Defined Threshold Value Research Articles

A physics-driven and machine learning-based digital twinning approach to transient thermal systems

PurposeIn this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.Design/methodology/approachTo achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.FindingsThe results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.Originality/valueThe present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

Professionals Evaluating Clients' Suitability for Digital Health and Social Care: Scoping Review of Assessment Instruments.

Increased digital health and social care services are generally considered to improve people's access to services. However, not everyone can equally access and use these resources. Health and social care professionals should assess clients' suitability for digital solutions, but to succeed, they need information about what to evaluate and how. This scoping review aimed to identify evaluation tools that professionals can use when assessing clients' suitability for digital health and social care. We summarized the dimensions and the practical usefulness of the instruments. The MEDLINE (Ovid), CINAHL, Web of Science, and ASSIA databases were searched in February 2023 following the Joanna Briggs Institute's Manual for Evidence Synthesis. Studies were included if they focused on health and social care clients and professionals, examined clients' suitability for using digital health or social care, and applied related assessment methods in the direct client work of professionals. Studies focusing primarily on instruments intended for research use without clear applicability to professionals' practical contexts were excluded. Details of the eligible studies were extracted, and qualitative content analysis according to the research objectives was performed. A total of 19 articles introducing 12 different assessment instruments intended for the health care context were included in the review. No instruments were found for evaluating the suitability for digital social care. The instruments contained 60 dimensions of the client's suitability for digital health, which reflected four perspectives: (1) skill-based suitability, (2) suitability based on general ability to maintain health, (3) suitability based on attitude and experience, and (4) suitability based on practical matters. The described practical usefulness of the instruments included professionals' possibility to (1) identify clients most in need of education and support, (2) direct and recommend the right clients for the right digital services, (3) ensure that clients can use digital health, (4) improve effectiveness and maximize the provision of digital health, (5) develop and redesign services, and (6) empower clients. Based on the diverse assessment instruments available and the dimensions they measure, there seems to be no comprehensive evaluation tool for assessing clients' prerequisites to use digital solutions. It is important to further develop comprehensive screening tools applicable to professionals' busy work (both in health and social care) with defined threshold values for suitability.

What you hear may not be what you see: Potential of citizen science methods to use bats as riverine forest quality indicators

AbstractMediterranean habitats will be one of the Eurasian ecosystems more strongly affected by Climate Change, especially their riverine systems. Monitoring these ecosystems, which are endemism hotspots and extremely sensitive to changes in rain regimes and extreme weather events like droughts, is of crucial importance. Decades of citizen science projects have proven their utility in highlighting ecological shifts and conservation action priority areas. The Bat Monitoring Programme (www.batmonitoring.org), for instance, has already been used to develop ecological indicators to evaluate the evolution and conservation status of Mediterranean ecosystems. However, using bats as ecological indicators for aquatic ecosystems has resulted in contradicting results, making its application a little controversial. In the present study, we compared two citizen science protocols (visual counting vs. passive acoustic monitoring) used in the Bat Monitoring Programme to test the utility of trawling bats as indicators of Mediterranean riverine habitat quality at both local and landscape scales. By doing so, we aimed to build a specific ecological indicator to determine habitat quality through visual and acoustic counts. Although both protocols presented similar positive significant responses to riverine forest quality, visual counts are suggested as the best sampling approach due to their simplicity and potential within citizen science projects. Moreover, for the first time, we defined threshold values of trawling bat activity to assign different levels of habitat quality to the sampled rivers. We applied them in NE Iberia to exemplify the benefits of using them in a Mediterranean region and discussed the potential, pros and cons of these two citizen science methodologies to establish a pan‐European river biomonitoring programme using trawling bats.

Methodology-related pseudohyperkalemia associated with marked muscle enzyme leakage in a dog.

