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The thermal efficiency of a flat‐plate solar collector is theoretically analyzed in this study. For this purpose, a novel computational technique called the Akbari‐Ganji method (AGM) is applied. The solution function derived using this method is examined for three different cases, including four‐term, six‐term, and eight‐term approximations. To validate the proposed method, the obtained results are compared with those from a published work, showing very good agreement. The thermal efficiency of the collector is comprehensively assessed under the influence of collector length, effectiveness coefficient, and heat loss coefficient. The results reveal that an increase in the effectiveness coefficient enhances the collector's thermal efficiency. Schematic of the air heating solar system (https://www.solarwall.com/technology/solar‐wall‐single‐stage/).

This study introduces a new approach by applying the Whale Optimization Algorithm (WOA) to create construction schedules using geometric data from Building Information Modeling (BIM). The algorithm utilizes 3D model information to establish stability criteria, which are organized in a Directed Design Structure Matrix (DSM). These criteria are integrated into the WOA Fitness function to enhance the constructability of schedules, where each schedule is symbolized as a unique whale. Through iterative WOA computations, the approach consistently achieves maximum constructability scores starting from randomly generated schedules, affirming the efficacy of this method. The results reveal that the proposed algorithm effectively produced fully executable project schedules from diverse inputs. Despite variations in computational times due to different input parameters, the experiments verified the consistent generation of schedules that are 100% executable. The entire process of whale optimization method for the current problem is summarized in the graphical image.

In this article, we prove the nonlinear stability of the quartic functional equation 1 6 f ( x + 4 y ) + f ( 4 x - y ) = 3 0 6 9 f x + y 3 + f ( x + 2 y ) (1) + 1 3 6 f ( x - y ) - 1 3 9 4 f ( x + y ) + 4 2 5 f ( y ) - 1 5 3 0 f ( x ) (2) (3)  in the setting of random normed spaces Furthermore, the interdisciplinary relation among the theory of random spaces, the theory of non-Archimedean space, the theory of fixed point theory, the theory of intuitionistic spaces and the theory of functional equations are also presented in the article.

Using viscosity approximation method, we study strong convergence to a common element of the set of solutions of an equilibrium problem and the set of common fixed points of a finite family of multivalued mappings satisfying the condition (E) in the setting of Hilbert space. Our results improve and extend some recent results in the literature.

We prove the stability of some functional equations in the random normed spaces under arbitrary t -norms.

We present a note on the paper by Brown and Wu (J Math Anal Appl 337:1326–1336, 2008 ). Indeed, we extend the multiplicity results for a class of semilinear elliptic system to the quasilinear elliptic system of the form: Here Δ p denotes the p-Laplacian operator defined by is a bounded domain with smooth boundary, is the outer normal derivative, the weight m ( x ) is a positive bounded function, and are functions which change sign in

In this paper we introduce a new algorithm based on viscosity approximation method for solving the generalized multiple-set split feasibility problem (GMSSFP)in an infinite dimensional Hilbert spaces . We establish the strong convergence for the algorithm to find a unique solution of the variational inequality which is the optimality condition for the minimization problem.

Evidence to date indicates that compassion and empathy are health-enhancing qualities. Research points to interventions and practices involving compassion and empathy being beneficial, as well as being salient outcomes of contemplative practices such as mindfulness. Advancing the science of compassion and empathy requires that we select measures best suited to evaluating effectiveness of training and answering research questions. The objective of this scoping review was to 1) determine what instruments are currently available for measuring empathy and compassion, 2) assess how and to what extent they have been validated, and 3) provide an online tool to assist researchers and program evaluators in selecting appropriate measures for their settings and populations. A scoping review and broad evidence map were employed to systematically search and present an overview of the large and diverse body of literature pertaining to measuring compassion and empathy. A search string yielded 19,446 articles, and screening resulted in 559 measure development or validation articles reporting on 503 measures focusing on or containing subscales designed to measure empathy and/or compassion. For each measure, we identified the type of measure, construct being measured, in what context or population it was validated, response set, sample items, and how many different types of psychometrics had been assessed for that measure. We provide tables summarizing these data, as well as an open-source online interactive data visualization allowing viewers to search for measures of empathy and compassion, review their basic qualities, and access original citations containing more detail. Finally, we provide a rubric to help readers determine which measure(s) might best fit their context.

Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

Breast cancer remains as a concerning global health issue, being the second leading cause of cancer deaths among women in the United States (US) in 2019. Therefore, there is an urgent and substantial need to explore novel strategies to combat breast cancer. A potential solution may come from the use of cancer nanotechnology, an innovative field of study which investigates the potential of nanomaterials for cancer diagnosis, therapy, and theranostic applications. Consequently, the theranostic functionality of cancer nanotechnology has been gaining much attention between scientists during the past few years and is growing exponentially. The use of biosynthesized gold nanoparticles (AuNPs) has been explored as an efficient mechanism for the treatment of breast cancer. The present study supposed a global systematic review to evaluate the effectiveness of biogenic AuNPs for the treatment of breast cancer and their anticancer molecular mechanisms through in vitro studies. Online electronic databases, including Cochrane, PubMed, Scopus, Web of Science, Science Direct, ProQuest, and Embase, were searched for the articles published up to July 16, 2019. Our findings revealed that plant-mediated synthesis was the most common approach for the generation of AuNPs. Most of the studies reported spherical or nearly spherical-shaped AuNPs with a mean diameter less than 100 nm in size. A significantly larger cytotoxicity was observed when the biogenic AuNPs were tested towards breast cancer cells compared to healthy cells. Moreover, biogenic AuNPs demonstrated significant synergistic activity in combination with other anticancer drugs through in vitro studies. Although we provided strong and comprehensive preliminary in vitro data, further in vivo investigations are required to show the reliability and efficacy of these NPs in animal models.