Dynamic Policy Research Articles

China’s policies: post-COVID-19 challenges for the older population

ABSTRACT On 7 December 2022, the State Council of China released ‘Measures to Further Optimize the Implementation of the Prevention and Control of the New Coronavirus Epidemic’. The previous three-year dynamic zero epidemic prevention policy was then replaced with a full liberalization policy. On 5 May 2023, the World Health Organization declared that COVID-19 no longer constituted a ‘public health emergency of international concern.’ However, given the ongoing prevalence of coronavirus, emerging mutations, and the liberalization of restrictions, there are increased risks of vulnerable people contracting new variants. Low vaccination coverage among older people with compromised immune systems, puts them at further risk. The policy shift will increase pressure on already stretched health infrastructure and medical resources. This short article adds to the current debate arguing that the Chinese government should take commensurate preventive measures, including strengthening medical facilities and equipment and targeting ongoing vaccination in older people.

Evaluating Adaptation Pathways in a Complex Multi‐Risk System

AbstractDisaster Risk Management (DRM) is complex due to interacting climate risks from interacting hazards and sectors. We develop a synthetic multi‐risk test case to explore the effects of these interactions on decision‐making under deep uncertainty. The test case accounts for changes in hazard impacts and occurrence frequency due to interactions between floods and droughts. Interactions between the shipping, housing and agricultural sectors are considered as well. We use this test case to explore the utility of the Dynamic Adaptive Policy Pathways for Multi‐Risk (DAPP‐MR) framework. DAPP‐MR has been introduced to assess DRM policies' effectiveness under deep uncertainties and to develop integrated adaptive strategies considering interactions across hazards, sectors and time. With the test case, we show that the stage‐wise approach of DAPP‐MR, which gradually increases the complexity of the analysis, can facilitate the evaluation process. Earlier stages of the analysis can be used to establish the direct cause‐effect relations, later stages allow us to identify whether additional interacting factors have a significant effect on the direct cause‐effect relations. As a result, decision‐makers can gain insights into dependencies and their relevance for developing short‐to long‐term strategies under deep uncertainty. We show that multi‐risk interactions can lead to non‐linear effects that influence the outcome of the policy analysis, for example, the indirect influence of a decision in one sector on another. Future work could investigate further improving the operationalization of this staged approach as well as extending the set of uncertainties, dynamics and decision‐rules accounted for in the multi‐risk test case.

Intricacies of French engagement in Sahel with peace prospects

The Sahel region faces an extreme human security crisis with complex security challenges rooted in historical, ethnic, and geopolitical ties. Decades of armed violence and unstable socio-political systems have led to deteriorating security conditions, with crisis hotspots diversifying across the region. The mobilization of destabilizing actors has necessitated external support, yet outcomes have proven inadequate. This research explores France's engagement in the region, transitioning with dynamic policy shifts towards a more “Sahelian solutions for Sahelian problems” oriented approach. However, externally led peacebuilding exercises have fallen short due to lack of coherence, worsening conditions in the region, leading to adverse effects like the emergence of a 'coup belt'. Thus, this study aims to identify sustainable peace alternatives by investigating key questions like: How has France's role in the Sahel transformed, particularly in response to policy shifts and troop withdrawals? Why does France remain a prominent security provider despite changes in its military posture? What are the limitations and outcomes of peacebuilding processes employed in the region by various actors? How effectively can peacebuilding discourses translate into tangible security improvements for the Sahelian people? Thus, this paper aims to demystify conflict dynamics in the region, analyzing France’s evolving role in the Sahel and evaluating collaborative efforts to address security threats and promote development, empowering local actors. Additionally, it explores peacebuilding theoretically to understand Sahel's ground realities. Through a review of past interventions, it highlights limitations in fostering positive peace, offering recommendations for coherent policies and collaboration in the Sahel. In this regard, the paper explores contemporary events through peace, security, and policy lenses.

End to End secure data exchange in value chains with dynamic policy updates

Data exchange among value chain partners offers a competitive advantage, but it also exposes sensitive data to increasing risks of compromise. To protect this data, securing it during storage and transmission is essential, allowing only the data producer and final consumer to access or modify it. This can be particularly challenging for Industrial Internet of Things (IIoT) devices, which often have limited processing and memory capabilities. To address these challenges, End-to-End (E2E) security mechanisms are needed to prevent data breaches resulting from value chain attacks. Additionally, value chain environments are dynamic and involve multiple data generation and consumption entities, requiring the ability to update access policies as needed. This paper presents a Ciphertext-Policy Attribute-Based Encryption (CP-ABE) data exchange system that provides E2E security for value chains, considering the most relevant security and industrial requirements. The proposed solution can protect data according to access policies while also allowing those policies to be updated without breaking E2E security or overloading field devices. The experimental evaluation demonstrates the feasibility of the proposed solution for IIoT platforms.

Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022

BackgroundInfluenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations.MethodsThe multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise.ResultsDuring the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2–4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0–1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time.ConclusionThe ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.

The Influence of Transformative Learning, Affective Commitment, Digital Transformation, Capability Dynamic, Ambidexterity, and Education Policy Toward Educational Leadership: A Case from Indonesia

Purpose: The implications of external environment changes and the presence of a new generation of students in the school to the school leadership are often overlooked. Very few studies elaborate on the need to shape educational leadership concepts and practices to deal with the changes. The study intention is to identify the influences of transformative learning, affective commitment, digital transformation, capability dynamic, and the ambidexterity to educational leadership, and to craft the educational leadership based on Indonesia context. Design/Methodology/Approach: The study is using Covariance Based Structural Equation Modelling (CB-SEM), the data is collected through a Likert based questionnaire from 271 respondents and it is processed by SPSS and AMOS. Findings: Transformative Ethics Agile (TEA) Leadership as educational leadership is influenced by Ambidexterity, transformative learning, affective commitment, and educational policy. Digital transformation and capability dynamic have insignificant influences, however ambidexterity mediating the influence of dynamic capability to TEA Leadership. Research Implications: The research contributes to renewing educational leadership concepts by putting emphasis on transformative, ethics, and agile elements. The variables that must be put into attention for leading school during changing time, specifically ambidexterity, transformative learning, affective commitment, and educational policy. The research highlights the importance of context on educational leadership. Originality/Values: The research contributes to the identification of variables that influence the effectiveness of school leadership and by doing that able to facilitate the strategy of leadership development for school management. The values of the research are on the mapping of factors contribute to educational leadership and lead the practical aspects of managing school in the changing time.

Model analysis and data validation of structured prevention and control interruptions of emerging infectious diseases.

The design of optimized non-pharmaceutical interventions (NPIs) is critical to the effective control of emergent outbreaks of infectious diseases such as SARS, A/H1N1 and COVID-19 and to ensure that numbers of hospitalized cases do not exceed the carrying capacity of medical resources. To address this issue, we formulated a classic SIR model to include a close contact tracing strategy and structured prevention and control interruptions (SPCIs). The impact of the timing of SPCIs on the maximum number of non-isolated infected individuals and on the duration of an infectious disease outside quarantined areas (i.e. implementing a dynamic zero-case policy) were analyzed numerically and theoretically. These analyses revealed that to minimize the maximum number of non-isolated infected individuals, the optimal time to initiate SPCIs is when they can control the peak value of a second rebound of the epidemic to be equal to the first peak value. More individuals may be infected at the peak of the second wave with a stronger intervention during SPCIs. The longer the duration of the intervention and the stronger the contact tracing intensity during SPCIs, the more effective they are in shortening the duration of an infectious disease outside quarantined areas. The dynamic evolution of the number of isolated and non-isolated individuals, including two peaks and long tail patterns, have been confirmed by various real data sets of multiple-wave COVID-19 epidemics in China. Our results provide important theoretical support for the adjustment of NPI strategies in relation to a given carrying capacity of medical resources.

Decentralized Identity Management in Cross-Social Network Photo Sharing

The widespread use of online social networks has resulted in a surge in photo sharing, but the vulnerability of photo privacy becomes apparent when these images are disseminated across multiple platforms. Current solutions often fall short in delivering efficient social network services without compromising user privacy. Introducing PhotoChain, a revolutionary blockchain-based framework designed to facilitate secure photo sharing across social networks. This framework incorporates advanced features, including dissemination control, face masking, photo integrity verification, access control, and dynamic privacy policy generation. Leveraging smart contracts, PhotoChain ensures a consistent consensus on dissemination control, while robust mechanisms for photo ownership identification are integrated to thwart illegal reprinting. A fully functional prototype has been implemented and rigorously tested, substantiating the framework's prowess in delivering security, efficacy, and efficiency for photo sharing across social networks. Keywords: Online social networks, PhotoChain, blockchain

Prevalence and associated factors of insomnia symptoms after ending China's dynamic zero-COVID policy: a cross-sectional survey of frontline nursing staff in Chinese hospitals.

After the Chinese government announced the end of the dynamic zero-COVID policy on January 8, 2023, the COVID-19 pandemic peaked. Frontline nursing staff are at high risk of infection transmission due to their frequent contact with COVID-19 patients. In addition, due to the ending of China's dynamic zero-COVID policy, frontline nursing staff have grappled with increased workload, fatigue, and more. This study aimed to explore the prevalence of insomnia symptoms in frontline nursing staff and its influencing factors following the end of the policy. Between January and February 2023, this study was conducted by the Wenjuanxing platform to survey frontline nursing staff in a hospital in Wuhu City, Anhui Province. All the nursing staff included in this study had a COVID-19 infection. The questionnaires included the Athens Insomnia Scale (AIS), PC-PTSD-5 Chinese Version Scale, the Fear of COVID-19 Scale, The 2-item Connor-Davidson Resilience Scale (CD-RISC-2) Scale, and the burden of COVID-19 Scale. Binary logistic regression methods were used to identify variables associated with insomnia symptoms. Among the 694 frontline nursing staff, 74.5% (517/694) exhibited insomnia symptoms. Fear of COVID-19 (p < 0.001), the burden of COVID-19 (p < 0.05), PTSD (p < 0.001), and higher technical titles (p < 0.008) were highly correlated with insomnia symptoms in frontline nursing staff. Psychological resilience (p < 0.001) was a protective factor for insomnia symptoms among frontline nursing staff. After ending China's dynamic zero-COVID policy, the prevalence of insomnia symptoms among frontline nursing staff is generally higher. This study highlights the association between insomnia symptoms and PTSD, fear of COVID-19, COVID-19 burden, and resilience. Psychological assistance is needed for frontline nursing staff to prevent insomnia symptoms and protect the mental health of frontline nursing staff after the end of China's dynamic zero-COVID policy.

Efficient criticality oriented service brokering policy in cloud datacenters

Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of these resources and the immense number of user workloads arriving simultaneously in the cloud, it is necessary to use an effective load distribution technique for scheduling the resources to achieve high performance and high user satisfaction. Service brokering policy and load balancing techniques are the two crucial areas to be focused on while selecting the datacenters and virtual machines, respectively. In this study, we have proposed a dynamic efficient criticality-oriented service brokering policy for load allocations among datacenters by considering task criticality, datacenter proximity, and traffic, the size of the datacenter, its present load and makespan value. The proposed methodology is examined against the current policies in the CloudAnalyst simulation tool and the analysis report confirms that our proposed policy gives priority to processing the urgent loads and chooses the optimum datacenter to diminish the load response time, datacenter processing time, minimizes the cost, achieves optimum resource utilization and workload balancing among resources.

Stochastic Economic Lot Scheduling via Self-Attention Based Deep Reinforcement Learning

The Stochastic Economic Lot Scheduling Problem (SELSP) is a difficult dynamic optimization problem with wide industrial applications. Traditional methods such as hyper-heuristics are manually designed based on substantial expert knowledge, which may limit their optimization performance. Recently, Deep Reinforcement Learning (DRL) is shown to be promising in automatically learning scheduling policies for SELSP. However, its performance is still quite far from that of hyper-heuristics, due to the lack of suitable deep models. In this paper, we propose a novel DRL method to learn dynamic scheduling policies for SELSP in an end-to-end fashion. Based on self-attention, our method can effectively extract useful features from raw state information, and is flexible in handling different numbers of products, which is not viable for previous methods. Experiments on a complex biopharmaceutical manufacturing process show that our method outperforms a recent DRL method and state-of-the-art hyper-heuristics. Moreover, the trained policy performs better in environments different from training with demand forecast errors and varying number of products, showing its strong robustness and generalization ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The Stochastic Economic Lot Scheduling Problem (SELSP) is an important problem for manufacturing enterprises, which is to optimally balance the production and inventory so as to minimize the total cost. However, SELSP is very challenging to solve due to the involvement of uncertain factors such as customer demands and machine failures. Traditional methods for solving SELSP, such as heuristic policies and hyper-heuristics, heavily rely on human experiences to design and hence the performance could be limited. This paper proposes a Deep Reinforcement Learning (DRL) based method to automatically learn scheduling policy for solving SELSP, which could alleviate the above limitation through a self-attention based feature extraction mechanism and reward based training. Experimental results on a realistic manufacturing process show that our method can deliver higher revenue than conventional manual policy and an existing DRL based method.

A mini review of reinfection with the SARS-CoV-2 Omicron variant.

