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New particle formation (NPF) often drives cloud condensation nuclei concentrations and the processes governing nucleation of molecular clusters vary substantially in different regions. The growth of these clusters from ∼2 to >10 nm diameters is often driven by the availability of extremely low volatility organic vapors (ELVOCs). Although the pathways to ELVOC formation from the oxidation of biogenic terpenes are better understood, the mechanistic pathways for ELVOC formation from oxidation of anthropogenic organics are less well understood. We integrate measurements and detailed regional model simulations to understand the processes governing NPF and secondary organic aerosol formation at the Southern Great Plain (SGP) observatory in Oklahoma and compare these with a site within the Bankhead National Forest (BNF) in Alabama, southeast USA. During our two simulated NPF event days, nucleation rates are predicted to be at least an order of magnitude higher at SGP compared to BNF largely due to lower sulfuric acid (H2SO4) concentrations at BNF. Among the different nucleation mechanisms in WRF‐Chem, we find that the dimethylamine (DMA) + H2SO4 nucleation mechanism dominates at SGP. We find that anthropogenic ELVOCs are critical for explaining the growth of particles observed at SGP. Treating organic particles as semisolid, with strong diffusion limitations for organic vapor uptake in the particle phase, brings model predictions into closer agreement with observations. We also simulate two non‐NPF event days observed at the SGP site and show that low‐level clouds reduce photochemical activity with corresponding reductions in H2SO4 and anthropogenic ELVOC concentrations, thereby explaining the lack of NPF. Plain Language Summary: In forested regions around the world, biogenic emissions have been reported to be key drivers of new particle formation (NPF), cloud condensation nuclei (CCN). However, at locations in the Midwest USA that are far from forests and influenced by croplands and urban sources, the processes driving NPF and CCN are not well understood. Using detailed regional model simulations, we show that dimethylamines (DMA) and sulfuric acid (H2SO4) are key nucleation drivers at the Southern Great Plain (SGP) site during our two simulated days in the springtime, and condensation of H2SO4 alone is not sufficient to explain the observed growth of molecular clusters from ∼2 nm diameters to >10 nm diameter. We show that anthropogenic extremely low volatility organics (ELVOCs) are critical for explaining the observed particle growth. In contrast, our simulated non‐NPF days at SGP are characterized by low‐level clouds, which reduce photochemical activity, H2SO4, and ELVOC concentrations, thereby explaining the lack of NPF. At the BNF forested site in southeast USA, we show that nucleation rates are limited by availability of H2SO4. Our study highlights the large heterogeneities in nucleation and particle growth mechanisms within the Midwest compared to southeast USA. Key Points: Dimethylamines (DMA) and sulfuric acid drive nucleation over the Southern Great Plain (SGP) region in the Midwest USAAromatic low volatile organic gases govern the growth of molecular clusters to larger sizesNucleation in forested regions of southeast USA is limited by sulfuric acid [ABSTRACT FROM AUTHOR]

One of the biggest problems in gaming AI is related to how we can optimize and adapt a deep reinforcement learning (DRL) model, especially when it is running inside complex, dynamic environments like "PacMan". The existing research has concentrated more or less on basic DRL approaches though the utilization of advanced optimization methods. This paper tries to fill these gaps by proposing an innovative methodology that combines DRL with high-level metaheuristic optimization methods. The work presented in this paper specifically refactors DRL models on the "PacMan" domain with Energy Serpent Optimizer (ESO) for hyperparameter search. These novel adaptations give a major performance boost to the AI agent, as these are where its adaptability, response time, and efficiency gains start actually showing in the more complex game space. This work innovatively incorporates the metaheuristic optimization algorithm into another field—DRL—for Atari gaming AI. This integration is essential for the improvement of DRL models in general and allows for more efficient and real-time game play. This work delivers a comprehensive empirical study for these algorithms that not only verifies their capabilities in practice but also sets a state of the art through the prism of AI-driven game development. More than simply improving gaming AI, the developments could eventually apply to more sophisticated gaming environments, ongoing improvement of algorithms during execution, real-time adaptation regarding learning, and likely even robotics/autonomous systems. This study further illustrates the necessity for even-handed and conscientious application of AI in gaming—specifically regarding questions of fairness and addiction. [ABSTRACT FROM AUTHOR]

Despite the increase in the efficiency of energy consumption in information and communication technology, software execution and its constraints are responsible for how energy is consumed in hardware hosts. Consequently, researchers have promoted the development of sustainable software with new development methods and tools to lessen its hardware demands. However, the approaches developed so far lack cohesiveness along the stages of the software development life cycle (SDLC) and exist outside of a holistic method for green software development (GSD). In addition, there is a severe lack of approaches that target the analysis and design stages of the SDLC, leaving software architects and designers unsupported. In this article, we introduce our behavior-based consumption profile (BBCP) external Domain-Specific Language (DSL), aimed at assisting software architects and designers in modeling the behavior of software. The models generated with our external DSL contain multiple sets of properties that characterize features of the software's behavior. In contrast to other modeling languages, our BBCP emphasizes how time and probability are involved in software execution and its evolution over time, helping its users to gather an expectation of software usage and hardware consumption from the initial stages of software development. To illustrate the feasibility and benefits of our proposal, we conclude with an analysis of the model of a software service created using the BBCP, which is simulated using Insight Maker to obtain an estimation of hardware consumption and later translated to energy consumption. [ABSTRACT FROM AUTHOR]

One of the biggest problems in gaming AI is related to how we can optimize and adapt a deep reinforcement learning (DRL) model, especially when it is running inside complex, dynamic environments like “PacMan”. The existing research has concentrated more or less on basic DRL approaches though the utilization of advanced optimization methods. This paper tries to fill these gaps by proposing an innovative methodology that combines DRL with high-level metaheuristic optimization methods. The work presented in this paper specifically refactors DRL models on the “PacMan” domain with Energy Serpent Optimizer (ESO) for hyperparameter search. These novel adaptations give a major performance boost to the AI agent, as these are where its adaptability, response time, and efficiency gains start actually showing in the more complex game space. This work innovatively incorporates the metaheuristic optimization algorithm into another field—DRL—for Atari gaming AI. This integration is essential for the improvement of DRL models in general and allows for more efficient and real-time game play. This work delivers a comprehensive empirical study for these algorithms that not only verifies their capabilities in practice but also sets a state of the art through the prism of AI-driven game development. More than simply improving gaming AI, the developments could eventually apply to more sophisticated gaming environments, ongoing improvement of algorithms during execution, real-time adaptation regarding learning, and likely even robotics/autonomous systems. This study further illustrates the necessity for even-handed and conscientious application of AI in gaming—specifically regarding questions of fairness and addiction.

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A hybrid optical amplifier (HOA) is designed and optimized for the transmission of 40 dense wavelength division multiplexed system (DWDM) channels modulated at 10 Gbps having 25 GHz spacing at the edge of the L and U wavelength bands over more than 250 km. Multi-parameter optimization process is used to achieve the highest gain and best gain flatness. Different power combinations distributed among four lasers of a total forward pumping power (1270 mW) and total backward pumping power of either 730 mW or 850 mW are investigated for their effect on the hybrid amplifier gain and flatness. The best power distribution among the pumps provides about 31 dB overall gain with a flatness about 0.8 dB and noise figure ∼5.7 dB. It is found that the red-shifted pumps' wavelengths should be used in the forward direction, while the blue-shifted pumps' wavelengths should be used in the backward direction. [ABSTRACT FROM AUTHOR]

This paper focuses on the monitoring and prediction of the energy consumption of IT equipment to make informed decisions in terms of energy efficiency. The challenge with current monitoring systems lies in their specialization, scalability and integration complexities. To overcome these challenges, we propose a monitoring and prediction system for energy consumption of IT equipment. The proposed solution combines an adaptable, cost-effiective and energy-Efficient embedded device with open source software and a service-oriented architecture (SOA), which offers flexibility and integration capabilities, facilitating the easy inclusion of several workstation working from different environments. Several traditional Linear Regression (LR) models were evaluated prediction models using a temporal window of hour taking into account several features. As a result of the LR models evaluation, it is established that the Bayesian Ridge model was the best model since it presented the lowest error and the highest coefficient of determination. Finally, two approaches were evaluated to predict energy consumption: a Kernel Density Estimation (KDE)-based mechanism proposed to generate predictor variables in order to predict future energy using the best LR model, and a KDE-based energy model. Numerical results show that the energy prediction using KDE for the energy measurements provides lower time response than the LR based mechanism for the available dataset. [ABSTRACT FROM AUTHOR]

