SSE Values Research Articles

Biodegradation of low, medium and high molecular weight phthalate by Gordonia sp. in a batch system: Kinetics and phytotoxicity analyses

ABSTRACTPhthalic acid esters (PAEs) are highly toxic compounds and can disrupt the hormonal balance of human, animal, and aquatic organisms. Due to the hazardous nature of such compounds, their removal from constituent wastewater before discharging into the environment is mandatory. This study focused on the biodegradation of dimethyl phthalates (DMP), di-n-butyl phthalates (DBP), and di-n-octyl phthalates (DnOP) by Gordonia sp. in a batch system. Initially, five different concentrations of DBP, DMP, and DnOP (200–1000 mg/L) were chosen individually as the sole carbon source to examine their effect on the biodegradation and biomass growth of Gordonia sp. Complete degradation of DBP and DMP was achieved up to 1000 mg/L initial concentration within 96 h, whereas in case of DnOP, the degradation value was only 83.5% at 120 h for the same initial concentration. The experimental data were fitted into various substrate inhibition kinetic models, and accurate predicted values of degradation of all the three PAEs were obtained using the Tiesser model in comparison with other models, which yielded the highest and lowest R2 and SSE values of 0.99 and 0.02 × 10−4, respectively. In addition, the phytotoxicity of PAEs degraded samples was assessed and more than 50% germination index value was observed for DMP and DBP degraded sample which established the treatment efficiency of Gordonia sp. in degrading DMP and DBP. Hence, high DMP and DEP degradation and phytotoxicity removal efficiency of Gordonia sp. demonstrate its potential for the treatment of PAEs contaminated wastewater.

OPTIMIZATION OF K-MEANS CLUSTERING USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR GROUPING TRAVELER REVIEWS DATA ON TRIPADVISOR SITES

K-Means Algorithm can be used to group tourists based on reviews on tourist destination objects. This algorithm has a weakness that is sensitive to the determination of the initial centroid. The initial centroid that is determined at random will decreasing the level accuracy, often gets stuck at the local optimum, and gets a random solution. Optimization algorithms such as PSO can overcome this by determining the optimal initial centroid. The optimal number of clusters (K) will be determined using the Elbow method by calculating the SSE value of the resulting cluster. The average Silhouette Coefficient (SC) is used to measure the quality of the clusters produced by the K-Means Algorithm with and without the PSO Algorithm. This study uses secondary data obtained from the UCI Machine Learning Repository with the name Travel Reviews Data Set which consists of 980 records and 10 attributes. The test results show that K=2 is the optimal number of clusters. The K-Means and PSO Algorithm gives an average SC value of 0.300358 which is better than without the PSO Algorithm of 0.300076. The optimal PSO hyperparameter generated is the number of particles=30, \varphi_1=2.2, and {\ \varphi}_2=3 at maximum iteration of 100.

Onion skin–derived sorbent for the sequestration of methylparaben in contaminated aqueous medium

Carbon-based adsorbents were produced from onion skin waste for the adsorption of methylparaben from contaminated water. The biomass-derived carbon was characterized using various established analytical techniques. The microscopic examinations revealed micro- and mesoporous structures with a partially disordered network of the graphenic carbon-like multilayer structure, confirmed by XPS and Raman spectra. XRD analysis revealed that the biomass-derived carbon is largely amorphous with the graphitic phase also confirmed. Aside from the prominence of sp2 hybridized carbon, FTIR analysis shows the existence of moieties and functional groups that may facilitate the sorption of methylparaben or other organic pollutants if explored. The adsorption isotherm revealed that the multilayer adsorption model (Freundlich) best fits experimental data with an SSE value of 0.454. A complex adsorption process is suspected between methylparaben and OSDC, and the physicochemical properties of the sorbate and sorbent played a huge role in the sorption process. The plausible interactions include van der Waals, hydrophobic bonding, hydrogen bonding, π-π stacking, and pore-filling mechanisms, leading to a hysteretic sorption process. The optimal removal efficiency and adsorption maxima of ~ 100% and ~ 8200 mg/g are obtainable at optimum process conditions. Therefore, waste valorization and adsorption performance achieved in this study suggest a sustainable and cost-effective pathway for pollution remediation.

