Narrow Results

A swarm of Unmanned Aerial Vehicles (UAVs) comprises multiple UAVs that are capable of completing tasks beyond the capabilities of a single UAV. Due to the unique challenges of UAV missions, these vehicles often operate far from base stations, making network connectivity crucial for the successful completion of UAV swarm missions. However, existing methods do not account for strategies to maintain the overall connectivity of the UAV network when nodes are under attack. To address this issue, we propose a method named Adaptive Communication Power Control (ACPC) that dynamically adjusts UAV communication power to mitigate potential connectivity losses caused by node failures or malicious attacks. This adjustment ensures that the network can maintain information exchange among the remaining UAVs even in the event of disruptions. Additionally, we introduce a novel evaluation method to assess the overall connectivity of the network and validate ACPC through simulations of UAV swarm missions where some UAVs experience failures. Using this evaluation method, we measured the network’s state before and after attacks and recovery and calculated the additional energy consumption required by the UAVs. The results indicate that our method can increase the resilience of the UAV network by up to 5.36 times, while only raising the total communication energy consumption to 1.53 times.

According to United Nations reports, the worldwide population is expected to reach around 9.6 billion by 2050. This forecast emphasizes the critical role of energy and raw materials that are needed to meet the tremendous demand for goods. Consequently, firms feel pressured to establish sustainable and resilient strategic plans to acquire new process technologies and expand their capacity on time. These decisions are made at the highest management level, supported by a set of capacity expansion portfolios, to improve their competitiveness, especially in capital-intensive industries such as chemical processing. This paper investigates the sustainable and resilient capacity expansion problem for such industries within a long-term horizon. The main objective is to develop a holistic capacity expansion planning framework that fits the chemical processing industries and can be used to generate resilient scenarios while enhancing their sustainability measures. To this end, a bi-objective mixed-integer programming model was developed to solve the sustainability–resilience–profitability dilemma. The results showed a controversial relationship between the profitability of capacity expansion investments and a company’s commitment to sustainability and resilience. Furthermore, capacity expansion decisions were shown to primarily depend on the importance assigned to maximize profit as a managerial choice. However, there is no clear trend in sustainability preferences based on the sustainability weighting choice.

In this study, we evaluate the transition from Industry 4.0 (I4.0) to Industry 5.0 (I5.0) using a multicriteria approach that integrates the Analytic Hierarchy Process (AHP) and Fuzzy Inference Systems (FISs). Key criteria identified through a comprehensive literature review include sustainability, human factors, resilience and organizational management, which are critical for the successful implementation of I5.0. AHP is utilized to rank these criteria and assign relative weights, reflecting their importance in the overall evaluation. FIS is employed to manage the inherent uncertainty and imprecision in decision-making within the context of I5.0. The results provide a holistic view of how companies navigate this transition, highlighting their strengths and weaknesses. This study offers valuable insights into the challenges and opportunities associated with adopting I5.0, emphasizing the utility of combining multicriteria approaches for strategic decision-making in dynamic industrial environments. These findings aim to support organizations in making informed decisions, fostering a more innovative, sustainable and human-centric industry. The combination of these methodologies provides a robust foundation for future research and practical applications in advancing I5.0.

We study the problem of universal resilience patterns in complex networks against cascading failures. We revise the classical betweenness method and overcome its limitation of quantifying the load in cascading model. Considering that the generated load by all nodes should be equal to the transported one by all edges in the whole network, we propose a new method to quantify the load on an edge and construct a simple cascading model. By attacking the edge with the highest load, we show that, if the flow between two nodes is transported along the shortest paths between them, then the resilience of some networks against cascading failures inversely decreases with the enhancement of the capacity of every edge, i.e. the more capacity is not always better. We also observe the abnormal fluctuation of the additional load that exceeds the capacity of each edge. By a simple graph, we analyze the propagation of cascading failures step by step, and give a reasonable explanation of the abnormal fluctuation of cascading dynamics.

