Cost Constraints Research Articles

Sustainable Integration of Green Hydrogen in Renewable Energy Systems for Residential and EV Applications

The surge in interest surrounding renewable energy stems from concerns regarding pollution and the finite supply of nonrenewable resources. Solar PV and wind hybrid renewable energy systems (HRES) are increasingly recognized as practical and cost-effective solutions, particularly in remote areas. However, the intermittent nature of solar and wind power presents a challenge. To address this, incorporating a hydrogen source into the system has been proposed. This study focuses on modelling and sizing a hybrid energy system tailored for remote areas, accommodating both home and electric vehicle loads. The simulation is conducted for Siliguri, West Bengal, India, with the goal of optimizing productivity, minimizing expenses, and considering economic factors using HOMER Pro software. The integration of green hydrogen-based power generation with photovoltaic and wind HRES emerges as an effective solution. Solar power, in particular, showcases promising opportunities for the electrolysis process and HRES systems. The presented work facilitates the modelling of a green hydrogen-based green energy system, taking into account capacity, cost, and emission constraints. Various case studies are conducted to enhance system efficiency and reduce the costs of energy (COE). In this paper, three cases of grid-connected and three cases of off-grid or grid-disconnected systems are considered for highlighting the benefits of hydrogen energy incorporation in both types of systems. This research contributes to sustainable energy solutions, advancing a greener and more efficient energy landscape, especially in addressing the recent development in load combinations of home and electric vehicle loads in both grid-connected as well as grid-disconnected system.

The material selection method for functional packaging of electromechanical products based on low carbon and cost constraint

Abstract Packaging is an important but easily overlooked part of the study of low-carbon lifecycle of electromechanical products, but with the significant growth of the express delivery business, the carbon emissions caused by packaging are increasingly noteworthy. In order to explore the carbon emissions of packaging lifecycle, the electromechanical products are used as the packaging object to discuss the connotation of functional packaging, low-carbon design boundary and research methods of packaging lifecycle and establish the calculation models of carbon footprint and cost of packaging lifecycle and the evaluation method of material selection. The case study of gasoline engines shows that material selection plays a decisive role in the packaging of electromechanical products based on low carbon and cost constraints.

A Snowmelt Optimization Algorithm Applied to Green Low Carbon Logistics Pathways Optimization Problems

INTRODUCTION: Efficient and accurate optimization of green and low-carbon logistics paths, as one of the key technologies of green and low-carbon logistics, can not only promote the high-quality development of the economy, but also reduce the negative impacts of logistics on the environment and increase the cost of logistics delivery.
 OBJECTIVES: To address the problems of slow convergence and easy to fall into local optimization in the current performance prediction research on talent team building.
 METHODS: This paper proposes a snowmelt heuristic optimization algorithm to solve the green low-carbon logistics path optimization problem. Firstly, the objective function of green low-carbon logistics path optimization is designed by analyzing the optimization cost and conditional constraints of the green low-carbon logistics path optimization problem; then, a method based on intelligent optimization algorithm is proposed by designing the position-order array coding and fitness function, combined with the snow-melting optimization algorithm; finally, the validity and superiority of the proposed method are verified by simulation experiments.
 RESULTS: The results show that the proposed method not only improves the convergence speed but also increases the optimization fitness value.
 Conclusion: The problem of slow convergence and easy to fall into local optimum in the solution of green low-carbon logistics path optimization problem is solved.

Simulation of four-point bending tests for the viscoelastic fracture properties of concrete

Understanding the fracture properties of concrete, such as crack propagation behavior and fracture energy, is crucial for designing and evaluating concrete structures. Experimental results are insufficient and cannot be directly employed for a comprehensive analysis of the fracture behavior of concrete structures under load, particularly when considering concrete as viscoelastic with the presence of cracks. Recognizing the time and cost constraints of traditional experimental testing, this research leverages numerical simulations as a cost-effective alternative to determine viscoelastic material parameters. Thus, the critical evaluation of concrete fracture properties, fundamental for the design and assessment of concrete structures, is addressed. Employing a finite element method for four-point bending tests, the study systematically investigates parameters such as initial crack depth, displacement acceleration, and time step. The material properties of concrete are described using viscoelastic models. The findings provide valuable insights into crack propagation behavior and deformation characteristics, emphasizing the significant influence of the modulus of elasticity on both maximum load values and displacement. These findings contribute to a deeper understanding of the structure's response and underscore the importance of considering these parameters in similar simulations. The study highlights the importance of considering these parameters in simulations to enhance the understanding of concrete fracture behavior. The paper's contributions can extend to optimizing concrete mixtures, formulating repair strategies, and improving structural assessments. Further research is suggested to improve the accuracy of simulations and investigate material properties under various conditions.