With the increase in voltage level and current capacity of the insulated gate bipolar transistor (IGBT), the avalanche effect has become an important factor limiting the safe operating area (SOA) of the device. The hole injection into the p/n junction on the backside of the IGBT after avalanche is the main feature that distinguishes the avalanche effect from other devices. In this paper, the avalanche breakdown characteristics of IGBT and the nature of current filament are investigated using theoretical analysis and numerical simulation, and the underlying physical mechanism controlling the nature of current filaments is revealed. The results show that the hole injection on the backside of the IGBT leads to an additional negative differential resistance (NDR) branch on the avalanche breakdown curve. The device's common-base current gain, αpnp, is a crucial factor in determining the current filament. As αpnp increases, the avalanche-induced current filament becomes stronger and slower, resulting in weaker avalanche robustness of the device. [ABSTRACT FROM AUTHOR]

This study presents a high-quality fiber ring laser created by integrating a semiconductor optical amplifier to generate amplified spontaneous emission with a four-subring resonator and utilizing the nonlinear polarization rotator effect. When the driven current of 400 mA, the laser exhibited a maximum power deviation of 0.204 dB and a wavelength deviation of 0.012 nm during a one-hour testing period. Furthermore, utilizing a delayed self-heterodyne measurement system, we measured the linewidth of the self-made fiber laser to be 23 kHz. [ABSTRACT FROM AUTHOR]

Organonitrates (ON) and nitroxy-organosulfates (NOS) are important components of secondary organic aerosols (SOAs). Gas-phase reactions of α-pinene (C10H16), a primary precursor for several ON compounds, are fairly well understood although formation pathways for NOS largely remain unknown. NOS formation may occur via reactions of ON and organic peroxides with sulfates as well as through radical-initiated photochemical processes. Despite the fact that organosulfates (OS) represent a significant portion of the organic aerosol mass, ON and NOS formation from OS is less understood, especially through nighttime heterogeneous and multiphase chemistry pathways. In the current study, surface reactions of adsorbed α-pinene-derived OS with nitrogen oxides on hematite and kaolinite surfaces, common components of mineral dust, have been investigated. α-Pinene reacts with sulfated mineral surfaces, forming a range of OS compounds on the surface. These OS compounds when adsorbed on mineral surfaces can further react with HNO3 and NO2, producing several ON and NOS compounds as well as several oxidation products. Overall, this study reveals the complexity of reactions of prevalent organic compounds leading to the formation of OS, ON, and NOS via heterogeneous and multiphase reaction pathways on mineral surfaces. It is also shown that this chemistry is mineralogy-specific.

The object of the study is a patient health monitoring system that uses service-oriented architecture (SOA) and artificial intelligence (AI) to integrate and analyze medical data. Such a system integrates data from a variety of sources, including medical devices, health apps, electronic health records, and wearables and physiological performance recorders, providing a comprehensive approach to health monitoring. Thanks to SOA, the system is able to process large arrays of data in real time, providing the opportunity to quickly process and analyze them. This allows medical professionals to get a comprehensive picture of patients’ health, taking into account both long-term trends and real-time indicators. One of the most challenging areas is ensuring effective integration and processing of disparate data from various medical devices and applications for accurate diagnosis and prognosis of diseases. It is also important to create a system that is easily scalable and can be adapted to the needs of different medical facilities and various monitoring systems. As a result of the research, it is concluded that the use of SOA allows creating flexible and scalable systems capable of integrating a wide range of medical devices and applications. The use of AI in these systems makes it possible to automatically detect deviations in health indicators, recognize pathologies in the early stages and predict the possible development of diseases. This is due to the fact that the proposed architecture has a number of features, in particular, the ability to collect, process and analyze large volumes of medical data in real time. Artificial intelligence algorithms provide high accuracy of diagnosis and forecasting thanks to the ability to quickly process complex data and find hidden patterns. Thanks to this, it is possible to obtain accurate and reliable indicators of the state of health of patients. Compared to similar known systems, it provides such advantages as increased efficiency of medical care, reduced risk of complications, early detection of diseases and a personalized approach to patient treatment, as well as the concentration of all data in one system. [ABSTRACT FROM AUTHOR]

