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Fulltext A dataset of tree heights in mangrove and non-mangrove trees in Malaysia derived from multiple measurement methods

Saliu IS, Wolswijk G, Satyanarayana B, Fisol MAB, Decannière C, Lucas R, et al.

Data Brief, 2020 Dec;33:106386.
PMID: 33102654 DOI: 10.1016/j.dib.2020.106386

The dataset contains tree height data collected in 200 mangrove and non-mangrove trees sampled in various sites in Malaysia. Different height measurement methods were performed, including visual measurements (stick, thumb rule) and precision field instruments (clinometer, laser rangefinder and altimeter), which were compared against benchmark values obtained using an unmanned aerial vehicle (UAV) and a Leica distometer. The core data have been analysed and interpreted in the paper by Saliu et al. ''An accuracy analysis of mangrove tree height mensuration using forestry techniques, hypsometers and UAVs '' [1], in which the accuracy of each method for tree height measurement was discussed.

Matched MeSH terms: Data Collection
Fulltext Ranking negative emissions technologies under uncertainty

Ng WY, Low CX, Putra ZA, Aviso KB, Promentilla MAB, Tan RR

Heliyon, 2020 Dec;6(12):e05730.
PMID: 33364497 DOI: 10.1016/j.heliyon.2020.e05730

Existing mitigation strategies to reduce greenhouse gas (GHG) emissions are inadequate to reach the target emission reductions set in the Paris Agreement. Hence, the deployment of negative emission technologies (NETs) is imperative. Given that there are multiple available NETs that need to be evaluated based on multiple criteria, there is a need for a systematic method for ranking and prioritizing them. Furthermore, the uncertainty in estimating the techno-economic performance levels of NETs is a major challenge. In this work, an integrated model of fuzzy analytical hierarchy process (AHP) and interval-extended Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed to address the multiple criteria, together with data uncertainties. The potential of NETs is assessed through the application of this hybrid decision model. Sensitivity analysis is also conducted to evaluate the robustness of the ranking generated. The result shows Bioenergy with Carbon Capture and Storage (BECCS) as the most optimal alternative for achieving negative emission goals since it performed robustly in the different criteria considered. Meanwhile, energy requirement emerged as the most preferred or critical criterion in the deployment of NETs based on the decision-maker. This paper renders a new research perspective for evaluating the viability of NETs and extends the domains of the fuzzy AHP and interval-extended TOPSIS hybrid model.

Matched MeSH terms: Data Collection
Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine

Nataraj SK, Paulraj MP, Yaacob SB, Adom AHB

J Med Signals Sens, 2020 11 11;10(4):228-238.
PMID: 33575195 DOI: 10.4103/jmss.JMSS_52_19

Background: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain-computer interface, i.e., thought-controlled wheelchair navigation system with communication assistance.
Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures ("minimum," "mean," "maximum," and "standard deviation") were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm.
Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.

Matched MeSH terms: Data Collection
Fulltext Rapid Discrimination of Halal and Non-halal Pharmaceutical Excipients by Fourier Transform Infrared Spectroscopy and Chemometrics

A Razak NF, Abd Karim RH, Jamal JA, Said MM

J Pharm Bioallied Sci, 2020 Nov;12(Suppl 2):S752-S757.
PMID: 33828373 DOI: 10.4103/jpbs.JPBS_364_19

Introduction: The appendage of "halal" to a product is not just a guarantee that the product is permitted for Muslims, but it has also become favorable lifestyle choice globally. However, the expansion of halal pharmaceutical market was hindered by lack of global halal standards for pharmaceutical ingredients and product integrity analytical methodology.
Objective: This work aimed to explore the possibility of using Fourier-transform infrared (FTIR) spectroscopy and chemometrics to develop multivariate models to authenticate the "halal-ity" of pharmaceutical excipients with controversial halal status (e.g., magnesium stearate).
Materials and Methods: The FTIR spectral fingerprints of the substance were used to build principal component analysis (PCA) models. The effects of different spectral pretreatment processes such as auto-scaling, baseline correction, standard normal variate (SNV), first, and second derivatives were evaluated. The optimization of the model performance was established to ensure the sensitivity, specificity, and accuracy of the predicted models.
Results: Significant peaks corresponding to the properties of the compound were identified. For both bovine and plant-derived magnesium stearate, the peaks associated can be seen within the regions 2900cm-1 (C-H), 2800cm-1 (CH3), 1700cm-1 (C=O), and 1000-1300cm-1 (C-O). There was not much difference observed in the FTIR raw spectra of the samples from both sources. The quality and accuracy of the classification models by PCA and soft independent modeling classification analogy (SIMCA) have shown to improve using spectra optimized by first derivative followed by SNV smoothing.
Conclusion: This rapid and cost-effective technique has the potential to be expanded as an authentication strategy for halal pharmaceuticals.