Would OTT-contents satisfy people’s demand? The Korean OTT-services demand analysis

ABSTRACT With the influence of the COVID-19 pandemic, Over-the-Top (OTT) services have gained considerable prominence in the traditional media market, generating increased interest in this domain. However, limited research has been conducted thus far on analysing consumer behaviour within the context of OTT service company business models. Then, this study aims to analyse the Korean over-the-top (OTT) services demand market with the G-K model, considering cost and time constraints. This study differentiate OTT services into two categories: advertisement video on demand (AVOD) and subscription video on demand (SVOD). Subsequently, an analysis of consumer demand is conducted based on each model. The results of the analysis reveal distinct behavioural patterns among users who engage with SVOD and AVOD services. Additionally, it was observed that SVOD consumers exhibit a greater inclination towards substituting their consumption patterns when opportunities for other leisure activities increase, as compared to AVOD consumers.

A model for aircraft cabin evacuation considering passenger type

Pedestrian safety evacuation in aircraft cabins has been a challenging problem because of the aircraft's unique characteristics, such as the diversity of passengers and the restricted evacuation environment. It is difficult to reproduce evacuation activities in aircraft cabin due to safety concerns and cost constraints. To fill this gap, an improved cellular automaton model of crowd evacuation for aircraft cabin is established by incorporating the characteristics of cabin space structures and passenger attributes. Passengers are divided into healthy individual passengers and disabled-healthy group passengers, whose movement mechanisms are quantified. Based on the constructed model, simulation experiments are conducted using the configuration cabin layout of B737-800 as an example. The results show that the evacuation time is prolonged with increased passenger density and the number of disabled passengers. Moreover, the overall evacuation time is insignificantly affected by whether disabled-healthy group passengers' seats are close to the aisle or window, and the evacuation efficiency is best when their seats are evenly distributed in the cabin. The evacuation time is the shortest when all cabin doors are open, and pedestrians are evacuated the slowest when the central emergency doors are closed. This study provides valuable insights into effective strategies for pedestrian evacuation and crowd emergency management of civil aircraft.

Single-sided magnetic resonance-based sensor for point-of-care evaluation of muscle

Magnetic resonance imaging is a widespread clinical tool for the detection of soft tissue morphology and pathology. However, the clinical deployment of magnetic resonance imaging scanners is ultimately limited by size, cost, and space constraints. Here, we discuss the design and performance of a low-field single-sided magnetic resonance sensor intended for point-of-care evaluation of skeletal muscle in vivo. The 11 kg sensor has a penetration depth of >8 mm, which allows for an accurate analysis of muscle tissue and can avoid signal from more proximal layers, including subcutaneous adipose tissue. Low operational power and shielding requirements are achieved through the design of a permanent magnet array and surface transceiver coil. The sensor can acquire high signal-to-noise measurements in minutes, making it practical as a point-of-care tool for many quantitative diagnostic measurements, including T2 relaxometry. In this work, we present the in vitro and human in vivo performance of the device for muscle tissue evaluation.

A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer.

The exploration of drug combinations presents an opportunity to amplify therapeutic effectiveness while alleviating undesirable side effects. Nevertheless, the extensive array of potential combinations poses challenges in terms of cost and time constraints for experimental screening. Thus, it is crucial to narrow down the search space. Deep learning approaches have gained widespread popularity in predicting synergistic drug combinations tailored for specific cell lines in vitro settings. In the present study, we introduce a novel method termed GTextSyn, which utilizes the integration of gene expression data and chemical structure information for the prediction of synergistic effects in drug combinations. GTextSyn employs a sentence classification model within the domain of Natural Language Processing (NLP), wherein drugs and cell lines are regarded as entities possessing biochemical relevance. Meanwhile, combinations of drug pairs and cell lines are construed as sentences with biochemical relational significance. To assess the efficacy of GTextSyn, we conduct a comparative analysis with alternative deep learning approaches using a standard benchmark dataset. The results from a five-fold cross-validation demonstrate a 49.5% reduction in Mean Square Error (MSE) achieved by GTextSyn, surpassing the performance of the next best method in the regression task. Furthermore, we conduct a comprehensive literature survey on the predicted novel drug combinations and find substantial support from prior experimental studies for many of the combinations identified by GTextSyn.

Timber supply through time – Copenhagen waterfronts under scrutiny

Dendrochronology is not a new method for attaining high-precision dates for archaeological and historic remains of timber. But the extent to which dendrochronology is utilized to attain detailed precision of the dating of complex wooden structures can suffer from the fact that the method is often applied in commercial archaeology, where the extent of analysis is severely limited by cost constraints. Instead of lamenting the potentially lost levels of detail that might have slipped through over the years, it is hoped that by presenting the potential of high chronological precision, that necessitates extensive sampling of timber and wood remains on archaeological sites, a new future will be promoted, in which new wide-ranging sampling strategies will become a more normal practice in archaeology, in both the research and commercial spheres. In this paper, I present some case studies where extensive tree-ring analysis of well-preserved wood remains have resulted in annual chronological detail, allowing an insight into the processes of building, and into the duration of structures that comprised the built environment of past peoples’ lives. In addition, we should not discuss precision dating for urban archaeological study without also touching on the subject of timber trade and timber provenance. Tree-ring studies are increasingly providing us with high precision provenance identification, not just for shipwrecks, barrels and other ‘portable’ objects. It is also allowing us to map trade in bulk structural timber. These analyses are providing us with insights into links between territories.

A study on siting of emergency shelters for dam failure floods considering population distribution and weather effects

In recent years, dam failures have occurred frequently because of extreme weather, posing a significant threat to downstream residents. The establishment of emergency shelters is crucial for reducing casualties. The selection of suitable shelters depends on key information such as the number and distribution of affected people, and the effective capacity and accessibility of the shelters. However, previous studies on siting shelters did not fully consider population distribution differences at a finer scale. This limitation hinders the accuracy of estimating the number of affected people. In addition, most studies ignored the impact of extreme rainfall on the effective capacity and accessibility of shelters, leading to a low applicability of the shelter selection results. Therefore, in this study, land-use and land-cover change (LUCC) and nighttime lighting data were used to simulate population distribution and determine the number and distribution of affected people. Qualified candidate shelters were obtained based on screening criteria, and their effective capacity and accessibility information under different weather conditions were quantified. Considering factors such as population transfer efficiency, construction cost and shelter capacity constraints, a multi-objective siting model was established and solved using the non-dominated sorting genetic algorithm II (NSGA- II) to obtain the final siting scheme. The method was applied to the Dafangying Reservoir, and the results showed the following: (1) The overall mean relative error (MRE) of the population in the 35 downstream streets was 11.16 %, with good fitting accuracy. The simulation results truly reflect the population distribution. (2) Normal weather screening generated 352 qualified candidate shelters, whereas extreme rainfall weather screening generated 266 candidate shelters. (3) Based on the population distribution and weather factors, four scenarios were set up, with 63, 106, 73, and 131 shelters selected. These two factors have a significant impact on the selection of shelters and the allocation of evacuees, and should be considered in the event of a dam-failure floods.

Pengembangan Bahan Ajar Digital Berbasis PBL untuk Meningkatkan Hasil Belajar Ekonomi Siswa

The objective of this research is to create digital teaching materials utilizing Problem-Based Learning (PBL) methodology. The goal is to help kids in class XI IPS 1 at MAN 2 Langkat learn more about economics. Based on what Borg and Gall said about research and development (R&D), this study is being done. The research and development implementation in this study utilized a total of seven procedures or stages. This simplification was necessitated by various factors, such as time and cost constraints. Additionally, Borg and Gall's perspective on limiting research and development to a smaller scale, including the number of research steps in a thesis or dissertation, also influenced this decision. The present study was conducted at MAN 2 Langkat, involving a sample of 34 students from class XI IPS 1. Based on the feasibility validation test conducted by experts on PBL-based digital teaching materials, the average score obtained was 85.73, indicating a high level of feasibility. The average posttest score for the experimental class which was treated with PBL-based digital teaching materials that was developed was 89,82, while the posttest score for the control class which was given teaching materials from the school averaged 86,29. These results indicate that PBL-based digital teaching materials are effective in use. Furthermore, the results of the 2-way ANOVA test stated that the 2-tailed Sig. was 0.229 < 0.05. Therefore, it can be concluded that there is no significant interaction in student economic learning outcomes between students with high and low literacy skills who are taught using PBL-based digital teaching materials and those who are not.

Horizontal well placement optimization based on matrix directional continuous element summation algorithm

During the course of actual oilfield development, judicious selection and design of well placement are paramount due to cost constraints and operating conditions. This paper introduces the Matrix Directional Continuous Elements Summation Algorithm (MDCESA), which is utilized to identify that segment with the largest summation for a given length in a 2D or a 3D matrix. An additional function that accounts for the distance between segments was added when searching for multiple segments to avoid intersections or overlaps between segments. The well placement optimization was transformed into a segment summation on a 3D matrix. Our findings reveal significant advancements in well placement optimization. Employing the MDCESA method, six producers were identified and their production performance was compared against two previously selected producers using a reservoir numerical simulator. The results demonstrated that the wells selected through MDCESA exhibited a substantial improvement in production efficiency. Specifically, there was an 11.6% increase in average cumulative oil production over a 15-year period compared to the wells selected by traditional methods. This research not only presents a significant leap in well placement optimization but also sets a foundation for further innovations in reservoir management and development strategies in offshore oilfields.

"Enhancing Customer-Centric Design In Three-Phase UPS Systems: Insights From Bengaluru's Industry Professionals And Innovation Trends"

This study investigates the impact of customer-centric design on three-phase UPS systems, focusing on industry professionals in Bengaluru. As demand for reliable and adaptable UPS systems increases, understanding how customer-focused design influences satisfaction is crucial. A mixed-method approach was used, starting with qualitative interviews of 30 industry experts to explore key themes, followed by a quantitative survey of 300 participants to assess customer satisfaction and design factors. The tools developed for this study included a semi-structured interview guide for qualitative data and a structured survey questionnaire for quantitative data collection, both validated through pilot testing. Key design factors such as reliability, scalability, ease of use, modularity, and energy efficiency were analyzed. Descriptive statistics revealed high mean scores for these factors, while correlation and factor analyses showed strong positive relationships with customer satisfaction, particularly for ease of use (r = 0.812) and scalability (r = 0.770). Thematic analysis emphasized the importance of AI-driven predictive maintenance and customization in improving system performance. The results indicate that larger companies more effectively implement customer-centric practices, while smaller firms face challenges such as cost constraints and technical limitations. The study recommends enhancing modularity, energy efficiency, and AI-driven maintenance to improve customer satisfaction, with a focus on helping smaller companies overcome these barriers. In conclusion, customer-centric design significantly enhances customer satisfaction in the UPS industry, and adopting these strategies is essential for long-term success and competitiveness.

"Enhancing Customer-Centric Design In Three-Phase UPS Systems: Insights From Bengaluru's Industry Professionals And Innovation Trends"

This study investigates the impact of customer-centric design on three-phase UPS systems, focusing on industry professionals in Bengaluru. As demand for reliable and adaptable UPS systems increases, understanding how customer-focused design influences satisfaction is crucial. A mixed-method approach was used, starting with qualitative interviews of 30 industry experts to explore key themes, followed by a quantitative survey of 300 participants to assess customer satisfaction and design factors. The tools developed for this study included a semi-structured interview guide for qualitative data and a structured survey questionnaire for quantitative data collection, both validated through pilot testing. Key design factors such as reliability, scalability, ease of use, modularity, and energy efficiency were analyzed. Descriptive statistics revealed high mean scores for these factors, while correlation and factor analyses showed strong positive relationships with customer satisfaction, particularly for ease of use (r = 0.812) and scalability (r = 0.770). Thematic analysis emphasized the importance of AI-driven predictive maintenance and customization in improving system performance. The results indicate that larger companies more effectively implement customer-centric practices, while smaller firms face challenges such as cost constraints and technical limitations. The study recommends enhancing modularity, energy efficiency, and AI-driven maintenance to improve customer satisfaction, with a focus on helping smaller companies overcome these barriers. In conclusion, customer-centric design significantly enhances customer satisfaction in the UPS industry, and adopting these strategies is essential for long-term success and competitiveness.