As hyperkalemia may be life-threatening, it is critical to recognize artifactually increased potassium concentrations. Pseudohyperkalemia may occur in myopathies when using the VetScan2 analyzer (VS2), but the degree of pseudohyperkalemia and relationships relative to creatine kinase activity (CK) are unknown. We aimed to determine what degree of muscle enzyme leakage, as reflected by increased serum CK activity, results in cases with falsely elevated potassium concentrations when measured by the VS2. We also sought to establish if a linear relationship exists between potassium concentrations measured by the VS2 and CK activity. Serum samples from dogs with increased CK activity and with CK activity within the reference interval and without clinically relevant biochemical alterations were used to create diluted samples having various CK activities. Potassium concentrations and CK activities were determined on VS2 and Cobas c501 (Cobas) analyzers. Wilcoxon signed rank, Bland-Altman, and Passing-Bablok analyses were used to compare potassium concentrations generated by the VS2 and Cobas analyzers. Least squares regression analysis was performed to evaluate if a linear relationship exists between VS2 potassium concentrations and Cobas CK activities. Potassium concentrations from the VS2 were higher (median and standard deviation (SD) = 5.2 +/- 0.46 mmol/L) than those from the Cobas analyzer (4.4 +/- 0.35 mmol/L; P < 0.000), and a positive mean bias was found (mean difference = 0.774 mmol/L; 95% Confidence Interval (CI) = 0.706-0.842; limits of agreement = 0.21-1.34). Passing-Bablok regression showed a positive proportional bias for potassium concentrations on the VS2 compared with paired Cobas results (Slope = 1.328; 95% CI = 1.100-1.500) but did not reveal systematic bias (Intercept = -0.714; 95% CI = -1.46-0.265). Least squares regression analysis showed a poor non-significant relationship (R2 = 0.19) between potassium measured by the VS2 and CK measured by the Cobas analyzer. A defined threshold value of CK activity at which potassium concentration begins to falsely increase when measured on the VS2 was not established as data widely varied. A poor non-significant relationship between potassium concentrations and CK activities did not allow prediction of the threshold at which falsely increased potassium concentrations would be expected on the VS2.

The LSTM-based automated phishing detection driven model for detecting multiple attacks on Tor hidden services

Phishing is a major problem on darknets. Phishing is the practice of attacking an unaware person by pretending to be someone else to steal their digital data. In anonymous platforms such as the dark web or deep web of Tor, detecting the attacker or phishing attacks is a much more complicated practice. Generic phishing attacks can be easy to spot. Today’s challenge is detecting the various attacks in the anonymous network is very hard. The intelligent factor of attacks can bypass traditional detection solutions. To solve the problem of complications in the Tor Network, this work focuses on the development of automated detection of vulnerable attacks in phishing-based Tor hidden services. The proposed model initially divides the attack parameters into three categories into Class A, Class B, and Class C based on technical perspectives and some defined threshold values. Next, the class A attacks (i.e. top level domain and protocol similarity) attacks are detected by a random forest (RF) classifier. Then, the class B attacks can be identified by the convolutional neural network (CNN). Finally, the LSTM model is applied for the accurate classification of multiple attacks in the Tor network. The experimental validation of the proposed model is tested using the CIRCL and AIL datasets. The experimental values highlighted the promising performance of the proposed model over other methods with a maximum overall detection accuracy of 95.60% and 95.77% on CIRCL and AIL datasets respectively. Therefore, the proposed model effectively detects multiple attacks in the Tor network under dynamic and real-time environments.

IoT Based Military Task Force Health Parameters and GPS Position Tracking System using Raspberry PI

In this project we present an Internet of Things based Soldier Health Status Detection and Location Tracking System using Internet of Things for the safety purpose of the soldier. The soldier is the critical unit of the nation and their life is valuable. Lot of soldiers are facing many problems such as communication with the control room and no proper medical help at a proper time which leads to the death of the soldier. To minimize such cases, we have proposed a continuous alert system to track location and monitor the health of the soldier. The proposed system is very useful in detecting location of the soldier in real time using GPS and communicating the health status parameter continuously using GSM module embedded in microcontroller. The tiny sensors can be fixed to the Soldier body or dress of the soldier to detect body parameter and transmit the information to the control room and other solider when there is a low body rate or when it falls than the defined threshold value. The soldier can send an alert message to the guardian and control room for the help in the panic situation using an application. The control room/guardian also uses android application to request the location of the solider automatically in the panic situation. The soldier can also request for the nearest hospital information in the emergency.

