On-line Phase Research Articles

Development and validation of a bioanalytical method for the quantification of methotrexate from serum and capillary blood using volumetric absorptive microsampling (VAMS) and on-line solid phase extraction (SPE) LC-MS

High-dose methotrexate is part of the polychemotherapy protocols for the treatment of Acute lymphoblastic leukaemia (ALL) with therapeutic drug monitoring (TDM) to adjust leucovorin rescue. An immunoassay is commonly used to analyse serum samples collected via venous blood sampling. However, immunoassays cannot distinguish between the parent drug and its metabolites. Besides, the blood volume required by venous blood sampling is high. Therefore, the aim of this project was to develop a fast, simple, reliable and cost-efficient micro sampling bioanalytical method using capillary blood to minimize the harm of children and to analyse both methotrexate and its metabolites. To achieve this aim, a LC-MS method with on-line solid phase extraction (SPE) for the simultaneous detection of methotrexate and its metabolites from capillary blood using volumetric-absorptive-microsampling (VAMS) technology was developed and fully validated. Besides, the method was also validated and modified for serum samples to compare the results with the immunoassay. A single-quadrupole MS detector was used for detection. Through the use of on-line SPE technology, a lower limit of quantitation of 0.03 µM for MTX and 7-OH-MTX and of 0.05 µM for DAMPA from a 10 μL capillary blood sample was achieved. The accuracy is between 90.0 and 104% and the precision between 4.7 and 12% for methotrexate and its metabolites, respectively. Because of the cross reactivity of the immunoassay a cross-validation was not successful. Besides, a correlation factor of 0.46 for MTX between plasma and whole-blood was found.A fast, simple, reliable and cost-efficient extraction and analysis LC-MS method could be developed and validated, which is applicable in ambulatory and clinical care.

Privacy-preserving compromised credential checking protocol for account protection

Hundreds of millions of accounts are sold on the Dark Web as a result of hacking. These stolen accounts can be used to maliciously log into the victim’s application, which is also known as credential stuffing attacks. Recently, to resist these attacks, several compromised credential checking (C3) services have been deployed to provide users with APIs to check whether their accounts have been exposed. However, these C3 services provide the security at the cost of high latency and bandwidth. There is also the problem implicitly trusting the server to properly handle the hash prefixes containing passwords. To solve these problems, we present an efficient C3 protocol for account protection, which enables a client to check whether its account appears in a database storing the compromised credentials, without disclosing the queried account to the server. Compared to existing C3 services, the proposed C3 protocol has 10∼20× and 17.8∼20.7% improvement in computational time for both the client and server during the online phase, respectively, while maintaining the same computational time for server during the preprocessing phase. Meanwhile, the proposed C3 protocol improves the communication cost of client-to-server by 17∼33× while maintaining the same communication cost of server-to-client.

An intelligent and interpretable rule-based metaheuristic approach to task scheduling in cloud systems

Metaheuristic algorithms can be very successful in solving scheduling problems. However, these methods can be slow for time-critical applications due to the iterative stochastic processes they perform. This problem becomes even more pronounced in dynamic environments such as cloud systems. This paper proposes an interpretable rule-based solution to minimize this problem. Combining metaheuristic task scheduling solutions and machine learning techniques, this method uses a two-phase mechanism, semi-offline and online. In the semi-offline phase, we first archive metaheuristic solutions to previously randomly generated or encountered task-scheduling problems. The basic idea of the proposed method is to reuse these previously obtained successful metaheuristic solution patterns for future similar problems. Finding similar solution patterns from this extensive archive dataset is done by automatically extracting rule sets through machine learning techniques. These interpretable rule sets are used to identify the type of task scheduling problem encountered in the online phase and to find the optimal solution pattern. The performance of this method, which dramatically reduces execution time and enables the use of metaheuristics in time-critical applications, has been tested and proven for various cloud task-scheduling scenarios.

