Two-step Methodology Research Articles

The impact of digital finance on resource allocation efficiency: evidence from China

ABSTRACT Digital finance presents new opportunities for enterprise development. This study develops a digital finance index utilizing data from Chinese prefecture-level cities and listed companies between 2011 and 2021, employing the OP and LP methods to assess resource allocation efficiency. The impact of digital finance on efficiency was analysed using a two-way fixed-effects model alongside an instrumental variable approach, while a two-step methodology was adopted to explore the underlying mechanisms. The findings demonstrate that digital finance significantly enhances resource allocation efficiency by broadening financing channels, improving corporate fund utilization, and reducing information asymmetry. Furthermore, the improvement in resource allocation efficiency attributed to digital finance is particularly pronounced in regions with higher per-capita income, larger urban populations, and state-owned enterprises. This study contributes a novel perspective on resource allocation efficiency and provides policy implications for effectively harnessing the opportunities afforded by digital finance in the contemporary era, thereby maximizing the benefits of the digital age and improving resource allocation efficiency.

Digital financial inclusion role to promote renewable energy technology innovation in Chinese prefectural cities: Moderating role of environmental governance

Renewable energy technology innovation is not only an important technical component of Chinese construction of low-carbon energy systems but also a key way to realize long-term, sustainable development. Digital inclusive finance plays an indispensable role in this process. Using panel data of 275 Chinese cities from 2011 to 2022, a two-step systematic methodology is used to assess the impacts and mechanisms of digital financial inclusion and the moderating role of different environmental governance models on qualitative and quantitative renewable energy innovation. The results indicate that digital inclusive finance has an incentive effect on both qualitative and quantitative renewable energy innovations, with a stronger incentive effect on qualitative behavior. This effect can act on renewable energy technology innovation by reducing financing constraints and incentivizing information disclosure mechanisms. Moreover, different forms of environmental governance have a positive, moderating effect, while the effect of top-down environmental governance is more manifested in quantitative renewable energy innovation. Furthermore, heterogeneity analyses show that the incentive effect of digital financial inclusion is more pronounced in the PV sector and the eastern regions. The findings provide a theoretical basis and practical insights that enable policy-makers to formulate relevant renewable energy technology innovation policies and promote low-carbon energy transition.

Enhanced performance of low-dielectric siloxane-polyimide copolymers: Synthesis and properties

Low-dielectric polyimides (PI) films with outstanding overall performance are attractive for next generation microelectronic applications. Herein, a series of two different FPIs and BPIs derived from 4,4′-diaminodiphenyl ether (ODA) and two different functional dianhydrides, 4,4'-(hexafluoroisopropylidene) biphthalic anhydride (6FDA) and 4,4′-(4,4′-isopropyldiphenoxy) biphthalic anhydride (BPADA), respectively, were synthesized by a conventional two-step methodology that included thermoimidatization. Additionally, employing aminopropyl-terminated polydimethylsiloxane PDMS (molecular weight = 1000) as the source of siloxane-oxygen bonds, a series of polyimide siloxane (SiFPIs and SiBPIs) copolymers were synthesized by varying the siloxane content (ranging from 0 to 5 wt%) within the copolymer structure. The effects of siloxane bond on the thermal, solubility, water resistance, dielectric and mechanical properties of polyimide were investigated systematically. The integration of siloxane significantly enhanced the solubility of these copolymers in polar aprotic solvents (DMAC, DMF, NMP), expanding their applicability in diverse processing environments. Crucially, these copolymers exhibited remarkably low dielectric constants—2.90 for SiFPI at 3% PDMS and 2.92 for SiBPI at 4% PDMS—coupled with low loss values, positioning them as prime candidates for advanced microelectronic applications. All the composite films have high thermal stability near pure PI. The introduction of PMDS into PI can improve the elongation at break and at the same time, the composite films still remained with higher modulus and tensile strength. Extensive hydrophobicity assessments through contact angle and water absorption studies underscored the copolymers’ superior moisture resistance, a critical parameter for their potential deployment in high-performance, environmentally sensitive electronic components.

Efficient catalysis for acidic methanol oxidation: Exploration of a Low-Platinum quaternary alloy catalyst via a Two-Step method

The quest for platinum-based catalysts with enhanced catalytic efficacy and durability during the methanol oxidation reaction (MOR) has been a focal point in energy conversion science. Traditional platinum-based catalysts are often constrained by their high platinum content, which escalates economic costs and complicates experimental protocols. In this study, we introduce a low-platinum-loaded catalyst synthesized via a two-step method, which incorporates a four-component alloy. This innovative approach significantly reduces platinum content while maintaining superior catalytic performance through precise control over the alloy’s composition and structure. The two-step synthesis methodology streamlines the conventional multi-step processes, enhancing the catalyst preparation’s efficiency and reproducibility. The resulting PtCoSnCu/N-C alloy catalyst exhibits remarkable catalytic activity in methanol solution, with mass activity and specific activity that are 9.73 and 9.75 times higher, respectively, than those of commercial platinum on carbon (Pt/C) catalysts. Notably, the optimized PtCoSnCu/N-C alloy catalyst demonstrates exceptional long-term stability, outperforming Pt/C in aging and extended stability tests. Density functional theory (DFT) calculations elucidate the optimization of the electronic structure and the synergistic effect of surface atoms in the PtCoSnCu/N-C catalyst, which mitigates the adsorption energy of CO molecules and enhances the utilization rate of active sites during the oxidation process. This research presents a paradigm-shifting low-platinum catalyst strategy for methanol fuel cells, establishing a theoretical foundation for catalyst design and optimization.

Environmental impact assessment of a direct methanol fuel cell and strategic mitigation guidelines

Direct methanol fuel cells (DMFCs) are gaining attention as a viable technology for portable and remote applications due to the benefits of methanol as fuel. However, the environmental implications of this technology have not been thoroughly evaluated. This study aims to conduct a comprehensive assessment of the environmental impact of a DMFC throughout its entire life cycle, and based on the findings, propose guidelines for minimizing the impact. To eliminate any potential biases, a two-step design methodology is applied. The first step involves obtaining the optimal preliminary design, for which a genetic algorithm is implemented. The second step is to conduct the environmental impact study on that optimal configuration. As a study case, a DMFC for a highly demanding application, such as an unmanned aerial vehicle, is selected, using green methanol as fuel. The results reveal that extraction and production stage has the greatest effect on the environment (74.1%), compared to use (25.8%) and transport stages (0.1%). Among the components of the stack, the catalysts have the most significant environmental footprint, 93.3% of the total impact of the stack during the extraction and production stage, while bipolar plates represent only 5.9%, despite comprising 86% of the stack’s mass.

Optimal Design of a Sensor Network for Guided Wave-Based Structural Health Monitoring Using Acoustically Coupled Optical Fibers.

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. Unfortunately, the FBG sensors and the equipment needed for interrogation is quite expensive, and hence their number is restricted. In the previous work by the authors, the number and location of the actuators was optimized for developing a SHM system with a single sensor and multiple actuators. But through the use of the phenomenon of acoustic coupling, multiple locations on the structure may be interrogated with a single FBG sensor. As a result, a sensor network with multiple sensing locations and a few actuators is feasible and cost effective. This paper develops a two-step methodology for the optimization of an actuator-sensor network harnessing the acoustic coupling ability of FBG sensors. In the first stage, the actuator-sensor network is optimized based on the application demands (coverage with at least three actuator-sensor pairs) and the cost of the instrumentation. In the second stage, an acoustic coupler network is designed to ensure high-fidelity measurements with minimal interference from other bond locations (overlap of measurements) as well as interference from features in the acoustically coupled circuit (fiber end, coupler, etc.). The non-sorting genetic algorithm (NSGA-II) is implemented for finding the optimal solution for both problems. The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly.