Evaluation of Mini Bibliometric Analysis, Moisture Ratio, Drying Kinetics, and Effective Moisture Diffusivity in the Drying Process of Clove Leaves using Microwave-Assisted Drying

Microwave-assisted drying (MAD) becomes a drying alternative for faster, high efficiency, and environmentally friendly. Clove plants are promising plantations because can be extracted into high-value essential oils. The purpose of this study was: (i) to evaluate the drying of clove leaves with MAD using six mathematical models, (ii) to investigate the effect of microwave power parameters and leaf size on clove leaf drying, and (iii) bibliometric analysis was used to prove the novelty of the study. Fresh clove leaves are grouped into large leaves (15.28±1.33 cm × 5.12±0.19 cm) and small leaves (14.00±1.36 cm × 4.09±0.39 cm) then dried with 4 different microwave powers namely 136, 264, 440, and 616 W. Six mathematical models (Page model, Verma model, Logarithmic model, Two-term Exponential model, Henderson and Pabis model, and Midilli model) were used to estimate the drying coefficient by following the non-linear regression method. The results showed that the Midilli model became the most accurate model to show the drying characteristics of clove leaves compared to 5 other confirmed mathematical models with SSE values ranging from 0.0001 - 0.0034. The k value of the Midilli model parameter indicates the MAD condition of the clove leaf with values ranging from 0.0002 - 0.0097. The De value indicates the moisture dispersal ability of the clove leaf with values that tend to fluctuate, namely around 4.6 × 10−12-9.9 × 10−11m2/s.

Mathematical Modelling of Dried Carica papaya Leaves

Background: Papaya leaves botanically classified as Carica papaya and are used medicinally in teas to aid in digestion. The Carica Papaya leaves are often considered as wastes, but they are actually beneficial for health. The study was aimed to achieve the suitable drying parameters of operation that makes the drying process to complete in less time without effecting the keeping quality of the product. Methods: The study was conducted using 3 drying methods which consisted of tray drying at temperatures 50°C, 60°C and 70°C, microwave drying at power levels 20% (180 Watts), 30% (270 Watts) and 40% (360 Watts), microwave vacuum drying at pressures of vacuum 400 mmHg, 500 mmHg and 600 mmHg and power levels 20% (180 Watts), 30% (270 Watts) and 40% (360 Watts). Twelve different mathematical drying models were compared based on their correlation coefficient, root mean square error and reduced chi-square to estimate the drying curves. Result: Experimental results showed that page model and weibulls distribution gave the best results, evidenced by the highest average correlation coefficient value of 0.9796, lowest average RMSE (Root mean square error) value of 0.0286 lowest average SSE value of 0.014 and the reduced average c² value of 0.0019. It is concluded that page model and weibulls distribution could sufficiently describe the tray drying, microwave and microwave vacuum drying respectively of carica papaya leaves.

PENGELOMPOKAN PRODUKSI TAMBAK GARAM DENGAN METODE CLUSTER K-MEANS DAN OPTIMASI CLUSTER MENGGUNAKAN ELBOW (STUDI KASUS: DINAS KELAUTAN KABUPATEN BANGKALAN)

Salt ponds in Madura are known by the Indonesian people as salt producers or it can be said that Madura is generally called a salt island, especially salt ponds in Bangkalan Regency in 2014 salt production increased from the previous year. But not long after that in 2020 and 2021 Salt Pond production decreased. The problem that occurs in the field of salt pond business in Kwanyar District, Bangkalan Regency is to analyze the problems that exist in salt ponds regarding the characteristics and potential of the salt pond business and the salt pond production system. And the problems that exist in the Maritime Service of Bangkalan Regency are difficult to classify salt pond production data. Responding to the problem of salt ponds in Bangkalan, a data mining system is needed to classify salt pond production. The grouping of salt ponds is grouped into 3 clusters. Where 3 groups (clusters) are used here, namely the need to increase production (cluster 1), it is enough to increase production (cluster 2), there is no need to increase production (cluster 3), which is useful for evaluating and providing solutions. The method used is the K-Means Clustering method, as well as finding an optimal group using Elbow Optimization. The results of the K-Means process of grouping the results obtained are group 1 = 65 data that need to increase production, group 2 = 55 data that simply needs to increase production, and group 3 = 21 data that doesn't need to increase production. Elbow Optimization Test to find the most optimal K, by conducting a 7 cluster trial. The result is K = 3 which is optimal from a comparison test with other Ks, looking at the resulting SSE and Elbow Garp values.