Dynamic risk analysis of evolving scenarios in oil and gas separator

Risk assessment models in the oil and gas (O&G) industry has begun to transform from time-static models like fault tree, event tree or bowtie to dynamic risk assessment (DRA) models, as the latter is able to better capture the real-time-dependent risk behavior of safety barriers. Currently, most existing works on DRA in O&G industry mainly rely on event-driven data, such as failures or accident data from similar systems for risk updating. These data can be collected only when accidents or near-misses have occurred, which limits the prediction capability of the DRA model. To address this drawback, a DRA model is developed in this paper that uses condition-monitoring data for risk updating. A significant advantage of using condition-monitoring data is that the risk can be updated before accidents or failures occur, giving the operation team more time to take preventive actions.The developed approach comprises of an offline and an online phase. In the offline phase, a conventional risk assessment is performed based on fault tree models to calculate the risks at the beginning of the operation. Through the offline analysis, we can also identify the most critical safety barriers by examining their contribution to the risk indexes. Critical safety barriers are selected for condition-monitoring. In the online phase, the condition-monitoring data are used to update the reliability of the safety barriers in real-time based on a sequential Markov Chain Monte Carlo (MCMC) algorithm using a Bayesian framework. The updated reliabilities of the safety barriers are, then, used in the offline risk assessment model for a DRA.The developed approach is applied for a DRA of liquid carryover from an oil and gas separator to downstream equipment, using real-time data from the separator's safety barriers. The results show that the developed method provides a more accurate representation of the system's performance, enabling early detection of potential failures and reducing uncertainty in risk estimates. The proposed DRA model demonstrated its effectiveness in predicting failures of safety barriers in real-time, giving operational teams ample opportunity to take corrective action. This leads to improved decision-making in the O&G industry, enabling timely response to changes in risk levels. The use of condition-monitoring data enhances the accuracy of risk estimations, representing a crucial advance in DRA applications in the O&G sector.

More efficient constant-round secure multi-party computation based on optimized Tiny-OT and half-gate

Constand-round secure multi-party computation enables a group of mutually distrustful parties to jointly compute a function of their inputs in a constant-round of communication. In recent years, the constant-round secure multi-party computation protocol has improved the efficiency of the online phase by completing the multiplication computation in advance of the online phase through the Beaver triples generated in the preprocessing phase. However, the preprocessing phase of these protocols is extremely overhead-intensive and there is room for improvement. In this paper, we propose an active, constant-round secure multi-party computation (MPC) protocol which can tolerate arbitrary n − 1 corruptions. At the high level, we follow the Tiny-OT protocol and the unauthenticated garbling for garbled circuits in the preprocessing phase presented by Carmit Hazay and Peter Scholl (ASIACRYPT 2017). Particularly, we first present a new optimized Tiny-OT protocol for generating Beaver (AND) triples which is more efficient than the previous protocol, and then combine the information generated by Tiny-OT to construct an unauthenticated garbled circuit. To be precise, we also reduce the number of commitments and hash function calls in the correctness check of AND triples, and avoid using additional OT operations to compute the circuit, thus further improving communication efficiency. Our new constant-round MPC protocol has a communication complexity O(|C|Bnk) per party. Moreover, we further provide solutions to the open problem of introducing the half-gate technique into multi-party distributed garbled, which will reduce half size of the garbled table per gate.

69 Transcranial Random Noise Stimulation Facilitates Phonemic Verbal Fluency and Convergent Thinking in Multilingual Healthy Adults.

