Flow-based System Research Articles

An Efficient Flow-Based Anomaly Detection System for Enhanced Security in IoT Networks.

The growing integration of Internet of Things (IoT) devices into various sectors like healthcare, transportation, and agriculture has dramatically increased their presence in everyday life. However, this rapid expansion has exposed new vulnerabilities within computer networks, creating security challenges. These IoT devices, often limited by their hardware constraints, lack advanced security features, making them easy targets for attackers and compromising overall network integrity. To counteract these security issues, Behavioral-based Intrusion Detection Systems (IDS) have been proposed as a potential solution for safeguarding IoT networks. While Behavioral-based IDS have demonstrated their ability to detect threats effectively, they encounter practical challenges due to their reliance on pre-labeled data and the heavy computational power they require, limiting their practical deployment. This research introduces the IoT-FIDS (Flow-based Intrusion Detection System for IoT), a lightweight and efficient anomaly detection framework tailored for IoT environments. Instead of employing traditional machine learning techniques, the IoT-FIDS focuses on identifying unusual behaviors by examining flow-based representations that capture standard device communication patterns, services used, and packet header details. By analyzing only benign traffic, this network-based IDS offers a streamlined and practical approach to securing IoT networks. Our experimental results reveal that the IoT-FIDS can accurately detect most abnormal traffic patterns with minimal false positives, making it a feasible security solution for real-world IoT implementations.

Development of a Spectrophotometric Flow-Based System for the Determination of Total Polyphenol Content in Legume Flours.

Conventional nutritional characterization of legume flours comprises costly and laborious analytical methods for nutrient quantification that establish the quality of seeds, including their macronutrient quantification, fiber, mineral, antioxidant content, and more. The quantification of total polyphenol content (TPC) in legumes is performed using different analytic methods, namely the Folin-Ciocalteu (FC) method, which is lengthy and employs potentially toxic reagents. Additionally, it is time-consuming and prone to human error if carried out in a conventional batch mode. To develop a semi-automatic method for a faster, greener, and more precise TPC quantification, resorting to flow injection analysis (FIA). The development of the FIA method was based on the FC method. The flow manifold was structured by performing an array of preliminary studies, which led to the establishment of the method that displayed the highest sensitivity (highest slope of the calibration curve). The method was applied to determine the TPC in various legume flours. This novel method resulted in a throughput of 1 analysis per minute, allowing to analyze 20 samples in triplicate within an hour, with a LOD of 4.32 mg L-1, a LOQ of 14.4 mg L-1, and an RSD of 4.4%. The results calculated from the proposed FIA method agreed with those of the reference procedure. By using the FIA system, a lower consumption of reagents was observed. Additionally, as there is no need to reach chemical equilibrium, a high throughput has been achieved, resulting in a faster and greener method for the determination of polyphenols in various types of legumes.

Preparation of PVP-BiOBr Adsorbent for Efficient Indigo Carmine Dye Removal Using Flow-Circulation Systems.

This work presents an adsorptive removal of indigo carmine (IC) dye using a polyvinylpyrrolidone capped bismuth oxybromide (PVP-BiOBr) adsorbent. PVP-BiOBr was synthesized via a simple precipitation method. The morphology and surface chemical structure of the adsorbent were characterized using XRD, SEM, FTIR, and BET analyses. The adsorption isotherm and kinetics were investigated to reveal the mechanism of dye removal. Prepared PVP-BiOBr has a crystallite size of 19.7 nm, with a mean particle size of ∼2 μm and a surface area of 5.14 m2 g-1. The optimum pH for this adsorptive process spanned the range of 4 to 9. Experimental data indicated applicability of the Langmuir isotherm model, and the study confirms a pseudo-second-order kinetics model. The maximum adsorption capacity for IC dye was 208.3 mg g-1. A flow-circulation system was developed for the treatment of IC dye contaminated water samples. PVP-BiOBr was packed inside a column and did not spill into the water sample after treatment. The removal efficiency was ≥90% after 25 min. The PVP-BiOBr adsorbent could be reused for three cycles. This work demonstrates that PVP-BiOBr is a promising candidate as an adsorbent for IC dye removal. Additionally, the flow-based system establishes an automated operation in continuous mode, which is viable for large scale applications.

