Model Of Resilience Research Articles

Halal supply chain resilience index: development and implementation of measurement tool

PurposeThis study aims to develop a measurement model of the halal supply chain resilience (HSCRES) index, which represents the capability of the supply chain (SC) to handle disruption caused by halal risks. A case study is conducted to apply the HSCRES index in the halal chicken SC in Yogyakarta, Indonesia, to test the proposed methodology.Design/methodology/approachA literature synthesis was conducted to establish the main capability and vulnerability factors and their relevant indicators. The indicators were validated using the confirmatory factor analysis approach. Then, applying an analytical hierarchy process involving ten experts – practitioners and academicians – the weight of each indicator was obtained. A survey of 20 employees of slaughterhouses, 35 sellers and 100 consumers was conducted to obtain the value of each indicator. Finally, the HSCRES index was calculated by comparing the total weighted capability value to vulnerability.FindingsThe results revealed that the resilience of halal chicken SC in Yogyakarta is at a good level, with an index of 3.459, and “halal team” is the most significant indicator. The findings also revealed several capabilities that need improvement, including dedicated halal facilities, employees’ halal competence and halal regulation. However, the lack of a halal certification board, lack of management commitment and packaging contamination were found as vulnerability indicators that need to be reduced.Research limitations/implicationsThe case of this study is limited to the halal chicken SC in Yogyakarta, Indonesia. As a consequence, the obtained results are limited to a specific context. The application of this method to different areas and objects enables the establishment of different capability and vulnerability indicators.Practical implicationsThe halal resilience measurement model offers a comprehensive understanding of the strengths and weaknesses of the HSC. The findings can help stakeholders improve preparedness for halal risks, deal with halal risks better and recover more quickly. Measuring the HSCRES index can be particularly useful for policymakers in developing evidence-based strategies to increase HSCRES.Originality/valueThe current study is the first to define and classify the contributing halal resilience attributes and also to calculate the halal resilience index.

Resilience model and reduction of suicidal inclinations among lesbians, gays, and bisexuals

Resilience model and reduction of suicidal inclinations among lesbians, gays, and bisexuals

A Systemic Model for Resilience and Time Management in Healthcare Academia: Application in a Dental University Setting

A Systemic Model for Resilience and Time Management in Healthcare Academia: Application in a Dental University Setting

ANN-RSM based multi-parametric optimisation and modelling of H2 and syngas from co-gasification of residues from oil palm plants

Despite their abundance, lignocellulose biomass co-gasification studies especially that of oil palm biomass are scarcely reported. In this study, the frond and trunk of the oil palm tree are co-gasified under different conditions. Experimental results were validated, modelled, and optimised with the aid of RSM and ANN for the syngas and H2 results. The optimum yield of syngas and H2 were found to be 49.01 %, and 23.26 % respectively when operated at 900°C with particle size of 2.6 mm and blending ratio of 1:1. ANOVA yielded satisfactory P-values in the case of RSM with 95 % confidence level. The Bayesian regularisation-based ANN with a 3–10–2 topology (3 inputs, 10 hidden neurons, and 2 outputs) has shown to be a very successful and resilient model, as indicated by its significant coefficient of determination R2 of more than 0.95. The selected ANN structure demonstrates an efficient framework for capturing complicated interactions among the data. The model's relevance is shown by its ability to provide statistically relevant predictions. Furthermore, its endurance under varying situations demonstrates its reliable effectiveness, implying a capacity to generalize effectively to new, existing data. Based on the findings of the suggested BP-based ANN, the proposed model may be used in co-gasification processing industries to make critical evaluations of process operating conditions.

The Development of Risk Assessments and Supplier Resilience Models for Military Industrial Supply Chains Considering Rare Disruptions

Background: Supply chain risk and resilience in non-profit-seeking industries involving governmental agencies and quasi-governmental agencies have been under-studied. This paper focuses on the military industrial supply chain to demonstrate the development of risk assessment and supplier resilience models considering one-off disruption events such as the COVID-19 disruption. Methods: We establish relevant resilience-based categories through a literature review, supported by the experiences of supply chain experts within the military industry. We quantify the severity of the identified resilience categories, their detectability, and their occurrence probabilities. The failure modes and effects analysis technique is used to evaluate the risk priorities for the resilience categories to develop a risk assessment model. The risk assessment model is then extended to a supplier resilience model by incorporating specific rare disruption factors, which can act as a scenario planning tool. Results: It is found that (i) the top four resilience sub-categories are financial, topical data, business continuity planning, and supply chain mapping, while cost reduction strategies and green material usage are the least important; (ii) the main areas requiring focus are topical data, supply chain depth awareness, business continuity management, and internal risk management; and (iii) suppliers have least resilience in the areas of ‘topical information’ and ‘business continuity strategy’. Conclusions: The tool developed can help military industrial supply chains identify the main areas to enhance resilience from multiple perspectives of severity, occurrence probability, detectability, and suppliers.

Comparative study of moisture and stress dependent unbound material modulus for flexile pavement response modeling

Moisture can significantly impact the resilient modulus of pavement base and subgrade. Although various resilient modulus models exist, their accuracy in predicting pavement responses have not been examined and verified. This study aims to compare the accuracy of pavement response modeling using different resilient modulus, considering moisture and stress dependent unbound material modulus. Comprehensive pavement information including pavement structure, viscoelastic property of asphalt concrete, unbound material properties, moisture gradient, water table, temperature gradient and FWD deflections were collected from Long-Term Pavement Performance (LTPP) program. Based on collected data, finite element models were developed to predict pavement deflections under FWD load and pavement responses under traffic loading, respectively. The results show that the moisture-suction-stress dependent (MSS) model has the best prediction accuracy as compared to field measurements, followed by the Witczak model, while the generalized constitutive model displayed the largest deviation. Under traffic loading, the critical pavement responses predicted from Witczak and generalized constitutive model can have a significant deviation from those predicted from MSS model. The generalized constitutive model was found to constantly overestimate the responses, and the deviation of Witczak model depends on other factors such as pavement structure. Using results from MSS as the baseline, these deviations were found to result in up to 50% errors in service life of selected pavements in this study. These findings highlight the importance of taking matric suction into account for accurate modeling of flexible pavement responses.

Multi-disciplinary seismic resilience modeling for developing mitigation policies and recovery planning

The multi-disciplinary data and information available at a community level comprise the foundation of natural hazard resilience modeling. These data enable and inform mitigation and recovery planning decisions prior to and following damaging events such as earthquakes. This paper presents a multi-disciplinary seismic resilience modeling methodology to assess the vulnerability of the built environment and economic systems. This methodology can assist decision-makers with developing effective mitigation policies to improve the seismic resilience of communities. Two complementary modeling strategies are designed to examine the impacts of scenario earthquakes from a combined engineering and economic perspective. The engineering model is developed using a probabilistic fragility-based modeling approach and is analyzed using Monte Carlo (MC) simulations subject to seismic multi-hazard, including simulated ground shaking and resulting liquefaction of the soil, to quantify the physical damage to buildings and electric power substations (EPS). The outcome of the analysis is subsequently used as input to repair and recovery models to quantify repair cost and recovery time metrics for buildings and as input to functionality models to estimate the functionality of individual buildings and substations by accounting for their interdependency. The economic model consists of a spatial computable general equilibrium (SCGE) model that aggregates commercial buildings into sectors for retail, manufacturing, services, etc., and aggregates residential buildings into a wide range of household groups. The SCGE model employs building functionality estimates to quantify the economic losses. The outcomes of this integrated modeling consist of engineering and economic impact metrics, which are used to investigate mitigation actions to help inform a community on approaches to achieve its resilience goals. An illustrative case study of Salt Lake County (SLC), Utah, developed through an extensive collaborative partnership and engagement with SLC officials, is presented. The results demonstrate the effectiveness of the proposed methodology in quantifying the loss and functional recovery of infrastructure systems, the impacts on capital stock, employment, and household income and the effect of various mitigation strategies in reducing the losses and functional recovery time subject to earthquakes with varying intensities.

A Review of Resilience and Fairness Concepts and Modeling in Humanitarian Logistics Using a Meta-analysis Approach

A Review of Resilience and Fairness Concepts and Modeling in Humanitarian Logistics Using a Meta-analysis Approach

Quasi-Static Numerical Simulations and Resilient Force Models of GFRP-Confined FRP Bar-Reinforced Concrete Piers

Quasi-Static Numerical Simulations and Resilient Force Models of GFRP-Confined FRP Bar-Reinforced Concrete Piers

Supply Chain Leadership in Crisis: The Military Model for Resilience

Supply Chain Leadership in Crisis: The Military Model for Resilience

Application of Multidisciplinary Community Resilience Modeling to Reduce Disaster Risk: Building Back Better

Application of Multidisciplinary Community Resilience Modeling to Reduce Disaster Risk: Building Back Better

Navigating Challenges: The Impact of Community Resiliency Model Training on Nurse Leaders

In response to heightened burnout and moral distress among nurse leaders post-COVID-19 pandemic, the Workforce Engagement for Compassionate Advocacy, Resilience, and Empowerment (WE CARE) program was initiated at our medical center. One aspect of the program was the implementation of Community Resiliency Model® (CRM) training. This paper explores the impact of a three-hour CRM® session on nurse leaders' resilience, burnout, and moral distress. Results indicate positive changes in resilience, burnout, and moral distress. These findings suggest that CRM® training is an effective intervention for enhancing nurse leader well-being and suggests broader implications for organizational well-being initiatives in healthcare settings.

Designing a resilient-sustainable integrated broiler supply chain network using multiple sourcing and backup facility strategies dealing with uncertainties in a disruptive network: A real case of a chicken meat network

Increasing supply and demand uncertainty, coupled with unforeseen disruptions, pose challenges to the resilience of today's critical sectors in the global food industry, including the broiler supply chain. This study introduces a resilient model to enhance the sustainability and resilience of the broiler supply chain in the face of uncertainties and disruptions. The model integrates backup facilities and employs multiple sourcing strategies to reinforce resilience. Using mixed integer linear programming with bi-objective, multi-period, and multi-product features, the model aims to minimize carbon dioxide (CO2) emissions from transportation while maximizing overall supply chain profit. The goal programming, and the ε-constraint methods optimize decision-making and yield Pareto solutions, achieving a balanced approach to conflicting objectives. Also, robust optimization and stochastic programming provide practical solutions for handling uncertainties. Validation and sensitivity analysis confirm that the proposed model optimizes the broiler supply chain, enhancing resilience, sustainability, and profitability.

Advancements in Dental Casting Materials & Their Forensic Significance

Abstract: This research explores various dental casting materials & their significance in forensic science. Traditional materials, such as- Dental stone, Plaster of Paris, & Dental wax have long been utilized in forensic investigations. However, advancements in materials science have led to the development of newer, more versatile options, such as- Epoxy resins, Polyurethane, & Alginate substitutes. Each material offers distinct advantages & limitations concerning ease of use, accuracy, & durability. Dental stone, a widely used & employed material, provides excellent detail reproduction & stability but lacks flexibility. Plaster of Paris, though economical, may not yield precise replicas due to its brittle nature. Dental wax, appreciated for its malleability & is susceptible to form deformation & degradation over time. In contrast, modern alternatives like- Epoxy resins & Polyurethane boast superior strength & dimensional stability, making them ideal for long-term storage of dental impressions. Epoxy resins, in particular, offer high fidelity reproduction of dental structures & resistance to moisture, temperature fluctuations. Polyurethane, known for its flexibility & durability, enables the creation of resilient dental models suitable for extensive forensic analysis. Furthermore, Alginate substitutes have gained popularity for their ease of use & compatibility with 3D Printing technology. These materials also offer quick setting times & adequate detail capture, facilitating rapid production of dental models for forensic examination. While, Traditional materials continue to serve a purpose, the adoption of advanced casting materials has revolutionized forensic odontology by enhancing- efficiency, precision & archival integrity

Machine learning for credit risk analysis across the United States

Money-Lending financial institutions face risk, which necessitates adopting a robust framework to manage it effectively. While traditional methods have been applied across the financial industry, the advent of artificial intelligence offers organizations the opportunity to utilize advanced methods to manage credit risk. This paper focuses on the application of machine learning techniques for credit risk analysis. Secondary data on information related to borrowers was extracted from Kaggle database simulating Credit Bureau data. Two ensemble models in random forest and gradient boosting were adopted for this study. The findings showed that percentage of income for loan repayment, borrower’s income, and interest rates on loans are the most important features for determining defaulters. Furthermore, the evaluation results revealed that both the random forest and the gradient boosting algorithms performed well, with F1 scores of 92.9% and 93% respectively. It was recommended that financial institutions should priorities the verification and comprehensiveness of their data, as precise data is essential for developing resilient models.

Upcrossing‐based time‐dependent resilience of aging structures

The time-dependent resilience of an in-service aging structure provides quantitative measure of the structural ability to prepare for, adapt to, withstand and recover from disruptive events. Resilience models have been proposed in the literature to evaluate the resilience of aging structures subjected to discrete load processes, which are, however, not applicable to handle resilience problems considering continuous load processes. In this paper, a new method is developed to evaluate the time-dependent resilience of aging structures subjected to a continuous load process. The proposed method serves as the complement of the existing resilience models addressing discrete load processes, and takes into account the aging effects of the structural resistance/capacity and the nonstationarity in loads as a result of climate change. A structure suffers from a damage state upon the occurrence of an upcrossing of the load effect with respect to the resistance/capacity, leading to the reduction of the performance function, followed by a recovery process that restores the performance. The proposed method enables the time-dependent resilience to be evaluated via a closed form solution. It is also revealed that, the proposed resilience model takes an extended form of the existing formula for upcrossing-based time-dependent reliability, thus establishing a unified framework for the two quantities. The applicability of the proposed method is demonstrated through examining the time-dependent resilience of a residential building subjected to wind load. The effects of key factors on resilience, including the nonstationarity and correlation structure of the load process, as well as the resistance/capacity deterioration scenario, are investigated through an example. In particular, the structural resilience would be overestimated if ignoring the potential impacts of climate change, which is a relatively non-conservative evaluation.

