Disruptive Events Research Articles

Strengthening food supply resilience: Lean and Lean Six Sigma strategies for disruptions

ABSTRACT Recognizing that the food industry faces a myriad of challenges, ranging from natural disasters to geopolitical conflicts and pandemics, this study explores how Lean, and Lean Six Sigma (L&LSS) can fortify supply chains against such diverse disruptions. While the COVID-19 pandemic serves as a contextual backdrop, our focus is on the broader application of L&LSS methodologies in navigating disruptive events. This study analyzes responses from 120 food firms, utilising structural equation modelling to correlate L&LSS practices with adaptive performance during crises. Our findings indicate that L&LSS not only equip firms to maintain operational continuity amidst disruptions but also positions them to anticipate and adapt to future systemic shocks. By elucidating the positive correlation between L&LSS engagement and crisis resilience, this research contributes a strategic framework to the literature, advocating for a pre-emptive embrace of L&LSS practices as essential to navigating the unpredictable nature of global crises.

Assessing the Moisture Resilience of Wood Frame Wall Assemblies

Resilience has been used as a building performance metric that measures the building’s capability of absorption, response, and recovery from one or a series of disruptive events, e.g., extreme weather events or power outage events. With respect to resilience, in relation to the moisture performance of the building envelope (moisture resilience), this aspect has not yet been thoroughly explored nor defined. Given the expected increase in annual precipitation in certain regions of Canada as induced by climate change effects occurring both currently and in the future, the moisture resilience of building envelops will require immediate attention given that wall assemblies of buildings are predicted to be subjected to excessive moisture loads in the coming years. In this study, the moisture resilience of wood frame wall assemblies to mould growth was described from three aspects: (i) absorption—the ability of the wall to maintain a low level of relative humidity on the OSB; (ii) response—the fluctuation of the relative humidity on the OSB; and (iii) recovery—the rate at which the relative humidity recovers to an acceptable level. The metrics used to demonstrate the relative impact of these factors on moisture performance were also developed. The results have revealed a robust correlation between moisture performance and the relative influence of various newly defined aspects of moisture resilience.

A room of one’s own? The consequences of living density on individual well-being and social anomie

Abstract The global housing affordability crisis and COVID shutdowns have put living space inequality back on the political agenda. Drawing on Durkheim’s theory of anomie and density, this paper argues that on how many square meters a society lives matters for how stable or anomic it develops. Using data from the Swiss Household Panel, we examine the selection, short-term, and dynamic effects associated with transitions to overcrowded and under-occupied dwellings. We conceptualize these transitions as disruptive events that require a reconfiguration of personal and social equilibria in individuals’ lives. While overcrowded housing leads to a heightening of emotional states and more tense internal household dynamics, people respond by adjusting their leisure activities and restructuring their support networks from strong to weak ties. Conversely, moving to an under-occupied dwelling is associated with melancholic emotional stabilization, but improves household balance and leads to consolidation of the core network of relatives at the expense of outer social circles. We conclude that the classical characterization of anomie as a mismatch between personal means and societal ends should be understood as a multifaceted phenomenon in which meso-level social networks can be a crucial means to cope with disruptions that arise at other levels.

AT 2021hdr: A candidate tidal disruption of a gas cloud by a binary super massive black hole system

