Real Production Research Articles

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.

Deep Reinforcement Learning and Discrete Simulation-Based Digital Twin for Cyber–Physical Production Systems

One of the goals of developing and implementing Industry 4.0 solutions is to significantly increase the level of flexibility and autonomy of production systems. It is intended to provide the possibility of self-reconfiguration of systems to create more efficient and adaptive manufacturing processes. Achieving such goals requires the comprehensive integration of digital technologies with real production processes towards the creation of the so-called Cyber–Physical Production Systems (CPPSs). Their architecture is based on physical and cybernetic elements, with a digital twin as the central element of the “cyber” layer. However, for the responses obtained from the cyber layer, to allow for a quick response to changes in the environment of the production system, its virtual counterpart must be supplemented with advanced analytical modules. This paper proposes the method of creating a digital twin production system based on discrete simulation models integrated with deep reinforcement learning (DRL) techniques for CPPSs. Here, the digital twin is the environment with which the reinforcement learning agent communicates to find a strategy for allocating processes to production resources. Asynchronous Advantage Actor–Critic and Proximal Policy Optimization algorithms were selected for this research.

STATNet: One-stage coal-gangue detector based on deep learning algorithm for real industrial application

Coal-gangue object detection has attracted substantial attention because it is the core of realizing vision-based intelligent and green coal separation. However, most existing studies have been focused on laboratory datasets and prioritized model lightweight. This makes the coal-gangue object detection challenging to adapt to the complex and harsh scenes of real production environments. Therefore, our project collected and labeled image datasets of coal and gangue under real production conditions from a coal preparation plant. We then designed a one-stage object model, named STATNet, following the “backbone-neck-head” architecture with the aim of enhancing the detection accuracy under industrial coal preparation scenarios. The proposed model utilizes Swin Transformer as backbone module to extract multi-scale features, improved path augmentation feature pyramid network (iPAFPN) as neck module to enrich feature fusion, and task-aligned head (TAH) as head module to mitigate conflicts and misalignments between classification and localization tasks. Experimental results on a real-world industrial dataset demonstrate that the proposed STATNet model achieves an impressive AP50 of 89.27 %, significantly surpassing several state-of-the-art baseline models by 2.02 % to 5.58 %. Additionally, it exhibits stronger robustness in resisting image corruption and perturbation. These findings demonstrate its promising prospects in practical coal and gangue separation applications.

Fault vibration model driven fault-aware domain generalization framework for bearing fault diagnosis

Deep learning methods can learn effective representations from the data, simplifying the fault diagnosis process and improving accuracy. However, the lack of data presents a significant challenge to effectively implementing fault diagnosis driven by deep learning. Simulation models can generate rich source domain data, which can be combined with domain adaptation methods to realize fault diagnosis tasks under sample scarcity. Nevertheless, there are some problems with existing simulation-driven fault diagnosis frameworks: (1) The choice of parameters for simulation models is usually empirical, which can lead to domain-specific components differences between the simulation data and the actual data. (2) It is difficult for the simulation models to generate the background noise of the real equipment, which will cause fault information to be covered, severely limiting the robustness of the fault diagnosis network. (3) Studies based on domain adaptation methods require a certain amount of target domain data for training, which is difficult to meet in real industrial production. To address these issues, this paper proposes a fault-aware domain generalization bearing fault diagnosis framework driven by fault vibration model, an analytical simulation model. Firstly, a spectral background estimation algorithm based on adaptive clutter separation (ACS) and maximum spectral envelope (MSE), termed ACS-MSE, is designed to estimate the potential resonance frequencies in the real system, guiding the parameters setting of the fault diagnosis vibration model and ensuring consistency between the simulated data and the actual data in the label space. Secondly, the normal actual signal is combined with the fault simulation signal to create a mixed signal that considers real background noise, and a fault-aware autoencoder is developed to extract the fault-related impact components covered by the background noise. Finally, a multi resonance frequencies domain generalization fault diagnosis network based on contrastive learning is designed to extract generalized fault features crossing different resonance frequencies and realize diagnosis tasks without real fault data. Experimental results on an open dataset and a custom dataset showed that the proposed method achieves higher accuracy compared to some advanced domain generalization and even domain adaptation methods. Ablation studies verify the importance and irreplaceability of the individual components of the proposed method.

Empirical comparison of assembly complexity assessment approaches in manufacturing

AbstractInvestigating assembly complexity is a continuing concern within the manufacturing field, as it may impact the quality and costs of products. A growing body of literature is aimed at developing novel and reliable methods to assess assembly complexity. The first aim of this paper is to provide an extensive literature review, identifying the main approaches to this problem. Three main approaches emerged: product-centred approach, information-centred approach and system-centred approach. A second goal is to conduct a comparative analysis of these approaches, highlighting their advantages and limitations and providing guidance for process and product designers seeking to control assembly complexity. For each approach, a representative method was chosen, analysed in detail, and applied to five different real products. As concluding findings it was observed that product-centred approaches provide a more objective analysis, focusing strictly on product features. On the other hand, information-centred approaches focus on quantity and variety of information handled by assembly operators, thus proposing a valuable link between complexity and effort required. Finally, system-centred approaches provide a holistic assessment of assembly complexity which is typically only qualitative.

Opportunities to reduce environmental burden by recycling fabric waste in a woollen fabric company

PurposeWool fiber is accepted as one of the natural and renewable sources and has been used in the apparel and textile industry since ancient times. However, wool fiber has the highest global warming potential value among conventional fibres due to its high land use and high methane gas generation. This study aimed to recycle the wool fabric wastes and also to create a mini eco-collection by using the produced yarns.Design/methodology/approachThis manuscript aimed to evaluate the fabric wastes of a woolen fabric producer company. Fabric wastes were opened with two different opening systems and fiber properties were determined. First, conventional ring yarns were produced in the company’s own spinning mill by mixing the opened fibres with the long fiber wastes of the company. In addition, opening wastes were mixed with different fibres (polyester, long wool waste, and Tencel fibres) between 25% and 70% in the short-staple yarn spinning mill and used in the production of conventional ring and OE-rotor yarns. Most of the yarns contained waste fibres at 50%. Recycled and virgin yarns were used as a weft and warp yarn and a total of 270 woven fabric samples were obtained and fabric properties were examined. Also, a fabric collection was created. A life cycle assessment (LCA) was made for one of the selected yarns.FindingsAt the end of the study, it was determined that it was possible to produce yarn and fabric samples from fiber blends containing high waste fiber ratios beyond 50%. All the woven fabric samples produced from conventional ring and OE-rotor yarns gave higher breaking, tearing and stitch slip strength values in the weft and warp direction than limit quality values of the company. In addition, abrasion resistance and WIRA steam stability properties of the fabric samples were also sufficient. Environmental analysis of the recycling of the wastes showed a possible decrease of about 9940034.3 kg CO2e per year in the global warming potential. In addition, fiber raw material expenses reduced yarn production cost about 50% in case of opened fabric waste usage. However, due to insufficient pilling resistance results, it was decided to evaluate the woven fabrics for the product groups such as shawls and blankets, where pilling resistance is less sought.Originality/valueThe original aspects of the article can be summarized under two headings. First, there are limited studies on the evaluation of wool wastes compared to cotton and polyester fibres and the number of samples examined was limited. However, this study was quite comprehensive in terms of opening type (rag and tearing), spinning systems (long and short spinning processes), fiber blends (waste 100% and blends with polyester, long wool waste and Tencel fibres) and yarn counts (coarser and finer). Recycled and virgin yarns were used as a weft and warp yarn and a total of 270 woven fabric samples were obtained using different colour combinations and weave types. All processes from fabric waste to product production were followed and evaluated. Life cycle assessment (LCA) and cost analysis was also done. The second unique aspect is that the problem of a real wool company was handled by taking the waste of the woolen company and a collection was created for the customer group of the company. Production was made under real production conditions. Therefore, this study will provide important findings to the research field about recycling, sustainability etc.

P‐80: 4K Real 240Hz Technology in Large LCD‐TFT TVs

This thesis presents the innovative 4K Real 240Hz technology independently developed by BOE in the realm of large‐scale TFT‐LCD televisions. Addressing the challenges posed by high refresh rates, including motion trailing, reduced charging efficiency, and elevated temperatures in Periphery Line Glass (PLG), we employed advanced technologies such as 2Frame OD (Over Driving), MLC (Multi‐Level Clock), and GPM (Gate Pulse Modulation). Furthermore, we utilized premium materials, including Low GAP Fast Response Liquid Crystals and the ION enhancement process, to craft 4K Real 240Hz products that offer exceptional image quality and swift responsiveness.

