel of antidepressants in plasma in a short run time. For Research Use Only. Not for Use in Diagnostic Procedures.

A facility location problem for extracurricular workshop planning: bi‐level model and metaheuristics

AbstractThis paper presents a facility location problem with capacitated multiservice facilities in which the allocation of the users is based on their preferences. The problem arises from an educational program, where a government agency or a nongovernmental organization (NGO) offers extracurricular workshops to elementary school students. These workshops promote the development of academic, social, or cultural skills of the students. This problem is formulated as a bi‐level program. The government/NGO is associated with the upper level, which aims to minimize the costs for opening workshops in different schools and the costs of allocating students to each workshop. The lower level is related to the students and seeks to optimize their global preferences for the offered workshops. To solve this problem, we propose a single‐level reformulation and two metaheuristics (a path‐relinking metaheuristic (PRM) and an iterated local search (ILS) procedure). The best performing method is the PRM that generates saturated initial random solutions following a drop heuristic approach. After different phases of improvement (with drop and exchange heuristics), the solutions undergo a path‐relinking procedure. The trajectory between the initial solution and the best solution found so far is explored using a randomized backward path‐relinking strategy that thoroughly explores the neighborhood of the best solution. Computational experimentation indicates that the proposed PRM metaheuristic is effective and efficient. The results obtained provide better upper bounds in shorter running times than the ones obtained by the single‐level reformulation or the ILS procedure. Finally, the application to a realistic case study of Apodaca municipality in Nuevo Leon, Mexico, shows that large‐scale instances can be solved efficiently using the proposed metaheuristic.

A new method to estimate soil organic matter using the combination model based on short memory fractional order derivative and machine learning model

In order to improve the quantitative inversion accuracy of soil organic matter in arid areas, the soils affected by different human disturbances in Xinjiang were used as research objects (Regions I, II and III). This study compared and analyzed the preprocessing hyperspectral data effects of GMFOD (global memory fractional order derivative) with 1759 memory length and SMFOD (short memory fractional order derivative) with different memory lengths (L = 1500, 1200, 1000, 800, 500, 300, 250, 200, 150, 100), machine learning models based on RBF (radial basis function) and ELM (extreme learning machine) were used to explore the best model for predicting soil organic matter content. Simulation results showed that (1) SMFOD had shorter computational runtime compared to GMFOD, and the 100 SMFOD had the shortest running time. When all orders of fractional order derivative were calculated, the time required for 100 SMFOD was 115.27 s, while the time required for GMFOD was 1025.32 s. These indicated that the running time of the former was 789.49% shorter than that of GMFOD. (2) Comparing the correlation coefficients of hyperspectral and soil organic matter in all fractional orders calculated based on GMFOD and SMFOD, it was found that the maximum correlation coefficient of 100 SMFOD and GMFOD differed by 0.0103, 0.0058 and 0.0287 in Regions I, II and III, which means that GMFOD only increased by 0.94%, 1.71% and 4.25%. These indicated that the maximum correlation coefficient of SMFOD and GMFOD was very similar at each fractional order. (3) The best model for inversion of soil organic matter in all three regions was SMFOD-CC1-RBF (CC1 denotes bands passed the 0.01 significance test), which was located at the fractional orders of 1.4, 1.8 and 1.1, respectively. Meanwhile, R2 (coefficient of determination), RMSE (root mean square error), RPD (ratio of the performance to deviation) were 0.8249, 0.7656, 1.9256 in Region I; were 0.7630, 1.4422, 1.9561 in Region II; and were 0.6799, 1.5343,1.8513 in Region III, which indicated that the higher-order SMFOD had better performance in inverting organic matter than the lower-order SMFOD, and the prediction ability of this model for soil organic matter in the three regions was good.

