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In this paper, we propose a novel evolving clustering algorithm for streaming data entitled EdgeCluster. The proposed algorithm is resource efficient, making it suitable for use at edge devices with limited storage and computational capacity. The EdgeCluster is capable of modeling and monitoring a streaming data phenomenon and identifying outlying behavior. In parallel with the monitoring, the EdgeCluster algorithm dynamically maintains the set of clusters that models the phenomenon's normal behavioral scenarios by taking newly arrived data into account and updating the clustering model accordingly. The EdgeCluster algorithm is evaluated and benchmarked to another resource-Aware stream clustering algorithm, EvolveCluster, in two experimental data scenarios using synthetic and real-world datasets. © 2024 IEEE.

With the international drive to deploy green energies and decarbonized intermediates in the place of fossil fuel sources, a large number of developed countries are actively preparing for a future where hydrogen plays a strategic role as an energy storage medium. Producing and using hydrogen requires the rapid expansion of a dedicated, economically viable industrial sector. Nevertheless, questions on how to safely store, transport and distribute hydrogen remain an important priority today. In countries with existing natural gas transport grids, the possibility to retrofit these networks to store and transport hydrogen-natural gas blends is being studied. A key challenge is to evaluate how pressurized H2 would interact with steel structures with regards structural embrittlement of the latter, with a view to exploiting existing transport infrastructures for storage and transport applications. In this work, we evaluate the H2-performance of a non-hydrogen service ×65 pipeline steel. The cracking susceptibility of this steel grade has been evaluated at 100 bar H2 using slow strain rate testing, Constant strain testing and fracture toughness measurements. Accompanying hydrogen permeation tests under pressure provide diffusion data and elucidate the discussion. Exposures were carried out in dry or wet H2 and with or without H2S contamination at levels representative of biogas. The results underline that the impact of dry or wet hydrogen on this grade are moderate. The presence of traces of H2S together with humidity could risk seriously degrading the mechanical performance of the ×65 steel grade. © 2023 The Authors, Correspondence Address: C. Mendibide; Institut de La Corrosion (French Corrosion Institute), Part of RISE - ZA Du Parc, Fraisses, Secteur Gampille, F-42490, France

Global energy transition efforts towards decarbonization requires significant advances within the energy sector. In this regard, hydrogen is envisioned as a long-term alternative fuel for gas turbines. Accordingly, the gas turbine industry has expedited their efforts in developing 100% hydrogen compliant burners and associated auxiliary components for retrofitting the existing gas turbines. The utilization of hydrogen in gas turbines has some underlying challenges such as corrosion mainly originating from increased steam content in the hot gas path. In addition to corrosion, the gas turbine compressor is vulnerable to fouling which is the most commonly occurring fault in gas turbine operating over certain time window. Both faults are susceptible to performance and health degradation. To avoid expensive asset loss caused by unexpected downtimes and shutdowns, timely maintenance decision making is required. Therefore, simple, accurate and computationally efficient fault detection and diagnostics models become crucial for timely assessment of health status of the gas turbines. The present study encompassed development of a physics-based performance model of a 100-kWe micro gas turbine running on 100% hydrogen fuel. The model is validated with experimental data acquired from test campaigns at the University of Stavanger. Data synthesized from experimentally validated performance model are utilized further for training machine learning algorithms. To identify an accurate algorithm, various algorithms such as support vector machine, decision tree, random forest algorithm, k-nearest neighbors, and artificial neural network were tested. The findings from fault diagnostics process (classification) revealed that ANN outperformed its counterpart algorithm by giving accuracy of 94.55%. Similarly, ANN also showed higher accuracy in performance degradation estimation process (regression) by showing the MSE of training loss as low as ~0.14. The comparative analysis of all the chos

The performance of lithium-ion batteries (LIBs) is sensitive to the operating temperature, and the design and operation of battery thermal management systems reply on accurate information of LIBs' temperature. This study proposes a data-driven model based on neural network (NN) for estimating the temperature profile of a LIB module. Only one temperature measurement is needed for the battery module, which can assure a low cost. The method has been tested for battery modules consisting of prismatic and cylindrical batteries. In general, a good accuracy can be observed that the root mean square error (RMSE) of esitmated temperatures is less than 0.8 °C regardless of the different operating conditions, ambient temperatures, and heat dissipation conditions.

