Specific Constraints Research Articles

Effect of Brand Image and Brand Trust on Purchase Intention of Fast-Moving Consumer Goods

An extensive analysis is performed in this research regarding how consumer relationships with brands influence brand trust and brand image, which represents a strong form of brand support rooted in consumer connections. This study examines how two consumer-brand relationship elements, specifically brand trust and brand image, influence buying intentions. Based on a review of the literature pertaining to consumer-brand dynamics, a theoretical framework is formulated that addresses the constructs of brand trust and brand image in their association with purchase intentions. In the current investigation, a quantitative approach is employed to rigorously assess the impact of brand trust and brand image on consumer purchasing intentions concerning fast-moving consumer goods. The data collected from a cohort of 167 individuals residing in the city of Madurai served as a basis for the empirical validation of the research hypothesis, which was established through the administration of a meticulously designed questionnaire. It was discovered that there exists a direct and significant correlation between brand trust, brand image, and purchase intention. This finding indicates that the connection between brand trust and brand image positively influences purchase intentions. Furthermore, it was uncovered that brand trust exerts a stronger effect on purchase intention than brand image alone. In summary, the findings highlight the substantial impact of consumer-brand relationships on purchasing intentions. The research recognizes specific constraints and provides suggestions for subsequent investigations within this field. As consumers increasingly exhibit intense loyalty and disdain towards brands, marketers must comprehend the importance of brands in the contemporary consumption landscape. This research indicates that marketers can enhance consumer purchase intentions and transform potential customers into actual buyers. Keywords: Brand trust, Consumer-brand relationships, Purchase intention , Brand image, Brand equity.

Mechanical problem solving by plush-crested jays: are tools special after all?

Tool use is taxonomically associated with high behavioural flexibility and innovativeness, and its prevalence is greater in primates and some bird species. This association, however, is not known to be causally determinant of tool-related competence since flexibility and innovativeness are often observed in the absence of tool use and vice versa. For this reason, it is interesting to explore whether animals that can be loosely categorized as outstanding, or ‘intelligent’ physical problem solvers, are also remarkable using tools innovatively, rather than tool use presenting special constraints. We investigate this problem using plush-crested jays (Cyanocorax chrysops), a corvid new to cognitive research that shows highly flexible and inquisitive behaviour in the wild and has not been reported to use tools. We tested jays in two tasks of apparent similar manipulative complexity and incentive, one involving a tool (T) and the other not (NT). In the NT task birds had to open a box with a transparent lid blocked by a latch to get a reward, whereas in the T task, they had to use a rake to pull out the reward from the box. Eight out of nine subjects succeeded in the NT task, whereas none of them learned to solve the T task. This is consistent with tool use involving dedicated competencies, rather than just high problem-solving proficiency.

Building information modeling in construction projects: the case of the Portuguese code of public contracts

Purpose This study aims to address gaps in the integration of Building Information Modelling (BIM) into procurement processes for construction works under the Portuguese Code of Public Contracts (CCP). The goal is to improve procurement procedures with practical recommendations benefiting both public and private sectors, and applicable in other countries, promoting efficient BIM use in construction projects. Design/methodology/approach The study involved a literature review and expert interviews to identify opportunities within the CCP related to construction procurement that impact BIM implementation during construction works. It critically analyzed these insights and proposed specific adjustments to the CCP to enhance BIM application in public construction works. Findings The study provides practical recommendations to address two main gaps identified in the literature: the neglect of non-specific digitalization factors influencing BIM implementation, and the lack of consideration for specific constraints in public procurement, particularly those dictated by legal frameworks like the CCP. These recommendations aim to optimize traditional procurement provisions for public construction, thereby improving BIM utilization on-site. Social implications The study’s social implications include enhanced transparency and efficiency in construction processes, leading to reduced delays and costs for both stakeholders and the public. By optimizing BIM practices, public resources can be used more effectively in construction projects. The research may also accelerate the adoption of advanced technologies in the industry and improve urban development through better-planned and executed infrastructure projects. Additionally, the study’s recommendations could serve as a model for other countries, fostering the adoption of higher construction standards on a global scale. Originality/value The study’s original contribution lies in its comprehensive examination of public procurement procedures, particularly within the CCP context, to enhance the utilization of BIM during construction works. It uniquely identifies literature gaps and provides practical recommendations addressing non-specific digitalization factors and legal constraints in public procurement.

