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To bypass the thermal data requirement for actual evapotranspiration (ETa) estimation in satellite remote sensing, two general approaches have been taken into practice based on previous efforts: (1) Multi-sensor data fusion for thermal sharpening and (2) the use of the process-based models such as the Penman-Monteith and Shuttleworth-Wallace equations augmented with satellite-based crop parameters. To address this issue, this study introduced an optical satellite data-based ETa estimation model, OPTRAM-ET, based on the optical trapezoid model (OPTRAM) estimates of soil moisture. The new model has been applied to Sentinel-2 and Landsat-8 images over 16 eddy covariance flux towers in the United States and Germany. The flux towers were chosen in a way to cover different ranges of landcover types, e.g., agriculture, orchard, permanent wetlands, and foothill forests. In order to assess the model in comparison to a thermal-based conventional method, the land surface temperature (LST)-vegetation index (VI) model was utilized. The results of the proposed OPTRAM-ET model showed promising performance in all the studied regions. While agricultural sites showed higher correlation due to their wider range of ETa values, error indicators were lower in foothill forests because soil moisture changes were smaller compared to irrigated and wet lands. In addition, the OPTRAM-ET model showed comparable performance to the conventional LST-VI model. The OPTRAM-ET model however does not need thermal data, and it benefits from higher spatial and temporal resolution data provided by ever-increasing drone- and satellite-based optical sensors to predict crop water status and demand. It is worth noting that the thermal sharpening step was excluded in this model which subsequently makes the model substantially less computationally demanding than a thermal-based model. Unlike the LST-VI model, which needs to be calibrated for each satellite image, a temporally-invariant region-specific calibration is possible in the OPTRAM-ET model. Importantly, the model requires further enhancement due to limitations caused by the simplified basic assumptions.

Introduction: Circulating microRNAs (miRNAs) serve as noninvasive diagnostic markers in many cancers. This meta-analysis aims to evaluate the diagnostic efficacy of circulating microRNAs for melanoma. Material and Methods: The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and ROC curve were evaluated using the Meta-Disc V.1.4 and Comprehensive Meta-Analysis V.3.3 software packages. To investigate the heterogeneity, the I2 and Chi-square tests were used. The publishing bias was evaluated using Begg’s rank correlation and Egger regression asymmetry tests. Results: A total of 9 articles covering 13 studies (more than 50 miRs individually and in combination) were included, containing 1,355 participants (878 cases and 477 controls). The overall pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and AUC were 0.78 (95% CI: 0.76–0.81), 0.80 (95% CI: 0.77–0.83), 4.32 (95% CI: 3.21-5.82), 0.17 (95% CI: 0.09-0.32), 28.0 (95% CI: 15.34-51.09), and 0.91, respectively. According to Begg's and Egger's tests, there was no publication bias (Begg's p = 0.160 and Egger's p = 0.289). Conclusion: Circulating miRNAs can serve as fair and non-invasive diagnostic biomarkers for melanoma. Additionally, specific miRNAs still need to be discovered for diagnosing melanoma.

Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop floors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factors in production processes. This study proposes a new approach for optimizing the hybrid flow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers’ flexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversification, and quality metrics, providing more versatile and better solutions for production and energy efficiency.

Introduction: The COVID-19 pandemic has widely affected medical tourism on a global scale, thereby reducing the number and volume of medical services. Given the importance of this topic, the present study aimed to determine the challenges of medical tourism after the prevalence of COVID-19 in the field of neurosurgery. Methods: The present descriptive study was conducted by the neurosurgery department of Isfahan University of Medical Sciences, Isfahan, Iran in the first quarter of 2022. Using the convenience sampling method and based on Morgan’s table, 500 patients with neurosurgical diseases registered in Medical Tourism companies were identified and included in the study. The data were analyzed in SPSS. Results: 142 (28.4%) out of 500 patients with COVID-19 were willing to come to Iran for neurosurgical treatment. The most important non-medical reasons included natural attractions (4.37±0.44), cost-effective accommodation (4.03±0.23), and support from a country of destination (place of residence) (3.75±0.22). The most important medical reasons included the short waiting list, the fast treatment response (4.26±0.76), the availability of qualified doctors (3.96±0.27), and the low-cost treatment (3.87±0.53). Conclusion: The present study focused on the functions and potentials of medical tourism in neurosurgery. It can be more successful by providing the right conditions to improve the current situation.

In this study, a numerical study is performed on the cooling phenomenon of three heat source electronic devices. The electronic devices are cooled in the form of natural heat transfer by the airflow in a porous medium. Electronic devices are installed on the boundary walls of a square environment. Cooling simulations are performed by drawing flow lines and constant temperature lines. Our main goal is to find the highest cooling rate in different Darcy numbers and different Rayleigh numbers in our investigation. The range of Darcy numbers and Rayleigh numbers is between 0.0001 to 0.01 and 1000 to 100,000, respectively. Our investigation showed the maximum cooling is obtained at the Darcy number of about 0.01. And also, by decreasing the value of Darcy number, a higher cooling rate for the hot boundary walls is achieved.