Smart factories are focusing on bridging the gap between physical and cyber-physical systems through software applications. This article proposed an efficient and sufficient data acquisition, storing and processing real-time monitoring information, response, and feedback in milling process monitoring. A methodology to enable integration between service-oriented IoT-based monitoring with interpreted information for open architecture CNC system was presented. The proposed system comprises four main layers: the perception layer, communication layer, application layer, and CNC machine. The developed system was validated through two case studies. First, the developed system successfully enabled data flow through the validation, from CNC machine back to CNC machine. Secondly, the reading of temperature, vibration, and electric current monitoring is higher for the worn cutting tool than the new cutting tool. Third, the percentage difference between new and worn cutting tools for temperature monitoring is up to 3.38%, and for vibration monitoring, it is up to 78.93%. Fourth, the electric current reading is proportional to cutting force as the reading of electric current on cutting insert is higher than reading before cutting tool insert with percentage differences more than 8.33% up to 20%. Based on the findings, it was summarized that the developed integrated monitoring system is feasible enough based on the performance and highly sensitive to any changes that occurred during the machining process, specifically on the cutting tool condition. In the future, this integrated monitoring system could be applied to other open CNC machine-based plug and play. [ABSTRACT FROM AUTHOR]

Electricity is becoming the most important resource for environmental development. Hence, Global warming has led to the acceptance of electric vehicles as the most efficient alter- native to internal combustion engines. The growing number of electric vehicles on the road cannot be charged by conventional fossil fuel power grids because they are neither economical nor efficient. Hybrid power systems for charging stations are the main source of charge for electric vehicles. Here hybrid involves two energy sources, namely wind and solar. Therefore, this paper aims to optimize the cost and reliability of the Hybrid Wind-Solar Power Charging Station for Electric Vehicles (HWSCSEV). Solar energy is converted into electric power by solar panels, and wind energy is converted into electric power by wind turbines with optimum reliability and minimum cost. A few recent metaheuristics techniques, such as the Osprey Optimization Algorithm (OOA), Pelican Optimization Algorithm (POA), Skill Optimization Algorithm (SOA), Velocity Pausing Particle Swarm Optimization (VPPSO) are successfully used for the cost and reliability calculation of the proposed model. Furthermore, the obtained results of these techniques are contrasted with each other outcomes for the best reliability and cost of the HWSCSEV. Finally, algorithms are effectively analyzed based on convergence rate, computational time, Friedman ranking test, and statistical results. [ABSTRACT FROM AUTHOR]

This document is a special issue of the journal Nonlinear Studies, featuring interdisciplinary perspectives in applied mathematics. It includes articles on consumer behavior, diffusion of innovation, numerical methods, game theory, and optimization, covering domains such as engineering, management, and physics. The papers have undergone peer review and offer original and high-quality research. The document compiles research contributions on reliability analysis, mathematical modeling, differential equations, and optimization problems, with references for further exploration. The authors express gratitude to the contributors and reviewers for their valuable contributions. [Extracted from the article]

Web service-based applications are applications that use Restful to exchange data over the network and interact directly with other applications. One of the advantages of web service-based applications is that many applications can access services on different platforms. This is very beneficial in the business sector if a company or organization builds applications on a large scale, and application development is carried out in stages. With the characteristics of web service-based applications, namely a neutral platform, it is very easy to integrate systems when the development process is carried out in stages. Therefore, this research will design a web service-based application based on business processes, especially Kredit Usaha Rakyat (KUR) between PT ABC Credit Guarantee Company and PT Bank XYZ. The methods used are Thomas Erl's SOA methods and SOMA. The results obtained are a method that will be a guide for producing web service-based KUR Online Guarantee applications with PT Bank XYZ. [ABSTRACT FROM AUTHOR]

The world presents us with two contradictory situations. Firstly, most people encounter the problem of lacking necessary hardware and/or software resources. Secondly, some individuals or organizations possess these resources but fail to utilize them to their full potential. The proposed solution is to provide underutilized resources to those who require them. However, it is important to ensure that there is no illegal copying or pirating of software. Instead, the owners of these resources can offer their services by processing data remotely. The Peer-to-peer (P2P) paradigm is used primarily for collaborative systems over a network. This article proposes a Hybrid P2P-Based collaborative architecture that employs HTTP transport to provide data remote-treatment services. In our peer-to-peer (P2P) solution, one peer provides a service, such as a software application capable of processing specific data, while another peer actively searches for this software and sends its own data to be processed by it. In this scenario, the second peer can utilize the software without encountering piracy issues, given that the first peer executes the software locally and adheres to the appropriate license. By employing HTTP for communication, it enables collaborative interaction among heterogeneous peer platforms. This system enables individuals without essential hardware and/or software resources to leverage the resources provided by others. The simulation results conducted using PeerSim simulator, are encouraging, indicating that the proposed architecture can serve as a reliable solution for collaboration between peers. The outcomes of the simulation demonstrate a significant level of satisfaction across the essential metrics we defined to assess the effectiveness of our solution. particularly in terms of responsiveness to requests, resulting in approximately 80% satisfaction and 20% dissatisfaction rates for requests. [ABSTRACT FROM AUTHOR]

In the present scenario of growing demand for high data rate, the need for free-space optical communication (FSOC) system around us has become an essential feature of communication systems. FSOC offers seamless communication with much higher data rate possessing advantages of both the optical fiber communication and wireless communication. The study of different mitigation techniques presently in use is important. One such method involves mitigation of attenuations by the use optical amplifiers of different types. Effects of different optical amplifiers have been studied in this present paper. The moderate turbulence scenario is considered with atmospheric and geometric losses at data rate of 10 GBPS for a 4-channel wavelength division multiplexing (WDM)-based FSOC system. The measuring parameters chosen are Q-factor, bit error rate (BER) and eye diagram. It has been observed that the erbium-doped fiber amplifier (EDFA) and the EDFA–EDFA hybrid amplifier give consistent good quality communication for long-distance transmission with large and clear eye opening and acceptable BER of 9.32E−10 and 8.21E−09 at maximum distance of 4 km, while the Raman amplifier and semiconductor optical amplifier (SOA) do not approach the maximum reach and fail to achieve acceptable performance beyond 2 km and 1 km, respectively. [ABSTRACT FROM AUTHOR]

Naphthalene is the most abundant polycyclic aromatic hydrocarbon (PAH) in vehicle emissions and polluted urban areas. Its atmospheric oxidation products oxygenated compounds potentially harmful for health and/or contributing to secondary organic aerosol (SOA) formation. Despite its impact on air quality, its complex structure and lack of data mean that no detailed scheme of naphthalene gaseous oxidation for SOA formation and speciation has yet been established. This study presents the construction of the first near-explicit chemical scheme for naphthalene oxidation by OH including kinetic and mechanistic data. The scheme redundantly represents all the classical steps of atmospheric organic chemistry (i.e. oxidation of stable species, peroxy radical formation and reaction and alkoxy radical evolution) integrating therefore fragmentation or functionalization pathways and the influence of NOx on secondary compound formation. Missing kinetic and mechanistic data were estimated using structure-activity relationships (SARs) or by analogy with existing experimental or theoretical data. The proposed chemical scheme involves 392 species (237 stable species, including 93% of the major molar masses observed in previous experimental studies) and 503 reactions with products. A first simulation reproducing experimental oxidation in oxidation flow reactor under high NOX conditions shows a simulated SOA mass in the same order of magnitude as experimentally observed, with an error of -12%. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform's real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond. [ABSTRACT FROM AUTHOR]

