Statistical Optimization Method Research Articles

Pelletization and characterization of agricultural by-products for water uptake

Industrial gas streams contain moisture of different levels and lowering their humidity is essential to ensure compliance with standards. Sorption has been an effective method to selectively remove water vapor from gaseous mixtures. Although various types of adsorbents have been employed in adsorption, cost-effective agriculture by-products, such as oat hull (OH), canola meal (CM) and their composite, are efficient materials for developing biosorbents. For industrial application, it is crucial to pelletize the biosorbents to control their size, shape, water uptake , and related qualities. This work investigated the effects of die temperature, compressive force, and biomass type on biosorbent pellet quality and interactions. In this regard, raw oat hull and canola meal and their 50%− 50% composition were pelletized under three levels of compressive (2000, 3000 and 4000 N) at 75, 85 and 95 °C as the die temperature. Increasing die temperature and compressive force enhanced quality of the produced pellets. Canola meal was superior in water uptake, while oat hull was important in strengthening pellets. Moreover, sodium lignosulfonate (LS Na ) as an additional binder improved pellet quality. By statistical optimization methods, four pelletized biosorbents (18.6% OH-81.4% CM-Without LS Na ; 100% CM-4% LS Na ; 54.5% OH- 45.5% CM-4% LS Na ; 91.9% OH-8.1% CM-4% LS Na ) were developed, which were promising for industrial application. The optimized biosorbents possessed relatively high density and strength, and their water uptake capacity was in the range of 0.32 – 0.38 g/g, which is comparable to or higher than commercial adsorbents. Therefore, the introduced biosorbents could be effective alternatives to the conventional materials for gas dehydration. • Canola meal and oat hull demonstrated relatively high water uptake capacity. • Pelletization enhanced raw biomass quality and produced uniform biosorbents. • Sodium lignosulfonate improved pellet quality and water uptake. • Four optimized biosorbents were developed and characterized for industrial usage.

Fragility of cardiovascular outcome trials (CVOTs) examining nutrition interventions among patients with diabetes mellitus: a systematic review of randomized controlled trials

PurposeThere is controversy regarding the optimal statistical method to interpret how robust is a statistically significant result. The fragility index (FI) and the reverse fragility index (RFI) are quantitative measures that can facilitate the appraisal of a clinical trial’s robustness. This study was performed to evaluate the FI and RFI of randomized controlled trials (RCTs) examining nutritional interventions in patients with diabetes mellitus, focusing on cardiovascular outcomes.MethodsA systematic search was conducted and relevant RCTs were identified in three databases. RCTs examining nutritional interventions (supplements or dietary patterns) in patients with DM with dichotomous primary endpoints involving cardiovascular outcomes were eligible. Data were extracted to compose 2 × 2 event tables and the FI and RFI were calculated for each comparison, using Fisher’s exact test. Risk of bias (RoB) of the included RCTs was assessed with the Cochrane RoB 2.0 tool.ResultsA total of 14,315 records were screened and 10 RCTs were included in the analyses. The median FI of the paired comparisons was 3 (IQR: 2–4) and the median RFI was 8 (IQR: 4.5–17). RoB and heterogeneity were low.ConclusionsRCTs examining nutritional interventions and cardiovascular outcomes among patients with diabetes mellitus appear to be statistically fragile. Τhe FI and the RFI can be reported and interpreted as an additional perspective of a trial’s robustness.Highlights• In the evidence-healthcare era, assessing how robust statistically significant results are remains a matter of controversy.• Recently, the fragility index (FI) and reverse fragility index (RFI) were proposed to assess the robustness of randomized controlled trials (RCTs) with 2 × 2 comparisons.• When applying the FI and RFI, RCTs examining nutritional interventions and cardiovascular outcomes among patients with diabetes mellitus (DM) appear to be statistically fragile.• Τhe FI and the RFI can be reported and interpreted as an additional perspective of a trial’s robustness.• RCTs implementing nutrition interventions among patients with DM can improve their methodology.