Psychometric Properties of the Qualiskope-A for Measuring Patient Satisfaction with Outpatient Medical Treatment: Use in Oncology and Transferability to Inpatient Care

The Qualiskope-A is a German-language PREM (Patient Reported Experience Measure) which, with the help of 27 items allocated to four scales, enables measurement of patient satisfaction with outpatient medical treatment along four dimensions of patient satisfaction. This study examined whether the questionnaire delivers reliable results in an oncological population and whether its application can be extended to inpatient care. Required data was collected as part of the PIKKO study. Initially, descriptive statistics and internal consistency (Cronbach's alpha) of the PREM's scales were analyzed. In addition, a sub-sample that assessed the same doctor at two consecutive measurement time points was observed with regard to test-retest reliability (Spearman correlation (rs) between both measurement time points). The measurement model of the Qualiskope-A was then examined using confirmatory factor analysis. To test the transferability to inpatient care, measurement invariance with regard to outpatients and inpatients was computed. A total of 476 patients was included in the study. Every score of the Qualiskope-A showed a left-skewed distribution in the sample and revealed pronounced ceiling effects. Cronbach's alpha coefficients were consistently>0,8. Within the test-retest group (n=197), a strong correlation (rs>0,5) was observed between the measurement time points. The fit indices calculated using confirmatory factor analysis showed a good model fit (CFI=0,958; RMSEA=0,026; SRMR=0,040; every factor loadings>0,6). The fit indices, calculated as part of the investigation of measurement invariance, consistently met the defined threshold values. The Qualiscope-A shows good reliability in the examined oncological sample. It can be used in both outpatient and inpatient settings (no indications of non-invariance were found). Due to pronounced ceiling effects, however, the item scaling should be revised.

I-MEREC-T: Improved MEREC-TOPSIS scheme for optimal network selection in 5G heterogeneous network for IoT

The Internet of Things (IoT) requires seamless mobility and services with low power consumption, low packet loss ratio, ultra-low latency, and high reliability. Ultra-dense deployment of small coverage radio access networks improves network throughput, latency, and packet loss ratio but increases the frequency of vertical handovers. Optimal network selection to ensure ultra-reliable, low-latency, seamless connectivity to provide massive machine-type communication to support a wide range of services for the emergence of IoT and Industry 5.0 through the next generation heterogeneous networks is one of the most challenging fields of research. The best network selection is one of the most crucial phases of the vertical handover decision algorithm in an ultra-dense heterogeneous network. Inadequate network selection can lead to failed handovers and ping pong handovers, which drastically affect the quality of services and the quality of experience. In this article, we have developed a new improved method based on improved removal effects of criteria- technique for order performance by similarity to ideal solution (I-MEREC-TOPSIS) network selection scheme. It is based on the removal effect of alternatives’ attributes. It is an intelligent integration of the I-MEREC weight method of alternative criteria and the TOPSIS alternative ranking multi-attribute decision-making technique. In the proposed scheme, optimal network selection is not only dependent on the best performance score of candidate networks but also on the statistically defined threshold value of servicing and target networks. In comparison to CRiteria Importance Through Intercriteria Correlation (CRITIC) TOPSIS, Entropy-TOPSIS, Statistical Variance Procedure(SVP) TOPSIS, and MEREC-TOPSIS, the proposed scheme has reduced ping-pong handovers and handover failures.

Feasibility and Preliminary Effectiveness of Behavioral Activation for Patients with Cancer and Depression in Japan.

Though the effectiveness of behavioral activation (BA) for patients with cancer and depression were reported, there is no evidence in Japan. This study aimed at examining the feasibility and preliminary effectiveness of BA for patients with cancer and depression in Japan. This pre-post study without a control group was conducted in patients with cancer and depression in Japan. The program completion rate was compared with those of previous studies to examine feasibility. To examine the preliminary effectiveness, outcomes were evaluated four times: before and immediately after the program, and two weeks and three months after the program ended. The primary outcome was the remission rate of depression using the 17-item version of the GRID Hamilton Rating Scale for Depression (HAMD17). Secondary outcomes were self-reported depression, anxiety, quality of life, changes in behavior, values, and perceived reward of activity and environmental factors. Pre- and post-program data were compared using paired-samples t-tests, and data obtained at four time points were analyzed using one-way repeated-measures analysis of variance. Of the 68 patients recruited from February 2018 to January 2022, 32 were registered. The completion rate was 75% (24/32), which was similar to previous studies. The total HAMD17 score significantly improved after the program. The remission rate of depression was 62.5% (20/32), which was above the defined threshold value (30%). All but two secondary outcomes significantly improved after the program (p < 0.05). The feasibility and preliminary effectiveness of BA for patients with cancer and depression in Japan were suggested.