Adv-BDPM: Adversarial attack based on Boundary Diffusion Probability Model

Deep neural networks have become increasingly significant in our daily lives due to their remarkable performance. The issue of adversarial examples, which are responsible for the vulnerability problem of deep neural networks, has attracted the attention of researchers in the study of robustness of these networks. To address the issues caused by the restricted diversity and precision of adversarial perturbations in neural networks, we introduce a novel technique called Adversarial Boundary Diffusion Probability Modeling (Adv-BDPM). This approach combines boundary analysis and diffusion probability modeling. First, we combined the denoising diffusion probability model with the boundary loss to design the boundary diffusion probability model, which can generate corresponding boundary perturbations for a specific neural network. Then, through the iterative process of boundary perturbations and its corresponding orthogonal perturbations, we proposed a decision boundary search algorithm to generate adversarial samples. The comparison experiments with black-box attacks in ImageNet demonstrate that Adv-BDPM has better attack success rate and perturbation precision. The comparison experiments with white-box attacks in CIFAR-10 and CIFAR-100 demonstrate that Adv-BDPM has better attack success rate, attack diversity for the same sample, and can effectively defend against adversarial training with shorter running time.

Innovative micellar-UPLC approach for concomitant determination of favipiravir and remdesivir with four co-administered cardiovascular drugs in their pharmaceuticals and spiked plasma: Greenness and whiteness assessment

Many COVID-19 patients have pre-existing cardiovascular diseases, which are strongly linked to the high incidence of severe COVID-19. The experts advised such patients to prioritize drug treatments as per the physician's recommendations to avoid aggravation of the basic diseases. Hence, the development of an analytical approach for the concurrent determination of drugs prescribed for the co-treatment of COVID-19 and cardiovascular diseases acquires significant importance in QC units and bioavailability centers. Herein, a micellar-UPLC approach was first devised for the concomitant analysis of aspirin, atenolol, atorvastatin, losartan, favipiravir, and remdesivir as co-administered drugs, either with or without salicylic acid. On ACQUITY UPLC C18 column, the aforesaid drugs were isocratically separated employing a micellar eluent system. This system consisted of 0.03 M Brij-35, 0.15 M sodium dodecyl sulfate, and 0.02 M sodium dihydrogen phosphate (pH 5.0) as the mobile phase A (90%) and n-propanol as the mobile phase B (10%). The eluent system was pumped at a flow rate of 0.50 mL.min−1 for 5.50 min, which was suddenly increased to 0.80 mL.min−1 for 2.50 min. The separated peaks were scanned at 240 nm. The suggested approach was validated following ICH requirements for linearity, robustness, accuracy, specificity, precision, and detection and quantitation limits. The suggested approach was applied successfully to commercial products and human plasma without any intrusion from excipients or plasma matrices. The suggested approach’s greenness was appraised employing five metrics namely: eco-scale, GAPI, NEMI, Raynie and Driver, and AGREE. Furthermore, the suggested approach’s whiteness features were investigated using the recently unveiled RGB12 model. The suggested approach outperformed the reported approaches in terms of greenness and whiteness. The sensitivity, high sample throughput, and short runtime (7.40 min) of the suggested approach assert its aptness for routine determination of the aforesaid drugs.

Quantum walk state transfer on a hypercube

We investigate state transfer on a hypercube by means of a quantum walk where the sender and the receiver vertices are marked by a weighted loops. First, we analyze search for a single marked vertex, which can be used for state transfer between arbitrary vertices by switching the weighted loop from the sender to the receiver after one run-time. Next, state transfer between antipodal vertices is considered. We show that one can tune the weight of the loop to achieve state transfer with high fidelity in shorter run-time in comparison to the state transfer with a switch. Finally, we investigate state transfer between vertices of arbitrary distance. It is shown that when the distance between the sender and the receiver is at least 2, the results derived for the antipodes are well applicable. If the sender and the receiver are direct neighbours the evolution follows a slightly different course. Nevertheless, state transfer with high fidelity is achieved in the same run-time.

