Adsorption prediction using an ANFIS may reduce the process costs in practical applications. MATLAB 2022 was used in this investigation to assess chromium (VI) adsorption data at different temperatures, doses of modified cellulose nanocrystals, and pH values. The best kind and quantity of membership function were chosen using the Box–Behnken experimental design approach to provide precise predictions with the least error. The RMSE was correlated with the number of MFs for each input by developing regression models based on analyzing every combination from the Box–Benkhen design. Five typical membership functions, Gaussian, triangle, Gaussian 2, trapezoid, and generalized bell-shaped, were used. ANOVA was used to demonstrate the significance of the regression models developed for the experimental data using trapezoid and trianglular MFs. The triangular MF produced the greatest accuracy in the Cr (VI) adsorption predictions (with a lower RMSE of 1.601 and R² of 0.998) when it was used in conjunction with the appropriate membership function numbers for each input (8, 8, and 4 for the trianglular membership function and 6, 6, and 3 for the trapezoid membership function) according to the ANFIS's predictions. Response surface plots were also used to evaluate the relationship between the triangular RMSE values and the membership function numbers. These findings demonstrate this material's potential to serve as a viable adsorption material for the focused elimination of contaminants, increasing the application of machine learning in sorption studies, and the remediation of novel pollutants.

Novel materials or techniques for treating wastewater are needed since the presence of developing pollutants in it presents a worldwide environmental issue. This investigation concentrated on turning moringa waste into activated carbon and using it to copper (II) adsorption. Analysis using thermogravimetric revealed the activated carbon's thermal degradation characteristics. BET analysis showed that it had mesoporous properties with increased surface area and pore volumes. The impacts of operating factors including pH, bed depth, concentration, and flow rate were examined using the column approach. The results of the experiment demonstrated that the adsorption capacity rose with input concentration and bed depth and declined with increasing flow rate. The ideal values were discovered to be 40 mg/L for concentration, 5 cm for bed height, and 6 for pH. 30% of the data were used for validation and testing when the ANN technique was developed, with the remaining 70% being used for training. The training dataset's R², MSE, ARE, and RMSE were 0.996, 0.011, 0.048, and 0.021. The curves were analyzed using ANN, and the results revealed that the best ANN architecture for representing the experimental data is consisting of [3 8 1] with the BR algorithm. These findings demonstrate the material's potential to serve as a viable adsorption material for the focused elimination of contaminants, increasing both the application of machine learning in sorption studies as well as the remediation of novel pollutants.

This study explores the relationship between nutrient concentration (NC) and epidermal thickness(d) of the leaves of hydroponically grown Brassica rapa and its attenuation coefficients (m) using a portable Time-Domain Optical Coherence Tomography (TD-OCT), which is a non-invasive imaging technique that uses low-coherence interferometry to generate axial scans of plants leaves by measuring the time delay and intensity of backscattered light. The portable TD-OCT system in this study has an axial and lateral resolution of 7 ?m and 3 ?m, respectively, a scanning depth of 12 mm, and a 1310 nm Super Luminescent Diode (SLD). Several studies suggest that the differences in d, and m indicate the status of plant health. The study used the Kratky method, a simple non-circulating hydroponic system, to cultivate Brassica rapa with varying NC (25%, 50%, 75%, 100% (control), and 125%). Each treatment group used two plants. The TD-OCT sample probe was placed on a fixed holder and was oriented vertically so that light was directed downward onto the leaf's surface to obtain the depth profile (A-scan). The distance between the probe and the leaf was adjusted to get the optimum interference signal. Five averaged A-scans were obtained per leaf on the 7th, 18th, and 21st days post-nutrient exposure. The logarithm of the averaged A-scan is linearly fitted to extract m. Results showed a positive correlation between NC and m which suggest that plants produce more chlorophyll and develop denser cells and increase m. There was no correlation obtained between NC and d. The study demonstrates the potential of TD-OCT as a non-destructive tool for assessing plant health and monitoring growth dynamics in hydroponic systems and m as a sensitive indicator of plant health as compared to d. Continued exploration of TD-OCT applications in agriculture can contribute to improving crop management strategies and promoting sustainable food production practices

