Analysis and Evaluation of the Status of Tourism Target in Kerman Province by Analytical Hierarchy Process (AHP)

Tourism as a renewable and huge economical resource is an important and effective factor for development. Survey of attractions, facilities and shortage of the eight tourism target areas in Kerman province by evaluation of the most important effective criterions for their development to enhancement of the level of quality in these areas are in the field of this paper. This research is practical and its method of survey is Descriptive – Analytic. Findings show that most of numerous areas have poor position for infrastructure and haven’t achieved to expected aims while most of them have many attractions and abilities for tourism attraction. Based on the results, the weight of quintet criterions include tourism attractions, suitable weather, access, facilities and historical value are 0.445, 0.262, 0.152, 0.089 and 0.052 respectively. Criterion tourist attractions have the most effectiveness on the priorities of the areas. Research findings show that among tourism areas in Kerman province, Sirch with weighted average 0.201 is the most favorite area for tourism development in Kerman province. Keywords: Tourism, Tourism Target Areas, Analytical Hierarchy Process (AHP), Kerman Provinc

Studying Short-Term and Long-Term Effects of OPEC Oil Basket Prices and Natural Gas on Liquefied Petroleum Gas (LPG) Traded on Energy Exchange of Iran

In this paper, we examine the short-term and long-term effects of OPEC oil basket prices and natural gas on liquefied petroleum gas (LPG) traded on Energy Exchange of Iran. tests of convergence (integration) and causality of variables have been used for 2-year period, from May 22, 2014 to July 21, 2016. The results of the study based on long-term relationship show that an increase of 1 percent in the logarithm of OPEC oil basket prices decreases 17.24 percent of the logarithm of the price of LPG. The direction of causality is from OPEC oil basket prices to LPG. Moreover, 1% increase in natural gas prices logarithm will increase 26.52 percent of the logarithm of the price of LPG. The direction of causality is from natural gas to LPG. Estimating the relationship between short-term error corrections for the logarithm of the price of LPG also confirms no statistically significant error correction component

The effects of low intensity aerobic exercise on blood pressur

چکیده: زمینه و هدف: کاهش فعالیتهای جسمی (کم تحرکی) و پرفشاری خون هر دو از عوامل خطر در ایجاد بیماریهای قلبی و عروقی و سکته های مغزی می باشند. کنترل فشارخون در محدوده طبیعی می تواند از ایجاد این عوارض پیشگیری کند یا آنها را به تعویق اندازد. استفاده از روشهای غیر دارویی یکی از اقدامات مهم در کنترل فشارخون می باشد. این مطالعه با هدف بررسی تأثیر ورزش پیاده روی با شدت پایین بر فشارخون کارمندان مبتلا به پرفشاری خون اولیه، انجام شد. روش بررسی: پژوهش حاضر یک مطالعه نیمه تجربی است که در آن 36 کارمند مبتلا به پرفشاری خون اولیه مراجعه کننده به مراکز بهداشتی درمانی شهر بروجن در یک برنامه ورزشی پیاده روی با شدت پایین به مدت 4 هفته، هر هفته 3 بار و هر بار به مدت 30-20 دقیقه شرکت کردند. سرعت پیاده روی در حدی تعیین شد که ضربان قلب در محدوده 60-50 درصد حداکثر ضربان قلب باشد. قبل از ورود به برنامه ورزشی، بلافاصله بعد از آن و یک هفته بعد متغیرهای پژوهش (فشارخون، نبض، وزن و شاخص توده بدنی) مورد اندازه گیری قرار گرفتند. داده ها با استفاده از آزمونهای آماری t زوجی و آنالیز واریانس با اندازه گیری‌های مکرر تجزیه و تحلیل شدند. یافته ها: میانگین سنی واحدهای مورد پژوهش 5±13/46 سال بود. قبل و پس از مداخله به ترتیب میانگین فشارخون سیستولیک 12±04/150 و 11±5/149 (05/0p>)، فشارخون دیاستولیک 6±6/88 و 5±6/84 (001/0

Exploitation of resources and cardiovascular outcomes in low-risk patients with chest pain hospitalized in coronary care units