Development and evaluation of a lateral flow-based portable optical system for determination of the pregnancy status of dairy cows

Our objectives were to develop and evaluate an integrated system consisting of a lateral-flow immunoassay (LFIA) and an electronic portable imaging device for determination of pregnancy status of cows based on plasma concentrations of pregnancy-specific protein B (PSPB). Experiment 1 was conducted to test the performance of the LFIA for PSPB (PSPB-LFIA) whereas experiment 2 was conducted to evaluate the performance of the integrated system including both the LFIA and imaging device. The PSPB-LFIA strips were made of nitrocellulose membrane with polystreptavidin, anti-mouse antibody, Europium-anti-PSPB conjugates, and biotin-PSPB. After adding buffer and plasma in a 96-well plate, strips were dipped to initiate flow and were read in a fluorescence microscope to estimate PSPB concentrations based on the test-to-control line signal (T/C ratio). The T/C ratio of standards was linearly associated with PSPB (R2 = 0.99 in both experiments) concentrations. To test the ability to identify pregnant cows of the PSPB-LFIA only or the integrated system, plasma samples were collected and transrectal ultrasonography (TUS) was conducted 29 to 35 d post AI in lactating Holstein cows (Experiment 1: n = 83; Experiment 2: n = 205). A cow was considered pregnant (Preg) if concentrations of PSPB in plasma obtained by ELISA were ≥2 ng/mL or if an embryo was visible by TUS. In Experiment 1, the accuracy of the PSPB-LFIA compared with ELISA was 92.7% (91.2% Se; 96.1% Sp; 98.1% PPV; 83.3% NPV) and compared with TUS was 90.4% (100% Se; 78.9% Sp; 84.9% PPV; 100% NPV). The agreement between LFIA and ELISA (kappa = 0.84; 95%CI 0.71-0.96) or LFIA and TUS (kappa = 0.80; 95%CI 0.67-0.93) as methods to classify cows as Preg or Non-Preg was high. In Experiment 2, the accuracy of the PSPB-LFIA compared with ELISA was 96.1% (93.8% Se; 100% Sp; 100% PPV; 90.5% NPV) and compared with TUS was 92.2% (99.0% Se; 84.7% Sp; 87.6% PPV; 98.8% NPV). The agreement between LFIA and ELISA (kappa = 0.92; 95%CI 0.86-0.97) or LFIA and TUS (kappa = 0.84; 95%CI 0.77-0.92) as methods to classify cows as Preg or Non-Preg was high. We conclude that a system integrating a fluorescence-based LFIA and an optical reader was effective for classifying cows as pregnant or not pregnant based on estimations of plasma concentrations of PSPB. This novel system serves as a platform for further development of on-farm pregnancy testing tools based on measurement of biomarkers of pregnancy in bodily fluids of cattle.

A microfluidic study of acetate conversion kinetics in a microbial electrolysis cell: The role of age, concentration and flow on biofilm permeability

This work addresses the need for kinetic studies to identify potentially exploitable mechanisms involved in generating current from flow-based bioelectrochemical systems (BES). Unlike most kinetic studies, which focus on electron transport, we focus on chemical mass transport by controlling the relevant experimental parameters. This is accomplished using a microfluidic 3-electrode setup that recorded output current (I) from a mature Geobacter sulfurreducens electroactive biofilm (EAB) while accurate control is applied over acetate concentration ([Ac]) and flow rate (Q). Additionally, the flow mode (tangential and perpendicular) is controlled to apply expansive or compressive sheer forces against the EAB. A detailed analysis of the effects of the control variables on the current is based on data collected for nearly 1 year, the longest timeframe for a microfluidic BES experiment to date. All experimental parameters affect output, but we find that age is the dominant factor. After nearly 1 year, current densities were as high as 29.5 A m−1, which is higher than in any reported 3-electrode experiment on G. sulfurreducens EAB. We conclude that flow-based deacidification of the EAB led to increases to outputs during early growth stages, whereas at later times, the increases were related to improved EAB permeability to acetate. Additionally, after 5 months each flow mode provokes complementary kinetic properties based on measurements of apparent enzyme/substrate affinity (KM(app)) and maximum current (Imax) values. Therefore, in addition to providing fundamental insights into BES functionality, these findings also open the door to practical applications and offer a road map to further experimental optimization.

Heparin Specifically Inhibits CRISPR/Cas12 Activation, Enabling Ultrasensitive Heparin Detection and Gene Editing Regulation.