The wet chemical auto–combustion technique was used to synthesize Barium Zirconate ceramic having the general formula BaZrO₃. Many strategies have been carried out to regulate the functional properties of the perovskite structured sample which was calcinated at 800℃ for 9 hours. Fourier transform IR spectroscopy, X – ray diffractometer, Scanning Electron Microscope (SEM) - EDAX, LCR meter and UV – Visible spectroscopy was employed to study about the structural, morphological, optical and electrical properties of the prepared cubic phase barium zirconate sample. The average value of the crystallite size was determined using data derived from XRD and was found to be 6.46nm by using Debye – Scherer formula. Lattice constant, crystallinity, unit cell volume, tolerance factor and x – ray density was also calculated. SEM - EDAX confirmed the elemental composition of the product and verified that they contained only the major constituents (Ba, Zr and O).The vibrational modes of the prepared sample was investigated using FTIR in the wavelength ranging from 400 - 4000Cm^-1. Energy bandgap was observed using Tauc’s plot, where a graph was prepared for photon energy(hυ) and (αhυ)². The powder sample was blended with PVA and made into pellet of 13mm diameter using a pelletizer to explore the dielectric parameters like dielectric constant, loss factor, etc, in the frequency ranging 100Hz to 4MHz at room temperature. With high dielectric constant and low dielectric loss factor, barium zirconate ceramics stands as an excellent material for several microwave applications.

The convergence of the Mann iteration scheme to fixed points of any class of mappings in any space is guaranteed only on the existence of a convexity structure defined therein.

For metric spaces, one notion of convexity is that propounded by Takahashi, and it has been generalized to the framework of metric spaces, establishing convex metric spaces as defined by Takahashi. For these particular spaces, a result has been published that establishes the condition for which the Mann iteration converges to fixed points of Banach contraction mappings defined therein.

On consideration of this result, the author of this work was inspired to formulate an analogous result that establishes the condition for which the Mann iteration converges to fixed points of Hardy–Rogers contraction mappings in the same framework. This result is novel in the sense that no such result has been published by any author. Additionally, it extends the cited existing result to a bigger class of contraction mappings.

Also, since Banach contraction mappings, Kennan contraction mappings and Chatterjee contraction mappings are special cases of Hardy–Rogers contraction mappings, this result produces three corollaries, each establishing a condition for the convergence of the Mann iteration for only one of the mentioned classes of contraction mappings.

Finally, this result is applied to the existence and approximation of solutions of Fredolm-type integral equations of the second kind; these equations arise in many problems in signal processing, theory of imaging and fluid mechanics, among other fields in the physical sciences and engineering.

In sum, the result to be presented extends a result on the convergence of the Mann iteration in convex Gb metric spaces and is applied to the existence and approximation of solutions of Fredolm integral equations, which find expressions in many models in science and engineering.

Many fitness devices lack the immersive features and interactions necessary to enhance user engagement and optimize athletic performance. To address this gap, we propose an innovative, open, and interoperable architectural solution that transforms sports equipment into immersive training platforms. The solution integrates videos of real-world routes with contextual data, such as speed and altitude variations, to recreate outdoor sports experiences indoors. The system dynamically synchronizes videos with fitness equipment, adjusting parameters such as resistance, incline, and inertia to simulate real-world conditions. The architecture is based on a dual-subsystem design. The first subsystem facilitates the creation and sharing of enriched videos in the cloud, fostering a collaborative community of users. The second subsystem focuses on delivering an immersive experience through advanced technologies, including cloud computing, wearables, SIG BLE standards for fitness equipment, and edge computing, all coordinated via a mobile application. These elements ensure seamless integration between videos, equipment dynamics, and user behavior, enabling personalized training based on historical and real-time data. Initial validation was conducted through prototypes and tests with real equipment, utilizing contextual data and basic device adjustments. While further advancements, such as AI-driven personalization, are still required, preliminary results demonstrate the architecture's feasibility and transformative potential. This scalable and flexible system fosters a collaborative ecosystem and drives innovation in the fitness industry.

Augmented reality (AR) is used more often in many maritime applications. In this paper, AR model is considered for improvement traffic monitoring in ports. It is useful for port authorities.

The model’s input is camera installed in the port. It provides a video stream over IP connection to the facility where the processing computer is placed. Ship’s detection is performed by YOLO (You only look once) artificial neural network. Developed YOLO detector detects small and large vessels. Hence, ships with automatic identification system (AIS) and non-AIS maritime traffic is detected. This creates realistic real world scenario for Mediterranean port traffic portfolio, which includes passengers’ ships, fishing ships, touristic ships, yachts, and other small-type of sailing objects.

Trajectories are estimated based on the central point of the detected vessel on the video stream from the surveillance camera. The intersection of the diagonals of the boundary box gives the central point of the vessel, the position of the central point remains remembered for each frame in the last 5 seconds. Position also depends on external influences. Hence, a linear regression is performed to get the direction of the vessel's movement between the memorized positions.