With a growing number of facilities able to monitor the entire sky and produce light curves with a cadence of days, in recent years there has been an increased rate of detection of sources whose variability deviates from standard behavior, revealing a variety of exotic nuclear transients. The aim of the present study is to disentangle the nature of the transient AT\,2021hdr, whose optical light curve used to be consistent with a classic Seyfert 1 nucleus, which was also confirmed by its optical spectrum and high-energy properties. From late 2021, AT\,2021hdr started to present sudden brightening episodes in the form of oscillating peaks in the Zwicky Transient Facility (ZTF) alert stream, and the same shape is observed in X-rays and UV from Swift data. The oscillations occur every sim 60-90 days with amplitudes of sim 0.2 mag in the g and r bands. Very Long Baseline Array (VLBA) observations show no radio emission at milliarcseconds scale. It is argued that these findings are inconsistent with a standard tidal disruption event (TDE), a binary supermassive black hole (BSMBH), or a changing-look active galactic nucleus (AGN); neither does this object resemble previous observed AGN flares, and disk or jet instabilities are an unlikely scenario. Here, we propose that the behavior of AT\,2021hdr might be due to the tidal disruption of a gas cloud by a BSMBH. In this scenario, we estimate that the putative binary has a separation of sim 0.83 mpc and would merge in sim 7times 10$^4$ years. This galaxy is located at 9 kpc from a companion galaxy, and in this work we report this merger for the first time. The oscillations are not related to the companion galaxy.

Sources and Radiations of the Fermi Bubbles

Two enigmatic gamma-ray features in the galactic central region, known as Fermi Bubbles (FBs), were found from Fermi-LAT data. An energy release, (e.g., by tidal disruption events in the Galactic Center, GC), generates a cavity with a shock that expands into the local ambient medium of the galactic halo. A decade or so ago, a phenomenological model of the FBs was suggested as a result of routine star disruptions by the supermassive black hole in the GC which might provide enough energy for large-scale structures, like the FBs. In 2020, analytical and numerical models of the FBs as a process of routine tidal disruption of stars near the GC were developed; these disruption events can provide enough cumulative energy to form and maintain large-scale structures like the FBs. The disruption events are expected to be 10−4∼10−5yr−1, providing an average power of energy release from the GC into the halo of E˙∼3×1041 erg s−1, which is needed to support the FBs. Analysis of the evolution of superbubbles in exponentially stratified disks concluded that the FB envelope would be destroyed by the Rayleigh–Taylor (RT) instabilities at late stages. The shell is composed of swept-up gas of the bubble, whose thickness is much thinner in comparison to the size of the envelope. We assume that hydrodynamic turbulence is excited in the FB envelope by the RT instability. In this case, the universal energy spectrum of turbulence may be developed in the inertial range of wavenumbers of fluctuations (the Kolmogorov–Obukhov spectrum). From our model we suppose the power of the FBs is transformed partly into the energy of hydrodynamic turbulence in the envelope. If so, hydrodynamic turbulence may generate MHD fluctuations, which accelerate cosmic rays there and generate gamma-ray and radio emission from the FBs. We hope that this model may interpret the observed nonthermal emission from the bubbles.

Factors associated with loneliness in Latin-American family care partners during the COVID-19 pandemic

BackgroundCOVID-19-related restrictions led to an increase in overall loneliness and social isolation. Before the pandemic, care partners reported higher levels of loneliness and higher loneliness prevalence compared to non-care partners. Because of the spread and severity of the infections, and the access to support spread, we expect a different impact of the COVID-19 pandemic on LATAM care partners.ObjectivesTo describe the loneliness levels of LATAM caregivers and to identify socioeconomic and health factors associated.DesignAn international online cross-sectional survey for care partners, embedded within the ‘Coping with Loneliness and Isolation during COVID-19’ (CLIC) Study conducted between June 2020– and November 2020.SettingWe analysed data from 246 family care partners living in Latin American countries (46% Mexico, 26% Chile,18% Brazil, and 10% from Argentina, Peru, Venezuela, Panama, Guatemala y Costa Rica).MeasurementsWe assessed loneliness using the 6-items of De Jong Gierveld loneliness Scale. We described the levels of overall, emotional, and social loneliness pre and during Covid, and reported the distribution of care partners who improved, worsened or maintained their levels of loneliness. Moreover, we used longitudinal multiple linear regression models with bootstraps errors of 1,000 iterations to identify factors associated with the levels of overall, emotional, and social loneliness during the pandemic.ResultsParticipants were mostly women, 50 years and older, in a partnership, highly educated and with finances meeting their needs, with good to excellent physical and mental health. Among the total of care partners, 55% perceived higher overall loneliness, 56% higher emotional loneliness, and 21% higher social loneliness during the pandemic in comparison with pre-COVID-19 levels. Perceived mental health was associated with the overall, emotional, and social loneliness.ConclusionsRegardless of their living and health situation, during the pandemic, loneliness increased in all groups of care partners. These should be taken in consideration when planning public health approaches for crises such as pandemics or other large-scale disruptive events.

Deep drilling in the time domain with DECam II: characterizing the light curves of candidates in the extragalactic fields

Abstract In this second paper on the DECam deep drilling field (DDF) program we release 2,020 optical gri-band light curves for transients and variables in the extragalactic COSMOS and ELAIS fields based on time series observations with a 3-day cadence from semester 2021A through 2023A. In order to demonstrate the wide variety of time domain events detected by the program and encourage others to use the data set, we characterize the sample by presenting a brief analysis of the light curve parameters such as time span, amplitude, and peak brightness. We also present preliminary light curve categorizations, and identify potential stellar variables, active galactic nuclei, tidal disruption events, supernovae (such as Type Ia, Type IIP, superluminous, and gravitationally lensed supernovae), and fast transients. Where relevant, the number of identified transients is compared to the predictions of the original proposal. We also discuss the challenges of analyzing DDF data in the context of the upcoming Vera C. Rubin Observatory and its Legacy Survey of Space and Time, which will include DDFs. Images from the DECam DDF program are available without proprietary period and the light curves presented in this work are publicly available for analysis.

Disruptive events in strategy and international business research

Disruptive events in strategy and international business research

Optimizing Agility in the Pharmaceutical Supply Chain Using Digital Twins to Cope with the Ripple Effect

The pharmaceutical supply chain is a multilayered and complex structure designed to deliver medicines to customers in a timely manner while ensuring optimal quality and quantity of medicines. The COVID-19 outbreak exposed the vulnerability and uncertainty of the pharmaceutical supply chain, so managing the risks in the pharmaceutical supply chain has become particularly important. This study demonstrates that digital twin (DT) technology can improve pharmaceutical supply chain agility and reduce the ripple effect caused by disruptions. The ripple effect refers to the effect that a sudden disruption at one node in the supply chain has on causing a chain reaction in the rest of the supply chain. The most used risk management method today is the Enterprise Resource Planning (ERP) system, which allows for real-time data sharing but has limitations in predicting and modeling the entire supply chain and lacks the ability to make quick decisions and simulate the entire supply chain. DT technology creates a virtual model of the supply chain, which enables continuous communication and information exchange between real assets and the virtual model, offering a promising alternative to risk management for pharmaceutical supply chains. risk management by providing a promising alternative. This study utilizes the AnyLogistix platform to construct a DT model and demonstrates the effectiveness of DT technology in reducing ripple effects and improving supply chain agility using quantitative analysis. This paper focuses on analyzing the pharmaceutical supply chain for 75mg aspirin tablets in London. A supply chain that operates normally, a supply chain that introduces a disruptive event (closure of the logistics center due to an earthquake), and a supply chain that applies a proactive strategy (activation of an alternate logistics center) to mitigate risk are simulated separately. The positive impact of DT technology on the supply chain is evaluated by analyzing the methods of key performance indicators (KPIs) such as inventory level, order fulfillment rate, and delivery time. The experimental results show that DT technology enhances the responsiveness, flexibility, speed, and proactivity of the supply chain, which significantly improves the agility of the supply chain. Proactive and effective strategies also reduce the financial impact of disruptions and service levels are quickly recovered. In addition, the proactive strategy stabilized inventory levels and reduced delayed orders. The above key metrics confirm the hypothesis that DT technology can improve supply chain agility and effectively reduce the ripple effect. Despite the significant conclusions drawn in this paper, there are some limitations. Firstly, sensitivity analysis and t-test were not used in the study for secondary testing, which resulted in the lack of reliability of the validation results. In addition, the possible interaction between the ripple effect and the bullwhip effect was not discussed, leading to a confounding factor in the experimental results. The above issues can be considered in further research in the future. Finally, this paper explores the possibilities and uses of introducing machine learning techniques and considers the supply chain risk factors of globalization. In summary, this study validates the potential of digital twin technology in improving agility and coping with ripple effects in pharmaceutical supply chains. As research continues, DT technology is expected to improve supply chain efficiency and reduce supply chain risk while ensuring patient safety.