Validation of an LC-MS/MS assay for rapid and simultaneous quantification of 21 kinase inhibitors in human plasma and serum for therapeutic drug monitoring

Kinase inhibitors have revolutionized cancer treatment in the past 25 years and currently form the cornerstone of many treatments. Due to the increasing evidence for therapeutic drug monitoring (TDM) of kinase inhibitors, the need is growing for new assays to rapidly evaluate kinase inhibitor plasma concentrations. In this study, we developed an LC-MS/MS assay for the rapid and simultaneous quantification of 21 kinase inhibitors. First, a literature search was conducted to ensure that the linear ranges of the analytes were in line with the reported therapeutic windows and/or TDM reference values. Subsequently, the assay was validated according to FDA and EMA guidelines for linearity, selectivity, carry-over, accuracy, precision, dilution integrity, matrix effect, recovery, and stability. The assay was fast, with a short run-time of 2 min per sample. Sample pre-treatment consisted of protein precipitation with methanol enriched with stable isotope-labeled internal standards (SIL-IS), and the mixture was vortexed and centrifuged before sample injection. Separation was achieved using a C18 column (3 μm,50 × 2.1 mm) with a gradient of two mobile phases (ammonium formate buffer pH 3.5 and acetonitrile). Analyte detection was conducted in positive ionization mode using selected reaction monitoring. The assay was accurate and precise in plasma as well as in serum. Extraction recovery ranged between 95.0% and 106.0%, and the matrix effect was 95.7%-105.2%. The stability of the analytes varied at room temperature and in refrigerated conditions. However, all drugs were found to be stable for 7 days in the autosampler. The clinical applicability of the analytical method (486 analyzed samples between 1 July 2022–1 July 2023) as well as external quality control testing results were evaluated. Taken together, the results demonstrate that the analytical method was validated and applicable for routine analyses in clinical practice.

Novel Ultra-Fast Liquid Chromatography Method for Simultaneous Quantification of Metformin Hydrochloride and Remogliflozin Etabonate

An advanced formulation having remogliflozin etabonate (REM) and metformin hydrochloride (MET) for the treatment of diabetes was recently approved. However, there hasn't been any information reported on a UFLC technique for simultaneously quantifying both active substances. Therefore, aim of the study was the development of an ultra-fast isocratic liquid chromatography method for quantification of MET and REM in API and dosage form. The reversed phase-UFLC method was developed with a very short run time of only 3 min with no interference due to blank. The compounds were separated using Shimadzu Sim-pack XR ODS (75 mm × 3 mm, 2.2 μ) column with mobile phase consisting of buffer (0.1% phosphoric acid):acetonitrile (30:70). The developed method was validated as per ICH-Q2(R1). The linearity was established in the range of 0.01-125 ppm and 0.01-25 ppm for MET and REM, respectively. The average assay was found 99.4% for MET and 99.1% for REM. The accuracy was observed 98.29-99.49% for MET and 98.39-99.73% for REM. The proposed method is very fast and simple however precise and accurate. This method is very useful for regular quality control laboratory to quantify MET and REM in API and dosages forms.

Establishment of a sensitive UPLC-MS/MS method to quantify safinamide in rat plasma.

A fast, simple, and sensitive ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method was established for the quantification of safinamide in rat plasma. Plasma samples were treated with acetonitrile for protein precipitation, and diazepam was used as an internal standard (IS). The analytes were separated on an Acquity UPLC C18 (2.1mm × 50mm, 1.7μm) chromatographic column with gradient elution using a mobile phase (0.1% formic acid-acetonitrile). Then, the eluates were detected by electrospray ionization (ESI) in positive ion mode. The analytes were quantified by multiple reaction monitoring (MRM) using the transition m/z 303.3→215.0 of safinamide and m/z 285.0→154.0 of IS. Safinamide had good linearity in the concentration range of 1.0-2000ng/mL, and the lower limit of quantitation (LLOQ) was 1.0ng/mL. The intra- and inter-day precision and accuracy of safinamide were less than 7.63%, while the average recovery rate was 92.98%-100.29%. The method was validated to be stable and had low noise, short chromatographic run time, wide linear range, small sample volumes, low sample injection volumes, and high sensitivity. Therefore, it can be used in pharmacokinetics and preclinical and clinical studies.

