Search results for: Food processing

Authors: Brian A. Smith, Ronald W. McClendon, Gerrit Hoogenboom

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The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding additional input terms that describe the date of an observation, increasing the duration of prior weather data included in each observation, and reexamining the number of hidden nodes used in the network. Models were created to predict air temperature at hourly intervals from one to 12 hours ahead. Each ANN model, consisting of a network architecture and set of associated parameters, was evaluated by instantiating and training 30 networks and calculating the mean absolute error (MAE) of the resulting networks for some set of input patterns. The inclusion of seasonal input terms, up to 24 hours of prior weather information, and a larger number of processing nodes were some of the improvements that reduced average prediction error compared to previous research across all horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17°C and 0.16°C, respectively, improvements of 7.4% and 5.9% over the existing model at these horizons. Networks instantiating the same model but with different initial random weights often led to different prediction errors. These results strongly suggest that ANN model developers should consider instantiating and training multiple networks with different initial weights to establish preferred model parameters.

Keywords: Decision support systems, frost protection, fruit, time-series prediction, weather modeling

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Authors: Akbar Cahyadhi Pratama Putra, Tantri Utami Widhaningtyas, M. Randy Aswin

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Indonesia, as an archipelagic nation, has a very long coastline with significant potential for marine resources, including mangrove ecosystems. The Lapindo mudflow disaster in Sidoarjo, East Java, resulted in mudflow being discharged into the sea through the Brantas and Porong rivers. The mud material transported by the river flow is feared to be dangerous because it contains harmful substances such as heavy metals. This study aims to map the mangrove ecosystem in terms of its density and assess the impact of the Lapindo mud disaster on the mangrove ecosystem, along with efforts to sustain its continuity. The mapping of the coastal mangrove conditions in Sidoarjo was carried out using remote sensing products, specifically Landsat 7 ETM+ images, taken during dry months in 2002, 2006, 2009, and 2014. The density of mangroves was determined using NDVI, which utilizes band 3 (the red channel) and band 4 (the near IR channel). Image processing to generate NDVI was performed using ENVI 5.1 software. The NDVI results were used to assess mangrove density on a scale from 0 to 1. The growth of mangrove ecosystems, both in terms of area and density, showed a significant increase from year to year. The development of mangrove ecosystems was influenced by the deposition of Lapindo mud in the estuaries of the Porong and Brantas rivers, where the silt provided a suitable medium for the growth of the mangrove ecosystem, leading to an increase in its density. The rise in density was supported by public awareness to mitigate heavy metal contamination, allowing for mangrove breeding near the affected areas.

Keywords: Archipelagic nation, Mangrove, Lapindo mudflow disaster, NDVI.

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Authors: Chutarat Boontho

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The purposes of this study were as follows to evaluate the economic value of Phu Kradueng National Park by the travel cost method (TCM) and the contingent valuation method (CVM) and to estimate the demand for traveling and the willingness to pay. The data for this study were collected by conducting two large scale surveys on users and non-users. A total of 1,016 users and 1,034 non-users were interviewed. The data were analyzed using multiple linear regression analysis, logistic regression model and the consumer surplus (CS) was the integral of demand function for trips. The survey found, were as follows: 1)Using the travel cost method which provides an estimate of direct benefits to park users, we found that visitors- total willingness to pay per visit was 2,284.57 bath, of which 958.29 bath was travel cost, 1,129.82 bath was expenditure for accommodation, food, and services, and 166.66 bath was consumer surplus or the visitors -net gain or satisfaction from the visit (the integral of demand function for trips). 2) Thai visitors to Phu Kradueng National Park were further willing to pay an average of 646.84 bath per head per year to ensure the continued existence of Phu Kradueng National Park and to preserve their option to use it in the future. 3) Thai non-visitors, on the other hand, are willing to pay an average of 212.61 bath per head per year for the option and existence value provided by the Park. 4) The total economic value of Phu Kradueng National Park to Thai visitors and non-visitors taken together stands today at 9,249.55 million bath per year. 5) The users- average willingness to pay for access to Phu Kradueng National Park rises from 40 bath to 84.66 bath per head per trip for improved services such as road improvement, increased cleanliness, and upgraded information. This paper was needed to investigate of the potential market demand for bio prospecting in Phu Kradueng national Park and to investigate how a larger share of the economic benefits of tourism could be distributed income to the local residents.

Keywords: Contingent Valuation Method, Travel Cost Method, Consumer surplus.

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Authors: Iftikhar Ahmad, Youssef Elkezza

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Sarir oilfield is in North Africa. It has facilities of oil and gas production. The assets of the Sarir oilfield can be divided into five following categories, namely: (i) Well bore and wellheads; (ii) Vessels such as separators, desalters, and gas processing facilities; (iii) Pipelines including all flow lines, trunk lines, and shipping lines; (iv) storage tanks; (v) Other assets such as turbines and compressors, etc. The nature of the petroleum industry recognizes the potential human, environmental and financial consequences that can result from failing to maintain the integrity of wellheads, vessels, tanks, pipelines, and other assets. The importance of effective asset integrity management increases as the industry infrastructure continues to age. The primary objective of assets integrity management (AIM) is to maintain assets in a fit-for-service condition while extending their remaining life in the most reliable, safe, and cost-effective manner. Corrosion management is one of the important aspects of successful asset integrity management. It covers corrosion mitigation, monitoring, inspection, and risk evaluation. External corrosion on pipelines, well bores, buried assets, and bottoms of tanks is controlled with a combination of coatings by cathodic protection, while the external corrosion on surface equipment, wellheads, and storage tanks is controlled by coatings. The periodic cleaning of the pipeline by pigging helps in the prevention of internal corrosion. Further, internal corrosion of pipelines is prevented by chemical treatment and controlled operations. This paper describes the integrity management system used in the Sarir oil field for its oil and gas production facilities based on standard practices of corrosion mitigation and inspection.

Keywords: Assets integrity management, corrosion prevention in oilfield assets, corrosion management in oilfield, corrosion prevention and inspection activities.

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Authors: Laila Salah Seada, Ashraf Ibrahim, Fawaz Al Rashid, Ihab Abdo, Hassan Kasim, Waleed Al Mansi, Saud Al Shabli

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Background and Objectives: Colorectal carcinoma is increasing among both men and women worldwide. It has a multifactorial etiology including genetic factors, environmental factors and inflammatory conditions of the digestive tract. A clinicopathologic assessment of colorectal carcinoma in Hail region is done, considering any changing patterns in two 5-year periods from 2005-2009 (A) and from 2012 to 2017 (B). All data had been retrieved from histopathology files of King Khalid Hospital, Hail. Results: During period (A), 75 cases were diagnosed as colorectal carcinoma. Male patients comprised 56/75 (74.7%) of the study, with a mean age of 58.4 (36-97), while females were 19/75 (25.3%) with a mean age of 50.3(30-85) and the difference was significant (p = 0.05). M:F ratio was 2.9:1. Most common histological type was adenocarcioma in 68/75 (90.7%) patients mostly well differentiated in 44/68 (64.7%). Mucinous neoplasms comprised only 7/75 (9.3%) of cases and tended to have a higher stage (p = 0.04). During period (B), 115 cases were diagnosed with an increase of 53.3% in number of cases than period (A). Male to female ratio also decreased to 1.35:1, females being 44.83% more affected. Adenocarcinoma remained the prevalent type (93.9%), while mucinous type was still rare (5.2%). No distal metastases found at time of presentation. Localization of tumors was rectosigmoid in group (A) in 41.4%, which increased to 56.6% in group (B), with an increase of 15.2%. Iliocecal location also decreased from 8% to 3.5%, being 56.25% less. Other proximal areas of the colon were decreased by 25.75%, from 53.9% in group (A) to 40% in group (B). Conclusion: Colorectal carcinoma in Hail region has increased by 53.3% in the past 5 years, with more females being diagnosed. Localization has also shifted distally by 15.2%. These findings are different from Western world patterns which experienced a decrease in incidence and proximal shift of the colon cancer localization. This might be due to better diagnostic tools, population awareness of the disease, as well as changing of life style and/or food habits in the region.

Keywords: Colorectal cancer, Hail Region, changing pattern, distal shift.

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Authors: Hamada A. Ahmed, Kadry M. Sadek, Ayman E. Taha

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The effects of basil and/or chamomile seed supplementation on the growth of Hubbard broiler chicks were evaluated. The antioxidant effects of these supplements were also assessed. 120 1-day-old broiler chicks were randomly divided into four equal groups. The control group (group 1) was fed a basal diet (BD) without supplementation. Groups 2, 3, and 4 were fed the BD supplemented with 10g basil, 10g chamomile, and 5g basil plus 5g chamomile per kg of food, respectively. Basil supplementation alone or in combination with chamomile non-significantly (P≥0.05) increased final body weight (3.2% and 0.3%, respectively) and weight gain (3.5% and 3.6%, respectively) over the experimental period. Chamomile supplementation alone non-significantly (P≥0.05) reduced final body weight and weight gain over the experimental period by 1.7% and 1.7%, respectively. In comparison to the control group, herbal seed supplementation reduced feed intake and improved the feed conversion and protein efficiency ratios. In general, basil seed supplementation stimulated chicken growth and improved the feed efficiency more effectively than chamomile seed supplementation. The antioxidant activities of basil and/or chamomile supplementation were examined in the thymus, bursa, and spleen. In chickens that received supplements, the level of malondialdehyde was significantly decreased, whereas the activities of glutathione, superoxide dismutase, and catalase were significantly increased (P<0.05). Supplementation of basil and/or chamomile did not affect blood protein levels, but had lipid-lowering effects as evidenced by reduced serum levels of total lipids, triglycerides, and cholesterol. In conclusion, supplementation of basil and/or chamomile improved growth parameters in broiler chicks and had antioxidant and blood lipid-lowering effects. These beneficial effects of basil and/or chamomile supplementation resulted in economically viable production of high-quality white meat containing no harmful residues.

Keywords: Herbal additives, basil, chamomile, broiler, growth performance, antioxidant.

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: Deep neural models, natural language inference, recognizing textual entailment, sentence-to-sentence relation.

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Authors: U. Waqas, M. F. Ahmed, A. Mehmood, M. A. Rashid

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In geophysical exploration surveys, the quality of acquired data holds significant importance before executing the data processing and interpretation phases. In this study, 2D seismic reflection survey data of Fort Abbas area, Cholistan Desert, Pakistan was taken as test case in order to assess its quality on statistical bases by using normalized root mean square error (NRMSE), Cronbach’s alpha test (α) and null hypothesis tests (t-test and F-test). The analysis challenged the quality of the acquired data and highlighted the significant errors in the acquired database. It is proven that the study area is plain, tectonically least affected and rich in oil and gas reserves. However, subsurface 3D modeling and contouring by using acquired database revealed high degrees of structural complexities and intense folding. The NRMSE had highest percentage of residuals between the estimated and predicted cases. The outcomes of hypothesis testing also proved the biasness and erraticness of the acquired database. Low estimated value of alpha (α) in Cronbach’s alpha test confirmed poor reliability of acquired database. A very low quality of acquired database needs excessive static correction or in some cases, reacquisition of data is also suggested which is most of the time not feasible on economic grounds. The outcomes of this study could be used to assess the quality of large databases and to further utilize as a guideline to establish database quality assessment models to make much more informed decisions in hydrocarbon exploration field.

Keywords: Data quality, null hypothesis, seismic lines, seismic reflection survey.

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Authors: Olivier Adam, Maciej Lopatka, Christophe Laplanche, Jean-François Motsch

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This article presents the results using a parametric approach and a Wavelet Transform in analysing signals emitting from the sperm whale. The extraction of intrinsic characteristics of these unique signals emitted by marine mammals is still at present a difficult exercise for various reasons: firstly, it concerns non-stationary signals, and secondly, these signals are obstructed by interfering background noise. In this article, we compare the advantages and disadvantages of both methods: Auto Regressive models and Wavelet Transform. These approaches serve as an alternative to the commonly used estimators which are based on the Fourier Transform for which the hypotheses necessary for its application are in certain cases, not sufficiently proven. These modern approaches provide effective results particularly for the periodic tracking of the signal's characteristics and notably when the signal-to-noise ratio negatively effects signal tracking. Our objectives are twofold. Our first goal is to identify the animal through its acoustic signature. This includes recognition of the marine mammal species and ultimately of the individual animal (within the species). The second is much more ambitious and directly involves the intervention of cetologists to study the sounds emitted by marine mammals in an effort to characterize their behaviour. We are working on an approach based on the recordings of marine mammal signals and the findings from this data result from the Wavelet Transform. This article will explore the reasons for using this approach. In addition, thanks to the use of new processors, these algorithms once heavy in calculation time can be integrated in a real-time system.

Keywords: Autoregressive model, Daubechies Wavelet, Fourier Transform, marine mammals, signal processing, spectrogram, sperm whale, Wavelet Transform.

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Authors: El-Said. I. Shabana, Mohammad S. Tayeb, Maher M. T. Qutub, Abdulraheem A. Kinsara

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Usually, phosphate deposits contain ²³⁸U and ²³²Th in addition to their decay products. Due to their different pathways in the environment, the ²³⁸U/²³²Th activity concentration ratio usually found to be greater than unity in phosphate sediments. The presence of these radionuclides creates a potential need to control exposure of workers in the mining and processing activities of the phosphate minerals in accordance with IAEA safety standards. The greatest dose to workers comes from exposure to radon, especially ²²²Rn from the uranium series, and has to be controlled. In this regard, radon (²²²Rn) was measured in the atmosphere (indoor and outdoor) of Al-Jalamid phosphate-mines working area using a portable radon-measurement instrument RAD7, in a purpose of radiation protection. Radon was measured in 61 sites inside the open phosphate mines, the phosphate upgrading facility (offices and rooms of the workers, and in some open-air sites) and in the dwellings of the workers residence-village that lies at about 3 km from the mines working area. The obtained results indicated that the average indoor radon concentration was about 48.4 Bq/m³. Inside the upgrading facility, the average outdoor concentrations were 10.8 and 9.7 Bq/m³ in the concentrate piles and crushing areas, respectively. It was 12.3 Bq/m³ in the atmosphere of the open mines. These values are comparable with the global average values. Based on the average values, the annual effective dose due to radon inhalation was calculated and risk estimates have been done. The average annual effective dose to workers due to the radon inhalation was estimated by 1.32 mSv. The potential excess risk of lung cancer mortality that could be attributed to radon, when considering the lifetime exposure, was estimated by 53.0x10^-4. The results have been discussed in detail.

Keywords: Dosimetry, environmental monitoring, phosphate deposits, radiation protection, radon-22.

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Authors: Natalia A. Salazar, Erika K. Méndez, Catalina Álvarez, Carlos E. Orrego

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Lyophilization, also called freeze-drying, is an important dehydration technique mainly used for pharmaceuticals. Food industry also uses lyophilization when it is important to retain most of the nutritional quality, taste, shape and size of dried products and to extend their shelf life. Vacuum-Induced during freezing cycle (VI) has been used in order to control ice nucleation and, consequently, to reduce the time of primary drying cycle of pharmaceuticals preserving quality properties of the final product. This procedure has not been applied in freeze drying of foods. The present work aims to investigate the effect of VI on the lyophilization drying time, final moisture content, density and reconstitutional properties of mango (Mangifera indica L.) slices (MS) and mango pulp-maltodextrin dispersions (MPM) (30% concentration of total solids). Control samples were run at each freezing rate without using induced vacuum. The lyophilization endpoint was the same for all treatments (constant difference between capacitance and Pirani vacuum gauges). From the experimental results it can be concluded that at the high freezing rate (0.4°C/min) reduced the overall process time up to 30% comparing process time required for the control and VI of the lower freeze rate (0.1°C/min) without affecting the quality characteristics of the dried product, which yields a reduction in costs and energy consumption for MS and MPM freeze drying. Controls and samples treated with VI at freezing rate of 0.4°C/min in MS showed similar results in moisture and density parameters. Furthermore, results from MPM dispersion showed favorable values when VI was applied because dried product with low moisture content and low density was obtained at shorter process time compared with the control. There were not found significant differences between reconstitutional properties (rehydration for MS and solubility for MPM) of freeze dried mango resulting from controls, and VI treatments.

Keywords: Drying time, lyophilization, mango, vacuum induced freezing.

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Authors: Chun-Cheng Lin

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Noise level has critical effects on the diagnostic performance of signal-averaged electrocardiogram (SAECG), because the true starting and end points of QRS complex would be masked by the residual noise and sensitive to the noise level. Several studies and commercial machines have used a fixed number of heart beats (typically between 200 to 600 beats) or set a predefined noise level (typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform SAECG analysis. However different criteria or methods used to perform SAECG would cause the discrepancies of the noise levels among study subjects. According to the recommendations of 1991 ESC, AHA and ACC Task Force Consensus Document for the use of SAECG, the determinations of onset and offset are related closely to the mean and standard deviation of noise sample. Hence this study would try to perform SAECG using consistent root-mean-square (RMS) noise levels among study subjects and analyze the noise level effects on SAECG. This study would also evaluate the differences between normal subjects and chronic renal failure (CRF) patients in the time-domain SAECG parameters. The study subjects were composed of 50 normal Taiwanese and 20 CRF patients. During the signal-averaged processing, different RMS noise levels were adjusted to evaluate their effects on three time domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS voltage of the last QRS 40 ms (RMS40), and (3) duration of the low amplitude signals below 40 μV (LAS40). The study results demonstrated that the reduction of RMS noise level can increase fQRSD and LAS40 and decrease the RMS40, and can further increase the differences of fQRSD and RMS40 between normal subjects and CRF patients. The SAECG may also become abnormal due to the reduction of RMS noise level. In conclusion, it is essential to establish diagnostic criteria of SAECG using consistent RMS noise levels for the reduction of the noise level effects.

Keywords: Signal-averaged electrocardiogram, Ventricular latepotentials, Chronic renal failure, Noise level effects.

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Authors: Lobat Taghavi, Najaf Hedayat

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Renewable water resources are crucial production variables in arid and semi-arid regions where intensive agriculture is practiced to meet ever-increasing demand for food and fiber. This is crucial for the Dez and Moghan command areas where water delivery problems and adverse environmental issues are widespread. This paper aims to identify major problems areas using on-farm surveys of 200 farmers, agricultural extensionists and water suppliers which was complemented by secondary data and field observations during 2010- 2011 cultivating season. The SPSS package was used to analyze and synthesis data. Results indicated inappropriate canal operations in both schemes, though there was no unanimity about the underlying causes. Inequitable and inflexible distribution was found to be rooted in deficient hydraulic structures particularly in the main and secondary canals. The inadequacy and inflexibility of water scheduling regime was the underlying causes of recurring pest and disease spread which often led to the decline of crop yield and quality, although these were not disputed, the water suppliers were not prepared to link with the deficiencies in the operation of the main and secondary canals. They rather attributed these to the prevailing salinity; alkalinity, water table fluctuations and leaching of the valuable agro-chemical inputs from the plants- route zone with farreaching consequences. Examples of these include the pollution of ground and surface resources due to over-irrigation at the farm level which falls under the growers- own responsibility. Poor irrigation efficiency and adverse environmental problems were attributed to deficient and outdated farming practices that were in turn rooted in poor extension programs and irrational water charges.

Keywords: water delivery, inequity, inflexibility, conflicts, environmental impact, Dez and Moghan

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Authors: S. Y. Lin, C. H. Kuan, C. H. She, W. T. Wang

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In this study, ultrasonic assisted machining (UAM) technique is applied in side-surface milling experiment for glass-ceramic workpiece material. The tungsten carbide cutting-tool with diamond coating is used in conjunction with two kinds of cooling/lubrication mediums such as water-soluble (WS) cutting fluid and minimum quantity lubricant (MQL). Full factorial process parameter combinations on the milling experiments are planned to investigate the effect of process parameters on cutting performance. From the experimental results, it tries to search for the better process parameter combination which the edge-indentation and the surface roughness are acceptable. In the machining experiments, ultrasonic oscillator was used to excite a cutting-tool along the radial direction producing a very small amplitude of vibration frequency of 20KHz to assist the machining process. After processing, toolmaker microscope was used to detect the side-surface morphology, edge-indentation and cutting tool wear under different combination of cutting parameters, and analysis and discussion were also conducted for experimental results. The results show that the main leading parameters to edge-indentation of glass ceramic are cutting depth and feed rate. In order to reduce edge-indentation, it needs to use lower cutting depth and feed rate. Water-soluble cutting fluid provides a better cooling effect in the primary cutting area; it may effectively reduce the edge-indentation and improve the surface morphology of the glass ceramic. The use of ultrasonic assisted technique can effectively enhance the surface finish cleanness and reduce cutting tool wear and edge-indentation. 

