Resilience Properties Research Articles

Are maximum yields sustainable? Effect of intra-annual time-scales on MSY, stability and resilience

The concept of Maximum Sustainable Yield (MSY) have been lying at the core of the theory of sustainable harvesting a fishery for decades and have become a key reference point for many fishing administrations, including the European Union. However, the existence of a MSY relies on the stability of a population equilibrium. This hypothesis, though always true in the original Schaeffer model, is still challenging in more realistic and recent population models. However, recent advances shows that fish population can exhibit complex dynamics that are ill described by the classical theory. In particular, processes occurring at intra-annual time scales can affect the stability of a population equilibrium even in a strictly single species case. Associated to stability, the resilience of the equilibrium (defined as an inverse return-time following a perturbation) also matters in a management purpose. Here, we introduce an analytical single population model in discrete time with a monthly time-step allowing temporal distinction between maturation and recruitment with density-dependent mortality and fishing exploitation. We show that, thanks to an appropriate population structure, we can easily derive inter-annual population equilibrium, and study their resilience and stability properties. Then, we show that under classical hypothesis concerning density-dependence, equilibrium stability is not guaranteed and that MSY can, in theory, be associated to unstable or low resilient states. However such destabilisation seems unlikely with realistic sets of parameters. Finally, a numerical illustration for sole (Solea solea) of the Bay of Biscay suggests that the value of MSY was sensitive to maturation period whereas viability, stability and resilience was more sensitive to timing of recruitment. The value of FMSY appeared robust to uncertainty concerning maturation and recruitment. We conclude by saying that even if the risk of destabilisation is low for real populations, the risk of decreased resilience near the border of extinction should be cared of.

Shelf Life of Aquaponically-grown Finstar Lettuce in Different Oxygen Transmission Rate Films

The effects of oxygen transmission rate of packaging material on the shelf life of aquaponically-grown Finstar lettuce was studied. Parameters of packaging headspace gas composition (oxygen and carbon dioxide concentrations), lettuce pH, percentage weight loss, total aerobic microorganisms, and color were analyzed every ten days for sixty days. Finstar lettuce was stored at 4○C in four different types of packages (treatments), including a clamshell package and three film bags with oxygen transmission rates (OTR) of 3.0-6.0 cc/(m2/24 hr/1 atm), 80-90 cc/(m2/24 hr/1 atm), and >225 cc/(m2/24 hr/1 atm). The percentage weight loss of the 3.0-6.0 OTR package (-0.76-1.05%) was lowest while the percentage weight loss of the clamshell package was highest (0.81-7.72%) among packaging treatments. Nearly ½ of the panelists rated lettuce as fresh enough to eat as is after 50 days of storage in 80-90 cc/(m2/24 hr/1 atm) films while lettuce packaged in the other treatments had less that 1/3 of the panelists judging the lettuce fresh enogh to eat as is. The long shelf-life may be attributed to Finstar having resilient genetic properties along with being greenhouse-grown which lessens the possibility of contamination compared to field-grown lettuces.

Graded 2D/3D Perovskite Hetero-Structured Films with Suppressed Interfacial Recombination for Efficient and Stable Solar Cells via DABr Treatment.

Several strategies and approaches have been reported for improving the resilience and optoelectronic properties of perovskite films. However, fabricating a desirable and stable perovskite absorber layer is still a great challenge due to the optoelectronic and fabrication limitations of the materials. Here, we introduce diethylammonium bromide (DABr) as a post-treatment material for the pre-deposited methylammonium lead iodide (MAPbI3) film to fabricate a high-quality two-dimensional/three-dimensional (2D/3D) stacked hetero-structure perovskite film. The post-treatment method of DABr not only induces the small crystals of MAPbI3 perovskite secondary growth into a large crystal, but also forms a 2D capping layer on the surface of the 3D MAPbI3 film. Meanwhile, the grains and crystallization of 3D film with DABr post-treatment are significantly improved, and the surface defect density is remarkably reduced, which in turn effectively suppressed the charge recombination in the interface between the perovskite layer and the charge transport layer. The perovskite solar cell based on the DABr-treatment exhibited a significantly enhanced power conversion efficiency (PCE) of 19.10% with a notable improvement in the open circuit voltage (VOC) of 1.06 V and good stability, advocating the potential of this perovskite post-treatment approach.

