Critical Value Research Articles

Optimization of Cu2O Thickness to Enhance Photocatalytic Properties of Electrodeposited Cu2O/FTO Photoanode

Currently, n-type cuprous oxide (Cu2O) is a promising material as photocatalyst because of its energy gap of 2 eV that absorbs visible light up to a wavelength of 600 nm. As a photoelectrode, the thickness of Cu2O is crucial, where the improper thickness may worsen the photocatalytic properties. This work aimed to enhance the photocatalytic properties of Cu2O electrodeposited on fluorine-doped tin oxide (FTO), called Cu2O/FTO, by optimizing the Cu2O thickness. The thickness of Cu2O was controlled by adjusting the deposition time in the electrochemical deposition of Cu2O/FTO. By changing the deposition time from 5 to 45 min, the morphology of Cu2O changed from a leaf-like shape to an irregular facet shape with highly dense coverage, and the average thickness increased from 370 to 1100 nm. The increasing Cu2O thickness resulted in the increasing light absorption. The Cu2O/FTO demonstrated anodic photocurrent, which increased with the Cu2O thickness up to a threshold value of 1000 nm (35 min deposition time). At a thickness of 1000 nm, Cu2O/FTO achieved the highest photocurrent (150 and 58 µA under irradiation of 365 and 470 nm, respectively) due to the highly dense morphology and high absorption. In addition, with a thickness of 1000 nm, the charge diffusion was still good. Further, the increase of Cu2O film thickness higher than 1000 nm caused low photocatalytic properties even though the morphology was highly dense, and the absorption was the highest. This condition could be due to the relatively too-high resistance of Cu2O that caused poor charge diffusion. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

A research on an improved fuzzy approximate entropy algorithm for EMG-based shoulder and neck muscle fatigue detection

A robust muscle fatigue algorithm plays a pivotal role in depicting the degree of muscle fatigue in both time-series EMG signal graphs and spectral graphs, aligning with human perception. While the fuzzy approximate entropy (fApEn) algorithm has been enhanced from the foundation of approximate entropy (ApEn) through the incorporation of fuzzy affiliation, concerns persist regarding the threshold value and the algorithm’s application range. This study extracts EMG signals across varied time durations and head-down angles, employing enhanced signal preprocessing techniques and optimizing the fApEn algorithm. Furthermore, real-time fatigue perceptions of subjects were recorded using the rating of perceived exertion. Experimental outcomes reveal that the EMG signal, post-wavelet analysis preprocessing, demonstrates promising noise reduction capabilities. Notably, the fApEn algorithm exhibits considerable enhancements through the identification of an optimal threshold using the gradient descent algorithm and a machine learning strategy.

Deep multimodal spatio-temporal Harris Hawk Optimized Pose Recognition framework for self-learning fitness exercises

Human pose recognition from videotapes has become an emerging research topic for tracking human movements. The objective of this work is to develop a deep multimodal Spatio-Temporal Harris Hawk Optimized Pose Recognition (STHHO-PR) framework for self-learning fitness exercises. The presented STHHO-PR framework uses audio modality and visual modality to classify the different poses. In audio modality, the VGG-16 network paradigm is used to extract the audio traits for fitness pose recognition. In visual modality, Harris Hawks Optimization (HHO) along with the Minimum Cross Entropy (MCE) method is employed to find out the optimum threshold values for body parts segmentation. These segmented body parts highlight the human joint points that are connected through the skeletonization process to extract the skeletal information. The extracted spatio-temporal features from audio modality and visual modality are optimally fused and used in the classification process. Weighted Majority Voting Ensemble (WMVE) classifier is adopted to build the classification model. This work is experimented with yoga videos acquired from publicly available datasets. The results show that the presented STHHO-PR framework outperforms other state-of-art procedures in terms of prediction accuracy.

