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| 1 | Comparison of dietary intakes of Canadian Armed Forces personnel consuming field rations in acute hot, cold, and temperate conditions with standardized infantry显示文摘Background:Dietary Reference Intakes are used to guide the energy intake of the Canadian Armed Forces(CAF)field rations provided to military personnel deployed for training or operations.However,the high energy expenditures likely to occur under harsh environmental/metabolically challenging deployment conditions may not be adequately considered.This study examined the Ad libitum energy and nutrient intakes of CAF personnel(n=18)consuming field rations in a resting thermoneutral environment and during a day of standardized strenuous infantry activities at varying environmental temperatures.Methods:Dietary intake was assessed using a measured food intake/food waste method during the experimental treatment and for 6 h after treatment.Four treatments were administered in a randomized counterbalanced design:exercise(as standardized infantry activities)in the heat(30℃),exercise in the cold(–10℃),exercise in temperate thermoneutral(21℃)air temperatures and a resting(sedentary)trial(21℃).Results:The average Ad libitum consumption of field rations was 70%of the provided total energy(2776±99 kcal/8 h)during all treatments.Even with an acute challenge of increased energy expenditure and temperature stress in the simulated field conditions,participants’energy intakes(1985±747 kcal/8 h)under hot,cold and temperate treatments did not differ from energy intake during the sedentary condition(1920±640 kcal/8 h).Participants’energy intakes(1009±527 kcal/6 h)did not increase during the 6 h posttreatment period when the stresses of the strenuous physical activities and the harsh environmental temperatures had subsided.Conclusions:These results should be considered when planning the provision of field rations for CAF personnel expected to be engaged in strenuous physical activities with prolonged exposure to temperature extremes. | Mavra Ahmed Iva Mandic Wendy Lou Len Goodman Ira Jacobs Mary R.L’Abbé | 2020 | Military Medical Research2020，7，1： | 1 |
| 2 | Increased synthesis rate of fibrinogen as a basis for its elevated plasma levels in obese female adolescents显示文摘 | Balagopal P Sweeten S Mavra S | | 0，，04： | 1 |
| 3 | Chondrogenesis, bone morpho- genetic protein - d and mesenchymal stem cells 显示文摘 | Miljkovic ND Cooper GM Mavra KG | 2008 | Osteoarthfitis Cartilage2008，16，10： | 1 |
| 4 | Change to physical properties of the cell wall and polyuronides in response to heat treatment of 'Fuyu persimmon that alleviate chilling injury 显示文摘 | Woolf A B Mavrae E A Spooner K J | 1997 | J Amer Soc Hort Sci1997，122，： | 1 |
| 5 | Spleen versus CNS immunoglobulin G in multiple sclerosis:an isoelectric focusing study 显示文摘 | MAVRA M NEWC0MBE J KEIR G | 1990 | Acta Neurologica Scandinavica1990，81，2： | 1 |
| 6 | Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms显示文摘Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early stage.Ductal carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and men.The number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling technique.Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations.In this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results.In this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor component.The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization.The resulting images’tumor portions are then isolated by a segmentation process,such as threshold detection.Furthermore,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of interest.For classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal patterns.Finally,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the SVM.Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases.Substantial results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies for normal and abnormal mammograms.Through ANFIS,promising results of mean square error(MSE)0.01866,0.18397,and 0.19640 for DCIS and LCIS differentiation during the training,testing,and checking phases. | Mavra Mehmood Ember Ayub Fahad Ahmad Madallah Alruwaili Ziyad AAlrowaili Saad Alanazi Mamoona Humayun Muhammad Rizwan Shahid Naseem Tahir Alyas | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 7 | A Hybrid Approach for Network Intrusion Detection显示文摘Due to the widespread use of the internet and smart devices,various attacks like intrusion,zero-day,Malware,and security breaches are a constant threat to any organization’s network infrastructure.Thus,a Network Intrusion Detection System(NIDS)is required to detect attacks in network traffic.This paper proposes a new hybrid method for intrusion detection and attack categorization.The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization.In the first step,the dataset is preprocessed through the data transformation technique and min-max method.Secondly,the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model’s performance.Next,we use various Support Vector Machine(SVM)types to detect intrusion and the Adaptive Neuro-Fuzzy System(ANFIS)to categorize probe,U2R,R2U,and DDOS attacks.The validation of the proposed method is calculated through Fine Gaussian SVM(FGSVM),which is 99.3%for the binary class.Mean Square Error(MSE)is reported as 0.084964 for training data,0.0855203 for testing,and 0.084964 to validate multiclass categorization. | Mavra Mehmood Talha Javed Jamel Nebhen Sidra Abbas Rabia Abid Giridhar Reddy Bojja Muhammad Rizwan | 2022 | Computers, Materials & Continua2022，，1： | 0 |
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