Search Results (44)

This research examines the macroeconomic determinants of inflation in Cambodia with an ARDL cointegration analysis. The results demonstrate a long-run negative association between inflation and exchange rates, tax revenue, and broad money. In the short run, growth in tax revenues dampens inflation, while money supply growth boosts it. Looking at the results, we can infer that expansionary fiscal policy (in particular, tax effort) and prudent monetary policy can control Cambodia’s currency and inflation. Policymakers should take into account the system of relationships among these macroeconomic variables to design such policies, which can cause price stability and long-term growth in the economy. Full article

This study examines the dynamic relationship between the gold markets of India and the United States from 2005 to 2025. Recognising gold’s role as a hedge and safe-haven during market uncertainty, we employ the Autoregressive Distributed Lag (ARDL) model to assess long-term co-integration and apply the Toda–Yamamoto causality test to evaluate directional influences. Additionally, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1) model is applied to examine volatility spillovers. Results reveal no long-term co-integration between the two markets, suggesting they function independently over time. However, unidirectional causality is observed from the U.S. to the Indian gold market, and the GARCH model confirms bidirectional volatility transmission, indicating interconnected short-run dynamics. These findings imply that gold market shocks in one country may affect short-term pricing in the other, but not long-term trends. From a portfolio diversification and risk management perspective, investors may benefit from allocating assets across both markets. This study contributes a novel empirical framework by integrating ARDL, Toda–Yamamoto Granger causality, and GARCH(1, 1) models over a two-decade period (2005–2025), incorporating post-COVID market dynamics. The combination of these methods, applied to both an emerging (India) and developed (U.S.) economy, provides a comprehensive understanding of gold market interdependence. In doing this, the paper offers valuable insights into causality, volatility transmission, and diversification potential. The econometric rigour of the study is enhanced through residual diagnostic tests, including tests of normality, autocorrelation, and other heteroscedasticity tests, as well as VAR stability tests. These ensure strong inference and model validity; more specifically, they are pertinent to the analysis of financial time series. Full article

Oil, as a key commodity in international markets, bears an importance for both producers and consumers. For oil-exporting countries, periodic fluctuations have a considerable impact on the economic status and the way monetary and fiscal policies should be conducted in the future. While most of academic efforts tried to link low-frequency real exchange rate with macroeconomic fundamentals for medium-/long-term inference, they omitted to gauge the volatile and complex high-frequency linkage between oil prices and exchange rate fluctuations. The inherent non-linear characteristics of such time series preclude the use of traditional tools or aggregated schemes based on lower frequencies for inference purposes. This work investigates the scale-based volatile linkage between daily international oil fluctuations and nominal exchange rate variations of an oil-exporting country, namely Algeria, by adopting a fractal regression approach to uncover the power-law, time-varying transmission and track its incidence in the short and long runs. Results show the absence of any short-term transmission mechanism from oil prices to the exchange rate, as the two variables remain decoupled but exhibit an increasing negative correlation when long scales are considered. Furthermore, the multiscale regression analysis confirms the existence of a scale-free, two-state Markov switching regime process generating short- and long-term impacts with sizeable amplitudes. The findings confirm the usefulness of monetary policy interventions to stabilize the local currency, as the source of Dollar–Dinar multifractality was found to be the probability distribution of observations rather than long-range correlations specific to oil prices. Full article

In the wave of digitalization, organizations are increasingly focused on whether to prioritize digital culture or organizational strategy for the use of artificial intelligence (AI); there are mixed opinions, particularly when AI-augmented HRM draws attention as a tool for achieving sustainable organizational performance (SOP) in developing countries. This study aims to explore the influence of digital culture and organizational strategy on AI-augmented HRM and SOP, focusing on the mediating role of AI-augmented HRM in these relationships. To investigate the hypothesized relationships, 219 sample data were gathered from employees associated with HRM-oriented activities in Bangladesh, and SPSS 23 and AMOS software were used to test the SEM model. The results proved that digital culture has an insignificant effect and organizational strategy has a significant effect on AI-augmented HRM, and AI-augmented HRM has a substantial effect on SOP and partially mediates the relationship between organizational strategy and SOP. Based on the results, we infer that the successful implementation of AI-augmented HRM can lead to organizational sustainability in developing countries, where organizational strategy plays a pivotal role rather than digital culture. This research incorporates the resource-based view (RBV) and dynamic capabilities theories, which are crucial for the groundbreaking development of the research model. The results suggest that managers and responsible authorities should prioritize organizational strategy over digital culture when implementing AI-augmented HRM systems to ensure sustainability in developing countries. However, in the long run, organizations also need to concentrate on generating digitally favorable environments. Full article