Keywords: Glass-ceramic, ultrasonic assisted machining, cutting performance, edge-indentation

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Authors: Satu Lautamäki

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This paper presents findings from a multidisciplinary course (bachelor level) implemented at Seinäjoki University of Applied Sciences, Finland. The course aims to develop innovative thinking of students, by having projects given by companies, using design thinking methods as a tool for creativity and by integrating students into multidisciplinary teams working on the given projects. The course is obligatory for all first year bachelor students across four faculties (business and culture, food and agriculture, health care and social work, and technology). The course involves around 800 students and 30 pedagogical coaches, and it is implemented as an intensive one-week course each year. The paper discusses the pedagogy, structure and coordination of the course. Also, reflections on methods for the development of creative skills are given. Experts in contemporary, global context often work in teams, which consist of people who have different areas of expertise and represent various professional backgrounds. That is why there is a strong need for new training methods where multidisciplinary approach is at the heart of learning. Creative learning takes place when different parties bring information to the discussion and learn from each other. When students in different fields are looking for professional growth for themselves and take responsibility for the professional growth of other learners, they form a mutual learning relationship with each other. Multidisciplinary team members make decisions both individually and collectively, which helps them to understand and appreciate other disciplines. Our results show that creative and multidisciplinary project learning can develop diversity of knowledge and competences, for instance, students’ cultural knowledge, teamwork and innovation competences, time management and presentation skills as well as support a student’s personal development as an expert. It is highly recommended that higher education curricula should include various studies for students from different study fields to work in multidisciplinary teams.

Keywords: Multidisciplinary learning, creative skills, innovative thinking, project-based learning.

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Authors: C. Paramasivam, K. B. Jayanthi

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An innovative approach to develop modified scaling free CORDIC based two parallel pipelined Multipath Delay Commutator (MDC) FFT and IFFT architectures for radix 2² FFT algorithm is presented. Multipliers and adders are the most important data paths in FFT and IFFT architectures. Multipliers occupy high area and consume more power. In order to optimize the area and power overhead, modified scaling-free CORDIC based complex multiplier is utilized in the proposed design. In general twiddle factor values are stored in RAM block. In the proposed work, modified scaling-free CORDIC based twiddle factor generator unit is used to generate the twiddle factor and efficient switching units are used. In addition to this, four point FFT operations are performed without complex multiplication which helps to reduce area and power in the last two stages of the pipelined architectures. The design proposed in this paper is based on multipath delay commutator method. The proposed design can be extended to any radix 2ⁿ based FFT/IFFT algorithm to improve the throughput. The work is synthesized using Synopsys design Compiler using TSMC 90-nm library. The proposed method proves to be better compared to the reference design in terms of area, throughput and power consumption. The comparative analysis of the proposed design with Xilinx FPGA platform is also discussed in the paper.

Keywords: Coordinate Rotational Digital Computer(CORDIC), Complex multiplier, Fast Fourier transform (FFT), Inverse fast Fourier transform (IFFT), Multipath delay Commutator (MDC), modified scaling free CORDIC, complex multiplier, pipelining, parallel processing, radix-2^2.

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Authors: Ladan Darougaran, Hossein Shahinzadeh, Hajar Ghotb, Leila Ramezanpour

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In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.

Keywords: Data aggregation, wireless sensor networks, energy efficiency, simulated annealing algorithm, genetic algorithm.

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Authors: H. Benjamin Fredrick David, R. Balasubramanian, A. Anbarasa Pandian

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Image retrieval is the most interesting technique which is being used today in our digital world. CBIR, commonly expanded as Content Based Image Retrieval is an image processing technique which identifies the relevant images and retrieves them based on the patterns that are extracted from the digital images. In this paper, two research works have been presented using CBIR. The first work provides an automated and interactive approach to the analysis of CBIR techniques. CBIR works on the principle of supervised machine learning which involves feature selection followed by training and testing phase applied on a classifier in order to perform prediction. By using feature extraction, the image transforms such as Contourlet, Ridgelet and Shearlet could be utilized to retrieve the texture features from the images. The features extracted are used to train and build a classifier using the classification algorithms such as Naïve Bayes, K-Nearest Neighbour and Multi-class Support Vector Machine. Further the testing phase involves prediction which predicts the new input image using the trained classifier and label them from one of the four classes namely 1- Normal brain, 2- Benign tumour, 3- Malignant tumour and 4- Severe tumour. The second research work includes developing a tool which is used for tumour stage identification using the best feature extraction and classifier identified from the first work. Finally, the tool will be used to predict tumour stage and provide suggestions based on the stage of tumour identified by the system. This paper presents these two approaches which is a contribution to the medical field for giving better retrieval performance and for tumour stages identification.

Keywords: Brain tumour detection, content based image retrieval, classification of tumours, image retrieval.

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Authors: Dongxiao Sun-Waterhouse, Shiji Nair, Reginald Wibisono, Sandhya S. Wadhwa, Carl Massarotto, Duncan I. Hedderley, Jing Zhou, Sara R. Jaeger, Virginia Corrigan

