Texas A&M University-Kingsville: AKM Digital Repository
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1689 research outputs found
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Effects of air flow in conjunction with acetic acid spray on microbial load of pork
No full textWithin the pork processing sector, the benefits of evaporative water loss on the surface of pork in conjunction with an organic acid wash needs investigating to provide information for possible microbial interventions. To observe the effects of evaporative water loss in conjunction with acetic acid sprays, sections of pork loins were subjected to a mixture of air flow and an acetic acid wash, including: 1) a control group with no acetic acid application or air flow; 2) a group with acetic acid and no air flow; 3) a group with air flow and no acetic acid spray; and 4) a group with both acetic acid and air flow. Once inoculated, loin segments were sampled throughout a 14-day experimental period, and changes in surface microbial load were observed through the use of standard bacterial plating methods. Results from the experiment indicated that there was a reduction in bacterial load between loins that received an antimicrobial intervention and those within the untreated control group; combinations of interventions did not differ (p > 0.05) in their ability to reduce overall microbial levels. Results of this experiment indicate that treating pork loin sections with acetic acid effectively in reduced microbial load, regardless of air flow, when compared to untreated loin sections
Characterization of Phytophthora nicotianae infection of sour orange roots
No full textPhytophthora nicotianae is an important soil-borne pathogen of citrus which cause foot and root rot. Although Phytophthora-resistant citrus rootstocks are available, such rootstocks are not well adapted to the soil and environmental conditions in the Rio Grande Valley (RGV) of South Texas. Sour orange (Citrus aurantium), the most widely used rootstock in the RGV, is considered tolerant to Phytophthora spp. induced diseases. Several studies have been conducted on the interaction between P. nicotianae with resistant and susceptible rootstocks. However,
limited information is available on the interaction of P. nicotianae on tolerant rootstocks, such as sour orange. This study aims to characterize the interaction of P. nicotianae on sour orange rootstock growing in-vitro, to understand the infection process by visualizing root colonization using microscopy and evaluating the expression of plant defense-related genes, PR1, PAL, POX, and RD21 over time by RT-qPCR. Live monitoring of P. nicotianae zoospores by microscopy revealed that spore attachment to sour orange root tissue occurs within 2 hours after inoculation. Microscopy imagery of a time course infection process showed that P. nicotianae colonization of sour orange roots initiates within 4 hours after inoculation, 24 hours hyphae are visible within the root tissues and at 48 hours post-inoculation, intercellular colonization and haustoria-like structures were observed. Gene expression of defense-related genes PR1, PAL, and POX is activated significantly (P-value < 0.05) at 24 hours post-infection, while RD21 is not induced. This work provides a better understanding of the interaction between P. nicotianae and sour orange rootstock, which will aid the elucidation of tolerance mechanisms to Phytophthora diseases
A correlational study of the L2 motivational self system and English as a foreign language learning
No full textThis correlational research examined the relationship between Dörnyei’s (2005, 2009) L2 Motivational Self System model (L2MSS) and L2 achievement of EFL Mexican learners at a university foreign language center. Male and female participants (413) answered a questionnaire concerning the L2MSS components, the Ideal L2 Self, the Ought-to L2 Self, Language Learning
Experience, and learners’ Intended Effort in EFL learning. L2 achievement was measured by the final grade of the English course. Spearman’s correlations found a consistent correlation of all L2MSS and Intended Effort, with a strong correlation between Language Learning Experience
and Intended Effort. Ideal L2 Self had a weak correlation with achievement. Ought-L2 Self and Instrumentality- Prevention had a weak negative relationship with achievement. Ought-L2 Self and Instrumentality- Prevention was related to achievement but negatively. A regression analysis established that Ideal L2 Self and Instrumentality - Prevention were correlated to achievement, but only eight percent of these motivational components explained or predicted achievement. As
the study revealed no consistent correlational values between L2MSS and EFL achievement, Language Learning Experience was the most influential component of the model for Intended Effort.
Regarding gender and EFL motivation, significant differences favored female learners in most L2 MSS components. Also, findings revealed the Ideal L2 Self dimension to have the highest mean and median value and the Ought-L2 Self the lowest in both genders. Language Learning Experience was the L2MSS component related to the frequency of class hours per
week as Language Learning Experience was present in intensive 10-hour on weekdays classes.