Matched MeSH terms: Data Collection
Fulltext An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Jameel SM, Hashmani MA, Rehman M, Budiman A

Sensors (Basel), 2020 Oct 14;20(20).
PMID: 33066579 DOI: 10.3390/s20205811

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.

Matched MeSH terms: Data Collection
Observation of the Production of Three Massive Gauge Bosons at sqrt[s]=13 TeV

Sirunyan AM, Tumasyan A, Adam W, Ambrogi F, Bergauer T, Dragicevic M, et al.

Phys Rev Lett, 2020 Oct 09;125(15):151802.
PMID: 33095594 DOI: 10.1103/PhysRevLett.125.151802

The first observation is reported of the combined production of three massive gauge bosons (VVV with V=W, Z) in proton-proton collisions at a center-of-mass energy of 13 TeV. The analysis is based on a data sample recorded by the CMS experiment at the CERN LHC corresponding to an integrated luminosity of 137 fb^{-1}. The searches for individual WWW, WWZ, WZZ, and ZZZ production are performed in final states with three, four, five, and six leptons (electrons or muons), or with two same-sign leptons plus one or two jets. The observed (expected) significance of the combined VVV production signal is 5.7 (5.9) standard deviations and the corresponding measured cross section relative to the standard model prediction is 1.02_{-0.23}^{+0.26}. The significances of the individual WWW and WWZ production are 3.3 and 3.4 standard deviations, respectively. Measured production cross sections for the individual triboson processes are also reported.

Matched MeSH terms: Data Collection
An improved simplified rules Fuzzy Logic Speed Controller method applied for induction motor drive

Talib MHN, Ibrahim Z, Abd Rahim N, Zulhani R, Nordin N, Farah N, et al.

ISA Trans, 2020 Oct;105:230-239.
PMID: 32475537 DOI: 10.1016/j.isatra.2020.05.040

Fuzzy Logic Speed Controller (FLSC) has been widely used for motor drive due to its robustness and its non-reliance to real plant parameters. However, it is computationally expensive to be implemented in real-time and prone to the fuzzy rules' selection error which results in the failure of the drive's system. This paper proposes an improved simplified rules method for Fuzzy Logic Speed Controller (FLSC) based on the significant crisp output calculations to address these issues. A systematic procedure for the fuzzy rules reduction process is first described. Then, a comprehensive evaluation of the activated crisp output data is presented to determine the fuzzy dominant rules. Based on the proposed method, the number of rules was significantly reduced by 72%. The simplified FLSC rule is tested on the Induction Motor (IM) drives system in which the real-time implementation was carried out in the dSPACE DS1103 controller environment. The simulation and experimental results based on the proposed FLSC have proved the workability of the simplified rules without degrading the motor performance.

Matched MeSH terms: Data Collection
Fulltext Effect of Addition of Polyaniline on Polyethylene Oxide and Polyvinyl Alcohol for the Fabrication of Nanorods

Al-Hazeem NZ, Ahmed NM

ACS Omega, 2020 Sep 08;5(35):22389-22394.
PMID: 32923796 DOI: 10.1021/acsomega.0c02802

For the first time, the fabrication of novel nanorods by the addition of polyaniline (PANI) to polyethylene oxide (PEO) and polyvinyl alcohol (PVA) polymers through electrospinning method is investigated. Field emission scanning electron microscopy observations reveal the formation of nanofibers and nanorods having diameters in the range of 26.87-139.90 nm and 64.11-122.40 nm, respectively, and lengths in the range of 542.10 nm to 1.32 μm. Photoluminescence (PL) analysis shows the presence of peaks which are characteristic of isotactic polymers (363-412, 529-691 nm), 412-529 nm for PVA/PEO and 363-691 nm for PVA/PEO/PANI. PL spectra also show peak bonding at a wavelength of 552 nm. Manufacture of nanorods by electrospinning method gives better options for controlling the diameter and length of nanorods.