Investigating the Tribological Behavior of Bioinspired Surfaces in Agro-waste and Alumina Reinforced AA6063 Matrix Composites

The tribological behavior of three bioinspired (BI) agro-waste ashes—bean pod ash (BPA), cassava peel ash (CPA), and coconut husk ash (CHA)—integrated as reinforcements in an alumina-reinforced AA6063 matrix hybrid and monolithic composite was investigated. Studies utilizing agro-wastes for bioinspired surfaces are limited despite these wastes posing significant environmental challenges. Consequently, there has been a growing effort to valorize these residues for sustainable engineering applications. This study uses a two-step stir-casting methodology to engineer bioinspired materials with lightweight properties, exceptional hardness, and wear-resistant characteristics. Microstructural investigations revealed a homogeneous distribution of natural and synthetic reinforcements within the AA6063 matrix. Lightweight AMCs were successfully fabricated, exhibiting densities ranging from 2.57 to 2.74 g/cm³ and hardness values between 81.28 and 107.47 BHN. The average surface roughness of these composites varied from 2.4 to 3.4 μm, with ANOVA tests confirming a statistically significant difference (p < 0.005). Wear rates and the coefficient of friction were observed to range from 2.52 x 10-3 to 5.50 x 10-3 mm³/m and 0.2476 to 0.5403, respectively. Reinforcing the AA6063 alloy with bioinspired agro-wastes significantly enhanced the hardness and tribological performance of the as-cast BI-AMCs. Notably, the wear rates and friction coefficients were significantly reduced, with the monolithic BI-AMCs demonstrating superior results. Furthermore, adding bioinspired agro-wastes, such as BPA, CPA, and CHA, improved the surface texture of AA6063 more effectively than Al2O3, leading to slightly smoother surfaces. These nuanced bioinspired tribological behaviors present opportunities for tailored load-bearing and aesthetic applications derived from agro-waste-based composites. This study advances our understanding of bioinspired tribological phenomena and paves the way for the development of innovative materials with diverse applications and enhanced performance.

Two-step method for predicting Young's modulus of nanocomposites containing nanodiamond particles

In this study, a sophisticated two-step methodology is proposed that incorporates the Kolarik and Hashin–Hansen models to precisely evaluate the effects of the interphase on the Young's modulus of nanodiamond (ND)-filled composites. Initially, the ND particles and their surrounding interphase were modeled as a core-shell structure, and the modulus of this structure was calculated. Subsequently, the core-shell particles were considered as a dispersed phase within a polymer matrix, leading to determination of the Young's modulus of the nanocomposite. The results obtained using the proposed method agreed well the experimental data of various nanocomposite samples. Furthermore, a robust correlation was indicated between the interphase properties and nanocomposite modulus. Notably, an interphase characterized by a thickness of 10 nm, modulus of 50 GPa, and a filler volume fraction of 0.02 increased the nanocomposite modulus to more than 3 GPa, assuming a matrix modulus of 1 GPa. Additionally, the findings revealed that ND particles with radii smaller than 4 nm had a remarkable influence on the nanocomposite modulus, whereas a high ND modulus had a minimal effect.

Ultrasound-Assisted Synthesis of Pyrazoline Derivatives as Potential Antagonists of RAGE-Mediated Pathologies: Insights from SAR Studies and Biological Evaluations.

In the context of age-related disorders, the receptor of advanced glycation end products (RAGE), plays a pivotal role in the pathogenesis of these conditions by triggering downstream signaling pathways associated with chronic inflammation and oxidative stress. Targeting this inflammaging phenomenon with RAGE antagonists holds promise for interventions with broad implications in healthy aging and the management of age-related conditions. This study explores the structure-activity relationship (SAR) of pyrazoline-based RAGE antagonists synthesized using an ultrasound-assisted green one-pot two-steps methodology. Our investigation identifies phenylurenyl-pyrazoline 2g as a promising candidate, demonstrating superior efficiency compared to the reference antagonist Azeliragon (IC50 = 13 µM). Compound 2g exhibits potent inhibition of the AGE2-BSA/sRAGE interaction (IC50 = 22 µM) and favorable affinity in Microscale Thermophoresis (MST) assays (Kd = 17.1 µM), along with a favorable safety profile, with no apparent cytotoxicity observed in vitro in the MTS assay. These findings underscore the potential of pyrazoline-derived RAGE antagonists as therapeutic agents for addressing age-related disorders.

Dengue vaccine endgame: why has the global response to exponential demand in the tropics been so slow?