CoRE: Constrained Robustness Evaluation of Machine Learning-Based Stability Assessment for Power Systems

Dear Editor, Machine learning (ML) approaches have been widely employed to enable real-time ML-based stability assessment (MLSA) of large-scale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems. In this letter, we propose a novel evaluation framework to analyze the robustness of MLSA against adversarial samples with key considerations for damage (i.e., the ability of the adversarial data to cause ML misclassification), bad data detection, physical consistency, and limited attacker's capacity to corrupt data. Extensive experiments are conducted to evaluate the robustness of MLSA under different settings.

Graphene Nanofoam Based Nanomaterials: Manufacturing and Technical Prospects

This article fundamentally reviews progress in the design and manufacturing of three-dimensional (3D) graphene-based nanocomposites for technical applications. The 3D graphene nanostructures have been manufactured using techniques like the template method, chemical vapor deposition, sol-gel, freeze-drying, hydrothermal technique, and other approaches. The nanofoam has been reinforced in polymers to achieve superior structural, morphological, and physical characteristics of the ensuing polymer/graphene nanofoam nanocomposites. The polymer/graphene nanofoam nanocomposites have been manufactured using the approaches like direct template method, in situ technique, infiltration process, and other methods. The 3D nanofoam- and polymer-based nanostructures have shown high specific surface area, suppleness, electron transport, thermal conduction, mechanical resilience, and other physical properties. The technical applications of hierarchical graphene nanofoams have been observed in the fields of radiation shielding, solar cells, supercapacitors, fuel cells, and other applications.

3D Printing of Resilin in Water by Multiphoton Absorption Polymerization

AbstractResilin is an elastic rubber‐like protein found in the cuticles of insects. It incorporates outstanding properties of high resilience and fatigue lifetime, where kinetic energy storage is needed for biological functions such as flight and jumps. Since resilin is rich in tyrosine groups, localized photopolymerization is enabled due to the ability to introduce di‐tyrosine bonds by a ruthenium‐based photoinitiator. Using Multiphoton Absorption Polymerization 3D printing process, objects containing 100% recombinant resilin protein are printed in water at a submicron length scale. Consequently, protein‐based hydrogels with complex structures are printed using space positioning voxel polymerization. The objects are characterized by dynamic mechanical analysis using nanoindentation. Printing parameters such as printing speed and laser power are found to enable tuning the mechanical properties of the printed objects. The printed objects are soft and resilient, similar to native resilin, while presenting the highest resolution of a structure made entirely of a protein and better mechanical properties of common hydrogels and poly(dimethylsiloxane). Moreover, topography and mechanical properties enable cell growth and alignment without cell adhesion primers, thus facilitating biological applications. The fabrication of 3D resilin‐based hydrogel will open the way for potential applications based on biomimicking and in creating new functional objects.

Preparation and characterization of hydrophilic and water‐swellable elastomeric nanocomposites

AbstractHighly polar and hydrophilic polymers; ethylene‐vinyl acetate (EVA), epichlorohydrin rubber (GECO), and polyethylene oxide (PEO) reinforced with fillers (carbon black (CB) and/or Silica) were used to prepare water‐swellable rubber nanocomposites. The study showed that although the high content of the GECO delayed vulcanization of the corresponding compounds, the sample filled with desired ratio of GECO/PEO/EVA or GPE, cross‐linked with peroxide exhibited the highest swelling performance of ⁓150%. The samples exhibited good re‐usability performance in the re‐swollen test, after drying. At prolonged water swelling, the tensile strength dropped drastically for compounds with high content of the GPE, due to weak filler‐matrix interactions. On the other hand, the incorporated GPE also increased the rebound resilience (%) property which is a key requirement in green tire fabrication. For example; peroxide cured sample coded SR3 (GPE‐15 phr CB) obtained ⁓30% resilience in non‐swollen state and ⁓53.5% after 1440 min of water‐swelling, which represents a dramatic development of over ⁓78% in rebound resilience. Thus, a proper balance between the GPE content, curing agent and the reinforcements may guarantee high water‐swelling performance and mechanical properties integrity for multifunctional applications such as wound healing, structural works, water collection from oil spillages, and for the development of water‐based sensors.

Validating the Contribution of Nature-Based Farming Solutions (NBFS) to Agrobiodiversity Values through a Multi-Scale Landscape Approach

Nature-Based Farming Solutions (NBFS) are envisaged practices that still strongly demand further context-specific scientific validation for their viable deployment at the local scale. In this context, our study deals with the test of a multi-scale system of landscape ecology indicators, interpreted as surrogates for the accounting of the contributions of NBFS to agrobiodiversity values and to the consequent environmental stability and resilience capacities of agroecosystems, recognized as pivotal for facing the ongoing climate change challenges. We here present the preliminary results obtained in a first pilot case study (Po Plain context). Landscape ecology analyses were undertaken at extra-local, local, and farm scales (with different levels of analytical detail), comparing the pilot farm to the surrounding conventionally managed context. A set of structural and functional indicators were tested, allowing a preliminary screening of the most suitable ones (good sensitivity to treatment changes, informative potential). Results suggested a multi-faceted positive contribution given by NBFS implementation and were the basis for orienting further NBFS implementation strategies based on vulnerability and resilience properties analysis. Further investigations are envisaged on wider datasets coming from other pilot case studies belonging to similar pedo-climatic conditions, in order to improve the informative potential of the here presented methodology.

Prediction of seismic acceleration response of precast segmental self-centering concrete filled steel tube single-span bridges based on machine learning method

The precast segmental self-centering concrete-filled steel tube (PSCFST) bridge is not only the ideal choice for fast and environmentally friendly construction but also has good seismic and resilience properties. Our research group has carried out shaking table test research on the PSCFST bridge, but due to the limitation of test equipment and site, no damage test has been carried out. To further study the seismic performance of PSCFST bridges when subjected to larger ground motions, machine learning (ML) models are developed to predict the seismic performance of PSCFST. A novel combined prediction model based on Conv1D-LSTM was proposed to predict the PSCFST bridge acceleration response. Two other commonly used ML methods including XGBoost and Random forest regression (RFR) are also used for comparison purposes. A database of ML prediction models is established based on 116 sets of input ground motion (GM) and superstructure acceleration response from shaking table tests. Then, the data of RSN292 60% GM were selected as the prediction test data. Furthermore, based on the Opensees platform, the PSCFST fiber finite element (FFE) model was established and validated by the shaking table test results, then the dynamic time history analysis of the ground motion with larger amplitude (greater than the input ground motion assignment of the shaking table test) was carried out. The superstructure acceleration response of 70%–120% of the RSN 292 GM is obtained by the FFE model and used as the data set for the ML prediction model. After that, the superstructure acceleration response is obtained through three prediction models. Comparing the simulation and prediction results shows that all the Conv1D-LSTM, XGBoost, and RFR models can reliably predict the acceleration response of the PSCFST bridge. In all cases, the Conv1D-LSTM model performed outperforms the XGBoost and RFR models. The determination coefficients (R2) of Conv1D-LSTM, XGBoost, and RFR model for the prediction of superstructure response are 0.9643, 0.8780, and 0.9623, respectively.

Phase-separation physics underlies new theory for the resilience of patchy ecosystems.

Spatial self-organization of ecosystems into large-scale (from micron to meters) patterns is an important phenomenon in ecology, enabling organisms to cope with harsh environmental conditions and buffering ecosystem degradation. Scale-dependent feedbacks provide the predominant conceptual framework for self-organized spatial patterns, explaining regular patterns observed in, e.g., arid ecosystems or mussel beds. Here, we highlight an alternative mechanism for self-organized patterns, based on the aggregation of a biotic or abiotic species, such as herbivores, sediment, or nutrients. Using a generalized mathematical model, we demonstrate that ecosystems with aggregation-driven patterns have fundamentally different dynamics and resilience properties than ecosystems with patterns that formed through scale-dependent feedbacks. Building on the physics theory for phase-separation dynamics, we show that patchy ecosystems with aggregation patterns are more vulnerable than systems with patterns formed through scale-dependent feedbacks, especially at small spatial scales. This is because local disturbances can trigger large-scale redistribution of resources, amplifying local degradation. Finally, we show that insights from physics, by providing mechanistic understanding of the initiation of aggregation patterns and their tendency to coarsen, provide a new indicator framework to signal proximity to ecological tipping points and subsequent ecosystem degradation for this class of patchy ecosystems.

Emulsification of castor oil using poly (N-vinyl-2-pyrrolidone) for functional finishing of cotton fabric

Castor oil (CAO)/poly (N-vinyl-2-pyrrolidone) (PVP) hybrid was synthesized at different reaction conditions including PVP/CAO weight ratio, temperature, and time. The results indicated that the optimum conditions to synthesis that hybrid is reacting of PVP having a molecular weight of 40,000 Da with CAO at a weight ratio of 30% and temperature of 150°C for 60 min. The synthesized hybrid was characterized via FTIR. Emulsification of such hybrid in water results in a white stable emulsion. The TEM analysis proved that the prepared emulsion of a particle size ranges from 320 to 370 nm. The technical feasibility to apply the produced emulsion for functional finishing of cotton fabric was studied. The results obtained indicated that treating cotton fabric with easy care finishing bath containing the synthesized emulsion results in enhancement in nitrogen content, tensile strength, whiteness index, stiffness, and antibacterial activities along with a decreasing in resiliency, wettability, and surface roughness properties of treated fabric, compared to the control sample. Moreover, incorporation of zinc oxide nano-particles (ZnO-NPs) or dihydroxybenzophenone (DHBP) in the above mentioned finishing bath enhances the functional properties of the finished fabric. Furthermore, dyeing fabric samples with different reactive and direct dyes followed by finishing using the aforementioned finishing bath, in absence of ZnO-NPs and DHBP, gives rise to an enhancement in the color strength of such samples compared to the dyed samples. The fastness to washing and perspiration of only the direct dye dyed/finished sample was improved. Almost all the dyed/finished samples exhibited an improvement in their fastness to the wet rubbing and alkaline perspiration along with a reduction in fastness to light. In addition, the SEM, as well as EDX analysis of treated fabric, was investigated.

A Preliminary Study and Characterization of Nasir Abad Hunza Marble Deposit Gilgit Baltistan

Marble is widely used in construction industry mainly in interior & exterior design of buildings due to its resilient physical, chemical & mechanical properties. The diverse geology of Gilgit Baltistan has naturally endowed this region with different varieties of marble deposits. These marble deposits need proper characterization to ensure their proper use in the construction industry. This research aims to conduct a preliminary study for the characterization of the Nasir Abad Hunza marble deposit. Samples from said deposit were subjected to different tests to study its chemical, physical and mechanical properties. Results of XRF analysis reveal that the deposit under study is dolomitic. The comparison of different physical properties of deposits with standard values confirms its originality. However, high variation in the strength of core samples was observed, which could result from incomplete crystallization by metamorphism. This variation in the strength of samples suggests strength-based definitions of deposit domains to propose the use of each domain separately.

Annihilating Entanglement Between Cones

Every multipartite entangled quantum state becomes fully separable after an entanglement breaking quantum channel acted locally on each of its subsystems. Whether there are other quantum channels with this property has been an open problem with important implications for entanglement theory (e.g., for the distillation problem and the PPT squared conjecture). We cast this problem in the general setting of proper convex cones in finite-dimensional vector spaces. The max-entanglement annihilating maps transform the k-fold maximal tensor product of a cone {textsf{C}}_1 into the k-fold minimal tensor product of a cone {textsf{C}}_2, and the pair ({textsf{C}}_1,{textsf{C}}_2) is called resilient if all max-entanglement annihilating maps are entanglement breaking. Our main result is that ({textsf{C}}_1,{textsf{C}}_2) is resilient if either {textsf{C}}_1 or {textsf{C}}_2 is a Lorentz cone. Our proof exploits the symmetries of the Lorentz cones and applies two constructions resembling protocols for entanglement distillation: As a warm-up, we use the multiplication tensors of real composition algebras to construct a finite family of generalized distillation protocols for Lorentz cones, containing the distillation protocol for entangled qubit states by Bennett et al. (Phys Rev Lett 76(5):722, 1996) as a special case. Then, we construct an infinite family of protocols using solutions to the Hurwitz matrix equations. After proving these results, we focus on maps between cones of positive semidefinite matrices, where we derive necessary conditions for max-entanglement annihilation similar to the reduction criterion in entanglement distillation. Finally, we apply results from the theory of Banach space tensor norms to show that the Lorentz cones are the only cones with a symmetric base for which a certain stronger version of the resilience property is satisfied.