Comprehensive identification and risk assessment of regulated and emerging mycotoxins in infant foods and related raw materials and risk management advice: A case study of an infant food company in China

This study evaluated muti-mycotoxins in 199 samples including processed infant foods and raw materials collected randomly from an infant food company and assessed their role in dietary exposure in infants and young children via probabilistic risk assessment. Approximately 79.6 % (74/93) of the processed infant foods and 65.1 % (69/106) of the raw materials were contaminated by mycotoxins, with a mean occurrence level of 3.66–321.8 µg/kg. Deoxynivalenol (DON) and tenuazonic acid (TeA) were the more prevalent mycotoxins detected, based on their higher frequencies and levels across samples. Co-occurrence of more than two mycotoxins was detected in 61.3 % (57/93) of the processed infant foods and 53.8 % (57/106) of the raw materials. Wheat flour and derived products (e.g., infant noodles and infant biscuits) were contaminated with higher contamination levels and a greater variety of mycotoxins than other samples (e.g., infant cereal and rice grains). The estimated daily exposure to OTA, DON, ZEN, and TEN was lower than the corresponding reference health-based guidance values, indicating acceptable health risks. However, the estimated dietary exposure to alternariol monomethyl ether (AME), alternariol (AOH), and tenuazonic acid (TeA) exceeded the corresponding thresholds of toxicological concern values, indicating potential dietary intake risks. Among the various samples, cereals and cereal-based infant foods emerged as the primary contributors to mycotoxin exposure. Further research is advised to address the uncertainties surrounding the toxicity associated with emerging Alternaria mycotoxins and to conduct cumulative risk assessments concerning multiple mycotoxin exposure in infants and young children.

The Effect of Crystal Arthropathy on the Diagnostic Criteria of Native Septic Arthritis.

Distinguishing between septic arthritis and crystal arthropathy flares can be challenging. The purpose of this study was to determine how the presence of synovial crystals affects the diagnostic criteria of septic arthritis. A retrospective review identified patients undergoing joint aspirations to rule out native septic arthritis. Differences between septic arthritis presenting with and without synovial crystals were analyzed. A receiver-operating characteristic curve was plotted for laboratory markers to determine the area under the curve, or diagnostic accuracy, for septic arthritis and to evaluate thresholds that maximized sensitivity and specificity. There were 302 joint aspirations in 267 patients. Septic arthritis was diagnosed in 17.9% (54/302). Patients with synovial crystals were less likely to have septic arthritis (4.2% [5/119] vs. 26.8% [49/183], P < 0.0001). Septic arthritis in patients with no synovial crystals was associated with fever and a higher synovial white blood cell (WBC) count, synovial polymorphonuclear cell percentage (PMN%), serum WBC, and C-reactive protein (CRP) ( P < 0.05). Septic arthritis in patients with synovial crystals was only associated with inability to bear weight and a higher synovial WBC and CRP ( P < 0.05). Synovial PMN% was considered nondiagnostic of septic arthritis (area under the curve 0.56) in patients with crystals while synovial WBC and CRP had acceptable (0.76) and excellent (0.83) diagnostic utility, respectively. The WBC and CRP value thresholds that maximized sensitivity and specificity for septic arthritis were greater in patients with crystals (21,600 vs. 17,954 cells/μL and 125 vs. 69 mg/L, respectively). The presence of synovial crystals reduced the likelihood of septic arthritis and altered the laboratory diagnostic criteria. PMN% was nondiagnostic in the setting of synovial crystals.

Quantitative evaluation of meniscus injury using synthetic magnetic resonance imaging.

Magnetic resonance imaging (MRI) can diagnose meniscal lesions anatomically, while quantitative MRI can reflect the changes of meniscal histology and biochemical structure. Our study aims to explore the association between the measurement values obtained from synthetic magnetic resonance imaging (SyMRI) and Stoller grades. Additionally, we aim to assess the diagnostic accuracy of SyMRI in determining the extent of meniscus injury. This potential accuracy could contribute to minimizing unnecessary invasive examinations and providing guidance for clinical treatment. Total of 60 (n=60) patients requiring knee arthroscopic surgery and 20 (n=20) healthy subjects were collected from July 2022 to November 2022. All subjects underwent conventional MRI and SyMRI. Manual measurements of the T1, T2 and proton density (PD) values were conducted for both normal menisci and the most severely affected position of injured menisci. These measurements corresponded to the Stoller grade of meniscus injuries observed in the conventional MRI. All patients and healthy subjects were divided into normal group, degeneration group and torn group according to the Stoller grade on conventional MRI. One-way analysis of variance (ANOVA) was employed to compare the T1, T2 and PD values of the meniscus among 3 groups. The accuracy of SyMRI in diagnosing meniscus injury was assessed by comparing the findings with arthroscopic observations. The diagnostic efficiency of meniscus degeneration and tear between conventional MRI and SyMRI were analyzed using McNemar test. Furthermore, a receiver operating characteristic curve (ROC curve) was constructed and the area under the curve (AUC) was utilized for evaluation. According to the measurements of SyMRI, there was no statistical difference of T1 value or PD value measured by SyMRI among the normal group, degeneration group and torn group, while the difference of T2 value was statistically significant among 3 groups (P=0.001). The arthroscopic findings showed that 11 patients were meniscal degeneration and 49 patients were meniscal tears. The arthroscopic findings were used as the gold standard, and the difference of T1 and PD values among the 3 groups was not statistically significant, while the difference of T2 values (32.81±2.51 of normal group, 44.85±3.98 of degeneration group and 54.42±3.82 of torn group) was statistically significant (P=0.001). When the threshold of T2 value was 51.67 (ms), the maximum Yoden index was 0.787 and the AUC value was 0.934. The measurement values derived from SyMRI could reflect the Stoller grade, illustrating that SyMRI has good consistency with conventional MRI. Moreover, the notable consistency observed between SyMRI and arthroscopy suggests a potential role for SyMRI in guiding clinical diagnoses.

