Experimental data on Helically Coiled Oscillating Heat Pipe (HCOHP) design and thermal performance

Experimental and derived data from three Helically Coiled Oscillating Heat Pipes (HCOHPs) charged with ethanol, methanol and deionized water working fluids respectively are presented. The data was obtained from prototypes of the HCOHPs fabricated out of copper and tested under laboratory conditions. The primary data presented covers the HCOHP aspects, charging of the working fluid and temperature measurements from Omega K-type Thermocouples installed on the evaporators, condensers, adiabatic sections, and on the cylindrical copper vessel integrated with it. The derived data covers the HCOHPs performances and thermal contact resistance experienced during laboratory testing. The data on the aspects and charging of the working fluid provides useful information for the validation of design parameters of other heat pipes. The measured temperature data and the derived performance data can used to validate the performance of heat pipes in other studies and to depict performance profiles in standard text and reference books. The nature of the data presented as a whole would be useful for comparative analysis involving heat pipes and other passive heat transfer devices

Factors Influencing Successful Small-Farm Operations in North Carolina

The overall goal of this research project is to identify and refine factors influencing successful small farm operations in North Carolina. Small farms account for 91 percent of all farms. Given the importance of small farm viability, this research project focuses on identifying ways to further enhance successful small farming in North Carolina. In an effort to further explain the factors that affect successful small-scale farming, researchers have identified factors that have underpinnings in 1) small-farm educational programming; 2) small-scale agricultural enterprises and production practices; 3) alternative marketing; and 4) risk management. Although this research project includes several surveys, for this phase of the project the survey instrument solicited production and financial data, attitudes and beliefs about farming, as well as demographic questions. The research instrument was distributed to a sampling frame that also included small farmers not identified as being successful. Outcomes of this project yielded possible ways to further enhance the success of small farms in North Carolina. Based on case study and questionnaire results, income was not found to be as important as believed and the overall, “love of farming,” seemed to be the driving force behind the farmer’s view of success and not profit. The small farm may represent an individual business enterprise but in reality represents a family business whose success is often measured in qualifiers indicators rather than business quantifiers.Small Farm, Agribusiness, Successful, Agribusiness,

Case Studies of Successful Small Scale Farming in North Carolina

The goal of this study focuses on determining factors that contribute to a successful small farm in North Carolina and on identifying ways to further enhance successful small farming. North Carolina farms vary extensively in size and other characteristics, ranging from very small retirement and residential farms to establishments with millions of dollars in sales. Farming continues to be a distinctive industry in part because most production, even among very large farms, is carried out on family-operated farms whose operators often balance farm and off-farm employment and investment decisions. The case studies of successful small farmers conducted in November 2007 were the primary sources of data. The North Carolina Cooperative Extension Program identified three “successful” farmers from its sampling frame to participate in the case studies. Researchers identified sets of variables associated with small farm success through various literature, published and unpublished reports and recommendations from experts in the field. After the variables were operationalized, a questionnaire was developed as a guide for conducting the case studies interview protocols. Each case study consisted of a one-visit protocol with electronic follow-up. Researchers conducted on-site interviews, and then toured the individual farms. The case study farmers used a diverse mix of enterprises including specialty crops and a combination of marketing strategies. The educational level ranged from post high school to Ph.D. although all farmers attended several workshops. All farmers minimized risk through diversity, contractual sales and insurance. Only one farmer used computers for record keeping and finance. The overall “love of farming” seemed to be the biggest driving force behind the farmer’s view of success.Small Farmer, Agribusiness, Agricultural Finance, Teaching/Communication/Extension/Profession,

Validation of Factors Influencing Successful Small Scale Farming in North Carolina

This phase of the research project involves developing a survey instrument to test the validity and predictive value of the variables identified in previous case studies. Given the importance of small farm viability, this research project focuses on identifying ways to further enhance successful small farming in North Carolina. The survey instrument was designed to solicit production and financial data, attitudes and beliefs about farming, as well as demographic questions. The results demonstrated that successful farmers indicators were the “love of farming” and “manageable debt”. Other strong indicators of successful farmers included a combination of marketing strategies that utilize technology such as websites as well as local farmers markets and educational level. Knowledge about the successful small farm is likely to provide valuable information about how to evaluate the “successfulness” of small farm operations and produce best practices models for small scale farm operations.Small Farmers, Agribusiness, Agribusiness, Farm Management,

The Role of Financial Institutions in Housing Delivery in the Kumasi Metropolis of Ghana: An Institutional and Client Analysis

The commitment of government in providing social housing in Ghana is non-existent in recent times.  As a result, the housing delivery in Ghana is driven by individual housing construction through the incremental process or acquisition from private developers which is challenged by financial constraints and the need to acquire alternative sources of funding becomes a necessity. This study sought to undertake an institutional and client analysis of the role of financial institutions in housing delivery and employed a case study research design within quantitative and qualitative research methodological paradigms to examine the research questions. A sample of 7 financial institutions which were involved in housing financing were purposively sampled and contacted for data using questionnaires and interview guides. By using questionnaires, a sample of 150 clients with links to the financial institutions were randomly selected and contacted for data. The study established that the contribution of financial institutions to housing delivery was unsatisfactory because of the limited options available, and the lack of consideration for the majority, but poor Ghanaians. It was also found that the financial institutions had failed to exert the impetus for improving housing delivery in the country due to low patronage by clients, incidence of non-settlement of loan amounts with interest, inadequate long term mortgage finance, lack of government support, high interest rate, strict demands for collateral security, long, boring, and expensive processes of arranging for mortgage finance or loans. The researchers therefore recommended among other remedial options that efforts should be made to improve the patronage and the establishment of long term mortgage financing with flexible interest rates and redemption options. Keywords: Financial institutions, housing delivery, low income earners, Ghana