To sum up, findings displayed a weak correlation between Ideal L2 Self and Instrumentality – Prevention with achievement, the strong association between Language
Learning Experience and Intended Effort, the female learners’ role in EFL motivation, and how required intensive EFL courses were for motivation in the L2MSS framework. With these results, this study sought to contribute to a better understanding of motivation, examining L2MSS in EFL Northeastern Mexican learners
The predictability of STAAR passage based on an at-risk indicator enrolled in rural school districts in the State of Texas
No full textStudents enrolled in public education schools in the State of Texas are required to participate in and secure passage of a norm-referenced state assessment in different grade levels and subject areas. The State of Texas Assessment of Academic Readiness (STAAR) is the norm-referenced assessment used for students in grades third through eighth, and enrolled in high school English I, English II, Algebra I, Biology, or U.S. History. Passage of these assessments are required for graduation of high school, and in some grade levels for promotion. The purpose of this study was to identify the predictability of English I STAAR passage based on an at-risk and special program indicators enrolled in Region 2 school districts in the state of Texas. The socioeconomic status of a student or receiving special education services is one of the special program indicators used to identify a student for academic performance and graduation data tracking. A student is considered to be at-risk for not completing graduation is they are identified as an English Language learner. Additional research is necessary to further support or make change to current education policy to ensure students are assessed in an equitable fashion, versus using a norm-referenced assessment due to students who receive an at-risk or special program indicators that students may be dispositioned to not to perform well on. The study determined that there is a strong correlation between three different special program indicators and the predictability of passage of the English I End of Course (EOC) STAAR assessment. There are other special program indicators that are used to identify the different applicable descriptors for a every student. The result of a predictability value are of great benefit to all education stake holders when employing a STAAR assessment to measure a student’s summative success. The study indicated a predictive correlation between an at-risk or special program indicator and passage of an English I End of Course STAAR assessment. With these results, a classroom teacher or campus leader should be able to foresee a student’s success and then adjust their instruction and environment. Those with a greater responsibility, such as the Texas Education Agency and the state legislators, are recommended to use this study to restructure their compliance piece of monitoring of student success through a high-stakes assessment. As educators, it is known that there is an unwritten and spoken truth of predicting a student’s success based on their at-risk or special programs indicator. A predictive measure is evident if a student economically disadvantaged, special education or an English language learner. District and campus leaders also have a responsibility to ensure content is being taught and mastered, and not just testing strategies. While there is data on predictive measures, there is limited data on predictive measure on the STAAR assessment, and especially limited after COVID. The results should be applied when creating and administering a STAAR assessment
Correction of offset mismatch in time-interleaved ADC using adaptive filter algorithm
No full textAnalog to Digital converter (ADC) plays an essential role in the mixed or digital signal processing. ADC is an electronic integrated circuit that converts an analog signal to a digital signal. Digital signals are less prone to additive noise. The microprocessor and some digital circuits perform the complex operations with digital signals. Some applications, such as telecommunication infrastructure and radar systems, must deal with super-high frequency. Time Interleaved Analog to Digital Converter (TI-ADC) is often used to increasing the sampling rate without compromising on resolution. It consists of multiple individual ADCs with a lower sampling rate to achieve high sampling rate. Due to the aging effect and changes in characteristics, the ADC's resolution may degrade, or sometimes it fails to meet accuracy. Changes between sub-ADCs characteristics can be recognized as mismatches, such as gain, offset, bandwidth, and timing mismatch. In this thesis, we devise the correction algorithm in the TI-ADC to remove offset mismatch. The proposed method is based on Adaptive Noise Cancelling (ANC) using Least Mean Square (LMS) adaptive algorithm
PVA based membranes crosslinked with SSA and TEOS as a protonic membrane in a single-celled Mfc
No full textA crosslinked Poly(vinyl alcohol) (PVA) membrane was fabricated using sulfosuccinic acid (SSA) as a proton source and tetraethyl orthosilicate (TEOS) as a crosslinking scaffold at varying ratios (PVA:SSA: TEOS, both between 5–40 % by mass, (aq. m %) at the same
crosslinking temperature. The crosslinked PVA membranes were bench tested as a protonic membrane electrolyte, to compare against Nafion-115™, for use in microbial fuel cell applications. The membranes were evaluated using ultraviolet-visible (UV-vis), and Fourier transforms infrared (FTIR) spectroscopy. By ion-exchange capacity, the water percent content, and the proton conductivity was determined. The ion exchange capacities were in the range of 0.02-5.75 mmol/monomer g of polymer depending on the weight percent of SSA, which was
also observed for the proton conductivity. The sensitivity dependence on proton conductance was a tradeoff between chemical stability to water. The greatest dependence was due to the degree of available-SO3- H+ for proton conduction and the limitation of water transport. The proton conductivities of all membranes were in the range of 1.45 mS/cm–1 to 5.43 mS/cm–1