Smart IoT-based systems often desire continuous execution of multiple latency-sensitive Deep Learning (DL) applications. The edge servers serve as the cornerstone of such IoT-based systems, however, their resource limitations hamper the continuous execution of multiple (multi-tenant) DL applications. The challenge is that, DL applications function based on bulky "neural network (NN) models" that cannot be simultaneously maintained in the limited memory space of the edge. Accordingly, the main contribution of this research is to overcome the memory contention challenge, thereby, meeting the latency constraints of the DL applications without compromising their inference accuracy. We propose an efficient NN model management framework, called Edge-MultiAI, that ushers the NN models of the DL applications into the edge memory such that the degree of multi-tenancy and the number of warm-starts are maximized. Edge-MultiAI leverages NN model compression techniques, such as model quantization, and dynamically loads NN models for DL applications to stimulate multi-tenancy on the edge server. We also devise a model management heuristic for Edge-MultiAI, called iWS-BFE, that functions based on the Bayesian theory to predict the inference requests for multi-tenant applications, and uses it to choose the appropriate NN models for loading, hence, increasing the number of warm-start inferences. We evaluate the efficacy and robustness of Edge-MultiAI under various configurations. The results reveal that Edge-MultiAI can stimulate the degree of multi-tenancy on the edge by at least 2X and increase the number of warm-starts by around 60% without any major loss on the inference accuracy of the applications., Accepted in Utility Cloud Computing Conference 2022

Knowledge on Water productivity (WP) of strategic crops, nationwide, will result in optimizing the consumption of agricultural water, proper cropping pattern and more financial benefits. In this study, a national web-based simulation portal was developed to evaluate the maximum achievable WP on a national scale. The National Water Portal (NWP) was consisting of four national databases (climatic, soil, crop and spatial data), a lump water balance model and a graphical user interface (GUI) to support computing the irrigation water requirements and evaluate the WP indicators at farm to national scale on the network. WP indicators defined as yield per crop evapotranspiration (WPETc), yield per net irrigation requirement (WPNIR), and the financial benefit per consumed water (WPEco) was calculated for the dominant strategic crop consist of winter wheat, barley, rice, maize, sugar beet and sugarcane. Net irrigation requirement was estimated using a lamp water balance model based on the dual crop coefficient approach presented by FAO 56. The results indicated that winter wheat and barley with NIR of 258 to 4235 m3 ha− 1 has the highest WPEco among the studied crops and rice with NIR of 4495 to 8907 m3 ha− 1 stands in the next category. WPEco for maize and forage maize (3747 to 7083 m3 ha− 1) was higher than WPEco for sugarcane and sugar beet. Sugar cane with NIR of 18318 m3 ha− 1 had distinguishably lower WPEco value among the studied crops because of its long growing season. The results suggested sugar cane to be replaced by with sugar beet (NIR from 5100 to 11896 m3 ha− 1) with 4 times higher WPEco.

Background: To investigate the cross-sectional and longitudinal relationships between vascular function and circulating progenitor cell (CPC) counts with respect to aging and exposure to risk factors. Methods: In 797 adult participants, CPCs were enumerated by flow cytometry as CD45med mononuclear cells expressing CD34 epitope and its subsets co-expressing CD133, and chemokine C-X-C motif receptor 4 (CXCR4+). Arterial stiffness was evaluated by tonometry-derived pulse wave velocity (PWV) and microvascular function was assessed as digital reactive hyperemia index (RHI). Results: In cross-sectional analyses, for every doubling in CD34+ cell counts, PWV was 15% higher and RHI was 9% lower, after adjusting for baseline characteristics and risk factors ( p for all < 0.01). There were significant CPC-by-age-by-risk factor interactions ( p Conclusion: A higher CPC count in the presence of risk factors is associated with better vascular function among younger individuals. There is no increase in CPC count with risk factors in older individuals who have worse vascular function. Moreover, a higher CPC count is associated with less vascular dysfunction with aging.

Ahmadinejad's rise to power in Iran's political structure was accompanied by a new round of economic policies. Iranian analysts have examined these policies from a variety of perspectives, while some believe that the economic orientation of this period was closely related to economic populism. Based on this, the main question of the preliminary research is that the economic policies of Mahmoud Ahmadinejad's presidential period are relative to the macroeconomics of populism? The present paper attempts, from the perspective of political economy, using Durran Bush and Edwards' macroeconomic popolism) in the form of a populist approach as a political strategy and at the level of economic policy-making) while revising Ahmadinejad's economic policies, an explanation of the relationship between these policies and populist approaches to the economy as well as its consequences for the economy of Iran. The research findings show that Ahmadinejad, on the basis of populist economic policy makers, promised to support his supporters of poverty and inequality and the revival of justice, he worked with oil revenues to implement expansionary economic policies in the first two years of his presidency. Which ultimately led to the spread of liquidity, inflation, and recession, after which, since 2009, due to lower oil revenues, the ninth government turned to stabilizing policies and reduced its subsidies and economic development plans.

Effective estimation of crop yield on a regional scale in a short time with low cost would only be possible utilizing remote sensing. Several approaches have been proposed based on remotely sensed data for estimating Aboveground Biomass (AGB). In this study, three satellite-based approaches, including Radiation Use Efficiency (RUE). The soil water atmosphere plant (SWAP) model, and FAO33, were evaluated in the irrigated wheat and barley fields in the Qazvin irrigation network, Iran. To this end, the leaf area index (LAI) and relative evapotranspiration (ETrel) were extracted from the Landsat data and incorporated into the methods. A comparative analysis was undertaken to evaluate the performance of the satellite-based approaches using percent absolute error (PAE). By updating SWAP with satellite-derived LAI and surface incoming solar radiation, the PAE decreased significantly. Results of different Spectral Indices (SIs) in the RUE method showed that NDVI performed best with PAE of 1.52 percent and RMSE of 664.6 kg ha−1. Also, the RUE method with RMSE of 664.6 kg ha−1 had 4.7 and 23.8 lower PAE compared to the SWAP (RMSE = 2221.4 kg ha−1) and FAO33 (RMSE = 4394.2 kg ha−1), respectively. However, this was not the only criteria for a well-performed method, because earlier AGB forecast was only feasible by making use of SWAP since the satellite-derived parameters were only incorporated into the model about one month before the harvest.

The lack of sufficient selectivity is the main limitation of chemiluminescence (CL) methods; because the CL reagent is not restricted to a specific analyte. This study investigated the preconcentration and determination of ethion using a flow injection CL (FIA-CL) method using a molecularly imprinted poly high internal phase emulsion (MIP-polyHIPE) adsorbent. Preliminary studies showed that ethion could be determined with high sensitivity in the Ru(bipy)

Introduction: Patients undergoing surgery may need transfusion of blood products for a variety of reasons. Therefore, this study aimed to determine the incidence of blood transfusion complications in patients undergoing surgery. Methods: The present study was performed as a cross-sectional study in 2020 at Shohadaye Tajrish Hospital in Tehran, Iran. A total of 1132 patients who had complications during surgery upon transfusion of blood and blood products from 2015 to 2020 were included in the census. To collect information, a checklist, including patients’ information, the type of product, and types of complications was used. SPSS software version 21 was used for data analysis. Results: In this study, 99.7% of the complications were acute, and in 91.1% of the cases, the severity of complications was mild. Of the confirmed complications, 46.4% were allergic reactions, and 43.8% were reported as non-hemolytic febrile reactions. A total of 91.1% of patients completely recovered after the onset of the complication, 6.2% had a partial disability, 0.4% had severe disability, and 0.3% died. Conclusion: The results showed that most patients had acute complications in terms of the type of complication and mild in terms of the severity, thus a completely regular program is recommended to control side effects related to blood and blood products transfusion.

Avoidance of resonance in fluctuation of milling tool is vital for reaching excellence quality and performance of the cutting operation. The cutting tool in resonance condition vibrates with considerable magnitude that causes to increase milling tool wear and manufacturing prices. Analytical study of primary resonances and bifurcation behavior of a micro-milling process, including structural nonlinearities, gyroscopic moment, rotary inertia, velocity-dependent process damping, static and dynamic chip thickness, is chief aim of this article. The milling tool is modeled as a 3-D spinning cantilever beam that is motivated by cutting forces. To get the analytical solution for frequency response function and bifurcation behavior of the system under primary resonances, the method of multiple scales is operated on converted ordinary differential equations that are obtained by applying assumed modes method on nonlinear partial differential equations of tool vibration. The effects of different process parameters and nonlinear terms on the frequency response of the tool tip oscillations are examined. In addition, the effects of detuning parameter and damping ratio on the bifurcation and behavior of the limit cycle under primary resonances are examined. The results shows that these parameters are the bifurcation parameters and Neimark, symmetry breaking, flip, and period-3 bifurcations occur when the detuning parameter is varied.

In the current study, the flow behavior due to the variation of the tail angle of attack and its deflection has been investigated on a body–tail with a controlling tail. Numerical computations were carried out at a constant velocity of 90 m/s with the tail angle of attack and deflection ranging from 0 to 30 degrees. It provides a deeper understanding of how the aerodynamics of the tail is influenced by the deflection of the tail and the variation of the angle of attack, along with body and nose vortices. The formation of structure and breakdown of vortices along with their effects on the pressure distribution over the tail and the corresponding normal force coefficients derived from the numerical results. As a result, due to increasing the angle of attack or the deflection angle, the high-pressure area extends over the surface of the controlling tail facing the flow. Besides, the effects of angles of attack on the normal force coefficients on the tail are more than deflection angles due to body effects and the corresponding generated vortices. A comparison of numerical results and experimental tests showed satisfying accuracy and provided reliable details on the flow behavior and the highest calculated error rate was 12.8%.

Leaf area index (LAI) is a key parameter for the calculation of crop biophysical and biochemical processes. Therefore, the accurate estimates of LAI has been always of great importance for agricultural researchers. Remote sensing has shown enormous potential in LAI estimation, however, more evaluations are necessary on choosing the best type of data. In this study, the spatial, temporal, and spectral resolutions of different remotely sensed data (Landsat 8, Sentinel-2, MODIS, and also field hyperspectral data) were evaluated for LAI estimation of wheat and barley. First, the 30-m Landsat 8, 10-m Sentinel-2, 250-m MODIS, and field-based point data were taken into account for assessing the goodness of the relationship between field LAI (collected using LAI-2200c) and Vegetation Indices (VIs) to investigate the effect of a difference in spatial resolution. Afterward, to assess the temporal resolution effects, the Sentinel-2 images were resampled to 30 m and were combined with Landsat 8 data. Also, hyperspectral VIs (HNDVI, HDVI, and HSR) were calculated using field data to evaluate the effects of spectral resolution. Results showed that the difference in spatial and temporal resolutions of the data did not have any considerable effect on improving the LAI-VI relationship. Nevertheless, there were some particular portions of the spectrum which had R2 of more than 0.8 which was a great improvement compared to multispectral data with R2 between 0.6 and 0.69. The best HNDVI and HSR were calculated from the 10-nm bands centered at 1 115 nm and 1 135 nm.

In heterogeneous distributed computing (HC) systems, diversity can exist in both computational resources and arriving tasks. In an inconsistently heterogeneous computing system, task types have different execution times on heterogeneous machines. A method is required to map arriving tasks to machines based on machine availability and performance, maximizing the number of tasks meeting deadlines (defined as robustness). For tasks with hard deadlines (e.g., those in live video streaming), tasks that miss their deadlines are dropped. The problem investigated in this research is maximizing the robustness of an oversubscribed HC system. A way to maximize this robustness is to prune (i.e., defer or drop) tasks with low probability of meeting their deadlines to increase the probability of other tasks meeting their deadlines. In this paper, we first provide a mathematical model to estimate a task’s probability of meeting its deadline in the presence of task dropping. We then investigate methods for engaging probabilistic dropping. We propose methods to dynamically determine task dropping and deferring threshold probabilities. Next, we develop a pruning system and a pruning-aware mapping heuristic, which we extend to engender fairness across various task types. We present the pruning mechanism as an independent component that can be applied to any mapping heuristic to improve the system robustness. To reduce overhead of the pruning mechanism, we propose approximation methods that remarkably reduce the number of mathematical calculations and improve the practicality of deploying the mechanism in heterogeneous or even homogeneous computing systems. We show the cost and energy gains of the pruning mechanism. Simulation results, harnessing a selection of mapping heuristics, show efficacy of the pruning mechanism in improving robustness (on average by ≃ 22%) and cost in an oversubscribed HC system by up to ≃ 33%.

Highlights •Patients with CAD and renal insufficiency (eGFR •CPC counts, an index of endogenous vascular regenerative capacity, may help stratify risk in patients with CAD and renal insufficiency. •Renal insufficiency is associated with lower CPC counts in old, but not young, patients with established CAD. •The increased risk of adverse outcomes with CAD and renal insufficiency is limited to patients with low, but not high, CPC counts.

Determination of optimal parameters of cutting tool is one of the most significant factors in any operation planning of metal elements, especially in micro-milling process. This article presents an optimization procedure, based on genetic algorithms, to optimize some parameters related to micro-milling tool including number of teeth, shank diameter, fluted section diameter, shank length, taper length, and length of fluted section. The aim of this optimization is maximizing the minimum value of cutting depth on the border of stability lobe diagrams, which is called allowable cutting depth, for chatter-free machining. Cutting tool is modeled as a three-dimensional spinning cantilever Timoshenko beam based on strain gradient elasticity theory. Structural nonlinearity, gyroscopic moment, rotary inertia, and velocity-dependent process damping are also considered in the cutting tool model. The values of natural frequency, damping ratio, and material length scale of the micro-milling tool are calculated using a system identification based on genetic algorithm to match the analytical response with recorded experimental vibration signal. Using beam model, the allowable cutting depth is increased in the optimization process for a specific range of spindle speed to avoid the chatter phenomenon. Analytical study of micro-milling process stability is carried out to determine the cost function of the genetic algorithm. A plot of the greatest fitness in each generation is sketched. In addition, stability lobe diagrams before and after optimization process are presented to show the efficiency of the optimized micro-milling tool. In the presented examples, the results of genetic algorithm may lead to design or find a micro-milling tool that its acceptable cutting depth increases up to 1.9313 times.