Corynebacterium megadyptis sp. nov. with two subspecies, Corynebacterium megadyptis subsp. megadyptis subsp. nov. and Corynebacterium megadyptis subsp. dunedinense subsp. nov. isolated from yellow-eyed penguins.

Novel Corynebacterium strains, 3BT and 7BT, were isolated from the oral cavities of young chicks of yellow-eyed penguins (hoiho), Megadyptes antipodes. A polyphasic taxonomic characterization of these strains revealed chemotaxonomic, biochemical and morphological features that are consistent with those of the genus Corynebacterium. The 16S rRNA gene sequence similarity values between the strains and their closest phylogenetic neighbour, Corynebacterium ciconiae CCUG 47525T were 99.07 %, values that are in line with their phylogenomic positions within the evolutionary radiation of the genus Corynebacterium. Digital DNA-DNA hybridization values and average nucleotide identities between the genome sequences of the two strains and related Corynebacterium species were well below the defined threshold values (70 and 95-96 %, respectively) for prokaryotic species delineation. The genome size of these strains varied between 2.45-2.46 Mb with G+C content 62.7-62.9 mol%. Strains 3BT and 7BT were Gram-stain positive bacilli that were able to grow in presence of 0-10 % (w/v) NaCl and at temperature ranging between 20-37 °C. The major fatty acids (>15 %) were C16 : 0 and C18 : 1 ω9c, and the mycolic acid profile included 32-36 carbon atoms. We propose that these strains represent a novel species, Corynebacterium megadyptis sp. nov. with 3BT (=DSM 111184T=NZRM 4755T) as the type strain. Phylogenomically, strains 3BT and 7BT belong to two lineages with subtle differences in MALDI-TOF spectra, chemotaxonomic profiles and phenotypic properties. The fatty acid profile of strain 3BT contains C18 : 0 as a predominant type (>15 %), which is a minor component in strain 7BT. Strain 7BT can oxidize N-acetyl-d-glucosamine, l-serine, α-hydroxy-butyric acid, l-malic acid, l-glutamic acid, bromo-succinic acid and l-lactic acid, characteristics not observed in strain 3BT. Therefore, we propose that these strains represent two subspecies, namely Corynebacterium megadyptis subsp. megadyptis subsp. nov. (type strain, 3BT=DSM 111184T=NZRM 4755T) and Corynebacterium megadyptis subsp. dunedinense subsp. nov. (type strain, 7BT=DSM 111183T=NZRM 4756T).

For quantitative criteria in alien species assessment

Impact or risk assessments of alien species can use qualitative criteria (such as verbally described categories) or quantitative criteria (numerically defined threshold values of empirically measurable quantities). According to a common misconception, the use of qualitative criteria in invasion biology is justified by uncertainty in the available data. Yet qualitative criteria have the effect of increasing uncertainty. In contrast, assessments using quantitative criteria are testable, transparent, highly repeatable and comparable. Most of these characteristics do not even depend on the availability of numerical data. Although quantitative criteria do not necessarily make assessments correct, they do make them correctable, which is the benchmark of science.

The aquatic particle number quandary

Optical surveys of aquatic particles and their particle size spectra have become important tools in studies of light propagation in water, classification of water masses, and the dynamics of trophic interactions affecting particle aggregation and flux. Here, we demonstrate that typical settings used in image analysis vastly underestimate particle numbers due to the particle – gel continuum. Applying a wide range of threshold values to change the sensitivity of our detection system, we show that macrogels cannot be separated from more dense particles, and that a true particle number per volume cannot be ascertained; only relative numbers in relation to a defined threshold value can be reported. A quandary thus presents itself between choosing a detection threshold low enough to accurately record orders of magnitude more particles on one hand or selecting a higher threshold to yield better image quality of plankton on the other. By observing the dynamics of coagulation and dissolution steps unique to cation-bridged gels abundant in aquatic systems, we find naturally occurring gels, and microscopic particles attached to them, to cause the ill-defined particle numbers. In contrast, the slopes in particle number spectra remained largely unaffected by varying sensitivity settings of the image analysis. The inclusion of fainter particles that are not typically captured by imaging systems provides a window into the true microscale spatial heterogeneity at scales relevant to small plankton organisms and processes that are dependent on particle density such as surface-associated chemical reactions as well as particle coagulation and aggregation dynamics.