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Attempts to add fibre and polyphenols (PPs) into popular beverages present challenges related to the properties of finished products such as smoothies. Consumer acceptability, viscosity and phenolic composition of smoothies containing high levels of fruit fibre (2.5-7.5 g per 300 mL serve) and PPs (250-750 mg per 300 mL serve) were examined. The changes in total extractable PP, vitamin C content, and colour of selected smoothies over a storage stability trial (4°C, 14 days) were compared. A set of acidic aqueous model beverages were prepared to further examine the effect of two different heat treatments on the stability and extractability of PPs. Results show that overall consumer acceptability of high fibre and PP smoothies was low, with average hedonic scores ranging from 3.9 to 6.4 (on a 1-9 scale). Flavour, texture and overall acceptability decreased as fibre and polyphenol contents increased, with fibre content exerting a stronger effect. Higher fibre content resulted in greater viscosity, with an elevated PP content increasing viscosity only slightly. The presence of fibre also aided the stability and extractability of PPs after heating. A reduction of extractable PPs, vitamin C content and colour intensity of smoothies was observed after a 14-day storage period at 4°C. Two heat treatments (75°C for 45 min or 85°C for 1 min) that are normally used for beverage production, did not cause significant reduction of total extracted PPs. It is clear that high levels of added fibre and PPs greatly influence the consumer appeal of smoothies, suggesting the need to develop novel formulation and processing methods if a satisfactory functional beverage is to be developed incorporating these ingredients.

Keywords: Apple fibre, apple and blackcurrant polyphenols, consumer acceptability, functional foods, stability.

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma, K. Jagannath, Achutha Kini U.

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In the present study, response surface methodology has been used to optimize turn-assisted deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness and surface roughness using response surface methodology and central composite design. In the development of predictive model, deep cold rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and the ball diameter are the significant factors on the surface hardness and ball diameter and numbers of tool passes are found to be significant for surface roughness. The predicted surface hardness and surface roughness values and the subsequent verification experiments under the optimal operating conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness and surface roughness is calculated as 0.16% and 1.58% respectively. Using the optimal processing parameters, the surface hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36% and the surface roughness is improved from 4.84µm to 0.252 µm, which resulted in decrease in the surface roughness by about 95%. The depth of compression is found to be more than 300µm from the microstructure analysis and this is in correlation with the results obtained from the microhardness measurements. Taylor hobson talysurf tester, micro vickers hardness tester, optical microscopy and X-ray diffractometer are used to characterize the modified surface layer. 

Keywords: Surface hardness, response surface methodology, microstructure, central composite design, deep cold rolling, surface roughness.

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Authors: Ashkan Ebadi, Adam Krzyzak

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Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and behavior, mainly in form of reviews, and making the best recommendations. The recommendations can facilitate user’s decision making process. Despite the wide literature on the topic, using multiple data sources of different types as the input has not been widely studied. Recommender systems can benefit from the high availability of digital data to collect the input data of different types which implicitly or explicitly help the system to improve its accuracy. Moreover, most of the existing research in this area is based on single rating measures in which a single rating is used to link users to items. This paper proposes a highly accurate hotel recommender system, implemented in various layers. Using multi-aspect rating system and benefitting from large-scale data of different types, the recommender system suggests hotels that are personalized and tailored for the given user. The system employs natural language processing and topic modelling techniques to assess the sentiment of the users’ reviews and extract implicit features. The entire recommender engine contains multiple sub-systems, namely users clustering, matrix factorization module, and hybrid recommender system. Each sub-system contributes to the final composite set of recommendations through covering a specific aspect of the problem. The accuracy of the proposed recommender system has been tested intensively where the results confirm the high performance of the system.

Keywords: Tourism, hotel recommender system, hybrid, implicit features.

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: Big data, bus headway prediction, machine learning, public transportation.

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Authors: Zia Amjad, Salem S. Alghamdi

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The study was conducted at the student educational farm at the College of Food and Agriculture in the Kingdom of Saudi Arabia. The aim of study was to characterize 154 Vicia faba L. accessions using agro-morphological traits based on The International Union for the Protection of New Varieties of Plants (UPOV) and The International Board for Plant Genetic Resources (IBPGR) descriptors. This research is significant as it contributes to the understanding of the genetic diversity and potential yield of V. faba in Saudi Arabia. In the study, 24 agro-morphological characters including 11 quantitative and 13 qualitative were observed for genetic variation. All the results were analyzed using multivariate analysis i.e., principal component analysis (PCA). First, six principal components (PC) had eigenvalues greater than one; accounted for 72% of available V. faba genetic diversity. However, first three components revealed more than 10% of genetic diversity each i.e., 22.36%, 15.86% and 10.89% respectively. PCA distributed the V. faba accessions into different groups based on their performance for the characters under observation. PC-1, which represented 22.36% of the genetic diversity, was positively associated with stipule spot pigmentation, intensity of streaks, pod degree of curvature and to some extent with 100 seed weight. PC-2 covered 15.86 of the genetic diversity and showed positive association for average seed weight per plant, pod length, number of seeds per plant, 100 seed weight, stipule spot pigmentation, intensity of streaks (same as in PC-1) and to some extent for pod degree of curvature and number of pods per plant. PC-3 revealed 10.89% of genetic diversity and expressed positive association for number of pods per plant and number of leaflets per plant. This study contributes to the understanding of the genetic diversity and potential yield of V. faba in the Kingdom of Saudi Arabia. By establishing a core collection of V. faba, the research provides a valuable resource for future conservation and utilization of this crop worldwide.

Keywords: Agro-morphological characterization, genetic diversity, core collection, PCA, Vicia faba L.

