Recovery Path Research Articles

The forest recovery path after drought dependence on forest type and stock volume

Drought legacy effects of forest ecosystems have been widely observed. However, the influence of forest type and stock volume on its recovery path is poorly understood. In this research, we first used the Standardized Precipitation Evapotranspiration Index to identify a drought event. Then, we applied the normalized difference vegetation index deficit and forest property maps derived from forest inventories to investigate the potential impacts of forest properties on forest recovery paths. The results showed that the legacy effects 1–3 years after a drought event were pervasive, but the forest recovery path was highly dependent on the forest type and forest stock volume. The recovery of forests with low stock volume densities (<60 m3 ha−1) was mostly stronger than that of forests with high stock volume densities (≥60 m3 ha−1) by the second year. Although all forests with different stock volume densities approximately returned to a normal status by the third year, they followed various paths to recovery. Natural coniferous forests in China that have a similar stock volume density (<60 m3 ha−1) took longer to recover than planted coniferous forests and exhibited a lower magnitude of recovery. These findings highlight that drought legacy effects are greater for natural coniferous forests with high stock volume densities, which provides insightful forest management information on how to speed up forest recovery with forest density control and type control.

Lightweight Path Recovery in IPv6 Internet-of-Things Systems

In an Internet-of-Things system supported by Internet Protocol version 6 (IPv6), the Routing Protocol for Low-Power and Lossy Networks (RPL) presents extensive applications in various network scenarios. In these novel scenarios characterized by the access of massive devices, path recovery, which reconstructs the complete path of the packet transmission, plays a vital role in network measurement, topology inference, and information security. This paper proposes a Lightweight Path recovery algorithm (LiPa) for multi-hop point-to-point communication. The core idea of LiPa is to make full use of the spatial and temporal information of the network topology to recover the unknown paths iteratively. Specifically, spatial and temporal information refer to the potential correlations between different paths within a time slot and path status during different time slots, respectively. To verify the effect of our proposal, we separately analyze the performance of leveraging temporal information, spatial information, and their composition by extensive simulations. We also compare LiPa with two state-of-the-art methods in terms of the recovery accuracy and the gain–loss ratio. The experiment results show that LiPa significantly outperforms all its counterpart algorithms in different network settings. Thus, LiPa can be considered as a promising approach for packet-level path recovery with minor loss and great adaptability.

A Perspective towards Multi-Hazard Resilient Systems: Natural Hazards and Pandemics

The concept of resilience has been used extensively across the sciences in engineering and the humanities. It is applied to ecology, medicine, economics, and psychology. The novel coronavirus disease (COVID-19) has posed an extraordinary challenge to the resilience of healthcare systems, communities, and nations and has profoundly altered our previous day-to-day operations. This paper presents a discussion of the definitions and characteristics of resilient systems. Scenarios are utilized to qualitatively explore key relationships, responses, and paths for recovery across different system types. The purpose is to develop an integrated approach that can accommodate simultaneous threats to system resilience, in particular, impacts from a natural hazard in conjunction with COVID-19. This manuscript is the first to advocate for more in-depth and quantitative research utilizing transdisciplinary approaches that can accommodate considerations across our built environment and healthcare system infrastructures in pursuit of designing systems that are resilient to both natural hazards and pandemic impacts.

In-silico studies on wild orange (Citrus macroptera Mont.) compounds against COVID-19 pro-inflammation targets

One-fifth of COVID-19 patients suffer a severe course of COVID-19 (SARS-CoV-2) infection; however, the specific causes remain unclear. Despite numerous papers that have been flooded in different scientific journals clear clinical picture of COVID-19 aftermath persists to remain fuzzy. The survivors of severe COVID-19infection having defeated the virus are just the starting of an uncharted recovery path. Currently, there is no drug available that is safe to consume to combat this pandemic. However, researchers still struggling to find specific therapeutic solutions. The present study employed an in silico approach to assessing the inhibitory potential of the phytochemicals obtained from GC-MS analysis of Citrus macroptera against inflammatory proteins like COX-2, NMDAR and VCAM-1 which remains in a hyperactive state even after a patient is fully cured of this deadly mRNA virus. An extensive molecular docking investigation of the phyto-compounds at the active binding pockets of the inflammatory proteins revealed the promising inhibitory potential of the phytochemicals. Reasonable physicochemical attributes of the compounds following Lipinski’s rule of five, VEBER and PAINS analysis further established them as potential therapeutic candidates against aforesaid inflammatory proteins. MM-GBSA binding free energy estimation revealed that Limonene was the most promising candidate displaying the highest binding efficacy with the concerned VCAM-1 protein included in the present analysis. An interesting finding is the phytochemicals exhibited better binding energy scores with the concerned COX-2, VCAM-1 and NMDA receptor proteins than the conventional drugs that are specifically targeted against them. Our in silico results suggest that all the natural phyto-compounds derived from C. macroptera could be employed in Post covid inflammation complexities after appropriate pre-clinical and clinical trials for further scientific validation. Communicated by Ramaswamy H. Sarma