Objective:The aim of the present study was to analyse the effects of the transcranial random noise stimulation (tRNS) technique when placed on the left dorsolateral prefrontal cortex (L-DPFC) and left inferior frontal gyrus (L-IFG), for the improvement of verbal fluency performance and creativity skills in a group of multilingual healthy adults.Participants and Methods:Fifty healthy adults, aged 18-47 years, participated in the study. All of them were native Spanish speakers of which three were bilingual (Spanish and English) and 47 were multilingual (Spanish, Basque and English). The study had a randomized, counterbalanced, double-blind, sham-controlled design. The participants were randomly allocated to either a tRNS active group or a placebo-control group. All participants were tested individually in one session divided into three parts: (1) baseline, (2) online, and (3) offline assessment. In the active condition, a 1.5mA current (100-500 Hz) was delivered for 20 minutes (online phase). Participants’ verbal fluency was assessed through semantic and phonemic verbal fluency tasks in three different languages (Spanish, Basque and English), while creativity was assessed in their native language with the Remote Associations Test (RAT, pre and post forms) for convergent thinking, and with the Unusual Uses subtest (UU, pre and post forms) for divergent thinking. In addition, the linguistic profile of the participants was evaluated with the Language Experience and Proficiency Questionnaire (LEAP-Q), and their processing speed and cognitive flexibility were assessed with the Stroop Color and Word Test (SCWT).Results:The results showed significant differences in phonemic fluency between the groups during the online assessment in Spanish (F= 5.31, p = 0.026), and in the offline assessments in Spanish (F=6.44, p = 0.015) and English (F=10.80, p = 0.002), with participants in the active condition performing better. While no differences were observed in the performance of the groups in verbal fluency in Basque, neither in the online (F=1.06, p = 0.31), nor in the offline assessment (F=2.62, p = 0.11). Furthermore, no significant differences were observed between groups in semantic verbal fluency tasks in any of the languages, neither during stimulation nor offline. However, there were no differences between conditions in the online (Spanish, F=0.86, p = 0.35; English, F=2.95, p = 0.09; and Basque, F=0.01 p = 0.94) and offline (Spanish, F=2.53, p = 0.11; English, F=0.74, p = 0.39; and Basque, F=1.39, p = 0.24) semantic tasks. In creativity, significant differences were observed between groups on the RAT (F=9.58, p = 0.003), while no differences were observed in the performance of any of the three dimensions of the UU (Originality, F=0.44, p = 0.51; Flexibility, F=0.42, p = 0.51; Fluency, F=0.69, p = 0.41). In the SCWT, statistically significant differences were only observed in the colour-word part (F=7.60, p = 0.008) during the online assessment, showing a better performance of the participants under the tRNS condition compare to the sham condition.Conclusions:The results obtained in this study suggest that the excitatory effects of tRNS over the L-DLPFC L-IFG could contribute to the improvement of phonemic verbal fluency and verbal convergent thinking, in healthy individuals.

Real-time Bayesian model calibration method for C/SiC mechanical behavior considering model bias

The mechanical behavior of C/SiC composites has significant nonlinear and dispersion. To obtain the real mechanical behavior of C/SiC composites in actual structure, a real-time calibration method for mechanical behavior of C/SiC composites based on the dynamic Bayesian network (DBN) is proposed. The epistemic uncertainty of the mechanical behavior is introduced in the form of model bias. The method comprises offline and online phases. Firstly, the surrogate model of the mechanical behavior is built based on the proper orthogonal decomposition and the Gaussian process model offline. Then, based on the DBN, model parameters can be continuously calibrated by the sensor data online. The validity of the method is verified by the uniaxial tensile test of C/SiC composites. The results show that the proposed method can real-time accurately calibrate and predict the mechanical behavior of all the C/SiC samples and the introduction of the model bias is of great significance.

Stochastic domain decomposition based on variable-separation method

This work proposes a stochastic domain decomposition method for solving steady-state partial differential equations (PDEs) with random inputs. Specifically, based on the efficiency of the variable-separation (VS) method in simulating stochastic partial differential equations (SPDEs), we extend it to stochastic algebraic systems and employ it in the context of stochastic domain decomposition. The resulting method, termed stochastic domain decomposition based on variable-separation (SDD-VS) alleviates the challenge commonly known as the “curse of dimensionality” notably by leveraging explicit representations of stochastic functions derived from physical systems. The primary objective of the proposed SDD-VS method is to obtain a separate representation of the solution for the stochastic interface problem. To enhance computational efficiency, we introduce a two-phase approach consisting of offline and online computation. In the offline phase, we establish an affine representation of stochastic algebraic systems by systematically applying the VS method. During the online phase, we estimate the interface unknowns of SPDEs using a quasi-optimal separated representation, facilitating the construction of efficient surrogate models for subproblems. We substantiate the effectiveness of our proposed approach through numerical experiments involving three specific instances, demonstrating its capability to provide accurate solutions.