Habibollah Saadat¹, Hossein Shiri², Zahra Salarpour², Tahereh Ashktorab² , Hamid Alavi Majd², Zahra Saadat¹, Hosein Vakili¹ 1Cardiovascular Research Center, Modarres Hospital, Shaheed Beheshti University of Medical Sciences, Tehran; 2Nursing School, Shaheed Beheshti University of Medical Sciences, Tehran, Iran Background: Most patients who present to medical centers due to chest pain do not suffer from acute coronary syndromes and do not need to be hospitalized in coronary care units (CCUs). This study was done to determine exploitation of resources and cardiovascular outcomes in low-risk patients with chest pain hospitalized in CCUs of educational hospitals affiliated with a major medical university. Methods: Over a 4-month period, 550 patients with chest pain who were hospitalized in the CCUs belonging to six hospitals affiliated to the authors' medical university were recruited by census method. Using Thrombolysis in Myocardial Infarction risk score, 95 patients (17.27%) were categorized as low-risk patients. This group was evaluated with respect to demographics, bed occupancy rate, mean hospitalization period, expenses during admission, and cardiovascular outcomes in the 30-day period postdischarge. Results: Mean (± standard deviation) hospitalization duration was 3.04 (±0.71) days. No significant difference was seen between the six surveyed hospitals regarding hospitalization duration (P = 0.602). The highest bed occupancy rate was seen in Taleghani and Shohada Tajrish hospitals and the lowest was in Modarres Hospital. The mean paid treatment expenses by low-risk patients was IRR 2,050,000 (US$205). Mean total hospitalization expenses was US$205. No significant difference was seen between the six surveyed hospitals (P = 0.699). Of the patients studied, 89.5% did not show any cardiovascular complications in 1 month and no deaths occurred. Conclusion: Given the high bed-occupancy rate by low-risk patients, associated high hospitalization costs, and the lack of cardiovascular complications in patients observed at 1-month follow-up after discharge, it is recommended that appropriate evaluations be performed in emergency units to prevent unnecessary admissions. Keywords: bed occupancy, hospitalization expenses, low-risk patients, chest pain, exploitation of resource

Impact of feature harmonization on radiogenomics analysis:Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images

Objective: To investigate the impact of harmonization on the performance of CT, PET, and fused PET/CT radiomic features toward the prediction of mutations status, for epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS) genes in non-small cell lung cancer (NSCLC) patients. Methods: Radiomic features were extracted from tumors delineated on CT, PET, and wavelet fused PET/CT images obtained from 136 histologically proven NSCLC patients. Univariate and multivariate predictive models were developed using radiomic features before and after ComBat harmonization to predict EGFR and KRAS mutation statuses. Multivariate models were built using minimum redundancy maximum relevance feature selection and random forest classifier. We utilized 70/30% splitting patient datasets for training/testing, respectively, and repeated the procedure 10 times. The area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess model performance. The performance of the models (univariate and multivariate), before and after ComBat harmonization was compared using statistical analyses. Results: While the performance of most features in univariate modeling was significantly improved for EGFR prediction, most features did not show any significant difference in performance after harmonization in KRAS prediction. Average AUCs of all multivariate predictive models for both EGFR and KRAS were significantly improved (q-value &lt; 0.05) following ComBat harmonization. The mean ranges of AUCs increased following harmonization from 0.87-0.90 to 0.92-0.94 for EGFR, and from 0.85-0.90 to 0.91-0.94 for KRAS. The highest performance was achieved by harmonized F_R0.66_W0.75 model with AUC of 0.94, and 0.93 for EGFR and KRAS, respectively. Conclusion: Our results demonstrated that regarding univariate modelling, while ComBat harmonization had generally a better impact on features for EGFR compared to KRAS status prediction, its effect is feature-dependent. Hence, no systematic effect was observed. Regarding the multivariate models, ComBat harmonization significantly improved the performance of all radiomics models toward more successful prediction of EGFR and KRAS mutation statuses in lung cancer patients. Thus, by eliminating the batch effect in multi-centric radiomic feature sets, harmonization is a promising tool for developing robust and reproducible radiomics using vast and variant datasets.</p

Radiation protection and secondary cancer prevention using biological radioprotectors in radiotherapy

Radiotherapy is the feasible treatment approach for many malignant diseases and cancers. New radiotherapy techniques such as ion therapy, stereotactic radiosurgery and intensity modulated radiation therapy deliver higher low dose radiation to large volume of normal tissues and are in debating as more secondary cancers inducers. A secondary cancer after radiotherapy is an important issue that reduces treatment efficiency and should be decreased. Radioprotective compounds are of importance in clinical radiation therapy for saving normal tissues. In the present study, we are so interest to introduce, suggest and review the application of biological radioprotectors in radiotherapy. We propose probiotics, prebiotics, gas, vitamin and nanoparticle producing microorganisms as new biological systems based radioprotectors to protect normal tissues. Also, we reviewed the main biological pathways, molecules and also radioadaptive response that act as radioprotectors. In this review we tried to address the secondary cancer induction by radiotherapy and also main biological radiation protection approaches, although there is a wealth of data in this subject.

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented