The purpose of this study is to examine the driving model of determinant factors that affects the performance of small and micro enterprises empirical evidence from Amhara Region, Ethiopia. The study used primary and secondary data from manufacturing, construction, urban agriculture, trade, and service entrepreneurs found in Bahirdar, Dessie & Gonder Cities using a purposive sampling technique. It also used the descriptive research design with a self-administered survey questionnaire. The Statistical analysis tools, SPSS and Amos, software were applied to analyze the data. Multiple regression model results revealed that access to credit, initial capital, working premises, industry category, market linkage, ICT adoption have a positive relationship and a major impact on the Amhara region's enterprise performance. Hence, the outcome variable, enterprise performance, is highly affected by all predicted variables. As the study finding shows, a lack of access to credit, , market linkage, working premises), initial capital, and information communication Technology problems are critical challenges of SMEs. The Regional Government, TVED Bureau, Micro Finance Institutions, , should take corrective action to provide sufficient credit and long-term loans to protect inflation. Moreover, this study proposed a sustainable cluster management approach that solves their challenges in an integrated way.

Agricultural extension and advisory services in Africa have significant impact on food security as well as economic and social development. Recent moves towards a pluralistic delivery system, including the emergence of private-sector led initiatives in many countries are subject of policy and academic discourse. Our study used an adapted, fit-for-purpose market systems development framework to review available research in extension and advisory services in selected sub-Sahara Africa countries. Using a literature survey methodology, we report evidence of multiple actors in extension delivery, findings that point towards evolution towards mixed delivery as well as objectives. While there are significant uptake of cost-recovery approaches among commercially-oriented farmers, many smallholder farmers still depended on donor-funded services. Our review adds to existing knowledge through incorporating a market systems development framework, which extends the often-used willingness to pay approach, and highlights the need for merger of the public and private-sector objectives to achieve developmental outcomes.

The purpose of this paper is to analyze market reflexivity in agricultural futures contracts with different maturities. To this end, we apply a four-dimensional Hawkes model to storable and non-storable agricultural commodities. We find market reflexivity for both storable and non-storable commodities. Reflexivity accounts for about 50 to 70 percent of the total trading activity. Differences between nearby and deferred contracts are less pronounced for non-storable than for storable commodities. We conclude that the co-existence of exogenous and endogenous price dynamics does not change qualitative characteristics of the price discovery process that have been observed earlier without consideration of market reflexivity.

Descriptive research design was used to select 200 respondents (i.e., 100 from each study places) following purposive sampling technique on two study areas (Market; Living). Personal observation and interview with retailers were done to triangulate the collected data from the formal survey. The collected data was analyzed using SPSS version 20 software program. Descriptive statistical measurements, Index and multinomial regression model was calculated to assess the relationship between customers point of emphasis during packaged water buying and respondents demographic variables. Majority of the respondent who frequently purchase packaged water generated a monthly income between 5000 and 10000 Birr. Television and radio was ranked first compare to other Media. Living place customers were more health concerned as compared to market place customers. Market place customer’s primarily give emphasis to price of packaged water. Almost all (97%) customers did not have the awareness towards packaged water standards. Only 86(43%) of the total respondents checked the chemical composition, of which 74(85%) of the respondents did not understand it. Customers sex, educational level and health status showed significant relationship with choice of packaged water quality -1.42(p<0.05), price -2.45(P<0.01) and health status -1.80(P<0.05) in market place and living places, respectively.

Increasing market participation among smallholder farmers have a big potential to uplift living standards of poor through increasing production and consumption pattern. Although, smallholder farming made 95% of total crop production in Ethiopia, they are exposed to a marketing bottleneck that hinders benefits from their produce. The objective of this study was analyzing factors determining smallholder Teff farmer decision to participate in output market and level of marketed output.The study used data from 190 respondents from four selected Teff dominant kebeles of Gena-Bossa districts in Dawro Zone, through structured questioner. This investigation was imperative because no adequate research has been done in study area in examining the hindering factors of farmers’ market participation. Moreover, in the prior study, different authors come up with varied outcomes in diverse country and geographic location concerning poor farmers’ market participation decision. The descriptive statistics and Heckman two stage econometric methods were employed to analyze data collected from sampled household. The significance of coefficient of inverse Mill’s ratio ( ) indicates the presence of self-selection bias and the effectiveness of applying Heckman two stage model. The results of study show that the smallholder decision to participate in output market were positively influenced by size of land holding, availability of family labor force, education status of household head, accessibility of credit service and access to market price information. On the other hand, size of family member, sex of house hold head being female and distance to market place discourage probability of Teff farmer market participation decision. Moreover, the second stage estimation reveals that, the education status of house hold head, size of farm land, amount of Teff crop produced, accessibility of market information, the size of family labor force and being member to farm cooperative increase the quantity of marketable output, whereas, large number of family size decline the level of Teff crops marketed. The policy that assist poor farmers in obtaining market skills; create affordable credit service; strengthen community based producer groups and capacitating the females socially and economically in the community believed to minimize the problems encountering small farmers in a way to market their crop

Time irreversibility, i.e. the lack of invariance of the statistical properties of a system under time reversal, is a fundamental property of all systems operating out of equilibrium. Time reversal symmetry is associated with important statistical and physical properties and is related to the predictability of the system generating the time series. Over the past fifteen years, various methods to quantify time irreversibility in time series have been proposed, but these can be computationally expensive. Here we propose a new method, based on permutation entropy, which is essentially parameter-free, temporally local, yields straightforward statistical tests, and has fast convergence properties. We apply this method to the study of financial time series, showing that stocks and indices present a rich irreversibility dynamics. We illustrate the comparative methodological advantages of our method with respect to a recently proposed method based on visibility graphs, and discuss the implications of our results for financial data analysis and interpretation.

2017 was an excellent year in terms of new drugs (chemical entities and biologics) approved by the FDA, with a total of forty-six. In turn, one of the highlights was the number of peptides (six) included in this list. Here, the six peptides are analysed in terms of chemical structure, synthetic strategy used for their production, source, biological target, and mode of action.

The purpose of this paper is to apply China’s economy growth prospects and its potential drivers of future. China's fast rise and its growth model have accelerated important existing structural trends in the global economy and made them decisive characteristics of the world economy. China's role in the world economy over the coming decades, an exercise which would not be possible without an investigation of the prospects for China's continued economic rise. On the one hand, China is a large export market for the United States. A lot of U.S. firms use China as the final destination of assembly in their global supply chain networks. China’s huge holdings of U.S. Treasury securities support the federal government finance its budget failures. However, some analysts contend that China consolidates a number of distortive economic policies such as protectionist industrial policies and an undervalued currency that undermine U.S. economic interests. They warn that efforts by the Chinese government to promote indigenous innovation, often through the use of subsidies and other distortive measures, could negatively affect many leading U.S. industries

The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs´ VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with σ-modification to assure robustness after combining control and monitoring variables. Lyapunov’s second method and Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with standard Proportional Integer controller (PIC) and direct APBC, show the proposed CAPBC effectiveness and robustness under normal and changing conditions

The purpose of this study is to investigate the impact of the voltage drop of a three-phase voltage source inverter in driving brushless motors. This study also proposes an enhanced Field Oriented Control scheme that accounts for the inverter voltage drop enabling efficient drive at very low motor-speeds. Experimental results are provided to verify the theoretical study and the proposed control scheme.

Responding to changing value creation processes in the sheet metal working sec-tor, where the complexity and interchangeability of products challenge traditional dif-ferentiation strategies, this empirical analysis explores integrating service-oriented and data-driven business models as new paths to ensure competitiveness, especially for small and medium-sized enterprises (SMEs). This study aims to capture the current state and challenges associated with the implementation of these business models in this sector. The research was conducted through semi-structured interviews with SMEs in the industry. The findings indicate that service-oriented and data-driven business models are not yet widely adopted and that manufacturing companies re-quire support in their implementation. Fields of action were identified for the industry. These are "Creating awareness and understanding", "Recognizing added value", "In-creasing company maturity" and "Understanding the change process". Cooperation between science and industry is essential in tackling these fields of action to ensure the successful integration of such business models in manufacturing companies. This pa-per identifies challenges in the fields of action that companies must address through a structured approach promoting awareness, recognizing value, improving organiza-tional maturity, and understanding the change process to successfully implement ser-vice-oriented and data-driven business models.

This study introduces a fruit harvesting mechanism powered by a single motor, designed for integration with unmanned aerial vehicles (UAVs). The mechanism performs reciprocating motion by converting linear motion into rotational motion. Consequently, the end-effector can execute multi-dimensional kinematic trajectories, including biaxial and rotational movements, synchronized with the motor's position. These axial and rotational motions facilitate the gripper's ability to reach, retrieve, and detach fruit from branches during the harvesting process. Notably, a critical consideration in designing this fruit harvesting mechanism is to generate the necessary torque at the end-effector while minimizing reaction forces and torque that could destabilize the UAV during flight. With these considerations in mind, this preliminary study aimed to harvest a Fuji apple and conducted dynamic analysis. We constructed a prototype of the single motor-driven fruit harvesting mechanism using a suitable servo motor. To assess its mechanical performance and evaluate its impact on the hexacopter, we developed both a specific test platform featuring a six-spherical-prismatic-spherical parallel structure and a virtual environmental flight simulator. Overall, the results demonstrate the successful harvesting of a Fuji apple weighing approximately 300 g by the single motor-driven fruit harvesting mechanism, with no adverse effects observed on the hexacopter's operation.

Land and property development process include a series of multifaceted activities ranging from purchasing to converting it for development purposes and everything in between. The process itself encompasses multiple stakeholders, drivers, and contributions from diversified public and private actors and transaction cost arises out of their complex interaction. Transaction costs incurred during any kind of human interactions (i.e. transactions). Every actor involved in the process wishes to maximize his achievement under various constraints and hence institutional arrangement (i.e. set of humanly devised rules to administer the constraints) is necessary for efficient management of the development process. Therefore, to devise an optimum outcome out of economic and social transactions in the property development process, cooperative and competitive relationships between individuals should be understood from a broader socio-political and governance structure. In this research, it is critically argued that land and property development process should implicate a multifaceted set of formal and informal rules or institutional arrangement to govern the intrinsic interaction, action and thereby reducing the related transaction cost. The argument is further reproachfully evaluated and implicated in the urban development process through the myopic lens of Transit-oriented development (TOD) pathway. A vigilant combination of descriptive and explanatory research approach is adopted to analyze the connection between theory and practice.

Liquidity providers for asset-pair pools with constant value distri- bution, such as Balancer pools, experience some impermanent loss whenever there is price divergence in the tokens’ fiat values. In this paper, we model this impermanent loss for geometric mean invariant pools by deriving a function of two parameters. We analyze our function graphically, showing that unevenly distributed pools with large weighting disparities best mitigate the risk of impermanent loss. We conclude by providing a general protocol for liquidity providers to choose a suitable pool based on their risk tolerance and profit goals.

