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.

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.

Dynamic systems which underly controlled systems are expected to increase in complexity as robots, devices, and connected networks become more intelligent. While classical stable systems converge to a stable point (a sink), another type of stability is to consider a stable path rather than a single point. Such stable paths can be made of saddles which draw in trajectories from certain regions, and then push the trajectory toward the next saddle point. These are chains of saddles are called stable heteroclinic channels (SHCs), and can be used in robotic control to represent time sequences. While we have previously shown that each saddle is visualizable as a trajectory waypoint in phase space, how to increase the fidelity of the trajectory was unclear. In this paper, we hypothesized that the waypoints can be individually modified to locally vary fidelity. Specifically, we expected that increasing the saddle value (ratio of saddle eigenvalues) causes the trajectory to slow to more closely approach a particular saddle. Combined with other parameters that control speed and magnitude, a system expressed with an SHC can be modified locally, point by point, without disrupting the rest of the path, supporting their use in motion primitives. However, even more complex trajectory shape modifications are possible. While some combinations can enable a trajectory to better reach into corners, other combinations can rotate, distort and round the trajectory in the region of a corner. In our example, modifying select saddle values produced a 32% decrease in the trajectory error of a complex trajectory produced using this system. This is an effect not visible in previous 1D studies, that can lead to different learnable and tunable representations of dynamic systems.

Channel bends are one of the most important characteristic features of natural streams. These bends often create the conditions for a hyporheic zone, which has been recognized as a critical component of stream ecosystems. The streambed vertical hydraulic conductivity (K_v), vertical hydraulic gradient (VHG) and Darcian flux (DF) in the hyporheic zone were estimated at 61 locations along a channel bend of the Beiluo River during July 2015 and January 2016. All the streambed attributes showed great spatial variability along the channel bend. Both upward fluxes and downward fluxes occurred during the two test periods, most of studied stream sections were controlled by downwelling, indicating stream water discharge into the subsurface. The average downward flux was higher at the downstream side than at the upstream side of the channel bend, especially in July 2015. The distribution of streambed sediment grain size has a significant influence on the variability of K_v; high percentages of silt and clay sediments generally lead to low K_v values. Higher K_v at the depositional left bank at the upstream site shifted toward the erosional right bank at the downstream site, with K_v values positively correlated with the water depth. This study suggested that the variabilities of K_v and VHG were influenced by the stream geomorphology and that the distribution of K_v was inversely related, to a certain extent, to the distribution of VHG across the channel bend. K_v and VHG were found to have opposite effects on the DF, and the close relationship between K_v and DF indicated that the water fluxes were mainly controlled by K_v.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

This research investigates the complex transformation of the Baltic Clean and Dirty Tankers markets from 1998 to 2023. Our study quantifies the market multifractality in two distinct periods, from 1998 to 2010 and 2010 to 2023. We compare multifractal measures of the original, surrogated, and shuffled data, which indicate high multifractality and a complex market structure influenced by temporal organization and inherent volatility. Shuffled data analysis emphasizes the critical role of temporal sequences within market behaviour. Additionally, the study delves into external factors such as regulatory changes, economic disturbances, technological innovation, and environmental concerns contributing to market complexity. A distinct shift in multifractal values between the two periods suggests an evolution from crisis-induced complexities to a market influenced by diversified and modern dynamics. Our findings offer a concise understanding of the multifractal nature of the tanker markets and how global crises can impact the markets, emphasizing the importance of considering systemic risks in freight market analysis.

An accurate assessment of the stage-discharge relationship in open channel flows is necessary and important to the design and management of hydraulic structures and engineering in practical hydrosystems such as rivers and streams. While the flow structures and patterns at open channel junctions are interesting and have been widely studied in the literature, this paper focuses further on the effect of flow junctions on stage-discharge relationship at mountain river confluences. In this study, both the flume and physical model experiments are designed and performed carefully to test and analyze the complex flow structures and characteristics at river confluences with different configurations and hydraulic conditions. The impacts of the flow junctions on the traditional stage-discharge relationship are analyzed in this study. The results of this study are discussed in the paper for the understanding of flow structures at flow junctions and the design and management of hydraulic structures in river engineering.

Finding a good MIMO system model also major issue in Wireless Communication system. It is facing with so many problem, one of the major problem is finding good system model in terms of capacity and transmitting antenna system. In this paper, we analyze the channel capacity of various MIMO system model with some constant SNR level and outage probability. We establish a novel idea for MIMO system models as consider as 2^N- MIMO system model and find-out change in channel capacity when different transmitting antennas with constant SNR and outage probability. The channel capacity ratio CC_R is presented here on the basis of 2^N- MIMO channel capacity model. Number of transmitted antenna presented in MIMO system is increases is well-known however paper shows change in capacity in simple form.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

Electricity consumption is often the hotspot of life cycle assessment (LCA) of products, industrial activities, or services. The objective of this paper is to provide a consistent, scientific, region-specific electricity-supply-based inventory of electricity generation technology for national and regional power grids. Marginal electricity generation technology is pivotal in assessing impacts related to additional consumption of electricity. China covers a large geographical area with regional supply grids; these are arguably equally or less integrated. Meanwhile, it is also a country with internal imbalances in regional energy supply and demand. Therefore, we suggest an approach to achieve a geographical subdivision of the Chinese electricity grid, corresponding to the interprovincial regional power grids, namely the North, the Northeast, the East, the Central, the Northwest, and the Southwest China Grids, and the China Southern Power Grid. The approach combines information from the Chinese national plans on for capacity changes in both production and distribution grids, and knowledge of resource availability. The results show that nationally, marginal technology is coal-fired electricity generation, which is the same scenario in the North and Northwest China Grid. In the Northeast, East, and Central China Grid, nuclear power gradually replaces coal-fired electricity and becomes the marginal technology. In the Southwest China Grid and the China Southern Power Grid, the marginal electricity is hydropower towards 2030.

Hypertension is a frequent risk factor for cardiovascular diseases and the prevalence rate is continuously rising. Multiple pathophysiological mechanisms are responsible for hypertension, necessitating a combination of drugs from different classes for best management. Combination therapy is five times more effective in decreasing blood pressure compared to escalating the dose of a single agent. As per the ACCOMPLISH trial analysis, coupling a renin-angiotensin system inhibitor with a calcium channel blocker is more effective than the combination of a renin-angiotensin system inhibitor with diuretics in reducing major cardiovascular events, cardiovascular-related mortality, and the progression of chronic kidney disease. The renin-angiotensin system can be suppressed by inhibiting the angiotensin-converting enzyme or angiotensin type 1 receptor (AT1R), resulting, in decreased vascular smooth muscle contraction, heart performance, and reduced aldosterone synthesis. Calcium channel blockers work by inhibiting L-type calcium channels, reducing heart performance via negative inotropic, chronotropic, and bathmotropic effects, along with reducing vascular smooth muscle contraction. Diuretics exert their antihypertensive impact by changing body fluid volume and electrolytes. Initially, hypertension treatment commences with a single medication but often, different classes of two or more antihypertensive medications are necessary for achieving the target blood pressure goal. Sometimes, for Resistant hypertension four different classes of antihypertensive medications are necessary to reach the target blood pressure goal.

The penetration of modern supermarkets is believed to be the cause of the declining role of traditional markets and street vendors in Indonesia. But the competition between state-promoted markets and traditional markets is rarely discussed. This investigation focuses on traditional markets as social institutions, which continuously have developed a variety of strategies in order to remain competitive in the midst of intense rivalry. Firstly, we will outline a theoretical understanding of the traditional markets positioning along the concepts of flexibility and market devices. Secondly, we empirically reflect the strategies of four traditional vegetable markets in the District of Malang, East Java Province, as case studies. We show that the traditional markets build flexibility without governmental support by: (1) specifying commodities, (2) segmenting customers, (3) managing the load time, (4) modifying transportation to operate more efficiently, and (5) minimizing transaction costs by leveraging social capital. We argue that traditional vegetable markets institutionally reduce the potential of transaction costs to be competitive and avoid problems of coordination by building social capital through networks. All in all, market institutions supported by market devices are flexibly capable of adapting to the pressures of both selfishness among actors and competition with other markets.

This paper examines the transformation of the Ukrainian stock market by analyzing daily returns from the PFTS stock index spanning 1995-2022. Segmented into sub-periods, it tests the hypothesis that the market's efficiency has grown over time. Various data properties, including persistence, volatility, normality, and resistance to anomalies, are explored using different statistical techniques and methods. Volatility, indicated by standard deviation, exhibits a general decreasing trend, suggesting a shift towards a more efficient market. Returns show no significant differences across sub-periods, except for 2005-2007. R/S analysis indicates the decrease of persistence in the market: a trend towards increased efficiency. Normality tests support a normal distribution of daily returns throughout sub-periods. An analysis of anomalies reveals the market's resilience to day-of-the-week effects, with no specific trends observed. The results suggest the absence of serious changes in data properties in the Ukrainian stock market over the time, means evolutionary processes over 25 years can’t be clearly observed. Reforms and changes have had limited impact, justifying continued reform for the market's evolution.

The present paper examines the impact of herding behavior on market capitalization in the Egyptian stock market before and after the disclosure of environmental-social-governance scores (ESG Score Index). This line of thought and research contributes to the literature on the interrelationships between ESG factors and market dynamics. Consequently, the evolving landscape of how to sustain investments in emerging markets like Egypt can be enlightened. Two main hypotheses were tested in the paper. The first hypothesis is on the expected significant impact of herding behaviour on market capitalization. The second hypothesis is on the expected positive impact of ESG on the relationship between herding behaviour and market capitalization. Panel data analysis using data extracted from financial statements of 22 Egyptian EGX 30 companies, over the period 2018-2023 will be used. This is to control for the cross-section and time characteristics of the research sample. The empirical findings reveal a positive significant effect of herding behavior on market capitalization before disclosing ESG scores. Another finding is that after the implementation of mandatory ESG disclosure, the relationship between herding behavior and market capitalization is strengthened. Moreover, it is empirically evident that companies with higher ESG performance attract more investments and higher valuations. The importance of ESG in shaping investment decision-making, enhancing transparency, and market dynamics has important policy implications.

Many studies in the current literature annotate patterns in stock prices and use computer vision models to learn and recognize these patterns from stock price-action chart images. Additionally, current literature also use Long Short-Term Memory Networks to predict prices from continuous dollar amount data. In this study, we combine the two techniques. We annotate the consolidation breakouts for a given stock price data, and we use continuous stock price data to predict consolidation breakouts. Unlike computer vision models that look at the image of a stock price action, we explore using the convolution operation on raw dollar values to predict consolidation breakouts under a supervised learning problem setting. Unlike LSTMs that predict stock prices given continuous stock data, we use the continuous stock data to classify a given price window as breakout or not. Finally, we do a regularization study to see the effect of L₁, L₂, and Elastic Net regularization. We hope that combining regression and classification shed more light on stock market prediction studies.

Nanotechnology has become a popular topic in agricultural and animal research, generating sig-nificant global interest. Nanoparticles (NPs)-enabled agriculture and animal husbandry (AHs) products have three main principles: crop production, protection, and nutrient enrichment. Re-search on this topic has grown exponentially in recent years, with an increasing number of peer-reviewed literature and patents on NPs-enabled agriculture and AH products. This study examines and collects data on the various applications of NPs-enabled agricultural products for crop production, protection, and livestock improvement from the core nanotechnological database. The search returned several types of NPs and products used in agriculture and AH, countries that utilize these products at a commercial level, and analysis of these data by phyton, R-statistical software. Our findings show remarkable growth of nanotechnology in agriculture, AH, and re-search fields for future sustainability goals in the era of climate change

The treasury of a DAO is purported to uphold the value of the DAO-issued token, and this study represents a pioneering effort to delve into these realms. We explore the relationship between the treasury and market capitalization in two DAO projects, namely Olympus DAO and KlimaDAO. Employing Granger Causality tests, we scrutinize the relationship between market capitalization and the treasuries of these DAOs. Furthermore, we use an EGARCH model to investigate the in-fluence of carbon offset tokens on KlimaDAO’s market capitalization, incorporating this variable into the model's conditional mean equation. The findings reveal a significant relationship between market capitalization and the treasuries of the DAOs, indicating a potential impact of market capitalization on treasuries. Additionally, the well-specified EGARCH model highlights the sig-nificance of carbon offset tokens, suggesting their potential to influence the overall market capi-talization of a DAO project and support its value.

The paper proposes a mechanism for the impact of changes in the key rate on the volume of newly issued loans. The volume depends on the price (interest rates on loans), and the price depends on the key rate and the actual consumption of loans in the previous period (generalized cobweb cycle). The model was estimated by a Kalman filter, adequacy was confirmed by simulation. It is possible to forecast the average rate on loans for a month in advance according to the information published by the Central Bank of the Russian Federation (CB). By playing various scenarios for changing the key rate, it was found that in quiet periods of economic development, the usual laws of supply and demand operate in the loan market and by raising the key rate, you can reduce inflation. In the turbulent (overheated) state of the economy, an increase in the key rate can, on the contrary, provoke an increase in the issuance of loans and unconventional manipulations with the key rate are required.

This paper aims to measure the effects of delisting on stock returns for Vietnam stock market. The study employs a sample of 118 stocks that were compulsorily delisted from the market between January 2011 and December 2021. Using an event study methodology, the empirical findings confirm that the delisting has negative effects on stock returns in the Vietnam stock market. Specifically, results derived from tests show that the average abnormal return of delisted stocks continuously declines during three trading days following the announcement of delisting. Moreover, it is found that the differences in cumulative abnormal returns between post-delisting and pre-delisting periods are significantly negative for all tracking periods. Apart from the negative effect of delisting on stock abnormal returns, we also find that the impact of delisting on stock returns for smaller companies is greater than bigger companies.

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”.

A new design scheme is proposed for twisting the walls of a microchannel, and its performance is demonstrated numerically. The numerical study was carried out for a T-shaped microchannel with twist angles in the range of 0 to 34π. The Reynolds number range was 0.15 to 6. The T-shaped microchannel consists of two inlet branches and an outlet branch. The mixing performance was analyzed in terms of the degree of mixing and relative mixing cost. All numerical results show that the twisting scheme is an effective way to enhance the mixing in a T-shaped microchannel. The mixing enhancement is realized by the swirling of two fluids in the cross section and is more prominent as the Reynolds number decreases. The twist angle was optimized to maximize the DOM, which increases with the length of the outlet branch. The twist angle was also optimized in terms of the relative mixing. The two optimum twisting angles are generally not coincident. The optimum twist angle shows a dependence on the length of the outlet branch but it is not affected much by the Reynolds number.

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.

Low-loss photonic waveguides in lithium niobate offer versatile functionality as nonlinear frequency converters, switches, and modulators for integrated optics. Combining the flexibility of laser processing with liquid phase epitaxy we have fabricated and characterized lithium niobate channel waveguides on lithium niobate and lithium tantalate. We used liquid phase epitaxy with K2O flux on laser-machined lithium niobate and lithium tantalate substrates. The laser-driven rapid-prototyping technique can be programmed to give machined features of various sizes, and liquid phase epitaxy produces high quality single-crystal, lithium niobate channels. The surface roughness of the lithium niobate channels on a lithium tantalate substrate was measured to be 90 nm. The lithium niobate channel waveguides exhibit propagation losses of 0.26 ± 0.04 dB/mm at a wavelength of 633 nm. Second harmonic generation at 980 nm was demonstrated using the channel waveguides, indicating that these waveguides retain their nonlinear optical properties.

Wall Shear Stress (WSS) abnormalities in carotid artery blood flow are key hemodynamic factors leading to arterial endothelial damage, atherosclerotic plaque formation, and carotid artery stenosis. Precise simulation of carotid artery blood flow WSS in vitro play an important role in understanding the underlying hemodynamic mechanism of carotid artery stenosis. This study presents a design for a microfluidic cell culture chamber with a variable cross-section and a step structure, leveraging microfluidic technology and principles of fluid dynamics. We propose an optimization strategy for microchannel dimensions based on Computational Fluid Dynamics (CFD) simulations. This strategy aims to replicate the distinct WSS waveform features found at various locations within the carotid bifurcation as observed in vivo. Our simulations indicate that with a step height of 0.09 mm, a width of 4 mm for the initial segment, and 5 mm for the subsequent wider channel, the device can effectively model the characteristics of low oscillatory WSS seen at sites of carotid sinus stenosis. Furthermore, it accurately represents the high-magnitude pulsatile WSS waveforms in the more uniform arterial sections downstream of the carotid sinus. The study also reveals that vortex formation and variations in low oscillatory WSS within the stepped section of the microchannel correlate with the step's height and width, as well as with the dimensions of the wider channel section. An increase in step width leads to a decrease in both the vortex area and the magnitude of negative WSS oscillations. When the step height is less than 0.03 mm, vortex formation is inhibited, challenging the simulation of low oscillatory WSS patterns. The variable cross-section microfluidic channel developed in this study provides a platform for simulating carotid artery WSS waveforms and facilitates research into the interplay between low oscillatory WSS at sites of carotid sinus stenosis and arterial endothelial dysfunction.

PIEZO channels PIEZO1 and PIEZO2 are the newly identified mechanosensitive, non-selective cation channels permeable to Ca2+. In higher vertebrates, PIEZO1 expresses ubiquitously in most of the tissues and cells while PIEZO2 expresses more specifically in the peripheral sensory neurons. PIEZO channels contribute to a wide range of biological behaviors and developmental processes, therefore driving a significant attention in the effort to understand their molecular properties. One prominent property of PIEZO channels is their rapid inactivation, which manifests itself as a decrease of channel open probability in the presence of a sustained mechanical stimulus. Lack of the PIEZO channel inactivation is linked to various mechanopathologies emphasizing the significance of studying this PIEZO channel property and the factors affecting it. In the present review, we discuss the mechanisms underlying the PIEZO channel inactivation, its modulation by interaction of the channels with lipids and/or proteins, and how the changes of PIEZO inactivation by the channel mutations can cause a variety of diseases in animals and humans.

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.

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.

We propose a deep-learning approach for the joint MIMO detection and channel decoding problem. Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners. However, due to the complex MIMO signal model, the optimal solution to the joint MIMO detection and channel decoding problem (i.e., the maximum likelihood decoding of the transmitted codewords from the received MIMO signals) is computationally infeasible. As a practical measure, the current model-based MIMO receivers all use suboptimal MIMO decoding methods with affordable computational complexities. This work applies the latest advances in deep learning for the design of MIMO receivers. In particular, we leverage deep neural networks (DNN) with supervised training to solve the joint MIMO detection and channel decoding problem. We show that DNN can be trained to give much better decoding performance than conventional MIMO receivers do. Our simulations show that a DNN implementation consisting of seven hidden layers can outperform conventional model-based linear or iterative receivers. This performance improvement points to a new direction for future MIMO receiver design.

Market air quality is very important to the economic lives of the people which is rarely researched, however, market activities particularly pedestrian traffic releases particulates which is detrimental to the health of the users and stakeholders. Thermo scientific MIE pDR-1500 particulate was used to monitor the quality of air within the market for eight (8) weeks, air pollutant of concern is PM2.5. ten (10) sample points were located in the market which covers ten (10) sample points for pedestrian traffic to represent the entire market environment spectrum. The analysis of PM2.5 measured daily during dry and wet season shows a clear seasonal variation of this particular pollutant as elevated concentration was measured during the dry season than the wet season. The assessment of PM2.5 concentration shows exceedances of the standards stated by WHO and NAAQS during the dry season which ranges from 47.9 μg/m3- 231.88 μg/m3 in the morning and 65.17 μg/m3- 1806.33 μg/m3 in the afternoon. From the findings, pedestrian traffic contributes immensely to air pollution in an open market, with this elevated concentration, prolonged exposure is highly detrimental to health. This study creates awareness to the pedestrians in an open market about air pollution and informs policy changes.

The combination of neural network and beamforming has been proved to be very effective in multi-channel speech separation. But its performance faces a challenge in complex environment. In this paper, an iteratively refined multi-channel speech separation method is proposed for the challenge, where the proposed is composed of initial separation and iterative separation. In initial separation, the time-frequency domain dual-path recurrent neural network neural network (TFDPRNN), minimum variance distortionless response (MVDR) beamformer and post-separation (also TFDPRNN) are cascaded for obtaining the first additional input in iterative separation. In iterative separation, the MVDR beamformer and post-separation are iteratively used, where the output of the MVDR beamformer is used as an additional input of the post-separation network and the final output comes from post-separation module. This iteration of the beamformer and post-separation is fully employed for promoting their individual optimization, which ultimately improves the overall performance of speech separation in multi-speaker scenarios. Experiments on the spatialized version of the WSJ0-2mix corpus show that our proposed method is significantly better than the current popular methods. In addition, the method also has a good effect on the dereverberation task.

Network Simulators (NS) is typically used to study services and applications in complex scenarios due to the infeasibility of deploying real testbeds. This is the case of Mobile Ad hoc Networks (MANETs) which can be extensively used in emergency situations or to gather information without infraestructure. An important factor to get realiable results in the MANETs’ simulations is the wireless channel module of NSs. In this report, we explain how thw wireless channel is implemented in NS-3 and it can be configured.

The purpose of this original study is to critically analyze the emergence and development of the national models of financial regulation; international standards and codes; regional and national financial regulation and supervision (the case of UK, USA, Sweden, EU and Finland). The research raises both academic and regulatory concerns. It aims at shaping changes (innovations) in the supervisory practices of national and international regulators in response to new developments in FinTech markets, digital products, financial instruments and risks. Secondly, it will stimulate more systematic work on regulatory databases, registration and reporting procedures in various economies in different financial markets. The author identifies five main systems of national financial regulatory markets: the multi-tiered, multi-agency US system; the Twin Peaks model (UK); the mega-regulatory model (Sweden). There is a thorough review of international standards and institutions that work for the stability of financial systems. The analysis of regional and national systems of financial regulation and supervision is based on the examples of the EU and Finnish institutions. National macro and microeconomic regulation and supervision have been examined with a focus on the US Federal Reserve and the US Treasury. National and international regulatory institutions have been evolving along several lines. First, minimum capital and credit risk requirements for banks (80s of the 20th century) are complemented in the 21st century by buffer reserves, liquidity and leverage standards. Second, regulation focuses on ensuring the sustainability of the national economy. The regulatory focus is on ensuring the sustainability of national and global financial systems. Third, there is an increase in the number of supervised institutions. Fourth, there is a division of functions between central banks (macroeconomic regulation) and one or two mega-regulators (microeconomic regulation and supervision). Fifth, there is a division of labour between the international financial institutions (BIS, IMF, WB) and national regulators. Sixth, the focus is on protecting consumers and investors, and countering money laundering and the financing of terrorism. Seventh, there is an understanding for a common approach by central banks to new financial technologies and cybersecurity.

Visible light communication (VLC) has attracted much attention in recent years among industrial and academic researchers. The large scale use of energy efficient light emitting diodes (LEDs) in lighting applications offers a unique opportunity to use license free visible light as the carrier in wireless communications. It has many potentials to complement radio frequency (RF) based communication systems. Researchers working on VLC have mainly concentrated on improving the communication speeds to match with Wi-Fi though the latest generation Wi-Fi standards offer significantly higher capacity at longer communication distances. Thus, market penetration is challenging for VLC as Wi-Fi is already quite mature and ubiquitous. Commercialization of VLC can only be successful if VLC can offer applications where VLC is more advantageous over the existing RF-based solutions. Once VLC enters the market through specific applications and becomes more mature in the process, the ecosystem may gradually adopt VLC in broader applications. In this article, the state of the art of the three promising VLC applications, their issues, and possible solutions have been discussed.

Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.

News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover the news sentiment and classify news according to its sentiments with the application of PhoBERT, a Natural Language Processing model designed for the Vietnamese language. A collection of nearly 40,000 articles on financial and economic websites is used to train the model. After training, the model succeeds in assigning news to different classes of sentiments with an accuracy level of over 81%. The research also aims to investigate how investors concern about the daily news by testing the movements of the market before and after the news is released. The analysis results show that there is an insignificant difference in the stock price as a response to the news. However, investors tend to overreact to negative and positive news in the Vietnam stock market.

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 structure of turbulent flow over non-flat surfaces is a topic of major interest in practical applications in both engineering and geophysical settings. A lot of work has been done in the fully rough regime at high Reynolds numbers where the effect on the outer layer turbulence structure and the resulting friction drag is well documented. It turns out that surface topology plays a significant role on the flow drag especially in the transitional roughness regime and therefore, hard to characterize. Survey of literature shows that roughness function depends on the interaction of roughness height, flow Reynolds number and topology shape. In addition, if the surface topology contains large enough scales then it can impact the outer layer dynamics and in turn modulate the total frictional force. Therefore, it is important to understand the mechanisms underlying drag increase from systematically varied surface undulations in order to better interpret quantifications based on mean statistics such as roughness function. In this study, we explore the mechanisms that modulate the turbulence structure over a two-dimensional (2D) sinusoidal wavy surface with a fixed amplitude, but varying slope. To accomplish this, we model the turbulent flow between two infinitely wide 2D wavy plates at a friction Reynolds number, $Re_{\tau}=180$. We pursue two different but related flavors of analysis. The first one adopts a roughness characterization flavor of such wavy surfaces. The second one focuses on understanding the non-equilibrium near surface turbulence structure and their impact on roughness characterization. Analysis of the different statistical quantifications show strong dependence on wave slope for the roughness function indicating drag increase due to enhanced turbulent stresses resulting from increased production of vertical velocity variance from the surface undulations.

The Inverted F Antenna (IFA) with the parasitic element on a finite conducting plane is proposed in the range frequency of 0.1 to 5.5 GHz and its characteristics are analyzed numerically. In this research, the parasitic element and the main IFA are investigated to obtain the resonance frequency for multiband operation purposes. The parasitic element is placed beneath adjusted to the main IFA to derive more frequency bands. The distance between the parasitic element and main horizontal element extremely affects the performance of the IFA. It is found that when the parasitic element is located closer to the conducting plane, this element is coupled by the current on the conducting plane. Consequently, the return loss bandwidth becomes narrower. Therefore, the gain of the proposed IFA becomes a bit higher. The antenna gain is about 8.21 dB at band #3 (λ_1.747), 7.43 dB at band #5 (λ_2.967) and 8.82 dB at band #6 (λ_4.023). This occurs when the calculation condition is antenna height h1 = 23 mm, h2 = 21 mm, horizontal antenna elements L = 173.2 mm, L1 = 140.9 mm and Lp = 152 mm, shorted element Ls = 30.7 mm, the distance between parasitic element and shorted element pyl = 5 mm. While the size of conducting plane is considered pxp+pxm by pyp+pym as 57.5+57.5 mm by 200+50 mm. In the numerical analysis, the electromagnetic simulator WIPL-D based on Method of Moment is used. The results show that the proposed IFA has UHF and SHF channel receiver which are suitable for advanced wireless service (band #3), mobile phones, Bluetooth, maritime service, radiolocation service (band #5) and radars, mobile phones, commercial wireless LAN (band #6).

The primary objective of this study is to examine the extent of financial integration between Latin American and US financial markets, particularly in light of recent efforts to foster integration through trade agreements. Spanning from January 1, 1990, to December 31, 2019, the sample focuses on major market indices and key sectors. Financial integration is quantified using a DCC multivariate GARCH model, incorporating a smooth transition model, structural breaks, and regression-based approaches. Results indicate increased comovement with the US for main market indices in Argentina, Chile, Colombia, Mexico, and Peru, while Brazil shows a decrease. Similar trends are observed in sectoral analyses. The study also reveals heightened correlation post-trade agreements. Structural break analysis highlights significant shifts in dynamic correlations for countries with US free trade agreements. These findings support the argument of increased financial integration, bearing significance for portfolio diversification and international policy formulation.

The widespread integration and growing dependence among currency, stock and commodity markets make these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we explore the spillovers and nonlinear dependences between the seven major foreign ex-change rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by em-ploying directional spillover network approach. We also use multi-scale de-composition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. Major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the EUR as the largest contributor of connectedness to the other series. The central role of the EUR is a net transmitter of connectedness to gold, oil, the GBP, JPY, and CHF.

Starting with BitTorrent and then Bitcoin, decentralized technologies have been on the rise over the last 15+ years, gaining significant momentum in the last 2+ years with the advent of platform ecosystems such as the Blockchain platform Ethereum. New projects have evolved from decentralized games to marketplaces to open funding models to decentralized autonomous organizations. The hype around cryptocurrency and the valuation of innovative projects drove the market cap of cryptocurrencies to over a trillion dollars at one point in 2017. These high valued technologies are now enabling something new: globally scaled, decentralized business models. Despite their valuation and the hype, these new business ecosystems are frail. This is not only because the underlying technology is rapidly evolving, but also because competitive markets see a profit opportunity in exponential cryptocurrency returns. This extracts value from these ecosystems, which could lead to their collapse, if unchecked. In this paper, we explore novel ways for decentralized economies to protect themselves from, and coexist with competitive markets at a global scale utilizing decentralized technologies such as Blockchain.

Ion channels serve many cellular functions including ion homeostasis, volume regulation, signaling, nutrient acquisition, and developmental progression. Although the complex lifecycles of malaria parasites necessitate ion and solute flux across membranes, whole genome sequencing of the human pathogen, Plasmodium falciparum, revealed remarkably few orthologs of known ion channel genes. Contrasting with this, biochemical studies have implicated channel-mediated flux of ions and nutritive solutes across several membranes in infected erythrocytes. Here, I review advances in the cellular and molecular biology of ion channels in malaria parasites. These studies have implicated novel parasite genes in the formation of at least two ion channels, with additional ion channels likely present at various membranes and parasite stages. Computational approaches that rely on homology to known channel genes from higher organisms will not be very helpful in identifying the molecular determinants of these activities. Given their unusual properties, novel molecular and structural features, and essential roles in pathogen survival and development, parasite channels should be promising targets for therapy development.

In this paper, we develop a tractable mathematical model and emulation framework for communicating information through water using acoustic signals. Water is considered as one of the most complex mediums to model owing to its vastness and variety in characteristics depending on the scenario, what kind of water-body (lakes, rivers, tanks, sea etc.) and geographical location of the water-body is considered. Our proposed mathematical model involves the concept of damped harmonic oscillators to represent the medium (water) and Milne’s oscillator technique to map the interaction between the acoustic signal and water. Wave equations formulated for acoustic pressure and acoustic wave velocity are used to characterize the travelling acoustic signal. Signal strength, phase shift and time-delay generated from the mathematical model are fed to a Simulink-based emulator framework to generate channel samples and channel impulse responses. The emulator uses wide sense stationary uncorrelated scattering (WSSUS) assumption and finite sum-of-sinusoids (SOS) with uniformly distributed phase to generate the channel samples. Using the emulator platform it is possible to generate amplitude variation profile, Doppler shift and spread experienced by any travelling signal through different underwater communication scenarios. Such an emulator platform can be used to simulate different communication scenarios, underwater network topologies, data to train different learning models and predict performance of different modulation, multiplexing, error correction and multi-access techniques for underwater acoustic communications (UWAC) systems.

in this paper, the optical channel performance with respect to varying scintillation index parameters is studied. Where different channel effects are studied and analyzed. In free-space optical (FSO) communication, the major drawback of the optical communication system is the atmospheric turbulence (AT). This effect is nonlinear with optical channel altitude being changed. Therefore, the power margenal (PM) required for such a channel is also inconstant respectively. In this thesis, we analyze AT effect on the optical channel with different altitudes. Such that we found the difference in the range of PM required for such optical link distance. In our result, the Bit Error Rate (BER) VS. PM required is measured. The optical channel model is Log-Normal considered and the BER threshold BER^th = 10^-10 is taken.

In line with Pomeau’s conjecture about the relevance of directed percolation (DP) to turbulence onset/decay in wall-bounded flows, we propose a minimal stochastic model dedicated to the interpretation of the spatially intermittent regimes observed in channel flow before its return to laminar flow. Numerical simulations show that a regime with bands obliquely drifting in two stream-wise symmetrical directions bifurcates into an asymmetrical regime, before ultimately decaying to laminar flow. The model is expressed in terms of a probabilistic cellular automaton evolving von Neumann neighbourhoods with probabilities educed from a close examination of simulation results. It implements band propagation and the two main local processes: longitudinal splitting involving bands with the same orientation, and transversal splitting giving birth to a daughter band with orientation opposite to that of its mother. The ultimate decay stage observed to display one-dimensional DP properties in a two-dimensional geometry is interpreted as resulting from the irrelevance of lateral spreading in the single-orientation regime. The model also reproduces the bifurcation restoring the symmetry upon variation of the probability attached to transversal splitting, which opens the way to a study of the critical properties of that bifurcation, in analogy with thermodynamic phase transitions.

Ion channels contribute fundamental properties to all cell membranes. The ion channels are highly diverse in conductivity, structure, location and function. However, many of them can be regulated by common mechanisms, such as voltage or (de-)phosphorylation. Considering primarily pain-related ion channels, this review covers more novel and less known features of ion channel function, also with the aim to transfer knowledge between fields, which get inevitably more separate due to their size.

Security is a major topic in recent researchers, and finding new methods deliver was the motivations for this research to emphasize and as an awareness to browsers developers to handle images with more care and to enhance exploit detection technology in the browser, as this paper proves that hackers now can attack users with just an image.

Phα1β a spider peptide, purified from the venom of Phoneutria nigriventer inhibits high voltage calcium channels and is antagonist of TRPA1 receptor two important pain transduction path-ways. In the last 15 years, we have showed, in preclinical tests with Phα1β native and recombi-nant in rodent models of pain the potential of Phα1β to become a new analgesic drug. Evaluation of new drug on human ERG channel is requirement of the regulatory agencies. For the hERG channel inhibition assay it was used the commercial kit FLIPR® Potassium Assay. It was tested the effect of Phα1β and as positive control dofetilide, a well characterized hERG block-er. Phα1β 56, 225, 450 and 900 pMol caused at a higher concentration a very weak inhibition of the hERG channel activity of about 13.47 % with channel inhibition at 50%, IC50 > 900 pMol. Dofetilide (0.0001 - 10 µM), a known hERG inhibitor, caused a well-known, concentra-tion-dependent inhibition of the hERG channel with a mean IC50 of 0.1642 µM (0.1189 – 0.2282 µM. For the cytotoxicity assay, Phα1β concentrations of 56, 225, 450 and 900 pMol incubated for 24 h with HEK293-hERG cells did not alter the viability of the HEK293-hERG cells. It is con-cluded that Phα1β even at high concentrations does not interfere with hERG channels.

The cardiac sodium ion channel (NaV1.5) is a protein with four domains (DI-DIV), each with six transmembrane segments. Its opening and subsequent inactivation results in the brief rapid influx of Na+ ions resulting in the depolarization of cardiomyocytes. The neurotoxin veratridine (VTD) inhibits NaV1.5 inactivation resulting in longer channel opening times, and potentially fatal action potential prolongation. VTD is predicted to bind at the channel pore, but alternative binding sites have not been ruled out. To determine the binding site of VTD on NaV1.5, we performed docking calculations and high-throughput electrophysiology experiments. The docking calculations identified two distinct binding regions. The first site was in the pore, close to the binding site of NaV1.4 and NaV1.5 blocking drugs in experimental structures. The second site was at the “mouth” of the pore at the cytosolic side, partly solvent-exposed. Mutations at this site (L409, E417, and I1466) had large effects on VTD binding, while residues deeper in the pore had no effect, consistent with VTD binding at the mouth site. Overall, our results suggest a VTD binding site close to the cytoplasmic mouth of the channel pore. Binding at this alternative site might indicate an allosteric inactivation mechanism for VTD at NaV1.5.

In this study, we employ the GARCH-MIDAS (Generalised Autoregressive Conditional Heteroskedasticity variant of Mixed Data Sampling) model to explore the predictability of commodity return volatility in relation to US climate policy uncertainty (CPU). Our analysis utilizes the 20-day annualized realized commodity volatility returns of nine global commodities (Aluminium, Cocoa, Coffee, Copper, Cotton, Rice, Soybean, Sugar, and Wheat) to construct the GARCH-MIDAS model, with Climate Policy Uncertainty (CPU) serving as the predictive variable. The outcomes of our investigation reveal a consistently positive and statistically significant relationship between CPU and the selected commodities. This implies that CPU effectively serves as a robust predictor of volatility in commodity returns. The implications drawn from our results suggest that climate change policies play a pivotal role in influencing economic activities within the commodity market. This underscores the substantial impact of climate change considerations on investment decisions and further emphasizes the integral role of climate change in shaping economic choices.

Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16th June 2016 to 9th July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.

Pine nuts are non-wood forest products (NWFP) with constantly growing market notwithstanding a series of phytosanitary issues and related trade problems. The aim of paper is to review the literature on the relationship between phytosanitary problems and trade development. Production and trade of pine nuts in Mediterranean Europe have been negatively affected by the spreading of Sphaeropsis sapinea (a fungus) associated to an adventive insect Leptoglossus occidentalis (fungal vector), with impacts on forest management activities, production and profitability and thus in value chain organization. Reduced availability of domestic production in markets with growing demand has stimulated the import of pine nuts. China has become a leading exporter of pine nuts, but its export is affected by a symptom associated to the nuts of some pine species: the ‘pine nut syndrome’ (PNS). Most of the studies embraced during the review are associated to PNS occurrence associated to the nuts of Pinus armandii. In the literature review we highlight the need for a comprehensive and interdisciplinary approach to the analysis of the pine nuts value chain organisation, where research on food properties and clinical toxicology be connected to breeding and forest management, forest pathology and entomology and trade development studies.

Comparison between hydraulic and hydrologic computational methods is conducted in this study, regarding prismatic open channels under unsteady subcritical flow conditions. One-dimensional unsteady flow continuity and momentum equations are solved using explicit and implicit finite difference schemes for a symmetrical trapezoidal cross section, where the flow discharge and depth are the dependent variables. The results have been compared to those derived from Muskingum-Cunge hydraulic/hydrologic method as well as the commercial software HEC-RAS. The results from explicit and implicit code compare well to those from commercial software and hydraulic/hydrologic methods for long prismatic channels, thus directing the hydraulic engineer to quick preliminary design of prismatic open channels for unsteady flow with satisfactory accuracy.

