Iterative schemes for numerical reckoning of fixed points of new nonexpansive mappings with an application

The goal of this manuscript is to introduce a new class of generalized nonexpansive operators, called (α,β,γ)-nonexpansive mappings. Furthermore, some related properties of these mappings are investigated in a general Banach space. Moreover, the proposed operators utilized in the K-iterative technique estimate the fixed point and examine its behavior. Also, two examples are provided to support our main results. The numerical results clearly show that the K-iterative approach converges more quickly when used with this new class of operators. Ultimately, we used the K-type iterative method to solve a variational inequality problem on a Hilbert space

Optimizing the Performance of Data Warehouse by Query Cache Mechanism

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Spice-derived bioactive compounds confer colorectal cancer prevention via modulation of gut microbiota

Colorectal cancer (CRC) is the second most frequent cause of cancer-related mortality among all types of malignancies. Sedentary lifestyles, obesity, smoking, red and processed meat, low-fiber diets, inflammatory bowel disease, and gut dysbiosis are the most important risk factors associated with CRC pathogenesis. Alterations in gut microbiota are positively correlated with colorectal carcinogenesis, as these can dysregulate the immune response, alter the gut’s metabolic profile, modify the molecular processes in colonocytes, and initiate mutagenesis. Changes in the daily diet, and the addition of plant-based nutraceuticals, have the ability to modulate the composition and functionality of the gut microbiota, maintaining gut homeostasis and regulating host immune and inflammatory responses. Spices are one of the fundamental components of the human diet that are used for their bioactive properties (i.e., antimicrobial, antioxidant, and anti-inflammatory effects) and these exert beneficial effects on health, improving digestion and showing anti-inflammatory, immunomodulatory, and glucose- and cholesterol-lowering activities, as well as possessing properties that affect cognition and mood. The anti-inflammatory and immunomodulatory properties of spices could be useful in the prevention of various types of cancers that affect the digestive system. This review is designed to summarize the reciprocal interactions between dietary spices and the gut microbiota, and highlight the impact of dietary spices and their bioactive compounds on colorectal carcinogenesis by targeting the gut microbiota

Applications in Home Improvement Retailer, Koctas

— It sounds like Koçtaş is a leader in the home improvement sector in Turkey, and they are focused on providing the best service and customer experience possible. They are also actively working to accelerate their digital investments and use their vast amount of customer data to innovate in the industry. One way they are using this data is by collecting and analyzing video camera images using AI. This allows them to detect humans and identify which products and shelves are most viewed in their stores. This information can then be used to optimize store layout and product placement for a better customer experience. Another way Koçtaş is innovating is through the implementation of kiosks that use Natural Language Processing (NLP) to interact with customers. These kiosks can understand and respond to questions asked by customers using AI, providing a more personalized and human-like experience. Finally, Koçtaş is using Dynamic Creative Optimization to create personalized advertisements for their customers. This method allows them to optimize the content and format of their ads based on the individual preferences and behavior of their customers, leading to more effective marketing. Overall, Koçtaş is using technology and data to drive innovation and provide a better customer experience in the home improvement industry

Cardiopulmonary resuscitation: outcome and its predictors among hospitalized adult patients in Pakistan.

Introduction:Our aim was to study the outcomes and predictors of in-hospital cardiopulmonary resuscitation (CPR) among adult Patients at a tertiary care centre in Pakistan.Methods:We conducted a retrospective chart review of all adult Patients (age \u3e or =14 years), who underwent CPR following cardiac arrest, in a tertiary care hospital during a 5-year study period (June 1998 to June 2003). We excluded Patients aged 14 years or less, those who were declared dead on arrival and Patients with a do not resuscitate order. The 1- and 6-month follow-ups of discharged Patients were also recorded.Results:We found 383 cases of adult in-hospital cardiac arrest that underwent CPR. Pulseless electrical activity was the most common initial rhythm (50%), followed by asystole (30%) and ventricular tachycardia/fibrillation (19%). Return of spontaneous circulation was achieved in 72% of Patients with 42% surviving more than 24 h, and 19% survived to discharge from hospital. On follow-up, 14% and 12% were found to be alive at 1 and 6 months, respectively. Multivariable logistic regression identified three independent predictors of better outcome (survival \u3e24 h): non-intubated status [adjusted odds ratio (aOR): 3.1, 95% confidence interval (CI): 1.6-6.0], location of cardiac arrest in emergency department (aOR: 18.9, 95% CI: 7.0-51.0) and shorter duration of CPR (aOR: 3.3, 95% CI: 1.9-5.5).Conclusion:Outcome of CPR following in-hospital cardiac arrest in our setting is better than described in other series. Non-intubated status before arrest, cardiac arrest in the emergency department and shorter duration of CPR were independent predictors of good outcome

