An investigation into the current utilisation and prospective of renewable energy resources and technologies in Libya

With the increase in energy demand and the international drive to reduce carbon emission from fossil fuel, there has been a drive in many oil-rich countries to diversify their energy portfolio and resources. Libya is currently interested in utilising its renewable energy resources in order to reduce the financial and energy dependency on oil reserves. This paper investigates the current utilisation and the future of renewable energy in Libya. Interviews have been conducted with managers, consultants and decision makers from different government organisations including energy policy makers, energy generation companies and major energy consumers. The results indicate that Libya is rich in renewable energy resources but in urgent need of a more comprehensive energy strategy and detailed implementation including reasonable financial and educational investment in the renewable energy sector

Review of State Policies on Teacher Induction

Outlines criteria and recommendations for state policies on providing mentoring support for new teachers and administrators, including universality, program standards on design and operation, mentor quality, program delivery, funding, and accountability

Determinants of Financial Performance of Commercial Dairy Farms.

Data from the 1993 Farm Costs and Returns Survey were used in a multi-variate analysis framework to determine factors associated with the financial performance of commercial dairy farm operations. Statistical equivalency tests revealed regional differences in the way extensive indebtedness, size of operation, and labor cost affect net farm incomes. Regional differences were also found in terms of how milk production per cow, per-unit cost of purchased feed, and level of adoption of capital intensive technologies affect per-unit returns. Examination of the variation in the net farm income of commercial dairy farms using the method of coefficients of separate determination identified the size of the operation, regardless of the location of the farm business, as the factor contributing the most to the variability in net farm income. On a per-unit-of-returns basis, factors found most important in explaining the variation in net returns per hundredweight of milk sold were cow's productivity, and per-cow forage production and purchased feed costs.financial performance, net farm income, technological adoption, Lorenz curve, Gini coefficient, Agricultural Finance, Livestock Production/Industries,

RISK MANAGEMENT THROUGH ENTERPRISE DIVERSIFICATION: A FARM-LEVEL ANALYSIS

Enterprise diversification is a self-insuring strategy used by farmers to protect against risk. This paper examines the impact of various farm, operator, and household characteristics on the level of on-farm diversification. Results provide evidence that larger farms are more specialized. Also, farmers who participate in off-farm income and farms located near urban areas are less likely to diversify. Additionally, results also show a significant positive relationship between diversification and farm/crop insurance and sole proprietorships. Finally, there is also evidence that farms that received government payments are more diversified than their counterparts.Farm Management, Risk and Uncertainty,

Wealth Accumulation by Farm Households: Evidence from a National Survey

Wealth affects the economic well-being of the farm households by enabling farm households to secure credit, facilitate intergenerational transfer, and provide for smoothing consumption expenditures in times of income shortfall. This paper examines the factors that are likely to influence wealth accumulation by farm households. Specifically, we use 2001 ARMS data and multivariate regression procedure to estimate two models; one for those farm households whose wealth originates primarily from the farm and another for households with both farm- and nonfarm wealth.Farm Management,

ADOPTION AND ECONOMIC IMPACT OF SITE-SPECIFIC TECHNOLOGIES IN U.S. AGRICULTURE

A Heckman's two-stage method is used in conjunction with data from the 1998 Agricultural Resource Management Study to estimate the likelihood of adopting a variable rate application technology (VRT) and the impact of such adoption on the per-acre costs of fertilizers and lime in cash grain production. Results highlight the importance of operator's level of human capital and attitude toward risk, along with size and location of farm in impacting VRT adoption decisions. Results also indicate no significant cost-savings attributable to VRT adoption.Farm Management, Research and Development/Tech Change/Emerging Technologies,

Energy-efficient embedded machine learning algorithms for smart sensing systems

Embedded autonomous electronic systems are required in numerous application domains such as Internet of Things (IoT), wearable devices, and biomedical systems. Embedded electronic systems usually host sensors, and each sensor hosts multiple input channels (e.g., tactile, vision), tightly coupled to the electronic computing unit (ECU). The ECU extracts information by often employing sophisticated methods, e.g., Machine Learning. However, embedding Machine Learning algorithms poses essential challenges in terms of hardware resources and energy consumption because of: 1) the high amount of data to be processed; 2) computationally demanding methods. Leveraging on the trade-off between quality requirements versus computational complexity and time latency could reduce the system complexity without affecting the performance. The objectives of the thesis are to develop: 1) energy-efficient arithmetic circuits outperforming state of the art solutions for embedded machine learning algorithms, 2) an energy-efficient embedded electronic system for the \u201celectronic-skin\u201d (e-skin) application. As such, this thesis exploits two main approaches: Approximate Computing: In recent years, the approximate computing paradigm became a significant major field of research since it is able to enhance the energy efficiency and performance of digital systems. \u201cApproximate Computing\u201d(AC) turned out to be a practical approach to trade accuracy for better power, latency, and size . AC targets error-resilient applications and offers promising benefits by conserving some resources. Usually, approximate results are acceptable for many applications, e.g., tactile data processing,image processing , and data mining ; thus, it is highly recommended to take advantage of energy reduction with minimal variation in performance . In our work, we developed two approximate multipliers: 1) the first one is called \u201cMETA\u201d multiplier and is based on the Error Tolerant Adder (ETA), 2) the second one is called \u201cApproximate Baugh-Wooley(BW)\u201d multiplier where the approximations are implemented in the generation of the partial products. We showed that the proposed approximate arithmetic circuits could achieve a relevant reduction in power consumption and time delay around 80.4% and 24%, respectively, with respect to the exact BW multiplier. Next, to prove the feasibility of AC in real world applications, we explored the approximate multipliers on a case study as the e-skin application. The e-skin application is defined as multiple sensing components, including 1) structural materials, 2) signal processing, 3) data acquisition, and 4) data processing. Particularly, processing the originated data from the e-skin into low or high-level information is the main problem to be addressed by the embedded electronic system. Many studies have shown that Machine Learning is a promising approach in processing tactile data when classifying input touch modalities. In our work, we proposed a methodology for evaluating the behavior of the system when introducing approximate arithmetic circuits in the main stages (i.e., signal and data processing stages) of the system. Based on the proposed methodology, we first implemented the approximate multipliers on the low-pass Finite Impulse Response (FIR) filter in the signal processing stage of the application. We noticed that the FIR filter based on (Approx-BW) outperforms state of the art solutions, while respecting the tradeoff between accuracy and power consumption, with an SNR degradation of 1.39dB. Second, we implemented approximate adders and multipliers respectively into the Coordinate Rotational Digital Computer (CORDIC) and the Singular Value Decomposition (SVD) circuits; since CORDIC and SVD take a significant part of the computationally expensive Machine Learning algorithms employed in tactile data processing. We showed benefits of up to 21% and 19% in power reduction at the cost of less than 5% accuracy loss for CORDIC and SVD circuits when scaling the number of approximated bits. 2) Parallel Computing Platforms (PCP): Exploiting parallel architectures for near-threshold computing based on multi-core clusters is a promising approach to improve the performance of smart sensing systems. In our work, we exploited a novel computing platform embedding a Parallel Ultra Low Power processor (PULP), called \u201cMr. Wolf,\u201d for the implementation of Machine Learning (ML) algorithms for touch modalities classification. First, we tested the ML algorithms at the software level; for RGB images as a case study and tactile dataset, we achieved accuracy respectively equal to 97% and 83.5%. After validating the effectiveness of the ML algorithm at the software level, we performed the on-board classification of two touch modalities, demonstrating the promising use of Mr. Wolf for smart sensing systems. Moreover, we proposed a memory management strategy for storing the needed amount of trained tensors (i.e., 50 trained tensors for each class) in the on-chip memory. We evaluated the execution cycles for Mr. Wolf using a single core, 2 cores, and 3 cores, taking advantage of the benefits of the parallelization. We presented a comparison with the popular low power ARM Cortex-M4F microcontroller employed, usually for battery-operated devices. We showed that the ML algorithm on the proposed platform runs 3.7 times faster than ARM Cortex M4F (STM32F40), consuming only 28 mW. The proposed platform achieves 15 7 better energy efficiency than the classification done on the STM32F40, consuming 81mJ per classification and 150 pJ per operation

TECHNOLOGY ADOPTION DECISIONS IN DAIRY PRODUCTION AND THE ROLE OF HERD EXPANSION

Technology adoption in dairy production allows for higher milk yield and lower per-unit costs. The importance of herd expansion and other factors to adoption was examined using a multinomial logit model and data from the USDA's 1993 Farm Costs and Returns Survey. Predicted probabilities of adoption were used to simulate the effect of herd expansion on milk production. Results identified age, size, and specialization in dairy production as important in increasing the likelihood of adopting a capital-intense technology. Education and size of operation positively impacted the decision to adopt a management-intense technology. Age, education, credit reserves, size, and increased usage of hired labor positively influenced the decision to adopt a combined capital-and management-intense technology.Livestock Production/Industries, Research and Development/Tech Change/Emerging Technologies,