Fault-tolerant partition resolvability of cycle with chord.

In the realm of connected networks, distance-based parameters, particularly the partition dimension of graphs, have extensive applications across various fields, including chemistry and computer science. A notable variant of the partition dimension is the fault-tolerant resolving partition, which is critical in computer science for networking, optimization, and navigation tasks. In networking, fault-tolerant partitioning ensures robust communication pathways even in the event of network failures or disruptions. In optimization, it aids in developing efficient algorithms capable of withstanding errors or changes in input data. In navigation systems, fault-tolerant partitioning supports reliable route planning and navigation services under uncertain or dynamic conditions. This paper focuses on the fault-tolerant partition dimension within the specific context of the cycle with chord graphs, exploring its properties and implications for enhancing the robustness and reliability of networked systems.

Contribution of lean production to the resilience of the Brazilian automotive supply chain

We propose a method to assess companies’ ability to cope with different types of disruptive events by developing resilience capabilities through Lean Production (LP) implementation. We use direct and reverse mathematical operations with two expert opinion-based matrices mapping relationships between LP practices and resilience capabilities, and resilience capabilities and types of disruptive events, obtained collecting opinions from 12 experts. We apply the method in two instances. First, using results from a survey with 135 practitioners from the Brazilian automotive industry regarding their adoption levels of LP practices. Direct matrix operations using this input indicated the development levels of eight resilience capabilities and the ability degree to cope with four types of disruptive events. Second, presenting four scenarios in which a company is exposed to high- and low-impact disruptive events of temporary and permanent nature, we perform reverse matrix operations to identify resilience capabilities and LP practices best fit to cope with each type of event. From the direct analysis of practitioners’ data two LP practices (supplier feedback and involved customers) and two resilience capabilities (provide a quick response to disruption and adapt to disruption easily) stand out. These practices and capabilities mainly prepare companies to cope with high impact disruptions of permanent nature.

Probing an LSTM-PPO-Based reinforcement learning algorithm to solve dynamic job shop scheduling problem

With the growth of personalized demand and the continuous improvement in social productivity, the large-scale and few-variety centralized production model is gradually transitioning towards a personalized model of small batches and multiple varieties, which makes the manufacturing process of the job shop increasingly complex. Furthermore, disruptive events such as machinery failures and rush orders in the job shop increase the uncertainty and variability of the production environment. Traditional scheduling methods are usually based on fixed rules and heuristic algorithms, which are difficult to adapt to constantly changing production environments and demands. This may lead to inaccurate scheduling decisions and hinder the optimal allocation of job shop resources. To solve the dynamic job shop scheduling problem (JSP) more effectively, this paper proposes a Reinforcement Learning (RL) optimization algorithm integrating long short-term memory (LSTM) neural network and proximal policy optimization (PPO). It can dynamically adjust scheduling strategies according to the changing production environment, achieving comprehensive status awareness of the job shop environment to make optimal scheduling decisions. First, a state-aware network framework based on LSTM-PPO is proposed to achieve real-time perception of job shop state changes. Then, the state and action space of the job shop are described within the context of the state-aware network framework. Finally, an experimental environment is established to verify the algorithm’s effectiveness. Training the LSTM-PPO algorithm makes it feasible to achieve better performance than other scheduling methods. By comparing the initial planning time with the actual completion time of the rescheduling decision under different dynamic disturbances, the efficiency of the proposed algorithm is verified for the dynamic JSP

Subrelativistic Outflow and Hours-timescale Large-amplitude X-Ray Dips during Super-Eddington Accretion onto a Low-mass Massive Black Hole in the Tidal Disruption Event AT2022lri

We present the tidal disruption event (TDE) AT2022lri, hosted in a nearby (≈144 Mpc) quiescent galaxy with a low-mass massive black hole (104 M ⊙ < M BH < 106 M ⊙). AT2022lri belongs to the TDE-H+He subtype. More than 1 Ms of X-ray data were collected with NICER, Swift, and XMM-Newton from 187 to 672 days after peak. The X-ray luminosity gradually declined from 1.5 × 1044 erg s−1 to 1.5 × 1043 erg s−1 and remains much above the UV and optical luminosity, consistent with a super-Eddington accretion flow viewed face-on. Sporadic strong X-ray dips atop a long-term decline are observed, with a variability timescale of ≈0.5 hr–1 days and amplitude of ≈2–8. When fitted with simple continuum models, the X-ray spectrum is dominated by a thermal disk component with inner temperature going from ∼146 to ∼86 eV. However, there are residual features that peak around 1 keV, which, in some cases, cannot be reproduced by a single broad emission line. We analyzed a subset of time-resolved spectra with two physically motivated models describing a scenario either where ionized absorbers contribute extra absorption and emission lines or where disk reflection plays an important role. Both models provide good and statistically comparable fits, show that the X-ray dips are correlated with drops in the inner disk temperature, and require the existence of subrelativistic (0.1–0.3c) ionized outflows. We propose that the disk temperature fluctuation stems from episodic drops of the mass accretion rate triggered by magnetic instabilities or/and wobbling of the inner accretion disk along the black hole’s spin axis.

Organisational resilience and recovery of tour operators: towards a challenge-metamorphosis model

ABSTRACT This study proposes the Challenge Metamorphosis Model (CMM) to understand the resilience and recovery of tour operators following the COVID-19 pandemic. CMM builds on challenges, crisis management, and resilience strategies to understand how businesses evolve or undergo a metamorphosis following disruptive external events. Twenty-five semi-structured interviews were carried out among Indian tour operators to understand the challenges they faced during the COVID-19 pandemic, their responses, and views on recovery and resilience. Data were analysed using thematic analysis followed by network analysis that applies Spradley’s semantic relationships to explore the latent relationships among three key concepts: challenges, recovery strategies, and resilience. Results revealed that tour operators employed a multiplicity of interrelated crisis and recovery strategies to deal with COVID-19 challenges, prompting a business metamorphosis in some cases. The findings have theoretical implications for crisis management and managerial strategies through the proposed CMM to strengthen the response and resilience of tour operators.

Decarbonization and waste reduction in a disrupted food supply chain: a step towards achieving food sustainability

PurposeThis study aims to reduce carbon emissions and minimize waste in the event of disruptions in a short and fast-food perishable such as fruits, vegetables, packaged food items, etc. supply chain through optimal investment in green and preservation technologies.Design/methodology/approach This study utilized a Hessian matrix approach to optimize decision variables with an objective to maximize the profit function.FindingsThe study demonstrates that investing in both green and preservation technology within a short and fast-food supply chain is highly beneficial for decarbonization and waste reduction and it leads to profit maximization. It has been shown with the help of a numerical experiments with investment in both green and preservation technology that total profit is 3.09% higher than without investment made in either technology.Practical implicationsThis study aids the industry in achieving food sustainability by minimizing waste of perishables and also minimizes carbon emissions which is essential for environmental protection. It assists industries in determining the optimal investment in preservation technology to minimize waste and in green technology to reduce emissions, thereby maximizing profits.Originality/valueThe current study formulates an inventory model that helps in decarbonization and waste reduction in food supply chain with the consideration of machine learning, demand disruption, preservation technology investment, screening of purchased items, waste disposal, a double triangular distribution deterioration rate, green technology investment, carbon emissions from various supply chain activities, carbon tax policy and fuel price variation over time for perishable food products in a two-warehouse system.

Primary care utilization in Bulgaria: some effects of the COVID-19 pandemic

Abstract Background Disruptions to primary care provision during the pandemic set back prior advancements in screening and chronic disease management. Delays in routine vaccination added to preexisting hesitancy and sustained the downward trend in vaccination rates. The study examines the magnitude and duration of primary care disruptions in Bulgaria between 2020 and 2022. Methods The impact of the pandemic on primary care is assessed using administrative data from the National Health Insurance Fund covering the annual number of primary care services. To evaluate the effect on healthcare utilization, the average number of check-ups per person during the pandemic is compared to a pre-pandemic period (2017-2019). Results After the onset of the pandemic, all visits declined significantly on an annual basis. In 2020, overall prophylactic check-ups dropped by 6-13% and remained lower than the baseline until 2022. Initial figures for 2023 suggest some recovery, while specific services are likely to experience an ongoing decrease. During the pandemic, the average number of prophylactic examinations for children fell between 6.9 and 9.3%. In 2022, there was a modest resumption, although these services did not reach the preceding levels. Adult prophylactic visits declined by approximately 10-12% between 2020 and 2021, but following a U-shaped pattern, they rebounded in 2022. Visits associated with chronic diseases experienced a drop of 7.8% in 2020 and continued to decrease in the next two years. Child vaccination was affected for a shorter period. In 2022, the coverage for most vaccine-preventable diseases was close to the pre-pandemic levels. However, the uptake of MMR and pneumococcal vaccines failed to demonstrate a clear path to recovery. Conclusions During the pandemic, a considerable drop in primary care utilization has been recorded. Effective recovery requires sustained service delivery and a rapid post-lockdown rebound to mitigate the negative impact on health outcomes. Key messages • Primary care needs additional recovery measures to prevent long-term harmful impacts on health outcomes. • Investing in service delivery innovations can boost resilience during disruptive events.

Evolution of Tidal Disruption Event Disks with Magnetically Driven Winds

We present a time-dependent, one-dimensional, magnetically driven disk wind model based on magnetohydrodynamic (MHD) equations, in the context of tidal disruption events (TDEs). We assume that the disk is geometrically thin, is gas pressure dominated, and explicitly accounts for magnetic braking and turbulent viscosity through an extended α-viscosity prescription. We find a particular wind solution for a set of basic equations that satisfies the necessary and sufficient conditions for vertically unbound MHD flows. The solution shows that the disk evolves with mass loss due to wind and accretion from the initial Gaussian density distribution. We confirm that the mass accretion rate follows the power law of time t −19/16 at late times in the absence of wind, which matches the classical solution of J. K. Cannizzo et al. We find that the mass accretion rate is steeper than the t −19/16 curve when the wind is present. Mass accretion is also induced by magnetic braking, known as the wind-driven accretion mechanism, which results in a faster decay with time of both the mass accretion and mass-loss rates. In the disk emission, the ultraviolet (UV) luminosity is the highest among the optical, UV, and X-ray luminosities. While the optical and X-ray emission is observationally insignificant without magnetic braking, the X-ray emission is brighter at late times, especially in the presence of magnetic braking. This provides a possible explanation for observed delayed X-ray flares. Our model predicts that late-time bolometric light curves steeper than t −19/16 in UV-bright TDEs are potentially compelling indicators of magnetically driven winds.

