Diversity Of Solutions Research Articles

The impact of various teaching methods on the knowledge of students of different genders: The case of mathematics word problems abstract

Despite significant progress in recent years, there is still evidence of gender differences and underrepresentation of women in leadership positions in mathematics. To promote equal opportunities for all students, it is essential to comprehend gender differences in math learning at school and identify potential barriers to educational equality. In this regard, our study examined the gender differences in solving word problems, and whether they relate to the solution strategy the students were taught.The study involved 100 10th-grade students divided into two groups and taught different solving approaches. One group was taught an algebraic approach using linear equations, while the other group was taught a combined approach, which incorporates the algebraic solution with a functional-graphic solution using a graph of a linear function.The research findings reveal gender differences in various aspects: Girls who learned the algebraic approach had better attainments than boys; Boys who learned the combined approach opted to solve problems with functions and graphs, whereas girls in the same group preferred an algebraic solution; Boys who learned the combined approach achieved significantly higher than boys who learned the algebraic approach.This study illuminates the importance of teaching word problems using diverse solution strategies because different populations of students respond differently to different strategies.

Urban agriculture as ecosystem services provider: A review

Background: Urban agriculture’s role in mitigating the urban environment deterioration as ecosystem services provider. Urban agriculture adds various functions to different landscape scales, providing numerous benefits if managed correctly. This paper aims to study the role of urban agriculture as ecosystem services provider in mitigating urban environment deterioration. Method: The method that will be used in this article is literature review from articles related to urban agriculture and ecosystem services from provisioning, regulating, services, and cultural. The review is compiled from urban agriculture’s function as ecosystem services provider, such as biodiversity conservation, microclimate regulation, aesthetic function, and food production. Result: In essence, urban agriculture is a diverse and vital solution for cities, and it takes various forms, from private gardens to community plots, addressing challenges like limited public funding and promoting greening initiatives. Urban agriculture plays a key role in biodiversity conservation, contributing to enhanced environmental benefits and the preservation of local habitats. Another crucial role in regulating the complex dynamics of the urban microclimate, offering ecological benefits such as reduced Urban Heat Island effects, wind protection, and pollutant absorption. Beyond its ecological impact, urban agriculture adds aesthetic value to urban spaces, creating visually appealing landscapes and promoting cultural integration. Moreover, it serves as a multifaceted strategy for sustainable urban development, addressing food security, resilience, and well-being. Conclusion: Amidst challenges, such as the threat to urban biodiversity and the need for proper green space management, urban agriculture emerges as a holistic approach, contributing to the visual, cultural, and environmental fabric of cities. Novelty/Originality of this article: This study offers a new perspective on urban agriculture as a multifunctional solution to address urban environmental degradation. By integrating agriculture into the urban landscape, the study reveals the transformative potential for creating greener, more resilient and sustainable cities.

Diverse exact solutions to Davey–Stewartson model using modified extended mapping method

In this study, we obtain solitary wave solutions and other exact wave solutions for Davey–Stewartson equation (DSE), which explains how waves move through water with a finite depth while being affected by gravity and surface tension. The study is conducted with the aid of the modified extended mapping method (MEMM). A variety of distinct traveling wave solutions are furnished. The obtained solutions comprise dark, bright, and singular solitary wave solutions. Additionally, Jacobi elliptic function solutions, exponential wave solutions, singular periodic wave solutions, rational wave solutions, and periodic wave solutions are also offered. To help readers physically grasp the acquired solutions, graphical representations of some of the extracted solutions are provided.

Building landscape ecological network with multi-scenario connectivity based on network fault tolerance index and networking technology in graph theory

Existing methods for constructing Landscape Ecological Networks (LEN) either focus on functional attributes or structural attributes and also do not address the dynamic impact of urban development on ecological spaces. To address these limitations, this paper proposed evaluating the maximum connectivity (k) of potential LEN, composed of existing ecological spaces, using a network fault tolerance index. We then used graph-based networking technology to construct the optimal LEN structure based on this index. Specifically speaking, first, we identified the existing LEN with the highest ecosystem service capacity and lowest ecological cost in the study area. Using four landscape pattern metrics, we comprehensively evaluated the fault tolerance index as the maximum connectivity k of potential LEN and determined that k = 7. We then modeled the existing LEN as a weighted graph and transformed the network optimization problem into a k-edge addition problem. This calculation yielded seven LEN schemes, each corresponding to different priority orders of new ecological corridors based on k value. This index captures changes in ecological space patterns and offers diverse LEN optimization solutions. It provides practitioners with flexible spatial strategies to address the dynamic impacts of urbanization.