Experimental Investigation into the Integration of Solid Desiccant Packed Beds with Oscillating Heat Pipes for Energy Efficient Isothermal Adsorption Processes

The heat of adsorption released during physical adsorption of water vapour on solid desiccants increases its surface vapour pressure consequently decreasing its adsorption capacity. In packed beds, this raises the bed temperature subsequently increasing the cooling load and energy required for the regeneration of the solid desiccants. In this study, we experimentally investigate helically coiled oscillating heat pipes (HCOHPs) using ethanol, methanol and deionized water respectively as working fluids integrated with packed beds of varying configurations towards isothermal adsorption. The results show average bed temperature reduction varied with heat output from the bed and the thermal performance of the HCOHPs. The fully packed bed (FPB) integrated with the ethanol HCOHP (EOHP) achieved maximum average bed temperature reduction of 14.0°C. The annulus packed bed (APB) integrated with the water HCOHP (WOHP) achieved a temperature drop of 10.1°C. Adsorption peak temperature reductions on the other hand were strongly dependent on HCOHP start-up. Maximum adsorption peak temperature reduction of 20.8°C in Mass Transfer Zone (MTZ) 1 was attained by the FPB-EOHP integrated system. For the APB, maximum adsorption peak temperature reduction of 13.2°C in MTZ 3 was recorded for Small APB (SAPB)-Methanol HCOHP (MOHP) integrated system. Adsorption rates in the FPB were influenced by the mal-distribution of flow within the bed and increased slightly on integration with the HCOHPs. Maximum rates of 1.47×10-06 kg/s was achieved by the FPB-EOHP. For the APB, the SAPB-WOHP achieved maximum adsorption rates of 1.21×10-05 kg/s. The adsorption rates in the Medium APB (MAPB) on the other hand did not appear to be influenced on integration with the HCOHPs. Overall, performances of the integrated systems were found to be influenced partly by the packed bed configuration, the HCOHPs' performance and the heat transfer resistance between the evaporators and the vessel walls. We recommend further optimization of the system parameters and investigation of its regeneration potential for future practical applications

An Alternative to the MVU Estimator to Estimate the Level of DC in AWGN

In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a particular statistical model, finding parameter values that maximize probability, observations, and the parameters are specified. The MLE can be seen as a special case of maximum post-positive estimation (MAP), which includes a uniform preventive distribution of parameters, or as a variant of the MAP that ignores the above and is therefore unregulated. Now let's look at an alternative to the MVU estimator, which is desirable in situations where the minimum variance unbiased (MVU) estimator does not exist or cannot be found, even if it exists. This estimator, which relies on the principle of maximum likelihood, is primarily the common method for obtaining a practical estimator. It has the clear advantage of being a crank turning procedure, which allows you to implement it for complicated estimation problems. A clear advantage of MLE is that it can be found numerically for a given data-set. The safest way to find the MLE is to search the grid, as long as the space between the searches are small enough, we are sure to find the MLE. Keywords: Maximum Likelihood Estimation, minimum variance unbiased, Estimator, Probability Distribution Function. DOI: 10.7176/ISDE/11-3-05 Publication date: June 30th 202

Physicochemical and biological properties of different Cocoa Pod Husk-based composts

The objective was to evaluate the properties of cocoa pod husk-based composts for potential application as soil amendments for crop production. The physicochemical and biological properties of the compost types were analysed. Four compost types were prepared by mixing cocoa pod husk, poultry manure and Panicum maximum in different proportions. A phytotoxicity test was carried out using maize (Zea mays L.) to test whether the compost types contain substances that inhibit seed germination or growth of the radicle. Bulk densities of the compost types were higher than 0.160 Mg m-3, an indication that the compost types as soil amendment will restrict root growth thereby inhibiting plant growth. The average pH of the compost types falls within the optimum range of 6.5 to 8.5 and thus, the composts are stabilized. The compost types had high nitrogen content, so when utilized as a soil amendment would improve the nitrogen content of soils. Copper concentrations in the compost types were far below the WHO/FAO permissible limit of 100 mg kg-1, therefore can be applied at high rates without any problem of copper accumulation in soil. Phytophthora palmivora and Phytophthora megakarya were not detected from the compost types, therefore the compost types could be used without Phytophthora disease infection. Germination percentage and germination index showed that the analyzed compost types achieved high percentages of the germinating capacity of maize seeds and had no phytotoxic substances. The cocoa pod husk-based composts showed substantially varied physicochemical and biological properties suitable to support plant growth. The results clearly showed that, CPHcomp3 made from CPH residues, poultry manure and Panicum maximum at the ratio 6: 1: 2 mixture is recommended for use as a soil amendment for crop production

Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security

Cyber Supply Chain (CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system which can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, and Procedure (TT and P), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. The experiment considers attack and TTP as input parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement