Zhanat Yerniyazova, Akmaral Mukasheva, Aigul Yesturliyeva
et al.
The purpose of the study is to identify patterns of human capital development in the oil and gas region of Western Kazakhstan and to assess the adaptive potential of educational programs of technical universities in the context of the transformation of industry competencies. The paper uses methods of comparative and correlation analysis, SWOT assessment of educational programs, content analysis of labor market data and expert interviews with representatives of industry enterprises. The results of the study indicate that the share of programs focused on the development of digital and environmental competencies is less than 35%. According to employers, graduates' qualifications do not fully meet modern production requirements. At the same time, there is a positive trend in the development of the areas of "digital engineering" and "automation of technological processes". The identified trends reflect not only structural changes in the field of education, but also deeper institutional shifts related to the digitalization of the economy. This contradiction between the supply and demand of personnel is due to the slow updating of educational standards and insufficient involvement of business in the development of training programs. The constructed model of the relationship between the level of digitalization of education and the quality of human capital made it possible to identify priority development trajectories until 2035: robotic systems, data management, energy-efficient technologies and sustainable production. The scientific novelty of the research lies in the proposal of an integrative model for analyzing the human capital of the oil and gas region, taking into account the digitalization of education and the sectoral characteristics of the regional labor market The results of the study can serve as a basis for the development of transformational models of engineering education in other resource regions of Central Asia.
Experiential focused on creating memorable and meaningful experiences for consumers, has emerged as a key strategy in promoting tourist destinations. particularly in destinations seeking to highlight their unique cultural characteristics. In this study, the influence of experiential marketing on the tourism demand of the floating islands Los Uros, Puno, an emblematic destination in Peru, is analyzed. The objective of the research was to evaluate how experiential marketing strategies impact tourism demand in this destination, focusing on the sensorial, affective and thought experiences that tourists perceive. The methodology adopted a quantitative approach, with a non experimental cross-sectional design, descriptive scope and a correlational analysis. The study population consisted of 158 tourists. To collect the data, a questionnaire based on a 5-point Likert scale was used, with a total of 21 items. Among the results, a significant positive correlation was found (r = 0.809, p < 0.001) between experiential marketing and tourism demand. Sensory (r = 0.780, p < 0.001), affective (r = 0.772, p < 0.001) and thought (r = 0.797, p < 0.001) were described as determining factors in tourists' decisions when visiting Los Uros. In addition, the multiple regression analysis indicated that experiential marketing explains 83% of the variability in tourism demand. In conclusion, experiential marketing strategies have a significant impact on tourism demand. It is recommended that marketing managers of Los Uros Island implement tactics that promote memorable experiences, prioritizing strategies that involve experiences that stimulate the thinking and emotions of tourists.
We study the problem of pure exploration in matching markets under uncertain preferences, where the goal is to identify a stable matching with confidence parameter $δ$ and minimal sample complexity. Agents learn preferences via stochastic rewards, with expected values indicating preferences. This finds use in labor market platforms like Upwork, where firms and freelancers must be matched quickly despite noisy observations and no prior knowledge, in a stable manner that prevents dissatisfaction. We consider markets with unique stable matching and establish information-theoretic lower bounds on sample complexity for (1) one-sided learning, where one side of the market knows its true preferences, and (2) two-sided learning, where both sides are uncertain. We propose a computationally efficient algorithm and prove that it asymptotically ($δ\to 0$) matches the lower bound to a constant for one-sided learning. Using the insights from the lower bound, we extend our algorithm to the two-sided learning setting and provide experimental results showing that it closely matches the lower bound on sample complexity. Finally, using a system of ODEs, we characterize the idealized fluid path that our algorithm chases.
The global coffee market has experienced significant price volatility in recent years, with rising prices influenced by multiple factors, including climate change, supply chain disruptions, increased production costs, and growing demand. This review investigates the causes of global coffee price increases, focusing on their impact on Ethiopia’s coffee sector, a major player in global production and export. Climate change, through extreme weather events, has resulted in fluctuating yields and reduced productivity, threatening the stability of Ethiopia’s coffee industry. Supply chain disruptions, including labor shortages and shipping delays aggravated by the COVID-19 pandemic, have further strained the market. Moreover, rising input costs, particularly for fertilizers and labor, have added pressure on coffee producers. While the price surge presents opportunities for increased export revenues, particularly in specialty coffee, these benefits are tempered by the challenges of climate variability, labor shortages, and rising production costs. To enhance resilience and sustain growth, Ethiopia must prioritize climate-smart agricultural practices, technological innovation, and market diversification. Government support, including infrastructure development and financial services are essential to strengthen the coffee sector’s competitiveness and ensure long-term sustainability in the face of global market fluctuations.
Nutrition. Foods and food supply, Food processing and manufacture
The over-the-counter (OTC) government bond markets are characterised by their bilateral trading structures, which pose unique challenges to understanding and ensuring market stability and liquidity. In this paper, we develop a bespoke ABM that simulates market-maker interactions within a stylised government bond market. The model focuses on the dynamics of liquidity and stability in the secondary trading of government bonds, particularly in concentrated markets like those found in Australia and the UK. Through this simulation, we test key hypotheses around improving market stability, focusing on the effects of agent diversity, business costs, and client base size. We demonstrate that greater agent diversity enhances market liquidity and that reducing the costs of market-making can improve overall market stability. The model offers insights into computational finance by simulating trading without price transparency, highlighting how micro-structural elements can affect macro-level market outcomes. This research contributes to the evolving field of computational finance by employing computational intelligence techniques to better understand the fundamental mechanics of government bond markets, providing actionable insights for both academics and practitioners.
Since the advent of generative artificial intelligence, every company and researcher has been rushing to develop their own generative models, whether commercial or not. Given the large number of users of these powerful new tools, there is currently no intrinsically verifiable way to explain from the ground up what happens when LLMs (large language models) learn. For example, those based on automatic speech recognition systems, which have to rely on huge and astronomical amounts of data collected from all over the web to produce fast and efficient results, In this article, we develop a backdoor attack called MarketBackFinal 2.0, based on acoustic data poisoning, MarketBackFinal 2.0 is mainly based on modern stock market models. In order to show the possible vulnerabilities of speech-based transformers that may rely on LLMs.
Babak Javadi, Zeinab Salimzadeh, Amir Hossein Akbari
et al.
Considering the evolution of the food industry and its challenges, like high perishability, managing the food industry supply chain is a key focus for researchers and decision-makers. Uncertainty in decision-making has gained importance, particularly in the yogurt industry, known for its complexity. This study addresses production and distribution planning, scheduling, and routing in the yogurt supply chain. The problem is characterized by multiple products, a single plant, multiple distribution centers, multiple periods, and various transportation methods. A mixed-integer non-linear programming (MINLP) model is used to minimize total costs, including production, setup, overtime, unmet demand, and transportation. Additionally, a robust fuzzy programming approach is applied under uncertainty, with linearization procedures proposed to convert it into a linearized mixed-integer programming formulation. The problem is tested with two data types: a sample problem in three sizes (small, medium, and large) and real data from Kalle Dairy Company, Iran. A Genetic Algorithm (GA) is developed to solve the problem, with necessary modifications made for its application. The GA's performance is compared to an exact algorithm (Branch & Cut), showing that the company's production policy adapts daily to meet demand precisely. The shift to smaller batch production and longer shelf life allows better stock allocation and avoids shortages in uncertain conditions. The company's policies adapt to severe fluctuations in the business environment, though this requires high costs, such as inventory maintenance.
O trabalho discute sobre a apropriação dos fundos públicos na educação pública no Brasil e o protagonismo das grandes corporações privadas nesse âmbito. É um recorte da dissertação defendida junto à Universidade Tecnológica Federal do Paraná, em 2023 e a ideia central é a Lei como um artefato tecnológico e seus propósitos moldados por teleologias pré-estabelecidas, refletindo disputas de poder de uma sociedade e período. Agrega à Teoria dos Artefatos na Ciência Jurídica e sua hermenêutica. A lei é analisada à luz das dimensões de CTS, através da análise crítica da Lei 14.533/2023, sobre a política para a educação digital no país.
Palavra-chave: Lei; Artefato Tecnológico; Não Neutralidade; CTS.
Special aspects of education, Labor market. Labor supply. Labor demand
Abstract As trade unions are active in corporations worldwide, their effects on the labor market have attracted global attention. However, there is scarce empirical evidence regarding how trade unions’ effects on the wage gap differ between the public and private sectors, especially in the Chinese context. Using national longitudinal survey data from the China Family Panel Studies for the years 2010, 2012, 2014, 2016, and 2018 and the Blinder-Oaxaca decomposition method, this study estimates unions’ effect on the wage gap between the public and private sectors in China. The results from the Ordinary Least Squares (OLS) method indicate the existence of a significant positive union wage premium nationwide. Additionally, the premium in the public sector is greater than that in the private sector. However, this effect becomes insignificant after accounting for individual heterogeneity using the fixed effects model. The decomposition results based on the OLS method indicate that the union coverage difference (the endowment effect) widens the wage gap between the public and private sectors; conversely, the union wage premium difference (the price effect) narrows the wage gap. These results indicate that a policy expanding union coverage in the private sector may effectively narrow the wage gap between both sectors.
Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual Market Dataset with market dynamics, stock ticker, and history state as context, leveraging a market dynamics modeling method that combines linear regression and Dynamic Time Warping clustering to extract market dynamics; ii) we present Market-GAN, a novel architecture incorporating a Generative Adversarial Networks (GAN) for the controllable generation with context, an autoencoder for learning low-dimension features, and supervisors for knowledge transfer; iii) we introduce a two-stage training scheme to ensure that Market-GAN captures the intrinsic market distribution with multiple objectives. In the pertaining stage, with the use of the autoencoder and supervisors, we prepare the generator with a better initialization for the adversarial training stage. We propose a set of holistic evaluation metrics that consider alignment, fidelity, data usability on downstream tasks, and market facts. We evaluate Market-GAN with the Dow Jones Industrial Average data from 2000 to 2023 and showcase superior performance in comparison to 4 state-of-the-art time-series generative models.
This paper empirically assesses predictions of Goodwin's model of cyclical growth regarding demand and distributive regimes when integrating the real and financial sectors. In addition, it evaluates how financial and employment shocks affect the labor market and monetary policy variables over six different U.S. business-cycle peaks. It identifies a parsimonious Time-Varying Vector Autoregressive model with Stochastic Volatility (TVP-VAR-SV) with the labor share of income, the employment rate, residential investment, and the interest rate spread as endogenous variables. Using Bayesian inference methods, key results suggest (i) a combination of profit-led demand and profit-squeeze distribution; (ii) weakening of these regimes during the Great Moderation; and (iii) significant connections between the standard Goodwinian variables and residential investment as well as term spreads. Findings presented here broadly conform to the transition to increasingly deregulated financial and labor markets initiated in the 1980s.
In many centralized labor markets candidates interview with potential employers before matches are formed through a clearinghouse One prominent example is the market for medical residencies and fellowships, which in recent years has had a large increase in the number of interviews. There have been numerous efforts to reduce the cost of interviewing in these markets using a variety of signalling mechanisms, however, the theoretical properties of these mechanisms have not been systematically studied in models with rich preferences. In this paper we give theoretical guarantees for a variety of mechanisms, finding that these mechanisms must properly balance competition. We consider a random market in which agents' latent preferences are based on observed qualities, personal taste and (ex post) interview shocks and assume that following an interview mechanism a final stable match is generated with respect to preferences over interview partners. We study a novel many-to-many interview match mechanism to coordinate interviews and that with relatively few interviews, when suitably designed, the interview match yields desirable properties. We find that under the interview matching mechanism with a limit of $k$ interviews per candidate and per position, the fraction of positions that are unfilled vanishes quickly with $k$. Moreover the ex post efficiency grows rapidly with $k$, and reporting sincere pre-interview preferences to this mechanism is an $ε$-Bayes Nash equilibrium. Finally, we compare the performance of the interview match to other signalling and coordination mechanisms from the literature.
The article presents directions for investing in the human capital of the Donetsk People's Republic (DPR). The data on the population of the DPR are provided. Further, the education and healthcare systems of the Republic are studied. The dynamics of changes in the number of students in secondary educational institutions of the DPR is considered. The demand and supply of labor in the DPR are also analyzed, the dynamics of the situation in the labor market is reflected. Materials and methods of research. The study is based on the analysis of statistical data, as well as publications in scientific journals, collections and reports. Research results and their discussion. The study aimed to identify the main areas for investing in the human capital of the DPR, to study data on the population, as well as education and healthcare systems, and to study the situation on the labor market. For this, an analysis of literary, statistical sources and Internet resources was carried out. Thus, among the main areas for investment in human capital, the following can be distinguished: education and training, healthcare, entrepreneurship and small business development, infrastructure and technology. The population of the DPR in 2022 amounted to 2202.4 thousand people. There are three levels in the education system of the Republic: general secondary, vocational and higher vocational education. The healthcare system is facing a severe shortage of staff. In 2022, the coefficient of tension in the labor market of the DPR was 0.9. Conclusion. Based on the results of the study, it can be concluded that ensuring the investment attractiveness of the human capital of the DPR is an important task. Human capital, as an independent resource, ensures the competitiveness and stability of the national economy.
The global markets provide enterprises with selling opportunities and challenges in stabilizing operational strategies. From the perspective of production management, it is important to improve the profitability of an enterprise by exploiting the different timing of the selling season in different markets to develop an operational strategy that is optimized and configured on a global scale. This paper examines the above issue with an insightful model of selling the product to two markets (a primary and a secondary market) with multiple risks of changes in the market environment and nonoverlapping selling seasons. We refer to this problem as the "global robust newsvendor" problem. We provide closed-form solutions of the optimal operation strategy for demand-independent and demand-related scenarios for the above two market stochastic systems. The closed-form solutions fully reflect the influence of the relationship between supply and demand on strategy selection. We find that the demand correlation and the lack of demand information will not substantially affect the operation strategy, and the enterprise's industrial chain and supply chain remain stable. However, the reduction of inter-market tariffs or logistics costs will cause changes, and the existence of the secondary market will lead to more capacity planning in the primary market. In addition, our model explicitly considers the impact of exchange rate uncertainty on operating strategies.
Sammendrag Innvandrerandelene i den eldre befolkningen vil øke sterkt de nærmeste årene. Tradisjonelt har innvandrere fra lavinntektsland hatt en tilbøyelighet til å forlate arbeidslivet tidligere enn norskfødte, også betinget på yrkesdeltakelse fram til tidligpensjonsalder. Reformene av det norske pensjonssystemet kan ha bidratt til å endre på dette. Vi studerer effekter av AFP-reformen i privat sektor og finner at de positive arbeidstilbudsresponsene var sterkere for innvandrere fra lavinntektsland enn for norskfødte. Blant personer med antatt rett på privat sektor-AFP anslår vi at forskjellen i yrkesdeltakelse ved alder 63–66 år mellom innvandrere fra lavinntektsland og norskfødte ble redusert fra ni til ett prosentpoeng.
Faced with huge market potential and increasing competition in emerging industries, product manufacturers with key technologies tend to consider whether to implement a component open supply strategy. This study focuses on a pricing game induced by the component open supply strategy between a vertically integrated manufacturer (who produces key components and end products) and an exterior product manufacturer (who produces end products using purchased key components) with different customer perceived value and different cost structure. This study first establishes a three stage pricing game model and proposes demand functions by incorporating relative customer perceived value. Based on the demand functions, we obtain feasible regions of the exterior manufacturer's sourcing decision and the optimal price decision in each region. Then the effects of relative customer perceived value, cost structure, and market structure on price decisions and optimal profits of the vertically integrated manufacturer are demonstrated. Finally, as for the optimal component supply strategy, we present a generalized closed supply Pareto zone and establish supply strategy Pareto zones under several specific configurations.
With the fast change of information and communication technologies and global economics manufacturing industry faces the challenges in both market and supply sides. The challenges in the market include short product life cycle, demand uncertainty, and product delivery. Accordingly, supply challenges are the dramatic increase of flexibility in productions and complexity in the supply chain, which result from the changes in the industry and rapid development of ICPT (Information, Communication, and Production Technologies). In this study, we consider a supply chain converged with ICPT, called Smart Manufacturing Supply Chain (SMSC). By investigating the attributes of SMSC, we identify the functional and structural characteristics of SMSC. Tactical supply planning in SMSC recognizes the ability of a pseudo real-time decision-making constrained by the planning horizon. In order to take advantages of SMSC a multi-objective multi-period mixed integer non-linear programming for closed-loop supply chain network design is presented. This model aims to minimizing overall costs environment effects and lead time. To solve the proposed model, considering uncertainties in the problem, the improved epsilon-constraint approach was adopted to transform the multi-objective model into a single-objective one. Then, the Lagrange relaxation method was employed for an effective problem-solving. In the following a case study in the real world was proposed to evaluate the models performance. Finally a sensitivity analysis was carried out to investigate the effects of important parameters on the optimal solution.
O livro de Paulo Sergio Tumolo – fruto de sua Tese de Doutorado – ocupa-se de tema ainda insuficientemente explorado na área de Trabalho e Educação, embora de fundamental importância para a educação dos trabalhadores: a formação sindical. Para tanto, o autor se detém na análise da dinâmica e da complexidade do processo de formação desenvolvido pela Central Única dos Trabalhadores-CUT. Busca, assim, apreender as múltiplas mediações que permeiam os princípios, as propostas e as ações da central no atual quadro de reordenamento do padrão de acumulação do capital e conseqüentes alterações no mundo do trabalho
Special aspects of education, Labor market. Labor supply. Labor demand
Arbeidsmarkedene i vestlige land har blitt polarisert over tid ved at middelklasseyrker har blitt automatisert bort. Denne prosessen er tidligere også dokumentert i Norge. Ettersom automatiseringen og digitaliseringen har fortsatt å øke, undersøker vi i denne artikkelen om de polariserende tendensene fortsatt pågår. Ved å analysere norske registerdata for perioden 2004-2018 finner vi stadig økende etterspørsel i toppen av yrkesfordelingen, men ikke økt etterspørsel lengre ned i fordelingen. Oppgraderingen og økt etterspørsel i toppen av arbeidsmarkedet, fortsatt fall i etterspørselen på midten, dempes av at relative lønninger har økt på toppen og falt på bunnen.