—Hospital information system, as an integrated system consists of complex, diverse, and heterogeneous information systems, which supports all clinical, administrative, and financial activities of patients; therefore, it faces interoperability problems, which is a critical feature of data sharing and integration. In this regard, a reference architecture will be presented for the integration of different systems in the hospital which comprises system, information and software architecture. This architecture emphasizes interoperability with eight layers of user interface and application, services, data collection and storage, integration, external application systems, communication and information infrastructure, management, and security/privacy, based on service orientation. It will be evaluated by ATAM method, based on ten scenarios. It shows the presented architecture will satisfy all functional and non-functional requirements such as interoperability between different information systems, reduction of information redundancy and development cost and time, scalability and accessibility.

Meta-heuristic approaches evaluate the optimum size and position of flexible alternative current transmission systems power systems. The seeker optimization algorithm (SOA) technique was used to solve power engineering optimization problems with better performance than traditional approaches. This article shows the application of SOA for optimal setting and allocation of thyristor-controlled series compensators (TCSCs) in a transmission line. TCSC devices are used to improve transmission systems’ capacity and voltage profile by controlling the transmission line reactance. The IEEE 30 bus system, as well as the Iraqi Super Grid 400 kV system, is used as a test system to illustrate the technique used. Results showed that the installation of TCSC unites the aims to reduce the voltage deviation, reduce the losses of active/reactive power, and increase transmission line reserves over thermal limits. TCSC devices are very effective for the better use of existing installations without sacrificing the stability margin. SOA is intended to identify the best location and size of TCSC devices that resolve the technological difficulties of reducing the TCSC devices’ costs.

The purpose of this research is to propose a System Architecture to facilitate the IT Talent ecosystem using a service-oriented approach. The need for this is important to support digital transformation in the IT Talent ecosystem. Human resources in the IT field are one of the key factors in implementing IT in organizations. However, the availability of IT human resources has not been able to meet the needs and challenges of the organization in synergizing IT and business. Meanwhile, on the other hand, the qualifications of IT human resources do not meet the existing competency standards. In this research, we use a service-oriented system development method. It consists of three stages, such as (1). Analysis and Observation, (2). Analysis from an in-depth interview, and (3) System Architecture Design, which includes Analysis Features of the Systems, Service Analysis and Identification, Specification of Architecture, and Layering. The novelty and findings of this research are a system architecture, which is called a middleware architecture, that can bridge entities in the IT Talent ecosystem to provide and use services to each other for support collaboration. In this study, we proposed a system architecture that acts as middleware to support collaboration and integration in the IT Talent ecosystem. We proposed TALENT-IT, which acts as a service bus mechanism. We used a service-oriented approach to develop this platform. The results of this study are: list of features, list of services, SOA layer, SOA architecture, and monetization feasibility and challenges. Doi: 10.28991/HIJ-2023-04-04-03 Full Text: PDF

Optical amplifiers are very essential part of any optical fiber communication system for attenuation free transmission of signals. For our research work, a 420-channel ultra-dense wavelength division multiplexing system with 12.5 GHz channel spacing and a data rate of 100 Gbps per channel is designed based on advanced modulation technique namely differential quadrature phase shift keying and investigated the suitable cascaded arrangement of the conventional optical amplifiers such as erbium-doped fiber amplifier, semiconductor optical amplifier (SOA) and RAMAN amplifier. The investigation has been done based on some parameters such as receiver sensitivity, probability of error, quality factor (Q-factor), eye closure, received output power, and bit error rate (BER) with varying distances and applied input power. The simulation results have reported that the cascaded arrangement of the RAMAN-SOA-RAMAN amplifiers is the best suitable arrangement for signal transmission using an optical fiber communication system with less effect of attenuation. Minimum receiver sensitivity of − 30 dBm, minimum probability of error of 0.33, minimum eye closure value of 1.24 dB, and maximum Q-factor of 16.5 dB with varying distances have been reported. Also, the minimum BER of 10−11 and maximum output power of − 31 dBm with varying applied input power has been reported. [ABSTRACT FROM AUTHOR]