The collision risk assessment is made based on the distance between the vessels and the direction and speed of the vessel's movement. If the continued movement of the vessel according to the estimated trajectories and speeds will result in the intersection of the motion vector, it is suggested to change the course or speed of the vessel in order to eliminate the potential danger of collision.

In order to be useful for port authorities, the goal is to visualize collision risks in AR environment. AR is installed on smart phone and employee of port authority can check for possible problems easily without need for powerful computers and desk.

Water is an essential resource for humans and a key component in the creation of concrete. However, the availability of this vital resource is being strained by the lack of fresh water brought on by population increase, climate change, and rising pollution. The concrete industry is increasingly using unusual waters, such as treated wastewater, brackish water, and industrial water, in response to the scarcity of fresh water. Although using these waters has benefits for the ecology and economy, there are concerns about how it may affect the quality of concrete. Despite the importance of the subject, there is a lack of scientific articles synthesizing the effects of all water characteristics and their interactions on concrete properties. With this in mind, this study aims to investigate the influence of the main characteristics on the strength, consistency and durability of concrete, using a qualitative approach based on a detailed synthesis of the literature. Our analysis of various water sources reveals that several key parameters in the mixing water significantly impact concrete. These include: pH - low pH leads to reinforcement corrosion, concrete cracking, and strength loss. High pH can degrade mechanical strength and cause setting and hardening issues. Chlorides promote reinforcement corrosion, cracking, loss of adhesion, and reduced mechanical properties. Sulfates contribute to swelling, cracking, strength loss, and aesthetic deterioration. Finally, suspended matter decreases mechanical strength, increases porosity, reduces durability, and hinders workability. Beyond their individual influences, the interaction between these different parameters is of particular importance, especially between ph and other parameters, as well as between chlorides and sulfates.

This study focuses on the analysis of composite skins made from carbon fiber, glass fiber, and aramid, materials commonly used in sandwich structures for marine applications. These composites are employed in both recreational boats and competitive racing vessels. The primary objective of the research was to evaluate the mechanical performance of these composites through tensile testing. The composite skins were modeled in a dog-bone shape, a geometry chosen to optimize stress distribution during experimental testing. Tensile tests were conducted to determine mechanical properties such as tensile strength, modulus of elasticity, and fracture strain. The data collected from these experimental tests were crucial for the subsequent modeling phase. Using the experimental results, a FEM (Finite Element Method) model for the composite skins was developed and validated. This numerical model allowed for the simulation of the mechanical behavior of the materials under various loading conditions, providing a deeper understanding of their structural performance. The validation of the FEM model was essential to ensure the accuracy of the simulations and to predict the behavior of the composites in real-world applications. The findings from this study offer significant contributions to the marine industry, suggesting possible improvements in the design and production of boats. The use of validated FEM models can lead to greater efficiency in the design phase, reducing costs while enhancing the safety and durability of the vessels. In conclusion, the research demonstrated the effectiveness of carbon fiber, glass fiber, and aramid composite skins for marine applications, highlighting how the combination of experimental testing and FEM modeling can significantly improve the understanding and optimization of composite structures.

Recent surveys by the WHO show that 50 million people are injured due to traffic accidents across the globe. This is primarily due to inattentive driving, unclear lane markings, poor visibility, and aggressive driving. These issues mentioned can be addressed by developing a smart device with advanced technology that avoids traffic accidents and secures drivers/passengers. This novel design utilizes ultra-high radio frequency identification that is fitted into road reflector studs, which function in a two-way system. The smart device consists of an RFID reader (ultra-high-frequency), an Arduino controller (ATmega328p), LEDs, cameras (OV7670), and a speed limiter, which create a safety network for the driver. Here, the RFID fitted into the reflector sends a signal to the nearby vehicle if the vehicle approaches the edge of the road, and the RFID scanner in the vehicle receives this signal, which alerts the driver using the Arduino controller, which decodes the signal and initiates the alert system so that the driver can get back into their lane. Also, the camera included in the smart device on the vehicle identifies barriers such as walking people, wildlife, and other harmful things in an effective manner through image classification with a deep learning method. This verifies whether the captured image is harmful to the vehicle or not. If the detected image is harmful, then it activates the speed controller present in the vehicle; thereby, the vehicle's speed will be controlled automatically. The proposed system is modeled and verified to have better results.