Search For Solutions Research Articles

Effect of Natural Weathering on the Mechanical Strength of Bamboo Bio-Concrete

The search for solutions that reduce the environmental impact of construction has driven the development of new materials. Bio-concrete represents a significant advance, presenting itself as an alternative to traditional concrete. Recent studies point to durability in outdoor conditions as one of the main challenges in its application. This paper presents natural durability studies performed on bamboo bio-concrete, produced with a bamboo particle volume of 50%. A surface treatment of applying resin externally was tested to reduce water ingress during weathering. The bio-concretes were exposed to natural and outdoor weather conditions for twelve months, and meteorological records were collected during the study period. The effect and influence of the external resin was investigated using visual surface analysis, uniaxial compression, modulus of elasticity and scanning electron microscopy. In terms of visual aspects, the resin was not effective in preventing loss of gloss, while in terms of microstructure, these samples showed better adhesion between the bamboo particles in the matrix. The compressive strength showed significant reductions of 60% (stress) and 73% (Young’s modulus) after twelve months of weathering. External resin could improve microstructures from surfaces to internal portions and more effectively preserve the mechanical strength of bio-concrete.

New emerging materials with potential antibacterial activities.

The increasing prevalence of multidrug-resistant pathogens is a critical public health issue, necessitating the development of alternative antibacterial agents. Examples of these pathogens are methicillin-resistant Staphylococcus aureus (MRSA) and the emergence of "pan-resistant" Gram-negative strains, such as Pseudomonas aeruginosa and Acinetobacter baumannii, which occurred more recently. This review examines various emerging materials with significant antibacterial activities. Among these are nanomaterials such as quantum dots, carbon quantum dots, metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and layered double hydroxides, all of which demonstrate excellent antibacterial properties. Interestingly, including antibacterial agents within the structure of these materials can help avoid bacterial resistance and improve the long-term efficacy of the materials. Additionally, the antibacterial potential of liquid solvents, including ionic liquids and both deep eutectic solvents and natural deep eutectic solvents, is explored. The review discusses the synthesis methods, advantages, and antibacterial efficacy of these new materials. By providing a comprehensive overview of these innovative materials, this review aims to contribute to the ongoing search for effective solutions to combat antibiotic resistance. Key studies demonstrating antibacterial effects against pathogens like Escherichia coli, Staphylococcus aureus, and multidrug-resistant strains are summarized. MOFs have exhibited antibacterial properties through controlled ion release and surface interactions. COFs have enhanced the efficacy of encapsulated antibiotics and displayed intrinsic antibacterial activity. Other nanomaterials, such as quantum dots, have generated reactive oxygen species, leading to microbial inactivation. This review aims to provide insights into these new classes of antibacterial materials and highlight them for addressing the global crisis of antibiotic resistance. KEY POINTS: • Nanomaterials show strong antibacterial effects against drug-resistant bacteria • Emerging solvents like ionic liquids offer novel solutions for bacterial resistance • MOFs and COFs enhance antibiotic efficacy, showing promise in combating resistance.

Nanocząstki krzemu w zrównoważonym rolnictwie

The increasing population, shrinking arable land, and climate change prompt the search for new solutions in agriculture. In a sustainable approach, agriculture should be based on improving the quality and quantity of yields while maintaining biodiversity and protecting the natural environment. Nanotechnology, present in many areas of our lives, offers opportunities to support the development of sustainable agriculture on many levels. Among the numerous solutions and nanomaterials, silicon, which is a natural component of the ecosystem, deserves special attention. In its nanoparticle form, it acquires new, unique properties. This article focuses on the significant role of silicon nanoparticles in organic farming, with particular emphasis on their function as nanofertilizers. The authors analyze the impact of silicon nanoparticles on plant growth and development and their potential in mitigating the negative effects of abiotic stress factors caused by drought, salinity, and exposure to metals. Additionally, the beneficial effects of silicon nanoparticles on plants growing under biotic stress conditions induced by microorganisms such as bacteria and fungi are presented. The paper includes a review of original research results conducted in recent years in this area, as well as possible mechanisms and strategies of silicon nanoparticle action at the physiological, cellular, and molecular levels. The issue of the safety of using nanoparticles in agriculture and the prospects for their further use as a factor enhancing the resistance and productivity of crops are also discussed.

Exploring Recent Advances in Lignocellulosic Biomass Waste Delignification Through the Combined Use of Eutectic Solvents and Intensification Techniques

Growing awareness of resource sustainability and waste management has driven the search for circular-economy solutions. Lignocellulosic biomass waste, the most abundant renewable carbon resource, offers green potential as an alternative to declining non-renewable fuels. However, due to its recalcitrant nature, it requires pre-processing to convert it into valuable products like energy and chemicals. Biorefineries play a key role in this process by promoting the integral use of biomass, by finding ways to utilize lignin, previously treated as waste. Common pretreatment methods are unsustainable, prompting research into eco-friendly solvents and advanced techniques like ultrasound- and microwave-assisted methods. Recent approaches have also explored the use of eutectic solvents, which, when combined with these intensification techniques, offer promising results. These green technologies improve delignification efficiency, which in turn improves the saccharification process, reduces solvent use, and minimizes environmental impact. Despite progress, challenges remain in making these methods economically viable and adaptable to diverse biomass types. This review article highlights recent advances in sustainable treatment technologies, including the combined use of eutectic solvents and process-intensification techniques, and the potential of the obtained lignin in various industrial applications. It also discusses future prospects for more environmentally friendly processes in biomass utilization.

STORAGE WASTE OF ASPARAGUS AS A VALUABLE SOURCE OF PHENOLIC COMPOUNDS

Summary. Waste from the fruit and vegetable industry dominates on the food waste market. With a tendency of increasing consumers’ interest in healthy food and growing consumption of fruits and vegetables, according to forecasts of analysts, the share of this waste will constantly increase. Therefore, nowadays the search for effective solutions of fruit and vegetable waste use, e.g. to obtain biologically active compounds, is extremely timely. Amount of waste during processing and storage of asparagus can reach up to 50%. However, these disposed residuals are rich in polyphenolic compounds, which are characterized by high biological value and can provide additional income. This work provides evaluation of phenolic compounds’ content in the waste generated during commercial processing of asparagus, at the stages of preparation for storage and after storage. Two varieties of Asparagus were studied, namely, varieties Prius F1 and Rosalie F1. It was established that the amount of phenolic compounds in asparagus waste reaches 67.73-74.77 mg×100g-1FW depending on the variety. Despite the differences in color, the studied varieties of asparagus did not differ significantly in terms of polyphenolic compounds’ total content. In the asparagus waste, content of phenolic compounds was estimated to be only 20-27% lower than in whole spears. Significant differences in the amount of polyphenolic compounds in asparagus waste produced during commercial processing before and after storage were not recorded. Asparagus waste of both studied varieties Prius F1 and Rosalie F1 thus may become a valuable source for obtaining polyphenolic compounds of high biological activity.

High-Performance Concrete from Rubber and Shell Waste Materials: Experimental and Computational Analysis

Waste and its environmental impact have driven the search for sustainable solutions across various industries, including construction. This study explores the incorporation of solid waste in the production of eco-friendly structural concrete, aiming to reduce pollution and promote ecological and sustainable construction practices. In this context, two types of eco-friendly concrete were produced using marine shells and recycled rubber as waste materials and compared with conventional concrete through experimental and computational approaches. The results demonstrated that the concrete with marine shells achieved a compressive strength of 32.4 MPa, 26.5% higher than conventional concrete, and a 1% reduction in weight. In contrast, the recycled rubber concrete exhibited a compressive strength of 22.5 MPa, with a 2 MPa decrease compared to conventional concrete, but a 4.3% reduction in density. Computational analysis revealed that porosity affects Young’s modulus, directly resulting in a reduction in the maximum achievable strength. This work demonstrates that it is feasible to produce eco-friendly structural concrete through the proper integration of industrial waste, contributing to decarbonization and waste valorization.

Telehealth Research Strategies in a Post COVID-19 Era: A Roadmap for the Coming Decade.

An Agency for Healthcare Research and Quality-funded Think Tank, held at Michigan State University, brought together a wide range of subject matter experts in telemedicine, telehealth, digital health, digital access, and health care. The authors of this article represented a group focused on the research needs, constructs, and strategies in the post-COVID era. While telemedicine and telehealth grew exponentially during the pandemic, the challenges that have been with us for decades, while ameliorated to some extent, remain. To showcase the State of Michigan as a model, the authors reviewed challenges and opportunities related to telehealth and telemedicine usage and categorized them into seven areas of highest priority. Based on a review of the literature and consultation in the fields of telehealth, telemedicine, and digital access, we identified seven key categories where research would be most effective going forward. These categories include research into the impact of telehealth on clinical services, telehealth's use in administrative activities, education and training for health care providers and patients, telehealth policy implications at state and federal levels, the impact of future technology and innovation on telehealth services, patient characteristics and their experiences, and the ethical aspects of telehealth in the future. We have formulated this overview of our findings to act as a roadmap for future telehealth research. We recommend that ongoing studies should explore each of the seven categories identified above. The search for solutions to overcome the challenges within these topics must not be constrained to research that has been conducted recently; many of these challenges have faced telehealth researchers for decades, and numerous solutions have been proposed over the years. Some of these proposals should be explored once again in light of technological and societal advances. The findings from these studies should be shared in ways and through venues that can make them accessible outside the spheres of academic research, but also by practitioners, policymakers, and patients. Third, research into telehealth in all its incarnations must be prioritized, and leadership is needed to ensure these areas of study are continued to be spotlighted after the noise of the COVID-19 pandemic grows quiet.

Hierarchical Optimization Configuration Strategy of Synchronous Condenser in High Penetration Wind Power Sending Systems

The increasing deployment of large-scale wind turbines in place of conventional generators is expected to lead to the dominance of asynchronous power sources in future power systems, further accelerating the trend toward grid electrification. As a result, the ability of power sources to support system voltage and frequency is gradually diminishing. Synchronous condensers (SCs), which are synchronous machines operating without prime movers, serve as effective devices for providing both dynamic voltage support and inertia. They can significantly enhance the system’s capacity to maintain voltage and frequency stability. However, most existing studies on the optimization of synchronous condenser configurations tend to focus on only one aspect at a time rather than addressing both simultaneously, limiting the full potential of these devices. Optimizing either the voltage or frequency in isolation often results in suboptimal improvements in the other. Moreover, the simultaneous optimization of both voltage and frequency can lead to non-convergent outcomes, complicating the search for an optimal solution. To address this, the paper proposes a hierarchical optimization strategy for synchronous condenser configuration aimed at enhancing both voltage and frequency stability. First, the connection sites for the synchronous condensers are determined based on short-circuit ratio (SCR) constraints. Next, an outer layer optimization model is developed to minimize the total installed capacity of the condensers while taking into account the SCR and transient overvoltage levels as constraints. Following this, an inner layer optimization model is introduced, incorporating a rate of change in the frequency and maximum frequency deviation as constraints. The model is solved using the bacterial foraging optimization algorithm (BFOA). Finally, a case study of a power grid with a high proportion of wind power validates the effectiveness of the proposed synchronous condenser configuration strategy. Compared to traditional methods, the total required capacity of synchronous condensers was significantly reduced.

A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing

With advancements in integrated space–air–ground global observation capabilities, the volume of remote sensing data is experiencing exponential growth. Traditional computing models can no longer meet the task processing demands brought about by the vast amounts of remote sensing data. As an important means of processing remote sensing data, distributed cluster computing’s task scheduling directly impacts the completion time and the efficiency of computing resource utilization. To enhance task processing efficiency and optimize the allocation of computing resources, this study proposes a Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm based on the original Siberian Tiger Optimization (STO) algorithm. The MSSTO algorithm integrates the Tent chaotic map, the Lévy flight strategy, Cauchy mutation, and a learning strategy, showing significant advantages in convergence speed and global optimal solution search compared to the STO algorithm. By combining stochastic key encoding schemes and uniform allocation encoding schemes, taking the task scheduling of aerosol optical depth retrieval as a case study, the research results show that the MSSTO algorithm significantly shortens the completion time (21% shorter compared to the original STO algorithm and an average of 15% shorter compared to nine advanced algorithms, such as a particle swarm algorithm and a gray wolf algorithm). It demonstrates superior solution accuracy and convergence speed over various competing algorithms, achieving the optimal execution sequence and machine allocation scheme for task scheduling.

Search for Durable Solution to the Rohingya Refugee Crisis in Bangladesh

Abstract The Rohingya, a Muslim minority living mainly in the northern Rakhine state of Myanmar, are considered illegal immigrants from Bangladesh, despite the fact that they have settled in Myanmar for hundreds of years. Myanmar recognized 135 ethnic groups under the 1982 Citizenship Act which excludes the Rohingya as an ethnic group in Myanmar and they became stateless. Myanmar Security Forces have committed grave violation of human rights against Rohingya. The latest attack in 2017 was more heinous than in 1978, 1992, and 2012. As of June 2018, The Rohingya refugee in Bangladesh was portrayed as a protracted refugee situation. In many protracted refugee situations, a durable solution such as local integration, resettlement and voluntary repatriations needed to be implemented to end the prolonged displacement. The aim of this paper is to discuss the most feasible durable solutions to be implemented to repatriate Rohingya refugees in Bangladesh.

INTELLECTUAL SUPPORT OF THE PROCESSES OF SEARCHING AND EXTRACTION OF PRECEDENTS IN CASE-BASED REASONING APPROACH

Context. The situational approach is based on the real-time decision-making methods for solving current problem situation. An effective tool for implementing the concept of a situational approach is an experience-based technique that widely known as сasebased reasoning approach. Reasoning by precedents allows solving new (latest) problems using knowledge and accumulated experience of previously solved problems. Since cases (precedents) describing a scenario for solving a certain problem situation are stored in the case library, their search and retrieval directly determine the system response time. In these conditions, there is a need to find ways of solving an actual scientific and practical problem aimed at optimizing case searching and extracting processes. The object of the paper is the processes of searching and extracting of cases from the case library. Objective. The purpose of the article is to improve the process of cases searching in CBR approach by narrowing down the set of cases permissible for solving the current target situation, and excluding from further analysis such cases that do not correspond to the given set of parameters of the target situation. Method. The research methodology is based on the application of rough set theory methods to improve the decision-making procedure based on reasoning by precedents. The proposed two-stage procedure for narrowing the initial set of cases involves preliminary filtering of precedents whose parameter values belong to the given neighborhoods of the corresponding parameters of the target situation at the first stage, and additional narrowing of the obtained subset of cases by the methods of rough set theory at the second stage. The determination of the R-lower and R-upper approximations of a given target set of cases within the notation of rough set theory allows dividing (segmenting) the original set of cases available for solving the current problem stored in case library into three subgroups (segments). The search for prototype solutions can be performed among a selected subset of cases that can be accurately classified as belonging to a given target set; which with some degree of probability can be attributed to the given target set, or within the framework of the union of these two subsets. The third subset contains cases that definitely do not belong to the given target set and can be excluded from further consideration. Results. The problem of presentation and derivation of knowledge based on precedents has been considered. The procedure for searching for precedents in case library has been improved in order to reduce the system response time required to find the solution closest to the current problem situation by narrowing the initial set of cases. Conclusions. The case-based reasoning approach is received the further development by segmenting cases in terms of their belonging to a given target set of precedents uses methods of the rough set theory, then the search for cases is carried out within a given segment. The proposed approach, in contrast to the classic CBR framework, uses additional knowledge derived from obtained case segment; allows modeling the uncertainty regarding the belonging / non-belonging of a case to a given target set; removing from further consideration cases that do not correspond to a given target set.