This paper presents a simple method for estimating the size of environmental capital (KN) assets that are otherwise mistaken as infinite. An illustration is provided for Australia’s air shed. The method draws on the perpetual inventory method (PIM) used in macroeconomics for measuring the size of manufactured capital (KM) stock. While the application of the PIM for measuring KM is based on net accumulation over time, with KN it involves net depreciation over time. The depreciation, however, can be negated by the resilience capabilities of KN assets based on their biophysical characteristics. Owing to sparse data, two proxy methods for estimating the resilience coefficient are developed. These proxies rely on emission targets and standards that have been discussed in Australia’s policy context.

This study identified the impact of a seismic shock on technological progress in earthquake-stricken areas (ESAs) using a synthetic control method. Technological progress was measured using the total factor productivity (TFP) and the TFP growth rate. The ESAs after the Wenchuan Earthquake in China were used as an empirical case study; the Solow residual model was used to assess the TFP and the TFP growth rate in 16 districts. Counter-factual dynamics for the ESAs were constructed to exclude the effect of the macro-economy. The research findings indicate that technological progress in the ESAs after the Wenchuan Earthquake improved as a result of reconstruction investments. However, there were differences in the speed of technological progress between ESAs. These differences may be attributed to the differences in the industrial characteristics between ESAs. The study concludes that the technological progress of the secondary industry, such as the manufacturing industry and building industry, is more resilient. This refers to the capacity to resist economic losses after the seismic shock, compared to the tertiary industry, such as the service industry and tourist industry. However, there was a larger long-term advancement in the technological progress in the tertiary industry compared to the secondary industry after the earthquake. With this understanding, ESA governments can implement appropriate strategies to meet both short-term needs and sustainable economic growth.

We study the tolerance of scale-free networks (following a power-law distribution P(k) = c⋅k^α) under degree segment protection and removal. We use percolation theory to examine analytically and numerically the critical node removal fraction p_c required for the disintegration of the network as well as the critical node protection fraction p_pc necessary to immunize the network against the disintegration. We show that when degree segment protection is prior to degree segment removal and 2 ≤ α ≤3, scale-free networks are quite robust due to the extremely low value of p_pc. Meanwhile, if we protect a degree segment with a fixed fraction of nodes, the threshold p_c has a generally downward trend as the degree sum of the segment decreases, but it is not strictly monotonic.

In the field of thermoplastic vulcanizate (TPV), experimental methods cannot quantify the relationship between the internal structure and performance of TPV, and are not conducive to the accurate design of TPV structure and performance, which is one of the problems to be solved in this field. In this study, a simple and effective two-dimensional micromechanical model was established based on the real microstructure of TPV by using the micromechanical method and the mechanical properties of TPV with different ethylene propylene diene monomer (EPDM) mass fractions were studied. The results show that with the increase of EPDM content, the maximum stress distribution area of TPV would change, the elastic modulus of TPV would gradually decrease, while the maximum stress of polypropylene (PP) phase would first decrease and then increase and strain corresponding to elastic–plastic change would also increase. The resilience of TPV increases with the increase of EPDM content and decreases with the increase of strain load. When the EPDM content is higher than 70%, the “S” bending deformation would occur at the thinnest part of PP matrix ligament.

This paper introduces the implementation of a computational agent-based financial market model in which the system is described on both microscopic and macroscopic levels. This artificial financial market model is used to study the system response when a shock occurs. Indeed, when a market experiences perturbations, financial systems behavior can exhibit two different properties: resilience and robustness. Through simulations and different scenarios of market shocks, these system properties are studied. The results notably show that the emergence of collective herding behavior when market shock occurs leads to a temporary disruption of the system self-organization. Numerical simulations highlight that the market can absorb strong mono-shocks but can also be led to rupture by low but repeated perturbations.

Resilience is the property that enables a system to continue operating properly when one or more faults occur. Nowadays, as software systems become more and more complex, their hardware execution platforms also become more heterogenous with larger scale. Software systems may fail due to some faults such as node breakdown, communication failure, or data processing failure. In this paper, we propose a ring-based resilience mechanism, which implements fault detection and recovery. (1) To solve the problem that the central server may have high burden of network traffic, we design a ring-based heartbeat algorithm for crash fault detection. (2) We also design a light-weight recovery mechanism to recover from crash faults as compared with the current system-specific mechanisms. To evaluate our mechanism, we use a 3D-online game system as a case study. By injecting faults, we test the effectiveness and overhead of the proposed mechanism. Compared with other mechanisms, the experimental results show that our mechanism can support resilience very well and is better at dealing with the crash fault caused by high cluster workload with acceptable overhead.

