User Capacity Research Articles

MATHEMATICAL MODELING OF SECURE MULTIPOINT VIDEO CONFERENCE SESSIONS USING MULTILAYER GRAPHS

The article describes a multilayer graph model for multipoint videoconference sessions. The model presented in this study allows to specify requirements for quality of video capture quality when transferred between terminals and multipoint control unit at the service layer, as well as assessing bandwidth performance and delay of datalink channels at the secure and transport network layer. The given multilayer graph model for videoconference system is designed taking into account the features of secure network configuration, in which several overlay network layers are distinguished over transportation networks. This model specifies a structure of multipoint videoconference session at service layer, at which videoconferencing terminals interoperate through server. Traffic flows generated during session are distributed according to routing rules at the secure network layer. A purpose to select the most optimal route configuration for the entire session includes a number of alternative methods to distribute traffic for each pair of client-server communication seems to be rather complicated. The study proposes an algorithm for searching the shortest path in the graph of a secure overlay network with a step-by-step projection fer each individual route section onto the underlying level of the transport network to find the shortest path within its boundaries. An example for solving a problem on finding route in regard to the overlay network requirements and the actual traffic flow through the transportation network is given. Minimization of the capacity usage of leased channels was chosen as the optimality criterion. Experiments were conducted to evaluate the session quality based on the objective methods for various video-stream parameters, and corresponding quantitative estimates of the required bandwidth in order to organize a session.

A Kantian Theory of Employment, or How Domestic Right Structures Contemporary Work: A Reply to Williams

This article shows how Kant’s framework of domestic right can be understood as a model for modern employment law, a suggestion made by Garrath Williams in a commentary on Kant’s Theory of Labour. I begin by exploring how Kant’s historical context informed his account of labor relations and argue that he made three key innovations: the theorization of domestic right, the linkage of work and political standing, and the development of contract right to make sense of the material dependency and formal equality of “free” contract workers. This account of contract-based work, however, sketches a dangerous “fantasy” of free contract that we see echoed in contemporary defenses of the gig economy. I show that this argument is central to understanding Kant’s evolving conception of the relationship between labor and citizenship, but I defend the claim that modern employment resembles domestic right more closely than contract right, by attending to both the formal structures of modern employment law and the lived experiences of professional employment in the digital age. Finally, I explore how drawing on the domestic model for understanding modern employment can help us to see the limits of both Kantian and contemporary accounts of the public good, by highlighting the ways that public law is deployed to formalize asymmetrical relations of dependence, to check their capacity for exploitation and domination. In so doing, public law acts to limit the scope of the state’s responsibility for both the formal and material conditions of equality, freedom, and flourishing, enforcing conditions in which citizens must rely on private employers for goods like health insurance, sick leave, and a minimum income.

Districting in last‐mile delivery with stochastic customers

AbstractDue to the peculiarities of city streets, last‐mile logistics is typically organized using territory‐based routing approaches, which divide the city into a set of districts and assign drivers to deliver in one or more of them. This allows drivers to develop a deep understanding of the characteristics of each district, and clients benefit from consistent service. However, these advantages must be carefully weighed against the flexibility of daily customer assignments, which enable planners to maximize driver utilization and minimize routing costs. In this paper, we propose a new holistic framework for defining districts, considering the impact on the quality of logistics decisions and fleet capacity usage. Specifically, we address how districting decisions affect the demand distribution within each district. Computational experiments on both simulated and realistic instances demonstrated a significant reduction in costs compared to benchmark techniques.

Mobile use in an age of interruption: Implications of capacity and structural interference for mobile users

We investigate the performance consequences of on-screen interruptions in mobile use. Drawing on capacity theory of attention, we conjecture that attention capacity may be lower in mobile use relative to PC use. A self-report instrument provides preliminary support for this conjecture. An online experiment provides additional support by showing that mobile users benefit from lower switching costs when they turn their attention to an interrupting task, while they suffer higher resumption costs when they resume a task that already demanded their attention. This study advances the understanding of mobile behavior while contributing to the broader literature on attention and interference.