Design of a stagger‐tuned high‐power low‐stress capacitive wireless power transfer system

AbstractCapacitive power transfer (CPT) has emerged as a promising alternative to traditional inductive methods. CPT offers advantages like cost‐effectiveness, reduced weight and volume, and greater tolerance to alignment errors. However, the high‐Q resonant circuits used as matching networks can be susceptible to high voltage stress, especially when transmitting substantial power. Consequently, designing matching networks for CPT systems necessitates consideration of multiple parameters, including practical constraints such as component losses and breakdown thresholds. In this work an innovative algorithm is presented for designing practical matching networks in CPT systems. The algorithm conducts a methodical search of potential solutions, and converges on component values that maximize power transfer efficiency whilst also minimizing component voltage stress. The proposed algorithm is demonstrated theoretically and experimentally.

New genetic gray wolf optimizer with a random selective mutation for wind farm layout optimization

The wake effect is a relevant factor in determining the optimal distribution of wind turbines within the boundaries of a wind farm. This reduces the incident wind speed on downstream wind turbines, which results in a decrease in energy production for the wind farm. This paper proposes a novel approach for optimizing the distribution of wind turbines using a new Genetic Gray Wolf Optimizer (GGWO). The GGWO employs a teamwork model inspired by wolf prey hunting, guided by four leaders: Alpha, Beta, Delta, and Omicron wolves, each with different hierarchical weights. To improve the competitiveness of the wolves, GGWO utilizes genetic algorithm operators such as crossover blending, normal mutation, and a new genetic operator called Random Selective Mutation (RSM), which improves solution search efficiency. The proposed GGWO is compared to other algorithms such as Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO). The studies examine varying wind speeds in magnitude and direction throughout the year, as well as different wind farm boundaries. The outcomes show that GGWO successfully identifies the ideal locations for wind turbines, scoring better scores in terms of total simulation duration and annual energy generation for the wind farm. It surpasses the performance of GWO, ABC, and PSO algorithms and exhibits comparable competitiveness with more intricate algorithms like ACO.

Super-Resolution Reconstruction of Remote Sensing Images Using Chaotic Mapping to Optimize Sparse Representation.

Current super-resolution algorithms exhibit limitations when processing noisy remote sensing images rich in surface information, as they tend to amplify noise during the recovery of high-frequency signals. To mitigate this issue, this paper presents a novel approach that incorporates the concept of compressed sensing and explores the super-resolution problem of remote sensing images for space cameras, particularly for high-speed imaging systems. The proposed algorithm employs K-singular value decomposition (K-SVD) to jointly train high- and low-resolution image blocks, updating them column by column to obtain overcomplete dictionary pairs. This approach compensates for the deficiency of fixed dictionaries in the original algorithm. In the process of dictionary updating, we innovatively integrate the circle chaotic mapping into the solution process of the dictionary sequence, replacing pseudorandom numbers. This integration facilitates balanced traversal and simplifies the search for global optimal solutions. For the optimization problem of sparse coefficients, we utilize the orthogonal matching pursuit method (OMP) instead of the L1 norm convex optimization method used in most reconstruction techniques, thereby complementing the K-SVD dictionary update algorithm. After upscaling and denoising the image using the dictionary pair mapping relationship, we further emphasize image edge details with local gradients as constraints. When compared with various representative super-resolution algorithms, our algorithm effectively filters out noise and stains in low-resolution images. It not only performs well visually but also stands out in objective evaluation indicators such as the peak signal-to-noise ratio and information entropy. The experimental results validate the effectiveness of the proposed method in super-resolution remote sensing images, yielding high-quality remote sensing image data.