CovXtreme : MATLAB software for non-stationary penalised piecewise constant marginal and conditional extreme value models

The covXtreme software provides functionality for estimation of marginal and conditional extreme value models, non-stationary with respect to covariates, and environmental design contours. Generalised Pareto (GP) marginal models of peaks over threshold are estimated, using a piecewise-constant representation for the variation of GP threshold and scale parameters on the (potentially multidimensional) covariate domain of interest. The conditional variation of one or more associated variates, given a large value of a single conditioning variate, is described using the conditional extremes model of Heffernan and Tawn (2004), the slope term of which is also assumed to vary in a piecewise constant manner with covariates. Optimal smoothness of marginal and conditional extreme value model parameters with respect to covariates is estimated using cross-validated roughness-penalised maximum likelihood estimation. Uncertainties in model parameter estimates due to marginal and conditional extreme value threshold choice, and sample size, are quantified using a bootstrap resampling scheme. Estimates of environmental contours using various schemes, including the direct sampling approach of Huseby et al. 2013, are calculated by simulation or numerical integration under fitted models. The software was developed in MATLAB for metocean applications, but is applicable generally to multivariate samples of peaks over threshold data. The software and case study data can be downloaded from GitHub, with an accompanying user guide.

Singular value thresholding two-stage matrix completion for drug sensitivity discovery

Incomplete data presents significant challenges in drug sensitivity analysis, especially in critical areas like oncology, where precision is paramount. Our study introduces an innovative imputation method designed specifically for low-rank matrices, addressing the crucial challenge of data completion in anticancer drug sensitivity testing. Our method unfolds in two main stages: Initially, the singular value thresholding algorithm is employed for preliminary matrix completion, establishing a solid foundation for subsequent steps. Then, the matrix rows are segmented into distinct blocks based on hierarchical clustering of correlation coefficients, applying singular value thresholding to the largest block, which has been proved to possess the largest entropy. This is followed by a refined data restoration process, where the reconstructed largest block is integrated into the initial matrix completion to achieve the final matrix completion. Compared to other methods, our approach not only improves the accuracy of data restoration but also ensures the integrity and reliability of the imputed values, establishing it as a robust tool for future drug sensitivity analysis.

Three-dimensional copula framework for early warning of agricultural droughts using meteorological drought and vegetation health conditions

ABSTRACT This study develops an early warning system for crop yield (CY) failure based on meteorological drought and vegetation health conditions. The framework combines three drought indices – the Standardized Precipitation Evapotranspiration Index (SPEI), standardized Normalized Difference Vegetation Index (stdNDVI), and standardized CY (stdCY) values – using copulas. Datasets for five major wheat-producing cities in Turkey between 2000 and 2022 are used for analysis. Results indicate that the time periods used to calculate SPEI and NDVI are critical in determining agricultural drought and CY conditions. The critical threshold values for SPEI and NDVI, with a 10% probability of causing agricultural drought, are found to be ~0.28 and ~0.42, respectively. Using a three-dimensional copula model resulted in more precise CY simulations than a two-dimensional model. The validation efforts showed that all of the observed CYs fell within the simulated range, indicating the robustness of the methodology in capturing drought impacts on CY conditions.

