Deregulation as a switch of regime: Price effects using a modified Hotelling rule

Concern over rising energy prices has led to support for government intervention to regulate the price of domestic non-renewable energy resources. However, US policy makers appear to be looking more towards competition, rather than regulation, to decide who will provide energy resources and how these will be priced. This paper models possible dynamic price effects occasioned by the removal of a regulatory constraint. The analysis focuses on a government decision to eliminate binding price ceilings on a non-renewable (energy) substitute when the incumbent resource is monopolistically controlled (using deregulation of natural gas as an illustration). Under certain conditions it is shown that one will get continuity of marginal revenue and a fall in the world price following decontrol (the expiration of a patent or a decision to grant mineral rights on federal land). In general, however, the model suggests that both marginal revenue and prices will be discontinuous and that the discontinuity is likely to result in a fall in price.     

Assessment of the sensory quality and shelf stability of selected Horro beef muscles in Ethiopia

The objective of this work was to assess sensory quality and retail life of Horro beef muscles in Ethiopia. Six muscles: M. rhomboideus (RM), M. infraspinatus (IS), M. longissimus lumborum (LL), M. semimembranosus (SM), M. biceps femoris (BF) and M. rectus femoris (RF) were considered. Sensory quality of the muscles was rated by a 9-member trained panel for palatability, tenderness, juiciness, amount of connective tissue (ACT), lean color and surface discoloration and measured by Warner-Bratzler Shear Force (WBSF). Retail life of the six muscles were evaluated for CIE 1976 L^∗a^∗b^∗ color values across 6 days. Significant differences (p < 0.05) between muscles for all sensory attributes and WBSF were found. Significant L^∗a^∗b^∗ color values of muscles and USDA quality grades by retail life were found (p < 0.05). Significant correlations (p < 0.05 or p < 0.01) of key parameters were also identified.     

An Approach for Integrating Valuable Flexibility During Conceptual Design of Networks

Energy and industrial networks such as pipeline-based carbon capture and storage infrastructures and (bio)gas infrastructures are designed and developed in the presence of major uncertainties. Conventional design methods are based on deterministic forecasts of most likely scenarios and produce networks that are optimal under those scenarios. However, future design requirements and operational environments are uncertain and networks designed based on deterministic forecasts provide sub-optimal performance. This study introduces a method based on the flexible design approach and the concept of real options to deal with uncertainties during conceptual design of networks. The proposed method uses a graph theoretical network model and Monte Carlo simulations to explore candidate designs, and identify and integrate flexibility enablers to pro-actively deal with uncertainties. Applying the method on a hypothetical network, it is found that integrating flexibility enablers (real options) such as redundant capacity and length can help to enhance the long term performance of networks. When compared to deterministic rigid designs, the flexible design enables cost effective expansions as uncertainty unfolds in the future.     

Detection and classification of large bowel obstruction from X-ray images using machine learning algorithms

Large bowel obstruction (LBO) occurs when there is a blockage or twisting in the large bowel that prevents wastes and gas from passing through. If left untreated, the blockage cuts off blood supply to the colon, causing sections of it to die which results in high rates of morbidity and fatality. The examination of clinical symptoms of LBO involves careful inspection of the cecum and colon. Radiologists use X-rays to inspect the clinical signs. Some research has been done to automate the detection of related abdominal and intestinal diseases. However, all these studies concentrate only on detecting Crohn's, ulcerative colitis, Acute Appendicitis, colorectal cancer, celiac diseases, liver diseases, and chronic kidney diseases. Automatic detection and classification of LBO has not been given due attention so far to the best of the authors knowledge. To address this challenge, we have designed a model for the detection and classification of LBO. The models development comprises of stages such as preprocessing, detection, segmentation, feature extraction, and classification. We used YOLOv3 for detection and used a gray scale level co-occurrence matrix (GLCM), and a convolutional neural network for feature extraction, while support vector machine (SVM) and softmax were used for classification. The proposed model achieved a diagnostic accuracy of 89% when feature extraction methods such as CNN and median filter with softmax classifier were used. CNN and Gaussian filter with soft max classifier achieved 91%, while CNN and anisotropic filter with soft max classifier achieved 92%. GLCM with threshold segmentation and Gaussian filter with SVM classifier achieved 87%, while CNN with watershed segmentation and Gaussian filter with SVM classifier achieved 97% and CNN-GLCM with watershed segmentation and anisotropic diffusion filter with SVM classifier achieved 98% for detection and classification of LBO. Finally, this paper presented a performance analysis of various machine learning approaches for detection and classification of LBO. Hence, our model is designed to assist human experts (Radiologists) in diagnosing LBO.     

Energy efficiency for rapeseed biodiesel production in different farming systems

Due to mounting concerns related to fossil fuel use and problems with their supply, the use of alternative sources of energy is increasing. One of the alternative sources is biomass and the European Union has adopted a biofuel directive that describes targets for the use of biofuels in the transport sector. The majority of biofuels produced in Europe comes from rapeseed. In this study, we focused on analyzing the efficiency of rapeseed biodiesel production. Energy efficiency in terms of Energy Return On Energy Invested (EROEI) was analyzed for two EU countries (Poland and The Netherlands) with different agro-ecological systems. Life Cycle Inventory (LCI) accounted for inputs, processes and outputs of energy in the biodiesel production system. Input parameters were derived from literature as well as from farmer’s interviews. The use of the outputs—straw, meal, and glycerin—were included in the LCI system boundary. The EROEI values ranged from 1.73 to 2.36 in Poland and from 2.18 to 2.60 in the Netherlands. The low number of respondents makes it risky to draw hard conclusions about these values but the patterns observed show that intensifying the production process and increasing yield bears very little or no benefit in terms of energy produced. Due to a higher amount of organic manure and consequently lower amount of artificial fertilizers used in crop growth in the Netherlands, the rapeseed biodiesel production system in the Netherlands is more efficient than in Poland. In both cases, the EROEI is quite low. More detailed spatial energy efficiency assessments are required to determine if and where sustainable production may be possible.