Multi-hop reasoning provides a means for inferring indirect relationships and missing information from knowledge graphs (KGs). Reinforcement learning (RL) was recently employed for multi-hop reasoning. Although RL-based methods provide explainability, they face challenges such as sparse rewards, spurious paths, large action spaces, and long training and running times. In this study, we present a novel approach that combines KG embeddings and RL strategies for multi-hop reasoning called path-based multi-hop reasoning (PMHR). We address the issues of sparse rewards and spurious paths by incorporating a well-designed reward function that combines soft rewards with rule-based rewards. The rewards are adjusted based on the target entity and the path to it. Furthermore, we perform action filtering and utilize the vectors of entities and relations acquired through KG embeddings to initialize the environment, thereby significantly reducing the runtime. Experiments involving a comprehensive performance evaluation, efficiency analysis, ablation studies, and a case study were performed. The experimental results on benchmark datasets demonstrate the effectiveness of PMHR in improving KG reasoning accuracy while preserving interpretability. Compared to existing state-of-the-art models, PMHR achieved Hit@1 improvements of 0.63%, 2.02%, and 3.17% on the UMLS, Kinship, and NELL-995 datasets, respectively. PMHR provides not only improved reasoning accuracy and explainability but also optimized computational efficiency, thereby offering a robust solution for multi-hop reasoning. Full article

Deep learning techniques have been widely investigated as an effective method for signal measurement in recent years. However, most existing deep learning-based methods still face difficulty in deploying on embedded platforms and perform poorly in real-time applications. To address this, this paper develops two accelerators, as the core of the signal measurement system, for intelligent signal processing. Firstly, by introducing the idea of automated framework, we propose a simplest deep neural network (DNN)-based hardware structure, which automatically maps algorithms to hardware modules, supports configurable parameters, and has the advantage of low latency, with an average inference time of only 3.5 μs. Subsequently, another accelerator is designed with the efficient hardware structure of the long short-term memory (LSTM) + DNN model, demonstrating outstanding performance with a classification accuracy of 98.82%, mean absolute error (MAE) of 0.27°, and root mean square errors (RMSE) of 0.392° after model compression. Moreover, parallel optimization strategies are exploited to further reduce latency and support simultaneous frequency and direction measurement tasks. Finally, we test the actual collected signal data on the XCVU13P field programmable gate array (FPGA). The results show that the time of inference saves 28–31% for the DNN model and 71–73% for the LSTM + DNN model compared to running on graphic processing unit (GPU). In addition, the parallel strategies further decrease the delay by 23.9% and 37.5% when processing continuous data. The FPGA-based and deep learning-assisted hardware accelerators significantly improve real-time performance and provide a promising solution for signal measurement. Full article