This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage.

Distributed control systems are part of the often invisible backbone of modern society that provides utility services like water and electricity. Their uninterrupted operation is vital, and unplanned stops due to failure can be expensive. Critical devices, like controllers, are often duplicated to minimize the service stop probability, with a secondary controller acting as a backup to the primary. A seamless takeover requires that the backup has the primary's latest state, i.e., the primary has to replicate its state to the backup. While this method ensures high availability, it can be costly due to hardware doubling. This work proposes a state replication solution that doesn't require the backup to store the primary state, separating state storage from the backup function. Our replication approach allows for more flexible controller redundancy deployments since one controller can be a backup for multiple primaries without being saturated by state replication data. Our main contribution is the partible state replication approach, realized with a distributed architecture utilizing a consensus algorithm. A partial connectivity-tolerant consensus algorithm is also an additional contribution.

The purpose of this work is to explore the role of the safe and optimal scheduling of thermal energy storage systems in intelligent buildings in promoting sustainable economic development under Digital Twins (DTs) technology. Phase Change Material (PCM) has high energy density, constant temperature storage, small footprint, and long service life. Here, PCM is first placed in the indoor building structure, and the DTs technology is introduced. In the development of intelligent buildings, the data generated by the energy storage system of intelligent buildings in the real space can be mapped to the virtual space in real time for simultaneous analysis. In addition, the PCM wall structure and thermal network DTs model are designed for the intelligent building. In addition, the PCW structure is used to build a thermal energy storage and dispatch model of the smart thermoelectric building based on DTs. Finally, the model is evaluated and analyzed experimentally. The analysis of system optimization power under different schemes indicates that the scheduling operation strategy of thermal energy storage of building walls can avoid overcharging or over-discharging batteries in the microgrid and reduce battery power consumption. Besides, the building wall energy storage capacity is always in the range of 0.2 ∼ 0.8 on the all-weather scale. Moreover, the model constructed here achieves significantly lower economic costs, environmental costs, and energy costs and a better energy-saving effect than the existing model. The model built here can serve as experimental reference for further digital energy storage in intelligent buildings and comprehensive energy utilization because of its superior safety performance and lower consumption.

This research focuses on the realization of rapid reconfiguration in a cloud manufacturing environment to enable flexible resource scheduling, fulfill the resource potential and respond to various changes. Therefore, this paper first proposes a new cloud and software-defined networking (SDN)-based manufacturing model named software-defined cloud manufacturing (SDCM), which transfers the control logic from automation hard resources to the software. This shift is of significance because the software can function as the “brain” of the manufacturing system and can be easily changed or updated to support fast system reconfiguration, operation, and evolution. Subsequently, edge computing is introduced to complement the cloud with computation and storage capabilities near the end things. Another key issue is to manage the critical network congestion caused by the transmission of a large amount of Internet of Things (IoT) data with different quality of service (QoS) values such as latency. Based on the virtualization and flexible networking ability of the SDCM, we formalize the time-sensitive data traffic control problem of a set of complex manufacturing tasks, considering subtask allocation and data routing path selection. To solve this optimization problem, an approach integrating the genetic algorithm (GA), Dijkstra's shortest path algorithm, and a queuing algorithm is proposed. Results of experiments show that the proposed method can efficiently prevent network congestion and reduce the total communication latency in the SDCM., QC 20220613

This work aims to study the role and current situation of Low-Carbon Technology (LCT) in realizing urban Carbon Neutrality goals and Sustainable Urban Development (SUD). This work focuses on the three main aspects of Carbon Neutrality by means of investigation: the new energy-supported LCTs, building-oriented Energy Conservation and Emission Reduction (ECER), and Carbon Capture, Storage, and Utilization (CCSU) technology. The research of the past ten years (2013–2022) is reviewed from the Web of Science, Google Academic, and other literature databases. Over 20,000 articles are selected by using the keywords "Carbon Neutrality Technology," such as "IEEE," "Elsevier," and other journal documents. Finally, more than 150 papers with high citation rates and relatively novel research results are selected for specific analysis. This work also establishes a case study of urban green development with multiple co-governance and a case study of building energy conservation and carbon reduction solutions. Finally, suggestions are put forward for achieving SUD. China's energy consumption and Carbon Dioxide (CO2) emissions have increased year by year, but the Carbon Peak has not yet been reached. About 85% of CCUS technologies extract CO2 from the air and use it for other emission reduction processes. In addition, the carbon storage time and storage efficiency need to be improved, and the general level can only reach about 60% of the storage efficiency. The secondary use of CO2 by technology is of practical significance to many industries and must be fully implemented from the perspective of the whole life cycle. Especially building energy consumption is a huge carbon emitter and poses a challenge for SUD. How to take effective measures to control emissions is still an arduous task.

© ESO 2014. Artículo firmado por 46 autores. This study makes use of the data provided by the Calar Alto Legacy Integral Field Area (CALIFA) survey http://www.califa.caha.es). Based on observations collected at the Centro Astronòmico Hispano Alemán (CAHA) at Calar Alto, operated jointly by the Max-Planck-Institut für Astronomie and the Instituto de Astrofísica de Andalucía (CSIC).CALIFA is the first legacy survey being performed at Calar Alto. The CALIFA collaboration would like to thank the IAACSIC and MPIA-MPG as major partners of the observatory, and CAHA itself, for the unique access to telescope time and support in manpower and infrastructures. The CALIFA collaboration thanks also the CAHA staff for the dedication to this project. The authors would like to thank the anonymous referee for comments that significantly improved the paper; Dimitri Gadotti for providing extensive help and advice with the image decomposition; Mike Dopita and Tim Heckman for their patience explaining the effects of shocks in superwinds; Daria Dubinovska for creating undistorted ACS PSF images from the Tiny Tim images and Carolin Villforth for further help with the ACS PSF; Lia Athanassoula for help interpeting the kinematic maps; Daniel Pomarede for help getting SD vision running; Jeremy Sanders and Roderik Johnstone for help interpreting the X-ray observations; Eva Schinnerer for pointing out the effect of beam smearing; all other interested researchers who have contributed with questions and comments following discussions and presentations of this work over the last 2 years. The numerical simulations were performed on facilities hosted by the CSC -IT Center for Science in Espoo, Finland, which are financed by the Finnish ministry of education. Funding and financial support acknowledgements: V. W. from the European Research Council Starting Grant (P.I. Wild SEDmorph), European Research Council Advanced Grant (P.I. J. Dunlop) and Marie Curie Career Reintegration Grant (P.I. Wild Phiz-e, We present optical integral field spectroscopy (IFS) observations of the Mice, a major merger between two massive (& 1011M⊙) gasrich spirals NGC 4676A and B, observed between first passage and final coalescence. The spectra provide stellar and gas kinematics, ionised gas properties and stellar population diagnostics, over the full optical extent of both galaxies with ∼1.6 kpc spatial resolution. The Mice galaxies provide a perfect case study highlighting the importance of IFS data for improving our understanding of local galaxies. The impact of first passage on the kinematics of the stars and gas has been significant, with strong bars likely induced in both galaxies. The barred spiral NGC 4676B exhibits a strong twist in both its stellar and ionised gas disk. The edge-on disk galaxy NGC 4676A appears to be bulge free, with a strong bar causing its “boxy” light profile. On the other hand, the impact of the merger on the stellar populations has been minimal thus far. By combining the IFS data with archival multiwavelength observations we show that star formation induced by the recent close passage has not contributed significantly to the global star formation rate or stellar mass of the galaxies. Both galaxies show bicones of high ionisation gas extending along their minor axes. In NGC 4676A the high gas velocity dispersion and Seyfert-like line ratios at large scaleheight indicate a powerful outflow. Fast shocks (vs ∼350 km s−1 ) extend to ∼ 6.6 kpc above the disk plane. The measured ram pressure (P/k = 4.8 × 106K cm−3 ) and mass outflow rate (∼ 8 − 20M⊙yr−1 ) are similar to superwinds from local ultra-luminous infrared galaxies, although NGC 4676A has only a moderate infrared luminosity of 3×1010L⊙. Energy beyond that provided by the mechanical energy of the starburst appears to be required to drive the outflow. Finally, we compare the observations to mock kinematic and stellar population maps extracted from a hydrodynamical merger simulation. The models show little, Unión Europea. FP7, European Research Council Starting, European Research Council Advanced, Marie Curie Career Reintegration, Mexican National Council for Science and Technology (CONACYT), Ramón y Cajal Program, Spanish Ministry of Economy and Competitiveness (MINECO), FP7 Marie Curie Actions of the European Commission, R. G. D. and R. G. B. from the Spanish, Agence Nationale de la Recherche, German BMBF, Spanish programme of International Campus of Excellence Moncloa (CEI), Emmy Noether-Programme of the German Science Foundation (DFG), I. M. P. from Spanish, Junta de Andalucía, FCT (Portugal), Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