Intercomparison and Assessment of Stand-Alone and Wavelet-Coupled Machine Learning Models for Simulating Rainfall-Runoff Process in Four Basins of Pothohar Region, Pakistan

The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the assessment of inherent uncertainty has been a prime focus for researchers. Therefore, alternative data-driven methods have gained widespread attention in hydrology. Moreover, scientists often couple conventional machine learning models with data pre-processing techniques, i.e., wavelet transformation (WT), to enhance modelling accuracy. In this context, this research work attempts to explore the latent linkage between rainfall and runoff in Pothohar region of Pakistan by developing a novel linkage of five streamline techniques of machine learning, including single decision tree (SDT), decision tree forest (DTF), tree boost (TB), multilayer perceptron (MLP), and gene expression modeling (GEP), with a more sophisticated variant of WT, i.e., maximal overlap discrete wavelet transformation (MODWT), for boundary correction of the transformed components of timeseries data. This study also implements these machine learning models in a stand-alone mode for a more comprehensive comparative analysis of performances. Furthermore, the study uses a combined-basin approach that divides Pothohar region into two basins to compensate for the complex topographic division of the study area. The results indicate that MODWT-based DTF outperformed other stand-alone and hybrid models in terms of modeling accuracy. In the first scenario, considering the Bunha-Kahan River basin, MODWT-DTF yielded the highest NSE (0.86) and the lowest RMSE (220.45 mm) and R2 (0.92 at lag order 3 (Lo3)) when transformed with daubechies4 (db4) at level three. While in the Soan-Haro River basin, MODWT-DTF produced the highest accuracy modeling at lag order 4 (Lo4) (NSE = 0.88, RMSE = 21.72 m(3)/s, and R2 = 0.91). The highly accurate performance of 3- and 4-days lagged models reflects the temporal consistency in hydrological response of the study area. The comparison of simple and hybrid model performance indicates up to a 55% increase in modeling accuracy due to data pre-processing with wavelet transformation

Comparative efficacy of phosphate solubilizing bacteria and synthetic phosphate fertilizers on the growth of wheat

Wheat is recognized as one of the most important dietary elements due to its high nutritious content and thus, has become greatest food option all over the world. Phosphorus (P) being major plant food nutrient plays a vital role multiple functions of plant growth and development. The current study was carried out to compare the performance of phosphate solubilizing bacteria (PSB) as bio-fertilizer with commercially available phosphate fertilizers on wheat crop. The trial was designed in randomized complete block (RCB) replicated thrice. 6 different sources of phosphate fertilizers (Di-ammonium phosphate as DAP, Nitrophos as NP, Single super phosphate as SSP, Restore as PSB, Marathon as PSB, Nitrogen (N2) fixing bacteria as PSB) followed by control were evaluated for agronomic, physiological and quality attributes of wheat. The results showed that most of the qualitative traits were significantly influenced by different treatments. However, application of N2 fixing bacteria was more significant in all treatments. Highest total viable count of colony-forming units (14.63×106 at 3-WAS & 17.70×106 after harvest CFU g-1), maximum tillers’ count (337 m-2), grains’ count (45.57 spike-1), grain yield (2714.3 kg ha-1), LAI (0.67 & 1.16 at 56 & 112 DAS), CGR (13.59 g day-1 m-2), photosynthesis rate (26.13 µ mol m-2 sec-1) and flag leaf sugar content (0.24%) were recorded on account of using N2-fixing bacteria applied as PSB. Moreover, NPK content in shoot, grain as well as uptake of NPK by grain were also received as highest in the same treatment. Based on research findings, it is concluded that application of N2-fixing bacteria as PSB (7.5 kg ha-1) might be increasing wheat production in Dera Ismail Khan and other areas of similar environment in Pakistan