Fast Path Recovery for Single Link Failure in SDN-Enabled Wide Area Measurement System

In the wide area measurement system (WAMS), the end-to-end transmission delay between the phasor measurement unit (PMU) and phasor data concentrator (PDC) is strictly constrained for real-time monitoring and protection applications. When a communication link failure happens, fast path recovery is required to reduce the impact of measurement losses. In this work, the promising software-defined network (SDN) technique is leveraged to compute the re-routing path in a global view upon a single link failure. More specifically, a hybrid fast path recovery algorithm (HFPR-A) is proposed based on the principle of simplicity: in some cases, the shortest path or approximate shortest path between PMU and PDC can be recovered by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adding only one edge</i> to the original forwarding tree; while in the other cases, the shortest paths can be recovered with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lower computational complexity</i> than the traditional Dijkstra’s algorithm. The proposed HFPR-A is implemented on the Ryu + Mininet testbed, and the simulation results on different IEEE benchmark test power systems show that the proposed HFPR-A could find shorter re-routing paths than the existing methods with a low-enough response time.

COVID-19 and the Energy Sector Development in ASEAN Countries: Assessing the Role of Fiscal Measures for Green Recovery Trajectory

The COVID-19 pandemic has had wide ramifications on many sectors, including energy sector. This draws the interest of numerous scholars who have sought to investigate the impact of the pandemic on the energy sector as well as the prospects for a green recovery trajectory. However, few of these authors have considered the role of fiscal stimulus that governments have provided in the context of green recoveries. This study aims to fill that gap, focusing on the case of ASEAN member states (AMS). For this purpose, we employ both statistical data from official sources and qualitative evidence collected through interviews and Focus Group Discussions (FGD) with officials from ASEAN countries. This study finds that green recovery stimulus in AMS is still limited. Although some of the fiscal supports that ASEAN countries have launched have had indirect effects on energy, policy makers in the region have not prioritized the green recovery at the center of recovery path. A major factor is fiscal limitations that force the governments of ASEAN countries to focus on strengthening the health sector and supporting vulnerable groups. Accordingly, this analysis highlights the importance of exploring other funding opportunities, including grants and concessional financing, to support green recovery in the region.

Spain: 2021 Article IV Consultation-Press Release; Staff Report; and Statement by the Executive Director for Spain

The Spanish economy is recovering from the deep recession caused by the COVID pandemic and employment is already above its pre-pandemic level. A highly successful vaccination campaign helped limit the impact of the recent wave of infections on hospitalizations and economic activity. Output is expected to return to its pre-pandemic level by the end of 2022. However, there is significant uncertainty around the recovery path related to the evolution of the pandemic and the duration of global supply bottlenecks. The pace of absorption and the effectiveness of use of Next Generation EU (NGEU) funds will also affect growth in the coming years. In the near term, the key policy challenge is to continue to provide targeted pandemic support while facilitating resource reallocation and a transformation to a greener and more inclusive economy.

Probabilistic Forecasts Using Expert Judgment: The Road to Recovery From COVID-19.

The COVID-19 pandemic has had a devastating effect on many industries around the world including tourism and policy makers are interested in mapping out what the recovery path will look like. We propose a novel statistical methodology for generating scenario-based probabilistic forecasts based on a large survey of 443 tourism experts and stakeholders. The scenarios map out pessimistic, most-likely and optimistic paths to recovery. Taking advantage of the natural aggregation structure of tourism data due to geographic locations and purposes of travel, we propose combining forecast reconciliation and forecast combinations implemented to historical data to generate robust COVID-free counterfactual forecasts, to contrast against. Our empirical application focuses on Australia, analyzing international arrivals and domestic flows. Both sectors have been severely affected by travel restrictions in the form of international and interstate border closures and regional lockdowns. The two sets of forecasts, allow policy makers to map out the road to recovery and also estimate the expected effect of the pandemic.