The building envelope is of great significance for building thermal comfort, and photovoltaic heat (PV/T) can be effectively utilized through phase change heat storage technology to reduce heating energy consumption. In this paper, a phase change heat storage structure with subzone rotation is proposed for the building envelope. A numerical model of a triplex-tube LHTES unit is established by using the enthalpy-porosity method and verified by experimental data. The study employs the Taguchi method to select rotation speed in different regions and fin/tube wall material as variables. The effects of different variables on heat release rate and solidification time are investigated, and the interaction of each parameter on solidification performance is analyzed through signal-to-noise ratio. The findings indicate that when the inner and outer tube speeds are 0.3 rpm and 0.5 rpm, respectively, compared to the initial model where both inner and outer tube speeds are 0.1 rpm, the average temperature response rate and the average heat release rate are increased by 51.47% and 61.04%, respectively. Meanwhile, the solidification time is shortened by 40.49%. However, the release of the total heat is reduced by 0.66%. The study concludes that increasing rotation speed or solidification consistency of PCM in different areas is of great value in enhancing overall solidification performance. Finally, the specific effect of increasing regional rotation speed on the solidification process is studied through temperature/flow rate monitoring in the unit.

Tiny machine learning (TinyML) represents an emerging research direction that aims to realize machine learning on Internet of Things (IoT) devices. The current TinyML research seems to focus on supporting the deployment of deep learning models on microprocessors, while the models themselves are trained on high performance computers or clouds. However, in the resource/time constrained IoT contexts, it is more desirable to perform data analytics and learning tasks directly on edge devices for crucial benefits such as increased energy efficiency, reduced latency as well as lower communication cost.To address the above challenge, this paper proposes a tiny federated learning algorithm for enabling learning of Bayesian classifiers based on distributed tiny data storage, referred to as TFL-BC. In TFL-BC, Bayesian learning is executed in parallel across multiple edge devices using local (tiny) training data and subsequently the learning results from local devices are aggregated via a central node to obtain the final classification model. The results of experiments conducted on a set of benchmark datasets demonstrate that our algorithm can produce final aggregated models that outperform single tiny Bayesian classifiers and that the result of tiny federated learning (of Bayesian classifier) is independent of the number of data partitions used for generating the distributed local training data.

Modern information technology (IT) infrastructures are becoming more complex to meet the diverse demands of emerging technology paradigms such as 5G/6G networks, edge, and internet of things (IoT). The intricacy of these infrastructures grows further when hosting containerized workloads as microservices, resulting in the challenge to detect and troubleshoot performance issues, incidents or even outages of critical use cases like industrial automation processes. Thus, fine-grained measurements and associated visualization are essential for operation observability of these IT infrastructures. However, most existing observability tools operate independently without systematically covering the entire data workflow. This paper presents an integrated design for multi-stage observability workflows, denoted as DistributEd obServability frameworK (DESK). The proposed framework aims to improve observability workflows for measurement, collection, fusion, storage, visualization, and notification. As a proof of concept, we deployed the framework in a Kubernetes-based testbed to demonstrate the successful integration of various components and usability of collected observability data. We also conducted a comprehensive study to determine the caused overhead by DESK agents at the reasonably powerful edge node hardware, which shows on average a CPU and memory overhead of around 2.5 % of total available hardware resource.

For efficient energy exchanges in smart energy grids under the presence of renewables, predictions of energy production and consumption are required. For robust energy scheduling, prediction of uncertainty bounds of Photovoltaic (PV) power production and consumption is essential. In this paper, we apply several Machine Learning (ML) models that can predict the power generation of PV and consumption of households in a smart energy grid, while also assessing the uncertainty of their predictions by providing quantile values as uncertainty bounds. We evaluate our algorithms on a dataset from Swedish households having PV installations and battery storage. Our findings reveal that a Mean Absolute Error (MAE) of 16.12W for power production and 16.34W for consumption for a residential installation can be achieved with uncertainty bounds having quantile loss values below 5W. Furthermore, we show that the accuracy of the ML models can be affected by the characteristics of the household being studied. Different households may have different data distributions, which can cause prediction models to perform poorly when applied to untrained households. However, our study found that models built directly for individual homes, even when trained with smaller datasets, offer the best outcomes. This suggests that the development of personalized ML models may be a promising avenue for improving the accuracy of predictions in the future.