Optimising the Economic Feasibility of High-Speed Maglev Systems: A Simulation-Based Approach for Variable and Parameter Analysis

This study introduces an advanced software platform and process for the quantitative national economic evaluation of high-speed maglev systems, overcoming limitations of traditional methods through parameter variation experiments and automated solution search. Utilising the adapted German standardised evaluation, this research demonstrates how integrated modelling, evaluation and optimisation software can deeply analyse the impact of various variables and parameters on economic outcomes. By employing an optimisation algorithm, the software not only determines critical evaluation parameters to ensure benefits exceed costs but also deduces optimised model variables. The macroeconomic benefit-cost ratio guides the optimal design concept, with the research finding a critical value for ensuring economic feasibility. The proposed solution achieves a 22% improvement in this ratio (1.106 vs. 0.909) compared to the existing Hefei-Wuhu route, highlighting its potential for large-scale maglev implementation. Future development directions include integration with micro-simulation systems, support for random behaviour, sensitivity analysis, data-driven machine learning and enhanced user interface design for broader applicability. The findings underscore the software’s capability to provide robust, data-driven insights for economic feasibility studies of high-speed maglev systems, presenting a significant step forward in infrastructure project evaluation.

Tourism as a Tool for Strengthening the “Soft Power” of Azerbaijan

Abstract The relevance of this topic is due to the fact that tourism today is a powerful and effective tool of “soft power” in Azerbaijan. The current state, principles and main directions of state policy in the field of tourism are considered. The importance of an integrated approach in the field of tourism research is emphasized. Scientific novelty lies in the analysis of new aspects and trends in tourism development, the search for new solutions and approaches to improving the tourist experience, as well as the application of new methods and technologies for the analysis and management of the tourism industry. The article provides recommendations for the development of tourism in Azerbaijan, which contributes to the strengthening of “soft power”.

Physiological adaptations in small-side games combined with speed-endurance training: analyzing heart rate and rate of perceived exertion

Background and Study Aim. Monitoring physiological responses during training is crucial for understanding how athletes adapt to various exercise intensities. However, the specific effects of combining Small-Sided Game (SSG) and Speed-Endurance Training (SET) on these parameters require the search for more effective solutions. This study aims to determine the effect of combined SSG and SET on heart rate (HR) and rate of perceived exertion (RPE). Material and Methods. This quantitative research employed an experimental method with a weekly pretest-posttest design. The study population consisted of eighty-two members of the Unesa Soccer Student Activity Unit. A sample of eighteen individuals was selected through simple random sampling. The sample had the following characteristics: age 20±0.69 years, weight 65±9.54 kg, height 165.22±4.31 cm, and BMI 22.82±1.92. Data were collected using the Polar H-10 device, and RPE interviews were conducted ten minutes after each training session. Data analysis was performed using paired sample t-tests and Wilcoxon tests. Results. The results showed a significant difference in HR for pair 1 (p = 0.037), but no significant differences for pair 2 (p = 0.058) or pair 3 (p = 0.076). However, the results for RPE indicated significant differences in pair 1 (p = 0.001), pair 2 (p = 0.004), and pair 3 (p = 0.002). The combination of SSG and SET led to an increase in HR during the first and second weeks. In the third week, HR decreased. RPE increased from the first to the second week. It then stabilized in the third and fourth weeks, indicating a consistent level of effort. The decrease in HR during the third week, despite the same effort level, suggests that the athletes successfully adapted to the training program. Conclusions. The combination of SSG and SET demonstrates potential as an effective training method for improving both physiological performance and adaptation in athletes. These findings offer valuable insights into how athletes respond to structured training programs, providing a foundation for future research and practical applications in sports training. The results contribute to a deeper understanding of the role that specific training combinations play in athlete development and recovery.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ И МАШИННОЕ ОБУЧЕНИЕ В ПЕДИАТРИИ - СОВРЕМЕННЫЕ ИССЛЕДОВАНИЯ И НЕИЗВЕДАННЫЕ ГОРИЗОНТЫ

The scientific article examines the implementation of digital technologies in the field of medicine. This review summarizes current data on the use of artificial intelligence and machine learning (AI-ML) in pediatrics based on global research. Currently, machine learning helps create models for predicting the severity of the condition in children with bronchiolitis, neonatal sepsis, bacterial infections, necrotizing enterocolitis, for screening autism, and internalizing disorders. The review highlights the variety of algorithms, analyzes the main methods with algorithms used in the development of artificial intelligence, and their application depending on specific tasks and requirements. As a result of the work, the relevance and necessity of using intelligent technologies in the modern world was determined. It was also found that despite the serious difficulties in implementing AI-ML systems, the prospects for their use encourage the search for solutions to overcome any obstacles. Highly qualified specialists from different parts of the world are constantly working on the development of this area.