Obfuscation is a technology that secures software artifacts from reverse engineering by making its cost prohibitively high. Intermediate level obfuscator implements the defensive mechanisms inside the software, and owing to high potency and resilience, can successfully secure the sensitive software components. This paper provides an analysis and parametrization of the obfuscator, as well as a method of fine-tuning and evaluating obfuscating transformations in terms of potency, resilience and cost.

Together with the spread of DevOps practices and container technologies, Microservice Architecture has become a mainstream architecture style in recent years. Resilience is a key characteristic in Microservice Architecture (MSA) Systems, and it shows the ability to cope with various kinds of system disturbances which cause degradations of services. However, due to lack of consensus definition of resilience in the software field, although a lot of work has been done on resilience for MSA Systems, developers still do not have a clear idea on how resilient an MSA System should be, and what resilience mechanisms are needed.

In this paper, by referring to existing systematic studies on resilience in other scientific areas, the definition of microservice resilience is provided and a Microservice Resilience Measurement Model is proposed to measure service resilience. And a requirement model to represent resilience requirements of MSA Systems is given. The requirement model uses elements in KAOS to represent notions in the measurement model, and decompose service resilience goals into system behaviors that can be executed by system components. As a proof of concept, a case study is conducted on an MSA System to illustrate how the proposed models are applied.

Many tissues undergo a steady turnover, where cell divisions are on average balanced with cell deaths. Cell fate decisions such as stem cell (SC) differentiations, proliferations, or differentiated cell (DC) deaths, may be controlled by cell populations through cell-to-cell signaling. Here, we examine a class of mathematical models of turnover in SC lineages to understand engineering design principles of control (feedback) loops, that may operate in such systems. By using ordinary differential equations that describe the co-dynamics of SCs and DCs, we study the effect of different types of mutations that interfere with feedback present within cellular networks. For instance, we find that mutants that do not participate in feedback are less dangerous in the sense that they will not rise from low numbers, whereas mutants that do not respond to feedback signals could rise and replace the wild-type population. Additionally, we asked if different feedback networks can have different degrees of resilience against such mutations. We found that all minimal networks, that is networks consisting of exactly one feedback loop that is sufficient for homeostatic stability of the wild-type population, are equally vulnerable. Mutants with a weakened/eliminated feedback parameter might expand from lower numbers and either enter unlimited growth or reach an equilibrium with an increased number of SCs and DCs. Therefore, from an evolutionary viewpoint, it appears advantageous to combine feedback loops, creating redundant feedback networks. Interestingly, from an engineering prospective, not all such redundant systems are equally resilient. For some of them, any mutation that weakens/eliminates one of the loops will lead to a population growth of SCs. For others, the population of SCs can actually shrink as a result of “cutting” one of the loops, thus slowing down further unwanted transformations.

In this paper, a new algorithm to select the relevant nodes — those that maintain the cohesion of the network — of the complex network is presented. The experiments on most of the real complex networks show that the proposed approach outperforms centrality measures as node degree, PageRank algorithm and betweenness centrality. The rationale of the algorithm for extracting relevant nodes is to discover the self-similarity of the network. As seen in the algorithm, throughout the extraction sequence of relevant nodes, differences are advised with node degree, PageRank algorithm and betweenness centrality. Finally, empirical evidence is considered to show that complex network robustness is a nonlinear function of the small-worldness measure.

In this paper, we explore the price dynamics of 16 representative records of USA Education Group stocks, encompassing two non-overlapping periods (before, during, and after COVID-19). Based on information theory and cluster analysis techniques, our study provides insights into disorder, predictability, efficiency, similarity, and resilience/weakness, considering the most diverse financial stakeholders. Our findings contribute significantly to understanding price dynamics in the education sector, encompassing non-crisis and crisis periods, and provide valuable information for financial risk management, highlighting the importance of considering financial assets’ efficiency and resilience/weakness during economic turbulence, such as the COVID-19 crisis.