Capecitabine is a chemotherapeutic agent used for the treatment of patients with metastatic cancers. This study aimed at determining the drug capecitabine in a simple chemiluminescence (CL) system of acidic potassium permanganate using the stopped-flow injection technique. Statistical methods were used to detect optimum conditions. The method showed two linear calibration ranges from 6.7 × 10-6 to 6.7 × 10-5 mol L-1 and from 6.7 × 10-5 to 2.7 × 10-3 mol L-1 with a detection limit of 1.5 × 10-6 mol L-1 . Chitosan-modified magnetic nanoparticles were studied in the drug-delivery experiments. According to the pH sensitivity of chitosan and low pH values in tumour cells, the chitosan-coated magnetic nanoparticles could provide a good targeting drug-delivery system to tumour sites. To evaluate the applicability of the method, the capecitabine-loaded magnetic chitosan nanoparticles were synthesized with two different cross-linkers; loading and releasing rates of the drug were investigated using the proposed CL method and an ultraviolet-visible light spectrophotometric method (absorption at 305 nm). The results showed a good correlation between the two methods, and it was found that the synthesized chitosan-modified magnetic nanoparticles could be used for pH-dependent release of capecitabine in cancer cells. Moreover, determination of capecitabine in tablets and synthetic samples was performed.

Background Dermoid cysts are benign congenital tumors that develop early in life. These tumors are classified by the presence of all 3 germ layers. Spinal intradural extramedullary teratoma is a rare disease, which is more common in children under 5 years of age than in adults. Case Description A 12-year-old girl with a dermoid cyst at the lower lumbar level presented with 2-month low back pain and intermittent lower extremity radicular symptoms on the right side. Magnetic resonance imaging scan of the spine revealed an intradural extramedullary mass lesion at L4-5. Surgical excision of the cyst was successfully performed. Surgical and histopathologic findings confirmed extramedullary ruptured matured teratoma. Postoperatively, the patient had remarkable clinical improvement. Conclusions Although dermoid cysts are uncommon, they should be considered in the differential diagnosis of spinal lesions in patients with lower back pain. It can be successfully treated with surgical excision.

A small low-power humidifier with a simple programmable on/off switch was used as a pulsed ultrasound generator. Using this tool, a novel sonochemiluminescence (SCL) method was developed to determine bentazone. To the best of our knowledge, no chemiluminescence method has been proposed to determine this pesticide. Only five studies have been proposed for SCL quantitative applications so far. Therefore, revealing new aspects of SCL promises to develop analytical methods for the quantitative determination of different substances. A molecularly imprinted polymerized high internal phase emulsion (MIP-polyHIPE) was synthesized, bentazone separated from aqueous solutions, and pre-concentrated by the MIP-polyHIPE foam. The adsorption of bentazone on the MIP-polyHIPE adsorbent was theoretically studied by density functional theory through molecular dynamics simulation. Both experimental and simulation results indicated removal and pre-concentration of bentazone by the MIP-polyHIPE adsorbent. Using the proposed SCL method and without pre-concentration process, a linear dynamic range (LDR) of 2.5 × 10

The aim of the reported work is to help design of decenary Multi-Valued Logic (MVL) circuits. This paper reports a work, in which, analog voltage-based circuitry is used to design MVL circuits. In this paper, some analog circuits are reported as elements that can be used in Multi-Valued Logic (MVL) circuitry. This article reported a MOSFET-Based Differential Amplifier (MBDA) as a key element in designing decenary MVL arithmetic unit. Operating voltage range and linearity of the gain are two important characteristics of this element. The operating voltage range for the MBDA is 0V to 5.5V as a output voltage.The achieved linear gain is within the range of 0.1V to 5.3V. Analog inverter and correction buffer circuits are reported based on MBDA. Analog inverter will be used in computational and logical decenary MVL circuits. The correction buffer is designed as an element to eliminate noises and signal drift at the output of the MVL gates and during data transfer.

