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Effect of Feeding a Low Level of Encapsulated Fish Oil to Dairy Goats on Milk Yield, Composition, and Fatty Acid Profile
No full textContradictory results have been found in the response of dairy goats to the supplementation of fish oil in their diet to improve the n-3 polyunsaturated fatty acids (PUFA) in milk. The responses to the inclusion of fish oil in the diet of ruminants either induced milk fat depression, increased milk fat content, and/or negatively impacted milk yield. The objective of this study was to determine whether including a low dose of encapsulated fish oil in the diet of goats can modify yield, milk composition, and the fatty acid (FA) profile of milk. Ten Alpine goats were divided into two homogeneous subgroups and assigned to either the control or experimental diet. The control animals received the basal diet without supplementation of fish oil, whereas the experimental group was given the same basal diet supplemented with encapsulated fish oil (1.14 g /kg of concentrate) for 56 days. Milk samples were analyzed for chemical composition and FA profile. The inclusion of encapsulated fish oil in the goat diet did not affect the yield and composition of goat milk. The effect of diet was not significant on the FA profile of goat milk, except that 20:0 was lower (P \u3c 0.05) in the milk of goats that received fish oil. The low dose of encapsulated fish oil supplement used in this study did not impact (P \u3e 0.05) the PUFA content of goat milk or milk composition and yield; however, the atherogenicity index (AI), which is beneficial to heart health, was lower (P \u3c 0.05) in the milk of goats that received fish oil as a supplement in their diet compared to the control
Evaluation of Friesian Holstein Bulls Fertility in Lembang and Singosari Artificial Insemination Center using West Java ISIKHNAS Data
No full textFriesian Holstein bull (FH) is one of dairy cattle in Indonesia. It is important to increase the production of dairy cattle to meet the need for milk consumption. The way to develop is through artificial insemination (AI). This study was aimed to evaluate the FH bull fertility by calculating the percentage of first service conception rate (%FSCR) of FH bulls in Lembang and Singosari AI Center using ISIKHNAS data in West Java for the year 2017 until 2018. The data included in this study after editing consisted of AI data (n=141176) and service records as well as pregnancy diagnosis information (n=98120). The study showed that the FH bull semen spread in West Java mostly from Lembang and Singosari AI Center. The fertility rate can be grouped into high fertile (HF), and low fertile (LF) level and also divided into two groups based on the number of AI services. The %FSCR of FH bulls semen that used for AI \u3c1000 services in Singosari 64.29% as HF level; 22.22% as LF level, while in Lembang had 74. 03% as HF; 21.15% as LF level. Then, the AI\u3e 1000 services in Lembang had %FSCR is 61.61% as HF level; 35. 28% as LF level. While Singosari had 63.11% as HF level; 33.78% as LF level. In conclusion, the FH bulls in Lembang and Singosari AI center had the HF of the %FSCR is about 53.13 until 74.03%. It is needed the more accurate assessment through genomic analysis to get the biomarker of HF bulls as a suggestion to improve the FH breeding cattle
Cost Optimization Modeling of Renewable Energy Sources in Smart Grid Using SCADA
Get PDFIn recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the termination of conventional energy resources. On the other hand, the depletion of fossil fuels like coal, petroleum, natural gas, etc., is creating an adverse effect on the environment. However, the cost of power generation from coal-fired plants is higher than the power generation\u27s price from renewable energy sources. Therefore, it has become a stumbling block balancing this increased energy demand using existing resources and new renewable resources that optimize the total power generation and the cost of power generation. However, power generation from the combined renewable energy sources confronts few complications, such as unstable power generated from the wind generator, the higher operational cost of the hydropower plant, etc. This experiment is focused on cost optimization during power generation through the pumped storage power plant and wind power plant. The entire modeling of cost optimization has been conducted in two parts. The mathematical modeling was done using MATLAB simulation while the hydro and wind power plant\u27s emulation was performed using SCADA designer implementation. The experiment was conducted using ranges of generated power from both power sources. The optimum combination of output power and cost from both generators is determined via MATLAB simulation within the assumed generated output power range. Secondly, the hydro-generator and wind generator\u27s emulation were executed individually through synchronizing the grid to determine each generator\u27s specification using SCADA designer, which provided the optimum power generation from both generators with the specific speed, aligning with results generated through MATLAB. Finally, the operational power cost (with no losses consideration) from MATLAB was compared with the local energy provider to determine the cost-efficiency. This experiment has provided the operational cost optimization of the hydro-wind combined power system with stable wind power generation using SCADA, which will ultimately assist in operations of large-scale power systems, remotely minimizing multi-area dynamic issues while maximizing the system efficiency
Demystifying Americanness: The model minority myth and the black-Korean relationship
No full textThe recent incidents involving both Black American and Korean American communities across the United States have reopened the old wounds between the two minority communities, recalling the two tragic incidents in the 1990s: the death of Latasha Harlins (1991) and the Los Angeles Uprising/Sa-I-Gu (1992). Revisiting and reevaluating these two cases, this article argues that the myth of true Americanness, channeled and reinforced through the concept of model minority, has not only shaped and sustained a contentious relationship between Korean immigrants and Black Americans but also intensified the racial tension among all racial and ethnic groups in the United States. We conclude that American people of all demographics must debunk the myth of model minority and challenge the false Americanism by embracing “deep diversity,” not merely distinctive group identities and outlooks, which offers a more diverse and rich interpretation of America as a whole
