Goal Function Research Articles

Dependence of wear intensity on the coating material of the hydraulic cylinder of the garbage truck’s sealing plate

The article is dedicated to the study of the influence of the coating material on the wear rate of the hydraulic cylinder of the mechanism of the garbage truck’s sealing plate. By means of a first-order planning of experiment with first-order interaction effects using the Box-Wilson method, an adequate dependence of the wear rate of the hydraulic cylinder of the mechanism of the garbage truck’s sealing plate on the coating material was determined. It is established that according to the Student’s criterion, among the studied factors of influence, the intensity of wear of the hydraulic cylinder of the mechanism of the garbage truck’s sealing plate is most affected by the iron content in the coating, the least – by the chromium content, and the nickel content affects only indirectly in interaction with the iron content. The response surfaces of the goal function – the intensity of wear of the hydraulic cylinder of the mechanism of the garbage truck’s sealing plate and their two-dimensional sections in the planes of the influence parameters are shown, which allow to clearly illustrate the specified dependence of this goal function on individual influence factors. The expediency of conducting further research to determine ways to further improve the wear resistance of the hydraulic cylinder of the mechanism of the garbage truck’s sealing plate is shown

A Linear Programming Approach to Ensuring Equitable Distribution of COVID-19 Vaccines in Lower Income Countries

Abstract The equitable dissemination of COVID-19 vaccinations in lower-income nations presents a crucial worldwide dilemma, necessitating smart allocation techniques to guarantee impartial and effective coverage. This paper introduces a mathematical approach that utilizes Linear Programming (LP) to efficiently distribute vaccination doses among lower-income countries. The model incorporates crucial variables such population magnitude, healthcare infrastructure capability, susceptibility to the virus, financial limitations, and worldwide vaccine availability. The linear programming (LP) model is constructed using decision variables that indicate the quantity of vaccination doses assigned to each country. The goal function seeks to optimize the fair allocation of resources by giving weights to countries according to certain criteria. Constraints are imposed to ensure compliance with global vaccine supply, population coverage targets, healthcare capacity, vulnerability concerns, and budget limits. The non-negativity constraints guarantee that the vaccination allocations will always be greater than or equal to zero. The model is designed to be flexible, taking into account dynamic factors such as emerging viral strains and fluctuating vaccination availability. It highlights the significance of timely data updates to facilitate decision-making. The LP solver, such as the simplex method or interior-point approaches, is used to determine the best solution that achieves a fair distribution while considering logistical limitations. The results obtained from the linear programming (LP) model offer practical and effective guidance for policymakers and health organizations. These insights help in distributing COVID-19 vaccinations in a way that maximizes their impact and reduces inequalities among lower-income countries. The approach recognizes the interdependence of global health and emphasizes the need for coordinated endeavors in addressing the pandemic. This mathematical model is a useful tool in the ongoing efforts to distribute COVID-19 vaccinations fairly and efficiently. It contributes to the worldwide aim of obtaining universal immunity and overcoming the problems posed by the epidemic in lower-icnome countries.

Speed gradient control over qubit states

We discuss the model of a quantum bit driven by an external classical field without decay in the rotating wave approximation. In such a model, the whole evolution of the qubit states takes place on the Bloch sphere. We reformulate the model as a unitless set of real ordinary differential equations and use the normalized external field as a feedback control parameter. The closed-loop algorithm is designed in the form of the speed gradient, driving the dynamical system towards minimizing a given nonnegative goal function expressed via the qubit variables. We investigate the achievability of the control goal, and focus on the most important features of the speed gradient algorithm applied to a quantum system in comparison with classical systems. Our approach is valid for the control over the ground and excited population levels, and over the qubit phase variables. The paper was presented at PhysCon2024.

