Cement, as a construction material, has low thermal resistance, inherent fire resistance, and is incombustible up to a certain degree. However, the loss of its mechanical performance and spalling are ...its primary issues, and it thus cannot retain its performance in refractory applications. The present study explores the performance of geopolymer formulations that have excellent fire resistance properties for potential refractory applications. This study is unique, as it investigates advanced solid geopolymer formulations that need only water to activate and bind. Various solid geopolymer formulations with fly ash as a precursor; potassium hydroxide and potassium silicate as activators; and mullite and alumina as refractory aggregates were studied for their compressive strength at up to 1100 °C and compared with their two-part conventional liquid alkaline geopolymer counterparts. Advanced solid geopolymer formulations with mullite and alumina as refractory aggregates had mechanical strength values of 84 MPa and 64 MPa post-1100 °C exposure and were further exposed to ten thermal cycles of 1100 °C to study their fatigue resistance and post-exposure compressive strengths. The geopolymer sample with mullite as a refractory aggregate yielded 115.2 MPa compressive strength after the fourth cycle of exposure. This sample was also studied for its temperature distribution upon direct flame exposure. All the geopolymer formulations displayed a drop in compressive strength at 600 °C due to viscous sintering and then a rise in strength at 1100 °C due to phase transformation. X-ray diffraction studies revealed that the formation of crystalline phases such as leucite, sanidine, and annite were responsible for the superior strengths at 1100 °C for the alumina- and mullite-based geopolymer formulations.
Globally, prostate cancer (PCa) is regarded as a challenging health issue, and the number of PCa patients continues to rise despite the availability of effective treatments in recent decades. The ...current therapy with chemotherapeutic drugs has been largely ineffective due to multidrug resistance and the conventional treatment has restricted drug accessibility to malignant tissues, necessitating a higher dosage resulting in increased cytotoxicity. Plant‐derived bioactive compounds have recently attracted a great deal of attention in the field of PCa treatment due to their potent effects on several molecular targets and synergistic effects with anti‐PCa drugs. This review emphasizes the molecular mechanism of phytochemicals on PCa cells, the synergistic effects of compound‐drug interactions, and stem cell targeting for PCa treatment. Some potential compounds, such as curcumin, phenethyl‐isothiocyanate, fisetin, baicalein, berberine, lutein, and many others, exert an anti‐PCa effect via inhibiting proliferation, metastasis, cell cycle progression, and normal apoptosis pathways. In addition, multiple studies have demonstrated that the isolated natural compounds: d‐limonene, paeonol, lanreotide, artesunate, and bicalutamide have potential synergistic effects. Further, a significant number of natural compounds effectively target PCa stem cells. However, further high‐quality studies are needed to firmly establish the clinical efficacy of these phytochemicals against PCa.
Natural bioactive compounds against prostate cancer.
Plastic pollution has emerged as a major global concern due to its enduring nature and limited recycling options. In response to this critical challenge, this paper presents a novel approach ...utilizing a Detection-Based Reward System (DBRS) alongside an innovative business model to promote effective plastic waste management, reduce plastic waste accumulation in the nature, and uphold environmental cleanliness. Leveraging the YOLOv5 algorithm for its exceptional accuracy, speed, and open-source availability, plastic bottle detection becomes a pivotal aspect of this system. Users seamlessly enroll in the system, triggering an automated detection process that computes reward points corresponding to their deposited plastic bottles. These reward points are meticulously stored within a centralized database. Beyond its operational facets, this comprehensive system encompasses a robust business model, strategically poised to capture widespread engagement with waste disposal practices, thereby contributing to the realization of Sustainable Development Goals (SDGs) geared towards fostering a healthier environment. Notably, the DBRS attains cutting-edge performance in plastic bottle detection, boasting an impressive mean Average Precision (mAP) of 0.973, underscoring its efficacy in tackling plastic pollution.
Aims: IoT-based trash collection system is a system that can automatically detect obstacles, which can be simplified as trash, and opens the Meta Bin lid to receive the trash. The main goal of the ...system is to make an environment where to find a digital and automatic way to the trash collection system.
Study Design: The existing research aims to design, simulate, and implement a new system that can play a vital role in terms of making the environment clean, in a large sense the world clean. Along with that a system that can carry not only a trash bin but also any portable devices as it can be operated automatically.
Place and Duration of Study: Department of Computer Science and Engineering and Department of Electrical and Electronic Engineering, American International University-Bangladesh (AIUB), Dhaka, Bangladesh between October 2022 and February 2023.
Methodology: In this work, we have designed a new model using Arduino, an IoT device, a servo motor, an ESP8266 Wi-Fi microchip, several DC motors, several IR Sensors, LEDs, etc. to build a system like Meta-Bin. Arduino IDE is used for program development.