Green Synthesis of Silver Nanoparticles with Antimicrobial Properties Using Phyla dulcis Plant Extract
No full textFoodborne illnesses caused by the consumption of food contaminated with foodborne pathogens at infectious doses are becoming a common health issue throughout the world. Metal nanoparticles with potential antimicrobial properties are an area that can be explored to discover novel antimicrobial agents. The traditional synthesis methods of metal nanoparticles involve the use of toxic chemicals and the generation of harmful byproducts. In this study, a greener method to synthesize silver nanoparticles (AgNPs) with potential antimicrobial properties was investigated. The aqueous extract of the medicinal plant Phyla dulcis Trev. (verbenaceae) was used as the reducing and stabilizing agent to synthesize AgNPs using microwave irradiation. The formation of AgNPs was confirmed using ultraviolet-visible spectroscopy by the appearance of characteristic surface plasmon resonance peaks in the 430-440 nm wavelength range. The size and stability of the AgNPs were studied using Zetasizer nano-series for 5 weeks after synthesis. The average particle size remained between 63 and 76 nm during the first 4 weeks and increased to 114 nm in the fifth week showing possible aggregation after the fourth week. The zeta potential remained between -20 and -24 mV throughout the 5 weeks showing relatively good stability. Scanning electron microscopy/energy dispersive X-ray spectroscopy showed the association of phytoconstituents with the AgNPs. X-ray photoelectron spectroscopy analysis confirmed the formation of metallic nanoparticles starting from silver nitrate. Finally, the AgNPs were tested to be effective against Escherichia coli O157:H7 (ATCC 43888), Salmonella Typhimurium (novobiocin and nalidixic acid resistant strain), Listeria monocytogenes (4b; ATCC 19115), and Staphylococcus aureus (ATCC 6538) strains, which are known to be common foodborne pathogens
Class of Integrals Involving Generalized Hypergeometric Function
Get PDFIn this paper, we establish some definite integrals involving generalized hypergeometric function, product of algebraic functions, Jacobi function, Legendre function and general class of polynomials. Certain special cases of the main results are also pointed out
Harriet Tubman - Melissa Waddy Thibodeaux
Get PDFMelissa Waddy-Thibodeaux (Actor/Playwright/Drama Instructor) has performed for over 40 years in various venues throughout the U.S. Her experience working with children and young adults, stems back to 1988 at the Ensemble Theatre where she worked closely with the late George Hawkins. She began the first children’s theater and began touring it in the early 1990’s to schools and libraries in and around the Houston area
Sentiment Analysis on Social Media Via Machine Learning
Get PDFSocial media are shaping users\u27 attitudes and behaviors through spreading information anytime and anywhere. Monitoring user opinions on social media is an effective solution to measure users\u27 preferences towards brands or events. Currently, supervised machine learning-based methods dominate this area. However, as far as we know, there is no comprehensive comparison of performances of different models to figure out which model will be better for individual datasets. The focus of this thesis is to compare the performance of different supervised machine learning models. In detail, we built six classifiers, including support vector machine, random forest, neural network, Adaboost, decision tree, and Naive Bayes on two datasets and compare their performance. Furthermore, we introduced feature selection to remove unrelated attributes to preprocess the data and compare performance by building classifiers on the preprocessed data. Experimental results show that without feature selection, there is no significant difference in the performance. After feature selection, random forest outperformed other classifiers
COVID-19 and the improvement of the global air quality: The bright side of a pandemic
No full textThe objective of this investigation is to study the impacts of the global response to COVID-19 on air pollution and air quality changes in major cities across the globe over the past few months. Air quality data (NO2, CO, PM2.5, and O3) were downloaded from the World Air Quality Index project for the January 2019–April 2020 period. Results show a significant reduction in the levels of 2020 NO2, CO, and PM2.5 compared to their levels in 2019. These reductions were as high as 63% (Wuhan, China), 61% (Lima, Peru), and 61% (Berlin, Germany), in NO2, CO, and PM2.5 levels, respectively. In contrast, 2020 O3 levels increased substantially, as high as 86% (Milan, Italy), in an apparent response to the decrease in titration by nitrogen monoxide and its derivatives. Significant differences in the weather conditions across the globe do not seem to impact this air quality improvement trend. Will this trend in the reduction in most air pollutants to unprecedented levels continue in the next few weeks or even months? The response to this and other questions will depend on the future global economic and environmental policies
Estimating Parameter of the Selected Uniform Population Under the Generalized Stein Loss Function
Get PDFThis paper deals with the problem of estimating scale parameter of the selected uniform population when sample sizes are unequal. The loss has been measured by the generalized Stein loss (GSL) function. The uniformly minimum risk unbiased (UMRU) estimator is derived, and the natural estimators are also constructed under the GSL function. One of the natural estimators is proved to be the generalized Bayes estimator with respect to a noninformative prior. For k = 2, we obtained a sufficient condition for an inadmissibility result and demonstrate that the natural estimator and UMRU estimator are inadmissible. A simulation investigation is also carried out for the performance of the risk functions of various competing estimators. Finally, this article represents a conclusion of our study