Effect of Turntable Rotation Rate on Drying Kinetics and Functional Properties of Lemon Peel during Microwave Drying

The aim of this study is to investigate the effect of the rotational rate of the turntable on drying kinetics of lemon peels and some functional and flow properties of lemon peel powders. Lemon peels were dried by microwave drying using different rates of rotation (0, 6.5, 9.5, and 12.5 rpm) at different microwave power levels (180W, 300W, 450W and 600W), and dried by oven drying and freeze-drying methods. Drying time was shortened by 72- 95% by microwave drying compared to oven drying. Microwave drying with rotation provided 5.6-23.8% reduction in drying time of peels compared to drying without rotation. Effect of rotation rate on drying time of lemon peels depended on the microwave power level. Page model provided lower SSE, RMSE, and higher R2 values within 5 different thin layer models. The effective moisture diffusivity value, ranging between 1.7x10-8 m2 s -1 -7.6x10-8 m2 s -1, was higher during microwave drying with rotation. The activation energy ranged between 21.3-22.7 W/g. Microwave drying provided higher bulk density, similar or lower water holding capacity and oil retention capacity values compared to freeze drying and oven drying. Freeze dried lemon peel powder had the lowest bulk density due to its porous structure. Microwave drying without rotation and the highest power level caused lower bulk density. At higher power levels, influence of turntable rotation on water holding capacity was more notable. Microwave drying technique can be used as alternative drying techniques to obtain high quality dried lemon peel powder if appropriate processing conditions are selected.

Application of PARAFAC Technique to Compare Fluorescence Property of Different Dissolved Organic Matters

Objectives : It provides a detailed procedure for analyzing dissolved organic matter (DOM) by the PARAFAC technique and aims to identify compare different fluorescent components of DOM with various water sources.Methods : PARAFAC modeling was performed based on the PARAFAC tutorial by Stedmon et al. [11] using a total of 160 samples originated from wetlands, river, wastewater treatment plant influent and effluent. The procedure includes data preprocessing, initial model creation, outlier analysis, split-half analysis, core consistency, and sum of squared error analysis (SSE). After finishing the model validation step, the samples were analyzed through the verified PARAFAC model.Results and Discussion : An initial model with 2 to 4 components was created, and as a result of outlier analysis, the four WWTP Influent samples were removed as outliers. As a result of SSE analysis, the model with two components has a relatively higher SSE value than the model with three or four components. In the case of core consistency, the model with 2, 3, and 4 components scored 99.6%, 94.9%, and 65.7%, respectively. As a result of the split-half analysis, the spectra of the model having the three components of the split data were the same, so it was finally determined that the number of three components was appropriate in the model. The DOM samples were analyzed by the developed PARAFAC model. It was confirmed that the ratio of humic components was higher in the order of WWTP effluent ≈ wetland > river > WWTP influent, and the ratio of protein components affected by microbial activity was in the order of WWTP influent > river > wetland ≈ WWTP effluent.Conclusion : In this paper, the procedure of PARAFAC modeling was described and the DOM fluorescence properties were compared. This research provides a practical method for increasing the utilization of the PARAFAC model in the water treatment research field. In addition, the result of the PARAFAC model is useful data to control DOM efficiently in water treatment facilities.

Prediction, Risk Assessment and Comparison of Selected Emerging Markets' Stock Indices During COVID-19 Pandemic Using the Coherent Measure