IntroductionThere has been a dramatic global surge in dengue fever (DF) in recent years, with a projected 100-400 million cases annually and large outbreaks expected every 3-4 years. This concerning scenario is in sharp contrast to the very low availability of dengue vaccines to meet this spiraling demand, particularly affecting developing countries in Latin America and Africa. In our study, we aim to identify current vaccine development constraints, from a technological perspective, based on clinical trials and patent landscape data.MethodsThis study was conducted in a two-step methodology. First, candidates and launched products and methods used in the development or as part of DF vaccines were identified from the Cortellis Drug Discovery Intelligence (CDDI) platform. Second, the Derwent Innovation database was used to retrieve patent documents related to dengue vaccines. Data were collected in the period 2014-2024 (to identify the candidate and or launched vaccines) and 2018-2022 (to identify patent documents) with claims involving applications for dengue vaccine development. We presented these descriptive data in the following format: quantitative and qualitative assessments describing the vaccine development scenario and the claimed vaccine technologies.ResultsOur study indicated that 65% of dengue vaccines are still in phase 1 of development. The few current dengue vaccines that have reached phase 3 (1) and launched (2) have shown limited levels of individual protection for one or more dengue serotypes and there is an anemic pipeline of the next generation of more effective tetravalent dengue vaccines. Although the intelligent use of multiple dengue vaccine formulations in immunization campaigns can result in effective protection against all serotypes at the population level, the implementation of this is complex and challenging. The results of our study thus provide evidence of a worrisome global situation regarding the development of new dengue vaccine candidates.ConclusionTo reverse this scenario, there is an urgent need to implement new knowledge governance approaches and business models to stimulate an exponential increase in global vaccine development funding for dengue and several other vaccines.

A novel technique including two steps for modulus prediction in polymer halloysite nanotube composites

A two-step methodology has been developed utilizing the models of Paul and Takayanagi to determine the modulus of polymer halloysite nanotube (HNT) products. Initially, HNTs and the adjacent interphase are considered as pseudoparticles, and their modulus is evaluated using the Paul model. Subsequently, the modulus of a nanocomposite, consisting of a polymer medium and pseudoparticles, is predicted by Takayanagi equation. The impacts of various factors on the modulus of the products are analyzed, and the results from the two-step method are compared with experimental data from different samples. It has been observed that the modulus of samples progressively increases with an increase in interphase depth. Also, a higher interphase modulus contributes to an enhanced modulus of samples. Nevertheless, excessively high interphase moduli (Ei > 60 GPa) result in only a marginal improvement in the modulus of nanocomposites. Additionally, narrower HNTs are advantageous for producing stronger samples, though the modulus of the nanocomposites slightly diminishes at very high HNT radii (R > 55 nm). The outputs of two-step method agree with the experimental moduli of various HNT-filled systems.

Multiperiod Dynamic Portfolio Choice: When High Dimensionality Meets Return Predictability

Multiperiod portfolio choice is the central problem in active asset management. Multiperiod dynamic portfolios are notoriously difficult to solve, especially when there are hundreds of tradable assets as well as a large number of state variables. In this article, we develop a novel two-step methodology to solve the multiperiod dynamic portfolio choice problem with high dimensional assets in the presence of return predictability conditional on a large number of state predictors. Specifically, in the first step, we propose the new Risk-Premium Projected-PCA (RP-PPCA) method to reduce the dimension of tradable assets. This method achieves Dimension Reduction (DR) by estimating latent factors with explanatory power in both time series variation and expected return in high-dimension-low-sample-size data. In the second step, we use dynamic programming to solve the multiperiod portfolio choice problem, and in each recursive step, we adopt an Adjusted semiparametric Model Averaging (AMA) method to avoid the curse of dimensionality associated with a large set of state variables while remaining computationally efficient. Thus, we name this two-step approach DRAMA, which stands for a combination of a new dimension reduction method and an adjusted semiparametric model averaging method. Analytically, we show that the portfolios constructed by the DRAMA are approximately optimal under mild assumptions. Moreover, our numerical results based on empirical data from US stock markets show that the proposed portfolios have excellent out-of-sample performances.