The rotational disruption of porous dust aggregates from ab initio kinematic calculations

The size of dust grains in the interstellar medium follows a distribution where most of the dust mass is made up of smaller grains. However, the redistribution from larger grains towards smaller sizes, especially by means of rotational disruption, is still poorly understood. We aim to study the dynamics of porous grain aggregates undergoing an accelerated rotation, namely, a spin-up process that rapidly increases the angular velocity of the aggregate. In particular, we aim to determine the deformation of the grains and the maximal angular velocity up to the rotational disruption event by caused by centrifugal forces. We precalculated the porous grain aggregate by means of ballistic aggregation analogous to the interstellar dust as input for subsequent numerical simulations. We performed three-dimensional (3D) N-body simulations, mimicking the radiative torque spin-up process up to the point where the grain aggregates become rotationally disrupted. Our simulations results are in agreement with theoretical models predicting a characteristic angular velocity, $ disr $, on the order of $ rad\ $, where grains become rotationally disrupted. In contrast to theoretical predictions, we show that for large porous grain aggregates ($ nm $), the $ disr $ values do not strictly decline. Instead, they reach a lower asymptotic value. Hence, such grains can withstand an accelerated rotation more efficiently up to a factor of 10 because the displacement of mass by centrifugal forces and the subsequent mechanical deformation supports the buildup of new connections within the aggregate. Furthermore, we report that the rapid rotation of grains deforms an ensemble with initially 50:50 prolate and oblate shapes, respectively, preferentially into oblate shapes. Finally, we present a best-fit formula to predict the average rotational disruption of an ensemble of porous dust aggregates dependent on the internal grain structure, total number of monomers, and applied material properties.

A Two-zone Accretion Disk in the Changing-look Active Galactic Nucleus 1ES 1927+654: Physical Implications for Tidal Disruption Events and Super-Eddington Accretion

The properties of slim accretion disks, while crucial for our understanding of black hole growth, have yet to be studied extensively observationally. We analyze the multiepoch broadband spectral energy distribution of the changing-look active galactic nucleus 1ES 1927+654 to derive the properties of its complex, time-dependent accretion flow. The accretion rate decays as Ṁ∝t−1.53 , consistent with the tidal disruption of a 1.1 M ⊙ star. Three components contribute to the spectral energy distribution: a central overheated zone resembling a slim disk, an outer truncated thin disk, and a hot corona. Photon trapping in the slim disk triggered by the high initial Ṁ was characterized by a low radiation efficiency (3%), which later more than doubled (8%) after Ṁ dropped sufficiently low for the disk to transition to a geometrically thin state. The blackbody temperature profile T ∝ R −0.60 for the inner overheated zone matches the theoretical expectations of a slim disk, while the effective temperature profile of T ∝ R −0.69 for the outer zone is consistent with the predictions of a thin disk. Both profiles flatten toward the inner boundary of the disk as a result of Compton cooling in the corona. Our work presents compelling observational evidence for the existence of slim accretion disks and elucidates the key parameters governing their behavior, paving the way for further exploration in this area.

Diverse policy generation for the flexible job-shop scheduling problem via deep reinforcement learning with a novel graph representation

In scheduling problems common in the industry and various real-world scenarios, responding in real-time to disruptive events is important. Recent methods propose the use of deep reinforcement learning (DRL) to learn policies capable of generating solutions under this constraint. However, current DRL approaches struggle with large instances, which are common in real-world scenarios. The objective of this paper is to introduce a new DRL method for solving the flexible job-shop scheduling problem, with a focus on these type of instances. The approach is based on the use of heterogeneous graph neural networks to a more informative graph representation of the problem. This novel modeling of the problem enhances the policy’s ability to capture state information and improve its decision-making capacity. Additionally, we introduce two novel approaches to enhance the performance of the DRL approach: the first involves generating a diverse set of scheduling policies, while the second combines DRL with dispatching rules (DRs) constraining the action space, with a variable degree of freedom depending on the chosen policy. Experimental results on two public benchmarks show that our approach outperforms DRs and achieves superior results compared to three state-of-the-art DRL methods, particularly for large instances.