In this study, we modeled the log-return of three emerging markets' stock indices, namely, Shanghai SSE, Russia MOEX, and Bombay Stock Exchange Sensex using the generalized hyperbolic family of distributions. We found the generalized hyperbolic family of distributions as the best fit for describing the probability density based on AIC and likelihood ratio test. The coherent risk measure, i.e., the expected shortfall, predicted using the best fit probability distribution, was used as a market risk quantification metric. During the COVID-19 period, the Indian stock market showed maximum market risk, followed by the Russian. The Chinese market showed the least market risk. Our experiment demonstrated a significant (p = 0.000) difference in the three markets concerning the coherent risk at different probability levels from 0.001 to 0.05 in the COVID-19 period using the Jonckheere-Terpstra test. The coherent market risk increased substantially in the Indian and Russian markets during the COVID-19 pandemic compared to the pre-COVID-19 period. However, in the Chinese market, we found that the coherent risk decreased during the COVID-19 period compared to the pre-COVID-19 period. We carried out the empirical study using the adjusted daily closing values of SSE, MOEX, and Sensex from July 2018 to July 2021 and dividing the data sets into pre-COVID-19 and COVID-19 periods based on the first emergence of the COVID-19 case.

Noise pollution prediction and seasonal comparison in urban parks using a coupled GIS- artificial neural network model.

Noise pollution is a challenging environmental issue in densely built urban areas and requires a holistic understanding of its sources and alleviation processes. Taking Isfahan City in Iran as a typical case, this study developed a combined GIS-artificial neural network (ANN) model to predict the spatio-temporal contribution of low-width parks to poise pollution mitigation. The 30-min equivalent sound level was measured at 100 stations in six urban parks (with a total area of 55.84ha) under stable and controlled winter and summer conditions. The noise level predicting variables were hypothesized to be the area of vegetation cover; NDVI-based vegetation density and standard deviation (std); vegetation height; and road coverage measured within 100-, 200-, and 300-m radius buffer rings drown around each noise sampling station. These predictors were introduced to a multi-layer perceptron ANN model to identify and compare the most important noise alleviation variables among the selected predictors. The mean noise levels ranged from 67.23 to 70.57dB. The number of vehicles showed an insignificant temporal difference, indicating that the noise source was relatively constant between the seasons. The ANN model performed satisfactorily in both seasons with SSE values of < 0.03. The Mann-Whitney U test showed a significant difference in the predicted noise levels between summer and winter. This study highlighted the efficiency of the combined GIS-ANN model in predicting distant-dependent urban processes, especially noise pollution whose levels and variability are essential in formulating urban land-use management.

Parameter estimation study of polymer electrolyte membrane fuel cell using artificial hummingbird algorithm

This study represents a comprehensive investigation of the performance of Artificial Hummingbird Algorithm for parameter estimation for Polymer Electrolyte Membrane Fuel Cell. With this purpose, four commercial fuel cell systems which were widely preferred in the literature such as NedStack PS6 (Case-I), 250 W fuel cell stack (Case-II), Horizon 500 W (Case-III), and BCS 500 W (Case-IV) were chosen. In order to compare the performance of this algorithm, seven well-known optimization techniques including Artificial Bee Colony, Salp Swarm Optimization, Particle Swarm Optimization, Gray Wolf Optimization, Genetic Algorithm, Harris Hawks Optimization, and Whale Optimization Algorithm were used. The sum of the squared errors, computational speed, and statistical measurements were calculated for the performance comparison. In this context, the best SSE values were found as 2.06556, 5.25017, 0.02477, 0.01170 for Case-I, Case-II, Case-III, and Case-IV, respectively. The best standard deviation value was found as 1 e−6 for the Case-III. Based on the obtained results, the Artificial Hummingbird Algorithm established itself as a competitive optimization technique for parameter estimation study of PEMFC in terms of computational speed and robustness.

Impact of pre-freezing and microwaves on drying behavior and terpenes in hops (Humulus lupulus)