Results: This is an automated trash bin, which has a different level of trash collection capacity with proper identifications and LED light indications. Also, a continuous notification system is enabled here. After testing the implemented system, the system gives an accurate result in every possible way and has an accuracy rate of more than 95%.
Conclusion: After the successful implementation of this research, we hope that there will be an autonomous, well-decorated, digital, and user-friendly system available for the citizens of Bangladesh. This will be immensely helpful for all kinds of people and to ensure a clean environment, this research might play a vital role soon.
This article reports the development of a very simple chemical sensor for nitroaromatic compound from a couple of novel bio-based polymers polycurcumin methacrylate (PCUMA) and polycurcumin acrylate ...(PCUA). The polymers were characterized by spectroscopic methods and the thermal behavior was observed by thermo gravimetric analysis. Molecular weight of the polymers was determined by gel permeation chromatography. The polymers exhibit purely electronic conduction and was confirmed by Wagner polarization technique. Sensitivities of the polymers were observed by monitoring the impedance response and current–voltage characteristics in presence of the vapor of analytes. The conduction level of PCUMA in presence of the analytes was found to be remarkably high compared to that of PCUA. PCUMA showed one order decrease in logZ values in impedance measurements and 90.44% increase in current density in current–voltage characteristics in presence of picric acid.
A field experiment was conducted to find out the effect of different doses of ipil-ipil (Leucaena leucocephala ) (Lam.) de Wit. tree green leaf biomass on rice yield and soil chemical properties. ...Four different treatments such as T0: Recommended fertilizer dose (Urea 195 kg/ha, TSP 50 kg/ha, MOP 142 kg/ha, Gypsum 75 kg/ha and Zinc Sulphate 4 kg/ha), T1: 5 t/ha, T2: 7.5 t/ha, and T3: 10 t/ha ipil-ipil tree green leaf was used in this study in a Randomized complete block design with three replications. The results showed that the treatment T3 was performed better than recommended fertilizer dose in case all yield contributing characters of rice except grain yield. The highest (5.29 t/ha) rice grain yield was obtained in recommended fertilizer dose followed by 10 t/ha, 7.5 t/ha and 5 t/ha ipil-ipil tree green leaf biomass amendment having 4.80, 3.16 and 2.36 t/ha respectively. The highest grain yield that was obtained from recommended fertilizer dose was 10.21% higher compared to the highest dose (10 t/ha) of ipil-ipil tree green leaf biomass. It was mentioned that among the different doses of ipil-ipil tree green leaf biomass 10 t/ha performed the best over others. The ipil-ipil tree green leaf biomass was also significantly influenced on some essential nutrient status which is very important for rice production. The highest amount of total N, available P, exchangeable K and available S were found in the treatment T3 and the lowest in the treatment T1. Therefore, it can be concluded that the ipil-ipil tree leaf has beneficial effects and could be combined with inorganic fertilizer for sustainable crop yield and maintaining soil fertility.Res. Agric., Livest. Fish.2(3): 385-394, December 2015
Point clouds obtained with 3D scanners or by image‐based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point cloud denoising ...largely rely on local surface fitting (e.g. jets or MLS surfaces), local or non‐local averaging or on statistical assumptions about the underlying noise model. In contrast, we develop a simple data‐driven method for removing outliers and reducing noise in unordered point clouds. We base our approach on a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds. Our method first classifies and discards outlier samples, and then estimates correction vectors that project noisy points onto the original clean surfaces. The approach is efficient and robust to varying amounts of noise and outliers, while being able to handle large densely sampled point clouds. In our extensive evaluation, both on synthetic and real data, we show an increased robustness to strong noise levels compared to various state‐of‐the‐art methods, enabling accurate surface reconstruction from extremely noisy real data obtained by range scans. Finally, the simplicity and universality of our approach makes it very easy to integrate in any existing geometry processing pipeline. Both the code and pre‐trained networks can be found on the project page (https://github.com/mrakotosaon/pointcleannet).
Point clouds obtained with 3D scanners or by image‐based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point cloud denoising largely rely on local surface fitting (e.g. jets or MLS surfaces), local or non‐local averaging or on statistical assumptions about the underlying noise model. In contrast, we develop a simple data‐driven method for removing outliers and reducing noise in unordered point clouds. We base our approach on a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds. Our method first classifies and discards outlier samples, and then estimates correction vectors that project noisy points onto the original clean surfaces. The approach is efficient and robust to varying amounts of noise and outliers, while being able to handle large densely sampled point clouds. In our extensive evaluation, both on synthetic and real data, we show an increased robustness to strong noise levels compared to various state‐of‐the‐art methods, enabling accurate surface reconstruction from extremely noisy real data obtained by range scans. Finally, the simplicity and universality of our approach makes it very easy to integrate in any existing geometry processing pipeline. Both the code and pre‐trained networks can be found on the project page (https://github.com/mrakotosaon/pointcleannet).
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