The gut microbiota plays an important role in the physiological activities of the host and affects the formation of important economic traits in agricultural animals. We investigated the effects of cecal microbiota on chicken weights using the Guizhou yellow chicken as a model. We collected experimental cohorts from chickens with high (HC, n=16) and low body weights (LC, n=16) and integrated microbial 16S rRNA gene sequencing and non-targeted serum metabolome data to explore the effect and its metabolic mechanism of cecal microbiota on market weight. The genera Lachnoclostridium, Alistipes, Negativibacillus, Sellimonas and Ruminococcus torques group were enriched in the HC group while Phascolarctobacterium was enriched in the LC group (P<0.05). Metabolomic analysis determined that pantothenic acid, luvangetin (2H-1-benzopyran-6-acrylic acid) and menadione (vitamin K) were significantly higher in HC serum while beclomethasone dipropionate (a glucocorticoid) and chlorophene (2-benzyl-4-chlorophenol) were present at higher levels in the LC group. The microbes enriched in HC that were significantly positively correlated with metabolites including pantothenic acid and menadione and negatively correlated with beclomethasone dipropionate and chlorophene. These results indicated that specific cecal bacteria in Guizhou yellow chickens alter the host metabolism and growth performance. This study provides a reference for revealing the mechanism of cecal microbe actions that affect chicken body weight.

The Metropolitan Economic Freedom Index (MEFI) ranks cities based on their support of free market enterprise. In its current state, MEFI purports to measure three constructs (government spending, taxation, and labor market freedom) with three equally weighted variables for each one, assuming perfect substitutability of variables. This study investigates the statistical consistency of MEFI through Confirmatory Factor Analysis. Multiple models investigate current variable selection by providing a potentially better indicator of labor market freedom, aggregation assumptions by removing the requirements for fixed and equal weights, and statistical consistency by evaluating the fit between the data and models. Results indicate that the current MEFI model is not statistically consistent with the data, that weighting of variables should not be equal, that variable selection should be investigated, and that constructs should be re-imagined. The models investigated provide an initial starting point for redefining MEFI.

Management of Australia’s largest river system, the Murray-Darling Basin (MDB) is controversial, addressing conflicting demands from stakeholders, including irrigators, the agriculture industry, indigenous groups, and the environmental lobby. Oversight is concentrated in a single agency, the MDB Authority, but climate change and the water market have contributed to uncertainty over the Basin’s environmental sustainability. This paper reports on preliminary research in South Australia’s Riverland, one of Australia’s most important wine, citrus, nuts, and stone fruit production areas. It focuses on the Renmark Irrigation Trust (RIT), supplying water to irrigators, primarily horticulturalists. It investigates the chief risks perceived and future-plans in face of concerns over variable water flows and economic uncertainty. Using sequential mixed methods, a questionnaire survey supplied information from a sample of irrigators, covering land use, on-farm environmental actions, risks, decision making, recent and proposed changes to farm management, and attitudes to the MDB Plan and the water market. Focus groups and interviews then enabled key topics to be addressed in greater depth. The RIT’s contribution to river restoration is highlighted, with its plans for additional on-farm water stewardship investigated. There are high levels of uncertainty regarding future viability of smallholdings, including unintended consequences from the water market (e.g., increasing areas under water hungry almonds), the role of water brokers, and environmental viability of the river system linked to climate change. More optimistic voices point to increased diversity of production, resilience of the local horticultural industry, and new entrants in the form of hobby and part-time producers.

The high penetration of intermittence resources in the energy market accelerates significantly the decarbonization process but, on the other hand, the electrical system has to face the problem of unbalances. Renewable Energies Sources (RES) are hard to precisely forecast, and power plants are not able to predict the amount of energy that they can provide far from the real time delivery. In this frame, the intraday market gets a fundamental role allowing agents to adjust their position close to the delivery time. In this work we suggest an agent-based model of intraday market combined with genetics algorithms to understand what the best strategy could be adopted by players in order to optimize the market efficiency in terms of welfare and unsold quantity. In the first part we show the effect on the market prices of different scenarios in which players aim at maximizing their revenues and selling/buying all their volumes. In the second part we show the effect of a particular genetic algorithm on the model, focusing on how agents can adapt their strategy to enhance the market efficiency. Comparative analyses are also performed to investigate how the welfare of the system increases as well as the unsold quantity decrease when genetic algorithm is introduced

Buyer practices have changed as individuals are figuring out how to live with the new truth of COVID-19. Take-out and conveyance orders have expanded, and our customer has added new items to their menu because of new client inclinations. With every one of the continuous changes, the customer had numerous unanswered inquiries, for example, Smartbridge has broad involvement with café innovation development Café TECHNOLOGY CAPABILITIES :Are the most famous items as yet unchanged after COVID? :Which are the most sold item blends now? :What is the acknowledgment of new things? :What are clients purchasing alongside new things? :How have liquor deals changed? The customer previously had reports that followed item deals and operational measurements, notwithstanding, there was a need to get a more profound knowledge into item examination. The customer expected to recognize what items and introductions were being sold all the more frequently, measure the acknowledgment of new items, and figure out what items clients buy together to improve advertising efforts, advancements, and deals. he E-business industry is filling immensely in the Indian market. The modest 4G web bundles in India clearly gives a push to these ventures. Thus, as Covid19 first hit in Quite a while, individuals got terrified to go out from their homes in light of the fact that, in their mind, it's a dread of Covid. They even wonder whether or not to go out to purchase fundamental (FMCG) products. Frenzy purchasing additionally has seen and to stay away from this dread of COVID-19, individuals are offering inclinations to the E-Commerce destinations to purchase fundamental products and a few clients are new which joined to purchase fundamental merchandise during this Pandemic Lockdown period. Numerous clients are moving their purchasing conduct from disconnected retail locations to online stores. This paper examines the customer buying pattern during lockdown.

Created in 1905, the Société des Artistes Meridionaux organized annual exhibitions in Toulouse, in order to promote regional arts and (re)created a "Latin" - and modern - style. Based on a corpus of 11,486 artworks exhibited at the SAM between 1907 and 1939, this paper seeks to measure the contours and particularities of the Salons des Artistes Meridionaux, using a computational approach.

The purpose of this study is to examine the driving model of determinant factors that affects the performance of small and micro enterprises empirical evidence from Amhara Region, Ethiopia. The study used primary and secondary data from manufacturing, construction, urban agriculture, trade, and service entrepreneurs found in Bahirdar, Dessie & Gonder Cities using a purposive sampling technique. It also used the descriptive research design with a self-administered survey questionnaire. The Statistical analysis tools, SPSS and Amos, software were applied to analyze the data. Multiple regression model results revealed that access to credit, initial capital, working premises, industry category, market linkage, ICT adoption have a positive relationship and a major impact on the Amhara region's enterprise performance. Hence, the outcome variable, enterprise performance, is highly affected by all predicted variables. As the study finding shows, a lack of access to credit, , market linkage, working premises), initial capital, and information communication Technology problems are critical challenges of SMEs. The Regional Government, TVED Bureau, Micro Finance Institutions, , should take corrective action to provide sufficient credit and long-term loans to protect inflation. Moreover, this study proposed a sustainable cluster management approach that solves their challenges in an integrated way.

We examine the profitability of the momentum and contrarian strategies in three South Asian markets i.e. Bangladesh, India and Pakistan. We also analyze, whether credit risk influences momentum and contrarian return for these markets from 2008 to 2014. We use Distance-to-default (DD) of Kealhofer, McQuown and Vasicek (KMV) model as a measure of credit risk. We calculate the credit risk and form the momentum and contrarian strategies of the firms on the basis of high, medium and low risk. We find that in all three markets, the momentum and contrarian returns are significant for medium and high credit risk portfolios and no momentum and contrarian returns for low credit risk portfolios.

Futures markets are mainly used as a tool for price discovery and for risk management on the spot markets and enable diversification for international portfolio investments. With this study we aim (1) to investigate the causality relationship between futures markets and spot market and (2) to examine the causality relationship between futures markets and spot markets in different countries. We are interested in both the futures markets - spot market relations and the interactions between the markets at international level. For variables we used the the BIST30 spot index and BIST30 futures contract representing the Borsa Istanbul market and the Dow-Jones 30 index and Dow-Jones 30 futures contract, which are the most important indices representing the US markets. Daily closing price data for the period between 2nd January, 2009 and 18th June, 2018 were analyzed using correlation, unit root test, causality test and regression equations. The results of the study show that the futures markets continue their price discovery role for both the spot markets and futures markets and are influential on other futures and spot markets at international level. These findings are important for investors wanting to invest in Turkey and in similarly considered emerging market economies. It will help investors take informed decisions by providing them with a more efficient price estimations utilizing the futures markets.

This paper examines the effect of chronotype on the delinquent credit card payments and stock market participation through preference channels. Using an online survey of 455 individuals who have been working for 3 to 8 years in companies in mainland China, the results reveal that morningness is negatively associated with delinquent credit card payments. Morningness also indirectly predicts delinquent credit card payments through time preference, but this relationship only exists when individuals’ monthly income is at low and average level. On the other hand, financial risk preference accounts for the effect of morningness on stock market participation. Consequently, an additional finding is that morningness is positively associated with financial risk preference, which contradicts previous finding in the literature. Finally, based on the empirical evidence, we discuss the plausible mechanisms that may drive these relationships and the implications for theory and practice. The current study contributes to the literature by examining the links between circadian typology and particular financial behaviours of experienced workers.

In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literatur A key finding of this study is that risk preferences at individual bank level can lead to unique interbank market structures which are suggestive of the capacity that the market responds to surprising

Depression and anxiety are the most common mental health disorders worldwide. Internet-Delivered Cogni-tive Behaviour Therapy (ICBT) can reduce barriers to care for broad cross sections of the population. How-ever, People of Diverse Ethnocultural Backgrounds (PDEGs) other than White/Caucasian underutilize mental health services and are underrepresented in clinical trials of psychological interventions. To address this research gap, we adapted an evidence-based ICBT program for PDEGs. The current pilot study explores the effectiveness, satisfaction, and engagement in the adapted ICBT by PDEGs (N=41) when benchmarked against a sample of PDEGs (N=134) drawn from a previous non-adapted version of the ICBT program. Inntent-to-treat analyses showed that the adapted ICBT program is effective in reducing anxiety and de-pression symptoms among PDEGs. Large within-group pre-to-post-treatment Cohen’s effect sizes of d = 1.23, 95% CI [0.68, 1.77] and d = 1.24, 95% CI [0.69, 1.79] were found for depression and anxiety, respectively. Further, 81.8% of the PDEGs who received the adapted ICBT program reported high overall satisfaction, 90.9 % reported increased confidence in managing symptoms, and 70.7% of participants completed the ma-jority of the psychoeducational lessons in the ICBT program. No statistically significant differences in clinical outcomes, engagement, and satisfaction were found between the pilot study and benchmark sample. Future directions for ICBT research with PDEGs are described.

A potassium bromide KBr material, which has been widely used as the key element in the Fourier spectrometers and as the output window of the IR-lasers, has been studied with good advantage via applying the carbon nanotubes in order to modify the potassium bromide surface. The laser oriented deposition method has been used to place the carbon nanotubes at the matrix material surface in the vertical position at the different electric field varied from 100 to 600 V×cm-1. The main idea of the improvement of the spectral properties of the potassium bromide structure is connected with the fact that the refractive index of the carbon nanotubes is substantially less than the refractive index of the studied material, and the small diameter of the carbon nanotubes allows one to embed these nano-objects in the voids of the lattice of the model matrix systems. Moreover, the mechanical characteristics and wetting features of the potassium bromide structures have been investigated under the condition mentioned above. Analytical and quantum-chemical simulations have supported the experimental results.

Green and low-carbon development has become an important embodiment of China's path to becoming a strong agricultural country. How to enable agricultural enterprises to better adopt green technologies and conduct production and operation with the concept of sustainable development has become a key link. This article utilizes a structural equation model (SEM) to analyze the role paths and mediating effects of organizational learning ability and policy orientation in agricultural enterprises' market orientation and willingness to adopt green technologies[ The definition of green technology in this paper refers to Guo Kesha, Tian Xiaoxiao. Green technology and green transformation of industrial development mode [J]. Tianjin Social Sciences, 2024 (02) :99-107, including green manufacturing (production) technology and green service technology.]. The results show that market orientation, organizational learning ability, and policy effectiveness have significant direct positive impacts on the willingness to adopt; market orientation has a significant positive impact on agricultural enterprises' organizational learning ability, organizational learning ability has a significant positive impact on policy orientation, and market orientation has a significant positive impact on policy effectiveness; the results of mediation analysis also indicate that learning ability has a significant mediating effect between market orientation and willingness to adopt, and policy effectiveness has a significant mediating effect between market orientation and willingness to adopt. Through difference analysis, it is believed that the mediating effects of the three paths are equally important, and organizational learning ability and policy effectiveness play a partial mediating role in agricultural enterprises' willingness to adopt green technologies. Based on this, policy suggestions are proposed to improve enterprises' own organizational learning ability, increase the promotion of policies related to green technologies, improve relevant policies for the application of green technologies, and increase research and development efforts for green technologies.

Measuring the impact of land use regulation on the land market involves identifying and classifying relevant impact factors related to the land market. The objective of this study was to identify land market impact factors in the context of the introduction of land use regulation in Nepal. A desktop review was carried out to identify preliminary set of impact factors, which were reclassified through intuitive analysis based on the degree of thematic closeness. Perspective-based impact factors were identified through the qualitative analysis of primary data collected through semi-structured interviews with the Nepalese land market stakeholders. These independently derived impact factors were compared with the desktop literature review impact factors resulting in fourteen land market impact factors across four dimensions, including transaction cost, valuation, mortgage availability, taxation, and compensation across the economic dimension; lot size, subdivision restrictions, and coordination across the institutional dimension; awareness, expectation and proximity across the social dimension; and risk reduction, quality of residential land, and suitability of zoning classification across the environmental dimension. There was significant overlap and commonality across factors identified from both the literature review and semi-structured interviews. The land market impact factors determined in the study may be adapted and generalized across other countries and could be utilized to better understand the impacts of land policy decisions on urban planning and development. Further research is recommended on the process to operationalize the use of these factors to quantify the impact of land use regulation on different land markets.

This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, with a focus on enhancing profitability while factoring in battery degradation. The study incorporates in the optimisation framework the cost of battery capacity degradation, the effects of capacity degradation and internal resistance degradation into dynamics. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximizing net revenues, profits considering capacity degradation, and profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. The paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.

With the recent advances in Machine Learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in literature with a lack of studies on the application of Deep Learning sequence models as Recurrent Neural Networks (RNN) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: a) we have proposed a Multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The Multi-head LSTM outperforms all the other models; b) We identify the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data; c) We made public the dataset we used for the experiments that include data from 8262 different public companies in the American stock market generated in the period between 1999-2018. Furthermore, we prove the efficacy of the Multi-head LSTM models in terms of lower false positives and better division of the two classes.

This article combines the traditional definition of portfolio risk with minimum spanning tree based “interconnectedness risk" to improve the equal risk contribution portfolio performance. We use betweenness centrality to measure an asset’s importance in a market graph (network). After filtering the complete correlation network to a minimum spanning tree, we calculate the centrality score and convert it to a centrality heuristic. We develop an adjusted variance-covariance matrix using the centrality heuristic, to bias the model to assign peripheral assets in the minimum spanning tree higher weights. We test this methodology using the constituents of the S&amp;P 100 index. The results show that the centrality equal risk portfolio can improve upon the base equal risk portfolio returns, with a similar level of risk. We observe that during bear markets, the centrality-based portfolio can surpass the base equal risk portfolio risk.

Aiming to investigate the impact of strategic knowledge disclosure on the continuous realization of digital innovation in enterprises, this study examines data from 168 listed companies across three high-tech industries in China from 2012 to 2021. By examining investor trust, consumer trust, worker trust, and rival trust as mediating variables within the market trust dimension, this research explores the impact mechanism of strategic knowledge disclosure on the sustainability of input and output in digital innovation. The findings reveal that strategic knowledge disclosure significantly promotes both input and output sustainability in digital innovation. Specifically, investor trust positively mediates the relationship between strategic knowledge disclosure and the sustainability of digital innovation input. Similarly, consumer trust and worker trust positively mediate the association between strategic knowledge disclosure and both input and output sustainability in digital innovation. However, it is important to note that rival trust negatively mediates the link between strategic knowledge disclosure and both input and output sustainability in digital innovation. The research findings possess significant theoretical value and provide valuable guidance for promoting the sustainable development of digital innovation in enterprises.

We aim to map the ESG patterns of emerging-market companies from 2018 to 2021 in order to determine whether the COVID-19 pandemic exerted any influence on sustainable corporate behavior. Thus, the ESG performances were assessed by applying the Kohonen neural network for clustering purposes at three main levels: i) ESG overall, including country and sectoral perspectives; ii) ESG thematic; and iii) ESG four-folded (stakeholder, perspective, management level, and focus strategic views). Our results show that emerging-market companies focus their ESG efforts on social and governance issues rather than on environmental. However, environmental, and social behavior differ more acutely than governance behavior across clusters. The ESG pillars country-level and eleven market-based economic sectors analysis corroborate the geographic and sector dependence of ESG performance. The thematic-level analysis indicate that operational activities and community issues received more attention, which suggests that emerging-market companies address distinct ESG topics according to their particularities and competitiveness. Furthermore, our empirical findings provide evidence that the ESG behavior of companies has changed over the course of the COVID-19 pandemic. Thus, our findings are relevant to policymakers involved in regulating ESG disclosure practices, investors focused on enhancing their sustainable investment strategies, and firms engaged in improving their ESG involvement.

Stock market prediction is a challenging task to perform, as we know the fluctuations that take place in the market makes it versatile and hard to predict the prices. In this research, we have explored the power of usage of hybrid ensemble algorithms to improve the predictive accuracy in the stock market forecasting. Our research comprises construction and evaluation of diverse hybrid models using different algorithms. The methodology presented in the paper involves comprehensive data preparation, feature engineering, and model normalization. Evaluating the different hybrid models, one stands out distinctly: LSTM (long short-term memory networks) + GRU (Gated recurrent units) + Conv1D (one-dimensional convolutional layer) hybrid. It illustrated its potential to revolutionize decision-making tools for investors and financial analysts in stock market analytics. The harmonious integration of algorithms not only underscores the effectiveness of hybrid modeling but also beckons for further exploration within the ever-evolving domain of predictive modeling, driven by the pursuit of precision and accuracy. This model shows a remarkable accuracy metrics including a Mean Absolute Error (MAE) of 0.95, a Mean Squared Error (MSE) of 2.1222, a Root Mean Squared Error (RMSE) of 1.52, and a R-squared (R2) score of 0.9982.

Chitin/chitosan and collagen are two of the most important bioactive compounds with applications in the pharmaceutical, veterinary, nutraceutical, cosmetic, biomaterials, and other industries. When extracted from fish and shellfish non-edible parts, by-catches, and invasive species, their use contributes to a more sustainable and circular economy. The present article reviews the scientific knowledge and publication trends along the marine chitin/chitosan and collagen value-chains and assesses how researchers, industry players, and end-users can bridge the gap between scientific understanding and industrial applications. Overall, research on chitin/chitosan remains focused on the compound itself rather than its market applications. Still, chitin/chitosan use is expected to increase in food and biomedical applications, while that of collagen is expected to increase in biomedical, cosmetic, pharmaceutical, and nutritional applications. Sustainable practices, such as the reuse of waste materials, contribute to strengthen both value-chains; the identified weaknesses include the lack of studies considering market trends, social sustainability, and profitability, as well as insufficient examination of intellectual property rights. Government regulations, market demand, consumer preferences, technological advancements, environmental challenges, and legal frameworks play significant roles in shaping both value-chains. Addressing these factors is crucial for seizing opportunities, fostering sustainability, complying with regulations, and maintaining competitiveness in these constantly evolving value-chains.

The article was devoted to topics related to the courier market, electromobility and renewable energy sources. This is the direction in which the car market is currently heading. The current situation of transport in Poland and the electrification trends of the courier market in Poland and beyond were presented and discussed. The article presents the conducted research and analyzes it for a selected company, which at the same time constituted the achievement of the goal and the solution of the research problem. Much attention was focused on conducting AHP analysis and simulating route changes for selected regions. The study contains an introduction and a summary discussing the most important conclusions.

With the view toward enlivening the Chinese automobile market, this work seeks to develop a framework to reveal the tastes of Chinese car users from online user-generated content (UGC) and identify future product improvement orientation. We construct an importance-satisfaction gap analysis (ISGA) model based on sentiment analysis technique. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users' satisfaction with different attributes based on the rules, and weigh the importance of attributes using TF-IDF method, which lay the statistic foundation for our ISGA model. We also take time into account and further analyze the ISGA model, aiming to provide new insight into the changing tastes of Chinese car users so as to suggest correct design positioning and resource allocation. The final evaluation results show that the method proposed in this paper, which integrates Chinese word-boundary-identified algorithm and sentiment lexicon matching rules, is well supported.

