Making Policy Decisions Research Articles

Effects of Injury Registry Data on Policy Making, Hospitalizations, and Mortality: Protocol for a Systematic Review and Meta-Analysis.

Initiated in 2021, a Brazilian project aims to establish a national injury registry, compiling comprehensive data on events and individuals across the country, irrespective of injury severity. The registry integrates information from prehospital and hospital care, diverse health systems lacking interoperability, and sectors such as firefighters and the police. Its primary goal is to enhance health surveillance by providing timely, high-quality information, guiding prevention strategies, and informing policy making. The project still aims to reduce long-term morbidity and mortality associated with injuries. A knowledge gap remains regarding the effects of injury registries in relation to policies and injury outcomes. This protocol outlines a systematic review and meta-analysis to answer "What is the effect of implementation and use of injury registry data on policy making, hospitalization, and mortality?" The systematic review follows PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, focusing on studies reporting results related to the implementation and use of injury registries, including trauma registries. Outcomes of interest include policy making, hospitalization rates or duration, and mortality. Registries within well-defined administrative boundaries will be included. Data will be collected from PubMed, Embase, Scopus, Web of Science, Lilacs, and references. Records will be independently screened by 2 reviewers, with any disagreements resolved through arbitration by a third reviewer. Homogeneous studies, with 3 or more evaluating the same outcome, may undergo meta-analysis. Subgroup analyses by registry type, injury groups, and other selected variables of interest will be conducted. Sensitivity analysis, risk of bias assessment, publication bias evaluation, and quality appraisal will also be performed. This systematic review will run from November 2023 to June 2024. No identical review was found. Search strategies were finalized, the bibliographic search started, duplicates were eliminated, and title and abstract screening began. Of 35 studies retrieved, 85 were excluded due to duplication, leaving 50 for selection. This study is timely, aligning with ongoing national efforts to implement an injury registry. By synthesizing available evidence, we will identify the potential of injury registries to guide the decisions of Brazilian policy makers. PROSPERO CRD42023481528; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481528. PRR1-10.2196/55029.

Foreign direct investment, total factor productivity, and economic growth: evidence in middle-income countries

This study examines the relationship between foreign direct investment and total factor productivity on economic growth in 90 middle-income countries. Because middle-income countries often face particular challenges in achieving sustainable economic development. Investigating how FDI and TFP contribute to or hinder economic growth in these countries can provide insight and help policymakers make policy decisions. We employ the dynamic system Generalized Method of Moments to analyze an unbalanced sample with 2714 annual observations from 1990 to 2020. The empirical results show that a percentage increase in foreign direct investment will increase economic growth in middle-income countries by 9.3%. In addition, Total Factor Productivity also has a positive relationship with economic growth due to improved labor quality and production innovations. Furthermore, the results indicate that Total Factor Productivity empowers the positive nexus between Foreign Direct Investment and economic growth. In addition, the main findings are also robust even though we employ alternative economic growth proxies. These findings support economic growth and industrialization theories but do not support labor market dynamics theories. Finally, this study contributes practical suggestions for sustainable economic development in middle-income countries.

How the political elite make decisions

AbstractThe political elite make policy decisions in noisy environments and under time pressure, and so are prone to using heuristics. There are conflicting schools of thought as to whether it is appropriate for them to do so. Experienced decision‐makers are thought to be more effective at using heuristics, so it is possible that for the political elite with experience in a particular context, heuristic decision‐making is appropriate. Yet, many politicians are asked to make decisions on matters about which they are not experts. To add to the debate, we facilitated a discussion with a highly experienced cohort of 21 current and former senior politicians, former advisers, and current and former senior bureaucrats. When presented with a carefully considered and innovative new transport network pricing policy, we sought to identify whether and, if so, how they used heuristics to make a decision. We found that they used heuristics (1) to decide whether to engage with the issue at all and (2) how to act, having made the decision to engage. We describe how these heuristics were used and discuss the implications for theory and public administration practice.Points for practitioners There is a growing body of evidence that the political elite use heuristics for decision‐making and that the use of heuristics is influenced by seven factors. We gained rare access to the political elite deciding on a politically risky issue and observed not only which heuristics they used, but how they used them. We observed a three‐step decision tree, incorporating the ‘wait‐and‐see’ heuristic being used to decide whether to act, and political empathy, or intuiting voter heuristics to help decide how to act. We outline five options for public administrators who think that the political elite are using heuristics inappropriately for decision‐making.

Spatiotemporal performance evaluation of high-resolution multiple satellite and reanalysis precipitation products over the semiarid region of India.

The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP. Both MSWEP and ERA5 had PODs of 70-100% in more than 90% of the grids and less than 35% of missing rainfall events in the entire Tamil Nadu. In the case of the rainfall estimation accuracy evaluation, the MSWEP exhibited superior performance, with lower RMSEs and biases ranging from - 25 to 25% at the annual and seasonal scales. In northeast monsoon (NEM), CHIRPS demonstrated a comparable performance to that of MSWEP in terms of the RMSE and PBIAS. These findings will help product users select the best reliable rainfall dataset for improved research, diversified applications in various sectors, and policy-making decisions.

Impact of the COVID-19 Pandemic and Lockdown on Cancer Diagnoses Using Swiss Cantonal Cancer Registry Data

Background/Objectives: This study aims to investigate differences in cancer diagnosis based on absolute case numbers and age-standardized incidence rate ratios (IRRs) in the pre-Coronavirus disease 2019 (COVID-19) years (2018/19) and the first two years of the COVID-19 pandemic (2020, 2021) in two Swiss cantons. Methods: Data of the Swiss cantonal cancer registry of the cantons of Zurich (ZH) and Zug (ZG) were used to descriptively investigate differences in annual and monthly absolute numbers regarding all-cancer and the five most common cancer types. Directly age-standardized monthly incidence rates (IRs) were calculated. Multivariate Quasipoisson regression models were fitted to determine the IRRs with 95% confidence intervals (95% CI). Results: Annual absolute numbers of all investigated cancers were similar in 2018/19, 2020, and 2021, except for prostate cancer (increase of 20.8% in 2021 compared to 2018/19). In 2020, there were generally more cancer diagnoses in January and February followed by a decrease in April and May. Compared to the pre-COVID-19 period, lower IRs were observed in 2020 for all-cancer (IRR = 0.96 [95% CI 0.96, 0.97]) and female breast cancer (0.92 [0.89, 0.96]), whereas higher IRs were observed in 2021 for all-cancer (1.02 [1.02, 1.02]) and prostate cancer (1.23 [1.18, 1.28]). Conclusions: Cancer detection and diagnoses decreased during the first year of the pandemic, especially during the most stringent lockdown phase in April. The findings of this study may inform the decisions of policymakers and public health system during future pandemics.

Disease X and COVID-19: turning lessons from India and the world into policy recommendations

Disease X is caused by pathogen X, an unknown infectious agent that can potentially trigger an epidemic or pandemic. Pathogen X might be any pathogen, including bacteria, viruses, parasites, fungi, and prions. WHO uses the term ‘Disease X’ for any new emerging disease caused by an unknown pathogen X. Disease X stands for any possible future pandemic in WHO’s shortlist of high-priority diseases. This review looks at the manifestations of the recent COVID-19 epidemic as the first Disease X to evaluate what has happened and to learn from what went wrong in India and worldwide. To this end, a summary is presented of response measures by governments, often lacking flows of information, discrepancies in the views of experts and decisions of policymakers, and undesirable variations in individual and collective behavior and their consequences. The elements of combating Disease X in a world with considerable inequalities in relevant knowledge, expertise, information, quality of governance, and financial possibilities are discussed. Based on this, recommendations are given for an innovative global pandemic preparedness system.

Prospects of Computational Intelligence in Society: Human-Centric Solutions, Challenges, and Research Areas

The future of society could be shaped by artificial intelligence (AI), which is a part of soft computing that deals with probabilistic reasoning, neural networks, fuzzy logic, and evolutionary algorithms. This paper focuses on issues that are faced by soft computing as it moves towards human-centric solutions, outlines the challenges for soft computing, and provides possible research directions for development. Currently, there is an increasing emphasis on human-centered solutions in various fields, such as the healthcare industry, transportation sector, financial institutions, and education systems in the world today. Using soft computing techniques in this way comes with its own advantages, especially when managing unclearness or ambiguity or unsureness existing within humans' decision-making processes. Soft computing based on using neural networks may help to develop computer systems capable of faking human behavior and preferences as they evolve over time by using fuzzy logic and evolutionary algorithms. This human-centric approach helps improve the user experience while creating more symbiotic ties between technology and society, leading to examples such as personalized medicine treatment plans, among others. Soft computing’s future in society will be, to a large extent, determined by the provision of human-friendly AI while addressing the ethical, legal, and social challenges of its applications. Rights on AI represent humanity and shall be accountable. Collaborating with interdisciplinary scientists and carrying out frontier studies on developing human-friendly AI systems from ethical, legal, and social perspectives and welfare has the potential to have an influence on community and policy-maker decisions, facilitate a suitable climate for soft computing and AI in society, leverage it for empowering individuals, communities, and societies, and to improve the quality of human life while respecting human values and dignity. It also paves the way for society to be more inclusive and equitable.

Based on Gated Recurrent network analysis of advanced manufacturing cluster and unified large market to promote regional economic development

This study evaluates the catalytic effects of advanced manufacturing industry clusters and unified large markets on regional economic development from a computer science perspective, revealing their underlying mechanisms. It employs a Gated Recurrent Network (GRN) model optimized with Gradient Boosting Decision Tree (GBDT) technology to conduct empirical analysis through comprehensive data collection and analysis. The primary objectives are to assess these catalytic effects, highlight the importance of innovation and environmental indicators, determine the contribution levels of various factors, and test the computational fit and predictive accuracy of the model. Key findings indicate that the GBDT-GRN model demonstrates a significant improvement in data computation accuracy, ranging from 20% to 52%, and an increase in response time by 23% to 52%. The model achieves a computational fit of 92% to 99% when analyzing regional economic development. The proposed GBDT-GRN model is highly accurate and reliable in evaluating catalytic effects, providing strong support for policy-making and business decision-making. Innovation and environmental indicators play a crucial role, with varying contributions from different factors. This study offers an effective solution for sequence data prediction problems, supports policy-making and business decisions, and points to promising directions for future research.

Integrating S1A microwave remote sensing and DSSAT CROPGRO simulation model for groundnut area and yield estimation

This study sought to corroborate microwave remote sensing and simulation models to efficiently delineate groundnut cultivation area and to estimate the yield by integration. Near real-time information on crop acreage and yield estimation is essential for making policy decisions. S1A SAR data were downloaded for entire crop growth period of groundnut during Kharif monsoon seasons (June – October) of 2019 and 2020 and were processed using MAPSCAPE RIICE software to extract groundnut cultivated area in the study districts of Tamil Nadu. Spectral dB curve groundnut generated using multi-date Sentinel 1 A SAR data showed a minimum at sowing, reached a peak at the pod development stage and decreased after that towards maturity. Groundnut area map was generated with a classification accuracy of 85.2 and 84.8 per cent with a kappa coefficient of 0.70, and total groundnut area of 104343 and 116199 ha was mapped during Kharif monsoon season 2019 and 2020, respectively. The mean agreement of 75.01 and 84.94 per cent was observed between DSSAT model simulated LAI and observed LAI at thirty monitoring locations in the study area during Kharif monsoon season 2019 and 2020, respectively, whereas agreement for yield was 82.11 and 83.70 per cent with RMSE of less than 20 per cent. Spatial distribution of groundnut LAI and yield was estimated by assimilating dB from satellite image and from DSSAT model, respectively. The estimated mean spatial LAI was 2.81 and 3.52, whereas mean spatial pod yield was 2124 and 2195 Kg ha−1 during Kharif monsoon season 2019 and 2020, respectively with RMSE of less than 20 per cent and R2 for integrating satellite products and simulation model for spatial estimates during both the year was >0.70, it shows the fitness of products towards increased accuracy of estimation.

An effective drift-diffusion model for pandemic propagation and uncertainty prediction

Predicting pandemic evolution involves complex modeling challenges, typically involving detailed discrete mathematics executed on large volumes of epidemiological data. Making them physics based provides added intuition as well as predictive value. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages. In this paper, the canonical susceptible-infected-recovered model is simplified, in the process generating quasi-analytical solutions and fitting functions that agree well with the numerics, as well as infection data across multiple countries. The equations provide an elegant way to visualize the evolution of the pandemic spread, by drawing equivalents with the similar dynamics of a particle, whose location over time represents the growing fraction of the population that is infected. This particle slides down a potential whose shape is set by model epidemiological parameters such as reproduction rate. Potential sources of errors and their growth over time are identified, and the uncertainties are mapped into a diffusive jitter that tends to push the particle away from its minimum. The combined physical understanding and analytical expressions offered by such an intuitive drift-diffusion model sets the foundation for their eventual extension to a multi-patch model while offering practical error bounds and could thus be useful in making policy decisions going forward.

Analisis Perbandingan Perilaku Konsumen Dalam Pengambilan Keputusan Berbelanja Di Toko Madura dan Minimarket Di Kabupaten Bangkalan

This research aims to determine differences in consumer behavior in making shopping decisions at Madura Stores and Minimarkets in Bangkalan Regency. This type of research is quantitative research. The population in this research is all consumers who shop at Madura Stores and Minimarkets in Bangkalan Regency, an unknown number. Meanwhile, for the sample in this study, researchers used 50 respondents. The data collection techniques used were questionnaires and observation. Data analysis techniques in this research used validity tests, reliability tests, Kolmogov-Smirnov normality tests, and paired sample t-tests using IBM SPSS 23. Based on the results of research that has been carried out, it is known that there is no significant difference between consumer behavior in making shopping decisions at Madura Stores and Minimarkets in Bangkalan Regency. With this information, it is hoped that it will be able to provide insight to the entire community in making policy decisions regarding the existence of Madura Shops and Minimarkets for shopping for necessities.

Incidence, antimicrobial susceptibility & out of pocket expenditure of severe enteric fever in Chandigarh, north India.

Background & objectives Burden estimates of enteric fever are required to make policy decisions on introducing typhoid vaccine in India. Incidence, antimicrobial susceptibility, and out-of-pocket expenditure (OOPE) of enteric fever are estimated in Chandigarh, India. Methods A hybrid (facility and community-based) surveillance system was set up at a secondary care hospital to enrol patients above six months of age, hospitalized with fever, from a defined catchment population from May 2018 to March 2020. Blood samples were collected and cultured using an automated system (BD BACTECTM blood culture system). The Salmonella Typhi and S. Paratyphi isolates were characterized for antimicrobial susceptibility. OOPE was recorded after 14 and 28 days of discharge. Results Blood samples were collected from 97 per cent of the 1650 study participants enrolled. The incidence of enteric fever was 226.8 per 1,00,000 person-years (PY), severe typhoid fever 156.9 per 1,00,000 PY, and severe paratyphoid fever 69.9 per 1,00,000 PY. Salmonella was highly susceptible to ampicillin, azithromycin, and ceftriaxone (99.25%) and least susceptible to ciprofloxacin (11.3%). The OOPE due to hospitalization of individuals infected with S. Paratyphi [INR 8696.6 (USD 116)] was significantly higher than the individuals infected with S. Typhi [INR 7309 (USD 97.5), P=0.01], and among cases who were hospitalized for more than seven days [INR 12,251 (USD 163.3)] as compared with those with a stay of 3-7 days [INR 8038.2 (USD 107.2)] or less than three days [INR 5327.8 (USD 71), P<0.001]. Interpretation & conclusions There was a high incidence of enteric fever, high OOPE, and resistance to ciprofloxacin.

Educational capitalisation: a co-formational feminist framework for conceptualising investment in for-profit education within the racialised and gendered political economy

ABSTRACT This paper examines the processes through which education becomes valued as capital within the racialised and gendered political economy. Using the empirical case of venture capital (VC) investment in for-profit education companies, principally education technology companies and for-profit school chains, it puts forward the concept of educational capitalisation. We conceptualise it as the set of uneven processes, practices, and socio-spatial relationships through which value is extracted from educational processes and practices, and, thus, education is valued in terms of expected monetary return on investment. While our conceptualisation focuses on VC investment, this framework could be used to outline other processes of extraction and valuation in education, including private equity, investment banking, venture philanthropy, and public-private partnerships. We conclude with a co-formational feminist framework for guiding future research, policymaking, and educational decision making that considers the financial, socio-technological, learning & teaching, and political-legal aspects of educational capitalisation, with ethical considerations embedded within each of these domains.

Public Views on Whether the Use of Pharmaceutical Neuroenhancements Should Be Allowed.

Some individuals are using drugs to try to enhance cognitive and social-affective functioning and asking physicians for off-label prescriptions for neuroenhancement (e.g., stimulants). Several medical societies released guidance on prescribing neuroenhancers, some of which refer to potential societal effects of neuroenhancement (e.g., distributive justice), besides risks and benefits to users. Which institutions (e.g., medical societies, government, universities) should make decisions on allowing neuroenhancement and whether they should consider potential societal effects are unclear. We examined whether and how much support for allowing pharmaceutical neuroenhancers was influenced by the institution and potential individual and societal effects of neuroenhancers. We conducted a discrete-choice experiment using a constructed representative sample of the US adult public. Multinomial logit models were used to analyze the data. Participants (n = 927) demographically resembled the US population. Risks of serious side effects (OR 0.20, CI 0.18-0.22) and a lack of benefits for users (OR 0.31, CI 0.26-0.38) had the largest negative effect on participants' support for allowing neuroenhancers. A risk of mild side effects had a moderate negative effect on participants' support for allowing neuroenhancers (OR 0.67, CI 0.62-0.74) and the prospect of more meaningful, long-lasting benefits for users a moderate positive effect (OR 1.74, CI 1.61-1.87). Positive or negative effects of neuroenhancers on the average well-being of people in society and on equality had moderate effects on participants' support for allowing neuroenhancers. For example, the odds of participants' support for allowing enhancers with a negative effect on societal well-being were around half (OR 0.45, CI 0.40-0.50) and the odds of allowing enhancers that worsen inequality were approximately 40% lower compared with enhancers without such effects (OR 0.62, CI 0.55-0.71). The odds of participants allowing neuroenhancers were slightly (10%) lower if enhancers reduced users' authenticity (OR 0.90, CI 0.84-0.97). The institution regulating neuroenhancers and neuroenhancers providing users with an unfair advantage did not affect participants' decisions. When presented with both individual and societal considerations, the public seems to support medical societies and other institutions making policy decisions about neuroenhancers based on risks and benefits for users, as well as, but to a lesser extent, effects on equality and societal well-being.

Can coastal and marine carbon dioxide removal help to close the emissions gap? Scientific, legal, economic, and governance considerations