Matched MeSH terms: Data Collection
Hybrid Harmony Search-Artificial Intelligence Models in Credit Scoring

Goh RY, Lee LS, Seow HV, Gopal K

Entropy (Basel), 2020 Sep 04;22(9).
PMID: 33286758 DOI: 10.3390/e22090989

Credit scoring is an important tool used by financial institutions to correctly identify defaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are the Artificial Intelligence techniques that have been attracting interest due to their flexibility to account for various data patterns. Both are black-box models which are sensitive to hyperparameter settings. Feature selection can be performed on SVM to enable explanation with the reduced features, whereas feature importance computed by RF can be used for model explanation. The benefits of accuracy and interpretation allow for significant improvement in the area of credit risk and credit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM to perform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tune the hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achieve comparable results as the standard HS with a shorter computational time. MHS consists of four main modifications in the standard HS: (i) Elitism selection during memory consideration instead of random selection, (ii) dynamic exploration and exploitation operators in place of the original static operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional termination criteria to reach faster convergence. Along with parallel computing, MHS effectively reduces the computational time of the proposed hybrid models. The proposed hybrid models are compared with standard statistical models across three different datasets commonly used in credit scoring studies. The computational results show that MHS-RF is most robust in terms of model performance, model explainability and computational time.

Matched MeSH terms: Data Collection
Fulltext Robust Wavelength Selection Using Filter-Wrapper Method and Input Scaling on Near Infrared Spectral Data

Silalahi DD, Midi H, Arasan J, Mustafa MS, Caliman JP

Sensors (Basel), 2020 Sep 03;20(17).
PMID: 32899292 DOI: 10.3390/s20175001

The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research. Nonetheless, this process allows the improvement in the chemical interpretability by emphasizing the chemical entities related to the chemical parameters of samples. With the complexity in the dataset, it may be possible that irrelevant wavelengths are still included in the multivariate calibration. This yields the computational process to become unnecessary complex and decreases the accuracy and robustness of the model. In multivariate analysis, Partial Least Square Regression (PLSR) is a method commonly used to build a predictive model from NIR spectral data. However, in the PLSR method and common commercial chemometrics software, there is no standard wavelength selection procedure applied to screen the irrelevant wavelengths. In this study, a new robust wavelength selection procedure called the modified VIP-MCUVE (mod-VIP-MCUVE) using Filter-Wrapper method and input scaling strategy is introduced. The proposed method combines the modified Variable Importance in Projection (VIP) and modified Monte Carlo Uninformative Variable Elimination (MCUVE) to calculate the scale matrix of the input variable. The modified VIP uses the orthogonal components of Partial Least Square (PLS) in investigating the informative variable in the model by applying the amount of variation both in X and y{SSX,SSY}, simultaneously. The modified MCUVE uses a robust reliability coefficient and a robust tolerance interval in the selection procedure. To evaluate the superiority of the proposed method, the classical VIP, MCUVE, and autoscaling procedure in classical PLSR were also included in the evaluation. Using artificial data with Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp, the study shows that the proposed method offers advantages to improve model interpretability, to be computationally extensive, and to produce better model accuracy.

Matched MeSH terms: Data Collection
Fulltext Translation and validation of the Arabic version of the General Medication Adherence Scale (GMAS) in Saudi patients with chronic illnesses

Naqvi AA, Mahmoud MA, AlShayban DM, Alharbi FA, Alolayan SO, Althagfan S, et al.