RBSS: A fast subset selection strategy for multi-objective optimization

Multi-objective optimization problems (MOPs) aim to obtain a set of Pareto-optimal solutions, and as the number of objectives increases, the quantity of these optimal solutions grows exponentially. However, a plethora of optimal solutions can impose significant decision stress on decision-makers. Subset selection, as the extension of a model, can extract a representative set of solutions, thereby alleviating the decision-makers’ choice pressure. In addition, extending a model undoubtedly incurs additional time costs. To cope with the foregoing issues, a fast subset selection method named ranking-based subset selection (RBSS) is proposed in this paper. It can efficiently select a small number of optimal solutions within an unbounded external archive and can be directly applied to any multi-objective evolutionary algorithm. This allows it to maintain good distribution and diversity with very little time investment. We employed a ranking-based approach to map the objective space to a ranking space (an integer space) defined by us and then selected the corresponding subset in the ranking space. The well-behaved mathematical properties of the ranking space and the advantages of using integer calculations accelerated the subset selection process. Experimental results indicate that compared to several state-of-the-art subset selection methods, RBSS is capable of selecting a set of representative and diverse solutions across different types of MOPs, while consuming significantly less time. Specifically, for problems where the Pareto front is a two-dimensional manifold and a one-dimensional manifold, the time consumption of RBSS is approximately only 0.028% to 27.5% and 4.6e−4% to 0.15% of that required by other algorithms, respectively.

Various exact solutions to the time-fractional nonlinear Schrödinger equation via the new modified Sardar sub-equation method

We aim to investigates the nonlinear Schrödinger equation including time-fractional derivative in (3+1)-dimensions by considering cubic and quantic terms The modified Sardar sub-equation method is used that lead to the discovery of a unique class of optical solutions. To transform the suggested nonlinear equation into an ordinary differential equation, we applied wave transformations, resulting in a set of nonlinear equations that offer diverse solution scenarios. The derived solutions encompass dark, wave, bright, mixed dark-bright, bell-shape, kink-shape, and singular soliton solutions. To enhance our understanding of the dynamic behavior exhibited by these solitons under varying time parameter values, visual simulations through a variety of graphs is presented. Furthermore, a comprehensive comparison is conducted, exploring a range of values for the conformable fractional order parameter. This comparison aims to highlight on the influence of fractional order variations on the solutions, contributing valuable insights into the nuanced dynamics of the system. Overall, this study serves to advance our understanding of nonlinear processes, and its potential applications in real-life phenomena. In the field of nonlinear optics, this equation can describe the propagation of optical pulses in nonlinear media. It helps in understanding the behavior of intense laser beams as they propagate through materials exhibiting nonlinear optical effects such as self-focusing, self-phase modulation, and optical solitons.

A modified Runge–Kutta optimization for optimal photovoltaic and battery storage allocation under uncertainty and load variation

The interest in incorporating environmentally friendly and renewable sources of energy, like photovoltaic (PV) technology, into electricity grids has grown significantly. These sources offer benefits, such as reduced power losses and improved voltage stability. To optimize these advantages, it is essential to determine optimal placement and management of these energy resources. This paper proposes an Improved RUNge–Kutta optimizer (IRUN) for allocating PV-based distributed generations (DGs) and Battery Energy Storage (BES) in distribution networks. IRUN utilizes three strategies to avoid local optima and enhance exploration and exploitation phases: a non-linear operator for smoother transitions, a Chaotic Local Search for thorough exploration, and diverse solution updates for refinement. The efficacy of IRUN is evaluated using 10 benchmark functions from the CEC’20 test suite, followed by statistical analysis. Next, IRUN is used to optimize the allocation of PVDG and BES to minimize energy losses in two standard IEEE distribution networks. The optimization problem is divided into two stages. In the first stage, the optimal size and the location of PV systems are calculated to meet peak load demand. In the second stage, considering time-varying load demand and intermittent PV generation, effective energy management of BES is employed. The effectiveness of IRUN is compared against the original RUN and other well-known optimization algorithms through simulation results. The comprehensive analysis demonstrates that IRUN outperforms the compared algorithms, making it a leading solution for optimizing PV distributed generation and BES allocation in distribution networks and the results show that the energy loss reduction reaches 63.54% and 68.19% when using PVand BES in IEEE 33-bus and IEEE 69 bus respectively.

Soliton solutions and sensitive analysis to nonlinear wave model arising in optics

In this study, we use analytical algorithms, specifically the auxiliary equation (AE) approach, the improved F-expansion method, and the modified Sardar sub-equation (MSSE) method to investigate complex wave structures for plentiful solutions associated with the fractional perturbed Gerdjikov-Ivanov (PGI) model with the M-fractional operator. The investigated model is a well-established mathematical model used to represent a variety of physical events in nonlinear dynamics and mathematical physics. By using the aforementioned techniques, we scrutinize some new optical wave solutions in the framework of dark, bright, periodic, combo, W-shaped, M-shape, V-shape, kink type, singular rational, exponential, trigonometric, and hyperbolic solutions. The acquired solutions address a wide range of optical solutions in the form of 3D plots, contour plots, and 2D plots, declaring the free parameters of such optical soliton solutions and comprehending their dynamic behavior. Also, the sensitive analysis of the selected model is analyzed. The main contribution of this study is to extract diverse solitary wave solutions of the adopted model. Some of the solutions are similar and some diverge from the previous solutions which justifies the novelty of the study. Finally, we discovered that the current technique provides a reliable instrument for investigating the analytic solutions of fractional differential equations. The proposed PGI model can be used to transmit ultra-fast pulses across optical fibers. This research goes beyond to the advancement of mathematical techniques for solving fractional differential equations and broadens their application to a wide range of real-world scientific and engineering problems.

Development and assessment of a floating photovoltaic-based hydrogen production system integrated with storage options

The integrated system approach utilized in the current study represents an innovative approach to harnessing solar energy through a floating photovoltaic-based integrated plant featuring a diverse array of energy storage options. As part of this research, a solar energy system integrating both floating and concentrating solar technologies was designed at the specified site. Despite the constraints presented by the cold temperature at the chosen location, particularly during the winter months when solar irradiation is restricted, strategies are employed, such as exploiting the high albedo effect caused by the freezing of the water body, optimizing the application of solar radiation, and assuring the system's resilience and energy efficiency. Floating photovoltaic and concentrated solar panels are integral components of this advanced system, which is supplemented by underground hot and cold storage units, underground hydro-pumped storage, a multi-effect desalination system with freshwater storage, a lithium bromide absorption cooling system, and an alkaline electrolysis system for hydrogen and oxygen storage. This comprehensive energy strategy includes diverse storage and production technologies. The thermodynamic analyses and assessments employing the Engineering Equation Solver and the System Advisor Model provide some quantitative performance evaluations. The study considers time-dependent operational characteristics, illustrates the water withdrawal rates, energy and exergy efficiencies of the heating space temperature, inverter efficiency, GHI irradiance, mass, and volume values of the heat transfer fluid in both hot and cold tanks within thermal energy storage, annual cooling generation over a year, power production, hydropower storage, and hydrogen storage over a year. The system's concentrated solar panels are projected to generate an annual AC output of 180.79 GWh, with a yearly water usage of 14,055 m3, while floating panels are predicted to provide 7000 kWh with a 0.85 performance ratio. The overall energy efficiency of the current system is determined to be 51.76 %, and the exergy efficiency is found to be 55.41 %, respectively. Optimizing energy efficiency, utilizing diverse storage solutions, and utilizing aquatic environments minimizes environmental impact, and time-dependent analysis enhances perceptions of performance throughout different times and circumstances.

Watch the Skies: A Study on Drone Attack Vectors, Forensic Approaches, and Persisting Security Challenges

In the rapidly evolving landscape of drone technology, securing unmanned aerial vehicles (UAVs) presents critical challenges and demands unique solutions. This paper offers a thorough examination of the security requirements, threat models, and solutions pertinent to UAVs, emphasizing the importance of cybersecurity and drone forensics. This research addresses the unique requirements of UAV security, outlines various threat models, and explores diverse solutions to ensure data integrity. Drone forensics, a field dedicated to the investigation of security incidents involving UAVs, has been extensively examined and demonstrates its relevance in identifying attack origins or establishing accident causes. This paper further surveys artifacts, tools, and benchmark datasets that are critical in the domain of drone forensics, providing a comprehensive view of current capabilities. Acknowledging the ongoing challenges in UAV security, particularly given the pace of technological advancement and complex operational environments, this study underscores the need for increased collaboration, updated security protocols, and comprehensive regulatory frameworks. Ultimately, this research contributes to a deeper understanding of UAV cybersecurity and aids in fostering future research into the secure and reliable operation of drones.

Physics-informed Reinforcement Learning optimization of PWR core loading pattern

The nuclear sector represents the largest single source of carbon-free energy in the United States. Nevertheless, keeping existing nuclear assets competitive on the grid is key to their ability to continue to provide dispatchable clean energy along side renewable generation. Optimizing the fuel cycle cost through the optimization of core loading patterns is one approach to addressing this lack of competitiveness. However, this optimization task involves multiple objectives and constraints, resulting in a vast number of candidate solutions that cannot be explicitly solved. While stochastic optimization (SO) methodologies are utilized by various nuclear utilities and vendors for fuel cycle reload design, manual design remains the preferred approach. To advance the state-of-the-art in core reload patterns, we have developed methods based on Deep Reinforcement Learning for single and multi-objective optimization. Previous research has laid the groundwork for these approaches and demonstrated its ability to discover high-quality patterns within a reasonable timeframe. Moreover, RL methods have shown superiority against SO in the multi-objective settings. In this paper, we introduce an innovative paradigm by incorporating multi-objective RL, interpretable AI, and physics knowledge to further improve the performance of the algorithms. When the problem increases in complexity, the classical single-objective sometimes failed to even found feasible solutions. Leveraging physic-information becomes pivotal to find high-quality and more diverse solutions faster. These findings motivate incorporation of more physics to further aid in the quest of surpassing human intelligence. Future work will focus on scaling this method and addressing fuel performance and control rod limits to demonstrate that these new designs can be considered for a real reactor.

Deformation-Resistant Underwater Adhesion in a Wide Salinity Range.

Conventional adhesives experience reduced adhesion when exposed to aqueous environments. The development of underwater adhesives capable of forming strong and durable bonds across various wet substrates is crucial in biomedical and engineering domains. Nonetheless, limited emphasis placed on retaining high adhesion strengths in different saline environments, addressing challenges such as elevated osmotic pressure and spontaneous dimensional alterations. Herein, a series of ionogel-based underwater adhesives are developed using a copolymerization approach that incorporates "dynamic complementary cross-linking" networks. Synergistic engineering of building blocks, cross-linking networks, pendant groups and counterions within ionogels ensures their adhesion and cohesion in brine spanning a wide salinity range. A high adhesion strength of ≈3.6MPa is attained in freshwater. Gratifyingly, steady adhesion strengths exceeding 3.3MPa are retained in hypersaline solutions with salinity ranging from 50 to 200gkg-1, delivering one of the best-performing underwater adhesives suitable for diverse saline solutions. A combination of outstanding durability, reliability, deformation resistance, salt tolerance, and self-healing properties showcases the "self-contained" underwater adhesion. This study shines light on the facile fabrication of catechol-free ionogel-based adhesives, not merely boosting adhesion strengths in freshwater, but also broadening their applicability across various saline environments.