Branched Latent Neural Maps

We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input–output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedforward partially-connected neural network that structurally disentangles inputs with different intrinsic roles, such as the time variable from model parameters of a differential equation, while transferring them into a generic field of interest. BLNMs leverage latent outputs to enhance the learned dynamics and break the curse of dimensionality by showing excellent in-distribution generalization properties with small training datasets and short training times on a single processor. Indeed, their in-distribution generalization error remains comparable regardless of the adopted discretization during the testing phase. Moreover, the partial connections, in place of a fully-connected structure, significantly reduce the number of tunable parameters. We show the capabilities of BLNMs in a challenging test case involving biophysically detailed electrophysiology simulations in a biventricular cardiac model of a pediatric patient with hypoplastic left heart syndrome. The model includes a 1D Purkinje network for fast conduction and a 3D heart-torso geometry. Specifically, we trained BLNMs on 150 in silico generated 12-lead electrocardiograms (ECGs) while spanning 7 model parameters, covering cell-scale, organ-level and electrical dyssynchrony. Although the 12-lead ECGs manifest very fast dynamics with sharp gradients, after automatic hyperparameter tuning the optimal BLNM, trained in less than 3 h on a single CPU, retains just 7 hidden layers and 19 neurons per layer. The resulting mean square error is on the order of 10−4 on an independent test dataset comprised of 50 additional electrophysiology simulations. In the online phase, the BLNM allows for 5000x faster real-time simulations of cardiac electrophysiology on a single core standard computer and can be employed to solve inverse problems via global optimization in a few seconds of computational time. This paper provides a novel computational tool to build reliable and efficient reduced-order models for digital twinning in engineering applications. The Julia implementation is publicly available under MIT License at https://github.com/StanfordCBCL/BLNM.jl.

Urinary Free Cortisol Determination and Interferences Studies Using Liquid Chromatography Coupled to Tandem Mass Spectrometry after On-Line Solid Phase Extraction Based on TurboflowTM Chromatography.

(1) A 24 h urinary free cortisol (UFF) is one of the first-line exams recommended for the diagnosis of Cushing's syndrome. In a hospital hormonology department, this activity can exceed several hundred dosages per week. The UFF is generally determined via an immunoassay with an automate using a chemiluminescence or electrochemiluminescence detection system. To increase the cortisol concentration in the analyzed sample, the automated analysis is preceded by urine extraction, which does not prevent there from being some interferences due to other steroids with close structures. (2) This paper describes the development of on-line solid phase extraction coupled to liquid chromatography and mass spectrometry for the analysis of urinary free cortisol. The on-line extraction was based on the TurboflowTM chromatography coupled to the analytical column by two valves, easily available for the laboratories. (3) The choice of the Accucore Polar Premium® analytical column made it possible to avoid analytical interferences with exogenous or endogenous molecules having the same SRM transition (363 → 121) as cortisol. (4) The method was fully validated in the range of clinically relevant concentrations from the lower limit of quantification (LLOQ) to 411.75 nmol·L-1.

Wind turbine fault detection based on spatial-temporal feature and neighbor operation state

Wind turbines (WTs) are complex pieces of engineering. They are generally exposed to harsh environmental conditions. Thus, major changeable working conditions and multidimensional dynamic correlation arise, resulting in unsatisfactory fault detection accuracy. A WT fault detection method based on spatio-temporal features and neighbor operation state is proposed in this study to tackle the aforementioned challenge. A spatio-temporal feature extraction method based on accumulated mutual information is proposed to describe the dynamic correlation of multiple features during the offline phase. Then, the spatio-temporal features are applied to build the weighted k-nearest neighbor fault detection model. In the online phase, the weighted distance between the online sample and the neighbor operation state is adopted to identify the anomaly state. Moreover, a dynamic threshold based on the neighbor sample is designed to cope with the changeable operating conditions. The proposed methods are demonstrated using FAST data and supervisory control and data acquisition data, which consider different fault types. The experiment results show that compared with similar methods and traditional fault detection methods, the operational characteristics of WTs’ components can be better described by the proposed spatio-temporal feature extraction method, and the false alarm rates can be considerably reduced by the dynamic threshold.

An online transfer kernel recursive algorithm for soft sensor modeling with variable working conditions

Soft sensor technology has found widespread application in the real-time detection of challenging variables like product quality and key process parameters. However, changes in working conditions can lead to alterations in data distribution, resulting in model inaccuracies. Furthermore, data from new working conditions arrive sequentially and online. Due to technical difficulties, time-consuming measurements, and high costs, there is often a lack of sufficient new data for modeling in the initial phase. Moreover, industrial processes often exhibit dynamic, nonlinear, variable coupling and other factors, posing significant challenges for model establishment. To address these challenges, this study proposed an online transfer kernel recursive algorithm for soft sensor modeling under changing working conditions. The algorithm consists of an offline phase and an online phase. In the offline phase, the algorithm considers the dynamic features of the process and variable coupling, resulting in the establishment of a dynamic inner model. The original data is then projected onto a latent variable space. In the online phase, the proposed method incorporates the concept of online transfer learning and takes into account the nonlinearity of the process. This allows for the establishment of a kernel online recursive method in the latent variable space, integrating parameter transfer and sample transfer. Experimental results conducted on multiple industrial datasets validate the effectiveness of the proposed approach.

Beam SNR Prediction Using Channel Charting

We consider a machine learning approach for beam handover in mmWave 5G New Radio systems, in which User Equipments (UEs) perform autonomous beam selection, conditioned on a used Base Station (BS) beam. We develop a network-centric approach for predicting beam Signal-to-Noise Ratio (SNR) from Channel State Information (CSI) features measured at the BS, which consists of two phases; offline and online. In the offline training phase, we construct CSI features and dimensionality-reduced Channel Charts (CC). We annotate the CCs with per-beam SNRs for different combinations of a BS beam and the corresponding best UE beam, and train models to predict SNR from CSI features for different BS/UE beam combinations. In the online phase, we predict SNRs of beam combinations not being used at the moment. We develop a low complexity out-of-sample algorithm for dimensionality reduction in the online phase. We consider <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbors, Gaussian process regression, and neural network-based predictions. To evaluate the efficacy of the proposed framework, we perform simulations for a street segment with synthetically generated CSI. We investigate the complexity-accuracy trade-off for different dimensionality reduction techniques and different predictors. Our results reveal that nonlinear dimensionality reduction of CSI features with neural network prediction shows the best performance, and the performance of the best CSI-based prediction method is comparable to prediction based on using known physical location.

MOOP: An Efficient Utility-Rich Route Planning Framework Over Two-Fold Time-Dependent Road Networks

Utility-rich (e.g., more attractive or safer) route planning on city-scale road networks is a common yet time-consuming task. Although both travel time and utility on edges are time-dependent concurrently in real cases, they are overlooked in most prior work. In this paper, we focus on the route planning over two-fold time-dependent road networks, i.e., both travel time and utility on edges are varying over time. We aim to find a route from an origin to a destination by maximizing the accumulated utility score within a time budget. Moreover, to satisfy users' real-time requests, the fast response is usually mandatory. Here, we propose a novel two-phase framework called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MOOP</b> , i.e., <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>M</u></b> anaging <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>O</u></b> ffline data for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>O</u></b> nline route <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>P</u></b> lanning, to discover the near-optimal driving routes efficiently. Specifically, in the offline phase, we construct the auxiliary data structure, i.e., the edge table, to manage the time-dependent information about edges. In the following online phase, the route is generated sequentially by an iterative edge table visiting process. We evaluate the proposed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MOOP</b> thoroughly based on synthetic road networks and two real-world road networks in the city of Chengdu (4,819 nodes and 6,385 edges) and the city of Chongqing (5,056 nodes and 7,355 edges) in China. Results show that: (i) our framework can work adaptively for different time-varying utility patterns; (ii) the edge table is economic yet effective; (iii) our route planning algorithm outperforms other baselines in obtaining the highest utility value while costing the least running time.