Use of statistical methods among acupuncture randomized controlled trials was far from satisfactory

ObjectivesTo examine whether appropriate statistical methods were used in acupuncture randomized controlled trials (RCTs). Study Design and SettingWe searched PubMed to identify acupuncture RCTs with continuous outcome as primary outcome published in the core clinical journals and complementary and alternative medicine (CAM) journals between January 2010 and December 2019 (10 years). We compared statistical characteristics of included trials published in core clinical journals and CAM journals. ResultsWe included 262 RCTs, including 46 published in core clinical journals and 216 in CAM journals. Of included RCTs, only 132 (50.4%) clearly predefined the primary outcome, 72 (27.5%) specified the use of intention to treat or modified intention to treat population for primary analysis. In the 167 trials reported missing participant data (MPD), 118 (70.7%) used suboptimal methods (e.g., complete case analysis) for dealing with MPD; 11 (6.6%) conducted sensitivity analysis regarding MPD. Among the 161 trials with repeated measures design, only 21 (13.0%) used advanced statistical models (e.g., mixed-effects models) for handling repeated-measure data in the primary analysis. In the 72 trials involving multiple acupuncturists, only 4 (5.6%) adjusted acupuncturist variable or considered the clustering by acupuncturist in analysis. Trials in core clinical journals were more likely to predefine primary outcome (78.3% vs. 44.4%, P < 0.001), use multiple imputations for handling MPD (40% vs. 1.5%, P < 0.001), and use statistically advanced methods for assessing treatment effect at a single time point (26.1% vs. 2%, P = 0.001). ConclusionThe use of statistical methods among acupuncture RCTs is far from satisfactory. Our findings highlighted the need for researchers to carefully use the optimal statistical methods and for journal editors to strengthen the use of statistical methods.

Evaluation by Survival Analysis of Cold Pain Tolerance in Patients with Fibromyalgia and Opioid Use.

PurposeThe cold pressor test (CPT) is a clinical pain research method used to measure cold pain tolerance. During this test, participants immerse an extremity (ie, hand or foot) into cold water for as long as tolerable. The duration of the test (traditionally up to an experimentally imposed cut-off at 2 minutes) indicates the amount of cold pain tolerance by the participant. Prior research studies have investigated cold pain tolerance in patients with chronic pain. However, few of these studies have used survival analysis, which allows for proper handling of data censoring and is therefore, an optimal statistical method for CPT data analysis. The goal of the present study was to use survival analysis to evaluate cold pain tolerance in patients with fibromyalgia. Furthermore, we aimed to model relationships between psychological and clinical variables as well as opioid medication use and cold pain tolerance.Patients and MethodsA total of 85 patients with fibromyalgia (42 who were taking opioids) and 47 healthy pain-free controls provided CPT and questionnaire data (collected across 2 study sites) for a case–control study. We used survival analysis using Cox regression to evaluate group differences (patients vs controls) in cold pain tolerance and to evaluate cold pain tolerance relationships with psychological, clinical, and medication use.ResultsAs compared to healthy controls, patients with fibromyalgia exhibited significantly lower CPT survival (HR = 2.17, 95% CI: [1.42, 3.31], p = 0.00035). As indicated by Cox regression models, the significant group difference in CPT survival did not relate to our selected psychological and clinical measures (p > 0.05). The groups of non-opioid-taking patients and healthy controls showed consistent CPT survival across study sites. However, patients taking opioid pain medications showed differences in CPT survival across study sites.ConclusionBy using survival analysis, an optimal method for time-to-event pain measures such as the CPT, we confirmed previously identified reductions in cold pain tolerance in patients with fibromyalgia. While our selected psychological and clinical measures were not significantly associated with cold pain tolerance, our data suggest that opioid medication use may impart greater cold pain tolerance in some patients.

