Acceptable Response Time Research Articles

Room temperature detection of aspergillus flavus volatile organic compounds (VOCs) under simulated conditions using graphene oxide and tin oxide Nanorods (SnO2 NRs-GO)

This study investigated the application of a hybrid nanocomposite of tin oxide nanorods (SnO2 NRs) and graphene oxide (GO) for the chemoresistive detection of some volatile compounds (hexanal, benzaldehyde, octanal, 1-octanol, and ethyl acetate vapours) emitted by Aspergillus flavus under simulated conditions. The synthesised materials were characterised using various analytical techniques, including high-resolution transmission electron microscopy (HR-TEM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) analysis, and Fourier transform infrared spectroscopy (FTIR). Three sensors were fabricated: individual nanomaterials (i.e., SnO2 and GO) and composites (SnO2-GO). The results showed that SnO2 NRs had limited sensitivity as a sensor, while GO-based sensors responded to various analyte vapours. However, the incorporation of SnO2 NRs into GO layers resulted in synergistic effects and improved sensor performance. The sensors' sensitivity, selectivity, recovery, and response times were quantitatively determined from the sensors' response curves. The nanocomposite sensor demonstrated superior sensitivity and selectivity for analyte vapours with acceptable response and recovery times. In addition, the sensor was insensitive to humidity and showed robust performance up to 62% RH, although sensor drift occurred at 70% RH. This study highlights the promising potential of using SnO2 NRs-GO composite-based sensor for sensitive and selective detection of analyte vapours, which has significant implications for food safety and environmental monitoring applications.

IMANoBAS: An Improved Multi-Mode Alert Notification IoT-based Anti-Burglar Defense System

Burglary involves forced or unauthorized entry, which leads to damage or loss of property having monetary or emotional value and, more severely, puts lives at risk. The dire need for the safety of lives and properties has attracted so much research on burglary alert system using Internet of Things (IoT) technology. Most of the research focused on alerting the users of burglary attempts using any or a combination of two notification methods: SMS, call, and email. This study emphasizes three-mode notification that combines SMS, call, and email using the application of IoT technology in a burglary alert system, which uses a Passive Infrared (PIR) sensor for burglar detection to ensure that Homeowners or authorized personnel get alerts in events of imminent attempt to break-ins. The study also details the sensor integration with its supporting components, such as the central hub or microcontroller, buzzer, LED, and network interface in the development of the system. The software was developed to facilitate seamless integration with the hardware, ensuring timely and accurate event detection and subsequent alert generation using Arduino IDE programming language, a framework based on the C++ language. The system effected the 3-mode notification to ensure that users get notification in case of an imminent break-in since the failure of the three modes simultaneously is extremely rare. The system’s performance based on its responsiveness on the 3-mode notifications was evaluated, and an average of 83.56% responsiveness was obtained, indicating an acceptable response time.

Is this a violation? Learning and understanding norm violations in online communities

Using norms to guide and coordinate interactions has gained tremendous attention in the multi-agent community. However, new challenges arise as the interest moves towards dynamic socio-technical systems, where human and software agents interact, and interactions are required to adapt to human's changing needs. For instance, different agents (human or software) might not have the same understanding of what it means to violate a norm (e.g., what characterizes hate speech), or their understanding of a norm might change over time (e.g., what constitutes an acceptable response time). The challenge is to address these issues by learning the meaning of a norm violation from limited interaction data. For this, we use batch and incremental learning to train an ensemble of classifiers. Ensemble learning and data-sampling handle the imbalanced class distribution of the interaction stream. At the same time, the training approaches use different strategies to ensure that the ensemble models reflect the latest community view on the meaning of norm violation. Batch learning uses weight assignment, while incremental learning continuously updates the ensemble models as community members interact. Here, we extend our previous work by creating a different balance strategy for online learning and integrating interpretability to understand norm violations. Additionally, we evaluate the proposed approaches in the context of Wikipedia article edits, where interactions revolve around editing articles, and the norm in question is prohibiting vandalism. Lastly, we conduct ablation studies to compare the ensemble's performance against a single model approach and to examine the behavior of two data sampling techniques. Results indicate that the different machine learning frameworks can learn the meaning of a norm violation in a setting with data imbalance and concept drift, although with significant differences.

