Simple Monitoring Research Articles

Achilles tendon thickness and serum asprosin level significantly increases in patients with polycystic ovary syndrome.

In our study, we aimed to investigate the Achilles tendon thickness (ATT) and asprosin levels in patients with polycystic ovary syndrome (PCOS) and to evaluate the relationship of these parameters, which may be related to cardio-metabolic diseases. In our prospective cross-sectional study, 45 female patients with PCOS and 30 female healthy individuals similar in age were included. Serum dehydroepiandrosterone sulfate (DHEAS), total testosterone, anti-Müllerian hormone (AMH) and asprosin levels were measured using appropriate kits and homeostatic model assessment of insulin resistance (HOMA-IR), luteinizing hormone (LH) to follicle-stimulating hormone (FSH) ratio was calculated. ATT measurements were performed by two radiologists using a high-resolution ultrasound doppler system. Serum DHEAS, total testosterone, AMH and asprosin levels, HOMA-IR value, LF/FSH ratio, and ATT values were higher in patients with PCOS compared to healthy controls. Correlation analysis was performed between ATT and other parameters in patients with PCOS. In univariate analysis, parameters associated with ATT were detected as asprosin, DHEAS and AMH. In the linear regression analysis performed with significant parameters, asprosin and DHEAS levels were found to be associated with ATT. ATT values and serum asprosin levels were found to be significantly increased in patients with PCOS, and there is a very close positive relationship between ATT and serum asprosin levels. For this reason, it was thought that ATT measurement could be a cheap, simple and non-invasive monitoring parameter that can be used in the routine cardiometabolic follow-up of patients with PCOS.

Monitoring and Fault Diagnosis of Breathing Valves in Atmospheric Storage Tank under Fuzzy PID

The atmospheric storage tank breathing valve relies too much on manual experience and simple monitoring methods in monitoring and fault diagnosis, and cannot provide high-precision fault diagnosis and prediction. Therefore, this article introduces a monitoring and fault diagnosis system for atmospheric storage tank breathing valves based on Fuzzy PID (Proportional-Integrated-Derivative). The research aims to design the atmospheric storage tank breathing valve system into three modules. The monitoring module uses sensors for data collection, while the early warning module classifies the data based on the monitoring module to find the optimal classification boundary and achieve abnormal state recognition. The fault diagnosis module uses the fuzzy PID algorithm to thoroughly inspect and evaluate the abnormal conditions detected in the atmospheric storage tank, and finally evaluates the monitoring and fault diagnosis of the atmospheric storage tank breathing valve before and after improvement. The results showed that the diagnostic accuracy improved by using fuzzy PID was 35.2% higher than before. In summary, the diagnostic method proposed in this paper is superior to the traditional method and improves the reliability and performance of the system.

Platelet indices in children with type 1 diabetes mellitus: a simple glucoregulation monitoring tool.

Long-term hyperglycemia can lead to changes in the function and morphology of platelets. This study aimed to test the potential glucoregulation monitoring properties of platelet indices, mean platelet volume (MPV) and platelet distribution width (PDW), in children with type 1 diabetes mellitus (T1DM). The study included 453 patients below the age of 18 with T1DM treated at the Institute for Child and Youth Health Care of Vojvodina. Children were divided into two groups, according to their glucoregulation quality, i.e., glycated hemoglobin (HbA1c) levels. Descriptive and inferential statistical analyses were performed. MPV and PDW were found to be important in predicting poor glucoregulation, both in independent and conjoint analysis. Proposed cut-off values for MPV and PDW in the glucose control monitoring of children with T1DM were 7.6 fL and 14.4%, respectively. Our study showed that MPV and PDW have monitoring properties in terms of glucose control in children with T1DM. Additionally, our study emphasizes the importance of selecting the most convenient control group in order to avoid misleading conclusions.