Prognostic utility of left ventricular global longitudinal strain in patients with systemic amyloidosis

Abstract Background Myocardial deposition of amyloid proteins results in restrictive cardiomyopathy. Left ventricular global longitudinal strain (GLS) has emerged as a sensitive measure for detecting subclinical cardiac dysfunction over traditional echocardiographic parameters. However, multiple studies have provided differing conclusions regarding prognostic utility of impaired GLS in patients with systemic amyloidosis. Purpose We conducted a systematic review and meta-analysis to evaluate whether impaired GLS was associated with increased mortality or major adverse cardiovascular events (MACE) in patients with systemic amyloidosis. Methods We performed a literature search of Embase, Medline and Web of Science databases to identify studies that reported the association of GLS with clinical outcomes in patients with systemic amyloidosis (light chain or TTR amyloidosis). Outcomes of interest included all-cause mortality and MACE, defined as a composite of death or heart transplant or heart failure hospitalization. Unadjusted and adjusted hazard ratio (uHR and aHR respectively) were pooled using a random effects model. Heterogeneity among the studies was assessed using the Higgins I2 value. Results Out of 2139 initial citations, 28 observational studies with a total of 2713 patients were included in the analysis. The mean age ranged between 58–78 years and 62% of the patients were male. Most patients had cardiac amyloidosis (83%) and light-chain amyloidosis accounted for 69% of cases. Mean follow-up ranged between 1 and 5 years. GLS was significantly higher (less negative) (mean difference (MD) −3.69 [−5.94, −1.44], I2=87, p&amp;lt;0.01) in non-survivors compared with survivors. Similarly, patients who experienced MACE had a significantly higher mean GLS (MD −3.22, [−5.21, −1.22,], I2=82, p&amp;lt;0.01]. The risk of both mortality and MACE increased significantly for every −1% increase in GLS. In unadjusted models, a GLS above the defined threshold value was associated with a significantly higher risk of mortality (uHR: 1.66 [1.22, 5.21], I2=85.2, p&amp;lt;0.01) and MACE (uHR: 2.24 [1.28, 3.92], I2=39, p&amp;lt;0.01). In multivariable models an increase in GLS by −1% was an independent predictor of mortality (aHR: 1.09 [1.01,1.16], I2=53, p=0.02) and MACE (aHR: 1.24 [1.14,1.36], I2=0, p&amp;lt;0.01). Conclusion In patient with amyloidosis, the baseline left ventricular GLS may help identify patients with a higher risk of mortality and MACE. Funding Acknowledgement Type of funding sources: None.

A novel approach to incorporate investor’s preference in fuzzy multi-objective portfolio selection problem using credibility measure

Investment decision making is usually a multi-objective optimization problem in an uncertain environment. In a real-life scenario, an investor aims to choose a portfolio based on his/her preferences for each objective. Credibility theory has been attributed as one of the most effective ways to model uncertain portfolio attributes. LR fuzzy numbers based credibilistic moments have been used successfully in the recent past to model portfolio selection problems in uncertain environments. Traditionally, the preferences on portfolio attributes are reflected as user defined threshold values. This work proposes a novel way to integrate the investor’s preferences in the portfolio selection model. Investor’s preferences are specified as LR fuzzy numbers corresponding to the portfolio’s expected return and illiquidity distributions. The portfolio models formulated in this study attempt to attain them to the best possible extent. Four new models are presented considering uncertain portfolio attributes, namely, fuzzy return and fuzzy illiquidity. The models are solved using the MIBEX-SM Genetic Algorithm (GA) and ECE methodology. In order to demonstrate the efficient working of the proposed models, practical applications of portfolio selection problem using historical time series data of National Stock Exchange (NSE) is presented. Credibilistic Sharpe Ratio (CSR) has been used to compare the efficiency of models among themselves as well as with the Nifty−50 index.