Gyro-free attitude reorientation control of spacecraft with state and input constraints

This paper introduces an explicit reference governor-based control scheme to address the gyro-free spacecraft attitude reorientation problem, considering specific constraints such as pointing, angular velocity, and input saturation. The proposed control scheme operates in two layers, ensuring the asymptotic stability of the attitude while adhering to the aforementioned constraints. The inner layer employs output feedback control utilizing an angular velocity observer based on immersion and invariance technology. Through an analysis of the geometry associated with the pointing constraint, determination of the upper bound of angular velocity, and optimization of the control input solution, the reference layer establishes a safety boundary described by the invariant set. Additionally, we introduce the dynamic factor related to the angular velocity estimation error into the invariant set to prevent states from exceeding the constraint set due to unmeasurable angular velocity information. The shortest guidance path is then designed in the reference layer. Finally, we substantiate the efficacy of the proposed constrained attitude control algorithm through numerical simulations.

INTEGRATED OPTIMIZATION MODELS FOR CARRIER SELECTION AND ROUTE PLANNING IN MULTIMODAL TRANSPORT SYSTEMS

This paper presents a comprehensive approach to the optimization of carrier routes and warehouses within multimodal transport systems, with a focus on a localized approach that takes into account specific regional features and constraints. The study develops an economic and mathematical model that considers both operational efficiency and cost minimization by integrating different modes of transport such as road, rail, and sea. Through a comprehensive analysis of existing literature and application of advanced optimization algorithms, the study proposes a new framework that improves the decision-making process in route planning and carrier selection. The proposed model is validated with real-world examples, demonstrating its practical applicability and potential to significantly improve the efficiency of multimodal transportation systems under different scenarios.

Dynamic interplay of anisotropy and matter-geometry coupling in stellar structures: A comprehensive study on the stability of pulsars 4U 1820-30 and LMC X-4

This study investigates the behavior of dense celestial objects within the context of f(R,Lm,T) theory. We develop modified Einstein field equations that account for the anisotropic configured static interior spacetime. By applying two specific constraints associated with the radial metric function and anisotropic pressure, we find a couple of solutions to these highly non-linear gravitational equations. For both models, we encounter differential equations whose integration yield constants which are determined through matching conditions. Additionally, the requirement of vanishing radial pressure at the hypersurface also plays an important role in this context. Furthermore, we graphically evaluate certain conditions whose fulfillment ensures the model’s feasibility under different parametric values. For this, we take into account the observational data of two compact stars 4U 1820-30 and LMC X-4. We conclude both our models to be well-aligned with the required viability and stability criteria. We must highlight here that our study advances the understanding of how f(R,Lm,T) gravity affects the internal structure of compact stars, providing valuable insights for future explorations.

Air PM10,2.5 Removal by Urban Green Space Under Urban Realistic Stressors

Urbanization has significantly altered the ecological resources, functions, and services, thereby imposing specific constraints on particulate matter (PM) mitigation through green spaces. To investigate the effect of green spaces on mitigating PM10,2.5 under multiple urban stressors, this study employed combined remote sensing imagery and small-scale quantitative measurements to identify the PM within green space and street tree, and their PM differences with the square underlying surface according to a continuous scale of 60~3000 m. The results indicated that urban stressors significantly influenced air PM10 and PM2.5 mitigation, with stressors LST (land surface temperature) and RD (traffic road density) as key stressors on air PM10, while LST, ISA (impervious surface area), BH (building height), NDVI (normalized difference vegetation index), GA (green space area), and WA (water body area) were key stressors on air PM2.5. Furthermore, stressors exhibited a significant scale effect on air PM10,2.5 mitigation; for air PM2.5, stressors ISA, RD, BH and BD (building density) had a notable impact on air PM2.5 mitigation at 1500~3000 m scales, while NDVI, GA, and WA showed a significant impact at 450~600 m. For air PM10, stressors ISA, BH, NDVI, and GA revealed a continuous scale effect, with the key scales occurring at 450 m and 3000 m. In summary, urbanization stressors can combine to affect air PM10 and PM2.5 mitigation by green spaces, especially at different spatial scales, to provide practical guidance for urban planning.