Surrogate-assisted fully-informed particle swarm optimization for high-dimensional expensive optimization

Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be powerful optimization tools for tackling Expensive Optimization Problems (EOPs) where a limited number of function evaluations are available. However, many SAEAs are only designed for low- or medium-dimensional EOPs. Existing SAEAs are challenging to address High-dimensional EOPs (HEOPs) owing to the curse of dimensionality and lack of powerful exploitation capacity. To tackle HEOPs efficiently, a Surrogate-Assisted Fully-informed Particle Swarm Optimization (SA-FPSO) algorithm is proposed in this paper. Firstly, a generation-based Social Learning-based PSO (SLPSO) is adopted to explore the whole decision space with the help of the global surrogate model. Secondly, the fully-informed search scheme is incorporated into the framework of SLPSO to improve its exploitation capacity in the surrogate-assisted search environment. Thirdly, a local space identification strategy is proposed to determine the search range for the local surrogate-assisted search. Seven commonly used expensive benchmark functions with dimensions ranging from 30D to 300D are used to verify the performance of SA-FPSO for HEOPs. Experiment results indicate that SA-FPSO obtains superior performance over several state-of-the-art SAEAs both in terms of convergence speed and solution accuracy.

Gravity data inversion for parameters assessment over geologically faulted structures—A hybrid particle swarm optimization and gravitational search algorithm technique

AbstractInterpreting gravity anomalies caused by fault formations is associated with hydrocarbon systems, mineralized areas and hazardous zones and is the main goal of this research. To achieve an effective and robust model over the geologically faulted structures from gravity anomalies, we present a nature‐inspired hybrid algorithm, which synergizes the physics of the particle swarm optimization and gravitational search algorithm with variable inertia weights. The basic principle of developed particle swarm optimization and gravitational search algorithm method is to synergistically use the exploratory strengths of gravitational search algorithm with the exploitation capacity of particle swarm optimization in order to optimize and enhance the effectiveness by both algorithms. The technique has been tested on synthetic gravity data with varying settings of noises over geologically faulted structure before being applied to field data taken from Ahiri‐Cherla and Aswaraopet master fault present in Pranhita–Godavari valley, India. The optimization process is further refined through normalized Gaussian probability density functions, confidence intervals, histograms and correlation matrices to quantify uncertainty, stability, sensitivity and resolution. When dealing with field data, the true model is never known; in these circumstances, the quality of the outcome can only be inferred from the uncertainty in the mean model. The research utilizes a 68.27% confidence intervals to identify a location where the probability density function is more dominant. This region is then used to evaluate the mean model, which is expected to be more appropriate and closer to the genuine model. Correlation matrices further provide a clear demonstration of the strong connection between layer parameters. The results suggest that particle swarm optimization and gravitational search algorithm is less affected by model parameters and yields geologically more consistent outcomes with little uncertainty in the model, aligning well with the available results. The analysed results show that the method we came up with works well and is stable when it comes to solving the two‐dimensional gravity inverse problem. Future research may involve extending the approach to three‐dimensional inversion problems, with potential improvements in computational efficiency and search accuracy for global optimization methods.

Joint deployment of exclusive bus lanes and limited access bus lanes in a transportation network

Providing exclusive right-of-way to buses is considered an effective measure to enhance the transit system’s performance. However, the conversion of a General-Purpose Lane (GPL), which is shared by all transportation modes, to an Exclusive Bus Lane (EBL), reduces the available capacity for auto users and may result in an underutilized lane known as the empty syndrome lane. To address this deficiency, it is suggested that a special class of vehicles, such as High Occupancy Vehicles (HOVs) be permitted to drive in EBLs, but they may be required to pay tolls. This type of lane is called a Limited Access Bus Lane (LABL) and is designed to prioritize public transportation and restrict the entry of HOVs in bus lanes. The goal is to ensure transit passengers do not experience a decrease in the quality of service. This paper compares the results of four bi-level optimization models used to determine the optimal location of different combinations of special-use lanes in the network, namely: 1) only EBLs; 2) joint deployment of EBLs and HOV lanes; 3) joint deployment of EBLs and tolled HOV lanes; and 4) joint deployment of EBLs and LABLs. A base optimization problem is developed for the last proposed scheme (i.e., joint deployment of EBLs and LABLs), and the formulation is modified to solve three other network design problems. The proposed model is solved by applying a genetic algorithm. To analyze the suggested framework, a numerical test is conducted on the Sioux Falls network as a medium-sized network and the network of Zanjan as a real-sized network. Furthermore, by analyzing the numerical results of the four optimization problems, this study highlights that the joint deployment of EBLs and LABLs outperforms the other three schemes in terms of transit users’ travel time and the average volume capacity of bus routes.

Using 5G Standards for Smart Healthcare Applications and Designing an Artificial Intelligence‐Based Industry 4.0 Communication System

ABSTRACTThe introduction of Industry 4.0, to improve Internet of Things (IoT) standards, has sparked the creation of 5G, or highly sophisticated wireless networks. There are several barriers standing in the way of 5G green communication systems satisfying the expectations for faster networks, more user capacity, lower resource consumption, and cost‐effectiveness. 5G standards implementation would speed up data transmission and increase the reliability of connected devices for Industry 4.0 applications. The demand for intelligent healthcare systems has increased globally as a result of the introduction of the novel COVID‐19. Designing 5G communication systems presents research problems such as optimizing resource usage, managing mobility, ensuring cost‐efficiency, managing interference, and maximizing spectral efficiency. The fast advancement of artificial intelligence (AI) in several domains yields improved performance in contrast to traditional methods. Hence, including AI in 5G standards would enhance performance by catering to diverse end‐user applications. Initially, we provide an overview of concepts such as Industry 4.0, the 5G standard, and recent developments in the sphere of wireless communications in the future. The goal is to use 5G technology to look at current research problems. We present a new architecture for Industry 4.0 and 5G‐compliant smart healthcare systems. We develop and run the proposed model to investigate the current 5G methods using the Network Simulator (NS2). The results of the simulation show that 5G resource management and interference management approaches already in use face challenges including performance trade‐offs.

Probabilistic Risk Assessment for Data Rate Maximization in Unmanned Aerial Vehicle-Assisted Wireless Networks

The evolution of beyond fifth generation (B5G) wireless networks poses significant technical and economic challenges across urban, suburban, and rural areas, demanding increased capacity for users whose positions continually change. This study investigated the dynamic positioning of an unmanned aerial vehicle (UAV), acting as a mobile base station (MoBS) to enhance network efficiency and meet ground terminals (GTs) expectations for data rates, particularly in emergency scenarios or temporary events. While UAVs show great promise, existing research often assumes certainty in network architecture, overlooking the complexities of unpredictable user movements. We introduce a decision-making framework utilizing the ordered weighted averaging (OWA) operator to address uncertainties in GT locations, enabling the optimization of UAV trajectories to maximize the overall network data rate. An optimization problem is formulated by modeling GT dynamics through a Markov process and discretizing UAV movements while accounting for communication thresholds and movement constraints. Extensive simulations reveal that our approach significantly improves expected data rates by up to 48% compared to traditional fixed base stations (BSs) and predefined UAV movement patterns. This research underscores the potential of UAV-assisted networks to bolster communication reliability while effectively managing dynamic user movements to maintain optimal quality of service (QoS).

Collaborative optimization of multi-modal transport solutions for urban-rural bus routes.

Faced with the land use heterogeneity and trip distribution non-equilibrium along the corridor connecting urban district and rural area, the operation characteristics of the all-stop bus, the express bus and the shuttle bus running on the transportation corridor should be given full consideration, as well as the tradeoff between passenger trip cost and bus operation cost during the peak-hour period. In order to realize the reliable decision of bus resources configuration for urban-rural public transportation, a bi-objective programming model of multi-modal urban-rural bus operation scheme is established, where the decision variables include the departure frequency of different bus modals, the terminals of shuttle buses, and the stop scheme of express buses, under the constraints of departure interval, passenger volume, vehicle occupancy, section capacity usage and modal competition. To validate the feasibility of proposed optimization model, an urban-rural bus corridor from Rural Jasmine Ecological Park in Hanjiang District to West Xiangyang Bridge in Songqiao Town was chosen as a study case, and the operation organization scheme of three bus modals were solved by the mathematical solver Lingo using mixed integer programming. Meanwhile, the scheme differences under different subobjective weights were discussed, taking into account the uncertain passenger demand and operation supply. The result shows that the increase of passenger trip cost weight will increase the departure frequency of all three bus modals, while the increase of operation cost weight will decrease the total operating vehicles, but the stop frequency at middle stations for express buses will increase accordingly. Compared to the current operation scheme, the optimized scheme can greatly enhance the section capacity usage at a slight sacrifice of passenger trip time with fewer bus vehicles.

An ethical assessment of powered exoskeletons: Implications from clinical use to industry and military contexts.

Exoskeletons are technologies that can help to increase or improve mobility, dexterity, and strength. They can be used as assistive devices, to restore lost affordances, or for rehabilitation. While mechanical exoskeletons are passive and rely on the body's power for movement, powered exoskeletons are active mechanical systems that can assist or enhance a user's capacity, including in strength and performance. They also offer scope to augment or enhance beyond simple medical support, with potential in the future for superhuman power and strength. While these technologies present promising clinical opportunities, including for those who want to regain walking capacity, they also bring ethical questions, such as about data privacy and accessibility. In addition, the physical features of the technology can prove mentally, physically, and financially demanding, and may be deployed in contexts where user choice and autonomy is constrained. In this article, we discuss these issues, and raise some pertinent ethical questions, not all of which can be easily answered. We touch upon medical and therapeutic uses, for industrial and workplace settings, and in military contexts specially, given these are contexts where such technology may be required or even imposed. We argue that reasonable optimism for such technologies needs to be tempered by sufficient ethical assessment to identify and address barriers to research, development, and use. As well as managing any impacts and expectations for the health and wellbeing of users, the potential impact on autonomy and the risk of coercion, we have to consider what kind of data may be recorded or used, and the risk that these technologies could exacerbate existing inequalities or harms.

A Bi-Population Competition Adaptive Interior Search Algorithm Based on Reinforcement Learning for Flexible Job Shop Scheduling Problem

In this paper, a bi-population competition adaptive interior search algorithm (BCAISA) based on a reinforcement learning strategy is proposed for the classical flexible job shop scheduling problem (FJSP) to optimize the makespan. First, the scheduling solution is represented using a machine-job-based two-segment integer encoding method, and various heuristic rules are then applied to generate the initial population. Secondly, a bi-population mechanism is introduced to partition the population into two distinct sub-populations. These sub-populations are specifically tailored for machine assignment and operation permutation, employing different search strategies respectively, aiming to facilitate an efficient implementation of parallel search. A competition mechanism is introduced to facilitate the information exchange between the two sub-populations. Thirdly, the ISA is adapted for the discrete scheduling problem by discretizing a series of search operators, which include composition optimization, mirror search, and random walk. A Q-learning-based approach is proposed to dynamically adjust a key parameter, aiming to strike a balance between the capacity for global exploration and local exploitation. Finally, extensive experiments are conducted based on 10 well-known benchmark instances of the FJSP. The design of the experiment (DOE) method is employed to determine the algorithm’s parameters. Based on the computational results, the effectiveness of four improvement strategies is first validated. The BCAISA is then compared with fifteen published algorithms. The comparative data demonstrate that our algorithm outperforms other algorithms in 50% of benchmark instances. Additionally, according to the relative percentage deviation (RPD) from the state-of-the-art results, the BCAISA also exhibits superior performance. This highlights the effectiveness of our algorithm for solving the classical FJSP. To enhance the practical application, the scope of the ISA will be broadened in future work to more complex problems in real-world scenarios.

Iterative coupling of a fundamental electricity market model and an agent-based simulation model to reduce the efficiency gap

Fundamental optimization models can determine normative cost-optimal systems following theoretical assumptions of markets with perfect competition and perfect foresight. However, in real markets, deviations from ideal assumptions need to be considered for designing efficient policies: they may lead to higher costs than theoretically expected and contribute to the so-called “efficiency gap”. Agent-based simulation models can consider market inefficiencies but are not ideally suited for determining normative systems. This work aims to reduce the efficiency gap between theoretically cost-optimal system configurations and their implementations in non-perfect markets by coupling a fundamental electricity market model and an agent-based model. A fully automated iterative coupling is demonstrated, and its convergence behavior is investigated for two coupling parameters. Fast convergence is observed when integrating peak capacity usage from the previous agent-based simulation into the fundamental model. However, the efficiency gap is reduced only to limited extent as no information on hourly dispatch is transferred between the models. Conversely, when using hourly storage dispatch as coupling parameter, it takes longer to achieve convergence. In return, the efficiency gap is reduced to larger extent. The proposed iterative coupling thus allows the identification of realizable cost-optimal systems, which means that they are achievable under non-perfect market conditions.