Saudi Pharm J, 2020 Sep;28(9):1055-1061.
PMID: 32922135 DOI: 10.1016/j.jsps.2020.07.005

Purpose: The study aimed to translate and validate the Arabic version of General Medication Adherence Scale (GMAS) in Saudi patients with chronic diseases.
Methods: A multi-center cross sectional study was conducted for a month in out-patient wards of hospitals in Khobar, Dammam, Makkah, and Madinah, Saudi Arabia. Patients were randomly selected from a registered patient pools at hospitals and the item-subject ratio was kept at 1:20. The tool was assessed for factorial, construct, convergent, known group and predictive validities as well as, reliability and internal consistency of scale were also evaluated. Sensitivity, specificity, and accuracy were also evaluated. Data were analyzed using SPSS v24 and MedCalc v19.2. The study was approved by concerned ethics committees (IRB-129-25/6/1439) and (IRB-2019-05-002).
Results: A total of 282 responses were received. The values for normed fit index (NFI), comparative fit index (CFI), Tucker Lewis index (TLI) and incremental fit index (IFI) were 0.960, 0.979, 0.954 and 0.980. All values were >0.95. The value for root mean square error of approximation (RMSEA) was 0.059, i.e., <0.06. Hence, factorial validity was established. The average factor loading of the scale was 0.725, i.e., >0.7, that established convergent validity. Known group validity was established by obtaining significant p-value <0.05, for the associations based on hypotheses. Cronbach's α was 0.865, i.e., >0.7. Predictive validity was established by evaluating odds ratios (OR) of demographic factors with adherence score using logistic regression. Sensitivity was 78.16%, specificity was 76.85% and, accuracy of the tool was 77.66%, i.e., >70%.
Conclusion: The Arabic version of GMAS achieved all required statistical parameters and was validated in Saudi patients with chronic diseases.

Matched MeSH terms: Data Collection
Measurements of tt[over ¯]H Production and the CP Structure of the Yukawa Interaction between the Higgs Boson and Top Quark in the Diphoton Decay Channel

Sirunyan AM, Tumasyan A, Adam W, Ambrogi F, Bergauer T, Dragicevic M, et al.

Phys Rev Lett, 2020 Aug 07;125(6):061801.
PMID: 32845700 DOI: 10.1103/PhysRevLett.125.061801

The first observation of the tt[over ¯]H process in a single Higgs boson decay channel with the full reconstruction of the final state (H→γγ) is presented, with a significance of 6.6 standard deviations (σ). The CP structure of Higgs boson couplings to fermions is measured, resulting in an exclusion of the pure CP-odd structure of the top Yukawa coupling at 3.2σ. The measurements are based on a sample of proton-proton collisions at a center-of-mass energy sqrt[s]=13 TeV collected by the CMS detector at the LHC, corresponding to an integrated luminosity of 137 fb^{-1}. The cross section times branching fraction of the tt[over ¯]H process is measured to be σ_{tt[over ¯]H}B_{γγ}=1.56_{-0.32}^{+0.34} fb, which is compatible with the standard model prediction of 1.13_{-0.11}^{+0.08} fb. The fractional contribution of the CP-odd component is measured to be f_{CP}^{Htt}=0.00±0.33.

Matched MeSH terms: Data Collection
Fulltext Dataset of computed N-value and factual N-value traced for Soil Subsurface Profiling

Jusoh H, Sabariah Binti Abd Manan T, Beddu S, Osman SBS, Jusoh MNH, Mohtar WHMW, et al.

Data Brief, 2020 Aug;31:105868.
PMID: 32637485 DOI: 10.1016/j.dib.2020.105868

Soil requires load bearing impact assessment for stability. Therefore, this study aims to utilize the multi-channel analysis surface wave (MASW) for soil subsurface investigation and profiling around Peninsular Malaysia. The standard penetration test (SPT) was conducted for comparison between factual N-value and computed N-value from shear wave velocity (Vs ) obtained from MASW using the Imai and Tonouchi equation. The correlation coefficient (R) and coefficient of determination, (R2 ), showed strong relationship between factual N-value and computed N-value. The model of Vs and factual N-value data distribution is non-normal but the analyzed relationship shows a significant level of p-value < 0.05. The R2 for each location of Vs -N-value relationship are ranging from 0.5 to 0.9.