Heparin is a highly sulfated linear glycosaminoglycan that is used as an anticoagulant to prevent and treat thrombotic diseases. Herein, we find that heparin specifically inhibits the activation of the Cas12 protein through the competitive binding of heparin and crRNA to Cas12. Studies illustrate that heparin's high molecular weight and strong negative charge are critical parameters for its inhibitory effect. This unexpected finding was engineered for the detection of heparin, affording a low detection limit of 0.36 ng/mL for fluorometric quantification. We further developed a rapid lateral flow-based system named HepaStrip (heparin strip), allowing heparin monitoring in clinical samples within 20 min. Finally, in vivo investigations revealed that heparin can regulate gene editing with the clusters of the regularly spaced short palindromic repeat (CRISPR)/Cas12 system in Escherichia coli. Heparin blocks the formation of Cas12-crRNA ribonucleoprotein, allowing the application of CRISPR for rapid and field-deployable detection of heparin and unleashing the potential use of heparin in future anti-CRISPR applications.

FlowTransformer: A transformer framework for flow-based network intrusion detection systems

This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer leverages the strengths of transformer models in identifying the long-term behaviour and characteristics of networks, which are often overlooked by most existing NIDSs. By capturing these complex patterns in network traffic, FlowTransformer offers a flexible and efficient tool for researchers and practitioners in the cybersecurity community who are seeking to implement NIDSs using transformer-based models. FlowTransformer allows the direct substitution of various transformer components, including the input encoding, transformer, classification head, and the evaluation of these across any flow-based network dataset. To demonstrate the effectiveness and efficiency of the FlowTransformer framework, we utilise it to provide an extensive evaluation of various common transformer architectures, such as GPT 2.0 and BERT, on three commonly used public NIDS benchmark datasets. We provide results for accuracy, model size and speed. A key finding of our evaluation is that the choice of classification head has the most significant impact on the model performance. Surprisingly, Global Average Pooling, which is commonly used in text classification, performs very poorly in the context of NIDS. In addition, we show that model size can be reduced by over 50%, and inference and training times improved, with no loss of accuracy, by making specific choices of input encoding and classification head instead of other commonly used alternatives.

Single flow-based system for the automatic multiparametric nutrients (NPK & Fe) assessment in soil leachates

A multiparametric sequential injection system for the determination of phosphate, nitrite, nitrate, potassium, and iron(III) in a single manifold was developed. The main goal of the proposed method was to develop an efficient tool to assess a number of essential chemical compounds in soils, providing the corresponding information on soil fertility and, additionally, information on possible groundwater contamination. The method was applied for the quantification of the aforementioned parameters in simulated leachates produced in laboratory-scale core columns. The relative standard deviations of ten replicate analyses of a standard were: 6% for phosphate; 2% for nitrite; 2% for nitrate; 5% for potassium; and 6% for iron(III). The limits of detection and quantification were: 2.15 and 7.18 μmol/L for phosphate determination; 0.22 and 0.73 μmol/L for nitrite determination; 3.42 and 8.00 μmol/L for nitrate determination; 39 μmol/L (limit of detection) for potassium determination; and 0.46 and 1.85 μmol/L for iron(III) determination. The sequential injection system was successfully applied for the quantification of multiple soil chemical components (PO43−, NO2−, NO3−, K+, and Fe3+) in soil leachates. The analysis of a sample, involving all the analytes, has a duration of 28 min.

Multiplexed experimental strategies for fragment library screening against challenging drug targets using SPR biosensors

Surface plasmon resonance (SPR) biosensor methods are ideally suited for fragment-based lead discovery. However, generally applicable experimental procedures and detailed protocols are lacking, especially for structurally or physico-chemically challenging targets or when tool compounds are not available. Success depends on accounting for the features of both the target and the chemical library, purposely designing screening experiments for identification and validation of hits with desired specificity and mode-of-action, and availability of orthogonal methods capable of confirming fragment hits. The range of targets and libraries amenable to an SPR biosensor-based approach for identifying hits is considerably expanded by adopting multiplexed strategies, using multiple complementary surfaces or experimental conditions. Here we illustrate principles and multiplexed approaches for using flow-based SPR biosensor systems for screening fragment libraries of different sizes (90 and 1056 compounds) against a selection of challenging targets. It shows strategies for the identification of fragments interacting with 1) large and structurally dynamic targets, represented by acetyl choline binding protein (AChBP), a Cys-loop receptor ligand gated ion channel homologue, 2) targets in multi protein complexes, represented by lysine demethylase 1 and a corepressor (LSD1/CoREST), 3) structurally variable or unstable targets, represented by farnesyl pyrophosphate synthase (FPPS), 4) targets containing intrinsically disordered regions, represented by protein tyrosine phosphatase 1B (PTP1B), and 5) aggregation-prone proteins, represented by an engineered form of human tau (tau K18M). Practical considerations and procedures accounting for the characteristics of the proteins and libraries, and that increase robustness, sensitivity, throughput and versatility are highlighted. The study shows that the challenges for addressing these types of targets is not identification of potentially useful fragments per se, but establishing methods for their validation and evolution into leads.

