The study of threshold of pain sensation in rats born from normal mothers and mothers with metabolic acidosis

زمینه و هدف: اسیدوز متابولیک یکی از مهمترین بیماری هایی است که بر دستگاه عصبی تأثیر می گذارد. این مطالعه با هدف بررسی تغییرات احتمالی در آستانه حسی درد در فرزندان متولد شده از مادران مبتلا به اسیدوز متابولیک در مقایسه با فرزندان متولد شده از مادران سالم انجام شده است. روش بررسی: در این مطالعه تجربی 50 سر موش صحرایی ماده سالم به طور تصادفی به پنج گروه مساوی (شاهد بدون تیمار، شاهد با دوز حداقل، شاهد با دوز متوسط، اسیدوز با دوز حداقل و اسیدوز با دوز متوسط) تقسیم شدند. برای ایجاد اسیدوز از محلول کلرید آمونیوم به جای آب آشامیدنی و در گروه های شاهد از محلول کلرید سدیم استفاده گردید. تمام حیوانات با جفت گیری طبیعی باردار شدند و 20 روز قبل و تا پایان بارداری موش ها، برای گروه های با دوزهای حداقل و متوسط به ترتیب مقادیر 15/0 و 3/0 مولار از محلول های مورد نظر استفاده شد. آزمایشات درد ناشی از فرمالین روی فرزندان نر بالغ انجام شد. نمره درد در سه مرحله حاد، اینترفاز و مزمن ثبت و مقایسه گردید. یافته ها: نتایج بیانگر افزایش معنی دار نمره درد مراحل حاد و مزمن آزمون فرمالین در فرزندان نر متولد شده از مادران اسیدوز می باشد (05/0>P). نمره درد در مرحله اینترفاز در گروه های اسیدوز با گروه های شاهد تفاوت معنی داری نداشت(05/0>P). نتیجه گیری: نتایج نشان داد اثرات اسیدوز متابولیک باعث کاهش آستانه حسی درد در فرزندان متولد شده از مادران مبتلا به اسیدوز متابولیک و افزایش درد در آن ها شده است. بنابراین با جلوگیری از بروز اسیدوز متابولیک مادران باردار می توان از کاهش آستانه حسی درد در فرزندان آن ها جلوگیری به عمل آورد کرد

Investigation Effect of Shift Work on Job Burnout and Depression, Anxiety, Stress Scale in Military Personnel

Shift work has been recognized as an important tool for organizing of work in developing countries. The disturbed depression, stress accident are the most common health‐related effects of shift work. The military personnel shift worker during work, are exposed to stress and psychological pressure that certainly affect the efficiency of their work. The aim of this study was to Investigation Effect of shift work on job burnout and Depression, Anxiety, Stress Scale in military personnel. This cross-sectional study was carried out on 100 military personnel male in Southern Iran. Respondents were divided into two groups based on their working schedule (50 shift work personnel / 50 day work personnel). Data collection tools were a Depression, Anxiety, and Stress Scale (DASS-21), demographic characteristics and Maslach job burnout questionnaire. Convenience sampling was used as sampling method. Finally, Data analysis was performed with SPSS (version 20), descriptive statistics, One Way Anova test, ANCOVA and t-independent test. The results of showed that shift work has an impact on burnout and DASS-21 and mean obtained score for DASS-21 and job burnout in shift workers are more day work individuals. Analysis of variance test showed significant difference between job burnout in day workers and shift workers and job burnout were more in shift workers. Also significant difference between DASS-21 in day workers and shift workers and DASS-21 was more in shift workers. This study showed that shift work has an impact on burnout and scale DASS-21 shall is taken to Intervention actions in shift works

Big Data Analytics and Its Applications in Supply Chain Management

In today’s competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Hence, explosive growth in volume and different types of data throughout the supply chain has created the need to develop technologies that can intelligently and rapidly analyze large volume of data. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. BDA provides a tool for extracting valuable patterns and information in large volume of data. So, the main purpose of this book chapter is to explore the application of BDA in supply chain management (SCM)

Geological, geochemical and fluid inclusion investigations on the Duna Pb-Ba-(Ag) deposit, Central Alborz, North Central Iran

