Leather Quality of Some Sudan Desert Sheep and Goats

Received: 2015-07-21   |   Accepted: 2015-10-20   |   Available online:2016-04-23dx.doi.org/10.15414/afz.2016.9.01.15-21This trial is aimed to study leather properties that produced from Sudan Desert sheep and goats in relation to breed type and age category. Thirty pieces of fresh sheep and goats skins were collected randomly (15 for each) during January 2015. The collected skins were tanned and the produced leather properties were studied. The Statistix 8 program for variance analysis was used for data analysis. The study samples were taken according to the Complete Randomized Design. Sheep leather results revealed that, lamb's skin was significantly (p≥0.05) produced better quality leather than ram's and ewe's skins in elongation %, tensile strength (kg/cm2), cracking load (kg), thickness (mm), tear load (kg/cm), flexibility and moisture%. While it was yielded leather with the same characteristics to ram's and ewe's leather in Ash%, fat % and chrome%. Leather properties [elongation %, tensile strength (kg/cm2), cracking load (kg), tear load (kg/cm), flexibility and Ash%] were significantly (p≥0.05) affected by breed variation. On the other hand thickness (mm), moisture%, fat % and chrome% were not significantly (p≥0.05) affected by breed. Ram's skin was produced better quality leather than ewe's skins. Goat's leather results revealed that, kid goat's skin was significantly (p≥0.05) produced better quality leather than bucks and doe's skin in tensile strength (kg/cm2), cracking load (kg), thickness (mm), tear load (kg/cm) and flexibility degree. But kids and buck's skins were produced the same quality leather in elongation % and moisture% with significant variation (p≥0.05) to doe's leather. Kid's skin yields leather with the same characteristics to buck's and doe's leather in Ash%, fat % and chrome%. Generally Desert goats produce slightly better quality leather than Nubian goats. Leather prosperities [cracking load (kg), tear load (kg/cm), and Ash%] were significantly (p≥0.05) affected by breed variation. Elongation%, tensile strength (kg/cm2), thickness (mm), moisture%, fat %, flexibility and chrome% were not significantly (p≥0.05) affected by goats breed. Keywords: leather quality, Kabashi, desert sheep, Nubian goats, desert goats, SudanReferences ABUSUWAR, A.O., AHMED, E.O. and BLAL, A.M. (2012). Effect of Feeding Treated Groundnut Hulls with Molasses on Performance of Desert Sheep during Late Summer in Arid Rangelands of Western Sudan. International Journal of Theoretical & Applied Sciences, vol. 4, no. 2, pp. 122-127.AGEEB, A.A. (1992). Production and reproduction characteristics of a flock of Baggara goats of Southern Kordofan. Sud. J.Anim. prod., vol. 5, pp.1-24.ALI, M.A.M., et al. (2014). Pre-weaning Body Measurements and Performance of Desert Sheep (Tribal Subtypes Hamari and Kabashi) Lambs of Kordofan Region, Sudan. Malaysian Society of Animal Production. Mal. J. Anim. Sci., vol. 17, no. 1, pp. 35-45.BABEKER, E.A. and ELMANSOURY Y.H.A. (2013). Observations concerning haematological profile and certain biochemical in Sudanese desert goat. Journal of Animal and Feed Research, vol. 3, no. 1, pp.80-86.CRAIG, A.S., EIKENBERRY, E.F. and PARRY, D.A.D. (1987). Ultrastructural organization of skin: classification on the basis of mechanical role. Connective Tissue Research, vol. 116, pp. 213–223.DASKIRAN, I., KOR, A. and BINGOL, M. (2006). Slaughter and carcass characteristics of Norduz male kids raised in either intensive or pasture conditions. Pakistan J. Nut, vol. 5, no. 3, pp. 274-277.DEVENDRA, C. and COOP, I.E. (1982). Ecology and distribution of sheep and goat. world aninal Science. In: Coop, I.E (e. d.) Sheep and goat production, vol. 3. New York: Elsevier, pp. 1-14.DEVENDRA, C. and MCLEORY, G.B. (1987). Goat and sheep production in the Tropics. Intermediate Tropical Agriculture Series, London: Longman.EBRAHIEM, M.A., GALALLYN, H.A. and BASHIR, A.Y. (2015a). Skin/Leather Quality of Some Sudan Goats under Range Condition. Global Journal of Animal Scientific Research, vol. 3, no. 2, pp. 329-336.EBRAHIEM, M.A., et al. (2015b). The effect of breed on Skin/Leather Quality of Sudan Desert Sheep. Global Journal of Animal Scientific Research, vol. 3, no. 1, pp. 6-10.EL-HAG, F.M., et al. (2007). Supplementary feeding to improve Desert sheep productivity under dryland farming. Trop. Sci., vol. 47, no. 1, pp. 26-32.EL-HAG, F.M., FADLALLA, B. and MUKHRAR, H.K. (2001). Some production characteristics of Sudanese Desert sheep under range condition in North Kordofan, Sudan. Trop. Anim. Heal. and Prod., vol. 33, pp. 229-239.FAO (1999). Production Yearbook: volume 52. Rome: FAO.GALL, C. (1996). Goat breeds of the world. C.T. A. Weikersheim: Margraf Verlag.GOMEZ, K., and GOMEZ, A.A. (1984). Statistical procedure for the agriculture research. 