Accumulating evidence has suggested that miR-137 and its target genes, CACNA1C, and TCF4, are amongst the most robustly implicated genes in psychiatric disorders. This preliminary study is aimed at investigating the effects of genetic variations in miR-137 (rs1625579A/C), TCF4 (rs1261084C/T), and CACNA1C (rs10774053A/G and rs10466907G/T) on BD susceptibility. We recruited 252 BD patients and 213 healthy subjects as the control group. Genotyping was performed using PCR-RFLP and ARMS-PCR methods. Enhanced risk of BD was found under the codominant homozygous, dominant, and allelic models of TCF4 rs1261084C/T, codominant homozygous and allelic models of CACNA1C rs10466907G/T polymorphisms, as well as codominant homozygous, dominant, recessive, and allelic models of the CACNA1C rs10774053A/G. Moreover, both TT/AG/GT/AA and TT/GG/GT/AC genotype combinations strongly increased the risk of BD in the participants. The bioinformatics analyses revealed that rs1261084C/T and rs10466907G/T created and disrupted binding sites of some miRNAs in the 3′-untranslated region of TCF4 and CACNA1C genes. In contrast, the rs10774053A/G created a new binding site for a major splicing factor and might have an effective role in the function of the CACNA1C protein. We have found that all the studied SNPs are positively associated with BD susceptibility. Replicated studies on different ethnicities are required to confirm these findings.

Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGAs) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.

Patient: Male, 16-day-old Final Diagnosis: High-grade immature CNS teratoma Symptoms: Brain mass • vision changes Medication: — Clinical Procedure: Surgical removal Specialty: Neurosurgery Objective: Congenital defects/diseases Background: The central nervous system (CNS) is a rare point of origin for mature or immature teratomas. However, immature teratomas are extremely rare. CNS teratomas have been known for poor patient prognosis and recovery and also reduce survival. However, chemoradiotherapy has been reported to increase patient survival. Case Report: This study presents a rare giant immature teratoma invading a newborn infant’s brain tissue and CNS. The tumor was surgically removed, and in a further 1-year follow-up, it did not need chemotherapy or radiotherapy according to alpha-fetoprotein (αFP) level and other serum markers. The teratoma had affected multiple loci of his brain’s left hemisphere, including parietal, frontal, temporal, and occipital lobes. A teratoma was diagnosed lateral to the midline, which is not common in CNS teratomas, as they mainly occur in or near the mid-line. The tumor was excised completely. The patient was followed up for 1 year, and no further recurrence was observed. Conclusions: Early diagnosis and treatment of immature teratomas are essential in patient prognosis. Chemotherapy is not always needed, but complete surgical removal and patient follow-up are always a necessity. In addition, adequate follow-up of these patients is critical to evaluate their further treatment plan and recurrence risk.

The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day−1, coefficient of determination (R2) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET info:eu-repo/semantics/publishedVersion

Evapotranspiration is considered to be an important component of allocating water to agricultural sector; therefore, the more accurate this parameter is, the more optimized the water use can be. This study was conducted in order to evaluate the Landsat 8 and Sentinel-2 data (A and B), both separately and combined, in potential evapotranspiration (ETp) and single crop coefficient (Kc) estimations. Field measurements such as crop height, leaf area index (LAI), land surface temperature (LST), air temperature above canopy (AT), and spectral data were exploited in the evaluating process throughout the entirety of 2017–18 growing season under winter wheat and barley cultivations in the Agricultural Research Farms of the University of Tehran. The novel method of Multi-Sensor Data Fusion using the Priestly-Taylor equation was taken into practice for satellite-based ETp (MSDF-ET) calculation from the combination of MODIS thermal and Landsat 8 and Sentinel-2 multispectral data. Thermal images were downscaled by the means of the TsHARP algorithm. Thus, prior to ETp calculation, the thermal sharpening algorithm calculated using different spectral indices (SI) was assessed. The SI included NDVI, SAVI, SR, NDWI, NDWIg, and LSWI. The subsequent results were representative of the LSWI qualification under both Landsat 8 and Sentinel-2 conditions against thermal and spectral measurements. Also the satellite-based ETp strongly correlated with the ETp derived from the field data illuminating the promising accuracy of the MSDF-ET method in both Landsat 8 and Sentinel-2 data. In the end, the time series of Kc obtained from the combination of satellites were fairly indicative of the real-world variations under different vegetation cover and crop growth stages. Overall, using Landsat 8 and Sentinel-2 products in integration with each other could significantly result in more reliable decisions in agricultural water resources management.