Large-scale gene expression data clustering through incremental ensemble approach

Abstract DNA microarray technology monitors gene activity in real-time in living organisms. It creates a large amount of data that helps scientists learn about how genes work. Clustering this data helps understand gene interactions and uncover important biological processes. However, the traditional clustering techniques have difficulties due to the enormous dimensionality of gene expression data and the intricacy of biological networks. Although ensemble clustering is a viable strategy, such high-dimensional data may not lend itself well to traditional approaches. This study introduces a novel technique for gene expression data clustering called incremental ensemble clustering for gene expression data (IECG). There are two steps in the IECG. A technique for grouping gene expression data into windows is presented in the first step, producing a tree of clusters. This procedure is carried out again for succeeding windows that have distinct feature sets. The base clusterings of two consecutive windows are ensembled using a new goal function to form a new clustering solution. By repeating this step-by-step method for further windows, reliable patterns that are beneficial for medical applications can be extracted. The results from both biological and non-biological data demonstrate that the proposed algorithm outperformed the state-of-the-art algorithms. Additionally, the running time of the proposed algorithm has been examined.

Advancing the Sustainable Development Goals in Tourism Using Quality Control Tools

The UN Sustainable Development Goals are increasingly prevalent in research and practice. Because they help businesses build sustainability, quality control tools offer actionable solutions for addressing the Goals. However, tourism research has yet to systematically investigate the diversity of tools that facilitate business-level contributions to the Goals and Targets. This work addresses this gap through the lens of diffusion of innovations theory, an in-depth instrumental case study, and multiple qualitative methods. Findings contribute to research and practice by revealing 10 tools that enable Goal contributions, including 4 new tools hitherto unacknowledged for this purpose. Outcomes also include a literature-based and empirically enhanced framework of tools to address the Goals, and a fit-for-purpose toolkit that enables contributios to multiple Goals. Theoretically, findings reveal the characteristics of tools that facilitate Goal contributions, including Fordism (post-Fordist, Fordist), function (e.g., measurement), strength (e.g., basic/advanced), administering organization (e.g., internally/externally created), and industry applicability (e.g., sector-specificity).

A method and tool for Sustainability-driven Computer-Aided Process Planning with user-tuneable optimization

This paper introduces the methodology framework, and a cloud-based Sustainability-driven Computer-Aided Process Planning (s-CAPP) tool aimed at optimizing efficiency and sustainability aspects of manufacturing operations at shopfloor level, considering a user-tailored optimization goal function. Geometric data extracted from 3D models of the parts to be produced are coupled with process parameters and resource consumption data from the manufacturing equipment in order to analyze the metrics for productivity and sustainability accordingly. By leveraging comprehensive data resources in manufacturing, s-CAPP allows companies to balance their production plans between productivity and sustainability through a user-friendly interface. Its application in a furniture manufacturing case study demonstrates its potential for process planning by prioritizing between productivity or sustainability, showcasing promising results. The presented s-CAPP implementation assists the production managers in taking decisions regarding sustainability and productivity in the production of batches through the prompting of intuitive visual results, impacting on both economic and environmental sides. Keywords: sustainability-driven manufacturing, process planning, cloud-based computing, furniture manufacturing, process simulation, data-driven manufacturing, feature extraction

Development of Speech Recognition in Wireless Mobile Networks for An Intelligent Learning System in Language Education

Communication has been crucial to human existence, society, and globalization for millennia. Speech Recognition (SR) technologies include biometric evaluation, security, safety, medical care, and smart cities. Most research has primarily focused on English; others neglect other lower-asset dialects like Uzbek, neglecting its research unaddressed. This study examines the efficacy of peer and ASR response in wireless mobile networks-assisted pronouncing training. This study proposes a Deep Neural Network (DNN) and Hidden Markov Method (HMM) based ASR model to develop a voice recognition system utilizing a combination of Connected Time-based Categorization (CTC)-attending networks for the Uzbek words and their variants. The suggested method diminishes training duration and enhances SR precision by efficiently employing the CTC goal function in attentiveness modeling. The research assessed the results of both linguistic experts and native speakers on the Uzbek database, which was compiled for this research. The data were gathered through a pronunciation assessment and a discussion. The participant was further instructed in the classroom. Test outcomes indicate that the suggested method attained a word error rate of 13.1%, utilizing 210 hours of records as a learning dataset for the Uzbek dialect. The proposed technique can significantly enhance students' pronunciation qualities. It might inspire pupils to participate in pronunciation learning.