Optimization of Microchannels and Application of Basic Activation Functions of Deep Neural Network for Accuracy Analysis of Microfluidic Parameter Data.

The fabrication of microflow channels with high accuracy in terms of the optimization of the proposed designs, minimization of surface roughness, and flow control of microfluidic parameters is challenging when evaluating the performance of microfluidic systems. The use of conventional input devices, such as peristaltic pumps and digital pressure pumps, to evaluate the flow control of such parameters cannot confirm a wide range of data analysis with higher accuracy because of their operational drawbacks. In this study, we optimized the circular and rectangular-shaped microflow channels of a 100 μm microfluidic chip using a three-dimensional simulation tool, and analyzed concentration profiles of different regions of the microflow channels. Then, we applied a deep learning (DL) algorithm for the dense layers of the rectified linear unit (ReLU), Leaky ReLU, and Swish activation functions to train and test 1600 experimental and interpolation of data samples which obtained from the microfluidic chip. Moreover, using the same DL algorithm, we configured three models for each of these three functions by changing the internal middle layers of these models. As a result, we obtained a total of 9 average accuracy values of ReLU, Leaky ReLU, and Swish functions for a defined threshold value of using the trial-and-error method. We applied single-to-five-fold cross-validation technique of deep neural network to avoid overfitting and reduce noises from data-set to evaluate better average accuracy of data of microfluidic parameters.

Identification of New Sources for Earliness and Low Grain Moisture at Harvest through Maize Landraces’ Test-Cross Performance

Early maturity is a highly important factor in the interrelations between yield, grain moisture, and plant density, contributing to cost-efficient maize production. Landraces conserved in gene banks present a promising basis for enriching the diversity of early maize breeding material. To start and speed up the mobilization of the maize genetic resources maintained in the ex situ Maize Research Institute Zemun Polje gene bank collection, which are currently scattered, little studied, and underused, 63 landraces were selected as new potential sources of early maturity; their test-cross performance with two divergent early testers was evaluated. The majority of the landraces with a prevailed flint type (29) exhibited heterosis for yield when crossed with the Iowa Stiff Stalk Synthetic—Iowa Dent tester (102NS), out of which 20 top crosses expressed grain moisture below the defined threshold value (21.1%). The best performing landraces can be used as a starting point for a new pre-breeding programme for the broadening of flint maize breeding material. In parallel, nine landraces expressed simultaneous heterosis when crossed with the flint tester (14NS), exhibiting grain moisture above the threshold value. A simultaneous heterotic effect with two divergent inbred testers implies the existence of an independent heterotic pool. These findings will contribute to the broadening of maize breeding material for early maturity and low grain moisture at harvest, which are important goals in maize breeding.

FLOOD MONITORING AND EARLY WARNING SYSTEM USING RASPBERRY PI

The humans are still not able to battle the natural calamities besides huge development in technologies. The fact is that the natural calamities canneither be abolished nor be prevented. But the technology has been developed gigantically in order toprevent loss of life. This project is totally based on informing the civilians about the upcoming flood so that they can evacuate the danger area before the flood hits. For detecting the rise in water level Ultrasonic Sensor and Water Level Sensor is used. For detecting the change in humidity and temperatureHumidity and Temperature Sensor is used. The data from the DTH11 and HC-SR04 is read by the microcomputer and analyze the data in order to detectthe level of water. If the level of water is less than the defined threshold value then the microcomputer turnsthe LED and buzzer on. Furthermore, the data obtained from the microcomputer is uploaded to the database. The values of the sensors updating in real time can be monitored in database table. The content of the database table is now linked with the web API (Application Programming Interface) and trigger is set.And now when the level of water crossesthe thresholdvalue the trigger istriggered and the web API sends theSMS to the phone number registered to it.

Machine learning algorithm for precise prediction of 2'-O-methylation (Nm) sites from experimental RiboMethSeq datasets.

