Heuristic Techniques Research Articles

АНАЛИЗ ИНТЕРМЕДИАЛЬНОЙ ПОЭТИКИ ПРОЗЫ КАК СРЕДСТВО СОВЕРШЕНСТВОВАНИЯ ЛИТЕРАТУРОВЕДЧЕСКИХ КОМПЕТЕНЦИЙ СТУДЕНТОВ

This article describes in detail the methods, techniques and means of developing the concept of intermediality in practical seminars in a university classroom. The productivity of the interdisciplinary nature of the research is revealed, during which advanced provisions and conclusions of pedagogy, didactics, methods of teaching Russian literature, literary theory, and literary criticism were used. It is shown that during formative training it is advisable to use creative, heuristic, and reproductive reading methods. The use of the project method, the research method, conducting a heuristic conversation, organizing educational dialogue, and working in groups provided the necessary conditions for the development of students' subjectivity during the cognitive process. An effective technology for analyzing a literary text in the inextricable and organic unity of form and content is presented, providing a deep understanding of the originality of the poetics, style and problems of E. Tursunov’s film story “Kelin”. Methods for developing students’ skills in identifying various forms of manifestation of intermedial connections in a work and determining their role in the artistic world are identified and characterized. It was also established that during the study of E. Tursunov’s artistic creativity, the combination of various elements of the method of problematic presentation and creative reading ensured the effectiveness of the analysis of recreating intermedial connections in various types of art. Fragments of educational analysis of the forms of expression of intermediality in the works of Ermek Tursunov are offered using active and heuristic methodological techniques that contribute to the activation of students’ thinking and the development of their literary competencies. The consistent work of the teacher in a heuristic and interactive format is substantiated, promoting the involvement of students in the process of active discussions.

Influence of Heuristic Mathematics Teaching Method towards Students’ Performance in Public Secondary Schools in Moshi District, Tanzania

This study was conducted to investigate the influence of heuristic mathematics teaching methods on students' performance in public secondary schools in Moshi District, Tanzania. The study was guided by Constructivism learning theory, which was pioneered by Jean Piaget (1936). The study employed a quantitative research approach whereby a cross-sectional survey design was used. The study’s target population was 61 public secondary schools, 73 mathematics teachers, and 28,134 students in Moshi District. Six schools and twelve teachers were selected using a straightforward random selection procedure, and 394 pupils were chosen using Slovin's formula. Both mathematics teachers and students were given access to quantitative tools (questionnaires). The reliability of Likert scale items was determined by the Cronbach Alpha technique. The collected data was sorted and analysed by using the Statistical Package for Social Sciences (SPSS – version 20) computer software. The study found that heuristic teaching method has a positive contribution towards students’ performance. The researcher concluded that heuristic teaching method used by mathematics teachers had a large number of contributions to students’ learning of mathematics. According to the study's recommendations, the government should make sure that mathematics teachers have access to training programs on using heuristic teaching techniques to boost students' performance.

IMPROVING MATERIAL REQUIREMENTS PLANNING THROUGH WEB-BASED: A CASE STUDY THAILAND SMEs

In the small business industry, effective cost control is of paramount importance. A well-functioning Material Requirements Planning (MRP) process plays a vital role in enabling businesses to manage their costs efficiently. This research paper explores the improvement of MRP in the context of small businesses in Thailand through the implementation of a web-based application. The objective was to enhance the MRP process by developing an MRP system using ReactJS, NodeJS, and PostgreSQL. The system was evaluated using heuristic evaluation techniques and the results indicated a positive outcome, the mean value is 0.83. The developed web based MRP system proved beneficial for small businesses, as it effectively reduced stocking costs and facilitated efficient raw material procurement. This research provides valuable insights into the implementation of web based MRP systems, enabling small businesses to optimize inventory management and enhance operational efficiency.