(Invited) Charge Propagation in Highly Concentrated Iodine/Iodide Solutions as Potential Redox Electrolytes and Mediators for Redox Flow Batteries and Solar Cells

The technology of Redox Flow Batteries is an important option to store energy from the operating irregularly renewable energy sources. The perspectives concern stationary energy storage, including grid-scale energy storage, thanks to their high power performance, flexible design, and ease of scaling-up. The flow-based electrochemical energy storage systems utilize the appropriate electroactive species dissolved in externally flowing electrolytes which are ready to accumulate all (or part) of the charge. Among important issues is the search for highly efficient (i.e., capable of fast electron transfers) electroactive systems that would yield high power and energy densities during the systems’ operation. Highly concentrated iodine/iodide system can act as redox conducting electrolyte. Furthermore its charge mediating capabilities has been recognized in the Dye Sensitized Solar Cell technology due to the feasibilioty of fast charge propagation accompanied by efficient electron self-exchange or diffusional-type electron hopping.In the present work, we concentrate on utilization of highly concentrated iodine/iodide redox systems and their possibility to exhibit high rates of charge propagation. In practical terms, it can be combined with ZnI2, or other zinc salt, containing electrolyte. Reactions in the zinc/iodine (polyiodide) redox flow battery are as follows: Zn → Zn2+ + 2e- (E = −0.76 V vs SHE) at the negative electrode (anode), and 3I- → I3 − + 2e− (E = 0.54 V vs SHE) at the positive electrode (cathode) thus yielding a total theoretical potential output as high as ∼1.3 V. The increase of current density could be achieved not only by reducing the viscosity of the electrolyte, thus accelerating charge-carrier transport, but also – by referencing to my experience with the iodine/iodide couple as charge relay for dye-sensitized solar cells – through improvement of the dynamics of charge propagation in highly concentrated iodine/iodide solution via the catalyzed enhancement of rates of electron self-exchange (hopping) between iodine/iodide (polyiodide) redox species as well as by accelerating the interfacial kinetics at electrodes. This can be realized by choosing appropriate electrode materials and through their activation or modification. The electrochemical activities of the redox couples are usually significantly increased through application of nanostructured functionalized carbons. While dispersed in solutions they can improve electron transfers to the redox sites. The proposed chemistry has been first tested using the microelectrode methodology to determine mass-transport (effectively diffusional) coefficients for charge propagation, heterogeneous and homogeneous (electron self-exchange) rates of electron transfers. Unless catalyzed, both interfacial and bulk (self-exchange) electron transfers involving the iodine/iodide redox system are somewhat complicated; there is a need to break the I-I bond in the I3 -or I2 molecule; it has also been well-established that platinum (e.g. when deposited on the counter electrode) induces electron transfers within the iodine/iodide redox system [1]. In the presentation, we are going to explore the respective interfacial (electrocatalytic) phenomena on nanostructured metal oxides (e.g. zirconia), in addition to traces of expensive platinum or palladium nanoparticles (provided that catalytic centers are three-dimensionally distributed in the electrolyte phase), and we will utilize them to enhance iodine/iodide electron transfers to develop a new generation of redox mediators or ultra-fast components of redox electrolytes. Our results show that the catalyzed iodine/iodide system can reach extremely high electron self-exchange rates, namely on the level of109 – 1010 mol-1 dm3 s-1 . [1] I.A. Rutkowska I.A., M. Marszalek, J. Orlowska, W. Ozimek, S.M. Zakeeruddin, P.J. Kulesza, M. Grätzel, ChemSusChem 8 (2015) 2560-2568.

Electrochemical capillary-driven microfluidic DNA sensor for HIV-1 and HCV coinfection analysis

Electrochemical DNA sensors can be operated in either static or flow-based detection schemes. In static schemes, manual washing steps are still necessary, resulting in a tedious and time-consuming process. In contrast, in flow-based electrochemical sensors, the current response is collected when the solution flows through the electrode continuously. However, the drawback of such a flow system is the low sensitivity due to the limited time for the interaction between the capturing element and the target. Herein, we propose a novel electrochemical capillary-driven microfluidic DNA sensor to combine the advantages of static and flow-based electrochemical detection systems into a single device by incorporating burst valve technology. The microfluidic device with a two-electrode configuration was applied for the simultaneous detection of two different DNA markers, human immunodeficiency virus-1 (HIV-1) and hepatitis C virus (HCV) cDNA, via the specific interaction between pyrrolidinyl peptide nucleic acids (PNA) probes and the DNA target. The integrated system, while requiring a small sample volume (7 μL for each sample loading port) and less analysis time, achieved good performance in terms of the limits of detection (LOD) (3SDblank/slope) and quantification (LOQ) (10SDblank/slope) at 1.45 nM and 4.79 nM for HIV and 1.20 nM and 3.96 nM for HCV, respectively. The simultaneous detection of HIV-1 and HCV cDNA prepared from human blood samples showed results that are in complete agreement with the RT‒PCR assay. The results qualify this platform as a promising alternative for the analysis of either HIV-1/HCV or coinfection that can be easily adapted for other clinically important nucleic acid-based markers.

An explainable efficient flow-based Industrial IoT intrusion detection system

With the steady growth in the adoption of IoT in an industrial context, security challenges intensify. Security threats to IoT devices in a nuclear plant or an oil refinery are much higher risk compared to home appliances. In this paper, we present an explainable, efficient, and highly accurate machine-learning based intrusion detection system for industrial IoT, referred to as E2I3DS. This work successfully reduces the needed features within the WUSTL-IIOT-2021 dataset from 48 features to 11 only, while maintaining very high accuracy. Experiments utilizing the 11-feature dataset showed that proposed system has a higher accuracy of 99.97%, with a false positive rate of 0.01% and a false negative rate of 0.04%. The proposed system also proved to be very efficient with an excellent detection time of 0.1517 μs. In addition, the model was explained using Shapley additive explanations.

Investigating on the robustness of flow-based intrusion detection system against adversarial samples using Generative Adversarial Networks

Recently, Software Defined Networking (SDN) has emerged as the key technology in programming and orchestrating security policy in the security operations centers (SOCs) for heterogeneous networks. Typically, machine learning-based intrusion detection systems (ML-IDS) have been deployed and associated with SDN to leverage the features of a programmable network to defend against sophisticated cyberattacks in anomaly detection. Unfortunately, such ML-based IDSs are easily vulnerable to adversarial attacks due to the lack of diverse forms of malicious records in the training dataset. The missing data sample in the training phase can lead to a lower detection rate in real-world scenarios with adversarial settings. In this paper, we explore the ability of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), WGAN-GP with two timescale update rule (WGAN-GP TTUR), and AdvGAN in generating perturbed attack samples to bypass attack detectors. Then, this approach is used to continuously evaluate the robustness of ML-based IDSs and then upgrade them as a service in SDN. The experimental results on CICIDS2018 and InSDN datasets demonstrate that generated adversarial samples can be used to fool targeted IDS. Later, those created samples can supplement the original ones in retraining IDS to improve the resilience of the attack detector.

Comparison of flowcytometry-based scoring system for the diagnosis of early T precursor-acute lymphoblastic leukemia.

Early T cell precursor-acute lymphoblastic leukemia (ETP-ALL) is a hematolymphoid malignancy where the blasts demonstrate T cell differentiation markers along with stem cell and myeloid antigen expression. The differential diagnosis of ETP-ALL from non-ETP ALL and mixed phenotype acute leukemia is often challenging due to its overlapping immunophenotypic picture with co-expression of myeloid antigens. In this study, we endeavored to describe the immune-phenotype profile of ETP-ALL in our patients and compared the utility of four different scoring systems for better discrimination of these entities. This retrospective analysis included 31 ETP-ALL out of 860 acute leukemia cases consecutively diagnosed at the two tertiary care centers. Flowcytometry-based immunophenotype was reviewed for all the cases, and the utility of four flow-based objective scorings was assessed for the diagnosis of ETP-ALL. Receiver operating curves were drawn to compare the different flow-based scoring systems. The prevalence of ETP-ALL was 40% (n=31/77 T-ALL) in our study group, comprised mainly of adults with a median age of 20 years. The five-marker scoring system had the maximum area under the curve, followed by the seven-marker scoring system. A cut-off of ≥2.5 was more specific (sensitivity: 91%; specificity: 100%), while a score of ≥1.5 was more sensitive but slightly less specific (sensitivity: 94%, specificity: 96%). The WHO criteria for the diagnosis of ETP-ALL should be followed across all laboratories to avoid confusion and for better treatment stratification. Flow-based scoring systems can be objectively employed for better detection of cases.