The development of female entrepreneurs in Indonesia is an integral part of Muslim women's economic contributions and empowerment. However, there is a lack of reliable research about female entrepreneurship and how gender may affect the experiences of business ownership in Indonesia. Therefore, the aim of this study is to explore the challenges encountered by these women entrepreneurs on a daily basis. Qualitative in-depth interviews were conducted with 30 female Indonesian entrepreneurs. Participants were recruited using theoretical and maximum variation sampling techniques. Content analysis was then used to analyze the data. Results revealed high levels of variations, both within and between women, suggesting that the quality of business entrepreneurship and success depended largely on the personal characteristics of these women, rather than on any system of formal education or training. This study also found that many women displayed resilient coping strategies when dealing with business failures. As a consequence, they were able to thrive despite restrictive social, cultural and political constraints. The paper highlights the importance of the experiences of female entrepreneurs in a developing country and the need to integrate the development of female entrepreneurship as a part of women empowerment effort.

While the goals of long term sustainability and survival were thoroughly studied in family firms’ literature, to the best of our knowledge, rare studies had investigated the influence of values on the capacity of family firms to be resilient. In addition, this likely relationship is highly contingent on cultural and national contexts, as family values – on which family business values are dependent – are not the same across countries and World regions. These theoretical and empirical gaps motivated the present research which aims at investigating the probable influence of family firms’ values on the resilience of these firms in the unique context of Tunisia.

This qualitative research is based on a body of discourse collected from nine managers belonging to five Tunisian family businesses. Our research allowed us to highlight some key family business values in a new underexplored setting that is an oriental country with a culture driven by Islamic and Arab values. More importantly, our analysis shows that family values underlie both community and business values. Finally, our results underscore the importance of business values, community values and family values in the resilience of the firms studied.

While the UN’s proclaimed decade of family farming (2019-2029) unfolds, management research has still not sufficiently explored the enterprising family in agriculture. Our article aims at exploring the literature on agricultural family businesses in the field of management sciences, towards suggesting future research directions. We present an overview of the definitional efforts and specificities of these family businesses, followed by a systematic literature review over the past decade. Our analysis identifies three clusters of dimensions that underpin the existing knowledge: entrepreneurial behavior, succession process, and psychological dynamics, in relation with three major outcomes that are growth, resilience, and continuity. Building on the existing research limitations and the current research trends, we craft a comprehensive agenda for scholars to advance our understanding of enterprising families in agriculture.

We construct a model for liquidity risk and price impacts in a limit order book setting with depth, resilience and tightness. We derive a wealth equation and a characterization of illiquidity costs. We show that we can separate liquidity costs due to depth and resilience from those related to tightness, and obtain a reduced model in which proportional costs due to the bid-ask spread is removed. From this, we obtain conditions under which the model is arbitrage free. By considering the standard utility maximization problem, this also allows us to obtain a stochastic discount factor and an asset pricing formula which is consistent with empirical findings (e.g., Brennan and Subrahmanyam (1996); Amihud and Mendelson (1986)). Furthermore, we show that in limiting cases for some parameters of the model, we derive many existing liquidity models present in the arbitrage pricing literature, including Çetin et al. (2004) and Rogers and Singh (2010). This offers a classification of different types of liquidity costs in terms of the depth and resilience of prices.

In this paper, we propose a method to organize a tree-based Peer-to-Peer (P2P) overlay for video streaming which is resilient to the temporal reduction of the upload capacity of a node. The basic idea of the proposed method is: (1) to introduce the redundancy to a given tree-structured overlay, in such a way that a part of the upload capacity of each node is proactively used for connecting to a sibling node, and (2) to use those links connecting to the siblings to forward video stream to the siblings. More specifically, we prove that even if the maximum number of children of a node temporally reduces from m to m − k for some 1 ≤ k ≤ m − 1, the proposed method continues the forwarding of video stream to all of m children in at most 2^x hops, where x is the smallest integer satisfying m − k ≥ m/2^x. We also derive a sufficient condition to bound the increase of the latency by an additive constant. The derived sufficient condition indicates that if each node can have at least six children in the overlay, the proposed method increases the latency by at most one, provided that the number of nodes in the overlay is at most 9331; namely the proposed method guarantees the delivery of video stream with a nearly optimal latency.