Hierarchical Fuzzy MCDA Multi-Risk Model for Detecting Critical Urban Areas in Climate Scenarios

One of the issues of greatest interest in urban planning today concerns the evaluation of the most vulnerable urban areas in the presence of different types of climate hazards. In this research, a hierarchical fuzzy MCDA model is implemented on a GIS-based platform aimed at detecting the urban areas most at risk in the presence of heatwave and pluvial flooding scenarios. The proposed model aims to detect the urban areas most vulnerable to both the two climatic phenomena and the two types of hazards as independent events; it partitions the physical component of an urban settlement into two subsystems: buildings and open spaces, and it determines the criticality of a subzone of the urban area of study by evaluating the vulnerabilities of the two subsystems to the two phenomena. The use of a hierarchical fuzzy MCDA model facilitates the modeling of the two subsystems and the assessment of their vulnerability to the two phenomena, and it provides a computationally fast tool for detecting critical urban areas. The model was tested on a study area made up of the districts of the central-eastern area of the city of Naples (Italy); it was divided into subzones made up of individual census areas. The most critical areas are represented by the subzones with criticality values higher than a specific threshold.

Analysis of Electroosmotically Modulated Peristaltic Transport of Third Grade Fluid in a Microtube Considering Slip-Dependent Zeta Potential

Abstract The present study investigates electro-osmotically modulated peristaltic transport of third-grade fluid through a microtube taking into consideration the intricate coupling of zeta potential and hydrodynamic slippage. The analytical results encompass the mathematical expressions for dimensionless electrical potential distribution as well as series solutions for stream function and axial pressure gradient up to first order utilizing the perturbation technique for small Deborah number coupled with the Cauchy product for infinite series. Critical values and ranges of wavelength have been obtained where the axial pressure gradient vanishes. Moreover, pivotal values and ranges of wavelength have also been noted for the invariance of pressure gradient with respect to Deborah number as well as Debye–Hückel parameter. Trapping phenomenon has also been investigated by contours of streamlines wherein the zones of recirculation or trapped boluses are formed predominantly near the microtube walls. Additionally, the relative enhancement in hydrodynamic slippage amplifies the trapped bolus size, whereas a diminishing behavior on bolus size is observed by the electro-osmotic parameter.

A Low-Speed Position Sensorless Scheme From Standstill for Low-Cost SRM Drives Based on Triple Current Slope Difference Threshold

In this paper, a triple current slope difference threshold based low-speed sensorless scheme for low-cost SRM drives is proposed. Firstly, slope based methods, including the current slope difference method, are proposed and the advantages in terms of anti-interference ability over traditional instantaneous value based methods have been clarified. Secondly, the traditional single/double threshold methods and corresponding outgoing/incoming phase injection types are introduced and comprehensively compared. The limitations of conventional methods while adopted in low-cost SRM drives are stated. Accordingly, to solve the problem that the current cannot be obtained during the demagnetization stage in the resistor-based current sampling scheme, the triple current slope difference threshold method is proposed, whose 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> threshold indicates the zero-current position. Thirdly, a general threshold value determination method considering the three-phase unbalanced inductances is proposed, which only requires the unsaturated inductance vs. rotor position characteristics that is very easy to be obtained. The proposed method only needs to store six threshold values in the microprocessor and needs no complex calculations and additional hardware, which can be realized in low-cost drive systems. Finally, the experiments of both the steady-state and starting transient-state under over-load conditions are implemented to verify the feasibility and accuracy of the proposed method.

M3W: Multistep Three-Way Clustering.

Three-way clustering has been an active research topic in the field of cluster analysis in recent years. Some efforts are focused on the technique due to its feasibility and rationality. We observe, however, that the existing three-way clustering algorithms struggle to obtain more information and limit the fault tolerance excessively. Moreover, although the one-step three-way allocation based on a pair of fixed, global thresholds is the most straightforward way to generate the three-way cluster representations, the clusters derived from a pair of global thresholds cannot exactly reveal the inherent clustering structure of the dataset, and the threshold values are often difficult to determine beforehand. Inspired by sequential three-way decisions, we propose an algorithm, called multistep three-way clustering (M3W), to address these issues. Specifically, we first use a progressive erosion strategy to construct a multilevel structure of data, so that lower levels (or external layers) can gather more available information from higher levels (or internal layers). Then, we further propose a multistep three-way allocation strategy, which sufficiently considers the neighborhood information of every eroded instance. We use the allocation strategy in combination with the multilevel structure to ensure that more information is gradually obtained to increase the probability of being assigned correctly, capturing adaptively the inherent clustering structure of the dataset. The proposed algorithm is compared with eight competitors using 18 benchmark datasets. Experimental results show that M3W achieves superior performance, verifying its advantages and effectiveness.