Token-modification adversarial attacks for natural language processing: A survey

Many adversarial attacks target natural language processing systems, most of which succeed through modifying the individual tokens of a document. Despite the apparent uniqueness of each of these attacks, fundamentally they are simply a distinct configuration of four components: a goal function, allowable transformations, a search method, and constraints. In this survey, we systematically present the different components used throughout the literature, using an attack-independent framework which allows for easy comparison and categorisation of components. Our work aims to serve as a comprehensive guide for newcomers to the field and to spark targeted research into refining the individual attack components.

Gaia as Seen from Within

Through our three-way collaboration we sought to understand Gaia and its political implications from the bottom-up and from within. Here we introduce that view of Gaia and how the dialogue between a philosopher (Bruno), a scientist (Tim), and a historian and philosopher of science (Séb) turned into a research programme. This sets in context a previously unpublished piece by Latour: ‘There is nothing simple in a feedback loop – or why goal function is not the problem of Gaia’.

Innovative resource allocation mechanism for optimizing 5G multi user‐massive multiple input multiple output system

Abstract5G networks are essential in all locations owing to the multitude of advantages they provide. As a result, the number of users has increased dramatically. Nevertheless, these users require a variety of resources in order to function efficiently. Deep learning techniques have been created to improve the precision and dependability of resource allocation in the context of 5G networks. This research utilizes an efficient recurrent neural network (ERNN) to handle resource allocation for 5G multiuser (MU)‐massive multiple input multiple output (MIMO). In order to optimize the objective functions, the first application of the multi‐objective differential evaluation algorithm (MODEA) is used. The neural network is provided with these updated goal functions in order to allocate resources. ERNN evaluates the level of need for each individual user. By partitioning the resource at this level, it maintains a high throughput while distributing it to each user. In addition, the fairness index of the resource distribution system based on neural networks is established. The suggested method achieves a data transfer rate of 290 bits per second (bps) and a fairness index of 0.97% when used by 50 users. The findings of the proposed method exhibit superior performance compared to other existing methods in the field of 5G massive MIMO.

Parameter Estimation of Chikungunya in the Case of Incomplete Information about Its Model

This paper presents solutions to the problem of parameter estimation of mathematical model of Chikungunya with incomplete information about the model parameters. The process involves finding a set of unknown parameters from the given model such that the behavior of the predicted system reflects the original behavior (measurement) using the same scientific assumptions. To achieve this, we solve the differential equations which describe the relation between unknown functions and their derivatives using the fundamental theorem of ordinary differential equation (ODE) (i.e., the theorem of existence) to obtain solutions for the initial value problems. The solutions to the ODE give unknown parameters which functionally depend on the original unknown parameters. And these parameters have two constraints between them. We further find solutions to the constraints minimization problem to obtain estimations of a-parameters. But, the method of constraint minimization problem stipulates the ill-conditioned problem and makes it impossible to accurately identify a-parameters. It is necessary therefore to formulate the goal function using the Least squares method to determine the unknown parameters. This method works reliably well and converges at a large interval of initial guess values of parameters.