Measuring the adaptive capacity of rangeland users under drought stress in North-easternIran: application of social network analysis

Drought events have significant impact on ecosystems and the livelihoods of rural communities in Iran. So, the purpose of this study was the analysis and evaluation of dimensions of adaptive capacity (AC) against drought in Bajestan county, Khorasan Razavi province, Iran. A questionnaire was used to collect data for social network analysis (SNA) and AC by the full network method. Then, a combined SNA-SEM model was developed to determine what was the social response of rangeland users in dealing with drought. In this study, the AC of rangeland users was compared in two groups of villages covered by the collaborative management or Carbon Sequestration Project (CSP) and uncovered by CSP. The results confirmed that the villages under CSP had higher levels of AC and social capital. Among five capitals, indicators of economic resources and information, skills and management were more effective on AC. Among the SNA indexes, effsize, constrain, indirects, density and betweenes centrality are more effective on AC of rangeland users. Results showed that identifying the key actors, who have the power and influence to determine the information flow and financial inflows in these components, can help increase the AC of rangeland users to mitigate effects of drought.

Carbon monitoring, reporting and verification (MRV) for cleaner built environment: Developing a solar photovoltaic blockchain tool and applications in Hong Kong's building sector

Photovoltaic (PV) systems offer significant potential for net-zero emission targets, due to their flexibility and high exploitation capacity. However, effective carbon monitoring, reporting, and verification (MRV) methods are crucial for their life cycle. The present study introduces a novel blockchain-based carbon auditing tool aimed at supporting the life cycle assessment of PV systems in Hong Kong's building sector. Consequently, all relevant information throughout the PV system's life cycle is uploaded and securely recorded within the blockchain system. The decentralized network structure, distributed consensus, and cryptographic methods employed by blockchain technology ensure the traceability, immutability, and transparency of the recorded information. Furthermore, this study provides an analysis of the installation potential and life cycle environmental benefits of PV systems in Hong Kong's building sector. Finally, the feasibility of the proposed framework is demonstrated through case studies conducted in Hong Kong. These findings underline the reliability of the proposed blockchain-based tool as a technological foundation for carbon MRV in the Hong Kong building PV sector.

Comparison of Open-Source Networking Libraries for Unity Engine in Higher Education

This study presents a comparative analysis of Open-Source Software networking libraries for the Unity 3D engine in developing Virtual Reality applications for higher education. The performance of three OSS libraries—Mirror, FishNet, and Netcode—against the commercial Photon Fusion were evaluated. Performance measurement such as user capacity, performance and cross-platform capability were used as comparison. While Photon Fusion excels in performance compared to other OSS libraries, alternatives like FishNet present a cost-effective and customizable option, ideal for educational settings with particular needs or constrained budgets. The result of this study assists in selecting suitable networking solutions for VR educational applications, promoting their broader adoption in learning environments.

Improved multi-strategy artificial rabbits optimization for solving global optimization problems

Artificial rabbits optimization (ARO) is a metaheuristic algorithm based on the survival strategy of rabbits proposed in 2022. ARO has favorable optimization performance, but it still has some shortcomings, such as weak exploitation capacity, easy to fall into local optima, and serious decline of population diversity at the later stage. In order to solve these problems, we propose an improved multi-strategy artificial rabbits optimization, called IMARO, based on ARO algorithm. In this paper, a roulette fitness distance balanced hiding strategy is proposed so that rabbits can find better locations to hide more reasonably. Meanwhile, in order to improve the deficiency of ARO which is easy to fall into local optimum, an improved non-monopoly search strategy based on Gaussian and Cauchy operators is designed to improve the ability of the algorithm to obtain the global optimal solution. Finally, a covariance restart strategy is designed to improve population diversity when the exploitation is stagnant and to improve the convergence accuracy and convergence speed of ARO. The performance of IMARO is verified by comparing original ARO algorithm with six basic algorithms and seven improved algorithms. The results of CEC2014, CEC2017, CEC2022 show that IMARO has a good exploitation and exploration ability and can effectively get rid of local optimum. Moreover, IMARO produces optimal results on six real-world engineering problems, further demonstrating its efficiency in solving real-world optimization challenges.