COVID-19 has caused severe morbidity and mortality worldwide. After the end of the dynamic zero-COVID policy in China in December, 2022, concerns regarding reinfection were raised while little was known due to the lack of surveillance data in this country. This study reviews the probability, risk factors, and severity of severe acute respiratory syndrome coronavirus 2 Omicron variant reinfection, as well as the interval between infections, risk of onward transmission by reinfected cases, and the role of booster vaccination against reinfection. References for this review were identified through searches of PubMed and Web of Science up to September 24, 2023. The rate of reinfection ranges from 3.1% to 13.0%. Factors associated with a higher risk of reinfection include being female, having comorbidities, and being unvaccinated. Reinfection with the BA.4 or BA.5 variant occurs approximately 180 days after the initial infection. Reinfections are less clinically severe than primary infections, and there is evidence of lower transmissibility. The debate surrounding the effectiveness and feasibility of booster vaccinations in preventing reinfection continues. The reinfection rate during the Omicron epidemic is significantly higher than in previous epidemic periods. However, the symptoms and infectivity of reinfection were weaker than those of the prior infection. Medical staff and individuals at high risk of reinfection should be vigilant. The efficacy of booster vaccinations in reducing reinfection is currently under debate.

Group Dynamics and Public Policy Formation: Insights from Nigerian Society

This paper appraises the theoretical assumptions of group theory within the purview of public policy with a specific focus on the Nigerian context. It begins by providing conceptual clarity on policy, public policy, and public policy analysis to lay the foundation for the subsequent discussion. The paper then examines the underlying assumptions of group theory and critiques its applicability within public policy discourse. Through the analysis of secondary data, this paper explores the role of group theory in shaping policy formulation and implementation processes. It highlights the significant influence of group actions on government policies, illustrating how various interest groups exert pressure and influence decision-making processes. This paper concludes that public policy emerges as a result of collective agitation and interaction within a given society. It recognises that various groups play pivotal roles in shaping policy outcomes, yet their actions are typically governed by established rules, fostering a climate of peaceful competition among competing interests.

Dynamic curriculum learning for conversation response selection

Curriculum learning (CL) has demonstrated its potential to fine-tune pre-trained models for the response selection task. However, existing CL works exhibit a significant reliance on fixed data buckets and a pre-ordained learning schedule. This paper seeks to transcend these constraints by proposing a novel dynamic curriculum learning (DCL) algorithm that employs: (1) a new difficulty measurement that amalgamates linguistic features with model confidence, and (2) an automatic uncertainty-based learning scheduler with a dynamic data sampling policy. This unique approach enables the model to autonomously assemble rational data batches and adjust the curriculum stage by itself. Empirical experiments show that our DCL method reveals its superior performance over three strong baselines and the competitive method on three multi-turn dialogue datasets. The in-depth analysis demonstrates our approach dynamically adapts the CL process, which in turn makes effective use of existing data and alleviates data scarcity.

Spatiotemporal differentiation calendar for car and truck flow on expressways: A case study of Jiangsu, China

The transport objects and demands of cars and trucks differ, leading to different spatiotemporal characteristics in their flow patterns. Existing studies on expressway flow are mainly focused on the overall traffic, however, a decomposition analysis of cars and trucks is necessary to accurately explore traffic prediction, management, and dynamic toll policy. This study presents calendars and spatial distribution of car and truck flow based on toll fee data in Jiangsu Province, China. Additionally, the influencing factors are explored by using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The results show that: (1) car flow follows a regular pattern of low from Monday–Thursday and high from Friday–Sunday, while high from Monday–Friday and low from Saturday–Sunday for truck flow. This law is not obvious in the total flow calendar. (2) The car flow predominantly accumulates in the 0–20 km range, while the truck flow is concentrated within the 20–40 km range. (3) The spatial distributions are stable for both car and truck flow, characterized by higher flows in the southern part of Jiangsu and lower in the northern region. Gaps in truck flow between cities are greater than those in car flow, narrowing during holidays/festivals than on weekdays or weekends. (4) private vehicle possession and travel time to Shanghai are the main influencing factors on car flow during different periods, the number of research institutions and the added value of tertiary industry are positively correlated on weekdays and weekends, respectively. Total retail sales of consumer goods is the main factor affecting truck flow during different periods, total import and export goods can be attributed to weekdays, while per capita disposable income affects weekends and festivals. Finally, the findings of this study offer insights for the optimal implementation of toll policies.

FairPlay: A Multi-Sided Fair Dynamic Pricing Policy for Hotels

In recent years, popular touristic destinations face overtourism. Local communities suffer from its consequences in several ways. Among others, overpricing and profiteering harms local societies and economies deeply. In this paper we focus on the problem of determining fair hotel room prices. Specifically, we put forward a dynamic pricing policy where the price of a room depends not only on the demand of the hotel it belongs to but also on the demand of: (i) similar rooms in the area and (ii) their hotels. To this purpose, we model our setting as a cooperative game and exploit an appropriate game theoretic solution concept that promotes fairness both on the customers' and the providers' side. Our simulation results involving price adjustments across real-world hotels datasets, confirm that ours is a fair dynamic pricing policy, avoiding both over- and under-pricing hotel rooms.

Divergence-Guided Simultaneous Speech Translation

To achieve high-quality translation with low latency, a Simultaneous Speech Translation (SimulST) system relies on a policy module to decide whether to translate immediately or wait for additional streaming input, along with a translation model capable of effectively handling partial speech input. Prior research has tackled these components separately, either using ``wait-k'' policies based on fixed-length segments or detected word boundaries, or dynamic policies based on different strategies (e.g., meaningful units), while employing offline models for prefix-to-prefix translation. In this paper, we propose Divergence-Guided Simultaneous Speech Translation (DiG-SST), a tightly integrated approach focusing on both translation quality and latency for streaming input. Specifically, we introduce a simple yet effective prefix-based strategy for training translation models with partial speech input, and develop an adaptive policy that makes read/write decisions for the translation model based on the expected divergence in translation distributions resulting from future input. Our experiments on multiple translation directions of the MuST-C benchmark demonstrate that our approach achieves a better trade-off between translation quality and latency compared to existing methods.

THE DYNAMICS OF POLICY CHANGE AND AGILE GOVERNANCE: HOW INDONESIAN GOVERNMENT RESPONSE TO COVID-19 PANDEMIC

Indonesia and other countries must deal with the COVID-19 pandemic with limited information control amid uncertainty. Handling a pandemic that has become a crisis places great demands on the government to provide agile and dynamic policies. Variations of policies that the Indonesian government has issued raise doubts in the public sphere. The government changed several policies quickly and easily, giving the impression that they did not go through a thorough formulating process. It shows that the handling of COVID-19 requires a policy system that is quite complex due to the many interactions of various sectors. By using qualitative methods, this study attempts to discuss the dynamics of changing public policies by using the concept of incremental policy amid demands for agile governance implementation by looking at the cases of policies issued by the Indonesian government in response to COVID-19. The dynamic of the policy network will be explored further to dismantle the discussion of policies for handling COVID-19 in Indonesia. Therefore, the results of this research show the challenges in implementing agile principles simply by changing and adapting policies to empirical conditions. In its application, agile governance shows several dominant actors who gather resources and support, shape policy outcomes, and advance their interests.

Maximizing the net present value of a project under uncertainty: Activity delays and dynamic policies

We study a project with stochastic activity durations and cash flows; we model the uncertainty using discrete scenarios. The project entails precedence-related activities, each of which incurs a cash flow that may be positive (inflow) or negative (outflow). The problem is to find a scheduling policy that maximizes the expected net present value of the project. A scheduling policy decides the starting time of each activity under every possible realization of the unknown parameters. Ideally, one wants to expedite the inflows (e.g., incoming payments), while delaying the outflows (e.g., costs) as much as possible, without violating the project deadline. In this article, we devise an exact and a heuristic method to define policies within two new classes of scheduling policies. The first policy class generalizes all existing static policies in the literature and further illustrates the importance of intentional activity delays from both a theoretical as well as an empirical point of view. Whereas the literature on project scheduling has mainly focused on static policies, we also propose a second class of dynamic policies. We show that dynamic policies outperform static policies by means of extensive computational experiments.

The exceptions to child exceptionalism: Racialised migrant ‘deservingness’ and the UK's free school meal debates

Free School Meals (FSM) have been the site of renewed contestation and extensive campaigning in the last half-decade. Until recently, children in families with ‘no recourse to public funds’ because of their immigration status were excluded from accessing FSMs, despite being some of the most destitute in Britain. Through an analysis of campaign materials and interviews with advocates, we consider this dynamic policy terrain in light of the UK's ‘hostile environment’ and consider lessons for campaigners. By exploring imaginaries of childhood, poverty, and nation shaping FSM policies, we offer a warning that campaigns can end up justifying exclusions at the same time as aiming for more expansive support. How campaigns represent their causes has implications, which, in this case, is often through the exceptionalism and hyper-deservingness of childhood. We argue these representations reduce discussion to technical questions about who is ‘deserving’, thereby risking shoring up an exclusionary and hostile state.