A comparison study on the acetone sensing performance of preeminent nanostructures of SnO2 for diabetes diagnosis

Metal oxide semiconductor-based acetone sensors are of great need owing to their cost-effective easy production for real-time applications. Experiments on different morphologies of metal oxide nanostructures are gaining momentum for enhancing acetone sensing properties. 1D nanofibers and dangling bonds-rich facet exposed materials are eminent nanostructures in this field. In this article, we compare the acetone-sensing abilities of these nanostructures, including nanofibers and facet-exposed nanostructures. Acetone sensing qualities of the fabricated sensors were tested at different temperatures varying from 100 to 350 °C. Due to a significant number of dangling bonds on the surface, the octahedral nanoparticle sensor produced a higher response than the other sensors. At their respective operating temperatures, each of the manufactured sensors was capable of detecting a very low concentration of acetone (1 ppm). All of the samples had acceptable response and recovery times. The octahedral nanoparticle sensor’s excellent repeatability, reproducibility, and long-term stability made it a good choice for real-time detection of acetone in the exhaled breath of individuals with diabetes. In patients with diabetes, the exhaled breath exhibits an acetone concentration exceeding 1.8 ppm, whereas in healthy persons, this concentration typically falls between the ranges of 0.3–0.9 ppm.

RAaaS: Resource Allocation as a Service in multiple cloud providers

Cloud users require a set of specific computing needs for their applications, while cloud providers offer a variety of computing products and services on the Internet. These two cloud players make deals through the use of service level agreements (SLAs) where, for instance, prices and levels of quality of service (QoS) are defined. From the cloud user’s point of view, building a robust set of SLAs becomes a challenging problem when multiple cloud providers are present in the market. The allocation of resources in the cloud to run complex applications with guaranteed reliable, secure and acceptable response times is not an easy task, and this paper aims to tackle this problem. This work describes a resource allocation service that aims to optimize the user’s request of cloud resources (virtual machines — VMs) onto multiple Infrastructure-as-a-Service (IaaS) cloud providers. The Resource-Allocation-as-a-Service (RAaaS) proposed in this paper works as a standalone service between cloud users and cloud providers, and it relies on three different requirements: reliability, processing, and mutual trust. The proposed resource allocation service is carried out using the three very common types of VM billing models: on-demand, reserved and spot, where the spot cost model is employed to furnish low-cost resources for the application allocation to improve its reliability. The contributions of this paper are threefold: (i) a three-dimension SLA encompassing reliability, processing, and trust; (ii) an integer linear program (ILP) to schedule cloud-based VMs to applications considering the three-dimension SLA model, and (iii) a heuristic algorithm to mitigate possible QoS violations. Experimental results show that the proposed RAaaS procedure is capable of optimizing resource allocation considering multiple criteria in the SLA while mitigating the extra costs introduced by mutual trust between customers using redundant spot instances allocation.

CSD-RkNN: reverse k nearest neighbors queries with conic section discriminances

The reverse k nearest neighbors (RkNN) query is a prominent yet time-consuming spatial query used in facility siting, influential domain analysis, potential customer analysis, etc. Its aim is to identify all points that consider the query point as one of their k closest points. However, when k is relatively large (e.g. k = 1000), existing RkNN techniques often struggle to provide acceptable response times (within a few seconds). To address this issue, we propose a verification approach called conic section discriminance (CSD). This method serves to determine whether points belong to the RkNN set. With CSD, only a small fraction of candidates require costly k nearest neighbors (kNN) queries for verification, while the rest can be rapidly verified with O(1) complexity. Furthermore, we propose a Voronoi-based candidate generation approach to curtail the candidate set size. By leveraging the VoR-tree structure, we integrate these two approaches to form a novel RkNN algorithm named CSD-RkNN. A comprehensive set of experiments is conducted to compare CSD-RkNN with Slice as the state-of-the-art RkNN algorithm, and VR-RkNN as the original RkNN algorithm on VoR-tree. The results indicate that CSD-RkNN consistently outperforms the other two algorithms, especially when k is relatively large.