Generalized Distribution Network Data-Gathering Procedure for ADMS Deployment

The implementation of advanced distribution management systems (ADMS) in today’s distribution networks (DNs) is critical for efficient operation. However, ADMS deployment poses significant challenges, particularly in gathering the extensive and diverse data required to model DNs. This paper presents a generalized, systematic, and algorithm-driven procedure for optimizing the missing data-gathering process during ADMS deployment. The procedure identifies the required DN model data by layers, considers distribution power utility (DPU) data sources, identifies missing data, and evaluates methods and the missing data-gathering ways, considering cost, duration, and specific constraints for data gathering. The developed approach provides DPUs with a clear, structured, and proactive approach to data gathering, significantly reducing complexity and enhancing efficiency. The practical application of this procedure is demonstrated using a real-world unbalanced DN example from a North American DPU, showcasing its potential to streamline ADMS deployment and deliver tangible operational benefits.

Day-to-day dynamic traffic evolution in the urban traffic system with ride-sharing

The introduction of ride-sharing services has significantly diversified commuting options for urban residents. In multi-mode cities, residents select travel modes based on their perception of the costs for various available options, while these perceptions are updated daily. As a result, residents’ travel mode choices and mode-split traffic flows vary day by day. To capture the nonlinear evolution phenomenon of their mode choice behaviors and mode-split traffic flows, we focus on a linear monocentric city with the introduction of ride-sharing services and develop a deterministic discrete-time day-to-day dynamic evolution model. Our model incorporates residents’ limited perceptions to better reflect real-world scenarios. Moreover, considering one ride-sharing driver is allowed to pick up multiple passengers, specific constraints on the number of ride-sharing drivers and passengers (i.e., side constraints) are added to the model formulation. Thus, the proposed model is a discrete-time day-to-day dynamic asymmetric stochastic user equilibrium model with side constraints, which have rarely been studied. The uniqueness of optimal Lagrange multipliers corresponding to side constraints in the day-to-day dynamic evolution model is demonstrated, which makes us successfully extend related literature on static ride-sharing equilibrium to the study of dynamic stochastic ride-sharing user equilibrium problems. Furthermore, we consider the stability issue of the equilibrium and provide sufficient and necessary conditions for its asymptotic stability. Finally, numerical examples are conducted to validate the properties and effectiveness of our dynamic model.

Gradient-based differential neural network to time-varying constrained quadratic programming

This paper introduces a novel approach to solving time-varying quadratic programming (TVQP) problems with time-dependent constraints, using gradient-based differential neural networks (GDNN). We establish the theoretical framework for both conventional gradient neural networks (CGNN) and GDNN models, highlighting their effectiveness in addressing dynamic optimization challenges. Comparative theoretical analyses show that the proposed GDNN model achieves higher accuracy than the CGNN model, significantly reducing solution errors with exponential convergence. Moreover, the use of a sign-bi-power activation function (SBPAF) ensures reasonable convergence times for the GDNN model. Our approach is validated through simulations of TVQP problems under specific constraints. The results demonstrate that while both models are capable of solving these problems, the GDNN model outperforms the CGNN model in minimizing optimization errors (residual errors), especially when varying the scaling factor γ, the GDNN model also shows superior performance and more efficient convergence.

Multiple-Input Multiple-Output Synthetic Aperture Radar Waveform and Filter Design in the Presence of Uncertain Interference Environment

Multiple-input multiple-output synthetic aperture radar (MIMO-SAR) anti-jamming waveform design relies on accurate prior information about the interference. However, it is difficult to obtain accurate prior knowledge about uncertain intermittent sampling repeater jamming (ISRJ), leading to a severe decline in the detection performance of MIMO-SAR systems. Therefore, this article studies the robust joint design problem of MIMO radar transmit waveform and filter against uncertain ISRJ. We characterize two categories of uncertain interference, including sample length uncertainty and sample-time uncertainty, modeled as Gaussian distribution in different range bins. Based on the uncertain interference model, we formulate the maximizing SINR as a figure of merit, which is a non-convex quadratic optimization problem under specific waveform constraints. Based on the alternating direction method of multipliers (ADMM) framework, a novel joint design algorithm of waveform and filter is proposed. In order to improve the convergence performance of ADMM, the difference in convex functions (DC) programming is applied to the ADMM iterations framework to solve the problem of waveform energy inequality constraint. Finally, numerical results demonstrate the effectiveness and robustness of the proposed method, compared to the existing methods that utilize deterministic interference models in the uncertain ISRJ environment. Moreover, the spaceborne SAR real scene imaging simulations are conducted to evaluate the anti-ISRJ performance.

RC-DETR: Improving DETRs in crowded pedestrian detection via rank-based contrastive learning

The variants of DEtection TRansformer (DETRs) have achieved impressive performance in general object detection. However, they suffer notable performance degradation in scenarios involving crowded pedestrian detection. This decline primarily occurs during the training phase, where DETRs are constrained solely by pedestrian labels. This limitation leads to the production of indistinguishable image features between visually similar pedestrians and background elements, resulting in incorrect detections. To address this issue, this paper introduces a rank-based contrastive learning method, which constructs an additional and specific constraint for each indistinguishable training sample to produce distinguishable image features. Unlike previous methods that rely solely on pedestrian labels to achieve a consistent confidence score, our approach relies on multiple constraints and aims to ensure the correct rank of detection results, with confidence scores of pedestrians consistently surpassing those of background elements. Specifically, we first filter out some training samples that could interfere with our delineation of indistinguishable and distinguishable training samples. Then, based on the confidence score rank, we divide the rest of the training samples into distinguishable positive and negative training samples and indistinguishable positive and negative training samples. Finally, we combine these training samples into multiple positive and negative pairs and utilize these sample pairs to train DETRs via contrastive learning. Our method can be plugged into any DETRs and does not increase any overhead on inference. Extensive experiments on three DETRs show that our method achieves superior performance. Especially on the Crowdhuman dataset, our method achieved the state-of-the-art 38.9% MR.

Global Meta-Analysis of Innovation Attributes Influencing Climate-Smart Agriculture Adoption for Sustainable Development

Climate-smart agricultural technologies offer transformative potential for achieving Sustainable Development Goals, especially in mitigating extreme weather impacts and enhancing food security. Despite this potential, adoption rates remain limited due to various factors, with perceived complexity playing a significant role. This study conducted a systematic review and meta-analysis to assess the influence of perceived innovation complexity on adopting climate-smart technologies. Using frameworks of the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, we systematically reviewed 28 studies and conducted a meta-analysis of 15 studies across diverse geographic contexts. Our findings from the systematic review indicate inconsistent results on the impact of complexity on adoption due to the different items and scales used to measure the concepts of complexity across contexts, suggesting that there is a need for the development of a standardized scale to measure complexity. Results from the meta-analysis generated a summary effect size (r = 0.51, 95% CI = [0.05, 0.72], z = 6.78, p ≤ 0.0001), revealing a significant relationship between perceived complexity and adoption intent. The effect size of 0.51 indicates that higher complexity levels significantly decrease the likelihood of adoption intent for climate-smart technologies. Differences in CSA research trends across geographic regions highlight the need for tailored approaches to technology adoption that take into account the specific capabilities and constraints of each region. These findings provide valuable insights for policymakers, Extension professionals, and technology developers to design interventions to promote ease of use and enhance technology diffusion in sustainable farming practices and food security. These findings contribute to ongoing efforts to foster sustainable agricultural innovations, offering guidance to accelerate the global transition to more resilient farming systems.

Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling

Aneo is one of the first Nordic power companies to apply stochastic programming for day-ahead bidding of hydropower. This paper describes our experiences in implementing, testing, and operating a stochastic programming-based bidding method aimed at setting up an automated process for day-ahead bidding. The implementation process has faced challenges such as generating price scenarios for the optimization model, post-processing optimization results to create feasible and understandable bids, and technically integrating these into operational systems. Additionally, comparing the bids from the new stochastic-based method to the existing operator-determined bids has been challenging, which is crucial for building trust in new procedures. Our solution is a rolling horizon comparison, benchmarking the performance of the bidding methods over consecutive two-week periods. Our benchmarking results show that the stochastic method can replicate the current operator-determined bidding strategy. However, additional work is needed before we can fully automate the stochastic bidding setup, particularly in addressing inflow uncertainty and managing special constraints on our watercourses.

Controllable Game Level Generation: Assessing the Effect of Negative Examples in GAN Models

Generative Adversarial Networks (GANs) are unsupervised models designed to learn and replicate a target distribution. The vanilla versions of these models can be extended to more controllable models. Conditional Generative Adversarial Networks (CGANs) extend vanilla GANs by conditioning both the generator and discriminator on some additional information (labels). Controllable models based on complementary learning, such as Rumi-GAN, have been introduced. Rumi-GANs leverage negative examples to enhance the generator's ability to learn positive examples. We evaluate the performance of two controllable GAN variants, CGAN and Rumi-GAN, in generating game levels targeting specific constraints of interest: playability and controllability. This evaluation is conducted under two scenarios: with and without the inclusion of negative examples. The goal is to determine whether incorporating negative examples helps the GAN models avoid generating undesirable outputs. Our findings highlight the strengths and weaknesses of each method in enforcing the generation of specific conditions when generating outputs based on given positive and negative examples.

Eye Tracking and Human Influence Factors’ Impact on Quality of Experience of Mobile Gaming

Mobile gaming accounts for more than 50% of global online gaming revenue, surpassing console and browser-based gaming. The success of mobile gaming titles depends on optimizing applications for the specific hardware constraints of mobile devices, such as smaller displays and lower computational power, to maximize battery life. Additionally, these applications must dynamically adapt to the variations in network speed inherent in mobile environments. Ultimately, user engagement and satisfaction are critical, necessitating a favorable comparison to browser and console-based gaming experiences. While Quality of Experience (QoE) subjective evaluations through user surveys are the most reliable method for assessing user perception, various factors, termed influence factors (IFs), can affect user ratings of stimulus quality. This study examines human influence factors in mobile gaming, specifically analyzing the impact of user delight towards displayed content and the effect of gaze tracking. Using Pupil Core eye-tracking hardware, we captured user interactions with mobile devices and measured visual attention. Video stimuli from eight popular games were selected, with resolutions of 720p and 1080p and frame rates of 30 and 60 fps. Our results indicate a statistically significant impact of user delight on the MOS for most video stimuli across all games. Additionally, a trend favoring higher frame rates over screen resolution emerged in user ratings. These findings underscore the significance of optimizing mobile gaming experiences by incorporating models that estimate human influence factors to enhance user satisfaction and engagement.