The present research focuses on the endogenous development theory and investigates the relationships between economic growth (dependent variable) and renewable energy consumption, technological innovation, and export diversification (independent variables) in seven emerging economies known as the E-7. Previous studies have examined these factors individually but have not explored their combined impact on the E-7 economies. Therefore, this study contributes to the existing literature on the effects of renewable energy consumption, technological advancement, and export diversification on economic development. This study analyses the dynamic connections among these variables in seven selected emerging countries: Brazil, China, Indonesia, India, Mexico, Russia, and Turkey. Panel data from 1990 to 2022 are utilised, and various methodologies, including panel cointegration, the pooled mean group–autoregressive distributed lag (PMG-ARDL) estimator, and robustness tests, such as the fully modified ordinary least square and dynamic ordinary least square tests, are employed. Empirical inferences are drawn using the Dumitrescu–Hurlin panel causality (DHC) test, and the long-run relationships among the variables are validated using the Westerlund residual cointegration tests. The results from the PMG-ARDL estimator show that renewable energy consumption, technological advancement, and export diversification have a significant and positive impact on economic expansion, confirming the validity of the endogenous growth model in the E-7 countries. The control variable of the financial sector has a positive but insignificant effect on economic growth, while trade openness has a negative and significant effect. The DHC test results indicate a neutral feedback effect of renewable energy consumption on economic growth. The findings also reveal a unidirectional causal relationship between technological innovation and economic growth. Overall, these findings provide valuable insights for economic policymakers in the E-7 countries. By removing barriers to renewable energy consumption, technological innovation, and export diversification, policymakers can promote sustainable economic development. Full article

As deep learning has produced dramatic breakthroughs in many areas, it has motivated emerging studies on the combination between neural networks and cache replacement algorithms. However, deep learning is a poor fit for performing cache replacement in hardware implementation because its neural network models are impractically large and slow. Many studies have tried to use the guidance of the Belady algorithm to speed up the prediction of cache replacement. But it is still impractical to accurately predict the characteristics of future access addresses, introducing inaccuracy in the discrimination of complex access patterns. Therefore, this paper presents the LSTM-CRP algorithm as well as its efficient hardware implementation, which employs the long short-term memory (LSTM) for access pattern identification at run-time to guide cache replacement algorithm. LSTM-CRP first converts the address into a novel key according to the frequency of the access address and a virtual capacity of the cache, which has the advantages of low information redundancy and high timeliness. Using the key as the inputs of four offline-trained LSTM network-based predictors, LSTM-CRP can accurately classify different access patterns and identify current cache characteristics in a timely manner via an online set dueling mechanism on sampling caches. For efficient implementation, heterogeneous lightweight LSTM networks are dedicatedly constructed in LSTM-CRP to lower hardware overhead and inference delay. The experimental results show that LSTM-CRP was able to averagely improve the cache hit rate by 20.10%, 15.35%, 12.11% and 8.49% compared with LRU, RRIP, Hawkeye and Glider, respectively. Implemented on Xilinx XCVU9P FPGA at the cost of 15,973 LUTs and 1610 FF registers, LSTM-CRP was running at a 200 MHz frequency with 2.74 W power consumption. Full article

This study examines the impact of international trade activities on employment in the Portuguese textiles and apparel industry from 2010 to 2017. It finds evidence that imports and exports have a persistent, negative, and significant effect on overall job creation, with this impact intensifying over the long-run. Additionally, the increasing elasticity of substitution between imports and exports indicates that private companies of this industry have benefited from a win–win situation characterised by higher production volumes and lower marginal costs. By applying an unsupervised machine-learning method, followed by a discrete choice analysis to infer the firm-level propensity to possess green capital, we identify a phenomenon termed the green international trade paradox. This study also reveals that international trade activities positively influence green job creation in firms lacking green capital if and only if these players are engaged in international markets while negatively affecting firms already endowed with green technologies. As such, empirical results suggest that the export-oriented economic model followed over the last decade by the Portuguese textiles and apparel industry has not necessarily generated new domestic employment opportunities but has significantly altered the magnitude and profile of skill requirements that employers seek to identify in new workforce hires. Full article

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO₂ emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs. Full article

Aiming to provide solutions for problems proposed by the inaccurate segmentation of long objects and information loss of small objects in real-time semantic segmentation algorithms, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment-and-fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses an NVIDIA GeForce RTX 3080 Laptop GPU experiment on the road street view Cityscapes and CamVid datasets, with the average simultaneously occurring ratios reaching 77.1% and 77.4%, respectively, and the running speeds reaching 127 frames/s and 112 frames/s, respectively. The experimental results show that the proposed algorithm can achieve a real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance. Full article