Statistical extrapolation methods and empirical formulae for estimating extreme loads on operating wind turbine towers

The wind turbine tower is designed against extreme load during power production with the return period of 50 years. The statistical extrapolation method is widely used to estimate the extreme load of wind turbine tower and is recommended by the IEC Standard 61400–1. However, various statistical extrapolation methods were used in previous studies and their accuracy and efficiency have not been evaluated comprehensively, which is investigated in this research. The validation procedure is assessed by simulating the one-year load of the reference wind turbine. It is found the fitting before aggregation methods with peak loads extracted by block maxima or peak over threshold techniques and three-parameter Weibull fitting function have the best accuracy and robustness. 60 samples for each wind speed are enough and the data below rated wind speed can be neglected for the optimal statistical extrapolation methods. In addition, an empirical formula for the extreme load estimation of wind turbine tower is proposed considering both the mean and fluctuating moment caused by the thrust force on the rotor, drag forces on the tower and nacelle and the gravities of nacelle and rotor. The extrapolation factor is modelled based on the estimated extreme load by the statistical extrapolation method. The proposed empirical formula shows good accuracy for both short-term and long-term extreme loads of wind turbine tower at various wind conditions.

Skillful Seasonal Prediction of North American Summertime Heat Extremes

Abstract This study shows that the frequency of North American summertime (June–August) heat extremes is skillfully predicted several months in advance in the newly developed Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system. Using a statistical optimization method, the average predictability time, we identify three large-scale components of the frequency of North American summer heat extremes that are predictable with significant correlation skill. One component, which is related to a secular warming trend, shows a continent-wide increase in the frequency of summer heat extremes and is highly predictable at least 9 months in advance. This trend component is likely a response to external radiative forcing. The second component is largely driven by the sea surface temperatures in the North Pacific and North Atlantic and is significantly correlated with the central U.S. soil moisture. The second component shows largest loadings over the central United States and is significantly predictable 9 months in advance. The third component, which is related to the central Pacific El Niño, displays a dipole structure over North America and is predictable up to 4 months in advance. Potential implications for advancing seasonal predictions of North American summertime heat extremes are discussed.

Production and optimization of l-asparaginase by Streptomyces koyangensis SK4 isolated from Arctic sediment.

Actinomycetes isolated from the Arctic sediment were evaluated for the production of the enzyme l-asparaginase, an enzyme used to treat acute lymphoblastic leukemia. The most potent strain Streptomyces koyangensis SK4 was selected for l-asparaginase enzyme production by submerged fermentation. The effect of various fermentation parameters on enzyme production was analyzed statistically using the Plackett-Burman design and response surface method. Effects of eight parameters including temperature, pH, incubation time, inoculum size, agitation speed, the concentration of starch, l-asparagine, and yeast extract were studied on l-asparaginase production by the Arctic isolate S. koyangensis SK4. Factors such as temperature, pH, incubation time, agitation speed, and l-asparagine concentration were found to be important factors influencing l-asparaginase production. Maximum enzyme activity of 136 IU/ml was obtained at 20°C on the seventh day of incubation in the asparagine dextrose broth maintained at pH 7.5, agitation speed 125 rpm, and l-asparagine concentration of 7.5 g/L. The statistical optimization method described in this study proved effective for increasing the l-asparaginase production by Arctic actinomycetes.

Mxnorm: An R Package to Normalize Multiplexed Imaging Data.