Background: Understanding the factors that influence the market entry, exit, and stability of community pharmacies (i.e., market dynamics) is important for stakeholders ranging from patients to health policymakers and small business owners to large corporate institutions. The purpose of this article is to provide an update to a 15-year ecological comparison for the market dynamics of Minnesota community pharmacies from 2002 to 2017 [1]. Objectives: The first objective was to update the 15-year study [1] by describing the market dynamics of community pharmacies for each county in the state of Minnesota every five years over a 20-year period (i.e., 2002-2022). The second objective was to update the 15-year study by describing the number and proportion of community pharmacies for each county in the state of Minnesota every five years over a 20-year period (i.e., 2002-2022) by (1) ‘Business Organization Structure’ and (2) ‘Pharmacy Type.’ The third and fourth objectives were new for the 2022 study. In response to evolutionary trends in the pharmacy market, objective three described the number of Minnesota licensed pharmacies located in-state and out-of-state between 2002 and 2022. Objective four described the primary type of service provided by licensed pharmacies categorized as out-of-state in 2002 and in 2022. Methods: Data were obtained from the Minnesota Board of Pharmacy, US Census Bureau, and Minnesota State Demographic Center for 2002, 2007, 2012, 2017, and 2022. Descriptive statistics were used to tabulate and summarize the findings and test associations between study variables. Results: Over the 20-year study period, the total number of community pharmacies in Minnesota remained steady. Findings for 2022 were similar to those for 2017 regarding the effects of population density and metropolitan designation on the market dynamics of community pharmacies and regarding the organizational structures and types of pharmacies. The findings revealed significant growth in the number of proportion of pharmacies licensed in Minnesota that were located out-of-state. In 2002, there were 283 of these pharmacies (19% of the total). By 2022, this grew to 960 (46% of the total). Over that 20-year period, the number of mail order and medical supply service pharmacies rose from 196 to 413. The number of specialty pharmacy distributors rose from 37 to 230 and the number of compounding pharmacies rose from 33 to 202. Discussion: The findings confirmed the shift over the past 20 years from traditional retail convenience models to healthcare access models based on population health metrics. National scalability was another detected shift in order to have profitable margins for some services and was feasible due to advances in technologies. Conclusion: The findings showed that community pharmacy distribution in Minnesota’s 87 counties shifted between 2002 and 2022 from traditional retail convenience models to healthcare access models based on population health metrics and to nationally scalable models based on economic pressures. This signals the need for not only new approaches for tracking community pharmacy market dynamics but also adjustments by community pharmacies to remain relevant in a new environment of patient care services.

This paper analyzes the relationship between corporate social responsibility (CSR) and social impact within the oil industry in an emerging market (Peru). Following the emerging literature on the social impact field, and using the case study methodology, the findings show that the expected positive relationship is diverse depending on several factors. Specifically, the results reveal that companies must focus on community and environmental responsibility, together with stakeholders' involvement to achieve social impact. However, when companies are weak in any of the previous factors, then the social impact is only partially accepted or even withdrawn.

This article provides a comprehensive analysis of the global gas and liquefied natural gas (LNG) markets. The article begins by discussing the increasing demand for gas and LNG, particularly in Asia, as countries aim to transition to cleaner sources of energy. It explores how market volatility has led to energy security interventions and lasting economic and emissions impacts. It then explores the global supply and demand dynamics, highlighting the structural change expected in the market and the competition between Europe and Asia for limited new LNG supply. The article also focuses on Europe's increased flexibility and dependence on LNG imports, which have risen by 60% to 121 million tonnes, offsetting lower Russian pipeline imports. Furthermore, the article delves into global supply and demand dynamics, how the market is expected to remain tight, and how record gas and LNG prices have led to demand reductions. Additionally, it analyzes the future outlook and investment needs, highlighting the continued uptake of gas in heavy-duty transport and the need for further investment to avoid a supply-demand gap. The article concludes with an analysis of the implications for the future of the global gas and LNG markets.

The research is a deep study of the CBOE Volatility Index (VIX), which represent the market sentiments of the S & P 500 Index (SPX) over a period of 33 years. It evaluates how the two indexes perform in the stock market crash and how reliable VIX is in predicting market sentiments. The research distributes the market rally in two forms, and one is a trust rally, when investors are happy to see the market in a stable form, and investors' sentiments are positive towards the stock market. Second is a fear rally, where investors' sentiments are negative, and their outlook is bearish for various reasons. It observes the pattern of rallies, like the longest trust or fear rally, in which year and its duration. The research analyzes data to reach a meaningful conclusion and various inferences that can be drawn from the study of historical data.

Scientific and technological advances in telecommunications and onboard electronics, and advances in sustainability standards, dictated major changes to various industrial sectors, including the automotive industry, where hard and soft approaches to manufacturing are vying for market dominance. This work presents a prospective analysis of the autonomous vehicle (AV) market, analyzing three of the main US AV technology firms, Tesla, Waymo and Apple. Their designs and solutions are compared, and prospective scenarios were constructed based on an analysis of their strengths, weaknesses, opportunities and threats (SWOT). The results suggest that Tesla currently exhibits the greatest market leadership in the group studied. However, it was concluded that in the medium term, Waymo would surpass Tesla and assume market leadership. In the long run, it was concluded that Apple will overcome its rivals and dominate this market.

This paper proposes a mathematical model to simulate Day-ahead markets of large-scale multi-energy systems with high share of renewable energy. Furthermore, it analyses the importance of including unit commitment when performing such analysis. The results of the case study, which is performed for the North Sea region, show the influence of massive renewable penetration in the energy sector and increasing electrification of the district heating sector towards 2050, and how this impacts the role of other energy sources such as thermal and hydro. The penetration of wind and solar is likely to challenge the need for balancing in the system as well as the profitability of thermal units. The degree of influence of the unit commitment approach is found to be dependent on the configuration of the energy system. Overall, including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. The proposed model, together with the insights from the study case, can be specially useful for system operators for optimal operational planning.

Market channel choice makes important contributions to the incomes and other livelihood attributes among smallholder farmers in developing countries. Often considered from a number of perspectives, the dominant view articulated suggests an advantageous integration into formal market channels. This position is questioned as it has implications for smallholder farmers’ food security and rural incomes. Using a mixed methods approach, the study collected primary data from 174 smallholder farmers and applied both a descriptive and multinomial logit regression model to analyze factors contributing to cocoyam production and market channel choices among respondents in the study area. Findings indicate that financial returns and available markets were key factors in cocoyam production, while amount received was a driver of market channel choice among 89% of respondents who sold directly at farm-gate. Farmers’ age (p=0.044), household size (p=0.043), distance to market (p=0.021), additional income (p=0.017) and amount received (p=0.014) were significant variables (p<.05) in the determinants of market channel choice. The study recommended improving market information provision and strengthening farmer associations which will enable smallholder farmers in rural communities to make informed choices with respect to produce price, access other markets and consolidate their collective market bargaining position.

The quality of an educational system is reflected not only in the instructive performances of the educational actors, but also in the degree in which it ensures an adequate professional integration. The undergraduate of today, the graduate of tomorrow needs an as accurate as possible information regarding the labour market, which cannot be achieved only by individual means, but also by resorting to carrier counselling services. The motivation of this research is closely connected to the improvement of the level of communication between the two aspects – on the one hand the academic one, by its finite products, and the economic one, on the other hand – and the opportunities that the cooperation between the two aspects might offer: adjusting the demand-supply ratio on the labor market and above all, stopping massive emigration of young graduates of higher education.

For automobile insurance, it has long been implied that when a policyholder made at least one claim in the prior year, the subsequent premium is likely to increase. When this happens, the policyholder may seek to switch to another insurance company to possibly avoid paying for a higher premium. In such situations, insurers may be faced with the challenges of policyholder retention by keeping premiums low in the face of competition. In this paper, we seek to find empirical evidence of possible association between policyholder switching after a claim and the associated change in premium. In accomplishing this goal, we employ the method of association rule learning, a data mining technique that has its origins in marketing for analyzing and understanding consumer purchase behavior. We apply this unique technique in two stages. In the first stage, we identify policyholder and vehicle characteristics that affect the size of the claim and resulting change in premium regardless of policy switch. In the second stage, together with policyholder and vehicle characteristics, we identify the association among the size of the claim, the level of premium increase and policy switch. This empirical process is often challenging to insurers because they are unable to observe the new premium for those policyholders who switched. However, we used a 9-year claims data for the entire Singapore automobile insurance market that allowed us to track information before and after the switch. Our results provide evidence of a strong association among the size of the claim, the level of premium increase and policy switch. We attribute this to the possible inefficiency of the insurance market because of the lack of sharing and exchange of claims history among the companies.

Active front-end (AFE) rectifiers have been becoming widely employed in power systems to achieve unity power factor and harmonic mitigations. The typical modeling approaches applied for AFE rectifiers in the literature mostly relied on two baselines named the detailed model and the time average model. The former approach deals with the switching element model (SEM) leads to significant harmonics in currents with distorted waveforms. The latter approach uses the average-value model (AVM) to overcome the currents’ harmonics as well as provide fast responses. However, even the AVM baseline has contained problems during the starting stage (lack of control signals) and over the dead-time periods which causes serious issues in the implementation process. This paper presents an improved dynamic AVM for AFE rectifiers by precisely considering the issues mentioned above along with the practical starting procedure and desirable initialization. The studied AFE rectifier is developed using the voltage-oriented control (VOC) technique based on the different modeling methodologies including SEM, Conventional AVM, and the proposed AVM. The performance of all models is analyzed and compared using simulation results with MATLAB/Simulink R2023a Function blocks for all the algorithm parts and SimScape elements for the electrical circuit model. The simulation results illustrate that the performance of the proposed AVM approach can closely resemble the behavior of the SEM baseline with low harmonic distortion.

Deepfakes are manipulated or altered images, or video, that are created using deep learning models with high levels of photorealism. The two popular methods of producing a deepfake are based on either convolutional neural networks (CNN), or autoencoders. Deepfakes created using CNN comparatively show higher qualities of realism, yet oftentimes leave artifacts and distortions in the generated media that can be detected using machine learning and deep learning algorithms. In recent years, there has been an influx of periocular image and video data because of the increase usage of face masks. By wearing masks, much of what is used for facial recognition is hidden, leaving only the periocular region visible to an observer. This loss of vital information leads to easier misidentification of media, allowing deepfakes to less likely be identified as fake. In this work, feature extraction methods, such as Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and CNN, are used to train an ensemble deep learning model to detect deepfakes in videos on a frame-by-frame level based on the periocular region. Our proposed model is able to distinguish original and manipulated images with accuracies around 98.9 percent, which is an improvement to previous works by combining SIFT and HOG for deepfake detection in convolutional neural networks.