Flow-based intrusion detection system in Vehicular Ad hoc Network using context-aware feature extraction

Autonomous transportation systems have an immense impact on the way of our day-to-day commute or traveling. Connectedness is an inseparable part of these systems, and it is possible through Intelligent Transportation Systems applications. These applications use a network system known as Vehicular Ad hoc Network (VANET). However, the safety that is provided by VANETs can be easily compromised by malicious users. Hence there is a need for an Intrusion Detection System (IDS). We designed an IDS model that can collect network data cooperatively from vehicles and Roadside Units (RSUs). For training the core of our proposed IDS we generated synthetic network data with the help of the widely used Network Simulator 3 (ns-3) and Simulation of Urban Mobility (SUMO) simulation tools. We also generated separate test data that is not just a split part of the training data. This ensures the elimination of the misleading performance result of an overfitted model. We employed a multi-class IDS using Convolutional Neural Network (CNN) with a novel feature extraction method known as Context-Aware Feature Extraction-Based CNN (CAFECNN). The CAFECNN model takes advantage of the collected network flow data to detect both passive and active types of attacks. The results show that the proposed model is stronger in identification of hard-to-detect passive attacks compared to traditional machine learning methods. We also evaluated the model using real-time simulations and observed an immediate improvement in the performance of the network despite the intruders, especially in terms of packet delivery ratio and network throughput.

Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach

The constantly evolving digital transformation imposes new requirements on our society. Aspects relating to reliance on the networking domain and the difficulty of achieving security by design pose a challenge today. As a result, data-centric and machine-learning approaches arose as feasible solutions for securing large networks. Although, in the network security domain, ML-based solutions face a challenge regarding the capability to generalize between different contexts. In other words, solutions based on specific network data usually do not perform satisfactorily on other networks. This paper describes the stacked-unsupervised federated learning (FL) approach to generalize on a cross-silo configuration for a flow-based network intrusion detection system (NIDS). The proposed approach we have examined comprises a deep autoencoder in conjunction with an energy flow classifier in an ensemble learning task. Our approach performs better than traditional local learning and naive cross-evaluation (training in one context and testing on another network data). Remarkably, the proposed approach demonstrates a sound performance in the case of non-IID data silos. In conjunction with an informative feature in an ensemble architecture for unsupervised learning, we advise that the proposed FL-based NIDS results in a feasible approach for generalization between heterogeneous networks.

Ensemble of Bio-inspired Algorithm with Statistical Measures for Feature Selection to Design a Flow-Based Intrusion Detection System

In today's high-speed network, the existing Intrusion Detection System (IDS) approaches experience more false alarm rates with low detection capability. Nowadays, IDS needs to analyze a considerable amount of data. The larger the amount of data results in the longer the time to analyze it, which delays attack detection. The IDS usability is defined as its capability to trigger an alarm early enough to minimize the damage that an ongoing attack can cause and provide a reduced range of warning (false alarm). These underline the necessity of feature selection in IDS to identify the informative features and overlook the irrelevant or redundant features that affect the IDS's detection rate and computational complexity. It implies that anticipating an ideal number of features from a flow-based intrusion dataset can improve IDS accuracy. Therefore, this paper proposes an ensemble of a bio-inspired algorithm (Krill Herd Algorithm) with statistical measures (Information Gain) to select optimal features for a flow-based IDS. This ensemble technique has shown improvement in the detection rate, decreases the false alarm rate, and reduces the computation time of the IDS.

Flow-Based Chemiluminescence Microarrays as Screening Platform for Affinity Binders to Capture and Elute Bacteria.