IoT: A Decentralized Trust Management System Using Blockchain-Empowered Federated Learning

We propose a decentralized medical trust management system using blockchain-based federated learning for large-scale Internet of Things (IoT) systems. The proposed system enables health institutions to share data without revealing the privacy of data owners. Health institutions form coalitions and the leader of each coalition is elected based on the proposed proof-of-trust collaboration (PoTC) consensus protocol. The PoTC consensus protocol is based on a weight difference game where trust scores, trust consistency value, and trust deviation are factors used for evaluating nodes in the blockchain. The trust of a node is obtained either through direct trust or recommended trust evaluations. Each leader elects an aggregator who has the most credibility to manage the proposed federated learning system. The leaders become the federated clients as well as validators while the aggregator is the federated server. To ensure the decentralization of nodes, a consortium blockchain is employed. Extensive simulations are performed, which show that the proposed system not only demonstrates scalability and credibility without compromising the accuracy, convergence, and resilience properties against malicious attackers but also outperforms existing trust management systems. A security analysis is also conducted, which shows that the proposed system is robust against trust-related attacks.

Transfers as an effective instrument of public finance resilience

The article analyses the budgetary provision of the regions in the Volga Federal District. A vertical and horizontal imbalance of budgets has been revealed. Only two regions are not subsidized, with estimated budgetary capacity above the national average. To balance the budget system and strengthen its resistance to crisis phenomena in the economy, the resilience property of public finances was considered. It has been established that one of the instruments of resilience is interbudgetary transfers, the improvement of distribution methods of which makes it possible to increase the flexibility and adaptability of the regional economy in a period of environmental turbulence. The existing models for the use of intergovernmental transfers are identified and considered: 1) the introduction of blocks for the distribution of subsidies into the system of substantiation of budgetary appropriations based on the developed methods, 2) a binary model for managing the socio-economic development of the region, which performs the dual functioning of the transfer mechanism and priorities strategic development. In order to increase the territories resilience, a model for managing the transfer mechanism is proposed, which, unlike the existing ones, provides for taking into account the stage belonging of the territories, the features and patterns characteristic of each stage of the life cycle (four stages: education, development, maturity and decline).

Pseudomonas cultivated from Andropogon gerardii rhizosphere show functional potential for promoting plant host growth and drought resilience

BackgroundClimate change will result in more frequent droughts that can impact soil-inhabiting microbiomes (rhizobiomes) in the agriculturally vital North American perennial grasslands. Rhizobiomes have contributed to enhancing drought resilience and stress resistance properties in plant hosts. In the predicted events of more future droughts, how the changing rhizobiome under environmental stress can impact the plant host resilience needs to be deciphered. There is also an urgent need to identify and recover candidate microorganisms along with their functions, involved in enhancing plant resilience, enabling the successful development of synthetic communities.ResultsIn this study, we used the combination of cultivation and high-resolution genomic sequencing of bacterial communities recovered from the rhizosphere of a tallgrass prairie foundation grass, Andropogon gerardii. We cultivated the plant host-associated microbes under artificial drought-induced conditions and identified the microbe(s) that might play a significant role in the rhizobiome of Andropogon gerardii under drought conditions. Phylogenetic analysis of the non-redundant metagenome-assembled genomes (MAGs) identified a bacterial genome of interest – MAG-Pseudomonas. Further metabolic pathway and pangenome analyses recovered genes and pathways related to stress responses including ACC deaminase; nitrogen transformation including assimilatory nitrate reductase in MAG-Pseudomonas, which might be associated with enhanced drought tolerance and growth for Andropogon gerardii.ConclusionsOur data indicated that the metagenome-assembled MAG-Pseudomonas has the functional potential to contribute to the plant host’s growth during stressful conditions. Our study also suggested the nitrogen transformation potential of MAG-Pseudomonas that could impact Andropogon gerardii growth in a positive way. The cultivation of MAG-Pseudomonas sets the foundation to construct a successful synthetic community for Andropogon gerardii. To conclude, stress resilience mediated through genes ACC deaminase, nitrogen transformation potential through assimilatory nitrate reductase in MAG-Pseudomonas could place this microorganism as an important candidate of the rhizobiome aiding the plant host resilience under environmental stress. This study, therefore, provided insights into the MAG-Pseudomonas and its potential to optimize plant productivity under ever-changing climatic patterns, especially in frequent drought conditions.