The Duna Pb-Ba-(Ag) mine is located ca. 155 km north of Tehran in the central Alborz structural zone, northern Iran. The ore mineralization occurs as stratabound, epigenetic, E-W and WSWENE trending veins and veinlets in fracture-controlled as well as massive and open-space filling textures within a Permian dolomitic limestone host rock. Field observations as well as mineralogical and petrographic studies show dolomitization, silicification and possibly haematisation in the host rock. In the mineralization zone, galena and barite are the main minerals, followed by pyrite, quartz, chalcopyrite, tetrahedrite, sphalerite, calcite and supergene minerals such as covellite, malachite, azurite, cerussite, anglesite, and Fe-oxides. The chemical analyses of the highgrade ore samples show an average grade of 18.66 wt. % for Pb, 19.99 wt. % for Ba, and 120 ppm for Ag together with substantial quantities of Zn (0.15 wt. %), As (690 ppm), Cu (0.86 wt. %), Sb (0.25 wt. %), and Sr (0.56 wt. %). The amount of silver in some samples from the tunnel and discordant layers is up to 7030 ppm. The positive Eu/Eu⃰ ratio and the weak negative Ce/Ce⃰ anomaly in the ore samples were most likely inherited from magmatic water. The presence of minerals such as pyrite and chalcopyrite together with the co-precipitation of sphalerite and chalcopyrite suggest a high-temperature for mineralizing fluids. The homogenization temperatures of fluid inclusions from barite in concordant layers span between 135 and 165 ºC with salinities between 18.54 and 23.65 wt. % NaCl equivalent, while the homogenization temperatures of fluid inclusions from barite of discordant layers span between 113 and 285 ºC with salinities between 7.34 and 23.65 wt. % NaCl equivalent. The structural, geological, geochemical, and mineralogical studies together with the paragenesis of the ore minerals and fluid inclusion data allow consideration of the Duna Pb-Ba-(Ag) mine as a two stage mineralization scenario; 1st stage /older/MVT-type (Early Cimmerian tectonic phase), and the second stage/younger/Irish-type (Laramide orogenic movements). The structural data, high temperature of the fluid inclusions, positive Eu/Eu⃰ ratio and high silver content, especially in the discordant layers, indicate the involvement of a magmatic water mixed with meteoric and connate fluids comparable to the Irishtype mineralization in the second stage, which formed along brecciated zones of the thrust faults. The second stage of mineralization was most likely influenced by the Akapol granitoid intrusive mass, which overprinted the 1st stage/older/MVT-type

Analysis of RC Deep Beams Considering the Shear Deformations and Bar-concrete Interaction

In this paper, reinforced concrete (RC) deep beams (DBs) have been analyzed numerically and a new approach is proposed to the nonlinear numerical modeling of such structural members. The effect of shear deformations and the interaction between reinforcing steel bar and concrete are considered in modeling and analysis. In order to consider the effect of shear deformations, the Timoshenko beam theory has been applied to formulate the analysis method. In the modeling, the RC DB is divided into several sub-elements which are composed of concrete and reinforcing steel bars. Individual degrees of freedom have been assigned to each reinforcing steel bar. Thus, each reinforcing steel bar is able to slip relative to its surrounding concrete and the bond effect is simulated by nonlinear springs. To consider the interaction between reinforcing steel bar and concrete, the concrete segment acts as a beam element, and each reinforcing steel bar acts as a truss element. The reliability of this method has been confirmed by comparing the obtained results from the numerical analysis and the results of the experimental pushover test

Multi-Source AoI-Constrained Resource Minimization under HARQ: Heterogeneous Sampling Processes

We consider a multi-source hybrid automatic repeat request (HARQ) based system, where a transmitter sends status update packets of random arrival (i.e., uncontrollable sampling) and generate-atwill (i.e., controllable sampling) sources to a destination through an error-prone channel. We develop transmission scheduling policies to minimize the average number of transmissions subject to an average age of information (AoI) constraint. First, we consider known environment (i.e., known system statistics) and develop a near-optimal deterministic transmission policy and a low-complexity dynamic transmission (LC-DT) policy. The former policy is derived by casting the main problem into a constrained Markov decision process (CMDP) problem, which is then solved using the Lagrangian relaxation, relative value iteration algorithm, and bisection. The LC-DT policy is developed via the drift-plus-penalty (DPP) method by transforming the main problem into a sequence of per-slot problems. Finally, we consider unknown environment and devise a learning-based transmission policy by relaxing the CMDP problem into an MDP problem using the DPP method and then adopting the deep Q-learning algorithm. Numerical results show that the proposed policies achieve near-optimal performance and illustrate the benefits of HARQ in status updating

Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA tool

ORIGINAL ARTICLES Epidemiology Biostatistics and Public Health - 2020, Volume 17, Number 2Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA toolInvestigation of diagnostic value of artificialintelligence systems in the diagnosis of breastcancer based on histopathological imagesusing Meta-MUMS DTA toolABSTRACTBackground: Various artificial intelligence systems are available for diagnosing breast cancer based onhistopathological images. Assessing the performance of existing methodologies for breast cancer diagnosis is vital.Methods: The SCOPUS database has been searched for studies up to December 15, 2018. We extracted the data,including "true positive," "true negative," "false positive," and "false negative". The pooled sensitivity, pooled specificity,positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the curve of summary receiveroperating characteristic curve were useful in assessing the diagnostic accuracy. Egger's test, Deeks' funnel plot, SVE(Smoothed Variance regression model based on Egger’s test), SVT (Smoothed Variance regression model based onThompson’s method), and trim and fill methodologies were essential tests for publication bias identification.Results: Three studies with eight approaches from thirty-seven articles were found eligible for further analysis. Asensitivity of 0.95, a specificity of 0.78, a PLR of 7525, an NLR of 0.06, a DOR of 88.15, and an AUC of 0.953showed high significant heterogeneity; however, the reason was not the threshold effect. The publication bias wasdetected by SVE, SVT, and trim and fill analysis.Conclusion: The artificial intelligent (AI) systems play a pivotal role in the diagnosis of breast cancer usinghistopathological cell images and are important decision-makers for pathologists. The analyses revealed that theoverall accuracy of AI systems is promising for breast cancer; however, the pooled specificity is lower than pooledsensitivity. Moreover, the approval of the results awaits conducting randomized clinical trials with sufficient dat

Investigating the relationship of self-esteem and spirituality to homesickness among dormitory students of Razi University in Kermanshah

For downloading the full-text of this article please click here.Background and Objective: University is considered a positive opportunity for personal development; however, students often face challenges when they are at university. Homesickness is among the most frequently reported concerns of dormitory students. This study aimed to investigate the relationship of self-esteem and spirituality to homesickness among dormitory students of Razi University in Kermanshah, Iran in 1395.Method: The study is descriptive and correlational. All female and male students who were living in dormitories of Razi University in school year of 94-95 formed the statistical population. 322 of them were selected based on Morgan table, using multi-stage cluster sampling. Research instruments were self-concept questionnaire (1976), spirituality questionnaire by Corp and Downing (2009), and homesickness questionnaire by Archer (1998). The data were analyzed using descriptive statistics, correlation coefficient, and regression. In this study, all ethical issues were carefully observed and the authors declare no conflict of interest.Results: The results showed that there is a significant negative relationship between self-esteem and homesickness (p<.01); also spirituality and homesickness (p<.001). Data analysis also demonstrated that spirituality can predict the extent of homesickness among university students living in dormitories (p< .001).Conclusion: According to the findings of this research, spirituality and self-esteem can be perceived as complementary tools to reduce homesickness. It seems that when dealing with homesickness, these two variables should be taken into account.For downloading the full-text of this article please click here

Comparison of intracytoplasmic sperm injection outcomes in azoospermic men who underwent testicular sperm extraction vs. microdissection testicular sperm extraction: A cross-sectional study

Background: Outcomes of intracytoplasmic sperm injection (ICSI) may be different in azoospermic men who undergo testicular sperm extraction (TESE) vs. microdissection-TESE (micro-TESE). Objective: This study was conducted to compare the ICSI outcomes in men who underwent TESE vs. micro-TESE due to obstructive azoospermia and nonobstructive azoospermia, respectively. Materials and Methods: A total of 310 azoospermic men who underwent ICSI from September 2016 to September 2020 were enrolled in this cross-sectional study and divided into two groups (172 cases in the TESE and 138 cases in the micro-TESE group). The paternal and maternal age, and the fertilization, biochemical pregnancy, abortion and live birth rates were compared between the two groups. Results: Maternal mean age was significantly higher in the TESE group (34.9 ± 4.2 yr vs. 32.3 ± 5.7 yr). The fertilization and biochemical pregnancy rates were significantly higher in the TESE group, but the abortion rate was similar in the two groups. The live birth rate was higher in the TESE group, but this difference was not significant (p = 0.06). Also, the maternal and paternal age did not affect ICSI outcomes. Conclusion: Individuals who underwent TESE had higher fertilization and biochemical pregnancy rates than those who underwent micro-TESE, but the live birth rate was not significantly different. Keywords: Intracytoplasmic sperm injection, Azoospermia, Testicular sperm extraction, Microdissection testicular sperm extraction, Pregnancy outcome

Artificial Intelligence and Its Application in Optimization under Uncertainty

Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided