Hasil untuk "Trade associations"

A. Greif

P. Krugman

Ernst Maug

Alexander Kvist, Daniel S. Peterson, Lucian Bezuidenhout et al.

Abstract Background Walking while performing a concurrent cognitive task leads to cognitive-motor interference, resulting in slower and more variable gait. This is particularly the case in Parkinson’s disease (PD), where dual-task situations exacerbate walking impairments, increasing fall risk and reducing quality of life. Cognitive-motor impairment has been linked to excessive attentional demands due to reduced locomotor automaticity. Neuroimaging studies suggest over-reliance on prefrontal resources potentially reflecting compensatory mechanisms. However, few studies link automaticity, performance, and cognitive capacity to prefrontal activity, particularly in PD. In older adults (OA) and people with PD, this study aims to: (1) describe dual-task effects and prefrontal cortical activity during walking with and without a dual-task, (2) determine the connection between prefrontal cortical activity and step time variability as a measure of gait automaticity, (3) explore associations between prefrontal cortical activity and other measures of gait automaticity and prioritization and (4) investigate executive function as a potential moderator in the compensatory relationship. Methods Data from 44 OA and 37 people with PD walking with and without an auditory Stroop task were analyzed. Gait variables were measured using inertial measurement units, and prefrontal activity was assessed with functional near-infrared spectroscopy (fNIRS). Executive function was determined with a trail making test. Data analysis involved linear regression models to explore relationships between prefrontal activity, gait automaticity, and executive function. Results Most participants had a cognitive priority trade-off when dual-tasking, and the OA group had more prefrontal activity compared to the PD group during single-task and dual-task walking. For PD there was a significant positive relationship between step time variability and prefrontal activity (β = 0.38, T = 6.26, p < 0.01), while OA had a relationship between age and prefrontal activity (β = 0.53, T = 2.33, p = 0.04). Secondary analyses showed relationships between prefrontal activity and dual-task cost of gait speed (β = 0.25, T = 2.90, p = 0.02) and Stroop response time (β = 0.27, T = 3.10, p = 0.01) in PD, but not in OA. No moderation effects were detected in the relationship between gait automaticity and prefrontal activity. Conclusions In PD, loss of gait automaticity is linked to increased prefrontal activity, suggesting compensatory mechanisms. In OA, prefrontal activity during walking seems to be primarily age-related.

Thomas Zhang, Una Clancy, Ayush Singh et al.

Background Novel risk factors for stroke, such as occupation, are increasingly under exploration. We investigate if specific occupational exposures and settings increase the risk of developing small‐vessel disease (SVD), including SVD‐related strokes. Methods We performed a systematic review on stroke–occupation associations and then analyzed data from patients presenting to Lothian stroke services with mild ischemic stroke (modified Rankin Scale score ≤2). We performed magnetic resonance imaging and inquired about occupational status. We assessed relationships between high‐risk occupations (per Control to Substances Hazardous to Health guidelines) and standard occupational classifications (per Standard Occupational Classifications criteria) against white matter hyperintensity volumes, SVD score, and stroke subtype. Results Our systematic review identified 37 papers assessing occupations/broad occupational classifications (n=13), psychosocial work‐related factors (n=11), and occupational exposure to hazardous substances (n=13). We then analyzed data from 414 participants and found, after adjustment for age, hypercholesterolemia, socioeconomic status, years of education, hypertension, diabetes, and smoking history, that high‐risk occupations were associated with higher SVD scores (odds ratio, 1.64 [95% CI, 1.07–2.54]; n=357; P=0.02) but not for lacunar stroke subtype (odds ratio, 1.03 [95% CI, 0.64–1.67]; n=358; P=0.90) or white matter hyperintensity volume (% intracranial volume) (β=−0.003 [95% CI, −0.015 to 0.008]; n=357; P=0.60). Examples of high‐risk occupations include drivers, engineers, and skilled trade workers. No associations were found for standard occupational classifications. Conclusions This systematic review shows limited data on stroke–occupation associations. Our analysis showed that high‐risk occupations are associated with higher SVD scores but not stroke subtype. Registration URL: www.crd.york.ac.uk/PROSPERO; Unique Identifier: 42024466671.

Eslam Abdelhakim Seyam

This paper examines data-driven methods for the identification of high-cost patients in European health systems by assessing predictive accuracy and interpretability in generalized and regularized statistical models. We learn binary classification problems from a big health insurance dataset to identify individuals in the upper  and upper  of overall healthcare spending. Three modeling paradigms-Generalized Linear Models (GLM), Generalized Additive Models (GAM), and LASSO regression-are used and contrasted in terms of predictive accuracy as well as practical interpretability. We find that GAM consistently outperforms, yielding highest F1 values and lowest log loss, in capturing nonlinear associations in health care consumption better than GLM or LASSO. Frequency of surgeries, hospitalizations, and duration of insurance coverage prove to be key determinants of high-cost status, while demographic attributes like gender exert a moderate impact. The comparisons highlight the potential of utilizing interpretable yet adaptable models to enable proactive, risk-based interventions. By presenting evidence of predictive accuracy vs. interpretability trade-offs, the paper aids more efficient high-cost care management, providing pragmatic advice to European health systems to efficiently allocate assets in light of avoidable health care spending.

Aakash Sharma, Naveen Narasimha Murthy, Naval Kant Jogi et al.

Introduction: As per the World Health Organisation, tobacco tends to be the growing global cause of death (4.9 million people a year) worldwide and also is associated with many fatal diseases. The objective of the study is to determine the prevalence of tobacco use amongst the study group and also to evaluate their attitude towards quitting tobacco habit. Materials and Methods: A self-designed structured questionnaire was used by the single-trained examiner for interviewing the participants. Three hundred and six (298 males and 8 females) hamal workers of Navi Mumbai were surveyed after ethical approval by the institution. Data were collected through face-to-face interviews. Data obtained were analysed using the SPSS software version 16 (SPSS < 0.005 considered Inc. Chicago, IL, USA). Descriptive statistics were calculated with P as significant. Results: Out of 306 hamals, 245 (80.01%) were using tobacco amongst which 210 (85.7%) consumed on a daily basis and 23 were past tobacco users that is only 7.5% were able to quit. Out of the 268 present and past tobacco users, 134 (50%) tried to quit of which 111 (85%) were unsuccessful and 23 (15%) were successful. 82.5% of current tobacco users expressed a desire to quit but needed help. Conclusion: A high prevalence of tobacco consumption was seen amongst the surveyed hamals and most of them need help to quit the habit. Employers, trade associations and worker representatives can work in partnership with their state and local health departments in implementing evidence-based policies and programmes to reduce the prevalence of tobacco use amongst these working population.

Pei Li, Jianguo Du, Fakhar Shahzad

This study conducted empirical research on issues regarding the regional brand co-construction of featured agricultural commodities to realize the interests of stakeholders involved in constructing regional brands for featured agricultural commodities. The study was based on the notion of brand value co-creation and collaborative construction and included a case study of the current international trade for featured agricultural commodities of two regional brands in Jinlin Province, China. The data were collected through in-depth interviews and questionnaires with employees from the above two brands, and the results were obtained by applying structural equation modeling (SEM). The results show that regional resources form the tangible and cultural basis of the collaborative construction of regional brands. Subjective cognition (SC) is the cognitive basis for the co-construction of regional brands, while interest appeal is the inner driving force for the co-construction of regional brands. It examines the perspectives of various stakeholders, including the government, industry associations, enterprises, industrial chains, and consumers. In conclusion, we outlined pathways for the collaborative construction of regional brands for featured agricultural commodities and provided specific countermeasures and suggestions to address the identified problems.

Ruslan S. Mukhametov

This study examines the relationship between citizens’ participation in non-governmental organizations (NGOs) and their civic engagement in the Russian context. The research addresses why some individuals actively participate in political life while others remain disengaged, drawing on the neo-Tocquevillian tradition that views NGOs as “schools of democracy.” Based on this theoretical framework, several hypotheses are formulated regarding the impact of NGOs and government-organized NGOs (GONGOs) on different forms of civic activity, including electoral participation, protest behavior, and voluntary donations. The empirical basis of the analysis is provided by the seventh wave of the World Values Survey involving a representative sample of 1,810 respondents from the Russian Federation who were interviewed in 2017. Methodologically, the study employs regression analysis, incorporating dependent variables (indicators of civic engagement), independent variables (participation in NGOs), and control variables (sociodemographic and value characteristics). The findings demonstrate that participation in GONGOs is positively associated with electoral activity and the volume of donations but shows no significant effect on protest behavior. When operationalized in binary form, trade union membership is linked to reduced electoral participation and decreased donations to independent organizations. Women’s associations exhibit no statistically significant influence on civic engagement. The study concludes that the type and the institutional character of NGOs determine their impact on civic participation, redistributing citizens’ resources toward institutionalized and state-sanctioned forms of activity.

Daniel Aronoff, Robert M. Townsend

We present a model of a market that is intermediated by broker-dealers where there is multiple equilibrium. We then design a smart-contract that receives messages and algorithmically sends trading instructions. The smart-contract resolves the multiple equilibrium by implementing broker-dealer joint profit maximization as a Nash equilibrium. This outcome relies upon several factors: Agent commitments to follow the smart contract protocol; selective privacy of information; a structured timing of trade offers and acceptances and, crucially, trust that the smart-contract will execute the correct algorithm. Commitment is achieved by a legal contract or contingent deposit that incentivizes agents to comply with the protocol. Privacy is maintained by using fully homomorphic encryption. Multiple equilibrium is resolved by imposing a sequential ordering of trade offers and acceptances, and trust in the smart-contract is achieved by appending the smart-contract to a public blockchain, thereby enabling verification of its computations. This model serves as an example of how a smart-contract implemented with cryptography and blockchain can improve market outcomes.

José Ignacio Rivero-Wildemauwe

Two agents trade an item in a simultaneous offer setting, where the exchange takes place if and only if the buyer's bid price weakly exceeds the seller's ask price. Each agent is randomly assigned the buyer or seller role. Both agents are characterized by a certain degree of Kantian morality, whereby they pick their bidding strategy behind a Veil of Ignorance, taking into account how the outcome would be affected if their trading partner adopted their strategy. I consider two variants with asymmetric information, respectively allowing buyers to have private information about their valuation or sellers to be privately informed about the item's quality. I show that when all trades are socially desirable, even the slightest degree of morality guarantees that the outcome is fully efficient. In turn, when quality is uncertain and some exchanges are socially undesirable, full efficiency is only achieved with sufficiently high moral standards. Moral concerns also ensure equal ex-ante treatment of the two agents in equilibrium. Finally, I show that if agents are altruistic rather than moral, inefficiencies persist even with a substantial degree of altruism.

Muhammad Sukri Bin Ramli

We propose an interpretable machine learning framework to help identify trade data discrepancies that are challenging to detect with traditional methods. Our system analyzes trade data to find a novel inverse price-volume signature, a pattern where reported volumes increase as average unit prices decrease. The model achieves 0.9375 accuracy and was validated by comparing large-scale UN data with detailed firm-level data, confirming that the risk signatures are consistent. This scalable tool provides customs authorities with a transparent, data-driven method to shift from conventional to priority-based inspection protocols, translating complex data into actionable intelligence to support international environmental policies.

Jędrzej Maskiewicz, Paweł Sakowski

The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.

Bertil Rolandsson, Anna Ilsøe

A crisis refers to some sort of disruption of established practices, routines, or procedures occurring whenever a risk has been realized (Battistelli & Galantino 2019; Beck 1986, 2006). For social actors exposed to crisis in the labor market, this means that they will have to navigate some sort of uncertainty, trying to respond to the consequences unfolding in their surroundings as well as in their own activities (Aven & Renn 2009, p. 1). As they navigate a crisis, they will have to assess further vulnerabilities and damages for their own businesses and the variety of societal values that they adhere to, seeking out opportunities to manage both risks and prospects (Bundy et al. 2017). Doing so nevertheless is difficult and leaves social actors with a variety of tensions that they must address. Historically, researchers have linked the Nordic labor markets with strong social partners (employers’ associations, trade unions, and the state), able and willing to tackle such tensions. Due to high coverage by collective agreements and supportive welfare state arrangements, they have been able both to contribute to institutional stability and support adaption and changes (Alsos & Dølvik 2021; Andersen et al. 2014; Campbell et al. 2006; Kjellberg 2023). In the wake of, for instance, the recent COVID-19 crisis and the ongoing digital transformation of work, research as well as policy and public debates have nevertheless indicated that we may face new types of critical challenges in today’s society. It is important to point out that these challenges display a great variation. For instance, current crises often seem to be global in scale – for example, climate change, digitalization, and the coronavirus pandemic – making it difficult for national-level actors to handle the consequences on their own (Beck 2006). Also, some of the crises seem to have the character of a chock occurring at a specific point in time (e.g., a financial crisis, the pandemic), whereas others have been here for years and will last far into the future (e.g., climate change), although they might change gears on the way and interact with other types of crises (Björck 2016; Enggaard et al. 2023). In addition, the nature of these challenges has to do with the strategic choices of the social partners, and whether they, as actors in the labor market, perceive the crisis as controllable, and perhaps something that could be exploited to strengthen their position in the Nordic society or not (Boin & ‘t Hart 2022; Boin et al. 2008). These are all different conditions that play a role for Nordic social partners’ ability and willingness to act. We may ask ourselves in what way today’s crises and linked developments affect the Nordic labor markets and the Nordic social dialogue. Are the institutional foundations for adaption to various crises still able to provide means and measures to address future challenges in the Nordic countries? In this special issue, we address these questions, contributing to research on the future of Nordic working life during times of uncertainties by exploring the meaning and the impact of different types of crises and responses. The coronavirus pandemic constitutes a prominent crisis theme throughout this special issue, but the articles also address issues often framed as labor market crises due to digitalization and the emergence of platform work. The articles display a great diversity of both theoretical assumptions and empirical materials. The texts draw on different types of historical, comparative, quantitative, or qualitative data, revealing that the way we approach or conceptualize what we mean by a crisis is not self-evident. The articles are sensitive to the contextual differences, and the fact that involved actors and organizations in the Nordic labor markets face a variety of different risks and have different views of the crises they encounter. Authors of the articles thereby also recognize that most of the actors involved in managing the different crises in the Nordic labor markets differ when it comes to the amount of risk they are willing to take as they strive with different aims (Battistelli & Galantino 2019)

Amit kumar Vishwakarma, Yatindra Nath Singh

P2P trading of energy can be a good alternative to incentivize distributed non-conventional energy production and meet the burgeoning energy demand. For efficient P2P trading, a free market for trading needs to be established while ensuring the information reliability, security, and privacy. Blockchain has been used to provide this framework, but it consumes very high energy and is slow. Further, until now, no blockchain model has considered the role of conventional electric utility companies in P2P trading. In this paper, we have introduced a credit blockchain that reduces energy consumption by employing a new mechanism to update transactions and increases speed by providing interest free loans to buyers. This model also integrates the electric utility companies within the P2P trading framework, thereby increasing members trading options. We have also discussed the pricing strategies for trading. All the above assertions have been verified through simulations, demonstrating that this model will promote P2P trading by providing enhanced security, speed, and greater trading options. The proposed model will also help trade energy at prices beneficial for both sellers and buyers.

Diego Rincon-Yanez, Chahinez Ounoughi, Bassem Sellami et al.

Knowledge representation (KR) is vital in designing symbolic notations to represent real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) have emerged so far as a popular form of KR, offering a contextual and human-like representation of knowledge. In international economics, KGs have proven valuable in capturing complex interactions between commodities, companies, and countries. By putting the gravity model, which is a common economic framework, into the process of building KGs, important factors that affect trade relationships can be taken into account, making it possible to predict international trade patterns. This paper proposes an approach that leverages Knowledge Graph embeddings for modeling international trade, focusing on link prediction using embeddings. Thus, valuable insights are offered to policymakers, businesses, and economists, enabling them to anticipate the effects of changes in the international trade system. Moreover, the integration of traditional machine learning methods with KG embeddings, such as decision trees and graph neural networks are also explored. The research findings demonstrate the potential for improving prediction accuracy and provide insights into embedding explainability in knowledge representation. The paper also presents a comprehensive analysis of the influence of embedding methods on other intelligent algorithms.

Rashad Moqa, Irfan Younas, Maryam Bashir

<h4>Background</h4>Studies on genome-wide associations help to determine the cause of many genetic diseases. Genome-wide associations typically focus on associations between single-nucleotide polymorphisms (SNPs). Genotyping every SNP in a chromosomal region for identifying genetic variation is computationally very expensive. A representative subset of SNPs, called tag SNPs, can be used to identify genetic variation. Small tag SNPs save the computation time of genotyping platform, however, there could be missing data or genotyping errors in small tag SNPs. This study aims to solve Tag SNPs selection problem using many-objective evolutionary algorithms.<h4>Methods</h4>Tag SNPs selection can be viewed as an optimization problem with some trade-offs between objectives, e.g. minimizing the number of tag SNPs and maximizing tolerance for missing data. In this study, the tag SNPs selection problem is formulated as a many-objective problem. Nondominated Sorting based Genetic Algorithm (NSGA-III), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which are Many-Objective evolutionary algorithms, have been applied and investigated for optimal tag SNPs selection. This study also investigates different initialization methods like greedy and random initialization. optimization.<h4>Results</h4>The evaluation measures used for comparing results for different algorithms are Hypervolume, Range, SumMin, MinSum, Tolerance rate, and Average Hamming distance. Overall MOEA/D algorithm gives superior results as compared to other algorithms in most cases. NSGA-III outperforms NSGA-II and other compared algorithms on maximum tolerance rate, and SPEA2 outperforms all algorithms on average hamming distance.<h4>Conclusion</h4>Experimental results show that the performance of our proposed many-objective algorithms is much superior as compared to the results of existing methods. The outcomes show the advantages of greedy initialization over random initialization using NSGA-III, SPEA2, and MOEA/D to solve the tag SNPs selection as many-objective optimization problem.

Phyo Pa Pa Aung, Ji-Yong Lee

Agriculture plays a key role in Myanmar and it is the backbone of the country’s economy. Among the major export-earning crops in Myanmar, mung bean is one of the important, and it creates many opportunities for smallholders. About 90% of the total production of mung bean is exported for overseas or border trade and has extended markets, especially China, Vietnam and EU countries. This study aims to measure the level of technical efficiency of green mung bean producers and determine the factors influencing the technical efficiency of mung bean production in Tatkon Township, Myanmar. Data from 144 farms were analyzed using a DEA model and Tobit regression. The empirical results reveal that about 46% of farmers had an efficiency score of more than 0.90, which indicates that 54% of farmers were relatively inefficient in their production. The results also show that socioeconomics factors, such as age of farmers, farmers participating in associations and soil fertility, had a significantly positive impact on technical efficiency. Gender, education, access to credit and extension services had a positive impact on the technical efficiency of mung bean production in the study area. To reduce inefficiency, the government should consider providing more services to male farmers and older farmers to improve their capacities, as well as providing an extension of services, new technologies, credit and improved variety for mung bean production.