(proton), in the temperature range of 25–40◦C, depending on the crosslinking ratios. The power densities obtained were around 32 mW/cm2, surpassing slightly to that of Nafion-115™
Performance analysis of MUSIC algorithm for direction of Arrival estimation
No full textThe smart antenna system emerged as a new technology to improve the data rate in Wireless Communication systems. Due to the advancements in higher quality image sensors and the popularity of multimedia and social networks, the need for faster data rates became inevitable. The smart antenna system is composed of antenna arrays and signal processing algorithms. Antenna arrays capture the incoming electromagnetic waves, and these signals are used to determine the location of the radiating sources. Once the direction of arrival of the signals are determined, the antenna radiation pattern could be customized so that Maximum signals are radiated in the direction of interests and jamming signals can be nulled. It is important to determine the DOA of signals accurately and efficiently. Here, we investigated the performance of the Multi Signal Classification known as MUSIC, which is a super-resolution estimation technique. In this thesis, the overview for smart antennas, direction of Arrival and MUSIC algorithm are discussed and the performance of MUSIC algorithm to estimate direction of arrival is evaluated based on various parameters like array element number, element spacing, signal to noise, effect of signal incident angle, number of snapshots with MATLAB for experimental purpose. Based on analysis, array element numbers, element spacing, signal to noise ratio, number of the snapshot, it is shown that these parameters have crucial impact on estimation of direction of arrival of the signals
A comparative study of Scrum and traditional methodologies in automotive product design and development area of Midwest US
No full textThe purpose of this study is to manufacture products using the Scrum methodology in the automotive industry and prove its benefit in comparison to the traditional methodology. Continuous innovation and product development are the key factors for the growth of any
organization in the market today. But delivering new products requires meeting an ever-changing
and increasing demand for developing the competitive product.
Developing a new product can be challenging where it is difficult to manage and predict the outcome. This calls for methods that meet these challenges. The traditional approach, for example, the Waterfall methodology which follows a sequential path fails to meet the product’s
ever-increasing or changing requirements. As an alternative to this traditional approach, the agile
philosophy was born. This methodology is iterative and incremental, based on feedback. As a form of agile methodology, Scrum emerged as the new way of managing development within any given industry at the beginning of the 21st century. Although Scrum has been widely used in the Software industry, it is still new to the industrial/automotive manufacturing system.
Therefore, a framework was developed in this research to facilitate the application of Scrum methodology in this industry
Achieving connected k-coverage in wireless sensor networks using computational geometry-based approaches
No full textThe metrics, called coverage and connectivity, are often used to assess the sensor's sensing and communication capabilities in planar wireless sensor networks (PWSNs). A PWSN relies on the detection capabilities of the sensors to provide coverage. However, this is not sufficient for this type of network to function properly. In addition, having all the sensors connected, i.e., they are capable of interacting with each other, is essential for the proper operation of PWSNs. This research aims to solve the connected k-coverage problem in PWSNs by ensuring that all field locations are covered or within the sensing range of at least k sensors (k > 1). Here, we provide a solution to the connected k-coverage problem using computational geometry-based approaches. Our goal is to maximize the lifetime of PWSNs by achieving connected k-coverage with a minimum number of sensors. To begin, we propose to tile the whole field of interest with planar tiles, which are convex polygons that do not overlap with each other and do not leave any gap in the underlying field. Following this, we compute the planar sensor density that is required to achieve k-coverage of a planar field of interest using these convex polygonal tiles. In addition, we determine network connectivity by correlating the sensing range of sensors with their communication range. Moreover, we propose energy-efficient connected k-coverage protocols based on our planar convex polygonal tiles. Finally, we validate our conceptual analysis with facts from simulations
Material flow optimization in a lean environment
No full textThe year 2019 brought a different approach to technology and many individuals needed to do things remotely due to concerns with Covid-19. A remote workplace environment gained more ground as a common business practice. Several manufacturing industries looked for means
to continue production with little to no human presence. The concept of Internet of Things (IoT) gained more traction to facilitate running or controlling industrial processes and applications.
Internet of Things has vast applications, from home automation to data collection to process automation and the list is endless. The aim of this research was to show material flow optimization in a lean environment and to demonstrate a cheaper and affordable means to pull
storage data for production using readily available data formats. I also showed how various components can be integrated to achieve material flow process in a lean environment. I was able to develop a mini-stacker crane system using the gravity conveyor design approach. The
simulation software used was factory IO and the controller is siemens PLC