Verify and trust: A multidimensional survey of zero-trust security in the age of IoT

The zero-trust (ZT) model assumes that all users, devices, and network traffic should not considered as trusted until proven. The Zero-trust model emphasizes the importance of verifying and authenticating every user and device, and limiting access to resources based on the principle of least privilege. Under the principle of the zero-trust model, devices are granted access after they have been successfully presented with their authentication credentials and access rights based on different factors, such as user identity, device health, location, and behaviour. Access controls are then continuously evaluated and updated as user properties, locations and behaviour change. The zero-trust model can be applied in various domains (healthcare, manufacturing, financial services, government etc.) to provide a comprehensive approach to cybersecurity that helps organizations to reduce risk and protect critical assets. This paper aims to provide a comprehensive and in-depth analysis of the zero-trust model, its principles, and its applications, as well as to propose recommendations for organizations looking to adopt this approach. We explore the major components of the zero-trust framework and their integration across different practical domains. Finally, we provide insightful discussions on open research issues within the zero-trust model in terms of the security and privacy of users and devices. This paper should help researchers and practitioners understand the importance of a zero-trust framework and adopt the zero-trust model for effective security, privacy, and resilience of their networks.

The impact of morningness-eveningness on depression through a serial mediation model of resilience and anxiety.

Resilience has been recently considered one of the possible mechanisms for the association between morningness-eveningness and depression. Meanwhile, anxiety is closely associated with mood disorder, but its association with morningness-eveningness is unclear. Therefore, this study aimed to explore the mediating effects of resilience and anxiety on morningness-eveningness and depression as the possible mechanisms. This study included patient group and nonpatient group. Patient group consists of 743 patients with mood disorders [Major Depressive Disorder (MDD), 233; Bipolar Disorder I (BDI), 113; Bipolar Disorder II (BDII), 397] whereas nonpatient group consists of 818 individuals without mood disorder. The Composite Scale of Morningness, Connor-Davidson Resilience Scale, Self-Rating Depression Scale, and Beck Anxiety Inventory were used to evaluate morningness-eveningness, resilience, anxiety, and depression, respectively. Our model provided a good fit for the data. The association between morningness-eveningness and depression symptoms was partially serially mediated by resilience and anxiety in both the patient and nonpatient groups. The patient group exhibited significantly stronger morningness-eveningness toward resilience and anxiety than the nonpatient group. In the indirect effect of morningness-eveningness on depression, group differences exist only through each mediation of resilience and anxiety, not through serial mediation. Our results expand on the mechanism underlying the association between morningness-eveningness and depression. They highlight the importance of morningness-eveningness modification to increase resilience and the need to consider anxiety jointly in this process.

Strengths-Based Models of Resilience in Tertiary Education Students

Tertiary education students experience higher levels of distress than the general public and their age-matched peers. Preventive health programs targeting tertiary education students are needed to combat high levels of distress in this population. This article investigates strengths-based theories of resilience which state that improvements in coping with stress also improve resilience. It utilized hierarchical regression to determine the amount of variance in mental health and resilience outcomes associated with the stress management resources, namely paying attention to the present moment (mindfulness) and engaging in personally meaningful activities (engagement), in a sample of 1,072 Australian tertiary education students. The results found that mindfulness and engagement are negatively associated with symptoms of depression and anxiety and positively associated with psychological well-being and resilience in tertiary education students. We also found that the amount of variance in mental health outcomes associated with engagement and mindfulness is additive. When a second stress management resource was added to the modeling, the amount of variance in the measured mental health outcomes explained by the coping resources increased. These findings help to provide a conceptual framework that can be used to design resilience training programs in tertiary education students to address the high levels of mental health disorder in this population.

Digital Nomad and Analysis of Regional Economic Resilience of Tourism Sector in Bali Province After Covid-19 Pandemic

This study aims to analyze the impact of the pandemic on Bali's regional economic resilience and how the workcation trend brought by digital nomad tourists able to encourage tourism recovery in Bali. The research method used is descriptive-analytical, including examining documents and statements from experts that explained the response to the economic shocks that occurred in Bali Province. Data sources were obtained from tourism ministry performance reports, BPS data, news articles and accredited journals. The study revealed that the resistance phase is demonstrated by the disruption of the tourism sector when the COVID-19 pandemic broke out, causing economic Bali to decline. The reorientation phase is demonstrated by the ability of government and business actors to adjust and adapt from shocks. The workcation trend brought by digital nomads is suitable for application during the adaptation period. The recovery phase is demonstrated by the revival of tourism in Bali Province with an increase in the number of foreign tourists. One of them comes from digital nomad tourists who increase regional income. Based on analysis using the regional economic resilience model, Bali Province in 2023 will approach economically resilient status.