Evaluating the impact of a training program to support transitioning from the hospital to the community for people after stroke: a community case study

BackgroundThe transitions in care along the stroke recovery path are challenging, particularly in finding mechanisms to continue one’s recovery once at home. We aim to evaluate the impact of training physiotherapists and fitness instructors from one regional community together to deliver an evidence-based group exercise program starting in the hospital and transitioning to the community using an implementation approach.MethodsThe evidenced based exercise program Fitness and Mobility Exercise (FAME) for stroke was chosen as the intervention. Data from interviews with stakeholders (community centre and health authority hospital staff including a physiotherapy navigator) was transcribed and themes evaluated using the RE-AIM (Reach, Efficacy, Adoption, Implementation, Maintenance) framework. These data were supplemented by information collected as a quality assurance project within the health authority.ResultsTwo programs were established; one in the community centre (run over 15 months by fitness instructors) and one in the regional hospital (run over 12 months by a rehabilitation assistant under the direction from a physiotherapist). Transitions in care were facilitated by implementing the same evidence-based group exercise class in both the hospital and community setting, so people living with stroke could seamlessly move from one to another. An existing physiotherapist navigator service also was valued as a support for the transitions between the two centres for people with stroke. The hospital group accessed group-based physiotherapy service on average 31 days earlier than they were able to in a one-to-one format.ConclusionsThis case study described the implementation of the Fitness and Mobility Exercise (FAME) program in one community and the use of a physiotherapist navigator to assist transition between them. After a community training workshop, FAME programs were established within the health authority and the community centre. FAME program participants within the health authority benefited from reduced wait times to access hospital outpatient physiotherapy service. Improvements in function were measured in and reported by the people after stroke attending either the health authority or community centre FAME groups.

Digital Innovations in MSMEs during Economic Disruptions: Experiences and Challenges of Young Entrepreneurs

The spike in COVID-19 cases all over the world poses a massive impact on various sectors of society. The global economy is no exception, for it has been severely affected by the unprecedented public health crisis leading businesspeople to shutdown commercial establishments and dramatically shifting businesses into digital platforms. Immense challenges brought by the pandemic have turned into great opportunities among entrepreneurs from different parts of the globe. In pursuit of the recovery path, they considered digital tools to be instrumental to surviving the crisis and even thriving in the long term. This study explores drivers and barriers to digital innovations in micro, small, and medium enterprises (MSMEs) during economic disruptions from the perspective of Filipino young entrepreneurs. Researchers employed a qualitative method for the conduct of in-depth interviews with 46 online entrepreneurs in the Philippines and transcribed them for Inductive Content Analysis to generate themes. Results found the adverse effects of the pandemic, which reinforce entrepreneurial innovations of shifting businesses to digital platforms, categorized into intrinsic (e.g., personal and professional growth) and extrinsic (e.g., mobility restrictions, market conditions, and household economic status) motivations. Participants described the barriers to digital entrepreneurship, including skills needed for doing online business, market-related problems in digital platforms, availability of quality internet infrastructures, and the pandemic restrictions. The findings of this study contribute to the entrepreneurship literature and areas for an empirical investigation to develop programs that support entrepreneurs during economic disruptions. This would serve as a guidepost in the formulation of government policies and strategies for economic recovery through digital entrepreneurship while considering the perspectives of young entrepreneurs. This further recommends future related researches to empower entrepreneurs, particularly those in the developing economies during the pandemic and beyond.

Real-Time Resilience Optimization Combining an AI Agent With Online Hard Optimization

In the highly interdependent environment of a large city, failures in the Electrical Distribution System (EDS) can cause direct or indirect consequences to other critical infrastructures and the well-being of the citizens. To increase the resilience of the supply of electricity to the city, this work combines the pre-training of an AI agent and very fast calculation of the optimum recovery path after the number and location of the electrical faults are known. In the introduced Soft-Hard Optimal Convergence (SHOC) method, machine learning techniques are used to train an AI agent with thousands of off-line scenarios for optimum system restoration. In real-time, after the actual fault information is known, the agent will provide a subset of solutions (soft solution) to be considered for hard optimization algorithms. The Infrastructure Interdependencies Simulator (i2SIM) is used to assist the prioritization of the sequence of fault recovery and topological reconfiguration to minimize the black-out time of the most critical loads. A 70-node distribution system case is used to demonstrate the proposed methodology, with solution times in the order of seconds to find the optimum repair sequence and switches topological reconfiguration to optimize the city's resilience index.