The popularity of microservice architecture abbreviated MSA has attracted researchers and practitioners in recent years. MSA aims to divide the complex monolithic system from the service to the database into several autonomous blocks called microservices. In this way, updates and deployment of each service can be managed independently. The dissociation of data storage for each microservice is one of the particularities of this architecture. However, the manual implementation of certain tasks in the design and development of the MSA is time-consuming particularly with regard to data management. The objective of this article is therefore to automatically generate the data structure of a microservice from ReLEL in order to systematically derive the MSA databases. Our approach is based on the Praxeme methodology. It is realized in two stages. First, the semantic aspect is derived. Then, we develop ATL derivation rules to design the data structure. [ABSTRACT FROM AUTHOR]

Hospital information system, as an integrated system consists of complex, diverse, and heterogeneous information systems, which supports all clinical, administrative, and financial activities of patients; therefore, it faces interoperability problems, which is a critical feature of data sharing and integration. In this regard, a reference architecture will be presented for the integration of different systems in the hospital which comprises system, information and software architecture. This architecture emphasizes interoperability with eight layers of user interface and application, services, data collection and storage, integration, external application systems, communication and information infrastructure, management, and security/privacy, based on service orientation. It will be evaluated by ATAM method, based on ten scenarios. It shows the presented architecture will satisfy all functional and non-functional requirements such as interoperability between different information systems, reduction of information redundancy and development cost and time, scalability and accessibility. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) emitted from terrestrial plants contribute substantially to ozone (O3) and secondary organic aerosol (SOA) formation in the troposphere. Accurate estimation of BVOC emissions is highly challengeable with a variety of uncertainties, one of which is the use of default emission factors (EFs) particularly for underrepresented regions without local data. In this study, locally measured BVOC‐EFs in south China, a subtropical region with abundant vegetation, were used to update regional BVOC emissions as estimated by the Model of Emissions of Gases and Aerosols from Nature (MEGAN). These EFs were recently determined in situ with characterized dynamic chambers for the emissions of isoprene, monoterpenes, and sesquiterpenes from tree species. The Community Multiscale Air Quality (CMAQ) model was then employed to see how much the regional O3 and SOA production is altered with the updated BVOC emissions. Results revealed lower BVOC emission estimates in south China when using the localized EFs than the MEGAN default ones, particularly for sesquiterpenes with a notable average reduction rate of approximately 40%. Using the updated BVOC emissions improved model O3 predictions in all seasons when compared to surface O3 monitoring, yet the lower BVOC emissions resulted in modeled O3 and SOA concentrations decreased by up to −6 ppb and −1.5 μg m−3, respectively, throughout south China. This study highlights the significance of localized EFs in refining emission estimates and air quality predictions in regions with a wealth of vegetation. Plain Language Summary: Terrestrial plants can emit a variety of biogenic volatile organic compounds (BVOCs), such as isoprene and terpenes. These BVOCs significantly impact tropospheric ozone (O3) and secondary organic aerosol (SOA) formation, and therefore accurate BVOC emission estimates are crucial for air quality modeling particularly in regions with a wealth of vegetation. However, in many regions local BVOC emission factors (EFs) are unavailable and using the model default EFs may bias the regional BVOC emissions. Here we carried out in situ measurements of EFs with well characterized dynamic chambers in south China. With these local EFs, the BVOC emissions was estimated by the MEGAN model, revealing a reduction in BVOC emissions in south China than those using the default EFs. When compared the modeling results with those from surface monitoring network, model performance in the prediction of O3 was improved with updated BVOC emissions incorporated into the air quality modeling. Additionally, our modeling outcomes illustrated that by using the updated BVOC emissions, predicted O3 and SOA levels could be notably reduced in specific regions. This study highlights the importance of integrating region‐specific data to enhance the precision of BVOC emission estimates and improve air quality predictions, particularly in regions with abundant vegetation. Key Points: Locally measured Biogenic volatile organic compound (BVOC) emission factors were used to update BVOC emissions in south ChinaUpdated lower regional BVOC emissions improved model prediction of O3 when compared to surface monitoringModeled O3 and secondary organic aerosol concentrations became lower with the updated BVOC emissions in south China [ABSTRACT FROM AUTHOR]