Funshade: Function Secret Sharing for Two-Party Secure Thresholded Distance Evaluation

We propose a novel privacy-preserving, two-party computation of various distance metrics (e.g., Hamming distance, Scalar Product) followed by a comparison with a fixed threshold, which is known as one of the most useful and popular building blocks for many different applications including machine learning, biometric matching, etc. Our solution builds upon recent advances in function secret sharing and makes use of an optimized version of arithmetic secret sharing. Thanks to this combination, our new solution named Funshade is the first to require only one round of communication and two ring elements of communication in the online phase, outperforming all prior state-of-the-art schemes while relying on lightweight cryptographic primitives. Lastly, we implement our solution from scratch in portable C and expose it in Python, testifying its high performance by running secure biometric identification against a database of 1 million records in ~10 seconds with full correctness and 32-bit precision, without parallelization.

B-310 High Through-put Online Solid Phase Extraction of Immunosuppressive Drugs From Whole Blood on Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) in a Tertiary Hospital Laboratory

Abstract Background Cyclosporine A and Tacrolimus are immunosuppressive drugs that are commonly used in solid organ transplant and are characterized by a relatively narrow therapeutic index, potential drug interactions and significant intra- and inter-individual variability. Therapeutic drug monitoring is therefore beneficial for optimizing dosage regimens, preventing rejection of the graft and reducing toxicities in transplant recipients receiving immunosuppressive drug therapy. A commercial assay (RECIPE ClinMass LC-MS/MS Advanced kit) for simultaneous measurement of immunosuppressants in whole blood using LC-MS/MS methodology has been adapted for assay performance evaluation in our laboratory on the Agilent 1290 Infinity II liquid chromatograph coupled to an Agilent 6470 triple quadrupole mass spectrometer. Our study evaluated the analytical performance of Cyclosporine A and Tacrolimus on the RECIPE assay and compared it against our existing in-house LC-MS/MS assay. Methods The RECIPE ClinMass® LC-MS/MS Advanced Immunosuppressants assay kit consist of mobile phase solvents, precipitation reagents, stable isotopic labeled internal standards, solid phase extraction and analytical columns, lyohilized whole blood calibrators and quality controls. Drug analytes were extracted from whole blood using single-step protein precipitation followed by on-line solid phase extraction on the LC, eluted on isocratic chromatography and detected on positive ESI MS ionisation mode. The RECIPE assay run time is under 3 min. Performance validation parameters included within/total assay imprecision, linearity, recovery, limits of detection (LOD)/quantification (LOQ) and carry-over. Method correlation studies using fresh patient whole blood specimens (n = 212 Cyclosporine A; n = 120 Tacrolimus) were performed on existing in-house assay on Agilent 6460 LC-MS/MS and the RECIPE assay on Agilent 6470 LC-MS/MS systems. Results Within run and total imprecision for Cyclosporine A and Tacrolimus on the RECIPE assay were determined to be ≤4.4% and ≤7.5% on the Agilent 6470 LC-MS/MS. The assay demonstrated linearity across the analytical measurement range up to 1500 ug/L for Cyclosporine A and up to 55 ug/L for Tacrolimus. Analytical recoveries obtained for both drugs ranged between 93%–100%. The LODs were assessed to be 13.0 ug/L and 0.70 ug/L for Cyclosporine A and Tacrolimus respectively. LOQs were assessed to be 22.7 ug/L and 1.30 ug/L for Cyclosporine A and Tacrolimus respectively. Results of carry-over studies were insignificant. Participation in the CAP Immunosuppressive Drugs External Quality Assessment Survey showed good agreement with LC-MS/MS peers. Method correlation with in-house assay demonstrated good agreement: (1) Cyclosporine A: Passing Bablok regression RECIPE = 0.94 (In-house) + 17.2, Bland Altman mean bias of +2.0%, Spearman correlation coefficient of 0.985; (2) Tacrolimus: Passing Bablok regression of RECIPE = 1.03 (In-house) - 0.2, Bland Altman mean bias of -0.2%, Spearman correlation coefficient of 0.988 Conclusion The RECIPE ClinMass® LC-MS/MS Immunosuppressants assay performed within manufacturer’s specifications except for LOD/LOQs which were determined at higher concentrations. The assay is adaptable to contemporary LC-MS/MS systems and confer benefits from rapid turnaround time (&amp;lt;3 min), simplified specimen preparation procedures and ready prepared reagents. The advantages allow enhancements in workflow efficiency and ease of staff training in our laboratory and the assay is suitable for routine clinical use in high workload clinical laboratories.