A Fuzzy Inference-Based TCSC Control Technique to Improve Dynamic Power System Responses

With the expansion of transmission systems, the devices that contribute to the overall power system performance must be adequate to the increasing modeling complexity and requirements. In this sense, the Flexible AC transmission system (FACTS) devices are often employed to improve stability and power quality, while the Thyristor-Controlled Series Compensator (TCSC) is a common example, able to change the equivalent transmission line impedance, improving power flow. This work discusses two established techniques for the control of TCSC devices based on the lead-lag model, whose parameters are defined through metaheuristic techniques, such as Genetic Algorithm and Particle Swarm Optimization. The results found in these publications are implemented in the Matlab/Simulink environment and confronted with the proposed Fuzzy logic application, written with linguistic inference, simplified rules, and simple membership functions. The two published models and the proposed Fuzzy logic performed satisfactorily with very similar results in all scenarios simulated considering a Single Machine Infinite Bus (SMIB) equivalent system model. The big advantage of the use of Fuzzy logic is its modeling simplicity, unlike the heuristic techniques that require much more modeling time and, sometimes, a large number of iterations to achieve acceptable parameters.

Improved heuristics for solving large-scale Scanning Transmission Electron Microscopy image segmentation using the ordered median problem

The discrete ordered median problem can be applied in a wide variety of areas. The application of this problem in electron tomography image segmentation is currently being considered since high-quality images are obtained when this model is applied. However, its application means that a high computing time is required to obtain solutions for large-scale instances. The size of the images is of great importance in electron tomography experiments, since the larger the image size, the higher the quality of the image. With the goal of reducing the computation times, this paper introduces different heuristic procedures to obtain feasible solutions for the ordered median problem that provide high-quality images in low computing times. Moreover, some noticeable improvements for the heuristic techniques are developed, taking advantage of the particular versions of the ordered median function that have been proven to be especially suitable for electron tomography image segmentation.

Stereotipype Tokoh Ayah dalam Cerpen Guru Karya Putu Wijaya: Kajian Dekonstruksi Derrida

Abstract This research aims to describe stereotype The father figure in the short story Guru by Putu Wijaya is in accordance with Derrida's theory. The research method uses a deconstruction approach to Derrida's theoretical literary works. Deconstruction of literary works is able to analyze short stories from the language that has various meanings in a text. The data in this research is in the form of text documents containing fragments of characters, plot, caritas background in short stories. The data source comes from Jawa Pos, 05/08/2016 edition of teacher's short stories by Putu Wijaya. The data collection technique uses heuristic techniques, namely reading the initial stages by analyzing the elements contained in the story such as characters. The data analysis technique uses the hermeneutic technique of in-depth reading of literary works in the form of the social environment in the story and analyzing it in depth in accordance with deconstruction theory. Research result stereotype The character of the father can be shown based on dialogue or the character's speech, such as the environment, the character's reactions to other characters. It is known that the character of the father does not depict the attitude of a parent who is firm, wise and unemotional. And the meaning of opposition which shows my father's bad thoughts towards teachers is a form of criticism of the government.

Developing an Optimization Model for Minimizing Solid Waste Collection Costs

With the increase in population in cities, the number of solid waste to be collected has also increased. Because the garbage collection route must be traveled repeatedly, even minor improvements in these routes can result in a significant increase in fuel usage. Shortening the journey would provide a significant contribution to lowering fuel expenses in all towns, especially given the rising cost of fossil fuels. Furthermore, lowering fuel usage is critical for Turkey to meet its national objectives under the Paris Agreement. The Simulated Annealing (SA) algorithm, one of the heuristic optimization techniques used to identify the best solutions to complicated problems, is employed to solve the routing problem of solid waste collection vehicles in this study. This method, which was inspired by the metal annealing process, stands out for its ability to avoid regional minima while looking for the optimal solution. The applicant region was selected as the Kosova Neighborhood of Konya's Selçuklu District. The container distances needed for the method to execute were acquired by extracting the coordinates of the containers. Kosova Neighborhood was separated into 7 distinct regions due to the restricted capacity of rubbish collection vans. All regions were analyzed independently, and the best feasible routes were estimated using the SA algorithm approach, and the results were compared to the greedy algorithm findings. The SA algorithm outperformed the greedy algorithm by 26.49%.