Performance-based life-cycle assessments of a resilient bridge system equipped with smart bearings

A resilient bridge system with smart bearings, that is, shape memory alloy (SMA)-cable-based bearings, is proposed. Its superior resilience property over the conventional bridge system still lacks a practical assessment approach. This paper aims to conduct a comparative performance assessment of both resilient and conventional bridge systems regarding fragility and life-cycle loss aspects. The quasi-static test on the smart bearing prototype is firstly conducted. The cloud method is subsequently utilized to carry out a fragility assessment on the bridge system with uncertainties (e.g. damping, mass, friction factor, and length of slack cable). The result shows that the damage probabilities of the smart bearing at component level and the resilient bridge at system level are both dramatically alleviated although there is a moderate increase of the reinforced concrete pier at component level. Additionally, the analysis result reveals that the life-cycle economic loss of the resilient bridge system is remarkably reduced only at the cost of a slight increment in construction. This study tries to provide decision-makers with a more comprehensive understanding of the seismic superiority of the proposed resilient bridge system with smart bearings and to promote its application in engineering practice for resilience enhancement.

An Efficient Method to Assess Resilience and Robustness Properties of a Class of Cyber-Physical Production Systems

Widely available real-time data from the sensors of IoT infrastructure enables and increases the adoption and use of cyber-physical production systems (CPPS) to provide enterprise-wide status information to promptly respond to business opportunities through real-time monitoring, supervision and control of resources and activities in production systems. In CPPS, the failures of resources are uncertainties that are inevitable and unexpected. The failures of resources usually lead to chaos on the shop floor, delayed production activities and overdue orders. This calls for the development of an effective method to deal with failures in CPPS. An effective method to assess the impacts of failures on performance and create an alternative plan to mitigate the impacts is important. Robustness, which refers to the ability to tolerate perturbations, and resilience, which refers to the capability to recover from perturbations, are two concepts to evaluate the influence of resource failures on CPPS. In this study, we developed a method to evaluate the influence of resource failures on CPPS based on the concepts of robustness and resilience. We modeled CPPS by a class of discrete timed Petri nets. A model of CPPS consists of asymmetrically decomposed models of tasks. The dynamics of tasks can be represented by spatial-temporal networks (STN) with a similar but asymmetrical structure. A joint spatial-temporal networks (JSTN) model constructed based on the fusion of the asymmetrical STNs is used to develop an efficient algorithm to optimize performance. We characterized robustness and resilience as properties of CPPS with respect to the failures of resources. We analyzed the complexity of the proposed method and conducted experiments to illustrate the scalability and efficiency of the proposed method.

Advances in Resilience Engineering for Air Traffic Management Applications

As an emerging safety perspective, resilience engineering is attracting increasingly attentions in aerospace industry. Numerous researches have been carried out to explore ATM resilience properties from different aspects. Nevertheless, there appear to be significant variations on the research objects, issues concerned, methods employed and indexes used. As far as we know, there seems to be an absence of a dedicated literature review to explore the application status of resilience in ATM area. To this end, our work is concentrated on the research situation and progress of resilience in ATM. A wide range of databases are retrieved to obtain an extensive coverage of the literature. Subsequently, the research works are reviewed on the basis of different classifications followed by a brief introduction of related findings and implications. Attempting to provide some reference for further studies, promising directions are pointed out in view of the challenges and requirements. It is desired that this work may throw some light on further explorations of ATM resilience.

A Device and Method for Determining the Resilient and Elastic Properties of Human Cutaneous Scars

A Device and Method for Determining the Resilient and Elastic Properties of Human Cutaneous Scars