Enhancing IoT Security through a Green and Sustainable Federated Learning Platform: Leveraging Efficient Encryption and the Quondam Signature Algorithm.

This research paper introduces a novel paradigm that synergizes innovative algorithms, namely efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning, to effectively counteract random attacks targeting Internet of Things (IoT) systems. The incorporation of federated learning not only fosters continuous learning but also upholds data privacy, bolsters security measures, and provides a robust defence mechanism against evolving threats. The Quondam Signature Algorithm (QSA) emerges as a formidable solution, adept at mitigating vulnerabilities linked to man-in-the-middle attacks. Remarkably, the QSA algorithm achieves noteworthy cost savings in IoT communication by optimizing communication bit requirements. By seamlessly integrating federated learning, IoT systems attain the ability to harmoniously aggregate and analyse data from an array of devices while zealously guarding data privacy. The decentralized approach of federated learning orchestrates local machine-learning model training on individual devices, subsequently amalgamating these models into a global one. Such a mechanism not only nurtures data privacy but also empowers the system to harness diverse data sources, enhancing its analytical capabilities. A thorough comparative analysis scrutinizes varied cost-in-communication schemes, meticulously weighing both encryption and federated learning facets. The proposed approach shines by virtue of its optimization of time complexity through the synergy of offline phase computations and online phase signature generation, hinged on an elliptic curve digital signature algorithm-based online/offline scheme. In contrast, the Slow Block Move (SBM) scheme lags behind, necessitating over 25 rounds, 1500 signature generations, and an equal number of verifications. The proposed scheme, fortified by its marriage of federated learning and efficient encryption techniques, emerges as an embodiment of improved efficiency and reduced communication costs. The culmination of this research underscores the intrinsic benefits of the proposed approach: marked reduction in communication costs, elevated analytical prowess, and heightened resilience against the spectrum of attacks that IoT systems confront.

Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System.

The intelligent reflecting surface (IRS) is a two-dimensional (2D) surface with a programmable structure and is composed of many arrays. The arrays are used to supervise electromagnetic wave propagation by altering the electric and magnetic properties of the 2D surface. IRS can influentially convert wireless channels to very effectively enhance spectral efficiency (SE) and communication performance in wireless systems. However, proper channel information is necessary to realize the IRS anticipated gains. The conventional technique has been taken into consideration in recent attempts to fix this issue, which is straightforward but not ideal. A deep learning model which is called the long short-term memory (Bi-LSTM) model can tackle this issue due to its good learning capability and it plays a vital role in enhancing SE. Bi-LSTM can collect data from both forward and backward directions simultaneously to provide improved prediction accuracy. Because of the tremendous benefits of the Bi-LSTM model, in this paper, an IRS-assisted Bi-LSTM model-based multi-user multiple input single output downlink system is proposed for SE improvement. A Wiener filter is used to determine the optimal phase of each IRS element. In the simulation results, the proposed system is compared with other DL models and methods for the SE performance evaluation. The model exhibits satisfactory SE performance with a different signal-to-noise ratio compared to other schemes in the online phase.