A multi-scenario approach to continuously learn and understand norm violations

Using norms to guide and coordinate interactions has gained tremendous attention in the multiagent community. However, new challenges arise as the interest moves towards dynamic socio-technical systems, where human and software agents interact, and interactions are required to adapt to changing human needs. For instance, different agents (human or software) might not have the same understanding of what it means to violate a norm (e.g., what characterizes hate speech), or their understanding of a norm might change over time (e.g., what constitutes an acceptable response time). The challenge is to address these issues by learning to detect norm violations from the limited interaction data and to explain the reasons for such violations. To do that, we propose a framework that combines Machine Learning (ML) models and incremental learning techniques. Our proposal is equipped to solve tasks in both tabular and text classification scenarios. Incremental learning is used to continuously update the base ML models as interactions unfold, ensemble learning is used to handle the imbalance class distribution of the interaction stream, Pre-trained Language Model (PLM) is used to learn from text sentences, and Integrated Gradients (IG) is the interpretability algorithm. We evaluate the proposed approach in the use case of Wikipedia article edits, where interactions revolve around editing articles, and the norm in question is prohibiting vandalism. Results show that the proposed framework can learn to detect norm violation in a setting with data imbalance and concept drift.

Flexible humidity sensor for smart agricultural applications

In order to meet the increasing food demand, sensors that measure the ambient temperature and humidity in greenhouses are needed for more efficient vegetable and fruit production. For this purpose, two different flexible and resistant textile-based humidity sensors that can measure the humidity at higher levels (80%, 90%, and 100% relative humidity (RH)) were designed and printed directly on the four different greenhouse fabrics using silver and carbon conductive inks. Depending on the humidity value in the environment, the sensor performance was tested based on sensor electrical resistance measurements with respect to repeated bending/cyclic tests, rubbing, ultraviolet (UV) exposure/ weatherability (against UV and raining) tests in order to simulate greenhouse conditions for smart agriculture. Despite applying 1024 bending cycles, up to 20 rubbing cycles, and 10 times UV and rain exposure to the humidity sensors at high RH, no significant change was detected in the resistance values of the humidity sensors. Moreover, some important features of the sensors such as hysteresis, repeatability, response time have been also examined. According to hysteresis test results, humidity sensors show acceptable dynamic response and response time of the sensors are 15.8, 17.3, 24.8, and 25 s at 100% RH for G2S, W1S, B2C, and W1C, respectively. Statistical analyses showed that the sensor designs and type of conductive inks had significant effects on the performance of the humidity sensors and the best sensor performance was obtained with the polypropylene coated fabric using design II and silver based conductive ink. The fabricated textile based flexible humidity sensors detect the change in RH levels from 80% RH to 100% RH and achieve good durability, and repeatability even after prolonged UV exposure and raining. Thus, the developed textile-based flexible humidity sensor might be useful for future smart agricultural applications.

Automated clinical knowledge graph generation framework for evidence based medicine

To practice the evidence-based medicine, clinicians are interested to find the most suitable research for the clinical decision making. The use of knowledge graphs (KGs) in evidence-based clinical decision support systems is becoming increasingly popular. However, existing KG construction frameworks are not fully automated and contextualized, thus unable to adapt to new domains and incorporate constantly changing information into their knowledge base, resulting in loss of relevance over time. Furthermore, existing KGs construction frameworks don't generate KG that provide relevant information within an acceptable response time for evidence-based practitioners because the organization of constructed subgraphs is neither topic-specific nor evidence-based PICO (Participants/Problem P, Intervention-I, Comparison C, Outcome O) query-friendly. By employing concept extraction, semantic enrichment, optimized clustering, and state of art Recurrent Neural Networks (RNNs) with BioBERT based encoded representation to categorize PICO elements and predict relationships between concepts using huge corpus of publicly available literature on COVID-19 and cerebral aneurysm, this paper proposes a topic specific, PICO enabled, and fully automated framework to curate information and create KG of different clinical domains. The evaluation shows that the proposed framework achieves significant improvement over baseline models and has 93 %, and 82 % accuracy on aneurysm and COVID data set respectively for PICO classification. Also, the relationship extraction module has an accuracy of 96 % with precision and recall being 92 %, and 90 % respectively.