This paper delves into the dynamics of asset pricing within Bachelier’s market model (BMM), elucidating the representation of risky asset price dynamics and the definition of riskless assets. It highlights the fundamental differences between BMM and the Black-Scholes-Merton market model (BSMMM), including the extension of BMM to handle assets yielding a simple dividend. Our investigation further explores Bachelier’s term structure of interest rates (BTSIR), introducing a novel version of Bachelier’s Heath-Jarrow-Morton model and adapting the Hull-White interest rate model to fit BMM. The study concludes by examining the applicability of BMM in real-world scenarios, such as those involving environmental, social, and governance (ESG)-adjusted stock prices and commodity spreads.

Market uncertainty has a significant impact on market performance. Previous studies put much efforts in investigations on market uncertainty related to information asymmetry and risk. However, they have neglected the uncertainty inherent in market transactions, which is also an important aspect of market performance, besides quantity of transactions and market efficiency. In this paper, we put forward the concept of transaction entropy to measure the market uncertainty and see how it changes with price. The definition of transaction entropy is the ratio of total information entropy of all traders to quantity of transactions, reflecting the level of uncertainty in making transactions. And the transaction entropy is the lowest at equilibrium, it will be decreasing in a shortage market, and increasing in a surplus market. Additionally, we make a comparison of total entropy of the centralized market with that of the decentralized market, revealing that the price filtering mechanism could effectively reduce market uncertainty. Overall, the introduction of transaction entropy enriches our understanding of market uncertainty and facilitates a more comprehensive assessment of market performance.

The purpose of this paper is to identify factors that influenced customer satisfaction in the Moscow taxi market. The previous researches in customer satisfaction of transportation services laid the foundation of factor model. The resulting list of factors was revised by taxi market experts. The augmented model provided the basis for quantitative survey. The research sample included 310 respondents who have been using taxi services in Moscow during past 2 years. Survey data was analyzed using multiple choice regression method. Analysis results show significant differences between frequent and casual taxi users and a certain number of passenger experience factors that have no influence on customer satisfaction:for frequent users it is discount, for regular users - trip environment, and for rare clients - technical support&security and trip comfort.

Markets dominate the world’s food systems. Today’s food systems fail to realize the normative foundations of ecological economics: justice, sustainability, efficiency, and value pluralism. I argue that markets, as an institution for governing food systems, hinder the realization of these objectives. Markets allocate food toward money, not hunger. They encourage shifting costs on others, including nonhuman nature. They rarely signal unsustainability, and in many ways cause it. They do not resemble the efficient markets of economic theory. They organize food systems according to exchange value at the expense of all other social, cultural, spiritual, moral, and environmental values. I argue that food systems can approach the objectives of ecological economics roughly to the degree that they subordinate market mechanisms to social institutions that embody those values. But such “embedding” processes, whether through creating state policy or alternative markets, face steep barriers and can only partially remedy food markets’ inherent shortcomings. Thus, ecological economists should also study, promote, and theorize non-market food systems.

For voltage-gated Ca2+ channel (VGCC), its α2δ subunits are traditionally considered to be auxiliary subunits that regulates VGCC trafficking to the plasma membrane. The antiepileptic, antinociceptive and anxiolytic gabapentin (GBP) has previously been shown to bind the VGCC α2δ subunits with high affinity to disrupt VGCC trafficking. Yet, the interaction between GBP and α2δ still remains poorly understood from a structural point of view. For instance, it is not clear yet what the structural implication is of α2δ-1-bound GBP against VGCC trafficking. With a set of experimental data-driven structural analysis of the VGCC α2δ-1 and its ligand GBP, this article postulates for the first time that: 1), α2δ-1 bound GBP stabilizes the α2δ-1-GBP complex structure; 2), α2δ-1 bound GBP restrains the conformational flexibility of α2δ-1; 3), α2δ-1-bound GBP establishes an electrostatic axis consisting of Q535 (Gln535)-R241 (Arg241)-GBP (gabapentin)-D452 (Asp452), which constitutes an energetically favourable contribution towards the structural stability of the α2δ-1-GBP complex and helps restrains the conformational flexibility and local structural rigidification of α2δ-1; and 4), GBP-induced local conformational inflexibility and structural rigidification of α2δ-1 is one key step in the pharmacological disruption of VGCC trafficking by GBP.

The use of plant-based and microorganism-based biological inputs is a sustainable agricultural practice, promoting a suitable and better utilization of non-renewable resources in the environment. The benefits of using microorganisms are associated with both direct and indirect mechanisms, primarily related to improvements in nutrient absorption and availability, as well as plant growth. The main benefits of using biochemical pesticides are the promotion of sustainability and the management of resistance. Although the use of microorganisms and botanical metabolites is a promising agricultural alternative, it is still primarily concentrated in grain crops. However, due to the wide range of mechanisms of action of plant-based and microorganism-based biological inputs, there is an opportunity to expand their use in agriculture. In the Brazilian biological inputs market, various terminologies have been used to characterize biological-based products, with many of these terms being employed incorrectly and sometimes conflicting with the country’s legislation. Therefore, this review presents the classes of biological inputs and the application and evolution of the plant-based and microbiological-based product market in different crops in Brazil.

The procedures used to create modern cars require extensive thought in various relevant scientific domains. Arguably, the most challenging obstacle facing the automobile sector is managing production facilities by integrating software production lines and CI/CD. All this is determined by market demands, the engine of a vehicle, and the complexity of assembling the entire car and installing its corresponding embedded software. As a result, concerns about various types of global change have grown, as well as the lack of the ability to use fossil fuels, creating a substantial impact on the purchase and sale of modern automobiles. The research foundation is reflected in covering strategies for the deployment and administration of software, as well as opportunities for business improvement in particular production processes. This article strives to provide a summary of a scientific investigation of original equipment manufacturers, market segmentation, and the effects of global market changes on automotive manufacturing by examining the correlation between certain changes in the purchase of a specific brand and the powertrain of a vehicle. The research tries to examine numerous datasets from the United States of America and Washington State, on the basis of which we may estimate possible future changes in the automotive industry’s sales.

Purpose –This study examines the effect of ESG performance on auditor choice and audit opinion for Egyptian-listed firms. Design/methodology/approach – We use univariate and multivariate analyses of 612 firm-year observations for a sample of 68 firms listed on EGX100 over 2012-2022 using binary logistic regression models. Findings – Consistent with the ethical perspective of corporate social responsibility, we found that firms listed in the ESG index are more likely to assign one of the Big4 auditors, and less likely to receive a qualified opinion. In addition, we find that COVID-19 moderates the relationship between ESG performance, auditor choice, and audit opinion. Originality/value – Our results contribute to the growing interest in the implications of ESG performance for audit practices in emerging economies. Implications: This research has important implications for investors, the audit profession, firms, and regulators in Egypt. It provides substantial evidence that ESG performance can enhance financial reporting quality. Further, it indicates that binding guidelines and regulations are crucial to oversee corporate ESG performance, especially during crisis time to enhance investors’ protection and firms’ sustainability.

Virtual reality (VR) can play a key role in automotive marketing research, lowering costs and shortening the time it takes to bring a new product to market. However, there are still few VR applications that support automotive customers' experiences during the early stages of product development. Through a systematic review of literature and patents, this study aims to identify the challenges and opportunities for the application of virtual reality in car clinics, and to categorize them into attributes. We searched through the knowledge databases of PatentScout, ScienceDirect, Springer, and IEEEXplore. We found 72 patents with a high concentration in a few inventors. The United States of America presented the greatest number of records and the most common applications related to the apparatus for automatically reading respondents' reactions in a virtual environment. In terms of articles, we found 19 research papers that discussed sixteen categories identified as challenges and opportunities for automotive marketing research: 1) cost, 2) location to customers, 3) flexibility in interactions, 4) model transportation, 5) depth perception, 6) haptic perception, 7) motion, 8) movement perception/ physical collision, 9) color and texture, 10) sound feedback, 11) product interaction/manipulation, 12) visual-spatial, 13) graphic quality, 14) intuitiveness, 15) cybersecurity and 16) cybersickness. We conclude that the automotive industry can employ virtual reality for marketing research, but relevant elements such as hardware and software definition, stimulus quality, and research objectives, among others, must be considered.

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.

Does the choice of proxy for stock market development matter? This paper suggests that the growth effect of stock market development is sensitive to the choice of proxy and using alternative financial development indicators have practically no influence on the results. We found that using either the stock market capitalization to GDP ratio or the stock market returns; have a positive and significant effect on growth. However, we cannot make same conclusion when one uses either the ratio of total value of trades on the major stock exchanges to GDP or stock market turnover ratio to proxy for stock market development as the coefficient on these variables were found to be statistically insignificant. The indexes extracted from principal component analysis confirm the sensitivity of the effect to the choice of proxy. This finding suggest that stock market development is a conceptual terms, thus, representing it with single indicators make it impossible to identify which stock market development indicators have a significant positive growth effects.

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

YouTube Kids serves as a dynamic and user-friendly resource for young learners to learn English. Its extensive library of instructional materials, which includes captivating animations and interactive movies, provides a rich and varied learning environment. Through the integration of play and exploration into language study, YouTube Kids creates a comfortable and engaging atmosphere that allows kids to improve their English language skills at their own pace. In this research article, a narrative literature review was employed as a qualitative methodology. Based on the gathered relevant literature, YouTube Kids presented such positive and negative impacts as a channel for English language acquisition. YouTube Kids is indeed a beneficial tool for young learners who are trying to learn English, but at the same time, it does have some potential disadvantages, as some videos may not employ the same standard or correctness of English, which could expose kids to improper grammar or pronunciation. Parents and teachers need to carefully select and keep an eye on the content their kids watch to ensure that their children's YouTube Kids content supports their language learning objectives. Through careful supervision and curation, parents and educators may maximize the usefulness of the site as a medium for teaching young learners English language skills while also minimizing any potential downsides.