Electrochemical Monitoring of Super-Engineering Polymer Depolymerization: A Rapid and Simple Approach

The mechanical properties and processability of polymers are strongly influenced by their molecular weight, underscoring the critical importance of controlling depolymerization during storage in polymer synthesis and application. However, conventional methods of determining molecular weight, including viscosity measurements, gel permeation chromatography (GPC), and nuclear magnetic resonance (NMR) spectroscopy, typically necessitate substantial quantities of polymer samples and protracted measurement times. Moreover, certain polymers may undergo continuous degradation post-polymerization, rendering depolymerization analysis via traditional means highly challenging. Hence, there is an imperative need to develop novel analysis techniques that overcome these limitations and facilitate comprehensive depolymerization characterization.To surmount these challenges, we have developed a novel electrochemical method utilizing cyclic voltammetry to facilitate polymer depolymerization measurement. For measuring degree of depolymerization, we used thermal depolymerization process. As the polymer's molecular weight diminishes because of thermal depolymerization, the solution viscosity decreases correspondingly, thereby enabling unimpeded redox species diffusion and a proportional increase in steady-state current. By constructing a calibration curve of current versus thermal depolymerization time for a polymer, we can swiftly and accurately determine its molecular weight tendency in solution. Our investigations indicate that this electrochemical technique is more efficient and cost-effective as it necessitates smaller sample volumes and shorter measurement times than conventional methods, and it exhibits excellent potential for analyzing and maintaining polymer properties across various industries.[1] Kim, J. W., Kim, S. J., Song, I. Y., Lee, W. E., Heo, K., Kim, S. Y., ... & Kim, B. K. (2022). Simple and rapid electrochemical monitoring for depolymerization of polyamic acid. Analytica Chimica Acta, 1233, 340489.

Application of Passive RFID Technology

Passive Internet of Things (IoT) is an emerging IoT technology that enables communication and data exchange between items through various sensors and devices. Sensor nodes do not require built-in batteries or other energy supply devices, but instead use external signals for operation. Radio Frequency Identification (RFID) technology is a simple and low-cost solution for the passive Internet of Things, suitable for scenarios that require simple tracking and monitoring of items. The sensor devices in this technology can be used for a long time interregional operation reduces maintenance costs and improves work efficiency. Therefore, it is a promising technology that can bring tremendous changes to various industries. In recent years, this technology has been widely applied in many fields, including logistics and supply chain management, industrial automation, intelligent agriculture, and health monitoring. This article elaborates on the practical application of the latest passive RFID technology, providing reference for more application scenarios in the future.

Quantitative monitoring ofloxacin in beef by TLC-SERS combined with machine learning analysis

Ofloxacin is one kind of quinolone antibiotic drugs, the abuse of ofloxacin in livestock and aquaculture may bring bacterial resistance and healthy problem of people. The illegally feeding cattle with ofloxacin will help it keep health, but the sedimentation of ofloxacin could bring problem in food safety. The accurate, simple and instant monitoring ofloxacin from beef by portable sensor was of vital issue in food quality. A simple and reliable method was proposed for instant and quantitative detecting ofloxacin in beef, in which the thin-layer chromatography (TLC) -surface-enhanced Raman scattering (SERS) spectroscopy was in tandem with machine learning analysis base one principal component analysis-back propagation neural network (PCA-BPNN). The TLC plate was composed with diatomite, that was function as the stationary phase to separate ofloxacin from beef. The real beef juice was directly casted onto the diatomite plate for separating and detecting. The directly monitor ofloxacin from beef was achieved and the sensitivity down to 0.01 ppm. The PCA-BPNN was used as reliable model for quantitative predict the concentration of ofloxacin, that shown superior accuracy compared with the traditional model. The results verify that the diatomite plate TLC-SERS combined with machine-learning analysis is an effective, simple and accurate technique for detecting and quantifying antibiotic drug in meat stuff to improve the food safety.

A simulation-based approach to assess the stability of marine food-webs and inform Good Environmental Status

The European Union Marine Strategy Framework Directive (MSFD) recognises that maintaining marine food-webs in Good Environmental Status (GES) is fundamental to ensure the long-term provision of essential ecosystem goods and services. However, operationalising food-web assessments is challenging due to difficulties in i) implementing simple but complete monitoring programmes, ii) identifying thresholds in monitoring indicators that inform when perturbations are diverting food-web state from GES and iii) in providing an integrative and complete picture of the (health) status of food-webs. In this context, stability assessments of marine food-webs could be useful to identifying the indicators that best track perturbation-induced changes in food-web state and the threshold boundaries that should not be exceeded to minimise the likelihood of losing stability. Yet, there is still a lack of systematic methods to perform such assessments. Here, we evaluate the potential of a simulation-based protocol to be used as a methodological standard for assessing the stability of marine food-webs. The protocol draws on the principles of ecological stability theory and provides a framework for assessing the trajectories of individual indicators during perturbation regimes and their robustness in detecting stability thresholds for marine food-webs. We tested the protocol on an open-ocean and deep-sea food-web modelled with the Ecopath with Ecosim suite. We concluded that indicators that quantify transfer efficiency through the food-web and measure the average trophic level of the community are optimal proxies for trophic functioning and structure to assess the stability of the system. Furthermore, we show how the approach can be applied to i) determine the impact of a loss of stability on the balance between trophic levels and ii) identify the biological components of the food-web that are most affected in scenarios of stability loss. Our findings could be useful for the ongoing debate on how trophic models and derived indicators can play a concrete and practical role in the food-web assessments in European seas.