Modified polysulfone membrane facilitates rapid separation of plasma from whole blood for an effective anti-SARS-CoV-2-IgM diagnosis

During the outbreak of coronavirus, RT-PCR was the premier gold standard method for severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) diagnosis. However, the sophisticated procedure of RT-PCR persuades researchers to develop sustainable point-of-need immunoassay methods for tracing unwitting carriers of SARSCoV-2. Herein, by fabricating a modified polysulfone (MPSF) membrane, we developed an integrated radial flow immunoassay (IRFIA) platform as a point-of-care system, capable of multiplying the immunoassays at a short run time. The target molecule is the SARSCoV-2 IgM in separated plasma. Although the lateral flow immunoassay kits for the rapid identification of Covid-19 have already been commercially developed but, the proposed method is superior to the conventional lateral flow immunoassay. In the newly designed membrane system, we have combined the five membranes of prevalent lateral flow immunoassay (LFIA) strips in one polymeric membrane. The MPSF membrane is capable of separating plasma from whole blood sample, which will reduce the interference of red colour of hemoglobin with generated signal and enhance the immunoassay precision. The efficiency of plasma separation, reached the mean value of 97.34 v/v% in 5 s. Furthermore, the gel electrophoresis results of the separated plasma contrasted with centrifuged plasma sample, demonstrated more efficient separation by the membrane. Using the MPSF membrane, signal generation time reduced from about 20 min in conventional rapid test strip for Covid-19 to about 7 min in IRFIA platform. The sensitivity and specificity of the membrane platform were determined to be 89% and 90%, respectively and a Kappa coefficient of 0.79 showed reliable agreement between the RT-PCR and the membrane system.

Central composite design driven optimization of sustainable stability indicating HPLC method for the determination of Tigecycline and greenness assessment

Background: Tigecycline (TGC) is a recently developed antibiotic to battle resistant bacteria. The procedures outlined in the literature for analyzing TGC involve chemical solvents that could be hazardous. Therefore, this study aimed to create a sustainable and stable HPLC technique for quantifying Tigecycline in lyophilized powder. The powerful chemometric tool, experimental design (ED), will be applied to analyze the variables' interaction and impact on the selected analytical target profiles. Response surface methodology provides a tutorial on using the central composite design with three levels of variables and quadratic programming to optimize the design space of the developed method. Methods: The New HPLC method consisted of an aqueous buffer and ethanol as a green mobile phase run on a reversed-phase symmetry C18 column. A full resolution between the Tigecycline and its degradation product peaks was achieved in a short analytical runtime. Results: Further, the specificity, accuracy, precision, robustness and stability indicating power of the proposed approach were verified through stress degrading testing. Conclusions: Finally, the analytical eco-scale and the green Analytical Procedure Index (GAPI) were utilized to determine how environmentally friendly the recommended method was compared to other published approaches.

The effect of intelligent tour guide system based on attraction positioning and recommendation to improve the experience of tourists visiting scenic spots

With the advancement of technology and economic development, human living standards have been substantially improved. The demand for tourism has also increased. Smart tourism uses information technology to integrate tourism resources. Tourists are provided with the right travel solutions for their needs. To further enhance the tourist experience, a smart tour guide system based on attraction positioning and recommendation is proposed. The RankSVM+time algorithm and K-means clustering algorithm are used to achieve attraction recommendation. The tourist flow path planning method of scenic spots is combined with D*algorithm to realize dynamic programming of tourist routes. A smart tour guide system based on tourist demand analysis is established to achieve data interaction and attraction positioning. The tour guide system is built based on analyzing the needs of tourists, realizing the functions of data interaction and attraction location. Tour guide services are provided in the form of voice or image. The experimental data shows that the F-value of RankSVM+time algorithm reaches 0.75. The recommendation accuracy is higher than that of RankSVM+Markov algorithm. The shortest running time is 81 s, which is faster than other methods. The intelligent tour guide system also dynamically adjusts the route visit scheme when dynamic changes occur in the attractions. The results show that the intelligent tour guide system based on attraction location and recommendation is highly accurate, fast and adaptable, which can enhance the tourist experience of visitors.