EXPLORING CREATIVE THINKING SKILL

This study aims to describe students' abilities with visual-spatial and logical-mathematical intelligence in solving contextual mathematical problems. This qualitative research involved two eighth-grade students from a junior high school, each with a background in logical-mathematical and visual-spatial intelligence. The task consisted of two questions that asked students to create two park designs and two alternative payment methods for the "Suroboyo Bus." These findings show both subjects’ complete fluency and flexibility indicators in creative thinking. Subjects exhibit the ability to generate diverse solution pathways and apply various problem-solving strategies. Furthermore, the creative thinking skills displayed by the subject contribute to their comprehensive understanding of mathematical concepts and foster deeper engagement with the subject. The analysis results show that students with logical-mathematical intelligence were able to solve the problems well and came up with various alternative answers that other students did not consider. Students with visual-spatial intelligence were able to solve the problems successfully but did not find ideas to search for alternative answers beyond what was already presented. These findings indicate that both students with visual-spatial and logical-mathematical intelligence were capable of solving the given contextual problems, although there is still room for improvement

Surrogate-assisted evolutionary framework with an ensemble of teaching-learning and differential evolution for expensive optimization

Surrogate-Assisted Evolutionary Algorithms (SAEAs) integrate Evolutionary Algorithms (EAs) with surrogate models to reduce the actual number of expensive function evaluations and have been widely used in solving Expensive Optimization Problems (EOPs). However, because of the insufficient diversity of new solutions, SAEAs often fall into local optima and result in evolutionary stagnation when dealing with complex problems. To improve the diversity of new solutions, this paper introduces a Surrogate-Assisted evolutionary Framework with an ensemble of Teaching-learning and Differential evolution (SAF-TD), which successfully integrates the strengths of both EAs to produce more diverse and effective solutions. The main contributions are summarized as follows: First, a novel framework that effectively integrates two distinct EAs using a radial basis function surrogate model and strategic sampling is introduced. Second, a mutation strategy with a supervisory mechanism is proposed to enhance mutation effectiveness and avoid stagnation. Third, a volatility index derived from dimensional improvements is incorporated into parameter control to address fitness-dependent weaknesses. Experiments on five expensive optimization functions and the CEC2017 test suite across multiple dimensions were conducted to evaluate the performance of SAF-TD. The results demonstrate that SAF-TD is highly competitive compared with state-of-the-art SAEAs and exhibits excellent performance in solving EOPs.

Turbulence scaling from deep learning diffusion generative models

Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows and comprehending them poses a major challenge. This comprehension necessitates an understanding of the space of turbulent fluid flow configurations. We employ a diffusion-based generative model to learn the distribution of turbulent vorticity profiles and generate snapshots of turbulent solutions to the incompressible Navier-Stokes equations. We consider the inverse cascade in two spatial dimensions and generate diverse turbulent solutions that differ from those in the training dataset. We analyze the statistical scaling properties of the new turbulent profiles, calculate their structure functions, energy power spectrum, velocity probability distribution function and moments of local energy dissipation. All the learnt scaling exponents are consistent with the expected Kolmogorov scaling This agreement with established turbulence characteristics provides strong evidence of the model's capability to capture essential features of real-world turbulence.

Art, illusion, and recycled images in Johannes Pauli’s anecdotes on painters

This article considers selected book illustrations that were published in the 1530s by Augsburg printer Heinrich Steiner. It compares Steiner’s choice of images for Schimpff und Ernst (1534), a popular anthology of entertaining and serious anecdotes compiled by the Franciscan preacher Johannes Pauli, with woodcuts that Steiner had previously employed in newly translated, moral–philosophical texts by Cicero and Petrarch. Although all three texts problematize the art of painting according to classical and medieval narratives, which urge caution in viewing lifelike images, each proposes diverse solutions for regarding illusionistic works of art. Whereas prior studies focus on the intentions of individual authors, this article highlights the printer–publisher’s agency. One image recycled in disparate works speaks to art’s multifaceted functions for upper-class, lay readers. This article argues that Steiner’s selection of images inserts Pauli’s chapter on artists and illusionistic painting into ongoing debates in Reformation-era Germany about the proper role of images in religious ritual. This cross-reading of image–text pairings in the early 1530s offers insights into the complexity of period attitudes towards visual art, the painter’s profession, and the beholder’s responsibilities.