Do Wages Follow Productivity? Evidence from European Countries

Abstract Economic theory assumes that factors are paid according to their marginal productivity, however the empirical validity of this hypothesis has been questioned in some of the literature. We use data from Eurostat to examine the changes of labour remuneration and labour productivity during the period 2004-2020 in a panel of European countries. The results from the panel regression analysis confirm that in general there is a strong link between labour productivity and the compensation of employees. However, exploratory analysis shows that the sample is characterized with internal heterogeneity. Growth is higher in Central and Eastern Europe and for some countries from the region there is decoupling of productivity and labour remuneration. The latter has seen stronger growth, with the exception of Romania, where in real terms productivity has been growing faster than wages. Possible explanations of the decoupling are catching up and convergence, integration in the world economy, factor mobility, difference in labour market institutions and excessive labour demand, but the present research is inconclusive.

Degree of hydrolysis of chicken versus plant-based chicken analogues: An in vitro digestion comparison

The adoption of plant-based meat analogues is increasing as an alternative to real meat products among consumers because they offer a more ecologically friendly and sustainable source of protein while also alleviating the ethical concerns related to livestock rearing and slaughter (1). However, there are concerns regarding plant-based meat analogues in terms of its nutritional quality, particularly their protein digestibility. This study aims to compared between chicken and plant-based chicken analogues in terms of nutritional composition and degree of protein hydrolysis.Proximate analyses were performed for raw and cooked samples to assess protein, fat, and energy concentrations in two chicken samples (breast and thigh) and four commercial plant-based chicken (P-C1[Wheat Protein 37%, Pea Protein 10%], P-C2 [ Soya Protein 63%], P-C3 [ Soya and Wheat Protein 83%], and P-C4 [Soya Protein 30%, Pea Protein 2%]). As a first step, the proximate analyses were assessed for the averaged samples, then the product-cooking interactions was assessed using a one-way ANOVA followed by Tukey test (p <0.05). In vitro digestion was performed following the INFOGEST harmonised static in vitro digestion model (2) for cooked samples. After digestion, o- Phthaldialdehyde (OPA) assay was carried out to measure the degree of protein hydrolysis for each sample, and two-way ANOVA test was performed.Protein content of chicken was higher compared with the plant-based chicken, whereas fat content and energy concentrations were higher in plant-based chicken. The protein content of chicken was higher for raw and cooked samples (raw: 19.8 ± 0.38 g/100 g; cooked: 30.55 ± 4 g/100g), compared with plant-based chicken (raw: 13.8 ± 5.3 g/100 g; cooked: 23.4 ± 4.5 g/100g). Plant-based chicken have a higher fat content and energy concentrations for raw and cooked samples (raw:6.52 ± 1.5 g/100 g; cooked: 9.6 ± 3.17 g/100g) and (raw:189.4 ± 28.1 g/100 g; cooked: 291.3 ± 48.1 g/100g) respectively; compared to chicken fat content (raw:4.6 ± 2.7 g/100 g; cooked: 6.1 ± 4 g/100g) and energy concentration (raw:150.9 ± 25.4 g/100 g; cooked: 228.3 ± 20.7 g/100g). The product-cooking interactions showed a significant increase (P ≤0.0001) in the protein content (raw: 15.6 ± 2.3g/100g to 19.8 ± 0.74 g/100g; cooked: 21.4 ± 0.55 g/100g to 33.06 ± 0.71 g/100g), and fat content P<0.001 (raw: 2.7 ± 0.06 g/100g to 8.3 ± 0.27 g/100g; cooked: 3.29 ± 0.19 g/100g to 13.22 ± 0.33 g/100g) in both chicken and plant-based chicken samples. No significant product-cooking interactions on energy content was found. The results from a two-way ANOVA test of the OPA and the degree of hydrolysis analyses demonstrated a significant increase in the degree of hydrolysis of chicken samples compared with plant-based chicken (P<0.0001).The degree of hydrolysis and digestibility of chicken and chicken analogues was influenced by protein type, nutrient composition, and processing. These findings would provide substantial information for the improvement of plant-based chicken products with enhanced nutritional profiles. This work will be extended to investigate the availability and digestibility of individual amino acids.

Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network

The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles, and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles. By fusing band combination optimization with deep learning, this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line. By applying hyperspectral imaging and a one-dimensional deep learning algorithm, we detect and classify impurities in seed cotton after harvest. The main categories detected include pure cotton, conveyor belt, film covering seed cotton, and film adhered to the conveyor belt. The proposed method achieves an impurity detection rate of 99.698%. To further ensure the feasibility and practical application potential of this strategy, we compare our results against existing mainstream methods. In addition, the model shows excellent recognition performance on pseudo-color images of real samples. With a processing time of 11.764 μs per pixel from experimental data, it shows a much improved speed requirement while maintaining the accuracy of real production lines. This strategy provides an accurate and efficient method for removing impurities during cotton processing.