An enhanced approach for power quality improvement of unified power quality conditioner connected wind energy conversion system

AbstractThis manuscript proposed a hybrid system for enhancing the control strategy of a unified power quality conditioner connected to a wind energy conversion system. The proposed method is combined with honey badger algorithm (HBA) and reptile search algorithm (RSA). Therefore, it is known as the Enhanced HBA (EnHBA) technique. The key aim of the EnHBA technique is to alleviating the PQ issue exhibited in the WECS. The EnHBA approach is utilized in the non‐linear load condition to find the best solutions from the available searching space in light of the goal function and generates the output. During the load variation conditions the EnHBA technique manages the loss of power, total harmonic distortion (THD), and voltage instability issue respectively. In this way, the PQ system performances improved and moreover, the various qualities decreased with the support of the EnHBA method. The EnHBA techniques' performance is validated through the MATLAB platform and the implementation is calculated with the existing techniques. The method EnHBA technique displays better outcomes in all approaches like, particle swarm optimization, whale optimization algorithm and singular spectrum analysis (SSA). The proposed method's voltage sag and swell are lowered to 14% and 15%, respectively. The THD of the En HBA method is 10%, which is lesser than other existing PSO, WOA, and SSA methods.

Biased choice and incentive salience: Implications for addiction.

Before we can make any choice, we must gather information from the environment about what our options are. This information-gathering process is critically mediated by attention, and our attention is, in turn, shaped by our previous experiences with-and learning about-stimuli and their consequences. In this review, we highlight studies demonstrating a rapid and automatic influence of reward learning on attentional capture and argue that these findings provide a human analog of sign-tracking behavior observed in nonhuman animals-wherein signals of reward gain incentive salience and become attractive targets for attention (and overt behavior) in their own right. We then consider the implications of this idea for understanding the drivers of cue-controlled behavior, with focus on addiction as a case in which choices with regard to reward-related stimuli can become injurious to health. We argue that motivated behavior in general-and addiction in particular-can be understood within a "biased competition" framework: Different options and outcomes compete for attentional priority as a function of top-down goals, bottom-up salience, and prior experience, and the winner of this competition becomes the target for subsequent outcome-directed and flexible behavior. Finally, we outline the implications of the biased-competition framework for cognitive, behavioral, and socioeconomic interventions for addiction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Simulation-based optimization concept for integrated assessment models: Case study MEDEAS-world

Simulation based optimization concept has been so far presented at local and national energy systems planning levels. This method represents a systematic way of dealing with uncertainties and combinatorial complexity when using modelling tools of very large size with significant number of possible policy inputs, and could be useful to analyse policy recommendations in broader models integrating more dimensions than energy. For the first time, the concept is successfully demonstrated using a working integrated assessment model (IAM), although some attempts have been mentioned in literature previously. IAMs aim to link main features of society, economy and energy with the biosphere and atmosphere into one modelling framework. The concept has been demonstrated for the IAM MEDEAS-World using system dynamics platform Vensim (version 9.3.4.) for both simulation and optimization. For an optimization problem formulation, 12 decision variables are selected from numerous input model parameters, the gross domestic product per capita has been selected for a goal function from simulation result as quantifiable policy objective, while for the constraint CO2 emissions is selected as subject to binding targets. It is possible to compile the simulation code so that optimization running time is around 10 times shorter. Preliminary results show that it is possible to implement richer optimization problems without significant increases in the running time using personal computers. The main contribution of this work is to prove the concept of simulation-based optimization by a demonstration within a working IAM. This opens the way to promising future work to inform about optimal combinations of available policies within different storylines, as well as providing additional general understanding of the IAM which could allow to improve the model. The original Vensim code has been provided in the Appendix.

Top-Down Task Goals Induce the Retrieval State.