Adaptive crossover-based marine predators algorithm for global optimization problems

Abstract The Marine Predators Algorithm (MPA) is a swarm intelligence algorithm developed based on the foraging behavior of the ocean’s predators. This algorithm has drawbacks including, insufficient population diversity, leading to trapping in local optima and poor convergence. To mitigate these drawbacks, this paper introduces an enhanced MPA based on Adaptive Sampling with Maximin Distance Criterion (AM) and the horizontal and vertical crossover operators – i.e., Adaptive Crossover-based MPA (AC-MPA). The AM approach is used to generate diverse and well-distributed candidate solutions. Whereas the horizontal and vertical crossover operators maintain the population diversity during the search process. The performance of AC-MPA was tested using 51 benchmark functions from CEC2017, CEC2020, and CEC2022, with varying degrees of dimensionality, and the findings are compared with those of its basic version, variants, and numerous well-established metaheuristics. Additionally, 11 engineering optimization problems were utilized to verify the capabilities of the AC-MPA in handling real-world optimization problems. The findings clearly show that AC-MPA performs well in terms of its solution accuracy, convergence, and robustness. Furthermore, the proposed algorithm demonstrates considerable advantages in solving engineering problems, proving its effectiveness and adaptability.

Recent advances in Sb-based anodes for Li/Na/K-ion batteries and all-solid-state Li-ion batteries

In recent decades, lithium-ion batteries (LIBs) have emerged as a primary focus in the energy-storage field owing to their superior energy and power densities. However, concerns regarding the depletion of non-abundant lithium resources have prompted the exploration and development of emerging energy-storage technologies, such as sodium- (SIBs) and potassium-ion batteries (PIBs). In addition, all-solid-state LIBs (ASSLIBs) have been developed to address the issues of flammability and explosiveness associated with liquid electrolytes. Among the various alloy-based anodes, antimony (Sb) anodes exhibit high energy densities owing to their high theoretical volumetric capacities that are attributable to their high densities. However, Sb anodes exhibit poor cyclabilities owing to excessive volume changes during cycling. To mitigate this issue, researchers have investigated the use of diverse solutions, including solid electrolyte interface control, structural control, and composite/alloy formation. Herein, we review and summarize Sb-based anode materials for LIBs, SIBs, PIBs, and ASSLIBs developed over the past five years (2018-present), focusing on their reaction mechanisms and multiple approaches used to achieve optimal electrochemical performance. We anticipate that this review will provide a comprehensive database of Sb-based anodes for LIBs, SIBs, PIBs, and ASSLIBs, thereby advancing relevant studies in the energy-storage-systems field.

Bayesian network structure learning using scatter search

Learning the structure of Bayesian networks (BNs) from data is an NP-hard problem as the solution space grows super-exponentially with the number of nodes. Many algorithms have been developed to efficiently find the best structures, with score-based algorithms being those that use heuristics or metaheuristics to explore potential structures in the search space. This paper proposes a new score-based algorithm that relies on a well-known metaheuristic called scatter search, which, to the best of our knowledge, has not been used in learning BN structure. The core of scatter search is to maintain a reference set that stores both high-quality and diverse solutions, thereby continuously tracking and improving the high-quality solutions, while exploring different search directions indicated by the diverse solutions. By incorporating a distance metric in the learning process, the exploration can be more systematic than purely random, as is often the case in most existing algorithms. The effectiveness and efficiency of the proposed algorithm are evaluated through computational experiments. In addition to learning higher-score structures, the results show that scatter search provides a higher degree of robustness compared with benchmark algorithms.

Emerging infectious disease prevention: Veterinary action required.

Emerging and re-emerging infectious disease outbreaks have increased significantly over the past century, largely due to human actions, leading to distress, death and severe socio-economic implications. Coordinated actions by multiple sectors and disciplines are required to address these drivers. The challenge for the veterinary profession is to identify and prioritise the actions to which it can and should contribute. This review explores: the extent to which veterinary oaths promote attention to ecological sustainability; challenges faced during One Health implementation across diverse settings and possible solutions; and opportunities for veterinarians to assist with pandemic prevention by working to stop viral spillover at source. It aims to stimulate a discussion within the veterinary profession regarding how our current approaches: prevent or promote emerging infectious diseases and re-emerging infectious diseases; provide opportunities to improve our preventive contributions going forward; and can yield significant cobenefits.