This research focuses on the customer orientation of insurance brokers, whose activity is regulated by the Law of 26/2006 of July 17 on the mediation of private insurances and reinsurances. The goal is to ascertain whether the intermediation inherent in the insurance broker’s activity, which implies a customer-oriented service, entails a positive behaviour that transcends the immediate environment, reaching society. This study presents a comparative analysis between the insurance brokerage society, characterised by providing a personalised customer service, and banks’ advisory services on insurance. To this end, we study the evolution of the total volume of business and new production, compared for a portfolio of insurance products. The results presented in this research suggest that the customer values the advisory service provided by the broker. However, for a particular business segment in standardised insurance products and products related to banking assets, customers are more likely to resort to the bank’s services. In addition, the results indicate that the commission percentages applied by the entities operating in the banking insurance channel exceed those perceived by the insurance broker. With all this, intermediation in the development of the insurer’s activity can entail an ethical and social behaviour that involves customer-orientation and, possibly, social service, which does not always benefit the insurer.

The need for the most accurate and flexible system of revenue collection from internal sources has become a matter of extreme urgency and importance in e-governance. This need underscores the eagerness on the part of the Government to look for a new principle and policy of revenue collection or to become aggressive and innovative in the mode of collecting revenue from existing sources using the present system. The Boards of some Governments in Africa, even up to the moment are facing a lot of setbacks in performing their tasks due to the manual system of revenue collection from the public. This can be improved through an effective collection of revenue using the most accurate and flexible system. Tax is usually collected in the form of specific sales tax, general sales tax, corporate income tax, individual income tax, property tax and inheritance tax. Problems such as high cost of collection, fraud, underpayment, leakage in revenue, poor access to information, poor tracking of defaulters is at the increase. As a result of this, there is need to computerize the revenue collection system. Computerized systems have proven to introduce massive efficiencies and quick collection of revenue from the public. This research work demonstrates how to design and implement an automated system of revenue collection and how to maintain a secured database for collected tax information. This research delves into the study of how machine learning algorithms and Software-defined Networks improve the security of such automated systems.

The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

This study investigates the complex interplay between public sentiment, as captured through news titles and descriptions, and the stock prices of three major tech companies: Microsoft (MSFT), Tesla (TSLA), and Apple (AAPL). Leveraging advanced analytical methods including Pearson correlation, wavelet coherence, and regression analysis, this research probes the degree to which stock price fluctuations can be attributed to the polarity of media sentiment. The methodology combines statistical techniques to assess sentiment's predictive power on stock opening and closing prices, while wavelet coherence analysis unveils the temporal dynamics of these relationships. Results demonstrate a significant correlation between sentiment polarity and stock prices, with description polarity affecting Microsoft's opening prices, title polarity influencing Tesla's opening prices, and a positive impact of title polarity on Apple's closing prices. However, Tesla's stock showed no significant coherence, indicating a potential divergence in how sentiment affects stock behavior across companies. The study highlights the importance of sentiment analysis in forecasting stock market trends, revealing not only direct correlations but also lagged influences on stock prices. Despite its focus on large-cap tech firms, this research provides a foundational understanding of sentiment's financial implications, suggesting further investigation into smaller firms and other market sectors.

Despite the shift to private sector-driven affordable housing in Nigeria for decades, the housing deficit has continued to increase to the disadvantage of low-income families. This paper explores the enabling strategies for stimulating private-driven affordable housing in Nigeria. A case study of the Millard Fuller Foundation projects was undertaken and semi structured interviews were administered to 12 residents of the estates and the developer to explore their experience and highlight the considerations for designing appropriate strategies. The data generated was analysed using thematic analysis with the support of Nvivo. The study identifies four major components of construction costs- land, design, materials, and finance that policy improvement can target to stimulate private investment. It shows that developers are likely to adopt practices that will reduce these costs with repercussions for end-users. Mindful of this, and the concern to make returns on investment, strategies should aim to harmonise both developers’ interest and that of the end-users through widespread infrastructural development to make land available in all locations, and incremental owner-building approach so that end users can take decisions for their housing. Furthermore, access to the National Housing Fund (NHF) mortgages should be enhanced by recognising supplementary incomes in the loan origination procedures

The aims of this paper are 1) to explore the type of industry of SMEs that can survive for more than 5 years in ASEAN, and 2) to analyze the success factors of SMEs in ASEAN to be able to survive for more than 5 years. This paper employs a qualitative method, with a literature review and meta-analysis. From 2007 to 2017, data was extracted from Google Scholar, Emerald Insight, Sage Pub, and EBSCO databases. There are 14 factors that influence the success of SMEs in the main sectors, 20 factors in the manufacturing sectors, and 14 factors in the service sectors, depending on the type of industry. For instance: eCommerce, technology, and social media, international oriented, good management system, government support, working condition /environment, skill and competency labour/owner, market penetration/selection, policy, organizational performance, and characteristics, networking with government /supplier / customer, networking with government /supplier /customer, planned /strategy (business /market /operational /organizational). The findings of this literature review reveal several important factors that are critical to the success of SMEs in competing with other industries and can be developed for a longer period.

In Poland, there has recently been a lot of interest in the possible use of insects as a new food source. Since they have many nutritional and environmental advantages, insects are a promising and sustainable source of protein. Although, in many parts of the world insects are a highly valued food, in Poland the development of insect-based food has only recently started, and the availability of products on the market is constantly increasing. This study examines the market availability of food products with insect additives in Poland, considering both online and sta-tionary stores. The assortment of products was analyzed based on the country of origin, insect species, form of insects, and product category. Polish consumers have more access to in-sect-based food products than ever before, with whole insects, protein bars, and powdered ver-sions predominating the market's offers..

In Brazil, the production of corn and soybeans has been growing significantly in recent years. Considering that the strategies for investing in the production of these crops are conditioned by production costs and risk, the objective of this article is to propose a simulation model that indicates the trend of production costs for these commodities, considering the dispersion and correlation of selected key variable prices and corn and soybean production costs practiced between the years of 2018 and 2022. Fifty specialized companies in the state of Sao Paulo, Brazil, provided data for this study, and Monte Carlo simulations showed that the total cost of corn production/ha is between USD 600.00 and USD 1150.00, with a level of certainty of 84.7%, and soybeans are in the range of USD 260.00 to USD 420.00, with a level of certainty of 86.4%. The model evidenced a trend of decreasing production costs for the crops for the 2023/24 and 2024/25 harvests, as the input with the greatest impact (fertilizer) is showing a downward trend. On the other hand, costs related to labor, soybean seed, and fungicides are showing an upward trend, while dolomitic limestone corrective remains stable.

Research background – In the conditions of a modern developed market economy, a rather important characteristic of the activities of a public company is its activity in the stock market (market activity), which involves the development of a dividend policy, which, on the one hand, should contribute to the achievement of the main goal of financial management - maximizing the material well-being of shareholders, and, on the other hand, take into account the interests of all other stakeholders interested in the activities of a public company (including potential investors) to ensure its sustainable development in the long term. Fulfillment of this requirement is possible only within the framework of a neutral approach to the implementation of the dividend policy of a public company.Purpose – The article is devoted to the consideration of the conceptual foundations for analyzing the sensitivity of key indicators of the activity of a public company in the stock market (market activity) to the main determining factors in the context of a neutral approach to the implementation of its dividend policy.Design/methodology/approach – The methodological basis of the study was the concept of a neutral approach to the dividend policy of a public company and the concept of sensitivity analysis, developed earlier by the author of this article.Findings – It is shown that the key indicators of the market activity of a public company include the dividend yield ratio, dividend coverage ratio, the expected price of an ordinary share, the dividend yield of an ordinary share and the quote ratio of an ordinary share (price and earnings ratio per ordinary share). An analysis of the sensitivity of these indicators to the main determining factors in the context of a neutral approach to the implementation of the dividend policy of a public company involves the construction of appropriate elasticity models that allow determining the change in key indicators of market activity depending on changes in the factors that determine them. The constructed elasticity models of the key indicators of market activity listed above can be used in predictive and analytical assessments of their changes, and also make it possible to identify the causes of these changes by calculating the impact on the specified elasticity of the determinants contained in their models, performed by the appropriate methods of factor analysis, under the conditions of a neutral approach to the implementation of the dividend policy of a public company.Originality/value – The author comes to the conclusion that the elasticity models developed by him for key indicators of the activity of a public company in the stock market in terms of the main factors determining them are a fairly effective tool for managing the market activity of a public company in the context of a neutral approach to the implementation of its dividend policy.

We analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and that stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensify the spillover effects across international stock markets. Our results demonstrate that the EU, Ger-many, and the US acted as net transmitters of dynamic connectedness; however, Japan (JP), China (CH), and India (IN) acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

This research article provides criticism and arguments why the canonical framework for derivatives pricing is incomplete and why the delta-hedging approach is not appropriate. An argument is put forward, based on the efficient market hypothesis, why a proper risk-adjusted discount rate should enter into the Black-Scholes model instead of the risk-free rate. The resulting pricing equation for derivatives and in particular the formula for European call options is then shown to depend explicitly on the drift of the underlying asset, which is following a geometric Brownian motion. It is conjectured that with the generalized model, the predicted results by the model could be closer to real data. The adjusted pricing model could partly also explain the mystery of volatility smile. The present model also provides answers to many finance professionals and academics who have been intrigued by the risk-neutral features of the original Black-Scholes pricing framework. The model provides generally different fair values for financial derivatives compared to the Black-Scholes model. In particular, the present model predicts that the original Black-Scholes model tends to undervalue for example European call options.

Financial statement analysis is an analysis that uses analytical procedures to evaluate a company's financial health, risks, performance, and future growth potential. This article aims to find out the application of analysis of financial statements of PT. Astra Internasional, Tbk towards the decision-making of novice investors in the Indonesian capital market. This article is qualitative descriptive with literature studies. The results of this article can be concluded that the analysis of the financial statements of PT. Astra Internasional, Tbk can help novice investors in the Indonesian capital market in making investment decisions.

Exisitng studies argue life chances are, in part, vertically reproduced. Such a statement is applicable to the Chinese contexts as, but not limited to, parental hukou status, to some extent, determines the life chances their children receive. In this essay, the author would like to introduce human capital theory (HCT) and educational signalling theory (EST), and assess how the applications of each of these two models can enrich the understanding of vertical reproduction of individuals’ social mobility opportunities. The author would also present the limitations of each of these two models when addressing relevant Chinese contexts.

This study will investigate different signals and events/news that determined the stock market's movements. As we know, many factors affect the stock market on a daily, weekly, and monthly basis, e.g., rate of interest, exchange rate, and oil prices, etc. Our research will investigate the impact of daily events/news in the KSE-100 index due to several policies announced and events/news in the country because the daily movements in the stock market can be determined only by different signals and events/news. Time series data is collected daily for particular reasons from "The News" (Daily Newspaper, Sunday edition) from 2010 to 2019. The results of this study show that political and global news affects the stock market index ferociously. For investors, the investment in blue chips is not less than a safe haven. When day-to-day transactions are concerned, there is always a higher panic attack than the herd behaviour in the stock exchange. Investors tend to make prompt responses to negative rather than positive news, which makes them risk averters. Our finding also confirmed that the ARCH/GARCH model is better than the simple OLS method concerning stock market upheaval.

Prediction of stock groups values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree_based models, there is often an intense competition between Adaboost, Gradient Boosting and XGBoost.

An application of deep convolutional neural network and recurrence plot for financial market movement prediction is presented. Though it is challenging and subjective to interpret its information, the pattern formed by a recurrence plot provide a useful insight into the dy- namical system. We used a recurrence plot of seven financial time series to train a deep neural network for financial market movement predic- tion. Our approach is tested on our dataset and achieved an average of 53.25% classification accuracy. The result suggests that a well trained deep convolutional neural network can learn a recurrence plot and pre- dict a financial market direction.

The article discusses the issue of the sustainable development of the cross-border market for cultural services in a city divided by the state border. The article uses the example of Cieszyn and Český Těšín, a city divided following the decision of the Council of Ambassadors in 1920. The research carried out so far indicates the main constraints in the harmonious functioning of the cross-border market for cultural services in this city, such as: different cultural policies implemented on both sides of the city, language barriers as well as legal and administrative differences. Therefore, the authors undertook research aimed at recognising the role of Euroregional structures in stimulating the sustainable development of this region. On the basis of the analysis of the Cieszyn Silesia Euroregion's documentation and the results of qualitative and quantitative research, the article describes the role of the Euroregion in building a cross-border market for cultural services. Recommendations were also prepared that could constitute the principles of a common cultural policy not only for Cieszyn and Český Těšín, but also for other European cities in the Schengen Area, which, like Cieszyn and Český Těšín, have been divided by a state border.

The deepening of electricity reform results in increasingly frequent auctions and the surge of generators, it becomes difficult to analyze generators’ behaviors. Since it’s hard to find analytical market equilibriums, approximate equilibriums were obtained instead in previous studies by market simulations, which are strict to initial estimations and simulation results are chaotic in some cases. In this paper, a multi-unit power bidding model is proposed to reveal the bidding mechanism under clearing pricing rule by employing auction approach, for which initial estimations are non-essential. Normalized bidding price is introduced to construct generator's price-related bidding strategy. Nash equilibriums are derived depend on the marginal cost and the winning probability which are computed from bidding quantity, transmission cost and demand distribution. Furthermore, we propose a comparative analysis to explore the impact of uncertain elastic demand on the performance of the electricity market. The result indicates that, there exists market power among generators leading to social welfare decreases even under competitive conditions but elastic demand is an effective way to restrain generators’ market power. The feasibility of the models is verified by a case study. Our work provides decision support for generators and a direction for improving market efficiency.

This research contributes to the overall debate on education for sustainable development (ESD) by shed- ding lights on the contributing role of formal education to the contemporaneous dynamics of literacy, labor market participation and poverty reduction in Africa, with a focus on Burkina Faso. The study uses a semi-parametric recursive trivariate probit modeling approach, and data from the 2014 National Survey on Household Living Conditions in Burkina Faso. The results show that the embraced systemic approach in this analysis is statistically signicant as shown by the 95% condence intervals on the three correlation coeffcients in the model. Furthermore, education does improve literacy skills, however improved literacy skills in itself does not guaranty active labor market participation in Burkina Faso. Active labor market participation seem to be affected by labor market rates of return, and individual reservation wage (or income). When labor market rate of return is short of high literacy skilled individuals' reservation wage, then the natural response is a choice of inactivity in the labor market, by the later group. Simultaneously however, it is found that active labor market participation leads to poverty reduction; therefore, in addition to new industrial policies for structural transformation of the economy, policy makers in Burkina Faso should consider education and minimum wage reforms to give highly literate household members the incentive to be active in the labor market.

The Italian herbal products market is the most prosperous in Europe. The proof is represented by the use of these products in several marketing categories, ranging from medicine to nutrition and cosmetics. Market and legislation in Italy are at the same time cause and consequence of this peculiar situation. In fact, the legislation on botanical food supplements in Italy is very permissive and at the same time the market shows an overall satisfaction of users and strong feedback in terms of consumption, which brings a widening use of medicinal plants, formerly the prerogative of pharmaceuticals, to other fields such as nutrition. This review summarizes the market and normative panorama of herbal products in Italy, highlighting the blurred boundaries of health indications, marketing authorizations and quality controls between herbal medicines and non pharmaceutical products, such as food supplements, cosmetics and other herbal-based “parapharmaceuticals”.

The magnetic properties of 0.10 mm ultra-thin grain-oriented (UTGO) silicon steel and Fe-based amorphous (FBA) alloy under sinusoidal excitation were measured experimentally, and the magnetic field strength and iron loss of the two materials under different frequencies and magnetic densities were obtained. Based on the measured data, the magnetization and loss characteristics of the two materials were analyzed and compared. Furthermore, two Epstein square ring models of the same specification and different materials were designed, and the reliability of the models was verified. Then, the electromagnetic characteristics of the two Epstein square ring models at higher and lower frequencies were calculated by using the finite element method, and the iron loss was obtained and compared. The results show that the FBA alloy has good application characteristics at low frequency and low power, and the 0.10 mm UTGO silicon steel has good application characteristics at high frequency and high power. This research can provide important data basis to promote the application of these two materials in new energy equipment.

The (numerical) solution of Ordinary Differential Equations (ODEs) problems is of paramount relevance, ODEs system being an ubiquitous mathematical formulation of many physical phenomena (such as those involved in fluid dynamics, chemistry, biology, evolutionary-anthropology, ...): almost every dynamici phenomenon can be modeled by means of an ODEs system. The present paper is the first manifesto of FOODIE, a library aimed to numerically solve ODEs problems by means of a clear, concise and efficient abstract interface. FOODIE, meaning Fortran Object oriented Ordinary Differential Equations integration library, has manifolds aims: to provide a set to built-in numerical schemes that are accurate, robust, validated and efficient and to allow easy application of these schemes to (almost) all ODEs problems by means of an effective Abstract Calculus Pattern. The key idea is to allow the same solver-implementation to be applied to all ODEs problems thus avoiding the re-implementation of the ODEs solver for each different ODEs problem: code re-usability is consequently maximized, FOODIE being a general robust framework. Besides, the same framework also allows rapid development of new ODEs solvers due to the high abstraction level of the library itself. The present paper is the first announcement of FOODIE project: the current implementation is extensively discussed and its capabilities are proved by means of tests and examples.

This paper aims to provide an evaluation of how Value Added Tax (VAT) policy alterations influence market liquidity, trading volume, and stock performance. To fill the existing literature gap on the direct effect of (VAT) adjustments on stock market dynamic, particulate within the banking industry. The study employs an event study methodology to analyse the immediate and medium-term effects on bank stocks listed in markets subject to a (VAT) adjustments. The core of the study analysis is anchored around two pivotal events, the initial application of (VAT) on 1st January 2018, and the subsequent (VAT) increase to 15% on 1st July 2020 using MATLAB R2022b. The study analysis encompasses the period immediately before and after the introduction and subsequent increase (VAT) in kingdom Saudi Ariba (KSA) employing an eleven sample comprises the (KSA) largest banks with a ±7 days window period from the event. The analysis revels that the imposition of 5% (VAT) lead a substation downturn in trading volume and stock returns of 9.5%, and 39.5%, subsequently. While when the (VAT) was increased by 15% the market exhibited a mixed but ultimately positive response of 3.3% trading volumes increases and 128.7% average stock return recovery.

Emergence of COVID-19 had profound impact on global stock markets, including Indian mar-ket. Key indices like NIFTY50 and Sensex dropped by approximately 40% within two months in 2020, which also affected major mutual funds, leading to significant depreciation of their Net Asset Value. Securities and Exchange Board of India (SEBI) allows mutual funds to use deriva-tives for hedging purposes, capped at 100% of the scheme's net assets covering debt, equity, and derivatives. However, circular amendment by SEBI in January-2019 permitted covered call strat-egies on constituent stocks of Sensex and NIFTY50 for mutual fund schemes, excluding ETFs and Index Funds. This study investigated whether mutual funds increased their derivative us-age as risk mitigation strategy during COVID-19 pandemic, analyzing derivative holdings from 2019-2023. Surprisingly, findings indicated a decline in derivative holdings in 2020 compared to pre-pandemic levels. Statistical tests and performance evaluation metrics such as T-test, F-test, Treynor Ratio, Sharpe Ratio, and Jensen's calculations were employed to assess mutual fund performance. Although derivative-holding mutual funds didn't outperform benchmarks in aver-age returns, it showcased significantly reduced volatility, offering investors favorable risk-return trade-offs. research sheds light on derivative utilization in Mutual Fund industry, potentially in-forming regulators, fund managers, and investors in their decision-making

For a long time, a company's Product Development Process (PDP) was seen as supporting the operations department, although PDP decisions and mistakes have a considerable impact on market performance. This is critical even in agriculture where bad habits and practices in the PDP can lead rural producers to great losses. Therefore, this research investigated the effect on the market performance of rural products (banana) in the southern region of Brazil, based on two analyses: (i) how sustainability practices support the PDP phases and (ii) how the phases of the Product Life Cycle Assessment (LCA) mediate sustainability practices and PDP phases. This study presents a quantitative analysis using Confirmatory Factor Analysis (CFA) and hierarchical ordinary least squares (OLS) regression of data obtained from a survey of 110 rural producers who directly participate in the banana production and planning process in southern Brazil. Our results shows that sustainability practices support the PDP, and we confirm that the product development and post-development phase has an effect on market performance. In addition, we identified that in the pre-development phase of the PDP, dealing with rural products (bananas), the maturity stage of the LCA mediates sustainability. In the PDP development phase, we concluded that rural families that develop economic and environmental practices with their products in the market growth phase may have reduced results. As for the post-development phase of the PDP, we conclude that when companies invest in environmental and social practices, there is a complete mediation of the effect, where these practices lose strength if the product is in the introduction and maturity phases in the market. As originality, our study contributed to demonstrate value on the product life cycle for the product development process in agriculture using sustainability practices through a systemic approach, filling the gap in the literature due to the lack of research on these areas seen in an integrated way.