Hop buds (Humulus lupulus) are paramount to beer bittering, flavoring, and microbiological stability. To optimize post-harvest processing, fresh and pre-frozen hops were subjected to freeze-drying, hot air and microwave-assisted hot air drying. Pre-freezing occurred at − 80°C, prior to drying at 35°C, 50°C, and 65°C, with different microwave power (0 W, 100 W and 200 W, where 0 W represented conventional hot air drying). Results show that hops drying kinetics can be described using the predictive Page and Logarithmic mathematical models. Obtained R2, SSE, and RMSE values ranged between 0.999 and 0.982, 0.035–0.001, and 0.058–0.004, respectively. Irrespective of the drying condition, pre-freezing reduced drying time by 0.17–85.9 %. Pre-freezing hop buds increases the effective moisture diffusion coefficient, and it increases with higher drying temperature and microwave power, ranging between 5.91 × 10−10 m2 s−1 and 2.43 × 10−7 m2 s−1. SEM analyses indicate that pre-freezing causes structural damage to lupulin glands. The average concentration of myrcene, limonene, caryophyllene, and humulene for fresh hops were 15.08 mg g−1, 0.27 mg g−1, 3.09 mg g−1, and 6.52 mg g−1 respectively. For the dried samples under the various conditions, the concentration ranged from 12.20 mg g−1 to 0.53 mg g−1 (myrcene), 0.26 mg g−1 to 0.12 mg g−1 (limonene), 1.49 mg g−1 to 0.31 mg g−1 (caryophyllene), and 2.69 mg g−1 to 0.52 mg g−1 (humulene). Results affirm that pre-freezing plant material prior to drying can shorten postharvest processing times, and this method can potentially be applied to other industrial crops. This study highlights the importance of controlled postharvest processing to ensure industrial crop quality.

An Effective Initialization Method Based on Quartiles for the K-means Algorithm

Objectives: This study aims to speed up the K-means algorithm by offering a deterministic quartile-based seeding strategy for initializing preliminary cluster centers for the K-means algorithm, enabling it to efficiently build high-quality clusters. Methods: We have investigated various cluster center initialization approaches in literature and presented our findings. For the Kmeans algorithm, we here propose a novel deterministic technique based on quartiles for finding initial cluster centers. To obtain the preliminary cluster centers, we have applied our suggested approach to the data set. The initial cluster centers determined by our suggested method are then entered into the K-means algorithm. The proposed seeding method is evaluated on sixteen benchmark clustering data sets: five synthetic and eleven real data sets. Python is used to run the simulation. Findings: Based on empirical results from experiments, it is evident that our proposed cluster center initialization method allows the K-means algorithm to form clusters with SSE values comparable to the minimum SSE values produced by repeated Random or Kmeans++ initializations. Furthermore, our deterministic initialization strategy assures that the K-means algorithm converges faster than the Random and K-means++ initialization techniques. Novelty: In this study, we explore the potential of quartile-based seeding as a technique of accelerating the Kmeans algorithm. Needless to add, as our seeding method is deterministic, the requirement to run K-means repeatedly with different stochastic initializations is completely eliminated. Also, our initialization strategy assures that there is remarkable saving in execution time as compared to the Random and Kmeans++ initialization techniques. Moreover, it is found that after initializing with our offered method, the solution obtained with just a single run of K-means produces optimal clusters. Applications: Our proposed seeding technique will be helpful for initializing the K-means algorithm in time-sensitive applications, applications managing large amounts of data, and applications looking for deterministic cluster solutions. Keywords: Kmeans Algorithm; Initialization Method; Speeding Kmeans; Quartiles; Clustering; Deterministic Initialization Method

Klasterisasi Wilayah Kabupaten Jember Berdasarkan Tingkat Ketahanan Pangan

Food security is a condition of food proclamation based on the availability of sufficient food to be able to live a healthy, active and productive life in a sustainable manner. Based on data from the Global Food Security Index (GFSI), Indonesia is ranked 69th with a level of food availability with a value of 63.7% and a level of ability or food needs of 74.9%. Jember Regency as one of the regencies in East Java Province based on TP2AK food security data in 2020 is in good condition (very resistant). However, in the same year Jember Regency experienced natural disasters with a total of 92 incidents The World Food Program in 2009 explained that one of the indicators of food security is vulnerability to food insecurity which is influenced by natural disasters. Based on the disaster conditions experienced by Jember Regency, the research carried out is to group food security areas in the Jember Regency area based on transient indicators using the KMeans method. The results of the analysis of the optimal SSE cluster formed in K4 (4 clusters) with an SSE value of 24,809. Each of the 4 clusters formed has a food security category of very resistant, resistant, moderately resistant, and somewhat vulnerable.