Micro-milling is one of the micro-manufacturing techniques used for creating micro-scale features. In this article, a size-dependent formulation based on the strain gradient elasticity theory is developed to analyze the micro milling tool vibration. A new cutting forces formulation in rotating frame is presented in this paper. Considering structural nonlinearities , gyroscopic moment, rotary inertia , process damping and size effect, nonlinear equations of tool motion are derived using non-classical Timoshenko beam theory and Hamilton's principal. Partial differential governing equations of the tool are converted to ordinkary differential equations by using assumed modes method. Then the method of multiple scales is used to obtain the analytical solution for tool vibrations. The proposed approach is applied to investigate the chatter instability observed in micro-milling operations. To verify the presented model, simulated stability lobe diagrams are compared with the results obtained from experimental tests and literature. According to the results, neglecting size effect, gyroscopic and rotary terms in the tool model causes significant errors in prediction of the chatter in micro-milling process.

Improving the accuracy of single crop coefficient (Kc) for major crops at regional scales would significantly result in optimizing the consumption of agriculture water. In this study, a new approach is applied to generate Kc curves from satellite images based on the Priestley-Taylor equation. The equation mainly consists of shortwave radiation and thermal terms. Therefore in order to meet the equation’s requirements, the Landsat optical data was considered as the essential satellite-based input of the approach, yet thermal data was obtained from the MODIS imagery which was primarily downscaled using the TsHARP algorithm to fit the 30 m spatial resolution of Landsat; therefore, in this study the method is referred to as the MultiSensor Data Fusion for potential EvapoTranspiration calculation (MSDF-ET). The Kc trends were first evaluated for annual crops (forage maize, sugar beet, wheat, and barley) in two different regions (the Qazvin and Tehran-Karaj plains) using field observation data of Leaf Area Index (LAI). The method was then applied to the major crops in 31 selected plains with three different climatic conditions in Iran. Climatic conditions were categorized into cold (North West region), arid (Central region), and warm and humid (South West region). Eventually, crop water requirements (CWR) in different regions were compared considering long-term air temperature, wind, and relative humidity (RH) variations. Evaluation of the method in the Qazvin and Tehran-Karaj plains showed promising results. The Kc trend varied according to the LAI and temperature variability. Crop growth duration and especially Kc curves were successfully extracted using remotely sensed data in the studied areas. CWR in the zone with higher values of air temperature, notably in warm seasons, was higher in comparison with other climatic zones. Also lengths of the growing seasons were shorter. In conclusion, surface and groundwater resources management would be improved as the result of taking satellite data into account for Kc and subsequently CWR calculations.

Multispectral imaging using Unmanned Aerial Vehicles (UAVs) has changed the pace of precision agriculture. Actual evapotranspiration (ETa) from the very high spatial resolution of UAV images over agricultural fields can help farmers increase their production at the lowest possible cost. ETa estimation using UAVs requires a full package of sensors capturing the visible/infrared and thermal portions of the spectrum. Therefore, this study focused on a multi-sensor data fusion approach for ETa estimation (MSDF-ET) independent of thermal sensors. The method was based on sharpening the Landsat 8 pixels to UAV spatial resolution by considering the relationship between reference ETa fraction (ETrf) and a Vegetation Index (VI). Four Landsat 8 images were processed to calculate ETa of three UAV images over three almond fields. Two flights coincided with the overpasses and one was in between two consecutive Landsat 8 images. ETrf was chosen instead of ETa to interpolate the Landsat 8-derived ETrf images to obtain an ETrf image on the UAV flight. ETrf was defined as the ratio of ETa to grass reference evapotranspiration (ETr), and the VIs tested in this study included the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Land Surface Water Index (LSWI). NDVI performed better under the study conditions. The MSDF-ET-derived ETa showed strong correlations against measured ETa, UAV- and Landsat 8-based METRIC ETa. Also, visual comparison of the MSDF-ET ETa maps was indicative of a promising performance of the method. In sum, the resulting ETa had a higher spatial resolution compared with thermal-based ETa without the need for the Albedo and hot/cold pixels selection procedure. However, wet soils were poorly detected, and in cases of continuous cloudy Landsat pixels the long interval between the images may cause biases in ETa estimation from the MSDF-ET method. Generally, the MSDF-ET method reduces the need for very high resolution thermal information from the ground, and the calculations can be conducted on a moderate-performance computer system because the main image processing is applied on Landsat images with coarser spatial resolutions.