Constraint-Driven Complexity-Aware Data Science Workflow for AutoBDA

The internet of things, privacy, and technical constraints increase the demand for edge-based data-driven services, which is one of the major goals of Industry 4.0 and Society 5.0. Big data analysis (BDA) is the preferred approach to unleash hidden knowledge. However, BDA consumes excessive resources and time. These limitations hamper the meaningful adoption of BDA, especially the time and situation critical edge use cases, and hinder the goals of Industry 4.0 and Society 5.0. Automating the BDA process at the edge is a cognitive approach to address the aforementioned concerns. Data science workflow is an indispensable challenge for successful automation. Therefore, we conducted a systematic literature survey on data science workflow platforms as the first contribution. Moreover, we learned that the BDA workflow depends on diversified constraints and undergoes rigorous data-mining stages. These caused an increase in the solution space, dynamic constraints, complexity issues, and NP-hardness of BDA workflow. Graphplan is a heuristic AI-planning technique that can address concerns associated with BDA workflow. Therefore, as the second contribution, we adopted the graphplan to generate a workflow for edge-based BDA automation. Experiments demonstrate that the proposed method achieved our objectives.

Design of Switched Capacitor Multilevel Inverter with Selective Harmonic Elimination

In this paper, Selective Harmonic Elimination (SHE) technique is applied to control multilevel inverters that can be used to eliminate the low order dominant harmonics. This is considered a low frequency technique, in which the switching angles are predetermined based on solving a system of transcendental equations. Newton Raphson is one among the heuristic techniques utilised for selective harmonic elimination. The proposed SHE strategy is simulated along with Sinusoidal Pulse Width Modulation (SPWM) technique and the harmonic distortions are measured and compared for both techniques in MATLAB/Simulink software.

Artificial intelligence for improving public transport: a mapping study

The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

Adaptive squirrel coyote optimization-based secured energy efficient routing technique for large scale WSN with multiple sink nodes

In general, Wireless Sensor Networks (WSNs) require secure routing approaches for delivering the data packets to their sinks or destinations. Most of the WSNs identify particular events in their explicit platforms. However, several WSNs may examine multiple events using numerous sensors in a similar place. Multi-sink and multi-hop WSNs include the ability to offer network efficiency by securing effective data exchanges. The group of nodes in the multi-sink scenario is described through a distance vector. Though, the efficiency of multi-sink WSNs is considerably impacted by the routing of data packets and sink node placement in the cluster. In addition, many WSNs for diverse reasons existed in the similar geographical region. Hence, in this task, a secured energy-efficient routing technique is designed for a Wireless sensor network with Large-scale and multiple sink nodes. Here, the concept of an improved meta-heuristic algorithm termed Adaptive Squirrel Coyote Search Optimization (ASCSO) is implemented for selecting the accurate selection of cluster head. The fitness function regarding residual distance, security risk, energy, delay, trust, and Quality of Service (QoS) is used for rating the optimal solutions. The consumption of energy can be reduced by measuring the mean length along with the cluster head and multiple sink nodes. The latest two heuristic algorithms such as Coyote Optimization Algorithm (COA) and Squirrel Search Algorithm (SSA) are integrated for suggesting a new hybrid heuristic technique. Finally, the offered work is validated and evaluated by comparing it with several optimization algorithms regarding different evaluation metrics between the sensor and sink node.

Design and implementation of 2 KW OFF-GRID photovoltaic systems with maximum power point tracking using PSO algorithm operating under partial shading conditions

This paper presents the design and implementation of a metaheuristic control method for maximum power point tracking (MPPT) methodology in photovoltaic systems under partial shading conditions. To attain maximum efficiency, photovoltaic (PV) panels should operate at their maximum power point (MPP). Therefore, to maximize the power from the PV system, an MPP tracker is inserted between the PV arrays and the load to ensure that the operating point of the system is adjusted to be placed on the MPP. When the radiation distribution over PV panels is uniform, many traditional MPPT techniques can effectively monitor MPP. However, when the PV arrays are partially shaded, more MPPs are displayed, which usually results in the inability to find the overall MPP. To overcome this difficulty, this proposed work presents a particle swarm optimization algorithm-based MPPT scheme for PV systems. This PSO heuristic algorithm-based technique not only ensures the ability to find the global MPP but also gives a simpler control scheme. The feasibility of this proposed method is verified by various partial shading conditions using Matlab. The proposed method is implemented with a 2KW PV system and the power output is compared with the existing system.