Design and Analysis of Cybersecurity Information Sharing Mechanism Between Computer Security Incident Response Teams (CSIRT) in Indonesia on Blockchain Technology Through Hyperledger Composer and Interplanetary File System (IPFS)

Sharing cybersecurity information among the Computer Security Incident Response Team (CSIRT) is a crucial step in enhancing organizational cybersecurity. However, a primary challenge faced is the lack of trust among users regarding the confidentiality, integrity, and availability of shared information. This study proposes a new approach by designing a mechanism for sharing cybersecurity information among CSIRTs in Indonesia on blockchain technology using Hyperledger Composer. This approach offers an innovative solution by leveraging the advantages of blockchain technology. Through this approach, cybersecurity information can be shared in a decentralized manner, overcoming the weaknesses of centralized systems, and enhancing overall information security. Another advantage of blockchain technology is its high performance and scalability, enabling increased speed, and user capacity in the process of sharing information. By implementing a blockchain-based mechanism for sharing cybersecurity information, this research aims to ensure crucial aspects of information security, namely confidentiality, integrity, and availability. The contribution of this study is not only in enhancing organizational cybersecurity but also in providing an innovative solution to practical challenges in sharing cybersecurity information among CSIRTs.

Memetic discrete differential evolution with domain knowledge for blocking scheduling distributed flow shop with lot-streaming constraints

This article addresses the blocking distributed flow shop scheduling problem with lot-streaming (BDFSPL) with the objective of minimizing the maximum completion time, or makespan. In many realistic conditions, a job is divided into multiple continuous processed sublots. This pattern achieves operation overlap on consecutive machines and decreases the completion time of the whole job. Moreover, there are no buffers between machines, potentially causing finished sublots to be blocked on the current machine until the downstream machine is available. This is the first study to consider distributed production, blocking constraints, and lot-streaming together. To address this problem, we formulate a mathematical model for BDFSPL and propose a memetic discrete differential evolution (MDDE) algorithm. First, the MDDE algorithm partitions the entire population into three subpopulations based on solution quality, and each subpopulation evolves independently through adaptive scale and parameter adjustment. Second, four discrete mathematical operators are proposed to generate new individuals, and based on these operators, discrete mutation, crossover, and selection operations of MDDE are designed. Additionally, a crucial path-based local search strategy, informed by the domain knowledge of the best individual, is embedded into MDDE to enhance the exploitation capacity. To validate MDDE’s effectiveness, comprehensive experiments with several related algorithms on extensive testing instances are carried out, and the parameters of the MDDE algorithm are calibrated by employing a design of experiments. The comparison results demonstrate the effectiveness and efficiency of the proposed MDDE algorithm for the BDFSPL.

Coupled optimization of task sequence and hoist scheduling for electroplating production lines based on an improved salp swarm algorithm

Automatic electroplating production lines have been widely used in electronics industries to reduce the labour intensity and improve the production efficiency. In the multi-variety and low-volume electroplating production, it is known that the task loading sequence and hoist scheduling are coupled with each other, and they codetermine the production efficiency, while all the existing scheduling methods consider them separately, and thus the optimal production schemes become unavailable. Therefore, this paper develops a Task sequence-Hoist scheduling Coupled Optimization (THCO) model which simultaneously considers the requirements and practical constrains of task sequence and hoist scheduling, having an optimization objective of minimizing the maximum completion time. For this model, a double-layer code is developed and an Improved Salp Swarm Algorithm (ISSA) is developed by introducing three improvement strategies: the random spare strategy which is used to increase the population diversity, the nonlinear adaptive weight strategy which is used to balance the exploration and exploitation capacities, and a golden sine algorithm which is used to improve the convergence rate. Experiments based on 23 benchmark functions are then conducted. The obtained results show that ISSA has better convergence and solving quality than existing algorithms. Furthermore, several production cases prove that THCO can generate production schemes that better meet the requirements of production lines.