Monitoring of Single-Track Melting States Based on Photodiode Signal during Laser Powder Bed Fusion.

Single track is the basis for the melt pool modeling and physics work in laser powder bed fusion (LPBF). The melting state of a single track is closely related to defects such as porosity, lack of fusion, and balling, which have a significant impact on the mechanical properties of an LPBF-created part. To ensure the reliability of part quality and repeatability, process monitoring and feedback control are emerging to improve the melting states, which is becoming a hot topic in both the industrial and academic communities. In this research, a simple and low-cost off-axial photodiode signal monitoring system was established to monitor the melting pools of single tracks. Nine groups of single-track experiments with different process parameter combinations were carried out four times and then thirty-six LPBF tracks were obtained. The melting states were classified into three classes according to the morphologies of the tracks. A convolutional neural network (CNN) model was developed to extract the characteristics and identify the melting states. The raw one-dimensional photodiode signal data were converted into two-dimensional grayscale images. The average identification accuracy reached 95.81% and the computation time was 15 ms for each sample, which was promising for engineering applications. Compared with some classic deep learning models, the proposed CNN could distinguish the melting states with higher classification accuracy and efficiency. This work contributes to real-time multiple-sensor monitoring and feedback control.

Colorimetry/fluorescence dual-mode detection of Salmonella typhimurium based on self-assembly of MCOF with Au NPs nanozyme coupled AIEgen

Sensitive, accurate, simple and quick monitoring of Salmonella typhimurium (S. typhimurium) in food is significant for preventing food poisoning, but still remains a challenge. Herein, a colorimetry/fluorescence dual-mode sensing strategy was fabricated to detect S. typhimurium by integrating the self-assembly of magnetic covalent organic framework (MCOF) with gold nanoparticles (Au NPs) as the peroxidase-mimicking nanozyme and aggregation-induced emission luminogen (AIEgen). S. typhimurium could competitive bind to aptamer conjugated Au NPs (Au NPs@apt), inhibit the self-assembly of MCOF with Au NPs, and shield the catalytic activity of AuNPs. After adding H2O2 and TPE-4A, the dark green solution changed to light with increasing S. typhimurium concentration, on the contrary, the fluorescent signals were generated. As a result, in colorimetry/fluorescence modes, S. typhimurium could be detected in the linear ranges of 103–108 CFU mL−1 and 101–107 CFU mL−1, with LODs of 1000 and 10 CFU mL−1, respectively. Importantly, different colors consistent with various S. typhimurium concentrations can be accurately classified by smartphone app and linear discriminant analysis (LDA). The smartphone-assisted data interpretation can generate complementary colorimetry and fluorescence signals without any sophisticated equipment and achieve on-site detection. Moreover, the proposed strategy could be explored for S. typhimurium monitoring in milk with satisfactory recoveries (97.6–100.4 %) in colorimetry and fluorescence mode and good classification and prediction performance in smartphone/LDA system, suggesting the feasibility and potential applications of the sensing platform.

Simple low-cost guide to athlete fatigue monitoring

As the demands of training and competition increase so does the potential risk of injury and illness to the athlete whilst seeking to maximize their adaptive processes to promote optimal performance. Therefore, as a strategy to mitigate this risk, strength and conditioning coaches need reliable and valid monitoring tools to track an athlete’s status throughout training to ensure progression of adaptation, and that the athlete remains healthy throughout the adaptation process. The purpose of this article is to provide the reader an evidence-driven outline of basic, simple, and cost-effective monitoring tools which are reliable and valid to observe the fitness/fatigue paradigm and track overall athlete physical adaptation and health throughout the training process, suitable for most settings. A weekly example calculating sessional ratings of perceived exertion (sRPE), training load, monotony, and strain is provided along with a basic monitoring system as a guide for the reader.

