Advances of Electrochemical and Electrochemiluminescent Sensors Based on Covalent Organic Frameworks

Covalent organic frameworks(COFs),a rapidly developing category of crystalline conjugated organic polymers,possess highly ordered structures,large specific surface areas,stable chemical properties,and tunable pore microenvironments.Since the first report of boroxine/boronate ester-linked COFs in 2005,COFs have rapidly gained popularity,showing important application prospects in various fields,such as sensing,catalysis,separation,and energy storage.Among them,COFs-based electrochemical(EC)sensors with upgraded analytical performance are arousing extensive interest.In this review,therefore,we summarize the basic properties and the general synthesis methods of COFs used in the field of electroanalytical chemistry,with special emphasis on their usages in the fabrication of chemical sensors,ions sensors,immunosensors,and aptasensors.Notably,the emerged COFs in the electrochemiluminescence(ECL)realm are thoroughly covered along with their preliminary applications.Additionally,final conclusions on state-of-the-art COFs are provided in terms of EC and ECL sensors,as well as challenges and prospects for extending and improving the research and applications of COFs in electroanalytical chemistry.

Modeling of combined Bayesian networks and cognitive framework for decision-making in C2

The command and control（C2） is a decision-making process based on human cognition,which contains operational,physical,and human characteristics,so it takes on uncertainty and complexity.As a decision support approach,Bayesian networks（BNs） provide a framework in which a decision is made by combining the experts＇ knowledge and the specific data.In addition,an expert system represented by human cognitive framework is adopted to express the real-time decision-making process of the decision maker.The combination of the Bayesian decision support and human cognitive framework in the C2 of a specific application field is modeled and executed by colored Petri nets（CPNs）,and the consequences of execution manifest such combination can perfectly present the decision-making process in C2.

Optimal decision fusion given sensor rules

When all the rules of sensor decision are known, the optimal distributeddecision fusion, which relies only on the joint conditional probability densities, can be derivedfor very general decision systems. They include those systems with interdependent sensorobservations and any network structure. It is also valid for m-ary Bayesian decision problems andbinary problems under the Neyman-Pearson criterion. Local decision rules of a sensor withcommunication from other sensors that are optimal for the sensor itself are also presented, whichtake the form of a generalized likelihood ratio test. Numerical examples are given to reveal someinteresting phenomena that communication between sensors can improve performance of a senordecision, but cannot guarantee to improve the global fusion performance when sensor rules were givenbefore fusing.

Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several nations.Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese residents.Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems.Every time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will stop.The prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a model.Extended short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term dependencies.As an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for COVID-19.We provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring framework.We developed a data analysis methodology,including data preparation and dataset splitting.We examine two popular algorithms,LSTM and Decision tree on the official datasets.Moreover,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the pandemic.Key issues and challenges are discussed for future improvement.It is expected that the results these methods provide for the Health Scenario would be reliable and credible.

Modeling of cognitive framework in time-stressed decision making

An approach for modeling a human cognitive framework in time-stressed decision making is presented. The recognitive and metacognitive processes that represent the cognitive framework are modeled by the colored Petri nets （CPNs）. A structural and behavioral analysis method is adopted to obtain the static and dynamic property used to verify the CPNs model of the cognitive framework. Finally, an example from the command and control radar recognition system is used to evaluate the feasibility and availability of the CPNs model adopted in practical systems.

Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and wind.Energy resources are vital for all countries in terms of their economies and politics.As a result,selecting the optimal option for any country is critical in terms of energy investments.Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming.In the present work,the authors suggest fuzzy multi-characteristic decision-making approaches for renew-able energy source selection,and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous data.This study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique,which agrees with professional assessment scores to be linguistic phrases,fuzzy numbers,or crisp numbers.The hybrid methodology is based on fuzzy set ideologies,which calculate alternatives in accordance with professional functional requirements using objective or subjective characteristics.The best-suited renewable energy alternative is discovered using the approach presented.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

Actuator and sensor fault isolation in a class of nonlinear dynamical systems

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems.Actuator and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be determined.The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

Study of component-ware & application software framework based decision support system development

The view that the traditional method of DSS development is outdated, which results to the diversiform disadvantages of DSS product. Therefore the ideas of application software framework based development to the generation process of DSS is introduced and a modified flow chat of DSS development is proposed. Moreover, a formal description of the DSS software framework and its development is given. The analysis results indicates that not only does the new development flow ensure the DSS development global stability but also improves the software reusability level of the development process.