Engaging the retrieval state (Tulving, 1983) impacts processing and behavior (Long and Kuhl, 2019, 2021; Smith et al., 2022), but the extent to which top-down factors-explicit instructions and goals-versus bottom-up factors-stimulus properties such as repetition and similarity-jointly or independently induce the retrieval state is unclear. Identifying the impact of bottom-up and top-down factors on retrieval state engagement is critical for understanding how control of task-relevant versus task-irrelevant brain states influence cognition. We conducted between-subjects recognition memory tasks on male and female human participants in which we varied test phase goals. We recorded scalp electroencephalography and used an independently validated mnemonic state classifier (Long, 2023) to measure retrieval state engagement as a function of top-down task goals (recognize old vs detect new items) and bottom-up stimulus repetition (hits vs correct rejections (CRs)). We find that whereas the retrieval state is engaged for hits regardless of top-down goals, the retrieval state is only engaged during CRs when the top-down goal is to recognize old items. Furthermore, retrieval state engagement is greater for low compared to high confidence hits when the task goal is to recognize old items. Together, these results suggest that top-down demands to recognize old items induce the retrieval state independent from bottom-up factors, potentially reflecting the recruitment of internal attention to enable access of a stored representation.

Investigation of reciprocal rank method for automatic generation control in two-area interconnected power system

In this article, the design and performance analysis of Jaya-assisted reciprocal rank-based proportional–integral–derivative controller with derivative filter (PIDn) is proposed for the automatic generation control (AGC) problem of two-area interconnected power systems. The filter with a derivative gain is used to nullify the impact of noise in the input signal. The integral of time multiplied square error (ITSE) of frequency deviations, tie-line power deviations, and area-control errors is the foundation of the goal function developed for tuning controller parameters (ACEs). A single overall objective function is created by combining these sub-objectives. ITSEs of two areas, ITSEs of tie-line power deviation, and ITSEs of ACEs of two areas are weighted and summarized to form the overall goal function. Each sub-relative goal’s importance in the control design is evaluated using the weights in the overall objective function. The weights in this article are determined using the reciprocal rank approach in a methodical manner, in contrast to earlier techniques where weights were either assumed equally by ignoring the relative relevance of sub-objectives or chosen at random. The resultant total objective function is minimized using the Jaya method. Six different test cases involving various load disturbances in the interconnected areas are used to evaluate the performance of the suggested Jaya optimizer-assisted reciprocal rank technique-based controller. Additionally, the performance of various controllers tuned with the sine cosine algorithm (SCA), salp swarm algorithm (SSA), symbiotic organisms search (SOS), Nelder–Mead simplex (NMS), and Luus–Jaakkola (LJ) algorithms is compared with that of the Jaya-tuned controller. The time domain specifications are provided for each of the six test scenarios. To display the differences in frequencies and tie-line power, the results are also shown. Statistical analysis is also provided to assess the suggested controller’s overall efficacy.

Analytical and numerical optimization of broaching tool tooth distribution

This paper presents a detailed mathematical analysis of the effect of tooth distribution on the stability of broaching operations. Analytic and numeric techniques are employed to analyse a simplified one-degree-of-freedom mechanical model of variable pitch broaching, to assess its stability, and to find the optimal tooth distances maximizing robustness against harmful self-excited chatter vibrations. A novel modelling approach, considering a theoretical infinitely long broaching tool, draws parallels with variable pitch milling, and analytic formulas for tuning milling cutters are extended to broaching tools. For a further increase of robustness, and to take feasibility constraints into account, a goal function based on the semi-discretization and spectral collocation techniques is implemented in a direct numeric optimisation framework. A new, detailed derivation of the corresponding parameter gradients is presented to enable the use of gradient descent techniques. Since broaching is inherently a time-limited process, optimal parameters found in this manner are then validated via time domain simulations to show the desirable transient behaviours achievable by this ideal tuning of the tool geometry.

Efficacy of eSyM: Acute care utilization among patients with cancer who do versus do not report ePROs.