In this Policy Bridge, we present the key issues regarding the safety, efficacy, funding, and governance of coastal and marine systems in support of climate change mitigation. Novel insights into the likely potential of these systems for use in mitigating excess carbon dioxide emissions are presented. There may be potential for coastal blue carbon and marine carbon dioxide removal (mCDR) actions to impact climate change mitigation significantly over the rest of the 21st century, particularly post 2050. However, governance frameworks are needed urgently to ensure that the potential contribution from coastal and ocean systems to climate change mitigation can be evaluated properly and implemented safely. Ongoing research and monitoring efforts are essential to ensure that unforeseen side effects are identified and corrective action is taken. The co-creation of governance frameworks between academia, the private sector, and policymakers will be fundamental to the safe implementation of mCDR in the future. Furthermore, a radical acceleration in the pace of development of mCDR governance is needed immediately if it is to contribute significantly to the removal of excess carbon dioxide emissions by the latter half of this century. To what extent large-scale climate interventions should be pursued is a decision for policymakers and wider society, but adaptive legal, economic, policy, research, and monitoring frameworks are needed urgently to facilitate informed decision-making around any implementation of mCDR in the coming decades. Coastal and ocean systems cannot be relied upon to deliver significant carbon dioxide removal until further knowledge of specific management options is acquired and evaluated.

Relationship Between Financial Deepening Indicators and Stock Market Performance in Kenya

The purpose of this study was to find out the relationship between financial deepening indicators and the stock market performance in Kenya. The selected financial deepening indicators were; financial savings, private sector credit, broad money supply and intermediation ratio on the stock market performance. Johansen cointegration test was done indicating that the variables co-move towards a long-run equilibrium, a multivariate vector error correction model was run and the estimates obtained. The error correction term was also computed. Empirical results showed that all variables are adequately explained by their own lags and the lags of the other variables, the coefficients are also significant. The error correction model indicated that an increase in private sector credit by one unit in the previous quarter causes the stock market performance to increase by 48% in the current quarter. Variance decomposition tests and impulse response functions indicated how other variables respond to shocks in the other variables and the forecast errors for each of the predicted quarters. The implication of this study is that the policy makers who are; the Government, the Central bank of Kenya and the Capital Markets Authority ought to make policy decisions while considering the effect of the full market. This study concluded that private sector credit is the financial indicator variable that affects the stock market performance the most with a bidirectional relationship.

Maximizing Trade Benefits Through Strategic WTO Dispute Filings: A Quantitative Analysis of Optimal Initiation

The study investigates the asymmetrical distribution of bilateral dispute initiations at the World Trade Organization Dispute Settlement System (WTO-DSS) between specific country pairs. The study introduces a novel concept of dispute depth to examine how the asymmetrical bilateral dispute initiation affects bilateral trade among disputing dyads and to optimize the number of disputes that a country should file to maximize its trade benefit. By using the augmented Structural Gravity Model of trade and the PPMLHDFE estimator with extensive trade data for the years 1995–2022, across selected WTO member countries, the article demonstrates that an increase in the dispute depth decreases the bilateral trade between disputing dyads. Further, the study finds that the WTO member countries should strategically file no more than three disputes per year to maximize their trade gains. The study has important ramifications in the ambiguous world of WTO-DSS and paves the way for implementing the optimal number of disputes into trade dispute strategy, policy-making decisions and business environment of the constantly evolving international trade landscape.

Real Estate Insights: The current state and the new future of tokenization in real estate