Electric vehicle-based express service network design with recharging management: A branch-and-price approach

With soaring fuel prices and the imperative to reduce global carbon emissions, electric vehicles (EVs) possess unparalleled advantages in terms of sustainability. Consequently, numerous express service providers have commenced the implementation of EV-based express service networks. However, designing an effective service network for EV fleets necessitates considering recharging operations during the route planning process. In this study, we explore a problem involving the design of an EV-based express service network under the constraint of limited recharging resources, while taking into account power management for each EV throughout the entire operational cycle. To address this challenge, we present a mixed-integer optimization model incorporating a Dantzig–Wolfe reformulation. Additionally, we develop resource extension functions that enable an efficient dynamic labeling algorithm, incorporating dominance rules, to solve complex subproblems. We propose a branch-and-price framework that integrates an aggregated branching strategy and a heuristic upper-bound technique, enabling the attainment of exact solutions for instances involving large fleet sizes. Computational experiments conducted on realistic instances, involving up to 20 terminals and over 2000 EVs, substantiate the efficiency of our approach.

Employing the Powered Hybridized Darts Game with BWO Optimization for Effective Job Scheduling and Distributing Load in the Cloud-Based Environment

One of the most frequent issues in cloud computing systems is job scheduling, which is designed to efficiently reduce installation time and cost while concurrently enhancing resource utilisation. Limitations such as accessible implementation costs, high resource utilisation, insufficient make-span, and fast scheduling response lead to the Nondeterministic Polynomial (NP)-hard optimisation problem. As the number of combinations along with processing power increases, job allocation becomes NP-hard. This study employs a hybrid heuristic optimisation technique that incorporates load balancing to achieve optimal job scheduling and boost service provider performance within the cloud architecture. As a result, there are many less problems with the scheduling process. The suggested work scheduling approach successfully resolves the load balancing issue. The suggested Hybridised Darts Game-Based Beluga Whale Optimisation Algorithm (HDG-BWOA) assists in assigning jobs to the machines according to workload. When assigning jobs to virtual machines, factors such as reduced energy usage, minimised mean reaction time, enhanced job assurance ratio, and higher Cloud Data Centre (CDC) resource consumption are taken into account. By ensuring flexibility among virtual computers, this job scheduling strategy keeps them from overloading or underloading. Additionally, by employing this method, more activities are effectively finished before the deadline. The effectiveness of the proposed configuration is guaranteed using traditional heuristic-based job scheduling techniques in compliance with multiple assessment metrics.

Computer-aided cluster formation in wireless sensor networks using machine learning

There is a widespread use of cluster-based routing in wireless sensor networks since it is the most energy-efficient. Idealizing cluster heads, on the other hand, is NP-hard and hence requires heuristic or metaheuristic approaches. However, while outperforming algorithms, metaheuristics computation time restricts its ability to respond to routing requests as rapidly as algorithms can today. A network’s or an application’s parameters can’t be easily accommodated by routing methods. This paper offers the HMML, a combination model combining hybrid metaheuristics and machine-learning. Our HMML model makes use of an automated tuning metaheuristic (e.g. evolutionary algorithm) to fine-tune the heuristic technique for each specific configuration. For a variety of combinations, this is done. A network simulation is run using the modified heuristic algorithm in each configuration to arrive at a solution. As a result, a comprehensive dataset for a variety of conditions is produced (e.g., support vector machine). These characteristics include local (round-state), global (network-state), and application-specific aspects of the input feature vector. After training, the HMML model may be used to quickly cluster data. Machine learning’s capacity to generalize helps us comprehend the metaheuristic algorithm’s behavior in identifying optimal paths for previous configurations. Simulation studies show that HMML can adapt to varied applications while extending network life which increases upto 5% for total energy consumption.

Study of Al2124-SiC nanocomposites by an improved statistical nanoindentation methodology

In this work, we first review the previous work done on statistical nanoindentation by different researchers, highlighting the main problems that have been found and possible proposed solutions. In the second part, we study and report the statistical nanoindentation of three model samples, in the form of a soft Al2124 matrix embedded with hard SiC particles. Three different variants were selected: (1) 25% of SiC particles with 3 μm diameter; (2) 25% of SiC particles with 0.7 μm diameter; and (3) 17% of SiC particles with 0.3 μm diameter. We propose a novel heuristic wavelet technique to filter the measurement noise from the raw nanoindentation data as an attempt to obtain a more robust statistical nanoindentation methodology. Our results have shown that, when the nanoindentation data are filtered, it is not necessary to select a priori the number of peaks (phases) to be analyzed and, in some cases, a wide number of bin-sizes can be used without affecting the results. Finally, a finite element modeling have been used to analyze the response of the nanoindenter regarding the position of the hard particle. Our model shows that it is impossible to get the whole hardness value of the hard SiC particle by the statistical nanoindentation methodology.