Wind Farm Layout Optimization/Expansion of Real Wind Turbines with a Parallel Collaborative Multi-Objective Optimization Algorithm

The objective of this paper is to study the Wind Farm Layout Optimization/expansion problem. This problem is formulated here as a Multi-Objective Optimization Problem considering the total power output and net efficiency of Wind Farms as objectives along with specific constraints. Once formulated, this problem needs to be solved efficiently. For that, a new approach based on a combination of five Multi-Objective Optimization algorithms, which is named the Parallel Collaborative Multi-Objective Optimization Algorithm, is developed and implemented. This technique is checked on seven test cases; for each case, the goal is to find a set of optimal solutions called the Pareto Front, which can be exploited later. The acquired solutions were compared with other approaches and the proposed approach was found to be the better one. Finally, this work concludes that the proposed approach gives, in a single run, a set of optimal solutions from which a designer/planner can select the best layout of a designed Wind Farm using expertise and applying technical and economic constraints.

Comparative Study on Ground and Roof-Mounted Solar PV Systems

Solar photovoltaic (PV) systems are integral to sustainable energy solutions. The choice between ground-mounted and roof-mounted systems significantly impacts efficiency, cost, and installation feasibility. This study provides a comparative analysis of these two solar PV installation types to inform stakeholders and guide decision-making. This study aims to guide stakeholders, including policymakers, investors, and energy planners, in making informed decisions regarding solar PV system installations. By highlighting the strengths and weaknesses of each approach, this review contributes to the broader understanding of solar PV deployment strategies and their implications for sustainable energy development. A systematic literature review was conducted to gather data from various sources including academic journals, ScienceDirect, Google Scholar, PubMed, SpringerLink, Academia, and research gate. Key criteria for comparison included energy production efficiency, initial and ongoing costs, installation complexity, and long-term maintenance. The review revealed that ground-mounted systems generally offer higher energy production due to optimal tilt and orientation adjustments, and often result in lower maintenance costs due to easier access. Conversely, roof-mounted systems are usually less costly to install as they utilize existing infrastructure and may benefit from lower regulatory hurdles. However, they are constrained by roof space and orientation limitations and may face higher maintenance costs due to accessibility issues. Ground-mounted solar PV systems typically provide greater efficiency and easier maintenance but at higher installation costs. Roof-mounted systems are more cost-effective in terms of installation but may present limitations in energy production and maintenance. The choice between these systems should be guided by specific site conditions, budget constraints, and long-term energy goals.

Piezoelectric deicing system for aeronautics: Extensional mode actuator and power supply

AbstractRecent research shows a growing interest in low‐power avionic deicing systems, in particular, those based on piezoelectric actuators for their energy efficiency. The system generates micrometric vibrations in the structure to delaminate and break the ice with a low power requirement. However, designing the power supply and its control for driving piezoelectric actuators is challenging due to their distinctive capacitive behavior at most frequencies, especially in deicing applications requiring high operational frequency. This contribution addresses the two cross‐dependent parts of the deicing system, that is the piezoelectric actuator designed for breaking any kind of ice on a large area and its power supply made of a 1 kW/200 V/2 MHz auxiliary resonant commutated pole inverter (ARCPI). The choice of this converter is based on the specific constraints of the application, such as varying the operating frequency with the actuator working conditions, the long cables and necessary insulation between the actuator and its supply, the soft‐switching operation and reactive energy balancing for a good efficiency. Based on these criteria, the converter was developed and realized in the laboratory. Deicing tests confirmed effective operation with a power input density of 122 mW/cm2, using nine piezoelectric patches per dm2.

On the periodic behavior of the generalized Chazy differential equation.

We consider the periodic behavior of the generalized Chazy differential equation x⃛+|x|qx¨+k|x|qxx˙2=0, where q is a positive integer and k is a real number. We give a pure analysis on the existence of non-trivial periodic solutions for k=q+1 and the non-existence of them for k≠q+1. Our method is based on considering the projections of the orbits onto the phase plane (x,x˙). We find that a non-trivial periodic solution of the equation is equivalent to a closed curve formed by two equilibrium points and two orbits with some specific constraints in the corresponding planar system and that the existence of such closed curves can be obtained by the existence of real zeros of some returning map. Our conclusion covers all q, which completes a recent result.