Developments of Pyrrolo[2,3-d]pyrimidines with Pharmaceutical Potential

: In terms of fused heterocyclic compounds, pyrrolopyrimidines, and their substituted analogs are among the most extensively explored scaffolds. Based on the location of the nitrogen atom in the pyrrole ring, pyrrolopyrimidines have different isomers. This study deals only with the pyrrolo[2,3-d]pyrimidine isomer. Several techniques are represented and discussed in this review for producing pyrrolo[2,3-d]pyrimidine derivatives. The first one is the cyclization of the pyrimidine ring on the pyrrole ring through the reaction of β-enaminonitrile, β-enaminoester or β-enaminoamide of the pyrrole ring with different bifunctional reagents such as formic acid, acetic acid, acetic anhydride, formamide, isothiocyanate, urea, thiourea, and carbon disulfide. The second technique includes cyclization of the pyrrole ring on the pyrimidine ring via the treatment of pyrimidine, aminopyrimidine, diamino-pyrimidine, or triamino-pyrimidine with different reagents such as nitroalkenes, alkynes, aldehydes, and acid chlorides. In addition, different reaction methodologies like one pot, two-step, and threestep synthetic methodologies were reported. The last technique for producing pyrrolo[2,3-d]pyrimidine derivatives is through miscellaneous reactions. This review also includes the interactions of pyrrolo[2,3- d]pyrimidines at different active centers of the pyrrole ring with different reagents to form N-alkylated, Nglycosylated, C-5, and C-6 adducts. Besides, the interactions on the pyrimidine ring to form chloro, hydrazino, and amino-imino derivatives were also discussed. The amino-imino derivatives are key intermediates for the preparation of tricyclic pyrrolotriazolopyrimidines. Finally, the pharmaceutical and biological properties of some pyrrolo[2,3-d]pyrimidine derivatives have also been mentioned. This information can be utilized to design novel diverse pyrrolopyrimidine derivatives for recent challenges in pharmaceutical and medical studies to develop the already existing drugs or discover new ones.

Green growth governance and total factor energy efficiency: Economic growth constraint and policy implementation in OECD countries

This study aims to examine the nature and governance of green growth strategy in OECD countries. As the strategy implies a sustainable relationship between economic growth and energy efficiency, we considered economic, environmental, and policy factors to empirically analyze green total factor energy efficiency (GTFEE) performances for 2001–2020 period. For this purpose, we employed a two-step methodology. In the first step, we estimated GTFEE scores from an undesirable output model by using robust order-m frontier efficiency method. In the second step, we used GTFEE estimates and built a panel data model to investigate the impact of governance and environmental policy factors. The panel data regression results point to several implications. Firstly, market-based environmental stringency policies are found to be effective. Also, economic globalization is found to be effective only in market-based and mix policy cases. The findings indicate the key roles of market-based pricing mechanisms and economic actors for the inclusive governance of green growth. Secondly, economic growth is found to be negatively related with GTFEE which shows the trade-off between economic growth and energy efficiency. Lastly, the analysis by commitment periods of the Kyoto Protocol points to the importance of global consensus on the implementation of green growth strategy.

Validity test of the Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria compared with the American Society for Parenteral and Enteral Nutrition (ASPEN) criteria in inpatients at Dr. Cipto Mangunkusumo hospital

Background: Malnutrition experienced by numerous inpatients is linked to various complications. The Global Leadership Initiative on Malnutrition (GLIM), which established the latest malnutrition criteria, recommends a two-step methodology for diagnosing malnutrition in adult inpatients. This study aims to evaluate the validity of the GLIM criteria for the diagnosis of malnutrition in hospitalized patients at Cipto Mangunkusomo Hospital Jakarta by comparing them with the ASPEN criteria. Methods: This cross-sectional study was conducted at Cipto Mangunkusomo Hospital, Jakarta. Secondary data was taken from 100 inpatients from October 2021 to February 2022 selected by consecutive sampling technique. The diagnosis of malnutrition in the patients applied the ASPEN and GLIM criteria. Furthermore, data were analyzed using Cohen's Kappa and chi-square tests. Results: Of 100 inpatients, 63% were diagnosed with malnutrition according to the GLIM diagnostic criteria. Meanwhile, 48% of them were found to be malnourished according to the ASPEN criteria. The GLIM criteria have a specificity of 69.2%, a sensitivity of 97.9%, a PPV of 74.6%, and an NPV of 97.3%. Conclusion: The GLIM diagnostic criteria are valid as an instrument for diagnosing malnutrition but require further research to assess the severity of malnutrition.

A Review of CIGS Thin Film Semiconductor Deposition via Sputtering and Thermal Evaporation for Solar Cell Applications

Over the last two decades, thin film solar cell technology has made notable progress, presenting a competitive alternative to silicon-based solar counterparts. CIGS (CuIn1−xGaxSe2) solar cells, leveraging the tunable optoelectronic properties of the CIGS absorber layer, currently stand out with the highest power conversion efficiency among second-generation solar cells. Various deposition techniques, such as co-evaporation using Cu, In, Ga, and Se elemental sources, the sequential selenization/sulfidation of sputtered metallic precursors (Cu, In, and Ga), or non-vacuum methods involving the application of specialized inks onto a substrate followed by annealing, can be employed to form CIGS films as light absorbers. While co-evaporation demonstrates exceptional qualities in CIGS thin film production, challenges persist in controlling composition and scaling up the technology. On the other hand, magnetron sputtering techniques show promise in addressing these issues, with ongoing research emphasizing the adoption of simplified and safe manufacturing processes while maintaining high-quality CIGS film production. This review delves into the evolution of CIGS thin films for solar applications, specifically examining their development through physical vapor deposition methods including thermal evaporation and magnetron sputtering. The first section elucidates the structure and characteristics of CIGS-based solar cells, followed by an exploration of the challenges associated with employing solution-based deposition techniques for CIGS fabrication. The second part of this review focuses on the intricacies of controlling the properties of CIGS-absorbing materials deposited via various processes and the subsequent impact on energy conversion performance. This analysis extends to a detailed examination of the deposition processes involved in co-evaporation and magnetron sputtering, encompassing one-stage, two-stage, three-stage, one-step, and two-step methodologies. At the end, this review discusses the prospective next-generation strategies aimed at improving the performance of CIGS-based solar cells. This paper provides an overview of the present research state of CIGS solar cells, with an emphasis on deposition techniques, allowing for a better understanding of the relationship between CIGS thin film properties and solar cell efficiency. Thus, a roadmap for selecting the most appropriate deposition technique is created. By analyzing existing research, this review can assist researchers in this field in identifying gaps, which can then be used as inspiration for future research.

Investigation of the fast flux test facility transient behavior during a loss of flow without scram test

A Coordinated Research Project (CRP) on the benchmark analysis of Fast Flux Test Facility (FFTF) Loss of Flow Without Scram (LOFWOS) has been held under the sponsorship of the International Atomic Energy Agency. The CRP aims to improve understanding of loss of flow events in fast reactors, as well as to assess capabilities of existing computer codes against experimental data. FFTF is a 400 MWth Sodium-cooled Fast Reactor (SFR) owned by the U.S. Department of Energy, operated from 1980 to 1992. The CRP involves the LOFWOS Test #13, belonging to a series of unprotected transients performed as part of the Passive Safety Testing program. In this framework, the Nuclear Engineering Research Group of Sapienza University of Rome has developed an integrated multiphysics modelling based on a coupled approach with RELAP5-3D© (thermal-hydraulics) and PHISICS (neutron-kinetics) codes. The present paper discusses the results obtained by the authors after the participation in the benchmark open phase. As suggested by the organizers, a two-step methodology was followed. Firstly, calculations were run by imposing the reactor power as boundary condition and focusing on the FFTF thermal–hydraulic behaviour. A good accordance was detected between the numerical results and the experimental data, above all for what concerns the reference core outlet temperatures. Then, the simulations were repeated by using a coupled approach. Calculation outcomes demonstrated the capability of the selected code suite (PHISICS/RELAP5-3D®) to reproduce the reactor transient behavior and its suitability to be used as an effective numerical tool to simulate liquid metal fast reactors accidental scenarios where thermal–hydraulic and neutron-kinetic phenomena are strongly coupled.