Bi-Objective Incentive Mechanism for Mobile Crowdsensing With Budget/Cost Constraint

In recent years, mobile crowdsensing (MCS) has been widely adopted as an efficient method for large-scale data collection. In MCS systems, insufficient participation and unstable data quality have become two crucial issues that prevent crowdsensing from further development. Thus designing a valid incentive mechanism is essentially significant. Most of the existing works on incentive mechanism design focus on single-objective optimization with various constraints. However, in the real-world crowdsensing, it is common that several objectives to be optimized exist. Furthermore, constraints on budget or cost are often seen in MCS systems as the feasibility of implementing incentive mechanism is indispensable. This paper studies a bi-objective optimization scenario of MCS to simultaneously optimize total value function and coverage function with budget/cost constraint through a set of problem transformations. Then a budget- or cost-feasible bi-objective incentive mechanism is further proposed to solve the aforementioned bi-objective optimization problem through the combination of binary search and greedy heuristic solution under budget or cost constraint, respectively. Through both rigorous theoretical analysis and extensive simulations, the obtained results demonstrate that the mechanisms achieve computation efficiency, individual rationality, truthfulness, and budget or cost feasibility, while one mechanism obtains an approximation.

Robustness paradox of cascading dynamics in interdependent networks

Cascading failure process in interdependent networks has always been an important field of network cascading analysis. Unlike the previous studies, we take people's demand for minimizing travel costs into consideration in this article and propose a network dynamics model based on the cost constraint. On this basis, we pay attention to the characteristics of different layers in the interdependent network, and taking the real-world traffic network for example, we define different load propagation modes for different layers. Then, we carry out the simulation experiment on cascade failure in the artificial network. By changing the structure of the network and the parameters in the model, such as the capability value of the network side and the connectivity of the network, we are able to focus on the effects of traditional protection strategies during the simulation and obtain some interesting conclusions. It is generally believed that increasing the quantity of connections in the network or improving the quality of edges will enhance the network robustness effectively. However, our experimental results show that these methods may actually reduce network robustness in some cases. On the one hand, we find that the resurrection of some special edges in the network is the main reason for the capacity paradox, as these edges will destroy the stable structure of the original network. On the other hand, neither improving the internal connectivity of a single-layer network nor enhancing the coupling strength between interdependent networks will effectively improve network robustness. This is because as the number of edges increases, some critical edges may appear in the network, attracting a large amount of the network load and leading the network robustness to decrease. These conclusions remind us that blindly investing resources in network construction cannot achieve the best protection effect. Only by scientifically designing the network structures and allocating network resources reasonably can the network robustness be effectively improved.

METHODOLOGICAL FOUNDATIONS OF OPTIMAL SWARM MANAGEMENT OF UAVS IN ENERGY CROP SPRAYING WITH AGROCHEMICAL AGENTS

The work is devoted to research on the optimal management of a UAV swarm when spraying energy crops with toxic chemicals. The relevance of the work lies in the fact that, at present, the solution of chemical plant protection tasks using UAVs is based on the individual experience of a human operator, who manages a swarm of UAVs, as a rule, in a non-optimal way with significant time and cost costs. The purpose of the article is to develop methodological principles for optimal management of a UAV swarm when spraying energy crops with agrochemicals. In order to achieve the goal, the following tasks must be solved: the formulation of the task of optimizing the management of a UAV swarm is substantiated, taking into account the time and cost constraints for the performance of chemical plant protection tasks, a method of optimal management of a UAV swarm when spraying energy crops with toxic chemicals and foliar feeding is developed, which provides a solution tasks to maximize the number of sprayed plants in each field section under time and cost constraints, a structural diagram of the solution to the task of optimizing the UAV swarm management was developed for the high-quality practice of chemical plant protection tasks. The scientific novelty of the work consists in the application of the dynamic programming method in the distribution and management of a swarm of UAVs on each site when spraying energy crops with agrochemicals. The practical value of the work lies in the development of a structural scheme for solving the problem of optimizing the control of a UAV swarm, with the help of which the optimal number of UAVs in a given time sequence is calculated in special software. automated operator's workplace for high-quality performance of chemical protection and plant nutrition tasks.