Thresholds for meaningful improvement in WOMAC scores need to be adjusted to patient characteristics after hip and knee replacement

PurposeTo calculate unstratified and patient-specific meaningful improvement (MI) and patient acceptable symptom states (PASS) for the WOMAC total score in patients after total hip (THR) or total knee replacement (TKR). MethodsA retrospective observational cohort study. Anchor-based receiver operator characteristics curves were used to estimate MI and PASS thresholds. ResultsRecovery paths were specific to individual characteristics of patients. An unstratified 12-months MI threshold of 28.1 (PASS: 13.3) and 17.8 (PASS: 15.8) for patients after THR and TKR, respectively, would unfairly detect critical recovery paths. ConclusionsThresholds for treatment success need to be as patient-specific as possible.

The Startup Sequences of the Restarted Units Considering the Characteristics of the Recovery Path

The Startup Sequences of the Restarted Units Considering the Characteristics of the Recovery Path

Typical Application Scenarios and Economic Benefit Evaluation Methods of Battery Energy Storage System

Energy storage system is an important means to improve the flexibility and safety of traditional power system, but it has the problem of high cost and unclear value recovery path. In this paper, the typical application scenarios of energy storage system are summarized and analyzed from the perspectives of user side, power grid side and power generation side. Based on the typical application scenarios, the economic benefit assessment framework of energy storage system including value, time and efficiency indicators is proposed. Typical battery energy storage projects are selected for economic benefit calculation according to different scenarios, and key factors are selected for sensitivity analysis. Finally, the key factors affecting economic benefit of the energy storage system are analyzed.

PR-LTTE: Link travel time estimation based on path recovery from large-scale incomplete trip data

The widespread use of positioning devices has generated large-scale trip data, boosting the study of traffic modeling. For instance, New York City Taxi & Limousine Commission regularly releases over 165 million taxi trip records containing the end-point information of each trip every year. Such big datasets provide us potential new perspectives to tackle traditional issues in traffic modeling. In this paper, we propose to use such vehicle trip data, which do not contain information on the intermediate vehicle positions other than the end points, to study the link (road segment) travel time estimation problem. A method named PR-LTTE is developed to estimate the mean travel time for interested links in a road network based on path recovery, jointly modeling trip distance and trip travel time. The key idea is to iteratively alternate between 1) inferring the most likely path for a given trip by minimizing a loss function that considers both trip distance and trip travel time, and 2) computing the link travel time via the least-squares estimation with the inferred path. Our experiments on two large-scale trip datasets (including New York City taxi trip data and Chengdu DiDi trip data) show very promising results. PR-LTTE improves the accuracy of travel time estimation by more than 50% in most cases over state-of-the-art methods.

Self-care deficit versus the potential to improve self-care: explanation of the nurse's clinical judgments

Background: people who become functionally dependent due to serious illness and/or the worsening of a chronic illness have different recovery paths. The attribution of the nursing diagnosis of self-care deficit versus potential to improve self-care requires, on the part of the nurse, a rigorous clinical judgment, for which a set of diagnostic activities is necessary. Objectives: the present study sought to identify the assumptions used by expert nurses when formulating the clinical judgment of self-care deficit versus the potential to improve self-care Methodology: qualitative design. Data were generated in two focus group meetings that included eleven nurses experienced in caring for persons with functional dependence and self-care defict. Previously prepared structured questions were used to facilitate discussion. The focus group meetings were recorded in audio and transcribed. Inductive content analysis was used to identify emerging themes. The checklist Consolidated Criteria for Reporting Qualitative Research (COREQ) was also used. Results: nurses` decision-making depends: the factors of influencing the potential to improve self-care abilities of a person with functional dependence, the domain of influence of these factors, and three fundamental aspects to consider when evaluating a person with functional dependence. Conclusions: the absence of recovery potential can rarely be deduced. People without cognitive impairment or with mild cognitive impairment present highest potential for recovery.