SummaryMultiplexed imaging is an emerging single-cell assay that can be used to understand and analyze complex processes in tissue-based cancers, autoimmune disorders, and more. These imaging technologies, which include co-detection by indexing (CODEX), multiplexed ion beam imaging (MIBI), and multiplexed immunofluorescence imaging (MxIF), provide detailed information about spatial interactions between cells (Angelo et al., 2014; Gerdes et al., 2013; Goltsev et al., 2018). Multiplexed imaging experiments generate data across hundreds of slides and images, often resulting in terabytes of complex data to analyze through imaging analysis pipelines. Methods are rapidly developing to improve particular parts of the pipeline, including software packages in R and Python like spatialTime, imcRtools, MCMICR0, and Squidpy (Creed et al., 2021; Palla et al., 2021; Schapiro et al., 2021; Windhager et al., 2021). An important, but understudied component of this pipeline is the analysis of technical variation within this complex data source – intensity normalization is one way to remove this technical variability. The combination of disparate pre-processing pipelines, imaging variables, optical effects, and within-slide dependencies create batch and slide effects that can be reduced via normalization methods. Current state-of-the-art methods vary heavily across research labs and image acquisition platforms, without one singular method that is uniformly robust – optimal statistical methods seek to improve similarity across images and slides by removing this technical variability while maintaining the underlying biological signal in the data.mxnorm is open-source software built with R and S3 methods that implements, evaluates, and visualizes normalization techniques for multiplexed imaging data. Extending methodology described in Harris et al. (2022), we intend to set a foundation for the evaluation of multiplexed imaging normalization methods in R. This easily allows users to extend normalization methods into the field, and provides a robust evaluation framework to measure both technical variability and the efficacy of various normalization methods. One key component of the R package is the ability to supply user-defined normalization methods and thresholding algorithms to assess normalization in multiplexed imaging data. Core features, usage details, and extensive tutorials are available in the package documentation and vignette on CRAN and the software repository.

Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment

The recent emergence of sixth-generation (6G) enabled wireless communication technology has resulted in the rapid proliferation of a wide range of real-time applications. These applications are highly data-computation intensive and generate huge data traffic. Cybertwin-driven edge computing emerges as a promising solution to satisfy massive user demand, but it also introduces new challenges. One of the most difficult challenges in edge networks is efficiently offloading tasks while managing computation, communication, and cache resources. Traditional statistical optimization methods are incapable of addressing the offloading problem in a dynamic edge computing environment. In this work, we propose a joint resource allocation and computation offloading scheme by integrating deep reinforcement learning in Cybertwin enabled 6G wireless networks. The proposed system uses the potential of the MATD3 algorithm to provide QoS to end-users by minimizing the overall latency and energy consumption with better management of cache resources. As these edge resources are deployed in inaccessible locations, therefore, we employ secure authentication mechanism for Cybertwins. The proposed system is implemented in a simulated environment, and the results are calculated for different performance metrics with previous benchmark methodologies such as RRA, GRA, and MADDPG. The comparative analysis reveals that the proposed MATD3 reduces end-to-end latency and energy consumption by 13.8% and 12.5% respectively over MADDPG with a 4% increase in successful task completion.

Optimization of biodiesel production parameters from Prosopisjuliferaseed using definitive screening design

The concept of waste to valuable products is a hot topic with more explorations going on worldwide to minimize the environmental pollution and wastage of food-based feedstocks. In this work, biodiesel was produced from Prosopis julifera seed oil using ethanol as solvent and magnesium oxide nanocatalyst and the process was optimized by employing an advanced statistical optimization method; definitive screening design. The maximum biodiesel yield from Prosopis julifera seed was found to be 32.5%. Acid esterification and transesterification were applied to minimize the acidity. Acidity of the P. julifera oil was initially reduced to 1.52 mg KOH/g using acid catalyst H2SO4, and then to 0.88mg KOH/g by transesterification process using magnesium oxide.Optimum biodiesel conversion efficiency of 94.83% was achieved under 10:1 ethanol-to-oil ratio, 5% magnesium oxide concentration, 80 min reaction time, 45 °C reaction temperature and 1000 rpm agitation rate. The transesterification reaction was found to be highly affected by the ethanol-to-oil ratio and catalyst concentration. The results showed that the catalytic activity of the magnesium oxide was sufficient for the production of biodiesel from P. julifera seed oil.The fuel properties were evaluated according to ASTM standards. FTIR analysis confirmed the existence of functional groups with respect to the fingerprint region of P. julifera ethyl esters. The Definitive screening design method can be suggested as an alternative method for the optimization of process parameters within limited materials and number of experiments. The findings suggest that this method of production of biodiesel from P. julifera seed oil shall open up new possibilities for a novel natural biofuel.

