Assessment of the energy and exergy efficiencies of farm to fork grain cultivation and bread making processes in Turkey and Germany

Energy and exergy efficiencies of the wheat and rye bread and hamburger bunmaking processes are assessed based on data from Turkey and Germany. Amount of the land required to produce the same amount of wheat in Turkey is 3.34 times of that required in Germany; this ratio is 2.30 for the rye grain. These results show that the efficiency of the conversion of the solar energy into the grain mass is low in Turkey. Cumulative degree of perfection (CDP) for the wheat and the rye grain production is 3.73 and 4.96 in Turkey, and 11.26 and 10.46 in Germany. Specific energy utilization for rye bread production is almost the same in Turkey and Germany; but it is 12 % higher in Turkey for wheat bread and hamburger bun making. Hamburger bun production requires the maximum energy utilization due to the higher weight loss in baking. The rye bread production process requires the minimum energy utilization due to the lower energy input in the agriculture and higher efficiency in the flour production. The maximum exergy destructions occur during the milling and the baking steps

MORE ABOUT THE HIGH-MATURITY FOR BUSINESS PROCESSES: CERTAIN DISTILLED PRACTICAL IMPLICATIONS

ABSTRACT Today"s many organizations define, deploy, and implement their business processes (engineering, management, or other business processes) to achieve targeted cost, quality, and schedule objectives. Organizations developing software or software-intensive system products have constant interest in high-maturity business process improvements to build and deliver ever more complex and huge products ever improved, quicker, and economical for their customers. As high-maturity is principally there to provide reducing internal rework, shortening cycle-times and eliminating schedule crunches, and lowering costs, organizations" interests in the highmaturity persistently grow. In our work, we explored and elicited certain distilled practical implications for highmaturity. With intent, we collected data from people having relationships with certain business process-focused models and standards used for improving software and software-intensive system development and management business processes. After data collection and screening, we applied descriptive statistics and t-tests to discover meaningful and important points. As a result, we provided findings related with the relations between organizational business process maturity and business process modeling type, outputs and results of business processes, and tailoring of business processes in software engineering settings

Identifying critical success factors for wearable medical devices: a comprehensive exploration

For healthy living, the successful use of wearable medical devices such as smartwatches, smart clothes, smart glasses, sports/activity trackers, and various sensors placed on a body is getting more important as benefits of these devices become apparent. Yet, the existing knowledge about the critical success factors for wearable medical devices needs to evolve and develop further. The main objective of this research is to distill salient constructs to enhance the successful use of wearable medical devices. Specifically, the study aims to identify factors, associated items, and interactions of the relevant factors. A questionnaire has been developed and deployed. The data were collected from 1057 people specifically chosen to represent a wide range of the population. Comprehensive and meaningful inferences have been drawn. Principally, as a fusion of factor analysis and path analysis, a partial least squares structural equation modeling approach consisting of exploratory and confirmatory factor analyses has been applied. In order to assess internal generalization and to precisely identify additional constructs, quasi-statistics have been used. The analyses of data collected revealed 11 salient constructs with 39 items and 18 statistically significant relationships among these constructs. Consequently, composed of distilled constructs and their associations, a novel model with an explanatory power of 73.884% has been approved. Moreover, 13 additional factors were identified as a result of the applied quasi-statistics. This research is the first of its kind on account of its sample characteristics with applied comprehensive methodology and distilled results. This research contributes to the pertinent body of knowledge concerning the critical success factors for wearable medical devices with distilled results. These contributions notably advance the relevant understanding and will be beneficial for researchers and for developers in the field of wearable medical devices

Melkersson-rosenthal syndrome: a case report.

Melkersson-Rosenthal Syndrome (MRS) is a rare disorder consisting of a triad of persistent or recurrent orofacial edema, relapsing facial paralysis and fissured tongue. It is rarely possible to observe all aspects of the classical triad at the same time, since these symptoms may appear in different times of life cycle. The most common symptom is orofacial edema. Although etiology of MRS is unclear, various factors such as infections, genetic predisposition, immune deficiency, food intolerance and stress have been held responsible. MRS is diagnosed based on clinical features. This case report describes a 39 years old male patient with recurrent swelling of the upper lip. Clinical examinations showed classical triad of MRS. The diagnosis and treatment procedures were presented with special emphasis to the clinical features of this rare condition

Peripheral osteoma of the mandible: a case report.

Osteomas are benign tumors which are composed of mature compact or cancellous bone. They can be either peripheral, central or extraskeletal. The peripheral osteoma arises from surface of the bone (periosteal) whereas the central osteoma arises from the bone medullary (endosteal) and the extra-skeletal soft tissue osteoma usually develops within the muscle. Osteomas are most commonly found in the skull and facial bones. Multiple osteomas may be associated with Gardner's Syndrome. These lesions are usually painless and recurrence is uncommon after local excision. In this case report clinical, radiographic findings and treatment of a 24-year-old male patient with peripheral osteoma in the anterior mandible are presented

Convolutional Sparse Support Estimator-Based COVID-19 Recognition from X-Ray Images

Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.Scopu

Early Detection of Myocardial Infarction in Low-Quality Echocardiography

Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32.4 million people suffer each year. Early diagnosis and treatment of MI are crucial to prevent further heart tissue damages or death. The earliest and most reliable sign of ischemia is regional wall motion abnormality (RWMA) of the affected part of the ventricular muscle. Echocardiography can easily, inexpensively, and non-invasively exhibit the RWMA. In this article, we introduce a three-phase approach for early MI detection in low-quality echocardiography: 1) segmentation of the entire left ventricle (LV) wall using a state-of-the-art deep learning model, 2) analysis of the segmented LV wall by feature engineering, and 3) early MI detection. The main contributions of this study are highly accurate segmentation of the LV wall from low-quality echocardiography, pseudo labeling approach for ground-truth formation of the unannotated LV wall, and the first public echocardiographic dataset (HMC-QU)a MI detection. Furthermore, the outputs of the proposed approach can significantly help cardiologists for a better assessment of the LV wall characteristics. The proposed approach has achieved 95.72% sensitivity and 99.58% specificity for the LV wall segmentation, and 85.97% sensitivity, 74.03% specificity, and 86.85% precision for MI detection on the HMC-QU dataset.aThe benchmark HMC-QU dataset is publicly shared at the repository https://www.kaggle.com/aysendegerli/hmcqu-datasetScopu

Advance Warning Methodologies for COVID-19 Using Chest X-Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labeled by the medical doctors and 12544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.Scopu

Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection

Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their 'maximum motion displacement' plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over HMC-QU dataset show that the proposed approach can achieve 87.94% accuracy, 92.86% sensitivity and 87.64% precision in MI detection even though the echo quality is quite poor and the temporal resolution is low.Scopu

Major and minor salivary gland cancers: A multicenter retrospective study

Background: Most of the studies on salivary gland cancers are limited for various reasons such as being single-center, small number of patients, including only major or minor SGCs, or only including epidemiological data. Methods: A total of 37 medical oncology clinics from different regions of Turkey participated in this retrospective-multicenter study. The analyzed data included clinical and demographical features, primary treatment, metastasis localizations, and treatments and includes certain pathologic features. Results: The study included data from a total of 443 SGCs. 56.7% was in major salivary glands and 43.3% was in minor salivary glands. Distant metastasis in the major SGCs was statistically significantly more common than in the minor SGCs, locoregional recurrence was statistically significantly more common in the minor SGCs than in the major SGCs (p = 0.003). Conclusions: Epidemiological information, metastasis and recurrence patterns, treatment modalities, and survival analysis of the patients over 20 years of follow-up are presented