Background Covid‐19 has caused many complications for both the infected and those in need of medical care. This may be due to infection‐related prognosis worsening or the patients’ avoidance of referring to the hospital for fear of contracting the infection. The decline of acute referral to the ER of many significant conditions with severe results on both the well‐being and life‐expectancy is a serious concern. To address these concerns, we designed this study to evaluate the recent pandemic's impact on “in‐hospital mortality” caused by neurological disorders pre and postpandemic. Methods and material The inclusion criteria were any acute neurological condition and the consent of the patients eligible for our study. The definitions of all assessed conditions and the comorbidities are ICD‐10 based. Hypertension and diabetes mellitus, due to their high prevalence, were evaluated separately from other internal comorbidities. The total number of the enrolled patients was 1742, 671 of whom had been during the COVID‐19 pandemic, and 1071 had attended the ER during the prepandemic era. Results The overall mortality was significantly higher during the pandemic, and the covid infected had suffered higher mortality rates. (p‐value

A molecularly imprinted polymerized high internal phase emulsion (MIP-polyHIPE) adsorbent was used for selective separating and preconcentrating the anti-plaque drug, clopidogrel. For the first time in this study, chemiluminescence (CL) methods were evaluated for the determination of clopidogrel. The synthesis of adsorbents by the emulsion templating method showed that the sensitivity of the method can be increased up to 42 times. The determination of clopidogrel was evaluated by Ru(phen)32+-Cerium (IV), KMnO4-H2SO4, KMnO4-H2SO4-Na2SO3, and luminol-H2O2 CL systems. According to the results, only the Ru(phen)32+-Cerium (IV) CL system showed a reasonable sensitivity for clopidogrel. Using MIP-polyHIPE adsorbent, the linear range, the limit of detection, and relative standard deviation for clopidogrel in this system were respectively 1.0 × 10−9–8.0 × 10−8 mol L−1, 3.0 × 10−10 mol L−1, and 6.3% (n = 4, 1.0 × 10−8). The proposed method was employed for determining clopidogrel in pharmaceuticals and blood serum samples. The results showed the good sensitivity and accuracy of the proposed method.

Supplemental Digital Content is available in the text., Objective: The underlying pathobiology of the paradoxical relationship between obesity and adverse outcomes in coronary artery disease (CAD) is unclear. Our objective was to determine the association between obesity and circulating progenitor cell (CPC) counts—a measure of intrinsic regenerative capacity—in asymptomatic individuals and patients with CAD and its impact on the obesity paradox. Approach and Results: CPCs were enumerated by flow cytometry as CD45med+ cells expressing CD34+, CD133+, and CXCR4+ epitopes in 672 asymptomatic individuals (50 years of age; 28% obese) and 1277 patients with CAD (66 years of age; 39% obese). The association between obesity and CPCs was analyzed using linear regression models. The association of obesity and CPCs with cardiovascular death/myocardial infarction events over 3.5-year follow-up in patients with CAD was studied using Cox models. Obesity was independently associated with 16% to 34% higher CPC counts (CD34+, CD34+/CD133+, and CD34+/CXCR4+) in asymptomatic individuals. This association was not attenuated by systemic inflammation, insulin resistance, or secretion but partly attenuated by cardiorespiratory fitness and body composition. In patients with CAD, obesity was associated with 8% to 12% higher CPC counts and 30% lower risk of adverse outcomes. Compared with nonobese patients, only obese patients with high CPC counts (CD34+ cells ≥median, 1806 cells/mL) were at a lower risk (hazard ratio, 0.52 [95% CI, 0.31–0.88]), whereas those with low counts (

Using natural draft dry air cooling systems in the power plant cycle is one of the proposed solutions for less water consumption. But the wind blowing will cause decreasement of cooling system performance in the power plants that work with the Rankin cycle. Therefore, it is important to know the right amount of wind speed to make the right decision to prevent reducing generating power or provide the right solution to improve the performance of the power plant cooling system. There are many methods of calibration of sensors in the world. But using optimization techniques or stochastic methods that do not require physical facilities and additional costs is almost a new approach. Therefore, in this study, wind sensor was calibrated using Bayesian inference method. Bayesian inference is a statistical method which updates the probability of a hypothesis as more evidence or information is available. Data obtained from Shazand power plant. Finally, after calibration, it is perceived that the error between observations and system models has decreased about 95%. Keywords: Thermal power plant, Cooling tower, Bayesian inference, Wind, Sensor, Data, Calibration

Robustness of a distributed computing system is defined as the ability to maintain its performance in the presence of uncertain parameters. Uncertainty is a key problem in heterogeneous (and even homogeneous) distributed computing systems that perturbs system robustness. Notably, the performance of these systems is perturbed by uncertainty in both task execution time and arrival. Accordingly, our goal is to make the system robust against these uncertainties. Considering task execution time as a random variable, we use probabilistic analysis to develop an autonomous proactive task dropping mechanism to attain our robustness goal. Specifically, we provide a mathematical model that identifies the optimality of a task dropping decision, so that the system robustness is maximized. Then, we leverage the mathematical model to develop a task dropping heuristic that achieves the system robustness within a feasible time complexity. Although the proposed model is generic and can be applied to any distributed system, we concentrate on heterogeneous computing (HC) systems that have a higher degree of exposure to uncertainty than homogeneous systems. Experimental results demonstrate that the autonomous proactive dropping mechanism can improve the system robustness by up to 20%.