Starting from the model of a slow-rolling missile with six degrees of freedom (6 DOF) in the body frame, a 6 DOF model in the Resal frame is obtained, which is used to linearize the coupled commanded motion. Based on the linearized model, we obtain the structural scheme of the commanded object and define the quality parameters. The obtained parameters have a complex representation (with real and imaginary parts) due to the coupling between longitudinal channels. Then, the kinematic guidance equations, the seeker equations and the actuator specific to the slow-rolling single-channel missile are defined using a switching function. The guidance kinematic equations, the seeker equations and the actuator model are linearized in the Resal frame, and the structural diagram of the homing missile is constructed. Starting from this, the characteristic polynomial having complex coefficients is determined and then analyzed with the Frank-Wall stability criterion. Based on the analysis, a stability range is determined for the navigation constant and a minimum and possibly a maximum limit for the time to hit the target is obtained. The stability range defined for the navigation constant on the linear model is finally validated on the nonlinear model in the body frame.

In the last decade, three-dimensional (3D) printing has allowed the field of complex microfluidic channels to expand rapidly towards channels with variable cross-sections (i.e., beyond simple rounded media with a constant diameter) and channels. Whose trajectory can be outside a single plane and has gained numerous macro- and nanoscale manufacturing advances. Three-dimensional printing is being explored for various biomedical applications and the fabrication of nanomedicines using additive manufacturing techniques. Employing 3D-printed templates for macro-to-micro interfacing, a passively operated microfluidic device was designed. The research seeks to fabricate a biodevice that enables insulin microdosing, creating a subsystem like a microvalve and a microchannel matrix using additive manufacturing techniques. Our systems feature designs that enhance fluid dynamics without affecting the stability of the insulin. We propose using Humalog insulin in tropical climates (up to 30°C) without refrigeration for 28 days. The microvalve has a spiral trajectory with four outlet channels distributed along it. These channels align with the microchannels organized in a 10 mm x 15 mm matrix with 25 microchannels, each with a 770 µm internal diameter. The system delivers a dose of 2,3 mL in 6 minutes. We designed these systems for insulin micro-dosing, but they can also serve similar devices for other drugs.

Zn2+ has been implicated in the regulation of intestinal redox and microbial homeostasis, and we recently reported that transient receptor potential canonical (TRPC) 6 channel activity contributes to intracellular Zn2+ homeostasis in mammalian cells. This study aims to investigate the role of TRPC6-mediated Zn2+ influx in stress resistance of intestine. Expression profile of TRPC1-C7 mRNAs in the actively inflamed mucosa from inflammatory bowel disease (IBD) patients was analyzed using GEO database. Systemic TRPC3-knockout (KO) and TRPC6-KO mice were treated with dextran sulphate sodium (DSS) to induce colitis. The Zn2+ concentration and the mRNA expression levels of oxidative/inflammatory markers in colon tissues were quantitatively analyzed, and gut microbiota profiles were compared. TRPC6 mRNA expression level was increased in IBD patients and DSS-treated mouse colon tissues. DSS-treated TRPC6-KO mice, but not TRPC3-KO mice, showed severe weight loss and increased disease activity index compared with DSS-treated WT mice. The mRNA abundances of antioxidant proteins were basically increased in TRPC6-KO colon, with changes in gut microbiota profiles. Treatment with TRPC6 activator prevented the DSS-induced colitis progression accompanied by increasing Zn2+ concentration. We suggest that TRPC6-mediated Zn2+ influx activity plays a key role in stress resistance against IBD, providing a new strategy for treating colitis.

Transient Receptor Potential Vanilloid 4 (TRPV4) channel is a non-selective cation channel, mostly permeable to calcium (Ca2+), which participates to intracellular Ca2+ handling in cardiac cells. It is widely expressed through the body and is activated by a large spectrum of physico-chemical stimuli, conferring it a role in a variety of sensorial and physiological functions. With-in the cardiovascular system, TRPV4 expression was reported in cardiomyocytes, endothelial cells (ECs) and smooth muscle cells (SMCs) where it modulates mitochondrial activity, Ca2+ ho-meostasis, cardiomyocytes electrical activity and contractility, cardiac embryonic development, fibroblast proliferation but also vascular permeability, dilatation and constriction. On the other hand, TRPV4 channels participate in several cardiac pathological processes such as development of cardiac fibrosis, hypertrophy, ischemia-reperfusion injuries, heart failure, myocardial infarc-tion, and arrhythmia. In this manuscript, we provide an overview of TRPV4 channel implica-tions in cardiac physiology and discuss the potential of TRPV4 channel as therapeutic target against cardiovascular diseases.

Next generation in-to-out-of body biomedical applications have adopted optical wireless communications (OWCs). However, by delving into the published literature, a gap is recognised in modeling the in-to-out-of channel, since most published contributions neglect the particularities of different type of tissues. Towards this direction, in this paper we present a novel pathloss and scattering models for in-to-out-of OWC links. Specifically, we derive extract analytical expressions that accurately describe the absorption of the five main tissues’ constituents, namely fat, water, melanin, oxygenated and de-oxygenated blood. Moreover, we formulate a model for the calculation of the absorption coefficient of any generic biological tissue. Next, by incorporating the impact of scattering in the aforementioned model we formulate the complete pathloss model. The developed theoretical framework is verified by means of comparisons between the estimated pathloss and experimental measurements from independent research works. Finally, we illustrate the accuracy of the theoretical framework in estimating the optical properties of any generic tissue based on its constitution. The extracted channel model is capable of boosting the design of optimized communication protocols for a plethora of biomedical applications.

The Casiquiare River is a natural channel that connects two of the greatest rivers in the world, the Orinoco and the Amazon in the South American Continent. The aim of this paper is to present a review and synthesis of the hydrological and sedimentological knowledge of the Casiquiare River, including the first hydro-sedimentary balance of the Casiquiare fluvial system conducted at the bifurcation and at the mouth on September 9-12, 2000, during the expedition ‘Humboldt-Amazonia 2000’. Bathymetric, flow discharge and physico-chemical measurements were made at the inlet and at the outlet of the Casiquiare Channel. The main conclusions of this study indicate that Casiquiare is taking a significant proportion of flow (20 to 30%) from the Upper Orinoco basin to the Amazon basin. Throughout its 356 km-course, this chameleon channel undergoes significant morphological, hydrological and bio-geochemical variations between the inlet and the outlet, whose most visible witnesses are the increase in its width (3 to 4 times), flow (7 to 9 times) and its change in water color (white to black water), under the influence of tributaries coming from vast forest plains.

The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, the problems such as knowledge loss still occur during the process of transfer learning to a new target dataset. To solve these problems, there are various regularization methods based on knowledge distillation techniques. In this paper, we propose a transfer learning regularization method based on feature map alignment used in the field of knowledge distillation. The proposed method is composed of two attention based submodules: self-pixel attention (SPA) and global channel attention (GCA). The self-pixel attention submodule utilizes both the feature maps of the source and target models, so that it provides an opportunity to jointly consider the features of the target and the knowledge of the source. The global channel attention submodule determines the importance of channels through all layers, unlike the existing methods that calculate these only within a single layer. Accordingly, transfer learning regularization is performed by considering both the internal of each single layer and the depth of the entire layer. Consequently, the proposed method using both of these submodules showed overall improved classification accuracy than the existing methods in classification experiments on commonly used dataset.

This paper addresses the problem of low accuracy of conventional channel estimation and signal detection in complex fields like mines; and the problem of replacing the deep receiver at the receiver side with a deep neural network requires a large amount of data for training and slow training time. Combining the receiver side with traditional communication knowledge, a channel estimation and signal detection method based on the joint ReEsNet and SDRNet under the mine is proposed. The detailed step is to use ReEsNet network for interpolation of conventional channel estimation. Then, on the basis of ReEsNet, the output data is channel equalized and pilots removed, and the previously processed data is input to SDRNet to perform signal recovery. In the complex mine environment, the MSE values are 2e-2 for LS, 6e-3 for MMSE, and 8.44e-5 for ReEsNet at the 1500th sample data for interpolation compared to the conventional channel estimation. the MSE of ReEsNet is the smallest and is closer to the real channel estimation. Compared with the proposed joint channel estimation and signal detection methods, fewer trainable parameters reduce 97% of the parameters compared to CCRNet and 84% of the parameters compared to CNNNet; their detection results, however, are not very different.

KV channel-interacting proteins (KChIPs) belong to a family of Ca2+-binding EF-hand proteins that are able to bind to the N-terminus of the KV4 channel α-subunits. As the auxiliary subunit, KChIPs are critically involved in regulating the amplitude and gating properties of KV4 channels by modulating their cell surface trafficking, voltage-dependent activation, inactivation kinetics, and recovery rate from inactivation. IKs, ICa,L, and INa can also be regulated by KChIPs. KChIPs are predominantly expressed in the brain and heart, where they contribute to the maintenance of the excitability of neurons and cardiomyocytes by modulating the KV4 currents. Interestingly, all KChIPs can act as transcription factors to control the expression of genes involved in pain, memory, and circadian regulation. Altered expression of KChIPs has been implicated in the pathogenesis of many diseases, such as arrhythmia, heart failure, Alzheimer's disease, etc. In this review, we summarize the research progress of KChIPs in their structural properties, physiological functions, and pathological roles in disease progression, and provide an overview of the therapeutic potential of KChIPs as pharmacological targets for associated disorders.

