Correct Solution Research Articles

Energy landscapes for the quantum approximate optimization algorithm

Variational quantum algorithms (VQAs) have demonstrated considerable potential in solving NP-hard combinatorial problems in the contemporary noisy intermediate-scale quantum (NISQ) era. The quantum approximate optimization algorithm (QAOA) is one such algorithm, used in solving the maximum cut (Max-Cut) problem for a given graph by successive implementation of L quantum circuit layers within a corresponding Trotterized ansatz. The challenge of exploring the cost function of VQAs arising from an exponential proliferation of local minima with increasing circuit depth has been well documented. However, fewer studies have investigated the impact of circuit depth on QAOA performance in finding the correct Max-Cut solution. Here we employ basin-hopping global optimization methods to navigate the energy landscapes for QAOA ansätze for various graphs, and analyze QAOA performance in finding the correct Max-Cut solution. The structure of the solution space is also investigated using discrete path sampling to build databases of local minima and the transition states that connect them, providing insightful visualizations using disconnectivity graphs. We find that the corresponding landscapes generally have a single funnel organization, which makes it relatively straightforward to locate low-lying minima with good Max-Cut solution probabilities. In some cases below the adiabatic limit the second lowest local minimum may even yield a higher solution probability than the global minimum. This important observation has motivated us to develop broader metrics in evaluating QAOA performance, based on collections of minima obtained from basin-hopping global optimization. Hence we establish expectation thresholds in elucidating useful solution probabilities from local minima, an approach that may provide significant gains in elucidating reasonable solution probabilities from local minima. Published by the American Physical Society 2024

Turbulence image correction using focused light field camera

Under turbulent conditions, the images of objects may be severely blurred and become unrecognizable because of the complex aberrations introduced by turbulence. Using adaptive optics (AO) to compensate for these wavefront aberrations can theoretically solve the problem of image blurring under turbulent conditions. However, due to its small field of view and high cost, the AO system is difficult to widely use. In recent years, using the technology of light field photography to correct turbulence images has been proposed. However, the current studies are either based on the structure of the non-focused light field camera, which makes it hard to achieve good image quality, or on the need to redesign the core sensor of the non-focused light field camera, which greatly increases the cost and complexity. We believe that the structure of a focused light field camera has a good potential for turbulence image correction and propose a new method of achieving a corrected focused image using a focused light field camera in this paper. We propose to use phase estimation instead of depth estimation for a focused light field camera and phase maps instead of a depth map as the basis for achieving a focused image. By the proposed method, a corrected focused image with a large field of view under turbulent conditions is achieved without using independent wavefront correction devices or redesigning the light field camera. The experiments are given to verify the effectiveness of the proposed method under strong turbulent conditions in the laboratory and real turbulent conditions in the outdoors. The proposed method in this paper provides a new and low-cost solution for image correction for optical imaging systems under turbulent conditions, so it has a high application value.

Bi-directional DC-AC using BLDC motor for electric and hybrid electric vehicles applications with reduced number of switches

Electric scooters often have a single design and are difficult to produce at a greater level than other forms of transportation. A possible cut electric scooter is built with the optimization design of the vehicle combustible wealth and the pollution characteristics in order to arrange to a correct solution by resolving each and every challenge. The engines' electrical systems are comprised of lead acid batteries. For the sake of a straightforward simulation procedure, the engine operates in two states ON and OFF while the vehicle's speed is regulated in three ranges roughly corresponding to high, medium, and low. The planetary speed ratio and the final drive speed ratio are gathered in the computation method's general outline. Utilizing simulation MATLAB, the findings of optimization strategies are resolved. This technique improves the fuel plenty and discharge quality of the possibility cut electric automobiles.

AUTOMATIC SOLVING OF PHYSICS WORD PROBLEMS

We present a system that solves simple physics word problems (PWPs) stated in the English language. The main feature of the system is that it is deterministic and yields a correct solution based on real physics formulas. The program generates the solution in a tabular form, showing givens, unknowns, and solution steps. We performed a thorough analysis on the previous work in this field. Most of the research was accumulated in math word problem (MWP) solvers. We have found that these programs are not capable of solving problems from Ukrainian physics textbooks. We identified several types of physics problems: theoretical, value conversion, value comparison, unknowns finding, value change. We developed separate problem-solving strategies for each type. The program uses named entity recognition (NER), a technique in natural language processing (NLP), to identify key elements in the problem text. We created a set of rules for marking these entities. Then, problem type recognition is performed. Depending on the type, the list of entities is converted into the internal problem representation. Value conversion and comparison problems are easy to handle. We developed a recursive algorithm for solving unknowns-finding problems which turned out to be a simplified version of Stanford Research Institute Problem Solver (STRIPS) algorithm. However, developing a universal algorithm for solving value-change problems presents a significant challenge. We believe this problem type belongs to the NP-hard class, indicating inherent difficulty in finding optimal solutions. The interface of the program is a web-application. The user can type the problem text and see the solution on a web page. Additionally, the result of NER is presented. Constructing a general problem solver is challenging. While our program can solve basic physics problems, complex problems involving forces, energy, etc., remain unsolved. However, our solver has great potential for future development. We have thoroughly analyzed its capabilities and limitations and proposed ideas for future research.

Deep-learning-based motion correction using multichannel MRI data: a study using simulated artifacts in the fastMRI dataset.

Deep learning presents a generalizable solution for motion correction requiring no pulse sequence modifications or additional hardware, but previous networks have all been applied to coil-combined data. Multichannel MRI data provide a degree of spatial encoding that may be useful for motion correction. We hypothesize that incorporating deep learning for motion correction prior to coil combination will improve results. A conditional generative adversarial network was trained using simulated rigid motion artifacts in brain images acquired at multiple sites with multiple contrasts (not limited to healthy subjects). We compared the performance of deep-learning-based motion correction on individual channel images (single-channel model) with that performed after coil combination (channel-combined model). We also investigate simultaneous motion correction of all channel data from an image volume (multichannel model). The single-channel model significantly (p < 0.0001) improved mean absolute error, with an average 50.9% improvement compared with the uncorrected images. This was significantly (p < 0.0001) better than the 36.3% improvement achieved by the channel-combined model (conventional approach). The multichannel model provided no significant improvement in quantitative measures of image quality compared with the uncorrected images. Results were independent of the presence of pathology, and generalizable to a new center unseen during training. Performing motion correction on single-channel images prior to coil combination provided an improvement in performance compared with conventional deep-learning-based motion correction. Improved deep learning methods for retrospective correction of motion-affected MR images could reduce the need for repeat scans if applied in a clinical setting.

Exploring Failure Cases in Multimodal Reasoning About Physical Dynamics

In this paper, we present an exploration of LLMs' abilities to problem solve with physical reasoning in situated environments. We construct a simple simulated environment and demonstrate examples of where, in a zero-shot setting, both text and multimodal LLMs display atomic world knowledge about various objects but fail to compose this knowledge in correct solutions for an object manipulation and placement task. We also use BLIP, a vision-language model trained with more sophisticated cross-modal attention, to identify cases relevant to object physical properties that that model fails to ground. Finally, we present a procedure for discovering the relevant properties of objects in the environment and propose a method to distill this knowledge back into the LLM.

A new chromatographic response function with automatically adapting weight factor for automated method development

A critical factor for automated method development in chromatography is the maximization or minimization of an objective function describing the quality (and speed) of the separation. In chromatography, this function is commonly referred to as a chromatographic response function (CRF). Many CRFs have previously been introduced, but many have unfavourable properties such as featuring multiple optima, insufficient discriminatory power, and a too strong dependence on the weight factors needed to balance resolution and time penalty components. To overcome these problems, the present study introduces a new type of CRF wherein the relative weight of the time penalty term is a self-adaptive function of the separation quality. The ability to unambiguously identify the optimal gradient settings of this newly proposed CRF is compared to that of some of the most frequently used CRFs in a study covering 100 randomly composed in silico samples. Doing so, the new CRF is found to flawlessly lead to the correct solution (=linear gradient parameters providing the highest resolution in the shortest potential time) in 100 % of the cases, while the most frequently used literature CRFs were off-target for about 50 to 60 % of the samples, even when considering the availability of spectral peak shape data. Some slight alterations to the proposed CRF are introduced and discussed as well.

Programming as a mediator of mathematical thinking

We report on three episodes from a case study where upper secondary students numerically explore the definite integral in a Python environment. Our research questions concern how code can mediate and support students' mathematical thinking and what kind of sociomathematical norms emerge as students work together to reach a mutual understanding of a correct solution. The main findings of our investigation are as follows. 1) Students can actively use code as a mediator of their mathematical thinking, and code can even serve as a bridge that helps students to develop their mathematical thinking collaboratively. Further, code can help students to perceive mathematical notions as objects with various properties and to communicate about these properties, even in other semiotic systems than the mathematical language. 2) For the participating students, a common norm was that an acceptable solution is a sufficient condition for the correctness of the solution method although students were aware of a problem in their code, yet also other norms emerged. This demonstrates that learning mathematics with programming can have an effect on what kind of sociomathematical norms emerge in classroom.

Application of Model-Free and Model-Based Kinetic Methods in Evaluation of Reactions Complexity during Thermo-Oxidative Degradation Process: Case Study of [4-(Hydroxymethyl)phenoxymethyl] Polystyrene Resin

This work examined the possibilities and limitations of model-free and model-based methods related to decrypting the kinetic complexity of multi-step thermo-oxidative degradation processes (as a testing system, a [4-(hydroxymethyl)phenoxymethyl] polystyrene resin was used), monitored by thermal analysis (TGA-DTG-DTA) techniques. It was found that isoconversional methods could successfully determine the correct number of process stages and presence of multiple reactions based on derived Ea(α) profiles and identify the negative dependence of the rate constant on the temperature. These methods could not overcome the problem that arose due to mass transfer limitations. The model-based method overcame more successfully the problem associated with mass transfer because its calculation machinery had capabilities for the correct solution of the total mass balance equation. However, a perfect fit with the experimental data was not achieved due to the dependence on the thermal history of the contribution (ctb.) of a given reaction step inside a fitting procedure cycle. On the other hand, through this approach, it was possible to estimate the rate-controlling steps of the process regarding the influence of the heating rate. It was found that for consecutive reaction mechanisms, the production of benzaldehyde and gases in high yields was controlled by the heating rate, where low heating rates were strongly recommended (≤10 K/min). Also, it was shown that the transport phenomenon may be also the rate-determining step (within the set of “intrinsic” kinetic parameters). It was also established that external heat transfer controls the overall rate, where the “pure” kinetic control regime had not been reached but was approached when lowering the temperature and size of the resin particles.

On the solution of MHD Jeffery–Hamel problem involving flow between two nonparallel plates with a blood flow application

AbstractThe Jeffery–Hamel flow phenomenon appears in a variety of real‐world applications involving the flow of two nonparallel plates. BY using a similarity transformation derived from the equation of continuity, partial differential equations determining flow characteristics are translated into nonlinear ordinary differential equations. The problem involves the flow of a specific type of fluid, namely, an incompressible and electrically conducting fluid, between two nonparallel plates. The flow is assumed to be steady, two‐dimensional, and subject to certain boundary conditions. Specifically, the plates are impermeable, and the fluid adheres to a no‐slip condition, resulting in zero fluid velocity at the plates' surfaces. Moreover, the problem incorporates the effects of magnetic fields and pressure fluctuations, making it highly applicable to scenarios, such as blood flow through arteries in the human body, which can be modeled as a special case of the magnetohydrodynamic (MHD) Jeffery–Hamel problem referred to as the (MHD) blood pressure equation. This work compares two numerical approaches for solving the MHDs Jeffery–Hamel problem: B‐spline and Bernstein polynomial collocation. The given approaches are used to discretize and transform the equation into a system of algebraic equations. Matrix algebra techniques are then used to solve the resultant system. A complete error analysis and convergence rates for different grid sizes are derived for both methods and are used to compare the accuracy and efficiency of the two approaches. Both approaches produce correct solutions, according to the numerical findings, although the Bernstein polynomial collocation method is more efficient and accurate than the B‐spline collocation.

Improving Mathematics Performance in Algebra by Using A Videogame Style Points-Based Feedback System

Researchers have found that using videogame-based learning has not only led students to be more engaged in the classroom, but also perform better on exams. These researchers have generally focused on variables such as design of content and interactivity for the video games themselves. The present paper follows up our previous research (Somayyajula, Gajula and Leddo, 2019) that shows that awarding points for correct solutions to math problems boosted performance in elementary school students. In the present study, we performed a similar intervention with middle and high school students who solved algebra problems. A total of 28 Participants were asked to solve 20 algebra problems involving mixtures (an application of systems of linear equations) that were delivered via software. Two versions of the software were used. In one version, Participants were told simply whether they gave the correct answer after each problem. In the other, Participants were not only told whether they gave the correct answer but were also given 100 points for each correct answer and shown their cumulative point totals. Participants who received points scored, on average, 47% higher than those who received no points. Results extend our previous work and suggest that the points motivated the Participants to perform better, suggesting an easy and inexpensive way to boost educational achievement.