Adsorption of caesium and cobalt ions on the muscovite mica clay-graphene oxide-γ-Fe2O3-Fe3O4 composite

The muscovite mica clay-graphene oxide-maghemite-magnetite (γ-Fe2O3-Fe3O4) composite was first used for the adsorption of caesium(I) and cobalt(II). The presence of clay minerals, graphene oxide, maghemite, and magnetite was detected in the prepared composite by XRD, WD-XRF, Mössbauer spectroscopy, and ATR-FTIR. The SEM and TEM results show that the composite has a layered structure with irregularly shaped pores on the surface. It was found that the adsorption of ions depends on the initial concentration, pH (except for caesium), mass of adsorbent, temperature, and contact time. The maximum adsorption capacity for Cs(I) and Co(II) was 2286mg/g and 652mg/g, respectively, and was obtained at concentrations (Cs(I) = 12,630mg/L; Co(II) = 3200mg/L), adsorbent mass of 0.01g, pH (Cs(I) = 7; Co(II) = 5), temperature of 20 ± 1°C, and contact time of 24h. The high adsorption capacity of the composite could be due to a diversity of functional groups, a large number of active sites or the multilayer adsorption of caesium and cobalt ions on the surface of the composite. The Freundlich, Langmuir isotherms, and the pseudo-second-order kinetic model better describe the adsorption of these ions on the composite. The adsorption was non-spontaneous endothermic for Cs(I) and spontaneous endothermic for Co(II). The proposed mechanism of adsorption of Cs and Co ions on the composite is complex and involves electrostatic interactions and ion exchange. The ANFIS model proved to be quite effective in predicting the adsorption of Cs(I) and Co(II), as shown by the obtained values of R2, MSE, SSE, and ARE.

DRYING CHARACTERISTICS AND QUALITY ANALYSIS OF HOT AIR-ASSISTED RADIO FREQUENCY AND HOT-AIR DRYING OF JUJUBE ( Zizyphus jujube Miller cv. Jinsixiaozao )

Radio frequency (RF) drying is a rapid dehydration technique that reduces water activity and extends the shelf life of agricultural products. In this study, the drying characteristics of jujube during HARF and hot air (HA) were investigated, and quality analyses, such as color, vitamin C (VC) and total flavonoid content (TFC), of jujube were performed. The drying curves revealed that the drying time used to reduce the moisture content from 81.7 to 6.0% on a dry basis was 360 and 1320 min using HARF drying and hot air (HA) drying, respectively. The logarithmic model was the best to describe the HARF drying process with R 2, RMSE and SSE values of 0.998, 0.00508 and 0.000306, respectively. Quality analysis demonstrated significant differences in color, VC content and total flavonoid content between the HARF and HA treatments during the drying process and storage periods (P > 0.5), and the HARF-dried jujube product performed much better than that dried by HA. Compared with HA drying, HARF drying showed a shorter drying time, higher drying efficiency and better product quality, indicating that HARF drying was a more promising drying method for jujube with acceptable product quality.

Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm

Industrial and commercial use of fuel cells as a source of clean energy production are two important goals of researchers in the field of energy today. Therefore, researchers in this science are always looking for new methods for industrial production and at a reasonable price for fuel cells. This study presents a new well-organized methodology for model identification of a Solid Oxide Fuel Cell (SOFC) stack by providing optimal selection of the unknown variables in the model. The main objective here is to minimize the sum of squared error value between the designed model output voltage and the experimental data. Here, to provide an optimization process, a modified version of gray wolf optimization (MGWO) algorithm has been utilized. This algorithm is then utilized to improve the algorithm efficiency and to get better results. To show the system reliability, two scenarios based on temperature and pressure variations have been utilized. The technique has been finally compared with several other techniques to verify its prominence. With considering the achieved results, it can be observed that the sum of square error for different temperature values based on the proposed method is too small, such that for the temperatures with values 553.4 °C, 652.3 °C, 669.8 °C, 754.6 °C, and 800 °C the SSE value is 5.27 e−4, 2.66 e−4, 3.91 e−6, 4.19 e−3, 2.07 e−4, respectively. Furthermore, the pressure value variations from 1 atm to 5 atm with 1.44 e−3, 3.20 e−3, 5.84 e−3, 3.36 e−3, 2.67 e−3, respectively indicate its higher efficiency toward the other studied methods. Final results designate that the proposed technique delivers outstanding efficiency toward the compared methods.