Power-to-gas (P2G) is a promising solution to the issue of non-dispatchable renewable power generation. However, the high investment costs and low energy efficiency of P2G systems pose challenges. This study designs a green hydrogen-based Energy Storage as a Service (ESaaS) mode to improve the economic efficiency of P2G systems. In this ESaaS mode, the P2G system acts as an energy trading hub. The ESaaS operator manages the system and enables microgrids to access energy storage services. In return, the ESaaS operator generates revenue through electricity and hydrogen trading. To characterize stakeholders' behaviors, we develop a cost-minimizing model for multi-energy microgrid operation and a revenue-maximizing model for P2G investment and operation. To coordinate stakeholders involved in cooperation, we employ Nash bargaining-based model reformulation to determine investment and operation decisions and benefit distribution. Then, logarithm transformation and trading price discretization are introduced to efficiently solve the non-convex and nonlinear reformulated model. Simulation experiments using Shanghai's electricity market and meteorological data demonstrate that the ESaaS mode achieves 100% recovery of surplus renewable electricity and increases the proportion of renewable energy in microgrids from 59% to 83%. The results suggest that green hydrogen-based ESaaS is a feasible solution to increase the utilization rate of renewable energy and bring considerable economic and environmental benefits to all stakeholders.

It remains a significant technical and economic challenge to fully power large-scale grids with intermittent renewable energy (RE). Meanwhile, due to the rapid decrease in the cost of RE power generation technologies in recent years, the number of real-world implementations and studies dedicated to the optimal capacity sizing of renewable off-grid systems has increased. However, a common approach in the literature is to rely on typical single-year meteorological and demand data. A negative effect of this assumption is that it does not consider the RE inter-annual variability, which might cause blackouts or oversizing the system and large curtailments. This study employs 43 years of hourly solar, wind, and demand data, coupled with different microgrid configurations, to evaluate the impact of diverse simulation periods on the total system cost, optimal RE mix, and system reliability. Our findings indicate that extended simulation periods considerably increased renewable energy systems (RES) reliability and that the resulting configurations can be up to 94% more robust than those obtained using a single year of data. Additionally, the optimal energy storage requirements increased when considering longer simulation periods, indicating that short simulation periods could underestimate energy storage capacities in off-grid systems. The overestimations or underestimations resulting from optimizations based on single-year data directly affect the long-term sustainability, reliability, and cost-effectiveness of the RES.

This study investigated the global changes in online music listening behaviors in response to COVID-19 and its restrictions (such as quarantine, school and workplace closures, and travel restrictions). In addition, the research included an examination of how friendship networks and online communication motives have moderated the effect of COVID-19 on music listening behaviors. The causal inference methods: difference in differences (DiD) and two-way fixed effects (TWFE), were conducted to analyze the online music listening behaviors and social interactions of 37,328 Last.fm users in 45 countries before and after the first wave of confinement. It was found that in response to COVID-19, the quantity, variety, and novelty of music consumption decreased, shifting toward mainstream artists, whereas individuals with more online social connections and communications showed the reverse behavior. Our research shows that online social interactions and community development significantly impact listeners’ behaviors and can be used as a guide to developing new design strategies for digital media, such as music, movies, and games.