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures.

Cyto‐Knet: An instance segmentation approach for multiple myeloma plasma cells using conditional kernels

AbstractMultiple myeloma disrupts normal blood cell production, requiring early detection due to the increased risk of bone metastases. Although various artificial intelligence (AI) methods have been developed to assist pathologists, they often lack comprehensive metrics to measure both detection and segmentation errors. This study presents a hybrid framework that combines deep learning and heuristic techniques to achieve accurate instance segmentation of individual plasma cells in Giemsa‐stained cytology images. Our proposed network, called cyto‐Knet, incorporates an innovative color‐balancing method as a preprocessing step to standardize the appearance of cytological images. Our network leverages a 4‐class segmentation strategy with conditional kernels to enhance segmentation performance and accuracy. Additionally, a marker‐based watershed algorithm is employed in the postprocessing step to address the challenge of merged objects. Extensive validation at both pixel and object‐based levels demonstrates superior performance compared with state‐of‐the‐art techniques. Our method achieves pixel‐based metrics (precision, recall, and F1‐score) of approximately 0.90. The object‐based evaluation reveals an average Dice coefficient of 0.9130 and an aggregated Jaccard index of 0.8237. Importantly, our solution is designed for integration into an end‐to‐end system for diagnosis support and can be easily extended to other applications.

Hard kinetic modeling of the industrial reaction of hydrogenation of soybean oil optimized by heuristic problem-solving techniques

Partial hydrogenation of vegetable oils is an important process for generating fats with particular characteristics. In this study, the hard kinetic modeling of the nickel-catalyzed soybean oil hydrogenation reaction was optimized by three algorithms considering five reaction mechanisms. In three mechanisms, it was considered to have a formation of half-hydrogenated intermediate compounds, and two did not consider this possibility. The Particle Swarm Optimization (PSO) algorithm allowed for achieving lower errors between the experimental and modeled values compared to the Simulated Annealing (SA) and Sequential Simplex (SP) algorithms for the five evaluated reaction mechanisms. In addition, the optimized apparent rate constants values indicated that in these reactional conditions, the industrial reaction mechanism of hydrogenation follows some preferred reaction pathways, such as a preliminary isomerization of cis to trans fatty acids to be then hydrogenated to other fatty acids less unsaturated. These optimization algorithms proved to be efficient tools for investigating the kinetics of the partial hydrogenation reaction of soybean oil and offer promising perspectives for other industrial processes.

Less Form, More Meaning: A Case Study of al-Iẖtibāk in the Qur’an Through the Prism of Dependency Grammar

Ellipsis pervades the Qur’anic discourse. One pervasive form of it is al-iẖtibāk (“interchangeable ellipsis”). In terms of the interface between Qur’anic exegesis and Arabic rhetoric, the phenomenon of al-iẖtibāk is far more than a rhetorically heuristic technique for surface-structure analysis. It is a fruitful tool for pragmasemantic inferences of discursive implicatures defined as a fundamental mode of text construction and a logical hermeneutic of text consumption. The veracity of al-iẖtibāk has received little attention in non-Arabic research. Consequently, this article revisits the typology of al-iẖtibāk through the lens of Osborne’s dependency grammar to argue for its relevance to Qur’anic epistemology and axiology. Using a dependency grammar approach to al-iẖtibāk, the article conducts a qualitative content analysis of representative Qur’anic verses to highlight the symbiotic relationship between Qur’anic exegesis and Arabic rhetoric. Al-iẖtibāk is found to be related to givenness and newness of information structure which impact its interpretation and resolution—a finding in line with Winkler’s argument. Al-iẖtibāk proves to be a central discourse marker of interaction between the Qur’an and its recipients who take part in filling the elliptical slots co(n)textually.