After cut by basecutter, sugarcane stalks are carried to a chopper by the basecutter and the feeding/conveying rollers train of harvester. However, the conventional feeding/conveying channels of sugarcane chopper harvesters, when harvesting seriously lodged sugarcane, have numerous drawbacks, including easy clogging, and poor adaptability. In order to resolve these problems, a novel design and layout method of feeding/conveying and chopping channel are proposed in the paper. And a chopper centrally-mounted channel is designed for a sugarcane chopper harvester as well, which has been implemented in the HN4GDL-91 harvester. The proposed channel consists of a feeding/conveying rollers train, a chopper, and a billet conveying device. By mounting the chopper between the feeding/conveying rollers train and the billets conveying device, the harvester can avoid the chopper chopping back the billets, thus improving the harvest quality. The feeding/conveying rollers train is designed with only a pair of feeding rollers and a pair of conveying rollers, achieving a short channel path. Moreover, the structure of the basecutter and the mechanism carrying stalks to feeding rollers are studied via theoretical analysis and tests. The relationship between the basecutter and the chopper centrally-mounted channel is also conducted in systematic analysis. The results of the theoretical analysis show that the inclination angle of the disc of basecutter should be 2° greater than the angle of the feeding conveying channel (both angles are from the horizontal plane). The inclination angle of the disc of basecutter should go from 10° to 15°. A field experiment with four factors and three levels indicates that the field loss rate is 2.45%, the feed quantity is 16.66 kg/s, and the qualified rate of the billets is 98.02%, while the number of sugarcane stalks in a clump, the ground speed of harvester the rotational speed of the chopper, and the rotational speed of feeding/conveying roller are 10, 1.09 m/s, 390 r/min, and 230 r/min, respectively. It is concluded that the proposed chopper centrally-mounted channel could improve the conveying and harvesting performance significantly.

EMG analysis is becoming increasingly important in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

Deep convolutional neural network (CNN) has shown their great advantages in the single image deraining task. However, most existing CNN-based single image deraining methods still suffer from residual rain streaks and details lost. In this paper, we propose a deep neural network including the Multi-scale feature extraction module and the channel attention module, which are embed in the feature extraction sub-network and the rain removal sub-network respectively. In the feature extraction sub-network, the Multi-scale feature extraction module is constructed by a Multi-layer Laplacian pyramid, and is then integrated multi-scale feature maps by a feature fusion module. In the rain removal sub-network, the channel attention module, which assigns different weights to the different channels, is introduced for preserving image details. Experimental results on visually and quantitatively comparison demonstrate that the proposed method performs favorably against other state-of-the-art approaches

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.

During the last years the desire and the need for green buildings have been increased. The rise of energy cost, the higher transportation expenses and the international challenges along with the climate change are related with the green buildings market but are also affecting the cost of construction. The aim of this research is to discover and confirm the increased request of green properties and to investigate whether this is related to a new need or just a desire of the buyers. Moreover, the paper examines the people’s knowledge of greenness and sustainability and their wish to live and work in sustainable buildings. Finally, do the buyers will to pay extra in order to purchase a sustainable property and how sustainability can affect the market value and the construction industry. The participants who took part in this research were people that are living and working in Cyprus. One of the significant outcomes was the fact that people who have the knowledge and awareness related to sustainability are willing to pay extra to purchase green properties.

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..

The paper is an investigation on the impact of financial markets on the volatility of green bonds credit risk component, measured by the option-adjusted spread/swap curve (OAS) of the Global Bloomberg Barclays MSCI Green Bond Index, for both the non and pandemic periods. For these purpose, after observing the dynamic joint correlations between all the variables through a DCC-GARCH, we adopt GARCH(1,1) and EGARCH(1,1) models, putting the OAS as dependent variable. Our main results show that the conditional variance parameters are significant and persistent in both times, testifying the overall impact of the other markets on the OAS. In more detail, we highlight that the gamma in the two EGARCH models is positive: so the “green” credit risk volatility is more sensitive to positive shocks than negative ones. With reference to the conditional mean, we note that if during the non pandemic time only the stock market is significant, during the pandemic also conventional bonds and gold are impacting. To the best of our knowledge this is the first study that analyzes the specific credit risk component of green bond yields: we deem our findings useful to observe the change of green bonds creditworthiness in a complex market context.

The study examined key socio-economic characteristics of smallholder farmers identified for their contribution to market participation. These variables include gender, age, marital status, level of education, household size, additional income, membership of cooperative, herd size and use of ICTs. Using a structured questionnaire, primary data was collected from a total of 129 respondents which was analysed with the Statistical Package for the Social Sciences, and presented using cross-tabulations, percentages and count data. The result indicates that age, additional income, cooperative membership and use of ICTs were important variables which contributed to market participation among respondents. The study also made applicable recommendations as the findings may have relevance for future research, policy and practice for commercializing smallholder farmers.

The Estimation of systemic risk in India is still in its infancy stage. There are several methods which are available but none of the methods are fully compatible to forecast the systemic risk since under different circumstances the factors responsible for the risk differs. In this paper the systemic risk estimation in India being carried out based on spread in daily stock market price(Difference between the bid and ask price of a share) of the top 100 firms in India according to market capitalization for the period of July2007 to March 2016. The results were compared with the Financial Stability Report published by Reserve Bank Of India for the period of March 2010 to June 2016.The results clearly indicates that there exits relationship between market illiquidity represented by spread and risks associated with the Financial System. In most of the cases the Z score (deviation from the mean/Standard Deviation) of the spread has become negative which provides the spread which is farther from the mean, also a good indicator of volatility in market and risk to financial system. It is also seen that the Systemic Risk Survey conducted by Reserve Bank of India which started during October 2011 has supported the results.

In this paper, we focus on the impact of financial liberalization on stability of emerging stock market. We identify crises in a group of Latin American (Argentina, Brazil and Chile) and Asian countries (Philippines, Korea, Taiwan and Thailand) during 1975–2005. This paper aims to apply the methodology of CMAX method. Our results indicate that liberalization triggers more unstable stock market in the short run and generate several crises. Still, liberalization seems to generate more stable financial markets in the long run. Financial liberalization does not increase the frequency of crises in emerging countries and at long-term, crises are less several.

Purpose: The purpose of this study was to evaluate the dosimetric impact of multiple channels compared to a single channel in high dose rate (HDR) vaginal cuff brachytherapy (VCBT). Methods and materials: Sample plans of HDR VCBT from both single and multiple channel cylinders at 2.5, 3.0 and 3.5 cm in diameter were created and analyzed. The doses were prescribed either at the 0.5 cm depth with 5.5 Gy for 4 fractions or at the cylinder surface with 8.8 Gy for 4 frac¬tions, in various treatment lengths ranging from 3 to 8 cm. A 0.5 cm ring target volume called PTV_Eval was contoured for each plan and served as the target volume for dosimetric evaluation. Mean doses and the doses covering 90% (D90), 10% (D10), and 5% (D5) of PTV_Eval were calculated and compared. The same treatment lengths, same prescription regimens, same target volume contouring and same plan evaluation methods were repeated for the similar size single channel and multiple channel cylinders. Results: The mean doses of both regimens with the multi-channel cases (6.3 to 7.1 Gy) were comparable to single channel ones (6.4 and 7.5 Gy). The ranges of D90, D10 and D5 were from 3.654 to 5.04 Gy, 8.44 to 10.08 Gy and 8.84 to 10.86 Gy in the multi-channel cases, but from 5.36 to 5.76 Gy, 7.40 to 9.97 Gy and 8.16 to 11.93 Gy in the single channel cases, respectively. Conclusion: The single channel and multiple-channel cylinder deliver a similar mean dose, but with marked heterogeneity in D90, D5 and D10 for the same prescription dose and target volume. Multi-channel cylinders adequately cover the target volume, but given the heterogeneity, increased treatment time and cost are best suited for asymmetric target volumes.

The purpose of this article is to conduct a bibliometric review of literary production in terms of market interdependence. We investigate the effect of contagion on agricultural commodities and identify the commodities and methods used in the most cited publications over the decades. For this, we used the SCOPUS database, sorting with Rayyan, Excel, and finally, the Bibliometrix/R-project. The results showed that corn, wheat, soybeans, cotton, and sugar are the most studied commodities. There is no uniqueness concerning the methods used to identify interdependence and the contagion effect. Also, it is possible to perceive the growing scientific interest concerning the subject, with a diversity of authors and magazines. However, there is a direction of the and magazines most cited articles for analyzing agricultural commodities and energy, demonstrated a worldwide trend of studies relating natural resources, biofuels, clean energy, and efficiency. In addition, countries like China and the United States stand out in this scientific scenario as the countries that produce the most articles.

The article explores the task of making higher education more profession-oriented. In this context, we consider the technology of structuring and matching professional activities and content of professional education curricula with the help of ontology. This technology employs intelligent analysis of labor market and educational content matching with the aim to organize educational programs and verify professional competences based on their ontological properties. The article also considers development of a professional training cognitive map that can help design the student’s personalized educational trajectory factoring in the given parameters.

The popularity of functional gummies has increased, which is evident from the growing line of functional gummies from almost every nutraceutical companies. Sensory evaluation serves the purpose of determining which brand of functional gummy would capture the largest market share. Texture profile analysis was used to determine the mechanical properties of functional gummies. The brands of functional gummies that came under the scope of this study were denoted as Brand A, B, C, D, E, F and G. Fourier Transform Infrared Spectroscopy was utilised to detect organic material and functional groups in the functional gummies. Texture profile analysis gave valuable insights into the gummies’ mechanical properties which are cohesiveness, springiness, hardness, gumminess, and chewiness. Amongst the gummies that were studied, Brand F gummy has the highest value of cohesiveness of 0.92. Brand A gummy has a high springiness value of 1.0. Brand B gummy possesses the highest value of hardness, gumminess and chewiness of 12 532.2 g, 7617.6 N, and 6256.8 J, respectively. Qualitative sensory evaluation reveals that Brand G gummy has the best aesthetic qualities in terms of colour and appearance. Brand B gummy tastes the best while brand A gummy claims the top spot for gumminess and chewiness. Overall, the respondents in this study preferred brand A gummy over other brands.