Matched MeSH terms: Data Collection
Fulltext 3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging

Mathai A, Guo N, Liu D, Wang X

Sensors (Basel), 2020 Jul 29;20(15).
PMID: 32751165 DOI: 10.3390/s20154211

Transparent object detection and reconstruction are significant, due to their practical applications. The appearance and characteristics of light in these objects make reconstruction methods tailored for Lambertian surfaces fail disgracefully. In this paper, we introduce a fixed multi-viewpoint approach to ascertain the shape of transparent objects, thereby avoiding the rotation or movement of the object during imaging. In addition, a simple and cost-effective experimental setup is presented, which employs two single-pixel detectors and a digital micromirror device, for imaging transparent objects by projecting binary patterns. In the system setup, a dark framework is implemented around the object, to create shades at the boundaries of the object. By triangulating the light path from the object, the surface shape is recovered, neither considering the reflections nor the number of refractions. It can, therefore, handle transparent objects with a relatively complex shape with the unknown refractive index. The implementation of compressive sensing in this technique further simplifies the acquisition process, by reducing the number of measurements. The experimental results show that 2D images obtained from the single-pixel detectors are better in quality with a resolution of 32×32. Additionally, the obtained disparity and error map indicate the feasibility and accuracy of the proposed method. This work provides a new insight into 3D transparent object detection and reconstruction, based on single-pixel imaging at an affordable cost, with the implementation of a few numbers of detectors.

Matched MeSH terms: Data Collection
Fulltext Analysis of tuberculosis program management in primary health care

Juliasih NN, Soedarsono, Sari RM

Infect Dis Rep, 2020 07 07;12(Suppl 1):8728.
PMID: 32874460 DOI: 10.4081/idr.2020.8728

Background: This study discusses the analysis of Tuberculosis (TB) program management at the Perak Timur Primary Health Care (PHC) and the Sawahan PHC in Surabaya. Early detection and adequate treatment can prevent transmission and improve control programs.
Objective: This study aims to analyze management of the tuberculosis program at PHCs in Surabaya.
Methods: The research method used is qualitative research. Data collection was done by interviewing tuberculosis officers about TB program and carrying out observations at the PHCs.
Results: The study showed that case finding in the Perak Timur PHC and the Sawahan PHC was passive-active. The Perak Timur PHC has facilities for rapid molecular testing, while the Sawahan PHC have to go to a center for Health Laboratory if rapid molecular testing is needed. In terms of treatment, patients at the Perak Timur PHC would come according to an agreement with TB officer, while at the Sawahan PHC, patients have to come every Monday. Officer at the Perak Timur PHC tended to accommodate the needs of TB patients compared to officer at the Sawahan PHC. The level of adherence to taking medication in two PHCs is good but there are a number of patients who have not really understood the frequency of taking medication.
Conclusion: Generally, both PHCs have good TB program management but the Perak Timur PHC tends to be more flexible towards patients while the Sawahan PHC tends to be stricter towards patients.

Matched MeSH terms: Data Collection
The impact of adult trauma triage training on decision-making skills and accuracy of triage decision at emergency departments in Malaysia: A randomized control trial

Ghazali SA, Abdullah KL, Moy FM, Ahmad R, Hussin EOD

Int Emerg Nurs, 2020 07;51:100889.
PMID: 32622225 DOI: 10.1016/j.ienj.2020.100889

INTRODUCTION: Patients who visit emergency departments need to undergo a precise assessment to determine their priority and accurate triage category to ensure they receive the right treatment.
AIM: To identify the effect of triage training on the skills and accuracy of triage decisions for adult trauma patients.
METHOD: A randomized controlled trial design was conducted in ten emergency department of public hospitals. A total of 143 registered nurses and medical officer assistants who performed triage roles were recruited for the control group (n = 74) and the intervention group (n = 69). The skill and accuracy of triage decisions were measured two weeks and four weeks after the intervention group were exposed to the intervention.
RESULTS: There was a significant effect on the skill of triage decision-making between the control and the intervention group p

Matched MeSH terms: Data Collection
Fulltext A Precise Prediction Method for the Properties of API-Containing Tablets Based on Data from Placebo Tablets

Hayashi Y, Shirotori K, Kosugi A, Kumada S, Leong KH, Okada K, et al.