11001 Background: Patients (pts) receiving cancer treatment frequently experience burdensome symptoms that compromise outcomes and necessitate acute care. Prior clinical trials have demonstrated that electronic patient-reported outcome (ePRO)-based symptom management programs improve outcomes in controlled settings. Deploying these programs in routine care settings remains challenging. With funding from the Cancer Moonshot IMPACT Consortium, we created eSyM – an ePRO-based, EHR-integrated symptom management program – to facilitate the widespread adoption of active symptom management efforts. Methods: eSyM was deployed across six health systems from September 2019-August 2022 via a modified stepped-wedge cluster randomized pragmatic trial. Pts starting chemotherapy (CHEM) or undergoing surgery (SURG) for a suspected or confirmed thoracic, gastrointestinal, or gynecologic cancer were prompted to complete symptom questionnaires regularly; those reporting symptoms were offered additional supports. To assess eSyM efficacy, we studied pts who were eligible to use the program – comparing those who completed at least one symptom questionnaire to those who did not. Outcomes included emergency department (ED) visits and inpatient encounters (INPT) at 30 and 90-days. Odds ratios with 95% CIs were derived after adjusting to account for the propensity to report ePROs as a function of age, sex, race/ethnicity, employment, marital status, poverty, rurality, insurance, comorbidity, cancer, treatment goal, institution, and calendar time. Results: Among eSyM-eligible pts, 51% (N = 10,454/20,471) completed at least one symptom questionnaire (median 4 reports/patient) – 47% (3815/8187) for CHEM and 54% (6639/12,293) for SURG. Comparing symptom reporters to non-reporters, the proportion of CHEM+SURG pts experiencing an ED event was 5.3% vs. 7.1% at 30 days and 10.0% vs. 12.9% at 90 days; and the proportion experiencing an INPT event was 6.7% vs. 11.3% at 30 days and 14.0% vs. 19.5% at 90 days ( p< 0.001 for all). Adjusted ORs appear in the. Conclusions: After accounting for propensity to report symptoms, completing at least one symptom questionnaire was associated with lower odds of experiencing an ED or INPT encounter among CHEM and SURG pts across six diverse health systems. eSyM engagement reduced acute care utilization. This EHR-integrated symptom management solution is broadly available to health systems that use Epic. Clinical trial information: NCT03850912 . [Table: see text]

LEADERSHIP DAN MANAGERIALSHIP

This research aims to find out about leadership, managerial ship, and the similarities and differences between leadership and managerial ship. Even thought the two terms are closely related and are often used interchangeably, they have differences. A leader has the task of giving direction and giving subordinates the task of moving towards the same goal and manager function in the field of management implementation. The method used in this research is library research, data was obtained from various sources such as books, articles,s and journals about leadership and management. The results of the research are: 1) leadership is the art and ability that a person has in using his power to influence other people to carry out certain activities to achieve predetermined goals, 2) managerial ship is managerial ability in preparing implementation plans as well as placing employees in accordance respective position, 3) the similarity between leadership and managerial ship is that both have responsibility and subordinates, 4) the differences is that in leadership, a leader is tasked with directing and moving subordinates, whereas in managerial ship, a manager has duties according to management functions.

Optimal coordination of directional overcurrent protection relays using hybrid optimizer

In order to minimize or maximize an objective function, electrical engineering problems might benefit from optimization methodologies inspired by nature. This study employs a hybrid particle swarm optimizer (HPSO) to get rid of local optimum solutions and find the best global one for directional overcurrent protection relays (DOPR) protection. DOPR coordination is a highly constrained, mixed-integral nonlinear optimization problem. An adaptive defense method has been created to counteract the intentionally challenging design. Therefore, design variables such as time multiplier setting (TMS) and plug setting (PS) are utilized for each relay in the circuit. The goal function is to reduce the amount of time spent to trace the fault by all the relays. Reducing the time required for each relay to trace the fault is the aim of function. After analyzing and comparing two test studies of the IEEE benchmark using various optimization strategies, we found that the results for the IEEE 15-bus system has minimum time of 8.9 s, which is improved by 22.92% to 82.89%, and those for the IEEE 30-bus system minimum time of 22.45 s that is improved by 4.41% to 39.6%. In terms of overall DOPR operation and computational time needed to get the global optimum solution, the acquired results are demonstrably superior to those obtained using conventional and state-of-the-art methods.