Objective Constraints Research Articles

Repulsive Vector Couplings and Massive Strange Stars

ABSTRACTWe describe in this work the hypothetical nuclear matter QCD phase composed of up, down and strange quarks, introducing repulsive vector interactions, which yield an enhanced MIT bag model (vMIT). Repulsive interactions seem a crucial ingredient to describe stable (and massive hybrid) compact star configurations, to comply with recent observations of pulsars with masses above . The motivation of our study, based on a known and widespread theoretical approach, is to review some of its foundations in view of recent observational advances on pulsar properties with a higher precision measurements. We address in this contribution the stability of compact stars with vector interactions, highlighting also the role of the bag constant and its impact on the stellar incompressibility. Therefore we reassess the proposal formulated by Bodmer, Terazawa, Witten, Farhi, and Jaffe, which suggests that strange quark matter may actually be more stable than ordinary hadronic matter. On this line of investigation, we analyze the dimensionless tidal deformability constraints of the intriguing central compact object (CCO) within the supernova remnant HESS J1731‐347 and the GW170817 event as strange matter candidates.

A Network Calculus Model for SFC Realization and Traffic Bounds Estimation in Data Centers

Network Function Virtualization (NFV) is a promising technology that can transform how internet service providers deliver their services. However, recent studies have identified several challenges in adopting NFV. Two key challenges are central to the operation and capacity planning of NFV Data Centers (DCs): (i) Service Function Chain (SFC) realization —determining if a new request with a known profile can be accommodated—and (ii) Network Function Virtualization (NFV) traffic bounds estimation —estimating the total traffic that a data center can handle considering all service requests and their performance constraints. To address these challenges, we propose a model that leverages stochastic network calculus to effectively dimension an NFV DC while ensuring delay and availability bounds for all service requests. Our theoretical model provides a mathematical framework to assess the realization of a single SFC request without delving into the specifics of the realization process. We utilize established availability-aware Virtual Network Function (VNF) placement patterns to obtain traffic bounds essential to planning data center capacity. We analyze NFV data center traffic under various scenarios over a Fat-tree DC topology. The results demonstrate that data center capacity is significantly influenced by the VNF placement strategy. Additionally, for data centers hosting latency-sensitive services, Service Level Objective (SLO) constraints on availability and delay are crucial in determining the number of such requests that can be accommodated.

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

Vehicle platoon safety considering the vehicle cut-in: A coupled reinforcement learning and model predictive control approach

A longitudinal platoon control method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) and Model Predictive Control (MPC) is proposed to solve the problems of low following efficiency and system instability in longitudinal platoon control. Firstly, Dynamic Bayesian Network (DBN) and Long Short-Term Memory (LSTM) network are introduced to identify the driving behavior of bystanders and derive the MPC objective constraint function containing three indicators of following, comfort and fuel consumption according to the platoon dynamics equation. Secondly, the system prediction model and cost function are introduced into the action-critic network of TD3 to solve the problem of no model training in the traditional TD3 algorithm and to speed up the training speed and accuracy of the network. On this basis, a Bellman equation is proposed to calculate the time-domain difference error and the expected loss function to solve the TD3 network overestimation problem. Finally, the joint simulation platform is built to simulate the platoon driving conditions and compare with the DDPG optimization algorithm and the traditional MPC algorithm respectively. The results show that the improved TD3-MPC algorithm satisfies the constraints and the spacing error is controlled within 0.3 m, and the vehicle speed changes more smoothly in the scenario of speed fluctuation in the front vehicle, and the ride comfort of the platoon is improved, and it has better robustness in the scenario of vehicle cut-in. The experimental results show that when the vehicle speeds are 20, 40, and 60 km/h, compared to MPC, the average spacing errors are reduced by 27.56%, 25.64%, and 28.04%, respectively, and compared to DDPG-MPC, the average spacing errors are reduced by 6.59%, 8.77%, and 7.86%, respectively.

Machine learning-based multi-objective prestress optimization framework of suspend dome structure and case study

Intelligent optimization algorithms are widely employed for the prestress optimization of suspend domes to determine the optimal cable forces. This process involves numerous complex iterative calculations, with each iteration including a corresponding finite element analysis. Such a process is both cumbersome and time-consuming. In this study, a machine learning-based surrogate model integrated with an intelligent optimization algorithm was developed to address the prestress optimization problem in suspend domes. This optimization problem incorporates multiple objective constraints, including maximum vertical displacement, maximum lateral displacement of the support, uniformity of the cable force, and maximum cable force value. The efficiency and effectiveness of the proposed method were demonstrated through two case studies. Results show that the proposed method reduced computational time by 95 % and ensured convergence by effectively integrating the surrogate model with a multi-objective optimization strategy.

Adaptive fuzzy-based node communication performance prediction with hybrid heuristic Cluster Head selection framework in WSN using enhanced K-means clustering mechanism

The “Wireless Sensor Networks (WSN)” has gained a lot of interest among research scholars and has been utilized in various advanced applications in distinct fields. Along with the load balancing techniques, the clustering scheme also prolongs the network’s overall lifespan. The “Cluster Head (CH)” performs the task of load balancing between the nodes in the “Clustering algorithm”; hence, the CH selection procedure is regarded as a critical task in the case of the clustering algorithms. Depending on the CH selection and cluster nodes, the rate of energy consumed by these CHs will be reduced in the wireless sensor. CH selection is a promising solution for the transmission of information within various parameters. Thus, CH selection leads to an increase in the duration of the system and a reduction in the energy utilization by the nodes. Therefore, an “optimization-based CH selection” mechanism in WSN is developed in this paper along with an enhanced node communication performance prediction strategy to provide better communication between the “Sensor Nodes (SNs)” with limited energy expenditure. The node’s communication performance is predicted using the Adaptive Fuzzy, in which metrics such as bit rate, latency, throughput, loss, and packet delivery ratio are specified as the input to the network. Here, the parameters within the fuzzy network are tuned with the help of the recommended “Hybrid Position of Heap and African Buffalo Optimization (HP-HABO)”. Then, to perform efficient node clustering, the “Optimal K-Means Clustering (OKMC)” approach is executed and the CHs are formed using the developed HP-HABO. The objective function depends on the constraints like energy, distance, and predicted communication performance attained by forming these CHs. The performance of the developed CH selection mechanism is verified by analyzing the experimental outcome of the proposed technique with different optimization algorithms and previous works concerning the objective constraints.

Dissipative dark cosmology: From early matter dominance to delayed compact objects

We demonstrate a novel mechanism for producing dark compact objects and black holes through a dark sector, where all the dark matter can be dissipative. Heavy dark sector particles with masses above 104 GeV can come to dominate the Universe and yield an early matter-dominated era before big bang nucleosynthesis (BBN). Density perturbations in this epoch can grow and collapse into tiny dark matter halos, which cool via self-interactions. The typical halo size is set by the Hubble length once perturbations begin growing, offering a straightforward prediction of the halo size and evolution depending on one’s choice of dark matter model. Once these primordial halos have formed, a thermal phase transition can then shift the Universe back into radiation domination and standard cosmology. These halos can continue to collapse after BBN, resulting in the late-time formation of fragmented dark compact objects and sub-solar-mass primordial black holes. We find that these compact objects can constitute a sizable fraction of all of dark matter. The resulting fragments can have masses between 1020 and 1032 g, with radii ranging from 10−2 to 105 m, while the black holes can have masses between 108 and 1034 g. Furthermore, a unique feature of this model is the late-time formation of black holes which can evaporate today. We compare where these objects lie with respect to current primordial black hole and massive (astrophysical) compact halo object constraints. Published by the American Physical Society 2024

The factors driving industrial integration: A fuzzy set qualitative comparative analysis

The integration of modern service and advanced manufacturing industries represents deep industrial reform and profound change in quality concepts, mechanisms, and practices. Integrating these two industries is important for achieving high-quality development and is an area of interest for policymakers and academia. This study analyzes the data of 11 regions in 2021 in the Guangdong–Hong Kong–Macao Greater Bay Area using the fuzzy set qualitative comparative analysis method from the configuration perspective. This study explores the effects of multiple factors, such as economic efficiency, technological innovation, and the policy environment, on improving the coupling coordination level of the modern service and advanced manufacturing industries, and analyzes the path selection of industrial integration development. Overall, economic efficiency, technological innovation, and policy environment factors are not necessary conditions for the integration development of the modern service and advanced manufacturing industries. The multiple impacts of economic efficiency, technological innovation, and policy environment form a multi-restructuring path that drives the integration of the two industries. Owing to the economic development level and resource endowment of different regions, the path dictating the degree of integration between the two industries varies by region. From a holistic perspective, all regions should strive to coordinate the multiple conditions of economic efficiency, technological innovation, and policy environment to break through objective constraints such as the government's conditions and external environment based on their own organizational conditions and resource endowments. Additionally, all regions should strengthen exchanges, cooperation, and integration mechanisms and implement complementary development and distinctive development in core cities in the Greater Bay Area. The results clarify the factors affecting industrial convergence in the Greater Bay Area, enriching existing research methods and laying a foundation for a decision-making theory of industrial integration development.

Model‐free event‐triggered distributed coordination control for dual‐arm reconfigurable manipulators with unknown object constraints

AbstractIn this paper, a model‐free event‐triggered distributed coordination control of dual‐arm reconfigurable manipulators is presented for handling unknown object task. Firstly, based on Newton‐Euler algorithm and kinematic analysis, the dynamic models of the dual‐arm manipulators and grasped object are established, respectively. According to the load distribution method, the motion‐induced force is effectively distributed to each arm. Then the dynamics of single‐arm manipulator for handling task is obtained. Secondly, the adaptive algorithm based on gradient model is proposed to estimate the position of object's center of mass (COM). The fusion state variable function is improved to achieve coordination control, reflecting the tracking performance of the position and internal force. For the modeless reconfigurable manipulator, the radial basis function neural network (RBFNN)‐based observer is utilized to approximate the uncertain dynamics. Further, the aperiodic model‐free distributed coordination controller is obtained through event‐triggered mechanism. Next, the uniformly ultimately bounded (UUB) stability of dual‐arm reconfigurable manipulators system is proved through Lyapunov stability theory. Finally, the experimental results verify that the proposed coordination control approach is valid.

Object Constraint Language based test case optimization with modified Average Percentage of Fault Detection metric

AbstractTesting is one of the most time‐consuming and unpredictable processes within the software development life cycle. As a result, many test case optimization (TCO) techniques have been proposed to make this process more scalable. Object Constraint Language (OCL) was initially introduced as a constraint language to provide additional details to Unified Modeling Language models. However, as OCL continues to evolve, an increasing number of systems are being expressed by this language. Despite this growth, a noticeable research gap exists for the testing of systems whose specifications are expressed in OCL. In our previous work, we verified the effectiveness and efficiency of performing the test case prioritization (TCP) process for these systems. In this study, we extend our previous work by integrating the test case minimization (TCM) process to determine whether TCM can also benefit the testing process under the context of OCL. The evaluation of TCO approaches often relies on well‐established metrics such as the average percentage of fault detection (APFD). However, the suitability of APFD for model‐based testing (MBT) is not ideal. This paper addresses this limitation by proposing a modification to the APFD metric to enhance its viability for MBT scenarios. We conducted four case studies to evaluate the feasibility of integrating the TCM and TCP processes into our proposed approach. In these studies, we applied the multi‐objective optimization algorithm NSGA‐II and the genetic algorithm independently to the TCM and TCP processes. The objective was to assess the effectiveness and efficiency of combining TCM and TCP in enhancing the testing phase. Through experimental analysis, the results highlight the benefits of integrating TCM and TCP in the context of OCL‐based testing, providing valuable insights for practitioners and researchers aiming to optimize their testing efforts. Specifically, the main contributions of this work include the following: (1) we introduce the integration of the TCM process into the TCO process for systems expressed by OCL. This integration benefits the testing process further by reducing redundant test cases while ensuring sufficient coverage. (2) We comprehensively analyze the limitations associated with the commonly used metric, APFD, and then, a modified version of the APFD metric has been proposed to overcome these weaknesses. (3). We systematically evaluate the effectiveness and efficiency of OCL‐based TCO processes on four real‐world case studies with different complexities.

Adaptive finite‐time tracking control of nonlinear systems subject to input hysteresis and multiple objective constraints

AbstractIn this article, the problem of adaptive finite‐time tracking control for a class of nonlinear systems subject to backlash‐like input hysteresis and multiple objective constraints is investigated. For the purpose of realizing multiple objective constraints, a new time‐varying barrier function (TVBF) is introduced to ensure that all objective constraint functions are always within the defined range. Meanwhile, based on the combination of the command filter approach and adaptive backstepping control, an error compensation system (ECS) is supplied to reduce the impact of filter error on control performance. Additionally, the problem of “singularity” caused by hysteresis is avoided by linearizing the backlash‐like hysteresis model, and Nussbaum‐type function is also applied to reduce the influence of hysteresis on the stability of the system. Then, by combining multi‐dimensional Taylor network (MTN) technology and command filter backstepping approach, an adaptive finite‐time control strategy is designed. The proposed control strategy ensures that all the signals in the closed‐loop system realize finite‐time semi‐globally uniformly ultimately bounded (SGUUB), and the output signal of the system can track the reference signal greatly while adhering to multiple objective constraints. Finally, the effectiveness of the proposed control strategy is verified by a practical simulation example.

Farmers' intention to adopt low-carbon agricultural technologies to mitigate climate change

Climate change is one of the main crises in the world primarily due to greenhouse gas emissions. The agricultural sector is a key factor in reducing greenhouse gas emissions. Therefore, this study aims to analyze farmers' intentions to use low-carbon agricultural technologies. Based on a theoretical framework adapted from the comprehensive action determination model, data were collected through questionnaires distributed among 373 farmers in the southern region of West Azarbaijan province, Iran. Factors influencing farmers' intentions were identified using a structural equation model. The results indicated that attitude (β = 0.532), habits (β = 0.282), and objective constraints (β = 0.105) significantly influenced farmers’ intentions to adopt low-carbon agricultural technologies. The model predicted 68.4% of the variance in farmers' intentions. All predictors in the comprehensive action determination model demonstrated robustness and significant positive relationships, except for subjective constraints. Additionally, the model effectively explained normative processes, habitual processes, situational influences, and intentional processes among farmers. This research suggests policy solutions to promote the adoption of low-carbon agricultural technologies by farmers in response to climate change, thereby fostering sustainable agriculture in the region. When developing future plans for climate change adaptation, planners should use the comprehensive action determination model as a fundamental guideline. This model is essential for both mitigating climate change and enhancing behavioral intentions, which are critical objectives.

The Study of Marriage Postponement Process in the Youth: A Phenomenological Study

Aim: The purpose of this study was to study the phenomenological process of postponing marriage in young people in order to understand different aspects of this phenomenon and how young people perceive it. Methods: To achieve the research goal, a qualitative approach and a descriptive phenomenological method were utilized. The research population included single and never-married girls and boys living in Tehran between the ages of 28 to 34 years for girls, and 32 to 38 years for boys. Purposeful sampling was used with the convenient sampling and 13 individuals including 7 girls and 6 boys were selected as the sample.y. Data were collected through semi-structured interviews and analyzed by Colaizzi method. Finding: Finally, the research findings reported falls into 12 main themes and 61 sub-themes. The main themes are: economic concerns, idealism, fear of future marriage, the impact of premarital relationships, motivational processes, immaturity and readiness for marriage, the role of family barriers, socio-cultural factors, selection constraints, preconceived notions of marriage, positive consequences and negative consequences. Conclusions: The results showed that postponing marriage is a complex phenomenon which is affected by objective constraints, limiting mental perceptions and insufficient maturity and has many negative consequences for young people. It seems that the efforts of the counseling and psychological community to produce content in the fields of creating a culture of easy marriage process, reviving the values of marriage, correcting the misconceptions of young people, and dissemination of successful marriages in the social media and networks, is an effective step to remove mental barriers of marriage.

Barriers Encountered by Chinese Manufacturing SMEs in Pursuing High-Quality Development Under Environmental Objective Constraints

High-quality development under environmental objective constraints represents a paradigm shift from perceiving environmental constraints as mere external pressures to embracing them as endogenous catalysts for progress. This shift, however, mandates considerable technological advancements, resource reallocations, and managerial overhauls, all requiring substantial capital investments. Chinese manufacturing SMEs, despite their efforts to adapt, often face challenges that hinder this transformation. To effectively facilitate this transition, it is crucial to pinpoint the barriers and craft tailored strategies to mitigate their inhibitory effects. Through a combination of theoretical deductions and on-the-ground research, this study not only identifies these barriers but also employs the ISM-MICMAC methodology to unpack their hierarchical and influential nature. Remarkably, the findings highlight that overcome technological and political barriers loom large, while the often-discussed economic and organizational barriers seem comparatively less formidable.

Optical routing: link failure detection-aided optimal routing in elastic optical networks using hybrid deep learning with target-based feature pool formation

ABSTRACT The Elastic Optical Network (EON) is a kind of optical network where a large amount of data signals are given into the channel of optical fibre by modulating with the optical signal. One of the most crucial tasks for optical networks is resource allocation and routing, which has a significant impact on resource effectiveness and network capacity. Deep learning-based routing and resource allocation have piqued the interest of researchers to a considerable extent. Thus, this paper aims to develop efficient link failure detection in EONs with hybrid deep learning strategies. Initially, the data aggregation is performed to combine the multiple link information from EONs. The aggregated link data is given into the hybrid detection model named as Enhanced Deep Learning Network (EDLNet), where the One-Dimensional Convolutional Neural Network (1D-CNN) is used for extracting the features and used for generating the target-based feature pool. This feature pool is utilised in the Long Short-Term Memory (LSTM) network for detecting link failures. The parameter optimization is done by the hybrid 1DCNN-LSTM network with the support of the Hybrid Red Deer with Sewing Training-based Optimisation (HRD-STO) to achieve accurate detection results. The detected failed link is mitigated at the time of routing, and only reliable links are used to attain certain objective constraints. These objective constraints include shortest path, link reliability and stability, throughput and delay for enhancing the efficiency of the EONs. The replication analysis reveals the effectiveness of the proposed link failure detection model by comparing it with the conventional models.

A maintainable and iterative development approach of critical systems with FoCaLiZe

The life-cycle development of critical systems follows mainly a V-cycle process overlapped with proofs and/or verification techniques. At the first development stage, a critical system must be, precisely, and as completely as possible described. Thus, it is recommended to use a graphical modeling language standard such as the Unified Modeling Language (UML) and the Object Constraint Language (OCL), to which both customers and developers could contribute. Then, the graphical design with its formal specification (requirements) is mapped into a complete formal development environment, using an MDE (Model-driven engineering) approach. The formal environment has to assess the development until the generation of a secure code. In this context, we propose a life-cycle development approach that combines UML/OCL and the FoCaLiZe formal environment for the secure development of critical systems. The proposed approach ensures essential development constraints such as strict boundaries and traceability between development phases. It also ensures a secure maintenance phase using partial transformation techniques from UML/OCL to FoCaLiZe. The latter enhances the transformation from UML/OCL into FoCaLiZe to support incremental development and assist developers in correcting modeling errors. The proposed approach is mostly dedicated to secure and maintainable lifecycle development processes of critical systems. The formal environment assesses the development process until the generation of a secure code. To illustrate the proposed approach, we present the stages of development of a constrained system controlling military objects. The development of this system uses the iterative, incremental, and maintainability stages as described by the development approach, until the generation of secure code.

Generalized Formal Model-Verifier: A Formal Approach for Verifying Static Models

The field of software modeling has gained significant popularity in the last decades. By capturing the static aspects of the software requirements, model-driven engineering eases the development and maintenance of software. However, additional constraints, such as invariants on model elements, that the solution must conform to may be too complex to include in the structure of the model itself. External solutions are often used to describe static constraints on models, the most prevalent approach being the Object Constraint Language (OCL) and its formal variants. This paper proposes the Generalized Formal Model-Verifier (GFMV), which is a general approach for verifying static constraints on software models. GFMV employs different formal verification methods based on Kripke Structures. Kripke Structures are used to capture the static structure of the model, then the constraints are formalized using a first-order branching-time logic, the Computational Tree Logic (CTL). Finally, the NuSMV model checker is reused to verify whether the constraints formalized in CTL hold on the formal Kripke Structure. When compared to existing solutions, GFMV offers increased generality and formal proof that the constraints hold on the model. The expressive power and runtime-scalability of the approach are evaluated on a real-world example model and OCL invariants cited from literature.

Identifying and Improving Problems and Risks of Management Strategies Based on GQM+Strategies Metamodel and Design Principles

Due to the criticality of software and IT in today’s business environment, organisations often align business goals and IT strategies. One alignment method is GQM+Strategies[Formula: see text] (GQM+Strategies[Formula: see text] is a registered trademark (No. 302008021763 at the German Patent and Trade Mark Office and international registration number IR992843). Although GQM+Strategies employs a vertical refinement tree grid based on rationales to align business goals and IT strategies in each department and throughout the organisation, it allows multiple perspectives. This can lead to strategic problems and risks because the GQM+Strategies grid may be unclear. To address this deficiency, this study defines modelling rules with a metamodel specified using a Unified Modelling Language (UML) class diagram and employs design principles described by Object Constraint Language (OCL) to automatically configure GQM+Strategies grids. These design principles are used in an experiment as the evaluation criteria to assess potential strategic problems and risks. The results confirm that our method has the potential to support the construction of GQM+Strategies grids with a consistent perspective, aiding in the alignment of business goals and IT strategies throughout an organisation.

Representation of some principles of the Agile Manifesto in the Semat Essence Kernel

The Agile Manifesto (AM) provides principles guiding agile software development as an alternative to traditional software development processes. While attempts have been madeto adapt processes to the agile context, representation of AM principles remains underdeveloped and subjective. The Semat (Software Engineering Method and Theory) Essence kernel offers a common ground for representing software development endeavors. In this paper, we represent some AM principles using the language provided by the Semat Essence kernel to establish a common ground. Additionally, we define constraints in OCL (Object Constraint Language) to enhance the Semat Essence kernel, enabling the introduction of time management in our representation. Such a representation will allow us for adapting and assessing different processes in an agile context.

Regional carbon emission pathways under central-local coordination: Objective constraints and collaborative incentives

The commitment of carbon peak and carbon neutrality in China has attracted global attention. In high-emission countries, the coordination between the central and local governments will inevitably influence climate governance. This study establishes the Regional Collaborations Dynamic Integrated model of Climate and Economy, predicting carbon emission pathways and social cost of carbon for regions within China. The design of six scenarios incorporates objective constraints and collaborative incentives to identify the impact of central-local coordination on climate governance. Results indicate that objective constraints will mitigate 0.17–0.60 Gt carbon emissions in all regions, reaching carbon peak 5–10 years earlier. Collaborative incentives will contribute to achieving carbon neutrality for regions in the long term, especially the Yangtze River Economic Belt advancing 7 years. The combination of objective constraints and collaborative incentives will facilitate China reaching carbon peak in 2029 and achieving carbon neutrality in 2057. The social cost of carbon will vary across regions due to socioeconomic conditions, initiative choices, and mitigation efforts. Moreover, the analysis of structural uncertainty can help China to undertake timely climate actions and avoid excessive mitigation costs. Based on these findings, this study proposes policy suggestions to improve climate governance by dynamic objective management, innovative collaborative forms, enhancing synergistic effects.