Vulnerability-driven multi-objective topological optimization of aircraft protections

Enhancing the survivability of platforms is paramount for increasing their safety level and limiting the loss of lives. This is particularly important in the military field, where aircraft vulnerability has been studied for decades through specifically developed frameworks. So far, most of the contributions have focused on improving the accuracy of methods for assessing the vulnerability to ballistic projectiles, mainly by advancing our understanding of the penetration of the threat through the components of the platform. Although state-of-the-art solutions allow conducting accurate assessments, the information provided by such methods has not been fully exploited yet for automatically designing corrective solutions and improving the survivability of platforms. For this reason, this work proposes a vulnerability-driven multi-objective topological optimization algorithm for designing protective surfaces. A genetic algorithm is used to describe the Pareto front delimiting the space of solutions characterized by optimal vulnerability and weight. The number of clusters that constitute the protections proposed by the optimization algorithm is minimized by adopting a spatial filter that penalizes fragmented designs. The framework is demonstrated through a case study involving a portion of a realistic remotely piloted aircraft system impacted by ballistic projectiles.

Investigative Listening as a Praxis for Technical Evaluation and Corrective Procedure

Abstract: Associate editor Justin John Moniz discusses how investigative listening can support effective teaching practices in the voice studio setting. An emphasis is placed upon the processes surrounding the diagnosis and correction of vocal faults, communicating corrective solutions, and student self-regulation.

ANALISIS KESALAHAN KONSEP BERDASARKAN GAYA BELAJAR PADA MATERI OPERASI BENTUK ALJABAR

This research was motivated by conceptual errors based on the learning styles of class VII students at SMP Negeri 3 Huruna when completing algebraic operations. This research aims to describe students' conceptual errors based on learning styles in solving algebraic operations problems. This type of research is qualitative research with a descriptive approach. The informants in this research were 25 class VII students of SMP Negeri 3 Huruna. Data were analyzed using data reduction steps, data presentation, and drawing conclusions. The data collection techniques used were questionnaires, tests and interviews. The results of this research from all student answers show that students made 31.6% reading errors, 32% understanding errors, 25.6% transformation errors, 4.8% processing errors and 6% errors in writing the final answer. So it can be concluded that class VII students at SMP Negeri 3 Huruna experienced errors in understanding based on the visual learning style. Through this research, researchers suggest that teachers create a more enjoyable atmosphere for mathematics learning and provide students with the habit of solving mathematical problems using good and correct solution steps.

Need for structured conservation inputs: a geospatial approach for habitat prioritization to restrict disturbance regime in the protected areas of the Western Himalayan region

AbstractThe population boom increases land-use requirements, and shrinking regions are available to preserve natural biodiversity. Unfortunately, scientific knowledge does not seem to have and might never get a correct solution to how much territory will be sufficient for satisfying the various biodiversity conservation goals. Prioritization of habitat, primarily for threatened species, is a critical management challenge. Habitat management efforts are gradually being performed over broader geographic regions, emphasizing the necessity of spatial and strategic prioritization. Habitat prioritization is a technique popular for identifying critical areas needing conservation initiatives. To develop a strategic conservation framework, key issues must be identified based on the ground knowledge and present land use status, which are difficult to gather for highly rough terrain areas. Incorporating spatial data sets thus helps to get the desired knowledge of the site on a large scale. This study used a grid-based multicriteria approach to create a spatial conservation management framework for the Rajaji-Corbett landscape (RCL) in Northern India. A grid-based prioritization assessment was conducted to identify priority areas for the RCL, keeping invasive species and fire as significant threats. The indicators or criteria were selected by looking at present vital concerns in the landscape. We took 20 indicators for our multicriteria assessment for landscape prioritization of RCL using 2 × 2 km grids for the evaluation and classified them according to low, medium, and high priority areas using Arc GIS Pro. The results identified 931 sq. km area of this landscape needs immediate management interventions where frequent fire incidents and presence of invasive species are high. RCL is well known for its biodiversity wealth and assemblage of various wildlife populations.

Examining the Prevalence of Musculoskeletal Problems Related to Mobile Phone Usage and Providing Corrective Solutions to Dental Students of Jundishapur University of Medical Sciences, Ahvaz