2. ed. New York: Wiley and Sons.ISO2418 (2002). International Standards Organization. Leather chemical, physical and mechanical and fastness tests-Sampling location. ISO3376 (2002). International Standards Organization. Leather physical and mechanical tests – Determination of tensile strength and percentage extension. ISO3377-1 (2002). International Standards Organization. Leather physical and mechanical tests – Determination of tear load – Part 1: Single edge tear. ISO3378 (2002). International Standards Organization. Leather – Physical and mechanical tests – Determination of resistance to grain cracking and grain crack index. ISO4044 (2008). International Standards Organization. Leather chemical tests preparation of chemical test samples. ISO5398-1 (2007). International Standards Organization. Leather chemical determination of chromic oxide content – Part 1: Quantification by titration. ISO5402 (2002). International Standards Organization. Leather physical and mechanical tests – Determination of flex resistance by flexometer method.JACINTO, M.A.C., DA SILVA, S.A.G. and COSTA, R.G. (2005). Anatomic and structural characteristics of wool and non-wool sheep (Ovis aries L.) in regard to the physico-mechanical aspects of the leather. Brazilian Journal of Animal Science, vol. 33, no. 4, pp. 1001-1008.MUKHTAR, H.K. (1985). Constraints to desert sheep production in the sedentary and nomadic systems of North Kordofan. In: M.E. Lazim (ed.), Annual Research Report (1984–85), (El-Obeid Research Station, Agricultural Research Corporation (ARC), Wad Medani, Sudan), pp: 40-55.MCLEROY, G.B. (1961). The sheep of Sudan ecotype and tribal breeds. Sudan J. Vet. Sci. and Husbandry, vol. 2, pp. 101-161.OLIVEIRA, R.J.F., et al. (2007). Influence of genotype on physico-mechanical characteristics of goat and sheep leather. Small Ruminant Research, vol. 73, no. 13, pp. 181-185.PASSMAN, A. and SUMNER, R.M.W. (1983). Sumner Effects of breed and level of feeding on leather production from 18-month-old weathers. New Zealand Journal of Experimental Agriculture vol. 11, pp. 47-52.SALEHI, M., et al. (2014). Effects of type, sex and age on goat skin and leather characteristics. Animal Production Science, vol. 54, pp. 638-644.SARKAR, K.T. (1991). Hide and skins processing technique. In: How to processing raw hide and skins in the tannery. Madrus: Indian Leather Producer Association.SLTC (1965). Society of Leather Trades Chemists. Official methods of analysis. 4. rev. ed. Redbourn: Society of leather trades chemists.SSMO1 (2004). Sudanese Standard and Meterology Organization. Leather standards: for garment leather. Khartoum..SSMO2 (2008). Sudanese Standard and Meterology Organization Leather standards: for vegetable and chrome tanned upper leather. Khartoum.SSMO3 (2008). Sudanese Standard and Meterology Organization. Leather standards: for lining leather. Khartoum.SUDHA TB, et al. (2009). Comfort, Chemical, Mechanical, and Structural Properties of Natural and Synthetic Leathers Used for Apparel. Journal of Applied Polymer Science, vol. 114, pp. 1761-1767.STATISTIX 8 (2007). For the Plant Materials Program. Version 2.0. United States Department of Agriculture (USAD) and Natural Resources Conservation Service (NRCS). USA.TEKLEBRHAN, T., URGE, M. and MEKASHA, Y. (2012). Skin/leather quality of indigenous and crossbred (Dorper × Indigenous) F1 sheep. Dire Dawa: Haramaya University, School of Animal and Range Sciences.TIBIN, I.M., SALAH, S.A. and NOOR, I.A. (2010).Effect of management and feed supplementation on the reproductive performance of Hammari sheep under range conditions in North Kordofan, Sudan. In: Tropentag 2010: ETH Zurich, September 14-16, 2010, Conference on International Research on Food Security, Natural Resource Management and Rural Development. WILSON, T. (1991). Small ruminant production and the small ruminant genetic resource in tropical Africa. FAO Animal Production and Health Paper, no. 88, p. 181.YEHIA, E. (2002). Ecology of some herbaceous forage in North Kordofan. M.Sc. Thesis. Khartoum: Institute of Environmental Studies, University of Khartoum.YOUSIF, A. and FADL EL-MOULA, A. (2006). Characterization of Kenana cattle breed and its production environment. Food and Agriculture Organization. Agri., vol. 38, pp. 47-56