Autonomous parameter optimization for femtosecond laser micro-drilling.

There is a strong need for a highly efficient method to find the optimal conditions to achieve a desired result in laser processing, oftentimes from a multidimensional parameter space. In this study, we adopted Bayesian optimization as an efficient statistical optimization method robust to the inherent variations observed in typical laser processing results. Specifically, the intensity and spatial beam profile of a femtosecond laser processing system were optimized according to results obtained from an in situ optical microscope observation. In this way, we show that the optimum set of parameters to achieve a desired shape can be obtained autonomously and more than an order of magnitude faster than with a simple grid-search.

From demand forecasting to inventory ordering decisions for red blood cells through integrating machine learning, statistical modeling, and inventory optimization.

The demand and supply of blood are highly variable over time. Blood inventory management that relies heavily on experience-based decisions may not be adaptive to real demand, leading to high operational costs, wastage, and shortages. We combined statistical modeling, machine learning, and optimization methods to develop a data-driven demand forecasting and inventory management strategy for red blood cells (RBCs). We then used the strategy to inform daily blood orders. A secondary semi-weekly (twice per week) ordering strategy was developed to handle the last-mile split delivery problem for blood suppliers, characterized by multi-deliveries to the same location multiple times during a short period of time. Both strategies were evaluated using the TRUST database including all patient data across four hospitals in Hamilton, Ontario. We identified 227,944 RBC transfusions for 40,787 patients in Hamilton, Ontario from 2012 to 2018. The predicted daily demand from the hybrid demand forecasting model was not significantly different from the actual daily demand (paired t-test p-value=0.163); however, the proposed daily ordering quantity from the model was significantly lower than the actual ordering quantity (p-value <0.001). The proposed daily ordering strategy reduced inventory levels by 38.4% without risk of shortages, leading to an overall cost reduction of 43.0% (95% confidence interval [CI]: 42.3%, 43.7%) compared with the actual cost. The semi-weekly ordering strategy reduced ordering frequency by 62.6% (95% CI: 61.5%, 63.7%). The proposed data-driven ordering strategy combining demand forecasting and inventory optimization can achieve significant cost savings for healthcare systems and blood suppliers.

Opuntia ficus indica waste as a cost effective carbon source for lactic acid production by Lactobacillus plantarum

Opuntia ficus indica (OFI) waste was evaluated as a fermentation feedstock for lactic acid production using Lactobacillus plantarum.Dilute acid pretreatment of the OFI cladodes (OFIC) was performed for extracting maximum fermentable sugars by optimizing process parameters using statistical optimization method. The best results were obtained with HCl 1% (v/v), temperature 120 °C, residence time 40 min, granulation 350 µm and substrate loading 5% (w/v), the sugar concentration reached 24 g/L with low concentration of hydroxymethylfurfural. The feasibility of producing lactic acid from OFI fruit peel (OFIFP) as a source of carbon was also investigated. Lactobacillus plantarum was shown to have a capacity for lactic acid production from OFIC350 (granulation 350 µm) hydrolysate and OFIFP extract without detoxification. The highest lactic acid yields of 0.46 and 0.78 g/g were obtained from enzymatic hydrolysate of pretreated OFIC350 and OFIFP extract, respectively.

Deriving health utility indices from a food allergy quality-of-life questionnaire.

