Impacts of atmospheric stilling and climate warming on cyanobacterial blooms: An individual-based modelling approach

Harmful algal blooms of the freshwater cyanobacteria genus Microcystis are a global problem and are expected to intensify with climate change. In studies of climate change impacts on Microcystis blooms, atmospheric stilling has not been considered. Stilling is expected to occur in some regions of the world with climate warming, and it will affect lake stratification regimes. We tested if stilling could affect water column Microcystis distributions using a novel individual-based model (IBM). Using the IBM coupled to a three-dimensional hydrodynamic model, we assessed responses of colonial Microcystis biomass to wind speed decrease and air temperature increase projected under a future climate. The IBM altered Microcystis colony size using relationships with turbulence from the literature, and included light, temperature, and nutrient effects on Microcystis growth using input data from a shallow urban lake. The model results show that dynamic variations in colony size are critical for accurate prediction of cyanobacterial bloom development and decay. Colony size (mean and variability) increased more than six-fold for a 20% decrease in wind speed compared with a 2 °C increase in air temperature. Our results suggest that atmospheric stilling needs to be included in projections of changes in the frequency, distribution and magnitude of blooms of buoyant, colony-forming cyanobacteria under climate change

Individual-based modelling of adaptive physiological traits of cyanobacteria: Responses to light history

Adaptive physiological traits of cyanobacteria allow plasticity of responses to environmental change at multiple time scales. Most conventional phytoplankton models only simulate responses to current conditions without incorporating antecedent environmental history and adaptive physiological traits, thereby potentially missing mechanisms that influence dynamics. We developed an individual-based model (IBM) that incorporates information on light exposure history and cell physiology coupled with a hydrodynamic model that simulates mixing and transport. The combined model successfully simulated cyanobacterial growth and respiration in a whole-lake nutrient enrichment experiment in a temperate lake (Peter Lake, Michigan, USA). The model also incorporates non-photochemical quenching (NPQ) to improve simulations of cyanobacteria biomass based on validation against cyanobacteria cell counts and chlorophyll concentration. The IBM demonstrated that physical processes (stratification and mixing) significantly affect the dynamics of NPQ in cyanobacteria. Cyanobacteria had high fluorescence quenching and long photo-physiological relaxation periods during stratification, and low quenching and rapid relaxation in response to low light exposure history as the mixing layer deepened. This work demonstrates that coupling adaptive physiological trait with physical mixing into models can improve our understanding and enhance predictions of bloom occurrences in response to environmental changes

Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks

Computer vision-based path planning can play a crucial role in numerous technologically driven smart applications. Although various path planning methods have been proposed, limitations, such as unreliable three-dimensional (3D) localization of objects in a workspace, time-consuming computational processes, and limited two-dimensional workspaces, remain. Studies to address these problems have achieved some success, but many of these problems persist. Therefore, in this study, which is an extension of our previous paper, a novel path planning approach that combined computer vision, Q-learning, and neural networks was developed to overcome these limitations. The proposed computer vision-neural network algorithm was fed by two images from two views to obtain accurate spatial coordinates of objects in real time. Next, Q-learning was used to determine a sequence of simple actions: up, down, left, right, backward, and forward, from the start point to the target point in a 3D workspace. Finally, a trained neural network was used to determine a sequence of joint angles according to the identified actions. Simulation and experimental test results revealed that the proposed combination of 3D object detection, an agent-environment interaction in the Q-learning phase, and simple joint angle computation by trained neural networks considerably alleviated the limitations of previous studies11Ysciescopu

A study on acoustic behavior of poroelastic media bonded between laminated composite panels

A study on the acoustic behavior of double-walled panels, with sandwiched layer of porous materials is presented within Classical Laminated Plate Theory (CLPT) for laminated composite panels. For this purpose, equations of wave propagation are firstly extracted based on Biot's theory for porous materials, then the transmission loss (TL) of the structure is estimated in a broadband frequency. Secondly, TL coefficient of the structure is determined using Statistical Energy Analysis (SEA). In the next step, accuracy of the solution is shown with comparing the data obtained from these two presented models as well as the experimental results available in literature. Finally, the effects of parameters on sound transmission loss of double porous composite panels, especially at a high frequency range, are discussed. In addition, the results show that maximum sound energy is transferred through the waves frame (structure born) due to the porous layer bonded between the two composite panels. Therefore, material parameters that are principally related to solid phase of the foam such as Poisson's ratio, bulk density and bulk Young's modulus, have the most significant effects on the transmission loss. Meanwhile, the impacts of composite material panels and composite plies arrangement on sound transmission loss structures have been addressed in this paper

Investigation of the effects of tumor size and type of radionuclide on tumor curability in targeted radiotherapy