The recent years have been marked by rapid growth in the field of "ESG finance," leading to the emergence and expansion of ESG ratings and rating agencies. This study seeks to examine how financial investors respond to updates in ESG ratings provided by two prominent ESG rating agencies: MSCI and Refinitiv. Specifically, the goal is to determine whether any potential positive or negative alterations in a company's sustainability ratings directly affect its market value. The chosen methodology for this investigation is the Event Study, a model employed to assess the influence of economic events on corporate valuations by analyzing their Cumulated Average Abnormal Returns (CAARs). Over 840 instances of rating updates (events) were analyzed using a sample of 75 companies across various industries, all listed on major stock exchanges. The findings indicate that shifts in sustainability ratings, as evaluated by the two rating agencies, do not have a significant impact on companies' market capitalization. Moreover, these outcomes remain consistent over time, suggesting that financial markets are not assigning increasing significance to ESG ratings. Potential explanations are offered and discussed in relation to existing literature on the subject.

The purpose of this paper is to summarize and share the field experiment results of KEPCO's project consortium to create a TSO-DSO-DERA interaction scheme. The field experiment was conducted based on the prequalification algorithm proposed in the previous research from the same consortium and was designed to verify the validity of the algorithm under realistic grid conditions. In addition, during the course of the field experiment, it was found that points that were missed or not given much importance in the existing prequalification algorithm could affect the completeness of the overall system, and then practical improvements were made to improve it. The demonstration results confirm that the proposed algorithm is effective in real-world grid environments and can help DSOs ensure the reliability of the distribution system while supporting DERA's participation in the wholesale market using the proposed prequalification scheme.

The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various Machine Learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduces two datasets from different markets, one with over 300,000 entries from Germany to serve as a training base for deep prediction models and a second dataset from Romania containing more than 15,000 car quotes used mainly to observe local traits. As such, we include extensive cross-market analyses by comparing the emerging Romanian market versus one of the world’s largest and most developed car markets, Germany. Our study uses several neural network architectures that capture complex relationships between car model features, individual add-ons, and visual features to predict used car prices accurately. Our models achieved a high R2 score exceeding 0.95 on both datasets, indicating their effectiveness in estimating used car prices. Moreover, we experimented with advanced convolutional architectures to predict car prices based solely on visual features extracted from car images. This approach exhibited transfer-learning capabilities, leading to improved prediction accuracy, especially since the Romanian training dataset is limited. Our experiments highlight the most important factors influencing the price, while our findings have practical implications for buyers and sellers in assessing the value of vehicles. At the same time, the insights gained from this study enable informed decision-making and provide valuable guidance in the used car market.

This paper analyzes the collection of George Viau (1855-1939) from a computational perspective. Indeed, this dental surgeon, close to the Impressionists, collected several hundred works of art in the first half of the 20th century: the auctions of his collection, in 1907, 1930 and after his death, in 1942, 1943 and 1948, accounted for 642 artworks and, during the Occupation, the 1942 sale produced a total of more than 46 million francs. Thanks to statistics, econometrics, network analysis and cartography, it is possible to understand the salient features of the collection and its place in the world of Parisian auctions.

Abstract: The increased penetration of renewables in distribution power systems has motivated researchers to take significant interest in local energy transactions. The major goal of Local Energy Markets (LEM) is to promote the participation of small consumers in energy transactions and providing an opportunity for transactive energy systems. Such energy transactions in LEM are considered as a bi-level optimization problem in which all agents at upper and lower levels try to maximize their profits. But typical bi-level problem is very complex as it is inherently nonlinear, discontinued and strongly NP-hard. So, this article proposes the application of hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem of LEM. The proposed CE-CMAES secured the 1st rank in Testbed-2 entitled, “Bi-level optimization of end-users’ bidding strategies in local energy markets (LM)” at international competitions on Smart Grid Problems, held at GECCO 2020 and WCCI 2020. CE method is used for global exploration of search space and CMAES is used for local exploitation as its adaptive step-size mechanism prevents its premature convergence. A practical distribution system with renewable energy penetration is considered for simulation. The comparative analysis shows that the overall cost, mean fitness and Ranking Index (R.I) obtained from CE-CMAES are superior to those obtained from the state-of-the-art participated algorithms. Wilcoxon Signed Rank Statistical test also proves that CE-CMAES is statistically different from the tested algorithms.

Arguments of Mises and Hayek, who opposed the planned economy (PE), are used in the paper as a starting point for establishing the objective area of effective application of the PE. The abstract model of PE, based on the definitions of Mises and Hayek, leads to the conclusion that for the effective use of PE, it must be a part of mixed economy and it must produce a limited amount of essential goods of irreducible demand. These goods must be distributed among all members of society free of charge, evenly, without competition. Examples of a mixed economy are given that meet this requirement. Calculations of the personal benefit in the transition to a mixed model of the economy have been carried out. The positive and negative qualities of the planned and market methods of organization are considered. Mixed economy model combines these qualities in optimal construction. An analytical framework has been introduced for the construction of product characterization curves. Such curves provide criteria for determining the efficiency of manufacturing of this product in a planned economy. The general economic prerequisites for the usability of the PE are clarified. The applicability and advantages of the PE for the organization of the universal basic income (UBI) system are demonstrated. The possibility of using PE to solve the problem of guaranteed employment is mentioned.

Acceptance of SNAP benefits has increased in farmers’ markets (FM) over the years. However, getting SNAP recipients to take up the opportunity of extra dollars for healthy food options remains a challenge. Thus, we tracked the use of the Double-up Food Buck (DUFB) program among SNAP recipients who visit participating FMs in Utah to understand the unique characteristics of the program users. Our results identified varying types of program users (multiple and single market shoppers, more frequent and less frequent shoppers). While our findings indicate a higher DUFB transaction in token value among frequent users, larger markets are more likely to have higher DUFB transactions with a higher number of market visits compared to smaller ones. In conclusion, while all types of users may add to the total revenue generated by participating FM, additional initiatives are required to encourage frequent visits and uptake of the program for better access to healthy foods.

Recently natural gas (NG) global market attracted much attention in case it is cleaner than oil, and simultaneously in most regions is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces that made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and to pre-process selected input vector. Then a hybrid artificial neural network (ANN) which equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g. Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) selected as the most critical feature via the DM step. Then, the hybrid prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different error based evaluation statistics. Besides, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process.

Electric Vehicles (EVs) are increasing the interdependence of transportation policies and the electricity market. EMMEV (Electricity Market Model with Electric Vehicles) is an experimental agent-based model that analyses how carbon reduction policy in transportation may increase number of Electric Vehicles and how does that would influence on the electricity price. Agents are ESCOs (Energy Service Providers) which can distribute fuels and their objective is to maximize their profit. In this paper, EMMEV is used to analyze the impacts of the LCFS (Low Carbon Fuel Standard), a performance-based policy instrument, on electricity prices and EV sales. The agents in EMMEV/regulated parties in LCFS should meet a certain CI (Carbon Intensity) target for their distributed fuel. In case, they cannot meet the target, they should buy credit to compensate for their shortfall and if they exceed, they can sell their excess. The results, considering the assumptions and limitations of the model, show that the banking strategy of the agents contributing in the LCFS might have negative impact on penetration of EVs, unless there is a regular Credit Clearance to trade credits. It is also shown that the electricity price as result of implementing the LCFS and increasing number of EVs has increased between 2–3 percent depending on banking strategy.

In areas with strongly seasonal climates, local people often use complex strategies to manage agricultural production shortages, including diverse activities such as hunting, selling and consuming non-agricultural products, and wage labor. We surveyed all the households in a village in northern Laos to evaluate how such livelihood activities varied during years with differing agricultural production conditions. We compared two years with normal rice production conditions (2010, 2012) and one year with a severe rice shortage (2011) due to a rodent outbreak. Earning wages inside and outside the village was the most important activity for mitigating rice shortages, followed by selling livestock and using/selling non-timber forest products. Villagers also borrowed rice from a village rice bank. Most cash income was earned from selling rice. We concluded that a balance of traditional risk management activities under the swidden system (e.g., raising livestock) with the more recent rice bank system and wages from the market economy will be critical for the sustainable development of mountain villages in northern Laos. Permanent crops and monocultures tend to make local livelihoods more dependent on a single crop, but maintaining the traditional swidden system will help local people to manage agricultural production shortages.

Business-to-business (B2B) manufacturing companies are increasingly confronted with transformative trends such as sustainability, digitalization, and servitization. These trends are changing how product portfolios are developed, and value contributions are assessed, and therefore have disruptive potential. Dealing with these disruptive factors in Product Portfolio Management (PPM) is a largely unexplored topic. This study presents an empirical-qualitative exploration that contributes significantly to the field. The aim is to clarify the extent to which disruptive factors influence the evaluation and shaping of the product portfolio in B2B manufacturing companies. The Gioia method was used to evaluate 21 semi-structured interviews with experts from leading B2B manufacturing companies. Eight overarching challenges in PPM resulting from the disruptive factors were identified. Based on the eight overarching challenges and their associated causal relationships, two aggregated dimensions of action were derived: (1) increasing speed and flexibility by using generative artificial intelligence (AI) in a defined PPM process and (2) adjusting the product portfolio evaluation to consider various strategic drivers. These two dimensions of action call for future research to overcome the disruptive factors in PPM.

Fluorescent bioprobes are invaluable tools for visualizing live cells and deciphering complex biological processes by targeting intracellular biomarkers without disrupting cellular functions. In addition to protein-binding concepts, fluorescent probes utilize various mechanisms, including membrane, metabolism, and gating-oriented strategies. This study introduces a novel fluorescent mechanism distinct from existing ways. Here, we developed a B-cell selective probe, CDrB, with unique transport mechanisms. Through SLC-CRISPRa screening, we identified two transporters, SLCO1B3 and SLC25A41, by sorting out populations exhibiting higher and lower fluorescence intensities, respectively, demonstrating contrasting activities. We confirmed that SLCO1B3, with comparable expression levels in T and B cells, facilitates the transport of CDrB into cells, while SLC25A41, overexpressed in T lymphocytes, actively exports CDrB. The observation suggests that SLC25A41 plays a crucial role in discriminating between T and B lymphocytes. Furthermore, it reveals the potential for reversible localization of SLC25A41 to demonstrate its distinct activity. This study is the first report to unveil a novel strategy of SLC by exporting the probe. We anticipate that this research will open up new avenues for developing fluorescent probes.

A reliable Electronic Voting Machine (EVM) is proposed and implemented in this study, which is integrated with a biometric fingerprint scanner to ensure a secure election process. This biometric EVM includes features such as an interactive user interface, hack-free design and master lock. The EVM system has the capability of registering user data and storing them in a database through proper authentication. Moreover, the system proposed lowers the requirement for human resources. This paper provides a detailed description of the systematic development of the hardware and software used. The software part includes algorithm development and implementation. A thorough and in-depth understanding of the data and the communication protocols along with the pathways used for storage of data in the devices is provided. Additionally, the cost of the system is 62.82% less than the officially existing EVM machines of India. Furthermore, this study seeks to demonstrate the benefits of such an approach from a technological and a social standpoint.