Construction of smartphone-adapted signal visualization platform for dual-mode detection of H2S based on integrated metal-organic framework nanoprobes

Hydrogen sulfide (H2S) is a toxic contaminant and has great influence on many physiological processes. Due to various pathophysiological roles and environmental pollution problems, it is necessary to construct and develop simple and portable monitoring sensors for the precise detection of H2S. Herein, we developed a smartphone-adapted dual-mode detection platform by integrating the colorimetric and photothermal imaging analysis into a metal-organic framework-based chip (ZIF-8/Cu). Due to the nanoconfinement effect of ZIF-8, small-sized plasmonic CuS could be in-situ formed during the detection procedure of H2S and endowed the chips with excellent photothermal properties. By constructing a smartphone-adapted photothermal imager, the metal-organic framework-based chip could achieve a portable photothermal imaging analysis of H2S. Moreover, as the formed CuS was a good peroxidase-like nanozyme, the chips could also be used to trigger the enzymic catalytic reaction toward the chromogenic reaction of 3,3′,5,5′-tetramethylbenzidine (TMB)-H2O2, thus providing another colorimetric sensing mode by using a smartphone App. In this smartphone-adapted visualization platform, the portable chemosensors could simultaneously achieve double detection modes at one electrode, which provided a new pathway for the accurate detection of H2S and circumvented the false-positive or negative errors during the detection process. Besides, by using the finite difference time domain (FDTD) simulation method, the in-depth mechanism, including the plasmonic effect and spatial electromagnetic field distribution, was explored to provide a possible reason for the excellent sensing performance of the dual-mode visualization platform. This work provides a new insight into the construction of the accurate, portable and smart sensing platform in the visual screening of H2S.

Simultaneous detection of AFB1 and aflD gene by "Y" shaped aptamer fluorescent biosensor based on double quantum dots.

The developed method for simultaneous detection of aflatoxin B1 (AFB1) and aflD genes can effectively monitor from the source and reduce the safety problems and economic losses caused by the production of aflatoxin, which can be of great significance for food safety regulations. In this paper, we constructed a sensitive and convenient fluorescent biosensor to detect AFB1 and aflD genes simultaneously based on fluorescence resonance energy transfer (FRET) between quantum dots (QDs) and a black hole quenching agent. A stable "Y" shaped aptasensor was employed as the detection platform and a double quantum dot labeled DNA fragment was utilized to be the sensing element in this work. When the targets of AFB1 and aflD genes were presented in the solution, the aptamer in the "Y" shaped probe is specifically recognized by the target. At this time, both Si-carbon quantum dots (Si-CDs) and CdTe QDs are far away from the BHQ1 and BHQ3 to recover the fluorescence. The linear range of the prepared fluorescence simultaneous detection method was as wide as 0.5-500ng·mL-1 with detection lines of 0.64ng·mL-1 for AFB1 and 0.5-500nM with detection lines of 0.75nM for aflD genes (3σ/k). This fabricated fluorescent biosensor was further validated in real rice flour and corn flour samples, which also achieved good results. The recoveries were calculated by comparing the known and found amounts of AFB1 which ranged from 88.4 to approximately 115.32% in the rice flour samples and 90.7 ~ 102.58% in the corn flour samples. The recoveries of aflD genes ranged from 84.32 to approximately 109.3% in the rice flour samples and 89.48 ~ 100.99% in the corn flour samples. Therefore, the proposed biosensor can significantly improve food safety and quality control through a simple, fast, and sensitive agricultural product monitoring and detection system.

Identification and estimation of hydrological contributions in a mixed land‐use catchment based on a simple biogeochemical and hydro‐meteorological dataset

AbstractWater pathways and water contamination in mixed land‐use catchments are complex to understand. Runoff‐generating sources can be numerous and water pathways modified by anthropogenic elements. Monitoring surveys considering geochemical and microbial parameters, are often carried out on such catchment, but are often simple in terms of studied parameters. Nonetheless, they can be helpful to identify the specific signatures of the main runoff‐generating sources and estimate their contribution to total runoff at the outlet of mixed land‐use catchments. Based on a monthly biogeochemical monitoring program conducted between 2017 and 2019 in the Ratier catchment (19.8 km2) near Lyon (France), a step‐by‐step approach was developed to: (1) identify the main runoff‐generating sources using a perceptual model of the Ratier catchment, (2) identify the respective biogeochemical signatures of each source using this biogeochemical dataset and hydro‐meteorological indicators and (3) estimate their contribution to the stream total runoff using an End‐Member Mixing Analysis method. We identified three main runoff‐generating sources outside of rainy periods: a colluvium aquifer, a fractured gneiss aquifer and a saprolite layer. The monitored geochemical datasets were found divided into three groups matching these sources. Contributions of these sources were estimated based on representative tracer concentrations. Microbial parameters showed a homogeneous agricultural and anthropogenic contamination among the catchment surface water, but also deeper into the fractured gneiss groundwater. This approach showed the potential of using simple monitoring datasets to identify runoff‐generating sources and estimate their contribution to total runoff.