BackgroundThe Food Allergy Quality‐of‐Life Questionnaire‐Parent Form (FAQLQ‐PF) is widely used to assess food allergy–specific health‐related quality of life (FAQL), but cannot be used directly in cost‐utility analyses, which require health state utility (HSU) scores. Currently, limited evidence is available regarding the HSU of food‐allergic children/adolescents. This study aimed to develop mapping algorithms from the FAQLQ‐PF onto HSU scores generated by generic, preference‐based, health‐related quality‐of‐life (HRQL) instruments.MethodsCaregivers of children aged 7 to 17 years with a clinician diagnosis of IgE‐mediated food allergy, recruited via Allergy & Anaphylaxis Australia, completed an online FAQLQ‐PF questionnaire and proxy generic preference‐based pediatric instruments (Assessment of Quality of Life [AQoL]‐6D and Child Health Utility 9D [CHU9D]). Optimal statistical methods were based on series of goodness‐of‐fit statistics.ResultsMean FAQLQ‐PF total score, AQoL‐6D, and CHU9D utility scores of 238 food‐allergic children/adolescents were 3.49 (SD: 1.41), 0.78 (SD: 0.22), and 0.74 (SD: 0.22), respectively. The Spearman correlation coefficients of FAQLQ‐PF with AQoL‐6D and CHU9D were rho = −0.56 and rho = −0.45, respectively. Optimal mapping algorithms were generated from selected FAQLQ‐PF items, mapped onto AQoL‐6D or CHU9D utility scores, with AQoL‐6D demonstrating better performance.ConclusionsThis study generated mapping algorithms to help facilitate the use of FAQLQ‐PF for cost‐utility analyses, which are essential for health economic evaluation. External validation of the reported mapping algorithms is warranted.

A method for minimizing the zone of low water flow velocity in a bottom center drain circular aquaculture tank

AbstractBottom center drain circular tanks in which water is injected tangentially to the tank wall are widely used in aquaculture. However, a torus‐shaped region about the center drain can develop into an irrotational zone that generates low velocities and poor mixing. Many works have been devoted to improving tank hydraulics by making good use of the water flow pattern. However, mathematics‐friendly methods, such as the statistical optimization method, which is widely used in science and engineering, are rarely used in aquaculture tank designing. This work focuses on the minimization of the low velocity zone in a bottom center drain tank based on experimental planning and computational fluid dynamics. Model validation was carried out by comparison with measurements. The results show that the low velocity zone can be reduced measurebly by the proposed method.

189 Fundamentals, Common Mistakes, and Graduate Education in Statistics

Abstract Experimental design and statistical data analyses are fundamental components of animal science research. Proper design of experiments and adequate sampling permits testing hypotheses raised by researchers and sets the stage for collecting required data and subsequent statistical analysis. When designing experiments, researchers should respect rules of randomization of treatments to avoid statistical bias and permit proper inference to be drawn. Use of sample sizes that result in adequate statistical power to identify the hypothesized differences among factor levels of interest is key and should be driven by formal processes determining such. Best practices for data collection should be performed to obtain high quality data by reducing collection (e.g., mislabeling, improper technique) and measurement errors. With sound data, appropriate and optimal statistical methods should be used to generate valid results. The statistical method deployed should be chosen based on assumptions about residuals (e.g., normality, correlation, and homogeneity) and on the type of data (e.g., quantitative continuous or categorical). The appropriate statistical model used should also be consistent with the experimental design to validate the respective test statistics. The science of statistics is changing rapidly. With the development of high-throughput technologies, the generation of large datasets, high performance and sophisticated models and the interest in Big Data, the training of animal science graduate students in data management and rigorous statistical analyses is more important than ever. In order to meet the demands of current trends, animal science graduate students must be trained in several complex statistical and computational skills to meet the challenges imposed by these complicated, sophisticated and nuanced analytical methods. The livestock production sector will benefit from improved training, use of advanced and appropriate experimental designs, and collection and analysis of quality data in research.

A cascade reaction for the synthesis of d-fagomine precursor revisited: Kinetic insight and understanding of the system