Enhancing human activity recognition using features reduction in IoT edge and Azure cloud

The Internet of Things (IoT), cloud computing, and machine learning opened an opportunity for new smart systems. These technologies have triggered huge traffic and delay by continuously transmitting telemetry data to the cloud. IoT edge choice made decision-making closer to the environment, which decreases traffic and speeds up response time. Human activity recognition (HAR) systems, among other IoT applications, require systems with quick response time; reduce costs using constrained traffic to the cloud while maintaining accurate prediction results. This study proposes an application of HAR for predicting activities using up to three smartphone accelerometers. Three models are developed, trained, and deployed to achieve the necessary accuracy at the IoT edge and in the cloud, with an acceptable response time. Since each coordinate value from the three accelerometers has different importance in activity category prediction, focusing on the data from the most related values can help minimize the amount of information transferred from the edge to the cloud. Six models were trained in the cloud; three were deployed and tested at the edge with different features by selecting the most important ones using Principal Component Analysis (PCA). Different experiments showed that traffic and processing time decreased significantly based on the time required to predict HAR categories with acceptable accuracy. Since there is significant latency between the edge and the cloud and within the cloud, sending samples for verification save bandwidth, and processing requests locally at the edge speed up predictions. Results illustrate that the time required to serve one request from the environment where smartphones generate traffic through the internet connection to the cloud took about 5.8 s on average, including transmission delays and the prediction process. During this time, the model at the edge can serve 150 requests with the same accuracy using nine features. In addition, the edge can serve 286 requests in 5.8 s with 94.8 % accuracy when choosing the top four features at the edge.

Comparative spectroscopic study of Tb:Ce(Sal)3Phen complex inhibited PVA nanofibres for flexible moisture sensor

In the present work, we report a systematic study on optical alteration in Tb:Ce(Sal)3Phen, Tb(Sal)3Phen complexes, and TbCl3·6H2O inhibited in polyvinyl alcohol (PVA) polymeric nanofibres. We also report the feasibility of Tb:Ce(Sal)3Phen complex dispersed electrospun nanofibres for opto-humidity sensor. Structural, morphological, and spectroscopic properties of the synthesized nanofibres were systematically compared using Fourier transform infrared spectroscopy, scanning electron microscopy, and Photoluminescence analysis. Synthesized Tb(Sal)3Phen complex inhibited in nanofibres yields characteristic bright green photoluminescence of Tb3+ under UV excitations, which is at least two manifolds enhanced on the addition of Ce3+ ions in the same complex. The presence of Ce3+ ions, the salicylate ligand, and the Tb3+ ion help to expand the absorption range (290 nm–400 nm) and, subsequently, the photoluminescence in blue and green regions. Our analysis revealed the linear enhancement of photoluminescence intensity with the addition of Ce3+ ions. Upon exposing the flexible Tb:Ce(Sal)3Phen complex dispersed nanofibres mat in different humidity environments, photoluminescence intensity shows a linear variation. The prepared nanofibres film shows good reversibility, small hysteresis, cyclic stability, and acceptable response and recovery times i.e. 35 and 45 s. The humidity sensing mechanism was proposed on the basis of infrared absorption analysis of dry and humid nanofibres.

Fully Integrated Wearable Device for Continuous Sweat Lactate Monitoring in Sports.

The chemical digitalization of sweat using wearable sensing interfaces is an attractive alternative to traditional blood-based protocols in sports. Although sweat lactate has been claimed to be a relevant biomarker in sports, an analytically validated wearable system to prove that has not yet been developed. We present a fully integrated sweat lactate sensing system applicable to in situ perspiration analysis. The device can be conveniently worn in the skin to monitor real-time sweat lactate during sports, such as cycling and kayaking. The novelty of the system is threefold: advanced microfluidics design for sweat collection and analysis, an analytically validated lactate biosensor based on a rational design of an outer diffusion-limiting membrane, and an integrated circuit for signal processing with a custom smartphone application. The sensor covering the range expected for lactate in sweat (1-20 mM), with appropriate sensitivity (-12.5 ± 0.53 nA mM-1), shows an acceptable response time (<90 s), and the influence of changes in pH, temperature, and flow rate are neglectable. Also, the sensor is analytically suitable with regard to reversibility, resilience, and reproducibility. The sensing device is validated through a relatively high number of on-body tests performed with elite athletes cycling and kayaking in controlled environments. Correlation outcomes between sweat lactate and other physiological indicators typically accessible in sports laboratories (blood lactate, perceived exhaustion, heart rate, blood glucose, respiratory quotient) are also presented and discussed in relation to the sport performance monitoring capability of continuous sweat lactate.

A bi-objective model for territorial design

The clustering of spatial-geographic units, zones or areas has been used to solve problems related to Territorial Design. Clustering adapts to the definition of territorial design for a specific problem, which demands spatial data processing under clustering schemes with topological requirements for the zones. For small sized instances, once the geographical compactness is attended to as an objective function, this problem has been solved by exact methods with an acceptable response time. However, for larger instances and due to the combinatory nature of this problem, the computational complexity increases and the use of approximated methods becomes a necessity, in such a way that when geographical compactness was the only cost function a couple of approximated methods were incorporated giving satisfactory results. A particular case of this kind of problems that has had our attention in recent years is the classification by partitioning of AGEBs (Basic Geographical Units by its initials in Spanish). Some work has been made related to the formation of compact groups of AGEBs, but additional re-strictions were often not considered. A very interesting and highly demanded application problem is to extend the compact clustering to form groups with for some of its descriptive variables. This problem translates to a multi-objective approach that has to pursue two objectives to attain a balance between them. At this point, to reach a set of non-dominated and non-comparable solutions, a method has been included that allows obtaining the Pareto front through the Hasse diagram, which implies proposing a mathematical programming model and the synthetic resulting between compactness and homogeneity.

Dynamic Terrain Data Exchange in a Collaborative Terrain Editor

In a computer supported cooperative work (CSCW), data consistency between collaborating users is a crucial issue. Based on the type of the application, ensuring data consistency can be a lengthy process that takes time and affects the system’s performance. In most 3D application, terrain data are massive due to its size. Exchanging this data may be expensive and may cause significant delay. In a real-time collaborative terrain editor, this issue becomes more significant due to terrain data exchange is consistently occurred between collaborating users. We present a solution to perform a conflict-free dynamic terrain data exchange in a real-time collaborative terrain editor. Our objective is to develop a method that able to ensure data consistency amongst collaborating peers in real-time manner. The main idea of our method is to split the terrain into smaller patches and synchronize the changes efficiently by only exchanging the modified patches. We applied our solution to a collaborative terrain editor application to test its performance in a real-time collaborative editing session. The tests were done in multiple scenarios, using different patch model, brush size (in the terrain editor), and connection setup between server and collaborating clients. The result shows that our protocol is capable to maintain data consistency between collaborating clients in a real-time terrain edition session. The delay is varied and highly depends on the data size and client-server environment setup. The overall test shows that it is possible to perform a collaborative terrain editing with an acceptable response time delay. In this paper, we present our proposed method, the implementation, and the result data from the test.

Evaluation of the sample size requirements of machine learning models used in engine design and research

Machine Learning (ML) techniques have been effectively used to learn the intricate relationships between variables that play a significant role in the field of engine design. However, there are two challenges to this approach – (1) Identifying ML regression models that could capture the trends of a response variable, given a non-parametric training set of relatively small size, with an acceptable accuracy and response time, and (2) identifying the size of the dataset, with respect to the input parameters, to be used for training and validation of the ML models. There is not enough information in the literature to reach a consensus on the sampling size to be used for an engine design problem that would yield an acceptable measure of goodness-of-fit. This is evident from the varied size of the training/validation data size used within the engine research community, as will be elaborated on in the following sections. The objective of this paper is to provide an insight into the sampling size required by the different ML models to achieve an acceptable fit between the model and the data, to be used in three types of engine design/optimization problems – (1) conventional diesel combustion (CDC) engine performance over a wide range of speed and load, (2) cold-start operation of a direct-injected spark-ignition (DISI) engine, and (3) high-load performance of a dual-fuel reactivity-controlled compression ignition (RCCI) engine.

High Sensitivity, Wide Range Pressure Sensor Based on Layer-by-Layer Self-Assembled MXene/Carbon Black@Polyurethane Sponge for Human Motion Monitoring and Intelligent Vehicle Control

Flexible pressure sensors with high sensitivity, low cost, lightweight, and convenience are of significance in the field of human movement monitoring and sensing. This article demonstrated a piezoresistive pressure sensor prepared by layer-by-layer (LbL) self-assembly of MXene and carbon black (CB) on a polyurethane (PU) sponge. The electronegativity of MXene and the electropositive of CB enable them to combine more stably. The assembled sensor achieves reliable repeatability (over 3700 cycle testing), high sensitivity (−5.26 kPa <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{1}}$ </tex-math></inline-formula> ), wide sensing range (0–240 kPa), acceptable response time (300/300 ms), and good reproducibility under various pressures. MXene and CB greatly increase the electrical conductivity and measurement range of the piezoresistive sensors. The existence of microcracks allows the sensor to sensitively capture micropressure signals, such as human radial artery pulse and respiration. In addition, the large-area conductive framework permits the sensor to maintain high responsiveness over a huge strain range and to distinguish various degrees of human movement. Furthermore, the sensor has shown excellent performance in controlling smart devices through external pressure as a human–machine interface. The pressure sensor based on LbL self-assembled MXene/CB on a PU sponge has potential applications in motion signal detection, artificial intelligence, and other fields.

Design and implementation of a new intelligent warehouse management system based on MySQL database technology

In order to improve the material management level of enterprises, based on MySQL database technology, this paper makes an overall design of the warehouse management system, builds a MySQL database, and realizes the design and application of a new intelligent warehouse management system. Through the operation and test of the system, the results show that the system mainly realizes the five necessary functional modules of warehouse management: basic information management, system management, procurement management, warehousing management and inventory management. In the test, the system runs normally, the unit test and integration test can meet the expected requirements, realize the functions required by the user, and get the desired results within the user's acceptable response time (within 3S). It can provide automatic and comprehensive records for the whole process of material management of the enterprise, and provide real-time and correct information for all warehouse activities, resources and inventory levels.

Computational and Communication Infrastructure Challenges for Resilient Cloud Services

Fault tolerance and the availability of applications, computing infrastructure, and communications systems during unexpected events are critical in cloud environments. The microservices architecture, and the technologies that it uses, should be able to maintain acceptable service levels in the face of adverse circumstances. In this paper, we discuss the challenges faced by cloud infrastructure in relation to providing resilience to applications. Based on this analysis, we present our approach for a software platform based on a microservices architecture, as well as the resilience mechanisms to mitigate the impact of infrastructure failures on the availability of applications. We demonstrate the capacity of our platform to provide resilience to analytics applications, minimizing service interruptions and keeping acceptable response times.

The Start of Combustion Prediction for Methane-Fueled HCCI Engines: Traditional vs. Machine Learning Methods

In this work, 11 regression models based on machine learning techniques were employed to provide a fast-response and accurate model for the prediction of the start of combustion in homogeneous charge compression ignition engines fueled with methane. These regression models are categorized into linear and nonlinear types. Although the robust random sample consensus (RANSAC) model is a nonlinear type as well as SAM (simple algebraic model), the prediction accuracy is enhanced from 89.3% to 98.4%. Such accuracy is also achieved for the linear models, namely, ordinary least squares, ridge, and Bayesian ridge models. Indeed, due to the linear hypothesis (the correlation for the start of combustion prediction), the presented models have an acceptable response time to be used in real-time control applications like the electronic control units of the engines.

Novel analytical method based on Fourier Transform assisted Ultraviolet-Infrared dual resonance spectroscopy for aerosol speciation

Detecting aerosols have been treated as one of the most important environmental challenges among the scientists. However, the current existing methods for aerosl detection and recongition (such as spectroscopy) cannot often be considered as favorable detection system, mostly attributed to their low recovery detection limitations, high cost, not-real time, etc. So, in the current study an optical detection device with three main parts (light sources, a 20-membered optical detection array and a digital versatile disc) has been introduced by fourier transform assisted ultraviolet-infrared dual (double) uresonance spectroscopy. For this purpose, a square matrix 10 × 10 dimension with five independent 4 × 4 sub-matrices (D1-D5) was defined as the responsoble model. This selection was based on the selective classification (clustering) of the measured results at the molecular levels of the tested aerosols. Then, the middle infrared (Middle-IR) region was divided into nine parts with 13.33 nm resolution. About the SiO2 and water aerosols, determinant percentages of D3 and D1 matrixes pocessed partially high and significant values. Besides, correlation between the determinant percentages of D1 and D4 as well as D2 and D5 revealed the reproducibility of the introduced detection system and the suggested model. The reliability of the detection system was also evaluated via introducing different two-mixture standard samples of the areoles and predication of the types of the introduced samples into the detection system. Capability determination and selective probes of this method have also been investigated for different analytical concentrations of CH3COOH in water samples at the mid-IR region under similar conditions (Average population: 200 ± 5 particles per each m3 sample). Further, its behavior was evaluated by analysis of different volatile homologs such as HCOOH, CH3COOH and C2H5COOH with acceptable response times and correlation coefficents.