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Authors: Bernardo C. P. Albuquerque, Darym J. F. Campos

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Increasing our ability to solve complex engineering problems is directly related to the processing capacity of computers. By means of such equipments, one is able to fast and accurately run numerical algorithms. Besides the increasing interest in numerical simulations, probabilistic approaches are also of great importance. This way, statistical tools have shown their relevance to the modelling of practical engineering problems. In general, statistical approaches to such problems consider that the random variables involved follow a normal distribution. This assumption tends to provide incorrect results when skew data is present since normal distributions are symmetric about their means. Thus, in order to visualize and quantify this aspect, 9 statistical distributions (symmetric and skew) have been considered to model a hypothetical slope stability problem. The data modeled is the friction angle of a superficial soil in Brasilia, Brazil. Despite the apparent universality, the normal distribution did not qualify as the best fit. In the present effort, data obtained in consolidated-drained triaxial tests and saturated direct shear tests have been modeled and used to analytically derive the probability density function (PDF) of the safety factor of a hypothetical slope based on Mohr-Coulomb rupture criterion. Therefore, based on this analysis, it is possible to explicitly derive the failure probability considering the friction angle as a random variable. Furthermore, it is possible to compare the stability analysis when the friction angle is modelled as a Dagum distribution (distribution that presented the best fit to the histogram) and as a Normal distribution. This comparison leads to relevant differences when analyzed in light of the risk management.

Keywords: Statistical slope stability analysis, Skew distributions, Probability of failure, Functions of random variables.

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: Anti-spoofing, CNN, fingerprint recognition, loss function, optimizer.

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Authors: Maria Toader, Gheorghe Valentin Roman, Alina Maria IonescuMaria Toader, Gheorghe Valentin Roman, Alina Maria Ionescu

Abstract:

Crops diversity and maintaining and enhancing the fertility of agricultural lands are basic principles of organic farming. With a wider range of crops in agroecosystem can improve the ability to control weeds, pests and diseases, and the performance of crops rotation and food safety. In this sense, the main objective of the research was to study the productivity and chemical composition of some alternative crops and their adaptability to soil and climatic conditions of the agricultural area in Southern Romania and to cultivation in the organic farming system. The alternative crops were: lentil (7 genotypes); five species of grain legumes (5 genotypes); four species of oil crops (5 genotypes). The seed production was, on average: 1343 kg/ha of lentil; 2500 kg/ha of field beans; 2400 kg/ha of chick peas and blackeyed peas; more than 2000 kg/ha of atzuki beans, over 1250 kg/ha of fenugreek; 2200 kg/ha of safflower; 570 kg/ha of oil pumpkin; 2150 kg/ha of oil flax; 1518 kg/ha of camelina. Regarding chemical composition, lentil seeds contained: 22.18% proteins, 3.03% lipids, 33.29% glucides, 4.00% minerals, and 259.97 kcal energy values. For field beans: 21.50% proteins, 4.40% lipids, 63.90% glucides, 5.85% minerals, 395.36 kcal energetic value. For chick peas: 21.23% proteins, 4.55% lipids, 53.00% glucides, 3.67% minerals, 348.22 kcal energetic value. For blackeyed peas: 23.30% proteins, 2.10% lipids, 68.10% glucides, 3.93% minerals, 350.14 kcal energetic value. For adzuki beans: 21.90% proteins, 2.60% lipids, 69.30% glucides, 4.10% minerals, 402.48 kcal energetic value. For fenugreek: 21.30% proteins, 4.65% lipids, 63.83% glucides, 5.69% minerals, 396.54 kcal energetic value. For safflower: 12.60% proteins, 28.37% lipids, 46.41% glucides, 3.60% minerals, 505.78 kcal energetic value. For camelina: 20.29% proteins, 31.68% lipids, 36.28% glucides, 4.29% minerals, 526.63 kcal energetic value. For oil pumpkin: 29.50% proteins, 36.92% lipids, 18.50% glucides, 5.41% minerals, 540.15 kcal energetic value. For oil flax: 22.56% proteins, 34.10% lipids, 27.73% glucides, 5.25% minerals, 558.45 kcal energetic value.

Keywords: Adaptability, alternative crops, chemical composition, organic farming productivity.

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Authors: Heri Hermansyah, Annisa Kurnia, A. Vania Anisya, Adi Surjosatyo, Yopi Sunarya, Rita Arbianti, Tania Surya Utami

Abstract:

Lipase is one of biocatalyst which is applied commercially for the process in industries, such as bioenergy, food, and pharmaceutical industry. Nowadays, biocatalysts are preferred in industries because they work in mild condition, high specificity, and reduce energy consumption (high pressure and temperature). But, the usage of lipase for industry scale is limited by economic reason due to the high price of lipase and difficulty of the separation system. Immobilization of lipase is one of the solutions to maintain the activity of lipase and reduce separation system in the process. Therefore, we conduct a study about lipase immobilization with the adsorption-cross linking method using glutaraldehyde because this method produces high enzyme loading and stability. Lipase is immobilized on different kind of resin with the various functional group. Highest enzyme loading (76.69%) was achieved by lipase immobilized on anion macroporous which have anion functional group (OH^‑). However, highest activity (24,69 U/g support) through olive oil emulsion method was achieved by lipase immobilized on anion macroporous-chitosan which have amino (NH₂) and anion (OH^-) functional group. In addition, it also success to produce biodiesel until reach yield 50,6% through interesterification reaction and after 4 cycles stable 63.9% relative with initial yield. While for Aspergillus, niger lipase immobilized on anion macroporous-kitosan have unit activity 22,84 U/g resin and yield biodiesel higher than commercial lipase (69,1%) and after 4 cycles stable reach 70.6% relative from initial yield. This shows that optimum functional group on support for immobilization with adsorption-cross linking is the support that contains amino (NH₂) and anion (OH^-) functional group because they can react with glutaraldehyde and binding with enzyme prevent desorption of lipase from support through binding lipase with a functional group on support.