Cooperative Spectrum Sensing for Cognitive Radio-Wireless Sensors Network Based on OR Rule Decision to Enhance Energy Consumption in Greenhouses

Wireless sensors networks (WSNs) combined with cognitive radio have developed and solved the limited space of the frequency spectrum. In this paper, we propose different types of spectrums sensing and their own decisions depend on the probabilities that applied into fusion center, and how these probabilities’ techniques help to enhance the energy consumption of WSNs. In the same way, the importance of designing balanced distribution between the wireless sensors networks and their own sinks. This research also provides an overview of security issues in CR-WSN, especially in Spectrum Sensing Data Falsification (SSDF) attacks that enforces harmful effects on spectrum sensing and spectrum sharing. We adopt OR rule as four types of CRSN sensing protocolin greenhouses application by using Matlab and Netsim simulators. Our results show that the designing balanced wireless sensors and their sinks in greenhouses are very significant to decrease the energy, which is due to the traffic congestion in the sink range area. Furthermore, by applying OR rule has enhanced the energy consumption, and improved the sensors network lifetime compared to cognitive radio network.

DECISION FUSION FOR WIRELESS SENSOR NETWORKS UNDER NAKAGAMI FADING CHANNELS

Decision fusion rules for Wireless Sensor Networks (WSNs) under Nakagami fading channels are investigated in this paper. Considering the application limitation of Likelihood Ratio Test fusion rule based on information of Channel Statistics using Series expansion (LRT-CSS),and the detection performance limitation of the Censoring based Mixed Fusion rule (CMF),a new LRT fusion rule based on information of channel statistics has been presented using Laplace approximation (LRT-CSL). Theoretical analysis and simulations show that the proposed fusion rule provides better detection performance than the Censoring based Mixed Fusion (CMF) and LRT-CSS fusion rules. Furthermore,compared with LRT-CSS fusion rule,the proposed fusion rule expands the application range of likelihood ratio test fusion rule.

Metal–Organic Framework-Based Sensors for Environmental Contaminant Sensing

Increasing demand for timely and accurate environmental pollution monitoring and control requires new sensing techniques with outstanding performance, i.e.,high sensitivity, high selectivity, and reliability. Metal–organic frameworks(MOFs), also known as porous coordination polymers, are a fascinating class of highly ordered crystalline coordination polymers formed by the coordination of metal ions/clusters and organic bridging linkers/ligands. Owing to their unique structures and properties,i.e., high surface area, tailorable pore size, high density of active sites, and high catalytic activity, various MOF-based sensing platforms have been reported for environmental contaminant detection including anions, heavy metal ions,organic compounds, and gases. In this review, recent progress in MOF-based environmental sensors is introduced with a focus on optical, electrochemical, and field-effect transistor sensors. The sensors have shown unique and promising performance in water and gas contaminant sensing. Moreover, by incorporation with other functional materials, MOF-based composites can greatly improve the sensor performance. The current limitations and future directions of MOF-based sensors are also discussed.

Berlin Green Framework‑Based Gas Sensor for Room‑Temperature and High‑Selectivity Detection of Ammonia

Ammonia detection possesses great potential in atmosphere environmental protection,agriculture,industry,and rapid medical diagnosis.However,it still remains a great challenge to balance the sensitivity,selectivity,working temperature,and response/recovery speed.In this work,Berlin green(BG)framework is demonstrated as a highly promising sensing material for ammonia detection by both density functional theory simulation and experimental gas sensing investigation.Vacancy in BG framework offers abundant active sites for ammonia absorption,and the absorbed ammonia transfers sufficient electron to BG,arousing remarkable enhancement of resistance.Pristine BG framework shows remarkable response to ammonia at 50–110°C with the highest response at 80°C,which is jointly influenced by ammonia’s absorption onto BG surface and insertion into BG lattice.The sensing performance of BG can hardly be achieved at room temperature due to its high resistance.Introduction of conductive Ti3CN MXene overcomes the high resistance of pure BG framework,and the simply prepared BG/Ti3CN mixture shows high selectivity to ammonia at room temperature with satisfying response/recovery speed.