The present study proposes and thoroughly examines a novel approach for the effective hybridization of solar and wind sources based on hydrogen storage to increase grid stability and lower peak load. The parabolic trough collector, vanadium chloride thermochemical cycle, hydrogen storage tank, alkaline fuel cells, thermal energy storage, and absorption chiller make up the suggested smart system. Additionally, the proposed system includes a wind turbine to power the electrolyzer unit and minimize the size of the solar system. A rule-based control technique establishes an intelligent two-way connection with energy networks to compensate for the energy expenses throughout the year. The transient system simulation (TRNSYS) tool and the engineering equation solver program are used to conduct a comprehensive techno-economic-environmental assessment of a Swedish residential building. A four-objective optimization utilizing MATLAB based on the grey wolf algorithm coupled with an artificial neural network is used to determine the best trade-off between the indicators. According to the results, the primary energy saving, carbon dioxide reduction rate, overall cost, and purchased energy are 80.6 %, 219 %, 14.8 $/h, and 24.9 MWh at optimal conditions. From the scatter distribution, it can be concluded that fuel cell voltage and collector length should be maintained at their lowest domain and the electrode area is an ineffective parameter. The suggested renewable-driven smart system can provide for the building's needs for 70 % of the year and sell excess production to the local energy network, making it a feasible alternative. Solar energy is far less effective in storing hydrogen over the winter than wind energy, demonstrating the benefits of combining renewable energy sources to fulfill demand. By lowering CO2 emissions by 61,758 kg, it is predicted that the recommended smart renewable system might save 7719 $ in environmental costs, equivalent to 6.9 ha of new reforestation.

The development of Digital Twins has enabled them to be widely applied to various fields represented by intelligent manufacturing. A Metaverse, which is parallel to the physical world, needs mature and secure Digital Twins technology in addition to Parallel Intelligence to enable it to evolve autonomously. We propose that Blockchain combined with other areas does not simultaneously require all of the basic elements. We extract the immutable characteristics of Blockchain and propose a secure multidimensional data storage solution called BlockNet that can ensure the security of the digital mapping process of the Internet of Things, thereby improving the data reliability of Digital Twins. Additionally, to address some of the challenges faced by multiscale spatial data processing, we propose a nonmutagenic multidimensional Hash Geocoding method, allowing unique indexing of multidimensional information and avoiding information loss due to data dimensionality reduction while improving the efficiency of information retrieval and facilitating the implementation of the Metaverse through spatial Digital Twins based on these two studies. © 2022 The Author(s)

Peer-to-peer (P2P) renewable power sharing within a building community is a promising solution to enhance the community's self-sufficiency and relieve the grid stress posed by the increased deployment of distributed renewable power. Existing studies have pointed out that the energy sharing potentials of a building community are affected by various factors including location, community scale, renewable energy system (RES) capacity, energy system type, storage integration, etc. However, the impacts of these factors on the energy sharing potentials in a building community are not fully studied. Being unaware of those factors’ impacts could lead to reduced energy sharing potentials and thus limit the associated improvement in energy and economic performances. Thus, this study conducts a comprehensive analysis of various factors’ impacts on the energy sharing performances in building communities. Two performance indicators are first proposed to quantify the energy sharing performances: total amount of energy sharing and energy sharing ratio (ESR). Then, parametric studies are conducted based on real electricity demand data in three countries to reveal how these factors affect the proposed indictors and improvements in self-sufficiency, electricity costs, and energy exchanges with the power grid. Next, a genetic algorithm based design method is developed to optimize the influential parameters to maximize the energy sharing potentials in a community. The study results show that the main influential factors are RES capacity ratio, PV capacity ratio, and energy storage system capacity. A large energy storage capacity can enhance the ESR. To achieve the maximized ESR, the optimal RES capacity ratio should be around 0.4 ∼ 1.1. The maximum energy sharing ratio is usually smaller in high latitude districts such as Sweden. This study characterizes the energy sharing performances and provides a novel perspective to optimize the design of energy systems in energy sharing communitie

Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems’ environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems’ security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach’s feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems’ constituents to learn about security threats in their environments collaboratively. © 2022 by the authors., open access

In recent years, A-site ordered half-doped double-perovskite manganites RBaMn2O6 (R=rare earth) have attracted much attention due to their remarkable physical properties and a prospect of application as magnetoresistive, multiferroic, and oxygen storage materials. The nature of the ground state in RBaMn2O6 as well as sequence of phase transitions taking place at cooling are not yet well understood due to complexity in both experimental and theoretical studies. Here we address the origin of the ground-state structure in PrBaMn2O6 as well as its electronic and magnetic properties. Utilizing GGA+U approach and specially designed strategy to perform structural optimization, we show that the system has two competing AFM-A and AFM-CE magnetic structures with very close energies. The AFM-A structure is a metal, while AFM-CE is an insulator and the transition to the insulating state is accompanied by the charge Mn3+/Mn4+, and orbital 3x2 − r2/3y2 − r2 orderings. This orbital ordering results in strong cooperative Jahn-Teller (JT) distortions, which lower the crystal symmetry. Our findings give a key to understanding contradictions in available experimental data on PrBaMn2O6 and open up the prospects to theoretical refinements of ground-state structures in other RBaMn2O6 compounds. © 2022 Elsevier B.V.