A two-step dynamic factor modelling approach for forecasting inflation in small open economies

We build a dynamic factor model to forecast inflation in a small open economy. The model is estimated with both market and survey data, and a unique two-step methodology to incorporate exogenous factors. Estimations with market data provide a better fit for in-sample and out-of-sample values of inflation. More importantly, our model outperforms univariate and estimated DSGE models, the more common approaches to inflation forecasting that perform well for advanced economies. Our findings, therefore, suggest that a dynamic factor modelling approach for a small open economy such as Jamaica can be a good alternative to the preferred methods for forecasting inflation in advanced economies.

Conjointly active and passive modelings with deep neural networks as fully automated optimizations for upper-mid band 6G communications

Today wireless systems include the fifth and sixth generations (5G and 6G) technologies and are growing day by day that result in exponentially increasing data traffic. For providing a reliable and high performance radio frequency (RF) designs especially for 6G networks, amplifiers and antenna as active and passive components play important roles. In the 5G/6G communication systems, the propagation loss is considerably large and its compensation requires high output power generated from the amplifiers for guaranteeing the satisfied quality of transmitted signal. From another point of view, the installed antennas must be able to optimally manage the radiated signals and handle/compensate nonlinear performances of the RF circuitry. Hence, advanced modeling and multi-objective optimization algorithms are required for designing and optimizing high performance amplifiers and antennas in terms of output power, gain, efficiency, linearity, and bandwidth. Concurrently optimizing active and passive components is not straightforward and typically it requires additional efforts by the RF designers. To tackle this drawback, a two-step methodology is proposed: (1) configuring the initial structure of active and passive devices, and (2) sizing the configured devices. In this work, various methods are introduced for structuring the topology of circuits and then artificial intelligence, including machine learning and neural networks, is preferred among other surrogate modelling for sizing the designs. These neural networks are satisfied due to the accurate modeling responses and are able to provide an automated optimization process leads to employ multi-objective optimization methods. In this work, an automated optimization process for comprehensive design of high-performance amplifiers with antennas through bottom-up optimization (BUO) method and long short-term memory (LSTM)-based deep neural networks (DNNs) is proposed. At the output layer of DNNs, the multi-objective multi-verse optimizer (MOMVO) method is employed for optimizing various specifications of active device (i.e., amplifier), and passive device (i.e., antenna), concurrently. In the presented method, all the electromagnetic (EM) design rules are implemented which results in reducing simulation time in the harmonic balance simulation environment that also provides ready to fabricate layouts. The novelty consists of the all-inclusive style that (1) reduces the manual breaks, aka time-to-market, and (2) delivers ready-to-fabricate layouts of the device that exhibits global optimum performances, automatically. The validation of the proposed method is verified by designing and optimizing high power amplifier (HPA) with antenna in the frequency band from 9.0 GHz to 9.6 GHz, suitable for upper-mid band 6G communications.

Assessing electromagnetic field exposure levels in multi-active reconfigurable intelligent surface assisted 5G network

As 5G mobile networks continue to proliferate in dense urban environments, it becomes increasingly important to understand and mitigate excessive electromagnetic field (EMF) exposure. This study investigates how the downlink EMF exposure levels of 5G millimeter wave (mm-wave) mobile networks are influenced by the integration of multi-active reconfigurable intelligent surfaces (RISs), using a ray-tracing approach. Our research employs a comprehensive two-step methodology: Firstly, we introduce a new RIS-assisted 5G mm-wave network planning technique. This technique leverages a machine learning (ML) approach for the classification of multi-RIS clusters. The primary goal is to optimize coverage while minimizing the number of required RIS deployments. This is achieved by strategically placing RISs based on the ML classification, ultimately aiming to enhance network efficiency. Secondly, we conducted a thorough comparative analysis, evaluating the impact of both passive and active RISs on EMF exposure level throughout a dense urban environment. Passive RIS and active RIS differ in their adaptability to changing network conditions. The result shows that the influence of multi-active RISs on EMF exposure is significant (about 7.5 times higher) compared to passive RISs.