Event Related Brain Responses Reveal the Impact of Spatial Augmented Reality Predictive Cues on Mental Effort.

This article presents the results from a Spatial Augmented Reality (SAR) study which evaluated the cognitive cost of several predictive cues. Participants performed a validated procedural button pressing task, where the predictive cue annotations guided them to the upcoming task. While existing research has evaluated predictive cues based on their performance and self-rated mental effort, actual cognitive cost has yet to be investigated. To measure the user's brain activity, this study utilized electroencephalogram (EEG) recordings. Cognitive load was evaluated by measuring brain responses for a secondary auditory oddball task, with reduced brain responses to oddball tones expected when cognitive load in the primary task is highest. A simple monitor n-back task and procedural task comparing monitor versus SAR were conducted, followed by a version of the procedural task comparing the SAR predictive cues. Results from the brain responses were able to distinguish between performance enhancing cues with a high and low cognitive load. Electrical brain responses also revealed that having an arc or arrow guide towards the upcoming task required the least amount of mental effort.

Initial clinical experience with new technology: ePatch extended Holter monitoring

Abstract Introduction Holter monitor electrocardiography is used to detect potential cardiac arrhythmias such as atrial fibrillation (AF). However, conventional ECG monitoring, data analysis and reporting methods can be labor-intensive and inefficient, leading to high upfront costs and long turnaround times. At the same time, traditional Holter devices can be cumbersome to wear, while inconvenient processes may reduce patient compliance and satisfaction – which impacts diagnostic yield. This new device (ePatch®) is a simple patch application, enrollment and activation with no battery charging needed and no cable attachments (Figure 1). Up to 14 days of continuous ECG recording. Wearable in shower, during exercise and while sleeping. Single-channel, up to 14 days. Two-channel, up to 5 days Purpose Our goal is to evaluate the quality and diagnostic efficacy of ePatch® in different clinical settings. Methods Prospective study of patients with ePatch® placed in our center requested for study of undocumented palpitations, syncope or cryptogenic stroke. Clinical variables, diagnostic accuracy, and time to diagnostic event are analyzed. Results We enrolled the first 57 patients, of whom 27 (50%) were women, with a mean age of 67.8 years (range 27 to 89). The most common indication was undocumented palpitations (35.1%) and cryptogenic stroke (35.1%), followed by syncope (29.6%). All the patients had correct compliance, tolerance and the ECG monitored was of good quality. A conclusive diagnosis: (symptomatic sustained tachyarrhythmia, atrial-ventricular block with bradyarrhythmia, significant pauses or AF) was obtained in 5 patients (8.7% of the total). The average event time since ePatch registration started was 2.4 days (range 1 to 5 days). Conclusions The ePatch® is a new extended Holter device offering a simple, complete and efficient ECG monitoring system. This new technology is safe and has numerous advantages: spend less time on logistics and device handling, prepare your patients efficiently, eliminate the need for additional consumables and constant battery replacement. The ECG monitor of our study is of 5 days, but it can be used for up to 14 days. It is designed to address the challenges of traditional Holter monitoring.

The application of quantitative telomerase activity measurement as an important indicator to monitor the cardiomyocyte differentiation process of human induced pluripotent stem cells under defined conditions

The construction of an in vitro differentiation system for human induced pluripotent stem cells (hiPSCs) has made exciting progress, but it is still of great significance to clarify the differentiation process. The use of conventional genetic and protein-labeled microscopes to observe or detect different stages of hiPSC differentiation is not specific enough and is cumbersome and time-consuming. In this study, in addition to analyzing the expression of gene/protein-related markers, we used a previously reported simple and excellent quantitative method of cellular telomerase activity based on a quartz crystal microbalance (TREAQ) device to monitor the dynamic changes in cellular telomerase activity in hiPSCs during myocardial differentiation under chemically defined conditions. Finally, by integrating these results, we analyzed the relationship between telomerase activity and the expression of marker genes/proteins as well as the cell type at each study time point. This dynamic quantitative measurement of cellular telomerase activity should be a promising indicator for monitoring dynamic changes in a stage of hiPSC differentiation and inducing cell types. This study provided a quantitative, dynamic and simple monitoring index for the in vitro differentiation process of hiPSC-CMs, which was a certain reference value for the optimization and improvement of the induction system.