Tin diselenide (SnSe2) is a layered semiconductor with broad application capabilities in the fields of energy storage, photocatalysis, and photodetection. Here, we correlate the physicochemical properties of this van der Waals semiconductor to sensing applications for detecting chemical species (chemosensors) and millimeter waves (terahertz photodetectors) by combining experiments of high-resolution electron energy loss spectroscopy and X-ray photoelectron spectroscopy with density functional theory. The response of the pristine, defective, and oxidized SnSe2 surface towards H2, H2O, H2S, NH3, and NO2 analytes was investigated. Furthermore, the effects of the thickness were assessed for monolayer, bilayer, and bulk samples of SnSe2. The formation of a subnanometric SnO2 skin over the SnSe2 surface (self-assembled SnO2/SnSe2 heterostructure) corresponds to a strong adsorption of all analytes. The formation of non-covalent bonds between SnO2 and analytes corresponds to an increase of the magnitude of the transferred charge. The theoretical model nicely fits experimental data on gas response to analytes, validating the SnO2/SnSe2 heterostructure as a suitable playground for sensing of noxious gases, with sensitivities of 0.43, 2.13, 0.11, 1.06 [ppm]−1 for H2, H2S, NH3, and NO2, respectively. The corresponding limit of detection is 5 ppm, 10 ppb, 250 ppb, and 400 ppb for H2, H2S, NH3, and NO2, respectively. Furthermore, SnSe2-based sensors are also suitable for fast large-area imaging applications at room temperature for millimeter waves in the THz range. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

The increasing demand for computer data storage with a higher recording density can be addressed by using smaller magnetic objects, such as bubble domains. Small bubbles predominantly require a strong saturation magnetization combined with a large magnetocrystalline anisotropy to resist self-demagnetization. These conditions are well satisfied for highly anisotropic materials. Here, we study the domain structure of thin Nd2Fe14B lamellae. Magnetic bubbles with a minimum diameter of 74 nm were observed at room temperature, approaching even the range of magnetic skyrmions. The stripe domain width and the bubble size are both thickness dependent. Furthermore, a kind of bubble was observed below the spin-reorientation transition temperature that combine bubbles with opposite helicity. In this paper, we reveal Nd2Fe14B to be a good candidate for a high-density magnetic bubble-based memory. © 2022 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/"Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Open access publication funded by the Max Planck Society.

Shortening inter-vehicle distance can increase traffic throughput on roads for increasing volume of vehicles. In the process, traffic accidents occur more frequently, especially for multi-car accidents. Furthermore, it is difficult for drivers to drive safely under such complex driving conditions. This article investigates multi-vehicle longitudinal collision avoidance issue under such traffic conditions based on the Advanced Emergency Braking System (AEBS). AEBS is used to avoid collisions or mitigate the impact during critical situations by applying brake automatically. Hierarchical multi-vehicle longitudinal collision avoidance controller is proposed to guarantee safety of multi-cars using Vehicle-to-Infrastructure (V2I) communication capability in addition to radar for longitudinal vehicle control. High-level controller is designed to ensure safety of multi-cars and optimize total energy by calculating the target braking force. Vehicle network is used to get the key vehicle-road interaction data and constrained hybrid genetic algorithm (CHGA) is adopted to decouple the vehicle-road interactive system,which can obtain the maximum ground friction through vehicle-road data, and provide key predictive parameters for multi-vehicle safety controller. Lower level non-singular Fractional Terminal Sliding Mode(NFTSM) Controller is built to achieve control goals of high-level controller. Simulations are carried out under typical driving conditions. Results verify that the proposed system in this article can avoid or mitigate the collision risk compared to the vehicle without this system. © 1967-2012 IEEE.