Patient satisfaction with quality of primary health care in Benghazi, Libya

Introduction: The Libyan National Health System (LNHS) is debated for the paradox of its performance versus impact. It has poor performance, but the national health statistics are good and competitive. There are concerted efforts to manage health care services and to regain the lost trust. A primary health care (PHC) system that focuses on preventive and promotive care is the core focus of LNHS efforts. Objectives: To assess patient satisfaction with quality of PHC assessed in terms of (a) customer profile, (b) patient satisfaction, and (c) health care-seeking behavior. Methodology: A sample of nine health centers and seven polyclinics from various locations in Benghazi, Libya were selected for gathering information by structured face-to-face interviews. A total of 310 beneficiaries were interviewed by using an Arabic translation of the Charleston Psychiatric Outpatient Satisfaction Scale. Results: The beneficiaries appear to be quite satisfied with the quality of services. Geographical zone, marital status of beneficiary, and type of facility are satisfaction-related factors. There are preferences for facilities located within the City Centre over those located elsewhere. There is also an interaction effect of the geographical zone and the type of facility in creating differences in satisfaction. Conclusions: A customer-friendly facility concept that emphasizes reception, physician interaction, and cordiality shall add value. Polyclinics require more attention as does the Al Slawy area. A few utility services might also be considered.Keywords: exit interviews; Health For All by All; geographic zone; administrative and environmental factors; health-seeking behavior; interaction effect; type and location of facility; place of residenc

The effects of economic sanctions on foreign direct investment and multinational companies operating in Sudan

Little is known about the effects of economic sanctions when it comes to Foreign Direct Investment (FDI) and Multinational Companies (MNCs) that are affected by such sanctions. The mechanisms used by foreign investors and MNCs to survive in such economic environment, and how to overcome sanctions restrictions in the host countries are yet to be explored by researchers. Therefore, the purpose of this study is to analyse the effects of economic sanctions on FDI and MNCs that operate in Sudan. It also investigated the business decisions made by these MNCs’ management to overcome restrictions imposed by the sanctions. Multiple qualitative methods were employed in this study. The first method was in the form of online questionnaires, and the second method was in the form of case studies to elicit relevant information about the impacts of the economic sanctions on the MNCs that operate in Sudan. The results from the case studies show that the MNCs from the sanction imposing countries evaluated the cost of the sanctions with the expected benefits from the operations in Sudan, and they decided to divest their activities once the sanctions cost become higher than the benefits. On the other hand, the online questionnaire’s findings reveal that MNCs decided to diversify their businesses in the primary sectors in order to lessen the effects of the economic sanctions’ restrictions. Additionally, a great number of the MNCs indicated that they were going to divest their operations in Sudan due to the heavy cost of the sanctions on their operations. Therefore, this study highlighted the need for urgent policy interventions to encourage MNCs operating in Sudan to diversify their business to the manufacturing and industrial sectors instead of divestment and leaving the county. Future studies could examine the impact of FDI and MNCs divestment on the economy of the target country