Keywords: Adsorption-Cross linking, lipase, resin, immobilization.

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Authors: José Alberto García Fernández, Zhimin Du, Xinqiao Jin

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Nowadays, data center industry faces strong challenges for increasing the speed and data processing capacities while at the same time is trying to keep their devices a suitable working temperature without penalizing that capacity. Consequently, the cooling systems of this kind of facilities use a large amount of energy to dissipate the heat generated inside the servers, and developing new cooling techniques or perfecting those already existing would be a great advance in this type of industry. The installation of a temperature sensor matrix distributed in the structure of each server would provide the necessary information for collecting the required data for obtaining a temperature profile instantly inside them. However, the number of temperature probes required to obtain the temperature profiles with sufficient accuracy is very high and expensive. Therefore, other less intrusive techniques are employed where each point that characterizes the server temperature profile is obtained by solving differential equations through simulation methods, simplifying data collection techniques but increasing the time to obtain results. In order to reduce these calculation times, complicated and slow computational fluid dynamics simulations are replaced by simpler and faster finite element method simulations which solve the Burgers‘ equations by backward, forward and central discretization techniques after simplifying the energy and enthalpy conservation differential equations. The discretization methods employed for solving the first and second order derivatives of the obtained Burgers‘ equation after these simplifications are the key for obtaining results with greater or lesser accuracy regardless of the characteristic truncation error.

Keywords: Burgers’ equations, CFD simulation, data center, discretization methods, FEM simulation, temperature profile.

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Authors: Madiha El Awamie, Catherine Rees

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Natural preservatives have been used as alternatives to traditional chemical preservatives; however, a limited number have been commercially developed and many remain to be investigated as sources of safer and effective antimicrobials. In this study, we have been investigating the antimicrobial activity of an extract of Glycyrrhiza glabra (liquorice) that was provided as a waste material from the production of liquorice flavourings for the food industry, and to investigate if this retained the expected antimicrobial activity so it could be used as a natural preservative. Antibacterial activity of liquorice extract was screened for evidence of growth inhibition against eight species of Gram-negative and Gram-positive bacteria, including Listeria monocytogenes, Listeria innocua, Staphylococcus aureus, Enterococcus faecalis and Bacillus subtilis. The Gram-negative bacteria tested include Pseudomonas aeruginosa, Escherichia coli and Salmonella typhimurium but none of these were affected by the extract. In contrast, for all of the Gram-positive bacteria tested, growth was inhibited as monitored using optical density. However parallel studies using viable count indicated that the cells were not killed meaning that the extract was bacteriostatic rather than bacteriocidal. The Minimum Inhibitory Concentration [MIC] and Minimum Bactericidal Concentration [MBC] of the extract was also determined and a concentration of 50 µg ml^-1 was found to have a strong bacteriostatic effect on Gram-positive bacteria. Microscopic analysis indicated that there were changes in cell shape suggesting the cell wall was affected. In addition, the use of a reporter strain of Listeria transformed with the bioluminescence genes luxABCDE indicated that cell energy levels were reduced when treated with either 12.5 or 50 µg ml^-1 of the extract, with the reduction in light output being proportional to the concentration of the extract used. Together these results suggest that the extract is inhibiting the growth of Gram-positive bacteria only by damaging the cell wall and/or membrane.

Keywords: Antibacterial activity, bioluminescence, Glycyrrhiza glabra, natural preservative.

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Authors: H. Alabed, K. Abuzayan, J. Waterhouse

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The Muslim faith requires individuals to fast between the hours of sunrise and sunset during the month of Ramadan. Our recent work has concentrated on some of the changes that take place during the daytime when fasting. A questionnaire was developed to assess subjective estimates of physical, mental and social activities, and fatigue. Four days were studied: in the weeks before and after Ramadan (control days) and during the first and last weeks of Ramadan (experimental days). On each of these four days, this questionnaire was given several times during the daytime and once after the fast had been broken and just before individuals retired at night. During Ramadan, daytime mental, physical and social activities all decreased below control values but then increased to abovecontrol values in the evening. The desires to perform physical and mental activities showed very similar patterns. That is, individuals tried to conserve energy during the daytime in preparation for the evenings when they ate and drank, often with friends. During Ramadan also, individuals were more fatigued in the daytime and napped more often than on control days. This extra fatigue probably reflected decreased sleep, individuals often having risen earlier (before sunrise, to prepare for fasting) and retired later (to enable recovery from the fast). Some physiological measures and objective measures of performance (including the response to a bout of exercise) have also been investigated. Urine osmolality fell during the daytime on control days as subjects drank, but rose in Ramadan to reach values at sunset indicative of dehydration. Exercise performance was also compromised, particularly late in the afternoon when the fast had lasted several hours. Self-chosen exercise work-rates fell and a set amount of exercise felt more arduous. There were also changes in heart rate and lactate accumulation in the blood, indicative of greater cardiovascular and metabolic stress caused by the exercise in subjects who had been fasting. Daytime fasting in Ramadan produces widespread effects which probably reflect combined effects of sleep loss and restrictions to intakes of water and food.

Keywords: Drinking, Eating, Mental Performance, Physical Performance, Social Activity, Sleepiness.

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