Monitoring Training Load and Wellness of Female Footballers Transitioning Between Club and National Teams.

Brown, GA, Massard, T, Wignell, T, McCall, A, and Duffield, R. Monitoring training load and wellness of female footballers transitioning between club and national teams. J Strength Cond Res 37(11): 2235-2240, 2023-This study described the change in training and match internal loads, plus perceptual wellness during the transition between club and national teams (NTs) for female international footballers (soccer). Session count, session rating of perceived exertion (s-RPE), and perceptual wellness data were collected from 42 players from 1 NT between 2019 and 2021 for 7 NT tournaments. Data were collated into 7-day (1 week) microcycles to compare the 2 weeks before the first week of NT (transition in: NT-2, NT-1, NT1); and separately from the final NT week to the next 2 weeks in clubs (transition out: NTFinal, NT+1, NT+2). Data were analyzed using linear mixed-models, with session count, s-RPE or wellness as the outcome variable, week as the fixed effect, player name, and tournament as random effects. Tukey's adjusted post-hoc comparisons and Cohen's d effect sizes (ES) were compared between weeks. Significance level was set at p < 0.05. During the transition into NT, training and total session counts increased (p < 0.0001, ES > 0.67), training s-RPE increased (p = 0.012, ES = 0.35), whereas match count and s-RPE decreased (p < 0.05, ES < -0.34). During the transition out of NT, there were decreases in training session count (p < 0.05, ES < -0.37), match count, and total session count (p < 0.0001, ES < -0.86), and match and total s-RPE (p < 0.0001, ES < -0.63). Perceived wellness was not significantly different throughout (p > 0.05, ES = -0.34 to 0.35). Thus, training and match exposures differ between club and NTs, demonstrating these unique transitional periods require simple and effective load monitoring to inform training and recovery prescription when changing between club and NTs.

Discussion on the relationship between the power generation of single-crystal solar panels and various influencing factors

Solar panels absorb a large amount of light energy from sunlight and convert it into electrical energy for use. How to increase power and reduce energy loss in various aspects is also the focus of research and development to improve conversion efficiency of solar panels.Set up 3.6kW solar power generator by single-crystal material to produce the Direct Current (DC) power and it is converted into an Alternating current (AC) power through an inverter which meets the parallel conditions of AC power system and supplies loadings with the national power grid at the same time. Meanwhile, a simple monitoring system of solar power was established to rectify and record the data of solar power generation resources and related parameters by big data to analysis the settlement classification relationship. According to the power generation characteristics of the single-crystal solar panels of the power generation by sampling and related parameter data can be used to observe the relationship between solar power generation efficiency, density of illumination, temperature of panel and actual power generation. The power generation of single crystal solar cells is closely related to photos and temperatures and has a short delay effect by statistics theory and methods.

How do sex-specific BMI trajectories shape diabetes risk? A longitudinal analysis of Indonesian adults

IntroductionSex is a critical predictor of body mass index (BMI) trajectory over the life span, playing a role in disparities in the risk of diabetes. While there is some evidence...

Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept

Abstract Sediment concentration (SC) monitoring has always been a pressing issue in water resource management, as many existing instruments still face challenges in accurately measuring due to environmental factors and instrument limitations. A robust technology is worth presenting to apply in the field site. This study firstly uses mean-absolute-error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and Nash–Sutcliffe efficiency coefficient (NSE) to describe the performance of the proposed convolutional neural network (CNN). Moreover, adapting the ensemble learning concept to compare the multiple machine learning (ML) approaches, the CNN presents the highest predicted accuracy, 91%, better than SVM (79%), VGG19 (63%) and ResNet50 (35%). As a result, the proposed CNN framework can appropriately apply the monitoring needs. The primary purpose is to develop a simple, accurate, and stable SC monitoring technology. Instead of some complex architectures, a simple and small neural network is adopted to implement real-time application (RTA). Via our design, such a traditional but critical issue can be improved to a new state. For example, by incorporating the concept of the Internet of Things (IoT) with our design, the distributed computing system for large-scale environmental monitoring can be realized quickly and easily.