China combines green energy and industrial policy in its power market reform with various policy initiatives, including price support scheme for electricity from renewable sources and subsidies in the push for broader use of greener energy. This study focuses on the impacts of power market reform on the stock price volatility of listed power companies: 1) we use the Iterative Cumulative Sums of Squares (ICSS) algorithm to identify structural break points in stock prices; 2) we analyze the characteristics of stock price volatility based on the GARCH model; 3) we report the impact of power regulation on stock price fluctuations based on the Autoregressive Distributed Lag (ARDL) model. The result suggests three structural breaks in China’s power stock price volatility were related to the promulgation of power market reform policies. We find that industrial policies promote the reduction of power stock price fluctuations and its impact on power stock price volatility is consistent in the long run. However, our study also indicates the recent policies related to renewable energy do not have a very significant impact on the power stock market.

The paper deals with the problem of choosing the best O&M strategy for wind power plants. Current maintenance theory considers just production opportunities and minimizes the maintenance costs, but with the liberalization of the electricity market also the electricity price has become an important variable to take into account in the O&M scheduling. Another important variables that is often neglected by the existing maintenance theory is the weather condition. This paper proposes a new strategy that takes into account the electricity price and weather conditions, improves the expected profit of the systems, and reduce the overall maintenance and logistic costs. The maintenance schedule is formalized as an optimization problem where the discounted cumulative profit of a wind generation portfolio in a fixed-time horizon (e.g. two years ahead), subject to the technologically-derived maintenance time constraints is optimized. Both the theoretical and computational aspects of the proposed O&M strategy are discussed. Results show that taking into account market and weather opportunities in the design of the maintenance strategy, it is possible to achieve a more complete scheduling for a given set of wind power plants.

(1) Background: The Gárdos channel (KCNN4) and Piezo1 are the best-known ion channels in the red blood cell (RBC) membrane. Nevertheless, the quantitative electrophysiological behavior of RBCs and its heterogeneity are still not completely understood.; (2) Methods: Here we use state-of-the-art biochemical methods to probe for the abundance of the channels in RBCs. Furthermore, we utilize automated patch-clamp, based on planar chips, to compare the activity of the two channels in reticulocytes and mature RBCs. Besides this characterization, we performed membrane potential measurements to demonstrate the effect of channel activity and interplay on the RBC properties.; (3) Results: Both, Gárdos channel and Piezo1, albeit their average copy number of activatable channels per cell is in the single digit range, can be detected by transcriptome analysis of reticulocytes. Proteomics analysis of RBCs could only detect Piezo1 but not the Gárdos channel. Furthermore, they can be reliably measured in the whole-cell configuration of the patch-clamp method. While for the Gárdos channel the activity is higher in reticulocytes compared to mature RBCs, for Piezo1 the tendency is the opposite. While the interplay between Piezo1 and Gárdos channel cannot be followed using the patch-clamp measurements, it could be proved based on membrane potential measurements in populations of intact RBCs.; (4) Conclusions: We discuss the Gárdos channel and Piezo1 abundance, interdependencies and interactions in the context of their proposed physiological and pathophysiological functions, which are the passing of small constrictions, e.g., in the spleen, and their active participation in blood clot formation and thrombosis.

Data obtained by Direct Numerical Simulations (DNS) of pressure-driven turbulent channel flow are studied in the range 180 ≤ Reτ≤ 10,000. Reynolds number effects on the Mean Velocity Profile (MVP) and second order statistics are analyzed with a view of finding logarithmic behavior in the overlap region or even further from the wall, well in the boundary layer’s outer region. Values of Kármán constant for the MVPs and the Townsend-Perry constants for the streamwise and spanwise fluctuation variances are determined for the Reynolds numbers considered. A data-driven model of the MVP, proposed and validated for zero pressure gradient flow over a flat plate, is employed for pressure-driven channel flow by appropriately adjusting Coles’ strength of the wake function parameter, Π. There is excellent agreement between the analytic model predictions of MVP and the DNS-computed MVP as well as of the Reynolds shear stress profile. The skin friction coefficient Cf is calculated analytically. The agreement between the analytical model predictions and the DNS-based computed discrete values of Cf is excellent.

Our study provides guidelines on how to build long-term customer relationship in the non-contract mechanism context. More specifically, the findings show that special, social, and core benefits influence calculative commitment, and operational and special benefits influence affective commitment. This study also supports that calculative and affective commitment play a crucial role in understanding multi-channel agencies’ loyalty. In sum, this study revealed that calculative and affective commitment can be considered as partial or full mediators in the relationship between RBs (relational benefits) and loyalty. This study not only contributed to the existing SET (social exchange theory) and RBs paradigm but also provided practical implications for food distribution management.

Microcombs provide a potential compact and efficient light source for multi-Terabit-per-second optical superchannels. However, as the bandwidth of these multi-wavelength light sources is increased, this can result in low per-line power. Optical amplifiers can be used to overcome power limitations, but the accompanying spontaneous optical noise can degrade performance in optical systems. To overcome this issue, we propose wideband noise reduction for comb lines using a high-Q microring resonator, whose resonances align with comb lines. When applying the proposed distillation to a superchannel system with 18 Gbaud, 64-QAM sub-channels in a > 10 Tb/s optical superchannel, we find that noise-corrupted comb lines can reduce the optical signal-to-noise ratio required for the comb by ~ 9 dB when used as optical carriers at the transmitter side, and by ~ 12 dB when used as a local oscillator at the receiver side.

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.

In this paper, we investigate the “statics and dynamics” return and volatility spillovers transmission across developed and developing countries. Quoted against the U.S. dollar, we study twenty-three global currencies over 2005 – 2016. Focusing on the spillover index methodology, the generalised VAR framework is employed. Our findings indicate no evidence of bi-directional return and volatility spillovers between developed and developing countries. However, a unidirectional volatility spillover from developed to developing countries is highlighted. Furthermore, our findings document significant bi-directional volatility spillovers within the European region (Eurozone and non-Eurozone currencies) with the British Pound (GBP) and the Euro (EUR) as the most significant transmitters of volatility. The findings reiterate the prominence of volatility spillovers to financial regulators.

As e-commerce services and Internet technology have rapidly developed in recent years, many services and applications integrating these technologies can now be completed online. These commercial activities include online auctions, online ticketing and online payments. The client shops from the store online, andthe store delivers the goods to the client. The goods can be divided into digital products without entities, as well as actual entities. If it is a physical product, the store will deliver the package to the client through itslogistics. However, there have been many cases of switched goods purchased by clients in recent years. Earlier, some scholars proposed a security mechanism with a subliminal channel for E-cash and digital content. Only the sender and the receiver would know that the secret information was hidden in the signature. So the privacy of this subliminal message couldbe ensured. We apply this concept to the logistics environment to design secure logistics architecture with subliminal messages. The client can check the subliminal message of the received package, and know whether the package has been switched by malicious people. In addition, the proposed scheme also applies sensor technology;the client can check the GPS location, the temperature and humidity at any time during the delivery process. So intelligent logisticswouldthereby be achieved. This paper proposes an intelligent and secure package sensoring logistics system based on a subliminal channel. The proposed architecture uses the related mechanisms tosolve the problems of a logistics system, including how to achieve mutual authentication, data integrity, anti-switch package, package location and status tracing, resisting replay attacks, forward and backward secrecy, and non-repudiation issues.

The segmentation and de-noising are basic operations, required in every signal processing and classification system. The classical segmentation and de-noising approaches are time-invariant. Consequently, it results in the post processing of an unnecessary information and causes an increase in the system processing activity and power consumption. In this context, an efficient event-driven segmentation and de-noising technique is proposed. It is founded on the principles of level crossing and activity selection. Therefore, it can adapt its sampling frequency, segmentation window length and position along with the filter order by analyzing the input signal local characteristics. As a result, the computational complexity and the power consumption of the proposed system is reduced compared to the counter ones. The suggested system performance is compared with the classical one. It is done for the case of a multi-channel Electroencephalogram (EEG) signals. Results show a noticeable compression gain with an effective adaptation of the de-noising filters order. It aptitudes a significant computational gain, transmission data rate reduction and power consumption reduction of the proposed technique, compared to the counter ones. It shows that the proposed solution is an attractive candidate to embed in the new generation EEG wearables.

This paper presents the design and implementation of a multichannel bio-impedance spectroscopy system on field programmable gate arrays (FPGA). The proposed system is capable of acquiring multiple signals from multiple bio-impedance sensors, process the data on the FPGA and store the final data in the on-board Memory. The system employs the Digital Automatic Balance Bridge (DABB) method to acquire data from biosensors. The DABB measures initial data of a known impedance to extrapolate the value of the impedance for the device under test. This method offers a simpler design because the balancing of the circuit is done digitally in the FPGA rather than using an external circuit. Calculations of the impedance values for the device under test were done in the processor. The final data is sent to an onboard Flash Memory to be stored for later access. The control unit handles the interfacing and the scheduling between these different modules (Processor, Flash Memory) as well as interfacing to multiple Balance Bridge and multiple biosensors. The system has been simulated successfully and has comparable performance to other FPGA based solutions. The system has a robust design that is capable of handling and interfacing input from multiple biosensors. Data processing and storage is also performed with minimal resources on the FPGA.