Background: Targeted radiotherapy is one of the important methods of radiotherapy that involves the use of beta-emitting radionuclides to deliver a dose of radiation to tumor cells. An important feature of this method is the tumor size and the finite range of beta particles emitted as a result of radionuclide disintegration those have significant effects for the curability of tumors. Material and Methods: Monte Carlo simulations and mathematical models have been used to investigate the relationship of curability to tumors size for tumors treated with targeted 131I and 90Y. The model assumed that radionuclides are distributed uniformly throughout tumors. Results: The results show that there is an optimal tumor size for cure. For any given cumulated activity, cure probability is greatest for tumors whose diameter is close to the optimum value. There is a maximum value of curability that occurs at a diameter of approximately 3.5 mm for 131I. For 90Y maximum curability occurs at a tumor diameter of approximately 3.5 cm. Tumors smaller than the optimal size are less vulnerable to irradiation from radionuclides because a significant proportion of the disintegration energy escapes and is deposited outside the tumor volume. Tumors larger than the optimal size are less curable because of greater clonogenic cell number. Conclusion: With single radionuclide targeted radiotherapy, there is an optimal tumor size for tumor cure. It is suggested that single agent targeted radiotherapy should not be used for treatment of disseminated disease when multiple tumors of differing size may be present. The use of several radionuclides concurrently would be more effective than reliance on single radionuclide. This approach of using combination of radionuclides with complementary properties could hopefully prepare new measures and improve the efficiency of tumor therapy

A Custome Loyalty Model fo E-Commerce Recommendation Systems

The main objective of this research is to provide a customer loyalty model for e-commerce recommender systems. The proposed model is developed using Delone and McLean Information System success model and a set of factors which are identified from the literature. To test the research hypotheses of the developed model, a questionnaire survey is conducted and the data is collected from the 384 customers in a B2C website. We used SPSS and SmartPLS software for descriptive statistics and path analyses and to verify the proposed model.  The result of the Structural Equations Modeling showed that trust has a significant relationship with the customers’ satisfaction in the e-commerce recommendation systems. In addition, the results revealed that satisfaction with the recommended products can improve the customers’ loyalty in the B2C recommendation systems. The proposed model will help the e-commerce managers to improve their website recommendation systems and increase the sale of the products by achieving the customers’ loyalty in the online shopping websites

Comparison of Appetite-regulating Hormones and Body Composition in Pediatric Patients in Predialysis Stage of Chronic Kidney Disease and Healthy Control Group

Background: Protein-energy malnutrition (PEM) is a common complication in pediatric patients with chronic kidney disease (CKD). Components incorporated in the regulation of appetite and body composition appear to be of the focus in renal insufficiency and may influence the CKD-associated PEM. The purpose of this study was to investigate plasma levels of appetite-regulating hormones and their correlation with the body composition variables in a pediatric in predialysis stage of CKD. Methods: Thirty children with CKD in predialysis stage were selected and compared with 30 healthy sex- and age-matched controls. Blood samples were collected in fasting. Serum total ghrelin, leptin, and obestatin levels were measured using enzyme immunometric assay methods. Anthropometric parameters measurement and body composition analysis were done using the bioelectric impedance analysis (BIA) method. Results: Patients showed insignificant elevated total ghrelin (105.40±30.83 ng/l), leptin (5.32±1.17 ng/ml) and obestatin (5.07±1.09 ng/ml) levels in comparison with healthy participants. By using BIA, patients had significantly different Dry Lean Weight (P=0.048), Extra Cellular Water (P=0.045), Body Cell Mass (BCM) (P=0.021), Basal Metabolic Rate (P=0.033) and Body Mass Index (P=0.029) compared with controls. Furthermore, the total body water was slightly and the ECW was significantly higher in CKD participants. There were significant negative correlation between obestatin and BCM (r=-0.40, P=0.03) and fat free mass index (FFMI) (r=-0.40, P=0.029) in patients. Conclusion: It seems that our results are insufficient to clarify the role of appetite-regulating hormones in PEM in CKD patients. It is apparent that there are still many unknown parameters related to both appetite regulating and CKD-associated PEM

A survey on the effects of patient safety training programs based on SBAR and FMEA techniques on the level of self-efficacy and observance of patient safety culture in Iran hospital, Shiraz in 2022–2023

BACKGROUND AND OBJECTIVE: Patient safety and medical personnel self-efficacy are among the main factors involved in providing quality health services. Moreover, safety culture in an organization is considered one of the most critical factors regarding patients’ safety. Therefore, the present study aimed to determine the effects of patient safety programs based on Situation, Background, Assessment, Recommendation (SBAR) and Failure Model Effects Analysis (FMEA) techniques on self-efficacy and patient safety culture in Iran Hospital of Shiraz in 2022–2023. MATERIALS AND METHODS: This two-stage quasi-experimental study was conducted in 2022–2033. Considering inclusion criteria, the present study included 80 nurses working in Iran Hospital. The participants were divided into groups of SBAR (40 participants) and FMEA (40 participants). All the data were collected using a Hospital Survey on Patient Safety Culture questionnaire and Sherer General Self-Efficacy Scale. Then, the collected data were analyzed using SPSS 13, Fisher’s exact test, paired t-test, and independent t-test with a significant level of P < 0.05. RESULTS: The mean score of total patient safety culture between the two groups was insignificant before the intervention (P = 0.58). However, it was more significant in the FMEA group than the SBAR group after the intervention (P < 0/05). In addition, the mean self-efficacy score between the two groups was insignificant before the intervention (P = 0.80). However, after the intervention, the mean score of the FMEA group was significantly higher than the SBAR group (P < 0.05). CONCLUSION: According to the findings of this study, there is a meaningful relationship between patient safety training programs based on SBAR and FMEA techniques on patient safety and self-efficacy of nurses; however, FMEA training has more positive effects on self-efficacy and patient safety compared to other techniques. As a result, these techniques, along with other plans, are recommended to authorities in order to help improve patient safety