Objectives: The present study aimed to assess the prevalence of musculoskeletal problems related to mobile phone usage and provide corrective solutions for dental students of Jundishapur University of Medical Sciences, Ahvaz. Methods: The current study was conducted based on an analytical cross-sectional design. In order to collect relevant data, such as the presence of pain in the upper limbs, back, and neck, questionnaires and instrumental methods were used. Students were examined in two groups: the first group included 150 dental students up to the 4th semester, and the second group encompassed 150 dental students from the 5th semester onwards (3rd, 8th, 5th, and 6th year). Data collection instruments were demographic characteristic form, nomophobia questionnaire (NMP-Q), neck disability index (NDI), and Cornell hand discomfort questionnaire (CHDQ). Results: According to the results of the present study, the prevalence of nomophobia among dental students of Jundishapur University of Medical Sciences in Ahvaz was at an average level. The mean NDI score was 16.20, and the mean pain score among the students was 1.72. Moreover, 46.5% of students had pain in the hand area, and the highest amount of pain was reported in the F area. The mean score of Cornell's musculoskeletal disorders was reported as 2.72. Conclusion: Examining the relationship between the pain area and the year of entry to the university demonstrated a significant relationship between the year of entry to the university and the pain area. In this regard, the incoming students 2021-22 had the greatest amount of pain. According to the results of the corrective intervention on skeletal disorders in the wrist and neck region, the mean scores of the variables increased significantly after the intervention and corrective measures.

Droplet impact onto a porous substrate: a Wagner theory for early-stage spreading

An analytical model for droplet impact onto a porous substrate is presented, based on Wagner theory. An idealised substrate boundary condition is introduced, mimicking the effect of fluid entry into a genuinely porous substrate. The asymptotic analysis yields a solution for a small porous correction with free-surfaces and pressures compared with the impermeable case. On a global scale, it is found that the impact region on the substrate grows more slowly with porosity included due to loss of mass into the substrate. The spatial distribution of liquid volume flux into the substrate is also described. Locally near the turn-over regions, the expected jetting along the surface is calculated with the same volume flux but the jet is found to be slower and thicker than for an impermeable substrate.

How to Make Computer-Based Feedback More Productive: The Power of Erroneous Solutions

This research aims to expand our understanding of how to facilitate student feedback engagement processes in a computer-based formative assessment environment. In the present research, we designed a new type of elaborated feedback in terms of combining the correct solution and the erroneous solution, and the erroneous solution matched the student’s initial answer. Furthermore, we analyzed whether this feedback had a stronger positive effect than the other three types of feedback containing different complexities of correct information (i.e., Knowledge of Correct Response, Problem-Solving Cues, or Complete Correct Solutions). As predicted, students who received correct and erroneous solutions experienced more positive feedback perceptions, perceived lower extraneous cognitive load and higher germane cognitive load, and achieved higher transfer performance. This research is one of the first that provides empirical evidence for the positive impact of incorporating students’ errors into the feedback design, and this novel insight can extend current theories on how to optimize feedback design to promote students’ active processing and use of feedback.

Determining Sample Size Requirements in EFA Solutions: A Simple Empirical Proposal

In unrestricted or exploratory factor analysis (EFA), there is a wide range of recommendations about the size samples should be to attain correct and stable solutions. In general, however, these recommendations are either rules of thumb or based on simulation results. As it is hard to establish the extent to which a particular data set suits the conditions used in a simulation study, the advice produced by simulation studies is not direct enough to be of practical use. Instead of trying to provide general and complex recommendations, in this article, we propose to estimate the sample size that is needed to analyze a data set at hand. The estimation takes into account the specified EFA model. The proposal is based on an intensive simulation process in which the sample correlation matrix is used as a basis for generating data sets from a pseudo-population in which the parent correlation holds exactly, and the criterion for determining the size required is a threshold that quantifies the closeness between the pseudo-population and the sample reproduced correlation matrices. The simulation results suggest that the proposal works well and that the determinants identified agree with those in the literature.

VisionaryVR: An Optical Simulation Tool for Evaluating and Optimizing Vision Correction Solutions in Virtual Reality.

In the rapidly advancing field of vision science, traditional research approaches struggle to accurately simulate and evaluate vision correction methods, leading to time-consuming evaluations with limited scope and flexibility. To overcome these challenges, we introduce 'VisionaryVR', a virtual reality (VR) simulation framework designed to enhance optical simulation fidelity and broaden experimental capabilities. VisionaryVR leverages a versatile VR environment to support dynamic vision tasks and integrates comprehensive eye-tracking functionality. Its experiment manager's scene-loading feature fosters a scalable and flexible research platform. Preliminary validation through an empirical study has demonstrated VisionaryVR's effectiveness in replicating a wide range of visual impairments and providing a robust platform for evaluating vision correction solutions. Key findings indicate a significant improvement in evaluating vision correction methods and user experience, underscoring VisionaryVR's potential to transform vision science research by bridging the gap between theoretical concepts and their practical applications. This validation underscores VisionaryVR's contribution to overcoming traditional methodological limitations and establishing a foundational framework for research innovation in vision science.