Optimal test plans for accelerated life tests based on progressive type-I censoring with engineering applications

In reliability engineering studies, the accelerated life tests with optimal test design that balances cost constraints is particularly important since the lifetimes of electro-mechanical, electronic, and mechanical goods are typically modelled with this characteristic. Comparative optimal test plans for accelerated life tests are developed in a variety of applications, including engineering, biological, medical, and other related fields. In this study, we compare, under cost restriction with a specified budget, optimum multiple constant stress and step stress accelerated life tests based on progressive-type-I censoring. The goal is to assess the advantage provided by step-stress testing over constant-stress testing. In order to conduct an accelerated life test effectively, stress durations need to be carefully specified during the design stage due to limited resources in engineering practice. The estimated accuracy of the parameters of interest and the expense of the experiment are directly impacted by the stress durations. Under a prespecified budget and cost constraints, this article examines the optimal stress durations based on D-, E-, T-, C-, R-, and P-optimal criteria. These criteria are also contrasted together. We illustrate the application of the optimal durations using numerical and simulation examples. A study is conducted to determine how sensitive the optimal duration is to model parameter misspecification.

Advancing pneumonia virus drug discovery with virtual screening: A cutting-edge fast and resource efficient machine learning framework for predictive analysis

Pneumonia, a severe respiratory infection characterized by a significant morbidity and fatality rate, afflicts many individuals globally. The demand for highly effective antiviral medications has experienced a surge because of the emergence of novel pneumonia viruses, such as the COVID-19 coronavirus. Due to their inherent time and cost constraints, conventional drug development strategies sometimes need to be more manageable. Exploring alternative approaches is crucial to identifying and establishing effective therapy choices. This work introduces a computational methodology for analyzing the chemical space of medications targeting the pneumonia virus, employing Python-based data mining tools. Using computer-aided analysis in drug molecules aims to enhance the efficiency of identifying and evaluating potential new therapeutic candidates using Machine Learning (ML). The research successfully discovered two therapeutic compounds by utilizing the Bayesian Ridge approach, which is the most accurate with the least mean squared error, is less computationally expensive in terms of power, memory, and CPU, and is the fastest of the investigated approaches. It discovered the CHEMBL433378 and CHEMBL93653, with promising docking scores of −4.3 and −4.2, respectively. Additionally, both molecules demonstrated significant inhibitory activity against their respective targets, as seen by their IC50 values of 0.0018 and 0.001. Both compounds meet the criteria for the B. Mann Whitney U Test and Lipinski test.

РОЗВ’ЯЗАННЯ МУЛЬТИМОДАЛЬНОЇ ТРАНСПОРТНОЇ ЗАДАЧІ З УРАХУВАННЯМ ОБМЕЖЕНЬ ЛОГІСТИЧНИХ ПОКАЗНИКІВ

The implementation of military transportation by all types of transport involves planning, management, deployment of transport communications, distribution and preparation of types of transport, organization of the transportation of troops and military property, evacuation of the wounded and sick, damaged weapons and military equipment, and trophy property. As a result of these actions, the issue of timely delivery of material and technical means with the lowest costs should be resolved. The solution to the multimodal (three-index biplanar) transportation problem is presented to find the rational distribution and delivery of material and technical means. The proposed solution considers the limitations of logistics indicators, such as delivery time, the available carrying capacity of different types of vehicles, the total cost of transportation in the operational movement of storage points and consumers, changes in supply routes, and delivery vehicle types. Introducing an additional constraint into the three-index biplanar transport problem turns it into a linear programming problem of general form. The three-index transportation problem with extra logistical constraints is given a mathematical formulation. A form of input data presentation in the extended transport tables of transportation costs and time spent on cargo transportation is given. An easy way to solve this kind of problem using the simplex method is demonstrated in the Microsoft Excel spreadsheet processor "Solver" add-on. The article presents the results of solving a multimodal transportation problem with logistical constraints in Microsoft Excel using the example of the delivery of material and technical means by three types of transport (road, rail, and river), with time and cost constraints. The example uses conditional input data for the simulation of the delivery of cargo from three Ukrainian cities (storage points) to the other three (consumption points).