Forecasting Foreign Tourist Visits to West Nusa Tenggara Using ARIMA Method

West Nusa Tenggara (NTB) is one of the provinces in Indonesia that has its own charm in the world of tourism and is known as a pioneer of halal tourism. In addition to domestic tourists, NTB tourism always has an attraction for foreign tourists. This is evidenced by the increasing number of foreign tourists visiting NTB from year to year before the Covid-19 pandemic. This condition certainly has a positive impact on increasing NTB’s economic growth in the tourism sector and indirectly on the optimization of existing infrastructure. The purpose of the study is to predict the number of foreign tourist visits to NTB so that it can assist the government in making decisions in preparing adequate facilities and infrastructure in the event of a surge in tourist visits. The method used in this study is the Box-Jenkins-ARIMA model. The ARIMA method is based on 3 models that are formed from the results of plot data. The data used in this study is secondary data sourced from the Central Statistics Agency (BPS) of West Nusa Tenggara (NTB), from January 2010 to June 2019. The results show that the ARIMA (4,1,1) model is the most widely used model. This model is suitable for predicting the number of foreign tourists visiting NTB because this model produces the lowest SSE and MSE values compared to other models.

Biochar from the co-pyrolysis of Saccharina japonica and goethite as an adsorbent for basic blue 41 removal from aqueous solution

The effects of utilizing goethite (5%, 10%, and 20%) in co-pyrolysis with low-lignin macroalgae, Saccharina japonica, on the carbon sequestration potential, magnetic, physicochemical, and dye (basic blue 41, BB41) removal properties of the resulting biochar were investigated. Biochars exhibited more aromaticity, better magnetic properties, and insignificant alterations to their point of zero charges (11.07 ± 0.03 to 10.59 ± 0.01) with goethite increment. Optimum conditions for high organic matter conversion and carbon preservation occurred using 5% goethite. Adsorption experiments showed that BB41 adsorption was highly pH-dependent, equilibrated later (from 12 h to 24 h) after goethite modification, and was best fitted to the pseudo-second-order model (higher R2 and lower SSE values). Langmuir monolayer adsorption capacity for BB41 was the highest amongst carbonaceous adsorbents in the literature [1494 mg/g (pristine); 1216 mg/g (5% goethite)]; initial BB41 concentration of 2000 mg/L at 30 °C and pH 8. The main governing mechanisms involved ion exchanges, hydrogen bonding, π-π interaction and pore-filling. Overall, low goethite amount (5%), co-pyrolyzed with macroalgae, offers an economically and environmentally effective way to produce magnetic biochar with enhanced carbon sequestration potential and superb cationic dye removal performance for environmental remediation applications.

An efficient heap-based optimization algorithm for parameters identification of proton exchange membrane fuel cells model: Analysis and case studies

Proton Exchange Membrane fuel cells (PEMFCs) are a promising renewable energy source to convert the chemical reactions between hydrogen and oxygen into electricity. To simulate, evaluate, manage, and optimize PEMFCs, an accurate mathematical model is essential. Therefore, this paper improves the accuracy of a mathematical model for the PEMFC based on semi-empirical equations by proposing a meta-heuristic technique to optimize its unidentified parameters. Because the I–V characteristic curve of the PEMFC systems has a nonlinear and multivariable nature, conventional optimization techniques are difficult and time-consuming but modern meta-heuristic algorithms are ideally suited. Therefore, in this paper, a new improved optimization algorithm based on the Heap-based optimizer (HBO) has been proposed to estimate the unknown parameters of PEMFCs models using an objective function that minimizes the error between the measured and estimated data. This improved HBO (IHBO) effectively uses two strategies: ranking-based position update (RPU) and Lévy-based exploitation improvement (LEI) to improve the final accuracy to the SSE value with higher convergence speed. Four well-known commercial PEMFCs, (the 500 W BCS stack, NetStack PS6, H-12 stack, and AVISTA SR-12 500 W modular) are utilized to verify the proposed IHBO and compare it with 11 popular optimizers using various performance metrics. The experimental findings show the superiority of IHBO in terms of convergence speed, stability, and final accuracy, where IHBO could fulfill fitness values of 0.01170, 2.14570, 0.11802, and 0.00014 for the 500 W BCS stack, NetStack PS6, H-12 stack, and AVISTA SR-12 500 W modular, respectively.