Spatio-Temporal Pattern and Influence Mechanism of Cultivated Land System Resilience: Case from China

The study of cultivated land systems from the perspective of resilience is of great significance for the innovation of the research paradigm of cultivated land use and the rational utilization and protection of cultivated land. This study aims to explain the theoretical connotations of cultivated land system resilience (CLSR), construct an evaluation system and zoning rules for CLSR, and take 30 provinces of China as case study areas to explore the influencing factors of CLSR, so as to provide a reliable governance plan for the sustainable development of cultivated land. The results show that: (1) CLSR refers to a sustainable development ability that CLS—by adjusting the structure and scale of internal elements—absorbs and adapts to internal and external disturbances and shocks to the maximum possible extent, abandons the original inapplicable state, creates a new recovery path, achieves a new balance, and avoids system recession. (2) The overall CLSR of the 30 provinces showed an upward trend, and the degree of polarization of the distribution pattern was gradually intensified and experienced a transition process from “leading by resource and ecological resilience—equilibrium of each resilience—leading by production and scale structural resilience”. (3) In the north, east, and south coastal areas of China, CLSR mainly consists of the major evolution areas and the stable development areas; the potential excitation areas of CLSR are mainly concentrated in the central and western regions of China; the CLSR-sensitive lag areas and degraded vulnerable areas are mainly distributed in the northwest and southwest of China. (4) Water resource endowment has a strong influence on CLSR, while social economy mainly influences CLSR through ‘economic foundation-superstructures’ and ‘economic development-factor agglomeration’. (5) According to the different CLSR zones, CLSR was strengthened mainly from the aspects of driving factor agglomeration, building factor free-flow systems, and multi-means support.

Short-term impact of the Covid-19 pandemic on the global and Turkish economy

Background/aimThe Covid-19 pandemic is one of those rare events that affects everyone on earth and changes our lives. The pandemic, which has killed over four million people worldwide, is putting unprecedented pressure on governments to maintain essential health and social services, as well as keep their economies running, even as the virus threatens people’s daily life on every level. Thus, the purpose of this study is to discuss the short-term economic impact of the pandemic by assessing its costs using official economic data for both the world and Turkey. Furthermore, this research highlights possible economic, social, and political pathways for a postpandemic new world.Materials and methodsThis study is a review article that overviews and tracks the economic development of the Covid-19 pandemic from the start, synthesizes and compares current data of reliable institutions, and provides an overall assessment.Results The pandemic has certainly caused short-term and long-term damage to economies and living standards for many people. Although there are estimates on what this damage is, the exact degree of the damage is still unknown. However, it seems that the recovery will be gradual, long-lasting, and unpredictable due to the unprecedented uncertainty characteristic of the pandemic.ConclusionEarly economic growth projections show that there will be no ordinary recovery for the world economy since short-term countries’ recovery paths are different. It is likely to remain uneven and depend on the effectiveness of the vaccination process, fiscal policy support, public health management, and hard-hit sectors’ growth size in economies. Due to the uncertainty and lack of confidence, governments should ensure an equal and sustainable economic recovery from the Covid-19 pandemic by conducting flexible monetary and fiscal policies. However, without structural reforms, economies can not boost either in the short-term and long-term.

Impact of Railway Critical Incidents on Train Drivers and Effectiveness of Critical Incident Management and Support Protocols: A Recovery Trajectory Analysis.

Train drivers are regularly involved in railway critical incidents (CI) and critical incident management and support protocols (CIMSPs) have been developed to support them. This study describes the impact of CIs and evaluates the effectiveness of CIMSPs. We interviewed 74 train drivers 1 week, 1, 3, and 6 months after a CI. We performed Linear growth model analyses to describe recovery paths and the effect of CIMSPs, considering the effect of CI complexity, proximity to victims, social support, self-assessment of health, use of resources, and attribution of responsibility. CIs have a significant negative impact on train drivers. Recovery paths can be explained by the level of implementation of CIMSPs, and other contributing factors. CIMSPs are effective in reducing the negative impact of CIs and should be implemented rigorously by employers.

A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand

The sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. However, as of today, only a small number of studies have examined detailed hospitalization data from a planning perspective. The current study develops a comprehensive framework for understanding the critical factors associated with county level hospitalization and ICU usage rates across the US employing a host of independent variables. Drawing from the recently released Department of Health and Human Services weekly hospitalization data, we study the overall hospitalization and ICU usage—not only COVID-19 hospitalizations. Developing a framework that examines overall hospitalizations and ICU usage can better reflect the plausible hospital system recovery path to pre-COVID level hospitalization trends. The models are subsequently employed to generate predictions for county level hospitalization and ICU usage rates in the future under several COVID-19 transmission scenarios considering the emergence of new COVID-19 variants and vaccination rates. The exercise allows us to identify vulnerable counties and regions under stress with high hospitalization and ICU rates that can be assisted with remedial measures. Further, the model will allow hospitals to understand evolving displaced non-COVID hospital demand.