The synthesis of aldol adduct (3S,4R)-6-[(benzyloxycarbonyl)amino]-5,6-dideoxyhex-2-ulose, a precursor of the interesting dietary supplement, iminosugar d-fagomine, was studied in a cascade reaction with three enzymes starting from Cbz-N-3-aminopropanol. This system was studied previously using a statistical optimization method which enabled a 79 % yield of the aldol adduct with a 10 % yield of the undesired amino acid by-product. Here, a kinetic model of the cascade, including enzyme operational stability decay rate and the undesired overoxidation of the intermediate product, was developed. The validated model was instrumental in the optimization of the cascade reaction in the batch reactor. Simulations were carried out to determine the variables with the most significant impact on substrate conversion and product yield. As a result, process conditions were found that provided the aldol adduct in 92 % yield with only 0.7 % yield of the amino acid in a one-pot one-step reaction. Additionally, compared to previous work, this improved process outcome was achieved at lower concentrations of two enzymes used in the reaction. With this study the advantages are demonstrated of a modelling approach in developing complex biocatalytical processes. Mathematical models enable better understanding of the interactions of variables in the investigated system, reduce cost, experimental efforts in the lab and time necessary to obtain results since the simulations are carried out in silico.

Characterization and hydrolysis optimization of Sargassum cinereum for the fermentative production of 3G bioethanol

Third-generation biofuels are gaining commercial interest because of the rapid growth rate, high productivity of nutrients, and the carbon dioxide sequestration ability of the feedstock, algae. But the selection of algae, cultivation method, and pretreatment techniques remain challenging. The present work describes the high suitability of brown seaweed as a promising feedstock for the production of bioethanol. Sargassum cinereum is a carbohydrate-rich and fast-growing brown seaweed in all marine environments and abundantly available in Gulf of Mannar, India. Proximate, ultimate, and biochemical analysis has demonstrated the potential carbohydrates in the brown seaweed composition. Preliminary studies on S. cinereum revealed that the H2SO4-assisted hydrolysis had produced more fermentable sugars than the tested. A statistical optimization method with 16 runs was employed to optimize the H2SO4 loading concentration (% w/v), residence time (min), and incubation temperature (°C) for the enhancement of fermentable sugars from the biomass. Optimization results have shown a 133% increase in reducing sugars yield. Furthermore, the obtained sugars were fermented to ethanol at a yield of 0.465 ± 0.024 g ethanol/reducing sugars using Saccharomyces cerevisiae MTCC170. The observed high ethanol yield from seaweed sugars was closer to the theoretical yield based on glucose as a substrate.

The Method for Optimum Design of Water Rocket Flight Stability Performance Conditions Using CAE with T Method and Robust Parameter Design

A water rocket is a rocket system that obtains thrust by injecting water with compressed air of up to about 8 atmospheres. It is usually manufactured using a pressure-resistant PET bottle. The mechanical elements and principles contained in the water rocket have much in common with the actual small rocket system, and are suitable as educational and research teaching materials in the field of mechanics. Especially in the field of disaster prevention and mitigation, the use of water rockets is being researched and developed as a rescue tool in the event of a flood or earthquake as a disaster countermeasure. However, since the water rocket is a flying object based on the mechanical principle, it is important to ensure the accuracy and stability of the flight path. In this paper, a mechanical simulator is developed with a numerical calculation program based on the mechanical consideration of water rocket flight performance. In addition, the correlation between the flight distance obtained in the simulation and the estimated flight distance is analyzed by applying a multivariate analysis method and verifying the validity of the flight distance calculated from the result. Based on the verification results, we will apply a statistical optimization method to approach the optimization of flight stability performance conditions for water rockets.

Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories

Extracting travel patterns from large-scaled vehicle trajectories is the key step to analyze urban travel characteristics, which can also provide effective strategies for urban traffic planning, construction, management and policy decision. In this study, we adopt the DBSCAN (Density-Based Spatial Clustering of Application with Noise) algorithm by fusing spatial, temporal and directional attributes extracting from vehicle trajectories Furthermore, LCS (Longest Common Sequences) is adopted to estimate spatial similarity, and two measurements are also designed to evaluate the temporal and directional similarity between trajectories. Accordingly, a statistical feature-based parameter optimization method is proposed in the clustering process to achieve reasonable clustering results. Finally, trajectory data collected from Harbin city, China are used to validate the effectiveness of clustering method. A comparison of clustering results considering different combination of attributes is conducted to further demonstrate the advantage of the proposed model.