Affinity describes the non-covalent but selective interaction between an affinity binder (e.g., proteins, antibiotics, or antibodies) and its counterpart (e.g., bacteria). These affinity binders can serve to detect bacteria and respond to the need for selective concentration via affinity chromatography for trace analysis. By changing the pH value or salt and protein contents, affinity bindings can be reversed, and bacteria can be recovered for characterisation. Analytical microarrays use multiple affinity binders immobilised on the surface in a distinct pattern, which immensely reduces screening time for the discovery of superior binding motifs. Here, flow-based microarray systems can inform not only about binding, but also about desorption. In this work, we pioneer a screening assay for affinity binders against both gram-positive and negative bacteria based on an automated flow-based chemiluminescence (CL) microarray. Biotinylation of model organisms E. coli and E. faecalis enabled labelling with horseradish-peroxidase-coupled streptavidin, and detection with CL. Polymyxin B, an antibiotic against gram-negative bacteria, was found to bind both E. coli and E. faecalis. Simultaneous screening for desorption methods unexpectedly revealed methyl alpha-D-mannopyranoside as a promising buffer for desorption from Polymyxin B. This proof-of-principle study shows that our new platform greatly facilitates the screening of new affinity binders against bacteria, with promise for future automation.

Scenarios and sustainability of the economy-nitrogen-resource-environment system using a system dynamic model on the Qinghai-Tibet Plateau.

Nitrogen (N) plays a vital role in the development of crop production and animal husbandry in agricultural and pastoral areas. However, the irrational utilization of N resources and subsequent environmental issues with rapid economic development has attracted wide public attention. Coordinating the economy-N-resource-environment (ENRE) system is of great importance for regional sustainable development. In this study, the dynamics of the ENRE system of a typical agricultural and pastoral area on the Qinghai-Tibet Plateau (QTP) were simulated using the VENSIM software from 1998 to 2018. Four typical scenarios (current development scenario, economic development scenario, environment protection scenario and resource optimization scenario) are established to assess the sustainability level and the coupling coordination degrees (CCDs) of the three subsystems, i.e., the economy, N-resource and environment subsystems from 2019 to 2030. Our study indicates that the N flow-based system dynamics (SD) model connects the different subsystems of the ENRE system together well and allows different scenario simulations. From 2019 to 2030, the ENRE system is at a weak sustainability level during the simulation period, and the three subsystems are at slightly unbalanced stages of development in terms of CCD level. The sustainability and CCD levels of the four examined scenarios are as follows: resource optimization scenario>economic development scenario>environment protection scenario>current development scenario, with average values of 0.45, 0.37; 0.42, 0.36; 0.41, 0.35; and 0.39, 0.34, respectively. Under the resource optimization scenario, reducing N inputs to food production and consumption and reducing the planting area of cash crops can effectively improve the N use efficiency of the food chain in the N-resource subsystem (15.34% from 2019 to 2030 on average). Our results provide a reference for promoting sustainable development and formulating policies in agricultural and pastoral regions.

To evaluate the feasibility of cadmium/tellurium (Cd/Te) quantum dots for developing N-terminal Natriuretic Peptide (NT-proBNP) in-vitro diagnostics

ABSTRACT Quantum dots have been widely used for biomedical applications like imaging, targeted drug delivery, and in-vitro diagnostics for better sensitivity. In-vitro diagnostic, lateral flow-based assay systems are gaining attention in the field of biomarker analysis mainly due to ease of test and quick availability of results. In the study, the potential of water-soluble carboxylic (-COOH) functionalized photoluminescent Cadmium Telluride Quantum Dots (CdTe) nanoparticles for lateral flow-based detection of N-terminal Natriuretic Peptide (NT-proBNP) biomarker (for heart failure) detection has been evaluated. Monoclonal antibodies were conjugated with COOH functionalized CdTe with EDC-NHS coupling chemistry, and conjugation was confirmed using FTIR. The CdTe nanoparticle exhibited an emission maximum at 715 nm when it is excited with 375 nm. The COOH functionalized CdTe showed an antigen concentration-dependent linearity in the lateral flow applications when the dye was prepared freshly and used. However, a relative reduction in CdTe quantum dot fluorescence intensity with time was observed. Factors such as low stability could be due to the quenching of the fluorescence of CdTe. This limits its commercial viability as an in-vitro diagnostic tool; thus, modifications of the quantum dots are required to have a stable preparation for its commercial potential for quantifications.