Real Production Research Articles

The Rheology of Polyether Ether Ketone Concentrated Suspensions for Powder Molding and 3D Printing.

The main goal of the work was to use rheological methods for assessing the properties of a composition based on polyether ether ketone (PEEK) to determine the concentration limits of the polymer in the composition and select the optimal content of this composition for powder molding. The rheological properties of highly filled suspensions based on PEEK and paraffin, as well as in paraffin-polyethylene mixtures at various component ratios, were studied. These materials are designed for powder injection molding and 3D printing. Suspensions with a PEEK powder content above 50% are not capable of flow and, with increasing pressure, slide along the surface of the channel. For compositions with a higher content (60 and 70 vol.%) PEEK, independence of the storage modulus from frequency is observed, which is typical for solids and confirms the assignment of such suspensions to elastic-plastic media. The introduction of high-density polyethylene into the composition helps improve the technological properties of suspensions, expanding the range of fluidity, although it leads to an increase in viscosity. In suspensions with a mixed composition of the liquid phase, with increasing temperature, a decrease in the storage modulus is observed at 120 °C and, on the contrary, an increase at 180 °C. The latter may be a consequence of the evaporation of paraffin and the softening of PEEK due to the approach to the glass transition temperature of the polymer. Suspensions with 40% PEEK content have an optimal set of rheological properties for powder injection molding. A 3D printing filament was also obtained from a composition with 40% PEEK, which had good technological properties for FDM 3D printing. Products of satisfactory quality from suspensions with 50% PEEK can be produced by powder injection molding, but not by 3D printing. The selected compositions were used to obtain real PEEK products for practical applications.

Assessing Macroeconomic Tail Risk

Abstract Real gross domestic product and industrial production in the United States display substantial asymmetry and tail risk. Is this asymmetry driven by a specific structural shock? Our empirical approach, based on quantile regressions and local projections, suggests otherwise. We find that the tenth percentile of predictive growth distributions responds between three and six times more than the median to monetary policy shocks, financial shocks, uncertainty shocks, and oil price shocks, indicating a common transmission mechanism. We present two data-generating processes that are capable of matching this finding: a threshold vector autoregression model and a non-linear equilibrium model.

Enhancing prior-knowledge-based control with deep attention neural network for outlet moisture content of cut-tobacco

Monitoring and controlling the drying process is crucial, particularly for rotary dryers, to ensure optimal quality and efficiency. However, due to the process’s nonlinear behavior, delays, and input disturbances, achieving this can be quite challenging. Previous research has not adequately explored online forecasting for rotary dryer control in real production settings. To bridge this gap, our study focuses on tobacco drying in an actual plant and presents a novel online prediction model for moisture content. We conducted comparative experiments to validate the model’s practical application in production settings and demonstrate its effectiveness in enhancing drying control. Our approach, called MCTFormer, has proven to be scalable to high-dimensional problems. The results of our study indicate a 45% improvement in product quality through accurate prediction of outlet moisture content, better control over moisture levels, and reduced energy consumption.

Model selection for long-term load forecasting under uncertainty

Purpose The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors. Design/methodology/approach The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases. Findings From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment. Research limitations/implications These findings can help utilities to align model selection strategies with their risk tolerance. Originality/value To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

Impact of lipid droplet characteristics on the rheology of plant protein emulsion gels: Droplet size, concentration, and interfacial properties

Plant-based meat analogs are being developed to address environmental, sustainability, health, and animal welfare concerns associated with real meat products. However, it is challenging to mimic the desirable physicochemical, functional, and sensory properties of real meat products using plant-based ingredients. Emulsion gels consisting of lipid droplets embedded in biopolymer matrices are commonly used to create products with appearances, textures, and sensory attributes like meat products. In this study, the impact of soybean oil droplet characteristics (concentration, size, and charge) on the physicochemical properties of potato protein gels was studied. The oil droplets were either coated by a non-ionic surfactant (Tween 20) or a plant protein (patatin) to obtain different surface properties. The introduction of the oil droplets caused the protein gels to change from mauve to off-white, which was attributed to increased light scattering. Increasing the oil droplet concentration in the emulsion gels decreased their shear modulus and Young’s modulus, which was mainly attributed to the fact that the oil droplets were less rigid than the surrounding protein network. Moreover, increasing the oil droplet size made this effect more pronounced, which was attributed to their greater deformability. Competitive adsorption of proteins and surfactants at the oi-water interface in the Tween emulsion promoted emulsion instability. This research highlights the complexity of the interactions between oil droplets and protein networks in emulsion gels. These insights are important for the utilization of emulsion gels in the formulation of plant-based foods with improved quality attributes.

Effect of agriculture on economic growth in Algeria during the period 1991–2022

Purpose. This paper aimed to study the impact of both agricultural value-added and agricultural labour on the economic growth of Algeria (represented by the real Gross Domestic Product – GDP) during the period 1991–2022, using the co-integration method according to the bounds approach known as the Autoregressive Distributed Lag modeling (ARDL). Results. The study revealed a relationship between real agricultural value-added and real GDP in both the short and long terms. However, the impact ratio in the long term is somewhat weak, reflecting the weakness of agricultural production in contributing to total output formation and economic growth. Additionally, agricultural employment only affects real GDP in the long term. The error correction coefficient is negative and significant, with the speed of adjustment towards equilibrium in the long-term reaching approximately 52.19 % (about one year and 10 months). Scientific novelty. This study highlights the great importance of the agricultural sector as one of the solutions to economic diversification in order to achieve economic development in Algeria – a country that has suffered and continues to suffer from economic dependence on the hydrocarbon industry. However, this study stands out from previous research by addressing various programs and reforms implemented in the agricultural sector during the study period. Moreover, it measures the impact of the agricultural sector on economic growth by creating a logarithmic model with two main variables: agricultural value-added and agricultural employment, both of which effectively contribute to increasing agricultural production and thus economic growth. Practical value. This study emphasises the need for the government to prioritise and improve agriculture to move towards smart agriculture and reclaim wastelands. At the same time, the government must allocate new programmes to develop the agricultural sector and activate workers to increase agricultural productivity in order to achieve food security and transform the country towards a more prosperous and sustainable future.

A Study on the Priorities of Investment Determinants of Real Estate REITs Products

A Study on the Priorities of Investment Determinants of Real Estate REITs Products

The Determinants of Profitability of Western Balkan Countries Commercial Banks: A panel Data Approach

Commercial banks are considered to be important contributors to the country’s financial system and economic development. The aim of this study was to examine the impact of various factors on commercial banks’ profitability. Moreover, the study compares and analyzes the main determinants of the banks’ profitability in Western Balkan (WB) countries. The sample size includes all commercial banks in WB countries, covering the period 2010–2020. The collected data provides information about the two main measurements used to determine a bank’s performance: 1) return on assets (ROA) and 2) return on equity (ROE), which in our study were used as indicators varying from non-performing loans, the number of banks, real gross domestic product growth, the unemployment rate, and consumer price inflation (percent, period average). The estimated regression models with panel data revealed that real GDP growth, the unemployment rate, and inflation are positively correlated and significantly determine the bank’s performance. While two other factors (non-performing loans and the number of banks) were negatively associated with the performance of banks. Since the determining factors of external and internal profitability turn out to be almost the same for all the countries of the Western Balkans, during the observed period of 2010– 2020, the number of cases of non-performing loans was much higher in other countries of the region than in Kosovo. Once again, the banking system in Kosovo, unlike other countries, has scored ROE performance indicators that are almost three times higher than the average of the countries in the region. On the other hand, Kosovo has the highest unemployment rate in the region. While the weakest ROE performance was in Montenegro, which was improving year after year. Our findings have several important implications for policymakers and regulators, especially in Kosovo.

Determination of multi-residue pesticides in dairy products using single-step emulsification/demulsification clean-up strategy combined with low-pressure gas chromatography-tandem mass spectrometry

In this study, a simple, sensitive, and rapid method was developed for the simultaneous determination of 99 kinds of pesticides in fatty milk samples. This novel emulsification-demulsification clean-up approach, coupled with an automatic demulsification-dehydration cartridge, allowed rapid single-step clean-up operation and high throughput. It also achieved effective and selective removal of lipids. The analysis was performed using low-pressure gas chromatography-tandem mass spectrometry (LPGC–MS/MS). Based on the optimal conditions, the targeted pesticides showed good linearity in the range of 5–250 μg/kg, with recoveries of 70–120% at spiking levels of 5, 10, and 20 μg/kg in cow milk, goat milk, and almond milk, respectively. The limit of quantification for most pesticides was 5 μg/kg, and the RSDs were lower than 20%. Analysis of real dairy products obtained from local markets revealed a potential risk in plant-derived almond milk, but no significant risks were found for cow and goat milk.

Ultra-wideband terahertz thermal absorber with doped silicon double-layer square groove arrays

This paper introduces an ultra-wideband terahertz thermal absorber, composed of double-layer square groove doped silicon cubes, exhibiting polarization independence. Within the frequency range of 0.9–9.7 THz, the absorption efficiency exceeds 95 %, and within an incident angle range of 0–67°, the absorption rate still maintains above 90 % in the frequency range of 1.4–9.7 THz. Through finite element method (FEM), this study clearly demonstrates the distribution of electromagnetic energy within the thermal absorber and thoroughly elucidates its notable absorption efficiency via impedance matching theory. Compared to existing technologies, the proposed double-layer square groove thermal absorber shows superior performance in terms of absorption efficiency and bandwidth. This ultra-broadband terahertz thermal absorber is highly applicable in areas including biomedical imaging, thermal imaging, and wireless communication. Considering its excellent manufacturing tolerance, this thermal absorber is significant for application in real industrial production.

Data-fusion for in-situ monitoring and molten state identification during LPBF of NiCoCr medium-entropy alloy

Laser powder bed fusion (LPBF) is an additive manufacturing technology with high practical value. In order to improve the quality of the fabricated parts, process monitoring has become a crucial solution, offering the potential to ensure manufacturing stability and repeatability. However, a cardinal challenge involves discerning a precise correlation between process characteristics and potential defects. This paper elucidates the integration of an off-axis vision monitoring mechanism via a high-speed camera focused on capturing the single-track melting phenomenon. An innovative image processing method was devised to segment the plume and spatters, while Kalman filter was employed for multi-object tracking of the spatters. The features of both the plume and spatters were extracted, and their relationship with molten states was investigated. Finally, the PSO-XGBoost algorithm was utilized to identify five molten states, achieving an accuracy of 92.16%. The novelty of this approach resides in its unique combination of plume characteristics, spatter features, and computationally efficient machine learning models, which collectively address the challenge of limited field of view prevalent in real production scenarios, thereby enhancing process monitoring efficacy. Relative to existing methodologies, the proposed PSO-XGBoost approach offers heightened accuracy, convenience, and appropriateness for the monitoring of the LPBF process. This work provides an effective and novel approach to monitor the LPBF process and evaluate the part fabrication quality for complex and changeable working conditions.

Reduce Product Surface Quality Risks by Adjusting Processing Sequence: A Hot Rolling Scheduling Method

The hot rolled strip is a basic industrial product whose surface quality is of utmost importance. The condition of hot rolling work rolls that have been worn for a long time is the key factor. However, the traditional scheduling method controls risks to the surface quality by setting fixed rolling length limits and penalty scores, ignoring the wear condition differences caused by various products. This paper addresses this limitation by reconstructing a hot rolling-scheduling model, after developing a model for pre-assessment of the risk to surface quality based on the Weibull failure function, the deformation resistance formula, and real production data from a rolling plant. Additionally, Ant Colony Optimization (referred to as ACO) is employed to implement the scheduling model. The simulation results of the experiments demonstrate that, compared to the original scheduling method, the proposed one significantly reduces the cumulative risk of surface defects on products. This highlights the efficacy of the proposed method in improving scheduling decisions and surface quality of hot rolled strips.

The green hydrogen-water-food nexus: Analysis for Spain

This research presents a novel approach that analyzes the water requirements for hydrogen production associated with a nationally adopted Green Hydrogen strategy. Green Hydrogen production is one of the most relevant technologies for massive renewable energy integration in transport systems, industries, or heating systems. European Union has launched a strategic roadmap for green hydrogen generation and integration in the energy chain. Spain aims to be one of the most important producers and exporters and has approved a Hydrogen Roadmap for next years, but no previous research has focused on the water requirements. This research finds that Spain's Green Hydrogen Roadmap, and a similar European one, is based on electrolyzers' peak capacity, and this causes a large uncertainty in real hydrogen production and associated water requirements. Spain represents a case study that can be a reference worldwide as it suffers from increasing droughts, has considerable tourist pressure, and is one of the most important producers of greens and fruits. This study proposes a methodology to quantify actual hydrogen production and associated water requirements and proves that current large-scale planning based on peak generation will produce a large uncertainty and could directly impact water supply security. One of the most relevant findings is that hydrogen production and water requirements must be included in these strategies to avoid future water supply tensions. An analysis of alternative water supply using desalinated water analyzes its feasibility and the effects on final cost. The outcomes are an example to be applied in similar scenarios worldwide and prove the need for an integrated energy-water-food strategy to deploy green hydrogen systems.

The Impact of Fiscal Policy on Economic Growth in Lao PDR

There has been various studies research on the Impact of the Fiscal Policy on Economic Growth by testing the Wagner’s Law and Keynes theory. This study holds two objectives, the first objective is to review a performance of the implementation government expenditure, and second objective is to test on the government expenditure contributes to the economic growth in the Lao PDR. To reach these two objectives, this study applies two methodologies. The first methodology is descriptive approach to respond for the first objective, and second methodology is bound testing approach and vector error correction approach to respond for second. This study is applied for time series data in terms of annual data from 1981 to 2014 in all models. The source of the data is mostly the website of the Asia Development Bank (ADB) year 2016. The results of the first methodology shows that the performance of the government expenditure continued increase rapidly, especially in 1986, 1997 and policy changed in 2012-2013 in terms of current expenditure for salary and capital expenditure directly affected to real Gross Domestic Products growth. The results of the second methodology shows that the Keynes theory and Wagner’s Law are significant and satisfied for the Lao economy in the long run. It meant that government expenditure has directly contributed RGDP, and vice versa. However, in the short run are not significant and unsatisfied in any lag.

Monthly seller’s bargaining power index constructed using open-source data

Purpose A tangible representation of the housing market hotness, namely, the index derived via Carrilo 2013’s concept, is widely used but difficult to construct, as it requires listing data of a geography which is not only inaccessible but also cumbersome to summarize. The purpose of this paper is to serve as a methodological extension of Carrilo 2013, presenting a process by which the seller’s bargaining power index can be easily constructed using open-source data. Design/methodology/approach The seller’s bargaining power index was first constructed using the open-source data by Zillow Research. Then, results verification approach including visualization, Pearson correlation test, Granger causality test and linear regression were used with the goal to generate empirical evidence and verify if the constructed index conforms with established relationships derived in past work, thereby substantiate accuracy of the constructed index. Findings Monthly seller’s bargaining power index was constructed for US as a whole and 250 metropolitan statistical areas through an automated process. The constructed index conforms with established evidence in terms of seasonality, trend and magnitude. The index also forms positive correlation with building permit/housing value/housing value increase rate, and negative correlation with inventory. It requires the explanatory power of multiple linear features to simulate the parameter. Seller’s bargaining power index at a national level can serve as a general indicator of economic strength, in parallel to number of building permit, housing starts and real gross domestic product. Quantitatively, the housing market of the USA is as “hot” during the COVID-19 pandemic as it was in the years before 2008. A healthy housing market appears to be one in which 75%−80% of the deals are in favor of the seller (take-it-or-leave-it). When said percentage reaches 90%, the market should be considered to be in a “bubble.” Originality/value This paper provides an easy pathway for future studies to simulate housing market hotness, and bring upon the benefit of convenience at low cost and in monthly frequency rather than quarterly or annually.

Impact of exchange rate on economic growth in Nigeria

This study examined the effect of exchange rate dynamics on economic growth in Nigeria from 1985 to 2021. The study utilized secondary data sources from the World Development Indicators (WDI) and the Central Bank of Nigeria (CBN) Statistical Bulletin. It employed real gross domestic product as a measure of economic growth. In addition to the official exchange rate as a proxy for exchange rate dynamics, the study integrated other domestic factors that can affect economic growth, such as trade openness and external reserves. The econometric techniques employed in the study were unit root tests, cointegration, and autoregressive distributed lag (ARDL)/bound techniques. Following the outcome of the bound test, it was reported that a long-run relationship exists between economic growth, exchange rate, trade openness, and external reserves. The long-run ARDL results reveal that the exchange rate had a positive and statistically significant effect on real GDP in Nigeria, with a unit increase in the exchange rate raising the real GDP by 0.3986 units in the long run; trade openness had a positive and not statistically significant impact on real GDP in Nigeria, suggesting that a unit increase in trade openness increases real GDP by 0.0025 units in the long run; and external reserves had a positive and statistically significant impact on real GDP in Nigeria, indicating that a unit increase in external reserves increases real GDP by 0.4007 units in the long run. Based on the results, the study recommends that the government should develop policies that stabilize the exchange rate. This could involve enhancing foreign exchange reserves management, improving the transparency and predictability of foreign exchange interventions, and possibly considering a more flexible exchange rate regime that can adapt to external shocks while maintaining stability. Policies should focus on gradually opening up the economy to international trade through strategic trade agreements that protect key domestic industries while promoting sectors where Nigeria has a competitive advantage. This approach will help mitigate any negative short-term impacts while capitalizing on long-term benefits. Keywords: Exchange Rate, Trade Openness, External Reserves and Real Gross Domestic Product.