Optimization, Kinetic and Thermodynamic Study of Rhodamine B Removal by Zinc Chloride-activated Arachis hypogaea (Groundnut Husk) biochar

Adsorption of dye from industrial effluent before discharge into the environment has become a major challenge to the food, pharmaceutical, textile, photographic and cosmetic industries. This has led scientists to the search for suitable green solutions. Carbonized and zinc chloride activated Arachis hypogaea (groundnut husk) in a 1:1 ratio was used as an adsorbent for the removal of Rhodamine B from aqueous solution. The adsorbent was characterized by ash content, bulk density, moisture content, pH, Iodine number, surface area and functional group analyses. Key parameters such as initial pH, initial concentration of Rhodamine B dye, temperature and contact time were investigated. The equilibrium data obtained were correlated with Langmuir, Freundlich, Temkin and Dubinin-Radushkevich isotherms. It was found that both the Freundlich and Langmuir isotherms fit well to the data. The Langmuir isotherm model best describes the uptake of Rhodamine B onto zinc chloride activated groundnut husk biochar with R2 > 0.997. Maximum Rhodamine B. dye removal was observed at a pH of 11.0, with an adsorbent dosage of 1.0 g, a contact time of 125 min, an initial dye concentration of 20 mg/L and a temperature at 315 K. The efficiency of zinc chloride activated groundnut husk biochar for Rhodamine B removal was 99.55% for dilute solutions at 50 g/L. The Fourier Transform Infrared (FTIR) spectra recorded before and after activation showed the numbers of functional groups available for Rhodamine B. molecules to bind onto in the studied adsorbent. The kinetic data were best described by the pseudo-first-order and pseudo second-order models, while the thermodynamic studies indicated a spontaneous and endothermic nature in the adsorption of Rhodamine B by the zinc chloride modified groundnut husk. This research clearly details an innovative approach to utilizing carbonized and zinc chloride-activated Arachis hypogaea (groundnut husk) as an eco-friendly adsorbent for the removal of Rhodamine B from industrial effluents.

Selection of aerodynamic characteristics and engine parameters of a maneuverable aircraft under epistemic uncertainty

At the preliminary stage of aircraft design, it is usually required to solve the problem of insufficient initial data for applying traditional algoristic-type mathematical programming models. In many cases, numerous input parameters cannot be accurately specified at the time of making design decisions. If the inaccuracy of parameters is not taken into account, the actual values of target functions may differ significantly from the calculated ones when solving optimization problems. In this regard, the issue of current interest is the development of algorithms to improve the reliability of design decisions under conditions of epistemological uncertainty when experts engage in the formation of initial data. The paper considers the problem of selecting aerodynamic characteristics and engine parameters of maneuverable aircraft under conditions of uncertainty associated with inaccuracy of expert data. The applied problem under consideration is further complicated by the necessity to include “black box” models in the algorithms being developed. The paper proposes algorithms that apply the uncertainty theory together with “black box” models that implement optimization calculation techniques derived from previous engineering practice of aircraft design. Using these algorithms, experts are able to set uncertain parameters whereby the lack of data is factored in using uncertainty distribution functions. In cases of monotonicity of uncertain parameters in target functions, application of the uncertainty theory provides a significant reduction in computational costs compared to the method of statistical modeling in optimization calculations. The paper presents the results of computational studies of the developed algorithms. A genetic algorithm (mathematical optimization solver) is used to optimize the search for design solutions. Pareto frontiers are obtained for different confidence levels enabling to make design decisions including values of aerodynamic characteristics and engine parameters of maneuverable aircraft.