: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

Autonomous and cooperative vehicle systems represent a key priority in the automotive realm. Networked driving simulation can be utilized as a safe, cost-effective experimental replica of real traffic environments in order to support and accelerate the development of such systems. In networked driving simulation, different independent systems collaborate to achieve a common task: multi-driver traffic scenario simulation. Yet distinct system complexity levels are necessary to fulfill the requirements of various application scenarios, such as development of vehicle systems, analysis of driving behavior, and training of drivers. With myriad alternatives of available systems and components, developers of networked driving simulation are typically confronted with high design complexity. There are no systematic approaches to date for the design of networked driving simulation in accordance with the specific requirements of the concerned application scenarios. This paper presents a novel design method for networked driving simulation. The method consists mainly of a procedure model that is accompanied by a configuration software. The procedure model includes the necessary phases for the systematic design of application-oriented platforms for networked driving simulation. The configuration software embeds supportive decision-making processes that enable developers to apply the design method and easily create different system models. The design method was validated by generating system models and developing platforms of networked driving simulation for three different application scenarios.

This article delves into the complex landscape of Bitcoin halving events, analyzing the historical context and implications of these pivotal occurrences within the cryptocurrency ecosystem and on its sustainability within the landscape of financial technologies. Through an examination of past halving events in 2012, 2016, and 2020, this study meticulously scrutinizes the conditions and prices of Bitcoin before and after each halving. By synthesizing data and trends from previous events, this article aims to uncover patterns and insights that illuminate the impact of Bitcoin halving on market dynamics and sustainability, movement of the price level, reached peaks and price troughs. Furthermore, this research presents a comparative analysis of past halving events, drawing parallels and distinctions to elucidate the evolving nature of Bitcoin's price movements and market sentiment surrounding these milestone events. Looking towards the future, this study estimates predictions and expectations for the upcoming Bitcoin halving. By unraveling the intricate relationship between Bitcoin halving events and market behavior, this article seeks to provide a comprehensive understanding of the broader implications for Bitcoin's trajectory and the evolving landscape of the cryptocurrency market and its sustainability.

Evaluation of market connectedness and asymmetric volatility spillover has recently seen a surge in financial risk analytics and portfolio diversification. We carried out a meta-literature review on connectedness and spillovers, providing solid insight into the research field and robust guidelines for future investigation. The review consists of a quantitative bibliometric analysis of 594 papers and a qualitative content analysis of 77 papers covering 1991 to 2021. The results of the meta-citation analysis shows that Diebold's Spillover index (2007) is the predominant method in most works as far as market connectedness and spillover is concerned. With an extensive review, we answered the following objectives (1) Analyze the most influential authors, journals, and publications, (2) Understand the research streams and most studied streams, (3) Understand the theme structure and thematic evolution, and keyword trends, (4) Examine the pattern of collaboration, and most productive affiliations, (5) Explore future research directions and untapped areas. The content analysis revealed the following important research streams in the current literature (1) Asymmetries in the market connectedness, (2) Influence of Macro Factors in the market connectedness and spillover, (3) the Role of Oil in market spillovers and hedging portfolios, (4) Dynamic cross-market connectedness and spillovers. The ongoing financial turmoil and market advancements make market connectedness a vital research topic; thus, our work would significantly contribute to macroeconomic policymakers, researchers and hedging investors.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

In this paper we focus on an implicit assumption in the BSM framework that limits the scope of market network connections to seeking gains in the currency basis, i.e., on trading strategies between the numeraire and the stock and between the numeraire and the option, separately. We relax this assumption and derive the equivalent of the standard BSM approach under a more general market network framework in order to assess its implications. In doing so, we find that it is not possible to hedge the implicit option that allows one to directly trade the option and stock. This represents a potential challenge to the BSM framework, since the missing market network connection provides a potentially useful mechanism for risk-bearing portfolio managers to alter their portfolios.

The Capital Asset Pricing Model is a widely applied model to describe risky markets and to deduce their systematic risk. Its estimation, therefore, remains an important task in Econo-financial studies. Empirically, it focuses on the impact of return interval on the betas. Existing studies somehow turn around the same idea of measuring the value of the beta according to the uniform intervals of time during a fixed period. However, it is noticed easily, and especially in the last decade that many factors such as socio-political, and Econo-environmental ones have led to a perturbation in the timeline of the worldwide development, and especially in countries and regions having political changes. This led us to introduce a new idea of risk estimation taking into account the non-uniform changes in markets by introducing a non-uniform wavelet analysis. We aim to explain the Econo-political situation of Arab spring countries and the effect of the revolutions on the market beta. The main novelty is firstly the construction of a dynamic backward-forward model for missing data, and next the application of random non-uniform wavelets. The proposed procedure will be acted empirically on a sample corresponding to TUNINDEX stock as a representative index of the Tunisian market actively traded over the period January 14, 2016, to January 13, 2021. The chosen 5-years period is important as it constitutes the first 5-years-after the revolution and depends strongly on the Socio-Econo-political stability in the revolutionary countries.

This paper proposes a game-theoretic framework for quantifying effects of strategic bidding behavior of load aggregators (LAs) on wholesale electricity market equilibrium. The independent system operator (ISO) employs double-sided auction design to settle transactions in the wholesale electricity market. Generating companies (GENCOs) and LAs submit their bids to the ISO in the economic model of supply function equilibrium. Under the smart grid paradigm, a LA participates in the electricity market on behalf of end-consumers to minimize their total payment for purchasing electricity. In this paper, bi-level programming (BLP) method is employed to determine optimal bidding strategy of self-interested market participants. In the upper level, the profit of intended market participant is maximized and in the lower level, the ISO clears the market via a bid-based security constrained economic dispatch (SCED). Also, competition of GENCOs and LAs is modeled as a non-cooperative game. To examine the effects of strategic bidding behavior of LAs on market equilibrium, the proposed model is applied to the IEEE 9-bus and the IEEE 30-bus test systems. According to numerical results, to allow the LAs to behave strategically along with the GENCOs makes the former better off and the latter worse off, while the net effect of this on total social welfare turns out to be case-contingent.

Since the Sustainable Development Goals (SDGs) came into effect, both UNESCO and other international organisations recommend empowering youth to implement the SDGs in universities. Getting started with the SDGs at university level is of special relevance in pre-service teacher training since future teachers are powerful agents of change in the lives of young people. Future teachers need to acquire competencies in sustainability to be able to promote meaningful changes in sustainable behaviour. To that end, holistic approaches to facilitate their acquirement need to be developed. The aim of this study is to explore which teaching methodologies are suitable for the development of competencies in sustainability in Higher Education (HE) and how to empower students to take a leading role in implementing the SDGs in universities. The participants in the study are a group of 23 students in pre-service teacher training. The experimental educational model used for the development of sustainable competencies consists of a methodological sequence of Project-Oriented Learning (POL) and a Cross-disciplinary Workshop on Sustainable Food. This study provides evidence that a holistic approach is appropriate for developing sustainable competencies and contributes to empowering students to implement SDG 12 at their university.

This paper explores the application of machine learning (ML) algorithms in predicting trends in the fixed bond market, where traditional analytical methods have proven inadequate. Focusing on various ML techniques such as supervised and unsupervised learning, neural networks, and deep learning, the study evaluates their effectiveness in forecasting market movements. It details a series of experiments in which different ML models are rigorously trained and tested against historical bond market data. The findings reveal that models employing time series analysis and advanced deep learning show marked potential in accurately predicting bond market trends. Additionally, the paper delves into the challenges and limitations inherent in these ML approaches, including data requirements and the risk of model overfitting. Finally, it proposes directions for future research, emphasizing the integration of ML into broader financial market analysis.

Mirror Therapy (MT) and Cognitive Therapeutic Exercise (CTE) are two neurorehabilitation techniques based on neuroplasticity and designed to enhance the functionality of the upper limb. Therefore, the aim is to assess the effectiveness of the combination of CTE and MT with task-oriented training (TOT) on fine and gross motor skills of the affected hand, as well as the functionality of the affected upper limb, autonomy, independence, and quality of life (QoL) in patients with acute stroke. This longitudinal and multicenter study involved a sample of 120 participants selected randomly and consecutively. Three groups were formed, including a control group (CG) and two treatment groups, one with MT and the other with CTE. The functionality of the upper limb, fine and gross motor skills, the quantity and quality of use of the affected upper limb, autonomy, and QoL were evaluated using various scales validated in the Spanish population at the baseline, after 20 treatment sessions, and at three months of follow-up. ANCOVA analysis was conducted, revealing statistically significant differences between assessments in the CG compared to the experimental groups (GEs), indicating a notable improvement in overall functionality, fine and gross motor skills, autonomy, and QoL in the latter. However, there were no significant differences between the GEs. Therefore, the application of either of these experimental combinations is crucial for the motor recovery, functionality, and improvement of the quality of life of individuals who have suffered a stroke.

The article presents an induction generator connected to the power grid using the AC / DC / AC converter and LCL coupling filter. In the converter, both from the generator side and from the power grid side, three-level inverters were used. The algorithm realizing Pulse Width Modulation (PWM) in inverters has been simplified to the maximum. Control of the induction generator was based on the Direct Field Oriented Control (DFOC) method. At the same time, voltage control has been used for this solution. The MPPT algorithm has been extended to include the variable pitch range of the wind turbine blades. The active voltage balancing circuit has been used in the inverter DC voltage circuit. In the control system of the grid converter with an LCL filter, the number of measurements was limited to the measurement of power grid currents and voltages. Synchronization of control from the power grid side is ensured by the use of a PLL loop with the system of preliminary suppression of undesired harmonics (CDSC).

The research presents the Methodology of Improving the Accuracy in Text Classification in Light of Modelling the Latent Semantic Relations (LSR). The aim of this Methodology is to find the ways of eliminating the Limitations of Discriminant and Probabilistic methods for LSR revealing and customizing the Text Classification Process to the more accurate recognition of the text tonality. This aim should be achieved by using the knowledge about the text’s Hierarchical Semantic Context in the form of Corpora-based Hierarchical Sentiment Dictionary. The main scientific contribution of this research is the following set of approaches to improve the qualitative characteristics of Text Classification process: combination of the Discriminant and Probabilistic methods allowing to decrease the influences of the Limitations of these methods on the LSR revealing process; considering each document as a complex structure allowing to estimate documents integrally by separated classification of topically completed textual component (paragraphs); taking into account the features of Argumentative type of documents (Reviews) allowing to use the author’s subjective evaluation of text tonality for development the Text Classification methodology. Tonality, expressed by the Review’s author, has a significant, but not critical, effect on the qualitative indicators of Sentiment Recognition.