Nanocrystalline diamond (NCD) films are attractive for many applications due to their smooth surfaces while holding the properties of diamond. However, their growth rate is generally low using common Ar/CH₄ with or without H₂ chemistry and strongly dependent on the overall growth conditions using microwave plasma chemical vapor deposition (MPCVD). In this work, incorporating a small amount of N₂ and O₂ additives into CH₄/H₂ chemistry offered a much higher growth rate of NCD films, which is promising for some applications. Several novel series of experiments were designed and conducted to tailor the growth features of NCD films by fine-tuning of the gas-phase compositions with different amounts of nitrogen and oxygen addition into CH₄/H₂ gas mixtures. The influence of growth parameters, such as the absolute amount and their relative ratios of O₂ and N₂ additives; substrate temperature, which was adjusted by two ways and inferred by simulation; and microwave power on NCD formation, was investigated. Short and long deposition runs were carried out to study surface structural evolution with time under identical growth conditions. The morphology, crystalline and optical quality, orientation, and texture of the NCD samples were characterized and analyzed. A variety of NCD films of high average growth rates ranging from 2.1 μm/h up to 6.7 μm/h were successfully achieved by slightly adjusting the O₂/CH₄ amounts from 6.25% to 18.75%, while that of N₂ was kept constant. The results clearly show that the beneficial use of fine-tuning of gas-phase compositions offers a simple and effective way to tailor the growth characteristics and physical properties of NCD films for optimizing the growth conditions to envisage some specific applications. Full article

6G will connect heterogeneous intelligent agents to make them natively operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how artificial intelligence and machine learning models behave depending on (i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; (ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper, we investigate how to effectively share communication spectrum resources between a legacy communication system (i.e., data-oriented) and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an enhanced Mobile Broadband (eMBB) service, i.e., a user uploading a video stream to a radio access point, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the highest eMBB service data rate, subject to a targeted goal effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of a goal in the context of wireless communications. This includes the goal effectiveness, (i.e., the goal achievability rate, or the probability of achieving the goal), as well as goal cost (i.e., the network resource consumption needed to achieve the goal with target effectiveness). We argue and show, through numerical evaluations, that communication reliability and goal effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal effectiveness, a long-term optimization problem is formulated and solved via Lyapunov stochastic network optimization tools to guarantee the desired target performance. Finally, our numerical results assess the advantages of the proposed optimization and the superiority of the goal-oriented strategy against baseline 5G-compliant legacy approaches, under both stationary and non-stationary communication (and computation) environments. Full article

The consequences of climate change are arising in the form of many types of natural disasters, such as flooding, drought, and tropical cyclones. Responding to climate change is a long horizontal run action that requires adaptation and mitigation strategies. Hence, future climate information is essential for developing effective strategies. This study explored the applicability of a statistical downscaling method, Bias-Corrected Spatial Disaggregation (BCSD), in downscaling climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and then applied the downscaled data to project the future condition of precipitation pattern and extreme events in Cambodia. We calculated four climate change indicators, namely mean precipitation changes, consecutive dry days (CDD), consecutive wet days (CWD), and maximum one-day precipitation (rx1day) under two shared socioeconomic pathways (SSPs) scenarios, which are SSP245 and SSP585. The results indicated the satisfactory performance of the BCSD method in capturing the spatial feature of orographic precipitation in Cambodia. The analysis of downscaled CMIP6 models shows that the mean precipitation in Cambodia increases during the wet season and slightly decreases in the dry season, and thus, there is a slight increase in annual rainfall. The projection of extreme climate indices shows that the CDD would likely increase under both climate change scenarios, indicating the potential threat of dry spells or drought events in Cambodia. In addition, CWD would likely increase under the SSP245 scenario and strongly decrease in the eastern part of the country under the SSP585 scenario, which inferred that the wet spell would have happened under the moderate scenario of climate change, but it would be the opposite under the SSP585 scenario. Moreover, rx1day would likely increase over most parts of Cambodia, especially under the SSP585 scenario at the end of the century. This can be inferred as a potential threat to extreme rainfall triggering flood events in the country due to climate change. Full article