PurposeThis real estate insight provides a comprehensive analysis of the current state and future potential of tokenization in the real estate industry mentioning several challenges to overcome to take advantage of this technology. We highlight potential benefits, including enhanced liquidity, increased security and improved accessibility. Additionally, the real estate insight critically discusses potential drawbacks, such as regulatory challenges and technological risks, and explores the impact of tokenization on real estate prices.Design/methodology/approachThis real estate insight employs a comprehensive literature review alongside a qualitative analysis of various case studies to explore current implementations of tokenization within the real estate industry. Multiple applications of tokenization in the real estate industry are examined, including fractional ownership, property management and transaction processes. The study investigates the optimization potential of tokenization for asset liquidity in the real estate area, transaction transparency and security. It also critically discusses potential challenges, such as regulatory compliance, security vulnerabilities and market adoption.FindingsThe future of real estate tokenization, driven by blockchain technology and smart contracts, offers significant potential for growth, enhancing liquidity and accessibility through fractional ownership. Smart contracts automate and secure transactions, while evolving standards and regulatory frameworks in regions like North America, Europe and Asia support market expansion. Since its initial implementation with the St. Regis Aspen Resort STO, a stream of successful projects has highlighted the viability of tokenization. However, challenges remain, including the need for regulatory clarity, industry and customer education, displacements of market participants and jobs and environmental impacts. Integrating advanced technologies like AI and IoT can further streamline property management and investment decisions.Practical implicationsThe real estate insight’s practical implications extend to industry professionals, policymakers and technology developers. Professionals gain insights into how tokenization can enhance liquidity and security in the real estate sector, guiding strategic decision-making. For policymakers, understanding potential challenges like regulatory compliance and technological risks informs the development of supportive regulations. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Highlighting the need for robust security measures and regulatory compliance in tokenization systems may foster better design practices. Therefore, the real estate insight’s findings could significantly shape the future development of tokenization integration in the real estate industry.Originality/valueThis real estate insight offers original value through a comprehensive analysis of the current and future impacts of tokenization in the real estate industry. It examines various applications of tokenization and critically discusses the potential challenges. The focus on informing strategic decisions for professionals and policymakers enhances its utility as a resource. Additionally, by addressing both the benefits and drawbacks, this study contributes to the broader discourse on the societal implications of tokenization. In the context of rapid technological advancement, such thorough studies are rare, further underscoring the real estate insight’s originality.

A Comprehensive Review of the Smart Microgrids’ Modeling and Control Methods for Sustainable Developments

Estimation strategies and hierarchical control measures are required for the successful operations of microgrids. These strategies and measures monitor the processes within the control variables and coordinate the system dynamics. State-of-the-art frameworks and tools are built into innovative grid technologies to model different structures and forms of microgrids and their dynamic behaviors. Smart grids’ dynamic models were developed by reviewing different estimation strategies and control technologies. A Microgrid control system is made up of primary, secondary, and tertiary hierarchical layers. These architectures are measured and monitored by real-time system parameters. Different estimation schemes and control strategies manage microgrid control layers’ dynamic performances. The control strategies in the developed technologies dynamics were accessed in the grid environment. The control strategies were modeled for microgrids using six design layers: adaptive, intelligent, robust, predictive, linear, and non-linear. The estimation schemes were assessed using microgrid controllers’ modeling efficiency. Hierarchical control strategies were also developed to optimize the operation of microgrids. Hence, this research will inform policy-making decisions for monitoring, controlling, and safeguarding the optimal design strategies for modeling microgrids.

Comparative secured transactions law: how and to what extent do global standards affect the reform of Japanese inventory financing law

Abstract Since March 2021 Japan has been undertaking official legislative discussions at the Committee of Secured Transactions Law Reform under the Legislative Council of the Ministry of Justice Japan to draft a statute of secured transactions of personal property. These discussions are now in their final stages. Enhancing the reliability and feasibility of security devices for inventory financing is the core purpose of the proposed legislation. As a result of policy-making decisions, the conventional Japanese security device of ownership transfer for security purposes will not be replaced by a new regime which reflects the modern principles originating from the UCC9 and illustrated in the UNCITRAL Model Law on Secured Transactions. However, the significance of such global standards in the context of Japanese law has been two-fold. First, they illustrate what conventional Japanese law achieves through its own alternative rules. Second, the Council Committee is likely to propose several changes to refresh our conventional security device by adding new principles which have traditionally been of less concern to Japanese practitioners. In this article, the author substantiates the influence of these global standards on Japanese legislation. This article focuses on the functional approach taken by the ownership transfer type security interest, the rules relevant to publicity of security interests as modified by the first-to-register-or-possession rule as suggested by global standards, the concept of aggregate of inventory as collateral as modified by registration to cover perfection in security interests in goods joining the aggregate after a debtor's dishonest disposition, and the assignment of accounts receivables accompanied with ownership transfer of inventory which may be viewed as functionally close to security interests in proceeds of inventory.