An approach for Schools Management System on The Cloud Computing

                                        The schools management information system plays an essential role for the success of the school management. The main purpose of the management information system at initial steps of its development is to improve the efficiency of the office activities. In school environment it was used to store student and personnel data. The most concern was being focused on data entry and collation, rather than on data transfer or analysis. This paper proposed a framework based on cloud computing that provides detailed and summarized information on the critical areas of the management activities to guide schools administrators in planning and in decision-making. Such information system will be accessible anywhere anytime as data are stored in remote servers that are accessible to users over the internet. Unified Modeling Language (UML) is used in the development of the proposed framework. To evaluate the developed framework, a web based application was developed using the proposed framework, and then some International Organization for Standardization (ISO) qualification metrics were used to evaluate the developed web based application using some selected characteristics. The evaluation results show that the proposed framework is very effective. Through the evaluation, the proposed framework is found to represents a solution to most of the problems mentioned in the previous researches and this implies that the proposed framework can be adopted by schools for more efficient information management and more effective management decisions. &nbsp

Deep Machine Learning for Oral Cancer : From Precise Diagnosis to Precision Medicine

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.© 2022 Alabi, Almangush, Elmusrati and Mäkitie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.fi=vertaisarvioitu|en=peerReviewed

Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication

Background: Machine learning models have been reported to assist in the proper management of cancer through accurate prognostication. Integrating such models as a web-based prognostic tool or calculator may help to improve cancer care and assist clinicians in making oral cancer management-related decisions. However, none of these models have been recommended in daily practices of oral cancer due to concerns related to machine learning methodologies and clinical implementation challenges. An instance of the concerns inherent to the science of machine learning is explainability. Objectives: This study measures the usability and explainability of a machine learning-based web prognostic tool that was designed for prediction of oral tongue cancer. We used the System Usability Scale (SUS) and System Causability Scale (SCS) to evaluate the explainability of the prognostic tool. In addition, we propose a framework for the evaluation of post hoc explainability of web-based prognostic tools. Methods: A SUS- and SCS-based questionnaire was administered amongst pathologists, radiologists, cancer and machine learning researchers and surgeons (n = 11) to evaluate the quality of explanations offered by the machine learning-based web prognostic tool to address the concern of explainability and usability of these models for cancer management. The examined web-based tool was developed by our group and is freely available online. Results: In terms of the usability of the web-based tool using the SUS, 81.9% (45.5% strongly agreed; 36.4% agreed) agreed that neither the support of a technical assistant nor a need to learn many things were required to use the web-based tool. Furthermore, 81.8% agreed that the evaluated web-based tool was not cumbersome to use (usability). The average score for the SCS (explainability) was 0.74. A total of 91.0% of the participants strongly agreed that the web-based tool can assist in clinical decision-making. These scores indicated that the examined web-based tool offers a significant level of usability and explanations about the outcome of interest. Conclusions: Integrating the trained and internally and externally validated model as a web-based tool or calculator is poised to offer an effective and easy approach towards the usage and acceptance of these models in the future daily practice. This approach has received significant attention in recent years. Thus, it is important that the usability and explainability of these models are measured to achieve such touted benefits. A usable and well-explained web-based tool further brings the use of these web-based tools closer to everyday clinical practices. Thus, the concept of more personalized and precision oncology can be achieved.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication

Background: Machine learning models have been reported to assist in the proper management of cancer through accurate prognostication. Integrating such models as a web-based prognostic tool or calculator may help to improve cancer care and assist clinicians in making oral cancer management-related decisions. However, none of these models have been recommended in daily practices of oral cancer due to concerns related to machine learning methodologies and clinical implementation challenges. An instance of the concerns inherent to the science of machine learning is explainability. Objectives: This study measures the usability and explainability of a machine learning-based web prognostic tool that was designed for prediction of oral tongue cancer. We used the System Usability Scale (SUS) and System Causability Scale (SCS) to evaluate the explainability of the prognostic tool. In addition, we propose a framework for the evaluation of post hoc explainability of web-based prognostic tools. Methods: A SUS- and SCS-based questionnaire was administered amongst pathologists, radiologists, cancer and machine learning researchers and surgeons (n = 11) to evaluate the quality of explanations offered by the machine learning-based web prognostic tool to address the concern of explainability and usability of these models for cancer management. The examined web-based tool was developed by our group and is freely available online. Results: In terms of the usability of the web-based tool using the SUS, 81.9% (45.5% strongly agreed; 36.4% agreed) agreed that neither the support of a technical assistant nor a need to learn many things were required to use the web-based tool. Furthermore, 81.8% agreed that the evaluated web-based tool was not cumbersome to use (usability). The average score for the SCS (explainability) was 0.74. A total of 91.0% of the participants strongly agreed that the web-based tool can assist in clinical decision-making. These scores indicated that the examined web-based tool offers a significant level of usability and explanations about the outcome of interest. Conclusions: Integrating the trained and internally and externally validated model as a web-based tool or calculator is poised to offer an effective and easy approach towards the usage and acceptance of these models in the future daily practice. This approach has received significant attention in recent years. Thus, it is important that the usability and explainability of these models are measured to achieve such touted benefits. A usable and well-explained web-based tool further brings the use of these web-based tools closer to everyday clinical practices. Thus, the concept of more personalized and precision oncology can be achieved

Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication

Background: Machine learning models have been reported to assist in the proper management of cancer through accurate prognostication. Integrating such models as a web-based prognostic tool or calculator may help to improve cancer care and assist clinicians in making oral cancer management-related decisions. However, none of these models have been recommended in daily practices of oral cancer due to concerns related to machine learning methodologies and clinical implementation challenges. An instance of the concerns inherent to the science of machine learning is explainability. Objectives: This study measures the usability and explainability of a machine learning-based web prognostic tool that was designed for prediction of oral tongue cancer. We used the System Usability Scale (SUS) and System Causability Scale (SCS) to evaluate the explainability of the prognostic tool. In addition, we propose a framework for the evaluation of post hoc explainability of web-based prognostic tools. Methods: A SUS- and SCS-based questionnaire was administered amongst pathologists, radiologists, cancer and machine learning researchers and surgeons (n = 11) to evaluate the quality of explanations offered by the machine learning-based web prognostic tool to address the concern of explainability and usability of these models for cancer management. The examined web-based tool was developed by our group and is freely available online. Results: In terms of the usability of the web-based tool using the SUS, 81.9% (45.5% strongly agreed; 36.4% agreed) agreed that neither the support of a technical assistant nor a need to learn many things were required to use the web-based tool. Furthermore, 81.8% agreed that the evaluated web-based tool was not cumbersome to use (usability). The average score for the SCS (explainability) was 0.74. A total of 91.0% of the participants strongly agreed that the web-based tool can assist in clinical decision-making. These scores indicated that the examined web-based tool offers a significant level of usability and explanations about the outcome of interest. Conclusions: Integrating the trained and internally and externally validated model as a web-based tool or calculator is poised to offer an effective and easy approach towards the usage and acceptance of these models in the future daily practice. This approach has received significant attention in recent years. Thus, it is important that the usability and explainability of these models are measured to achieve such touted benefits. A usable and well-explained web-based tool further brings the use of these web-based tools closer to everyday clinical practices. Thus, the concept of more personalized and precision oncology can be achieved

Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer

Background: The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling. Objectives: This study compares the performance of a nomogram with a machine learning model to predict overall survival in tongue cancer. The nomogram and machine learning model were built using a large data set from the Surveillance, Epidemiology, and End Results (SEER) program database. The comparison is necessary to provide the clinicians with a comprehensive, practical, and most accurate assistive system to predict overall survival of this patient population. Methods: The data set used included the records of 7596 tongue cancer patients. The considered machine learning algorithms were logistic regression, support vector machine, Bayes point machine, boosted decision tree, decision forest, and decision jungle. These algorithms were mainly evaluated in terms of the areas under the receiver operating characteristic (ROC) curve (AUC) and accuracy values. The performance of the algorithm that produced the best result was compared with a nomogram to predict overall survival in tongue cancer patients. Results: The boosted decision-tree algorithm outperformed other algorithms. When compared with a nomogram using external validation data, the boosted decision tree produced an accuracy of 88.7% while the nomogram showed an accuracy of 60.4%. In addition, it was found that age of patient, T stage, radiotherapy, and the surgical resection were the most prominent features with significant influence on the machine learning model's performance to predict overall survival. Conclusion: The machine learning model provides more personalized and reliable prognostic information of tongue cancer than the nomogram. However, the level of transparency offered by the nomogram in estimating patients' outcomes seems more confident and strengthened the principle of shared decision making between the patient and clinician. Therefore, a combination of a nomogram - machine learning (NomoML) predictive model may help to improve care, provides information to patients, and facilitates the clinicians in making tongue cancer management-related decisions.Peer reviewe

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Peer reviewe