Data-driven analysis of parametrized acoustic systems in the frequency domain

A data-driven method combined with the formulations of boundary integral equations is developed for the frequency-domain analysis of parametrized acoustic systems, arising from the spatial discretization of the linear Helmholtz equation. The method derives surrogate models for the approximation of frequency response functions at selected field points via the construction of neural networks with radial basis function interpolation. This requires the offline collection of a training database consisting of discrete frequency-parameter samples and their associated response values, whereas the information about system matrices and interference with boundary element kernels are no longer necessary, leading to a matrix-free and non-intrusive nature. To ensure accurate fitting results, a tensor decomposition technique is employed to decouple the naturally formed tensor grid between the frequency and parameter inputs in the database. After establishing two sets of radial basis networks separately, the final reduced-order solutions as a continuous function of the frequency and parameters are represented by the linear combinations of tensor products. In the online phase, the original transfer function at any testing frequency-parameter location can be quickly predicted as direct output from the compact surrogate model. With the analytical solution, the traditional boundary element method and the fast multipole boundary element method as solvers for the generation of training data, two benchmark problems and one complex system are investigated. In all cases, the simplicity, versatility and efficiency of the proposed method are demonstrated through examples of multi-frequency solutions of acoustic systems with parametrized material and geometric properties.

Online solid phase extraction high-performance liquid chromatography – Isotope dilution – Tandem mass spectrometry quantification of organophosphate pesticides, synthetic pyrethroids, and selected herbicide metabolites in human urine

Analytical methods to quantify pesticide biomarkers in human population studies are critical for exposure assessment given the widespread use of pesticides for pest and weed control and their potential for affecting human health. We developed a method to quantify, in 0.2 mL of urine, concentrations of 10 pesticide biomarkers: four organophosphate insecticide metabolites (3,5,6-trichloro-2-pyridinol (TCPy), 2-isopropyl-6-methyl-4-pyrimidinol, para-nitrophenol, malathion dicarboxylic acid); five synthetic pyrethroid insecticide metabolites (4-fluoro-3-phenoxybenzoic acid, 3-phenoxybenzoic acid, cis and trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid (DCCA), cis-3-(2,2-dibromovinyl)-2,2-dimethylcyclopropane-1-carboxylic acid); and the herbicide 2,4-dichlorophenoxyacetic acid.he method is based on enzymatic hydrolysis of conjugated urinary metabolites, extraction and pre-concentration of the deconjugated metabolites using automated online solid-phase extraction, and separation and quantification using liquid chromatography-isotope dilution tandem mass spectrometry.Depending on the analyte, method detection limits were 0.1–0.6 ng/mL; mean accuracy, calculated as spike recoveries, was 91–102%, and total precision, given as percent variation coefficient, was 5.9–11.5%. Percent differences associated with three freeze-thaw cycles, 24-h benchtop storage, and short-term processed sample stability were <14%.Method suitability was assessed by recurring successful participation in external quality assessment schemes and by analyzing samples from subjects with suspected exposure to pesticides (n = 40) or who self-reported consuming an organic diet (n = 50). Interquartile ranges were considerably lower for people consuming an organic diet than for those potentially exposed for cis-DCCA (0.37 ng/mL vs 0.75 ng/mL), trans-DCCA (0.88 ng/mL vs 1.78 ng/mL) and TCPy (1.81 ng/mL vs 2.48 ng/mL). This method requires one-fifth of the sample used in our previous method and is suitable for assessing background exposures to select pesticides in large human populations and for studies with limited sample volumes.