Thermodynamical analysis with extended phase transition of AdS hairy black hole in gravitational decoupling theory

This article explores various thermodynamical aspects and the extended phase space of the AdS hairy black hole within the context of gravitational decoupling. This study focused on thermodynamic properties such as geometrical mass, Hawking temperature, entropy, specific heat, Gibbs free energy (both locally and globally) and P-V criticality at the horizon radius. The thermodynamic stability of the AdS hairy black hole has been established based on its positive heat capacity. The analysis of Gibbs free energy is utilized to investigate the behavior of the first-order phase transition. Thermodynamic pressure, denoted by P, is related to the cosmological constant by the formula P=−Λ8π . In addition, a numerical investigation has been carried out to examine the equation of the state parameter and its critical values.

Flow cytometric minimal residual disease measurement accounting for cytogenetics in children with non-high-risk acute lymphoblastic leukemia treated according to the ALL-MB 2008 protocol.

Quantitative measurement of minimal residual disease (MRD) is the "gold standard" for estimating the response to therapy in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Nevertheless, the speed of the MRD response differs for different cytogenetic subgroups. Here we present results of MRD measurement in children with BCP-ALL, in terms of genetic subgroups with relation to clinically defined risk groups. A total of 485 children with non-high-risk BCP-ALL with available cytogenetic data and MRD studied at the end-of-induction (EOI) by multicolor flow cytometry (MFC) were included. All patients were treated with standard-risk (SR) of intermediate-risk (ImR) regimens of "ALL-MB 2008" reduced-intensity protocol. Among all study group patients, 203 were found to have low-risk cytogenetics (ETV6::RUNX1 or high hyperdiploidy), while remaining 282 children were classified in intermediate cytogenetic risk group. For the patients with favorable and intermediate risk cytogenetics, the most significant thresholds for MFC-MRD values were different: 0.03% and 0.04% respectively. Nevertheless, the most meaningful thresholds were different for clinically defined SR and ImR groups. For the SR group, irrespective to presence/absence of favorable genetic lesions, MFC-MRD threshold of 0.1% was the most clinically valuable, although for ImR group the most informative thresholds were different in patients from low-(0.03%) and intermediate (0.01%) cytogenetic risk groups. Our data show that combining clinical risk factors with MFC-MRD measurement is the most useful tool for risk group stratification of children with BCP-ALL in the reduced-intensity protocols. However, this algorithm can be supplemented with cytogenetic data for part of the ImR group.

Validation of a Field-Portable, Handheld Real-Time PCR System for Detecting Pseudogymnoascus destructans, the Causative Agent of White-Nose Syndrome in Bats.

White-nose syndrome (WNS), caused by the fungus Pseudogymnoascus destructans, has decimated bat populations across North America. Despite ongoing management programs, WNS continues to expand into new populations, including in US states previously thought to be free from the pathogen and disease. This expansion highlights a growing need for surveillance tools that can be used to enhance existing monitoring programs and support the early detection of P. destructans in new areas. We evaluated the feasibility of using a handheld, field-portable, real-time (quantitative) PCR (qPCR) thermocycler known as the Biomeme two3 and the associated field-based nucleic acid extraction kit and assay reagents for the detection of P. destructans in little brown bats (Myotis lucifugus). Results from the field-based protocol using the Biomeme platform were compared with those from a commonly used laboratory-based qPCR protocol. When using dilutions of known conidia concentrations, the lowest detectable concentration with the laboratory-based approach was 108.8 conidia/mL, compared with 1,087.5 conidia/mL (10 times higher, i.e., one fewer 10× dilution) using the field-based approach. Further comparisons using field samples suggest a high level of concordance between the two protocols, with positive and negative agreements of 98.2% and 100% respectively. The cycle threshold values were marginally higher for most samples using the field-based protocol. These results are an important step in establishing and validating a rapid, field-assessable detection platform for P. destructans, which is urgently needed to improve the surveillance and monitoring capacity for WNS and support on-the-ground management and response efforts.

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

Heterogeneous wireless sensor network (HWSN) is a trending technology in both the industrial and academic sectors, consisting of a large number of interconnected sensors. However, higher energy consumption and delay are significant drawbacks of this technology in applications such as military, healthcare, and industrial automation. The main objective of this research is to enhance the energy efficiency of HWSN using a clustering technique. In this article, a novel approach, namely power optimization and hybrid data aggregation (POHDA), is proposed to address these challenges in HWSN. POHDA-HWSN focuses on power optimization and congestion avoidance through effective CH selection using hybrid data aggregation based on parameters such as residual energy, distance, mobility, threshold value of the node, and latency. By weight-based effective cluster head (CH) selection, the energy consumption, end-to-end delay, and overhead during communication are reduced in this network. The POHDA-HWSN approach considers specific parameters to compare the results and outcomes with earlier research such as HCCS-WSN, FMCA-WSN, and APCC-WSN. The results prove that the proposed POHDA-HWSN approach achieves higher energy efficiency and delivery ratio.

Analytical expressions for the first passage time distribution and hit distribution in two and three dimensions

The distribution of the time elapsed before a random variable reaches a threshold value for the first time, called the first passage time (FPT) distribution, is a fundamental characteristic of stochastic processes. Here, by solving the standard macroscopic diffusion equation, we derive analytical expressions for the FPT distribution of a diffusing particle hitting a spherical object in two dimensions (2D) and three dimensions (3D) in the course of unrestricted diffusion in open space. In addition, we calculate, analytically, the angular dependence of the FPT, known as the hit distribution. The analytical results are also compared to simulations of the motions of a random walker on a discrete lattice. This topic could be of wide pedagogical interest because the FPT is important not only in physics but also in chemistry, biology, medicine, agriculture, engineering, and finance. Additionally, the central equations often appear in physics and engineering with only trivial variations, making the solution techniques widely applicable.

IoT based weather monitoring system for temperature, humidity, rainfall and seismic detection in real time

This paper presents the design and implementation of an IoT-based weather monitoring system enhanced with seismic detection capabilities, employing Node MCU microcontroller and the Blynk mobile application platform. The system incorporates various sensors including a rain sensor, Light light-dependent resistor (LDR), a DHT11 sensor for temperature and humidity measurement, and an accelerometer (MPU6050) for earthquake detection. The hardware components are interconnected with the Node MCU microcontroller, facilitating real-time data acquisition from the sensors. Programming of the Node MCU is conducted using the Arduino IDE, enabling data transmission to the Blynk app via Wi-Fi connectivity. The Blynk app serves as the user interface, allowing seamless visualization and monitoring of weather parameters and seismic activity on smartphones. Key functionalities of the system include periodic data collection from sensors, transmission of sensor data to the Blynk server, and updating of graphical representations in the Blynk app interface. The accelerometer sensor is utilized to detect changes in acceleration, indicative of seismic disturbances. Threshold values are defined to trigger alerts within the Blynk app in the event of potential seismic activity. The implementation undergoes rigorous testing to ensure accuracy, reliability, and responsiveness under diverse environmental conditions. Considerations such as network stability, power efficiency, and data security are addressed to enhance system performance and longevity. The system demonstrates promising capabilities for real-time weather monitoring and seismic event detection, offering valuable insights for disaster preparedness and environmental analysis. IoT-based weather monitoring system integrated with seismic detection provides an effective solution for comprehensive environmental monitoring, leveraging the power of IoT technology and mobile applications for enhanced data accessibility and analysis. Further advancements and optimizations can be explored to extend the functionality and scalability of the system for broader applications in environmental science and disaster management.

Performance Evaluation of Collision Avoidance for Multi-node LoRa Networks based on TDMA and CSMA Algorithm

LoRa can be used as the communication technology for the intelligent monitoring system. However, LoRa is usually used for outdoor communication. The usage of LoRa as indoor communication has many challenges. One of the challenges is that collision happens when using standard LoRa devices with only one channel. The algorithms based on TDMA (Time-division Multiple Access) and CSMA (Carrier-sense Multiple Access) protocols can be used to address this challenge. These two algorithms can be modified by applying the device that is the center of the network (gateway) as a central control and the data transmitter (sensor node) as a passive device. The test was conducted on the Intelligent Laboratory Monitoring System to evaluate this design on a multi-node LoRa network. RSSI testing proves that the distance and building interference affect the signal strength or RSSI of sensor nodes, so the average RSSI value is -73.75 with an RSSI threshold of value -106. The gateway successfully collected each sensor node data with an average success of about 64.953%. The experiment results show the success rate of the CSMA-based algorithm is 10% versus 100% in TDMA-based algorithm; the delay is 4125 ms for CSMA-based and 428.3 ms for TDMA-based. This result means that the CSMA-based algorithm is more complex, takes more time to process the data than the TDMA-based algorithm, has a low success rate, and is more prone to collisions.