In the present work, the performance of the all-optical AND logic gate using four wave mixing in wide band semiconductor optical amplifier (SOA) has been analyzed for different modulation formats. The two binary inputs of all-optical AND gate are modulated by return to zero and non-return to zero (NRZ) modulation formats. The quality factor (Q-factor) has been examined on the basis of critical parameters of SOA. The obtained results show that the acceptable Q-factor (>6) is possible with the proposed design at input signal power even as low as 50 μW (−13 dBm), when the data is modulated in NRZ on-off keying (OOK) format. [ABSTRACT FROM AUTHOR]

The demand for larger bandwidth to carry high capacity data in optical networks is increasing. Thus, it is expected to replace all digital logic with optical logic soon. Privacy and data security are also of utmost importance in all-optical networks. Generally, all-optical encryption has been carried out using (i) highly nonlinear fiber (HNLF) and (ii) Semiconductor Optical Amplifier (SOA) with interferometric structures. However, the former is limited by the requirement of a lengthy fiber and the latter one with more number of nonlinear components. In this work, an all-optical encryption process using nonlinear effects and four-wave mixing (FWM) in SOA without interferometric structure is presented for different number systems. The basic idea of the proposed system is to use delay operations which are carried out by duo binary modulation units whereas the encryption using single SOA is possible by FWM nonlinear effect. This combination of novel design for different number systems is optimized through SOA structural parameters, pump and probe signal power, and wavelength. Through optimization, the performance improvement is assured by a good extinction ratio of 11.5 dB for the active region length of less than 200 µm. Compared to the existing techniques, the quality factor is 2.28 times and 1.52 times better than HNLF and SOA-MZI, respectively. [ABSTRACT FROM AUTHOR]

The impact of relative humidity (RH) on organic new particle formation (NPF) from the ozonolysis of biogenic volatile organic compounds (BVOCs) remains an area of active debate. Previous reports provide contradictory results, indicating both the depression and enhancement of NPF under conditions of high RH. Herein, we report on the impact of RH on NPF from the dark ozonolysis of cis-3-hexenyl acetate (CHA), a green-leaf volatile (GLV) emitted by vegetation. We show that RH inhibits NPF by this BVOC, essentially shutting it down at RH levels > 1%. While the mechanism for the inhibition of NPF remains unclear, we demonstrate that it is likely not due to increased losses of CHA to the humid chamber walls. New oxidation products dominant under humid conditions are proposed that, based on estimated vapor pressures (VPs), should enhance NPF; however, it is possible that the vapor phase concentration of these low-volatility products is not sufficient to initiate NPF. Furthermore, the reaction of C3-excited state Criegee intermediates (CIs) with water may lead to the formation of small carboxylic acids that do not contribute to NPF. This hypothesis is supported by experiments with quaternary O3 + CHA + α-pinene + RH systems, which showed decreases in total α-pinene-derived NPF at ~0% RH and subsequent recovery at elevated RH. [ABSTRACT FROM AUTHOR]

Despite the increase in the efficiency of energy consumption in information and communication technology, software execution and its constraints are responsible for how energy is consumed in hardware hosts. Consequently, researchers have promoted the development of sustainable software with new development methods and tools to lessen its hardware demands. However, the approaches developed so far lack cohesiveness along the stages of the software development life cycle (SDLC) and exist outside of a holistic method for green software development (GSD). In addition, there is a severe lack of approaches that target the analysis and design stages of the SDLC, leaving software architects and designers unsupported. In this article, we introduce our behavior-based consumption profile (BBCP) external Domain-Specific Language (DSL), aimed at assisting software architects and designers in modeling the behavior of software. The models generated with our external DSL contain multiple sets of properties that characterize features of the software’s behavior. In contrast to other modeling languages, our BBCP emphasizes how time and probability are involved in software execution and its evolution over time, helping its users to gather an expectation of software usage and hardware consumption from the initial stages of software development. To illustrate the feasibility and benefits of our proposal, we conclude with an analysis of the model of a software service created using the BBCP, which is simulated using Insight Maker to obtain an estimation of hardware consumption and later translated to energy consumption.