Analysis of epitranscriptomic RNA modifications by deep sequencing-based approaches brings an essential contribution to the general knowledge on their precise locations and relative stoichiometry in cellular RNAs. To reveal RNA modifications, several analytical approaches have been proposed, including antibody-driven enrichment, analysis of RT-signatures and specific chemical treatments. However, analysis and interpretation of these massive datasets, especially for low abundant cellular RNAs (e.g. mRNA and lncRNA) is not easy nor straightforward, since the insufficient specificity and selectivity are leading to massive false-positive and false-negative identifications. The main issue in the application of these methods relies on a subjective classification of potentially modified positions, mostly based on arbitrarily defined threshold values for different scores. Such approach using pre-defined scores' values was revealed to be appropriate for limited complexity datasets (for tRNA and/or rRNA analysis), but application to longer reference sequences requires much better classification algorithms. In this work we applied a machine learning algorithm (Random Forest, RF) to create a predictive model for analysis of 2'-O-methylated sites in RNA using RiboMethSeq datasets. Model's training was performed on a large collection of human rRNA datasets with well-known modification profiles and the performance of the prediction was assessed using experimentally defined profiles for other eukaryotic rRNAs (S.cerevisiae and A.thaliana). Application of this Random Forest prediction model for detection of other RNA modifications and to more complex datasets is discussed.

Computational intelligence-enabled prediction and communication mechanism for IoT-based autonomous systems

Autonomous systems and the Internet of Things (IoT) have become more sophisticated research areas and entered successfully into various daily living activities such as smart homes & buildings, autonomous cars, drones, robots, etc. A crucial and essential aspect of these systems is the precision and accuracy of the decision-making process, i.e., the decision support system. Likewise, developing a completely autonomous system is an open research problem. This paper proposes a computational intelligence-based prediction and communication mechanism for the independent system where IoT is used as a data collection tool. Initially, energy gauge (EG) devices collect helpful information about neighboring devices in the IoT networks. Then, information about the potential relaying devices is broadcasted by the concerned EG device, which uses every member device to adjust routing path(s) in the autonomous system. Furthermore, every EG device has an embedded computational intelligent decision support system that is used to precisely predict the criticality of a neighboring device (preferably relay) in the autonomous systems. Therefore, every device must ensure data transmission via the most reliable path(s), i.e., avoiding critical devices if possible. A device is assumed critical if either its residual energy or received signal strength indicator value is less than the defined threshold values for the autonomous systems. Additionally, the proposed mechanism has ensured a uniform traffic distribution of the transmitted packets in the autonomous system. The operational applicability of the proposed computational intelligence-enabled prediction mechanism in the autonomous system is verified by comparing it with the existing approaches. Simulation results show that the proposed scheme has enhanced the accuracy of the concerned autonomous systems more than other schemes.

Normal fault transmissibility characteristics under the transition condition of fault conduction and sealing observed in simulation experiments

In rift basins, normal faults conducting and sealing fluids are the main mechanisms for evaluating fault-related reservoirs, and the defined threshold value of fault conduction or sealing is based on statistics. However, fault transmissibility characteristics under a transition condition of fault conduction and sealing have not been clarified. This study used structural deformation and hydrocarbon charging as a foundation to quantitatively characterize fault transmissibility under the transition condition of fault conduction and sealing. This study examined faults occurring in sandstone–mudstone interbeds and used the fault throw and mudstone content as the main variables. The physical simulation results revealed that despite all models exhibiting simple “one-layer” fault architecture with only a fault core, their internal composition undergoes a sequential change from the mudstone drag, mudstone drag and sliding surface to the mudstone breccia and sliding surface. Similarly, when the fault is in the transition condition of fault conduction and sealing under the constraints of mudstone, a normal distribution is observed in terms of the number of oil-bearing layers, oil-bearing length, and oil-bearing area of the lateral fault conduction. However, when the shale smear factor (SSF) is too high, the “one-layer” fault architecture mainly comprises the mudstone breccia and sliding surface, which increases the vertical fault transmission and reduces the lateral fault conduction. Conversely, an excessively small SSF, where the fault zone is a “one-layer” fault architecture comprising mainly the mudstone drag or mudstone drag and sliding surface, will restrict the vertical fault transmission and reduce the lateral fault conduction. When the lateral fault conduction near the inflection point of the normal distribution reaches its threshold, the superposition of mudstone along the fault and the presence of mudstone breccia in the fault zone become critical for decelerating the vertical fault transmission and accelerating the lateral fault conduction. The study of normal fault transmissibility characteristics under the transition condition of fault conduction and sealing provides a solid theoretical foundation for the risk assessment of fault-related hydrocarbon accumulation.