An Interactive Expert System Based Decision Making Model for the Management of Transit System Alternate Fuel Vehicle Assets

Traditionally, the process used by public transportation entities to determine the acquisition strategy for new vehicle asset is based upon a broad range of criteria. Vehicle cost has been cited as one of the more critical factors which decision makers consider. It is currently a common practice to consider other factors (life-cycle cost, fuel efficiency, vehicle reliability, environmental effects, etc.) that contribute to a more comprehensive approach. This study investigates the next generation of advancements in decision making tools in the area of the application of methods to quantify and manage uncertainty. In particular, the uncertainty comes from the public policy arena where future policy and regulations are not always based upon logical and predictable processes. The fleet decision making process in most governmental agencies is a very complex and interdependent activity. There are always competing forces and agendas within the view of the decision maker. Rarely is the decision maker a single person although, within the transit environment, there is often one person charged with the responsibility of fleet management. The focus of this research examines the decision making of the general transit agency community via the development of an expert systems prototype tool. A computer-based prototype system is developed which provide an expert knowledge-based recommendation, based upon variable user inputs. The results shown in this study show that a decision making tool for the management of transit system alternate fuel vehicle assets can be modeled and tested. The direct users of this research are the transit agency administrations. The results can be used by the management teams as a reliable input to inform their urban transit buses expansion decision making process.

Outstanding Humidity Chemiresistors Based on Imine-Linked Covalent Organic Framework Films for Human Respiration Monitoring

Human metabolite moisture detection is important in health monitoring and non-invasive diagnosis.However,ultra-sensitive quantitative extraction of respiration information in real-time remains a great challenge.Herein,chemiresistors based on imine-linked covalent organic framework(COF)films with dual-active sites are fabricated to address this issue,which demonstrates an amplified humidity-sensing signal performance.By regulation of monomers and functional groups,these COF films can be pre-engineered to achieve high response,wide detection range,fast response,and recovery time.Under the condition of relative humidity ranging from 13 to 98%,the COFTAPB-DHTA film-based humidity sensor exhibits outstanding humidity sensing perfor-mance with an expanded response value of 390 times.Furthermore,the response values of the COF film-based sensor are highly linear to the relative humidity in the range below 60%,reflecting a quantitative sensing mechanism at the molecular level.Based on the dual-site adsorption of the(-C=N-)and(C-N)stretching vibrations,the revers-ible tautomerism induced by hydrogen bonding with water molecules is demonstrated to be the main intrinsic mechanism for this effective humidity detection.In addition,the synthesized COF films can be further exploited to effectively detect human nasal and oral breathing as well as fabric permeability,which will inspire novel designs for effective humidity-detection devices.

An Efficient Stabbing Based Intrusion Detection Framework for Sensor Networks

Intelligent Intrusion Detection System(IIDS)for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall.The efficiency of IIDS highly relies on the algorithm performance.The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms.Here,a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework(SILF),is proposed to learn the attack features and reduce the dimensionality.It also reduces the testing and training time effectively and enhances Linear Support Vector Machine(l-SVM).It constructs an auto-encoder method,an efficient learning approach for feature construction unsupervised manner.Here,the inclusive certified signature(ICS)is added to the encoder and decoder to preserve the sensitive data without being harmed by the attackers.By training the samples in the preliminary stage,the selected features are provided into the classifier(lSVM)to enhance the prediction ability for intrusion and classification accuracy.Thus,the model efficiency is learned linearly.The multi-classification is examined and compared with various classifier approaches like conventional SVM,Random Forest(RF),Recurrent Neural Network(RNN),STL-IDS and game theory.The outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy,precision,recall,F-measure,pvalue,MCC and so on.The proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion detection.Here,the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.