Traditional hydrometric data combined with environmental tracers such as water stable isotopes contributes to improve the understanding of catchment hydrology. Nevertheless, the application of isotopic tracers in headwater catchments of the tropical Andes with deep soils and permeable parent material influenced by recent volcanism remains limited. In this study, the stable isotopic composition of precipitation, soil water, wetlands, and streamflow was studied to provide insights into the hydrology of a small tropical Andean catchment with deep and permeable volcanic soils, ash layers, and fractured bedrock resulting from Holocene volcanic activity. Although local precipitation forms under isotopic equilibrium conditions, the stable isotopic composition of precipitation is influenced by atmospheric moisture recycling processes. The spatial and temporal variability of isotopic signals and the analysis of inverse transit time proxies (ITTPs) of surface (streamflow) and subsurface (soil and wetlands) waters indicate that vertical flow paths through the deep volcanic ash soils are dominant across the catchment. The strongly damped isotopic composition of these waters points to high soil and wetlands water storage, increasing the transit time or age of stream water in the hydrological system. These findings indicate that water mobilizing through subsurface flow paths–that is, volcanic soils, ash layers, and cracks in the fractured bedrock resulting from Holocene volcanism–is the main contributor to streamflow generation. Comparison with previously published work from Andean catchments and other volcanic areas shows the diversity of hydrological conditions that can be found as a result of pedological and lithological differences shaped by volcanic activity. Therefore, site-specific strategies may be needed to improve water resources management. © 2022 John Wiley & Sons Ltd.

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences., QC 20220608

Serverless computing emerges as an architecture choice to build and run containerized data-intensive pipelines. It leaves the tedious work of infrastructure management and operations to the cloud provider, allowing developers to focus on their core business logic, decomposing their jobs into small containerized functions. To increase platform scalability and flexibility, providers take advantage of hardware disaggregation and require inter-function communication to go through shared object storage. Despite data persistence and recovery advantages, object storage is expensive in terms of performance and resources when dealing with data-intensive workloads. In this paper, we present Floki, a data forwarding system for direct and inter-function data exchange proactively enabling point-to-point communication between pipeline producer-consumer pairs of containerized functions through fixed-size memory buffers, pipes, and sockets. Compared with state-of-practice object storage, Floki shows up to 74.95× of end-to-end time performance increase, reducing the largest data sharing time from 12.55 to 4.33 minutes, while requiring up to 50,738× fewer disk resources, with up to roughly 96GB space release., This work was partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P, the Ministry of Science under contract PID2019- 107255GB-C21/AEI/10.13039/501100011033, and PID-126248OB-I00, and the Generalitat de Catalunya under contract 2014SGR1051., Peer Reviewed, Postprint (author's final draft)

Prosumer operators are dealing with extensive challenges to participate in short-term electricity markets while taking uncertainties into account. Challenges such as variation in demand, solar energy, wind power, and electricity prices as well as faster response time in intraday electricity markets. Machine learning approaches could resolve these challenges due to their ability to continuous learning of complex relations and providing a real-time response. Such approaches are applicable with presence of the high performance computing and big data. To tackle these challenges, a Markov decision process is proposed and solved with a reinforcement learning algorithm with proper observations and actions employing tabular Q-learning. Trained agent converges to a policy which is similar to the global optimal solution. It increases the prosumer's profit by 13.39% compared to the well-known stochastic optimization approach., QC 20230614

This paper introduces a multi-period, two-dimensional vehicle loading and dispatching problem, called Two-Dimensional Vehicle Loading and Dispatching Problem with Incompatibility Constraints (VLDP). The problem concerns preparing a single-origin single-destination transportation plan of loading required orders to vehicles at the origin and dispatching the vehicles to deliver the orders to the destination within their due dates. The decision maker uses their own fleet of vehicles, with each vehicle having a fixed transportation cost per trip, and may outsource additional vehicles at a higher cost. VLDP involves constraints regarding the due dates of the orders, pairwise incompatibility of orders packed in the same vehicle, incompatibility of orders and vehicles, as well as area and weight capacity of the vehicles. An order can be delivered earlier than its due date, incurring an earliness penalty due to storage requirements at the destination. The objective is to minimize the total vehicle usage and earliness penalty costs. A Mixed-Integer Linear Programming model (MILP) is provided, as well as an Adaptive Large Neighbourhood Search (ALNS) algorithm. Results of computational experiments on instances derived from real-world data show the effectiveness of the ALNS algorithm. © 2022 Elsevier B.V., We gratefully thank Xinerji Technology for data and guidance and Selen Akyol for her assistance with the data preparation and computational study.