Testing the Dynamic Affiliation among Financial inclusion, Energy Poverty, Environmental Degradation and Economic Development in the Selected African countries: Evidence of Panel Data Analysis

Energy is necessary for "cooking, lighting, and other business activities" and plays a vital part in daily living, in order to eradicate poverty in developing nations, affordable energy availability is a must. The study investigates the dynamic affiliation among financial inclusion, energy poverty, environmental degradation and economic development in the selected African countries using panel data analysis. The study used panel data for Nigeria, Ghana, Gabon and Niger from 2005 to 2021. The study employed fixed effect model. The panel unit root tests show that real gross domestic product, automated teller machine, depositors with commercial banks, and energy poverty are integrated of order one, while CO2 emission is integrated of order zero. The fixed effect model shows that automated teller machine, depositors with commercial banks and CO2 emission have positive and statistically significant effect on the real gross domestic product in Nigeria, Ghana, Gabon and Niger. While Energy poverty has a negative, but statistically significant effect on the real gross domestic product in Nigeria, Ghana and Gabon. The study recommends that in order to reduce energy poverty and environmental degradation in the region, African nation particularly Nigeria, Ghana, Gabon, and Niger should make sure that their citizens have access to affordable, readily available electricity. Policymakers and governments in Africa must create inclusive, well-directed financial policies that take into account the disparities in income, governance, and laws among their nations.

Governance and Economic Growth in Selected Sub-Saharan African Countries

The variation in economic growth among nations has brought the issue of economic growth to the forefront. Growth in an economy is key for economic development. When "the country's total earnings increase", its population benefits. Economic growth is significant because it promote the competitiveness of any nation. One of the most striking challenges of economic growth in Sub-Saharan African (SSA) countries is poor governance, decline in the living conditions of the population, with the specter of widespread hunger plaguing people's perceptions. The objective of the study is to examine the effect of governance on economic growth in the selected Sub-Saharan African (SSA) countries. The technique adopted for this study is the Panel Corrected Standard Error Method. This method was employed because it controls for heteroscedasticity. The result shows that there exist a direct relationship between control of corruption, voice and accountability, political stability and absence of violence on real gross domestic product in SSA respectively, while regulatory quality and government effectiveness has a negative effect on real gross domestic product (RGDP) respectively. The study recommends that SSA government as well as stakeholders in the region needs to improve the quality of their regulations and also endeavor to improve on their effectiveness and efficiency.

Bitmap-Based Security Monitoring for Deeply Embedded Systems

Deeply embedded systems powered by microcontrollers are becoming popular with the emergence of Internet of Things (IoT) technology. However, these devices primarily run C/C++ code and are susceptible to memory bugs, which can potentially lead to both control data attacks and non-control data attacks. Existing defense mechanisms (such as control flow integrity (CFI), data flow integrity (DFI) and write integrity testing (WIT), etc.) consume a massive amount of resources, making them less practical in real products. To make it lightweight, we design a bitmap-based allowlist mechanism to unify the storage of the runtime data for protecting both control data and non-control data. The memory requirements are constant and small, regardless of the number of deployed defense mechanisms. We store the allowlist in the TrustZone to ensure its integrity and confidentiality. Meanwhile, we perform an offline analysis to detect potential collisions and make corresponding adjustments when if happens. We have implemented our idea on an ARM Cortex-M based development board. Our evaluation results show a substantial reduction in memory consumption when deploying the proposed CFI and DFI mechanisms, without compromising runtime performance. Specifically, our prototype enforces CFI and DFI at a cost of just 2.09% performance overhead and 32.56% memory overhead on average.

Drivers of international fiscal spillovers

AbstractThis paper investigates the drivers of international fiscal spillovers across 62 countries for the period covering 1970–2021. Using the local projections method at the country‐pair level, fiscal spillovers are estimated as the cumulative response of the real gross domestic product (GDP) growth in spillover destination countries to a unit shock in the real government spending growth in spillover source countries. Pairwise estimation results (for 3782 country pairs) suggest that there are statistically significant fiscal spillovers for 36% of country pairs, whereas this ratio is 49% for country pairs within the euro area. For the median country pair, a unit shock of real government spending growth in the spillover source country results in about 0.09% of the increase in the real GDP growth of the spillover destination country, whereas this fiscal‐spillover estimate goes up to 0.42% when the spillover source is in the euro area, and the spillover destination is an oil producing country. A secondary investigation based on the Heckman selection model is used to identify the drivers of fiscal spillovers across country pairs, where the existence of statistically significant fiscal spillovers is shown to be connected to the proximity between countries. The size of fiscal spillovers is further shown to increase with the initial (as of 1970) country size, trade openness, and government size of the spillover source country, whereas it decreases with the initial country size and trade openness of the spillover destination country. Important policy suggestions follow.