Enabling Resource Awareness in Integrated Sensor Grid Framework Using Cross Layer Scheduling Mechanism

Researches related to wireless sensor networks primarily concentrate on Routing, Location Services, Data Aggregation and Energy Calculation Methods. Due to the heterogeneity of sensor networks using the web architecture, cross layer mechanism can be implemented for integrating multiple resources. Framework for Sensor Web using the cross layer scheduling mechanisms in the grid environment is proposed in this paper. The resource discovery and the energy efficient data aggregation schemes are used to improvise the effective utilization capability in the Sensor Web. To collaborate with multiple resources environment, the grid computing concept is integrated with sensor web. Resource discovery and the scheduling schemes in the grid architecture are organized using the medium access control protocol. The various cross layer metrics proposed are Memory Awareness, Task Awareness and Energy Awareness. Based on these metrics, the parameters-Node Waiting Status, Used CPU Status, Average System Utilization, Average Utilization per Cluster, Cluster Usage per Hour and Node Energy Status are determined for the integrated heterogeneous WSN with sensor web in Grid Environment. From the comparative analysis, it is shown that sensor grid architecture with middleware framework has better resource awareness than the normal sensor network architectures.

Multisensor Information Fusion for Condition Based Environment Monitoring

Destructive wildfires are becoming an annual event,similar to climate change,resulting in catastrophes that wreak havoc on both humans and the envir-onment.The result,however,is disastrous,causing irreversible damage to the ecosystem.The location of the incident and the hotspot can sometimes have an impact on earlyfire detection systems.With the advancement of intelligent sen-sor-based control technologies,the multi-sensor data fusion technique integrates data from multiple sensor nodes.The primary objective to avoid wildfire is to identify the exact location of wildfire occurrence,allowingfire units to respond as soon as possible.Thus to predict the occurrence offire in forests,a fast and effective intelligent control system is proposed.The proposed algorithm with decision tree classification determines whetherfire detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to optimize the complex environment.The experimental results of the proposed model have a detection rate of 98.3.Thus,providing real-time monitoring of certain environ-mental variables for continuous situational awareness and instant responsiveness.

金属有机框架荧光传感器在食品中抗生素类兽药残留检测的研究进展

金属-有机框架材料(metal-organic frameworks,MOFs),是一类由金属离子或者是次级构筑单元与有机配体通过配位键自组装形成的材料,是近年快速发展的一种新型功能性检测材料。MOFs具有比表面积大、孔道尺寸可调和结构可控等优势,在构建高选择性和高灵敏度的发光传感器领域有着广阔的应用前景。该文对发光MOFs的不同荧光发光机理进行阐明和详细分类,总结了发光MOFs作为荧光传感器在抗生素类兽药残留检测中的应用实例,最后就目前存在的问题提出建议,并对未来的发展前景进行了展望。

ZIF-8封装AuNCs荧光传感器用于铁离子的检测

介绍一个适合于本科教学使用的综合创新实验,设计合成了一种荧光传感器Au NCs@ZIF-8,并将其用于铁离子(Fe^(3+))的检测。MOFs具有高效吸附、聚集检测物的特点,因此选择将Au NCs封装到ZIF-8中,从而实现目标物的定量检测。封装后,由于Au NCs具有AIE特性,所以Au NCs@ZIF-8的荧光强度明显增强。ZIF-8是一种多孔材料,加入Fe^(3+)后,Fe^(3+)能够进入ZIF-8内部,导致Au NCs的荧光被Fe^(3+)猝灭,成功用于实际水样中Fe^(3+)的检测。尽管该实验步骤复杂并涉及新型纳米材料的合成和表征,但它成功地整合了无机化学、分析化学以及材料化学的相关知识,且操作难度适宜。这为提升学生的动手能力和科技前沿理解提供了一次宝贵的机会。