The analytical model of crimping springback for large-diameter longitudinal welded pipe

The crimping technique is widely used in the manufacturing process of large-diameter longitudinal welded pipe. In the bending process, the springback is the critical problem affecting the shape, which is the key index of forming quality. So, crimping faces more challenges in accurate springback prediction for difficult-to-form metals such as pipeline steels and non-circular nature of the punch. This paper has established an analytical model of crimping springback for the longitudinal welded pipe. Firstly, the crimping process mathematical model which considered the impact of elastic modulus attenuation on springback calculation during plastic deformation is established, and the cross-section bending moment is calculated based on Hill’s bending theory. Secondly, a semi-analytical and semi-numerical method for calculating the crimping shape is presented. Finally, the comparison of the crimping shape of the real product shows that the calculation accuracy and efficiency of this method can meet the requirements of engineering applications. The relative error of the springback angle calculated by the analytical model is −5.84%, and the absolute error is 0.3°. This method can accurately predict the springback of longitudinal welded pipe after crimping and provide a reference for crimping process optimization.

Navigating Economic Ripples: How Real Exchange Rate Volatility Shapes Real Gross Domestic Product with Evidence from Tanzania

This article aimed at examining the effects of the real exchange rate on real gross domestic product (GDP) in Tanzania using a data set spanning from 1964 to 2019. The Gregory–Hansen test was employed to check the long-run relationship between the real exchange rate and real economic growth. Structural vector autoregressive and vector error correction models were employed to establish the effects of the real exchange rate on real economic growth, while fully modified least-squares and Granger causality tests were used for robustness check and determination of the direction of causality between variables, respectively. Findings show a long-run relationship between the exchange rate and economic growth. In addition, when the Tanzanian shilling appreciates, it significantly and negatively affects real GDP. A 1% appreciation leads to a 0.13% decrease in GDP in the long run and 0.01% in the short run. Merchandise exports had a positive effect, while inflation had a negative effect both in the long and short run. The impulse response function shows that the real exchange rate affects the real GDP through the merchandise export channel and inflation. This implies that the appreciation of the Tanzanian shilling makes merchandise exports more expensive, reduces foreign exchange inflows and thus reduces actual output. Stabilising the exchange rate, targeting inflation, improving productive capacity and promoting export-led growth are important.

Modelling The Relationship Between Cyclical Output and Cyclical Unemployment: Empirical Validation of Okun’s Law - The Case of Morocco.

The aim of this study is to analyse the dynamic relationship between economic growth and unemployment in Morocco. By examining the validity and asymmetry of Okun's law. Based on quarterly data covering the period from 2000 to 2022, we adopt a dynamic modelling of Okun's law, with the aim of estimating its elasticities during recessions and expansions using an ARDL model. The results of the study show the existence of an inverse relationship between GDP and unemployment with a strong persistence of the unemployment rate. The asymmetry study showed that unemployment reacts more strongly to negative deviations in real GDP than to positive deviations. In terms of policy implications, the study recommends the need to reform the structure of the Moroccan economy so as to allow the integration of a greater number of workers, while stimulating economic growth through the promotion of labour-intensive investment. Involving the Moroccan economy in a process of structural transformation is a crucial element, and investment in areas such as innovation, R&D or digital technology can help to increase real gross domestic product and consequently reduce the unemployment rate.

Gamification Approach for Interactive Learning of Lean Manufacturing Activities

The gamification approach is popular method in education as it has been widely utilized in many fields. The project focuses on designing a game for interactive learning of lean manufacturing activities to create an alternative learning method that would gain interest and efficiency in learning. Integration of gamification approach and interactive learning method was used to illustrate a virtual lean manufacturing education and training by means of users’ interaction towards provided interactive learning materials and participation in playing the designed game. Input of the project which is learning materials was developed by analysing numerous articles regarding common lean manufacturing tools and practices. Interactive elements such as animation, sound effects, lecture voice, and videos were applied in each learning material to deliver an adequate understanding of each topic. Construction of the game platform is being developed using ActivePresenter software which includes simulation videos of the production line using FlexSim software to visualize a real production floor which provides a clearer picture of the production system for users. The game should include a basic assessment of lean manufacturing practices by considering its complexity and difficulty level for users with inadequate or does not have experience in the field. The outcome of the project is expected to successfully design a basic game platform for lean manufacturing education by means of interactive learning. Users will be able to learn lean manufacturing systems using the designed interactive learning materials included in the games and self-assess themselves as the game publishes the correct and best answer option in case they answer wrongly.