Pharmaceutics, 2020 Jun 28;12(7).
PMID: 32605318 DOI: 10.3390/pharmaceutics12070601

We previously reported a novel method for the precise prediction of tablet properties (e.g., tensile strength (TS)) using a small number of experimental data. The key technique of this method is to compensate for the lack of experimental data by using data of placebo tablets collected in a database. This study provides further technical knowledge to discuss the usefulness of this prediction method. Placebo tablets consisting of microcrystalline cellulose, lactose, and cornstarch were prepared using the design of an experimental method, and their TS and disintegration time (DT) were measured. The response surfaces representing the relationship between the formulation and the tablet properties were then created. This study investigated tablets containing four different active pharmaceutical ingredients (APIs) with a drug load ranging from 20-60%. Overall, the TS of API-containing tablets could be precisely predicted by this method, while the prediction accuracy of the DT was much lower than that of the TS. These results suggested that the mode of action of APIs on the DT was more complicated than that on the TS. Our prediction method could be valuable for the development of tablet formulations.

Matched MeSH terms: Data Collection
Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant

Ansari M, Othman F, El-Shafie A

Sci Total Environ, 2020 Jun 20;722:137878.
PMID: 32199382 DOI: 10.1016/j.scitotenv.2020.137878

Sewage treatment plants (STPs) keep sewage contamination within safe levels and minimize the risk of environmental disasters. To achieve optimum operation of an STP, it is necessary for influent parameters to be measured or estimated precisely. In this research, six well-known influent chemical and biological characteristics, i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD), Ammoniacal Nitrogen (NH3-N), pH, oil and grease (OG) and suspended solids (SS), were modeled and predicted using the Sugeno fuzzy logic model. The membership function range of the fuzzy model was optimized by ANFIS, the integrated Genetic algorithms (GA), and the integrated particle swarm optimization (PSO) algorithms. The results were evaluated by different indices to find the accuracy of each algorithm. To ensure prediction accuracy, outliers in the predicted data were found and replaced with reasonable values. The results showed that both integrated GA-FIS and PSO-FIS algorithms performed at almost the same level and both had fewer errors than ANFIS. As the GA-FIS algorithm predicts BOD with fewer errors than PSO-FIS and the aim of this study is to provide an accurate prediction of missing data, GA-FIS was only used to predict the BOD parameter; the other parameters were predicted by PSO-FIS algorithm. As a result, the model successfully could provide outstanding performance for predicting the BOD, COD, NH3-N, OG, pH and SS with MAE equal to 3.79, 5.14, 0.4, 0.27, 0.02, and 3.16, respectively.

Matched MeSH terms: Data Collection
Search for a Narrow Resonance Lighter than 200 GeV Decaying to a Pair of Muons in Proton-Proton Collisions at sqrt[s]=13 TeV

Sirunyan AM, Tumasyan A, Adam W, Ambrogi F, Bergauer T, Dragicevic M, et al.

Phys Rev Lett, 2020 Apr 03;124(13):131802.
PMID: 32302170 DOI: 10.1103/PhysRevLett.124.131802

A search is presented for a narrow resonance decaying to a pair of oppositely charged muons using sqrt[s]=13 TeV proton-proton collision data recorded at the LHC. In the 45-75 and 110-200 GeV resonance mass ranges, the search is based on conventional triggering and event reconstruction techniques. In the 11.5-45 GeV mass range, the search uses data collected with dimuon triggers with low transverse momentum thresholds, recorded at high rate by storing a reduced amount of trigger-level information. The data correspond to integrated luminosities of 137 and 96.6 fb^{-1} for conventional and high-rate triggering, respectively. No significant resonant peaks are observed in the probed mass ranges. The search sets the most stringent constraints to date on a dark photon in the ∼30-75 and 110-200 GeV mass ranges.

Matched MeSH terms: Data Collection
Fulltext Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Hannan MA, Lipu MSH, Hussain A, Ker PJ, Mahlia TMI, Mansor M, et al.

Sci Rep, 2020 Mar 13;10(1):4687.
PMID: 32170100 DOI: 10.1038/s41598-020-61464-7

State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.

Matched MeSH terms: Data Collection

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