Overcoming the strength-ductility trade-off dilemma is paramount for advanced materials engineering. Herein, we prepared 7075 aluminium alloys with superior strength and ductility via additive friction stir deposition (AFSD) and subsequent heat treatment. Compared with the commercial base material, the heat-treated 7075 aluminium alloy maintained a high ultimate tensile strength of 556 MPa, while the uniform elongation increased from 12.2% to 26.7%, exhibiting the highest strength-ductility synergy reported among commercial Al-Zn-Mg-Cu alloy systems. Grain boundary sliding was activated via the equiaxed grains to accommodate substantial plastic strain. This method provides a promising and cost-effective pathway for developing strength-ductility on Al-Zn-Mg-Cu alloys.
Materials of engineering and construction. Mechanics of materials
Oscar Lahuerta, Claudio Carretero, Luis Angel Barragan
et al.
This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction heating system. By utilizing observable variables, specifically the voltage and current waveforms from the inductor system, the proposed architecture aims to accurately estimate key electrical parameters, i.e., equivalent resistance and inductance, which vary over time due to the nonlinear magnetic properties of the induction load. To assess the performance of the proposed PINN architecture, a comparison with results obtained using an extended Kalman filter was conducted, which serves as a benchmark for this type of task. In addition, the robustness of both approaches was assessed by introducing varying levels of uncertainty in the observable variables. Finally, the effectiveness of both methods was validated through the analysis of experimental measurements collected from a functional prototype.
Konstantinos Bertsouklis, Eireni Dima, Konstantina-Stamatina Arfani
et al.
Employing rare or threatened species in ornamental horticulture offers a dual benefit by promoting climate adaptation and enhancing species conservation. <i>Kosteletzkya pentacarpos</i>, an endangered halophytic species, holds potential for introduction into the nursery industry, but efficient propagation methods are lacking. The present study investigated the in vitro propagation of the species using nodal explants excised from aseptic seedlings. A two-stage in vitro culture system was tested with thidiazuron (TDZ) promoting shoot initiation at low concentrations, while higher levels induced callus formation. Transferring micro-shoots to hormone free-, Murashige and Skoog medium (MS) promoted the highest shoot multiplication and elongation. The effect of sodium chloride (NaCl) on in vitro culture was also assessed, with MS media containing up to 5.0 g L<sup>−1</sup> NaCl supporting successful culture establishment. Spontaneous rooting was observed during various stages of the culture process. Micro-shoots were rooted at 100.0% on half strength MS medium with or without indole-3-butyric acid, and all plantlets were successfully acclimatized in a peat–perlite substrate (1/1, <i>v</i>/<i>v</i>). Thus, the present protocol provides an efficient system for the large-scale propagation of <i>K. pentacarpos</i> serving as a valuable tool for its conservation and the potential use in the nursery industry.
As interest in autonomous maritime technology continues to grow, various collision avoidance algorithms for autonomous vessels have been developed. However, evaluating and comparing the performance of these algorithms presents challenges due to the significant influence of factors such as the number of obstacles, specific encounter scenarios and obstacle arrangements. To address these challenges, the present study employs a Monte Carlo simulation technique to quantitatively evaluate the performance of ship collision avoidance algorithms in port congestion zones. To accurately reflect the conditions of real congested harbor areas with high ship traffic, four major encounter scenarios were defined, with the positions, velocities, and heading angles of obstacles randomly generated within predefined ranges to incorporate randomness into the simulation environment. Using the developed Monte Carlo simulation technique, the performance of the Worst Case Velocity Obstacle (WVO) algorithm and a hybrid algorithm combining WVO with modified Artificial Potential Field algorithm(APF) were qualitatively evaluated. The simulation results revealed the limitations of the WVO algorithm, particularly in scenarios involving crossing and converging encounters, due to frequent heading changes and passive rudder actions. In contrast, the hybrid algorithm, which incorporate WVO algorithm with modified APF method, demonstrated improved collision avoidance performances, including maintaining greater safe distance and reducing collision occurrence through actively using rudder angles.
Detecting dynamic and amorphous objects like fire and smoke poses significant challenges in object detection. To address this, we propose Dual-Path Cascade Stochastic DETR (Dual-Path CSDETR). Unlike Cascade DETR, our model introduces cascade stochastic attention (CSA) to model the irregular morphologies of fire and smoke through variational inference, combined with a dual-path architecture that enables bidirectional feature interaction for enhanced learning efficiency. By integrating object-centric priors from bounding boxes into each decoder layer, the model refines attention mechanisms to focus on critical regions. Experiments show that Dual-Path CSDETR achieves 94% AP50 on fire/smoke detection, surpassing deterministic baselines.
This paper presents the Dynamic Simultaneous Multi-threaded Architecture (DSMT). DSMT efficiently exe-cutes multiple threads from a single program on a SMT processor core. To accomplish this, threads are generated dynamically from a predictable flow of control and then executed speculatively. Data obtained during the single context non-speculative execution phase of DSMT is used as a hint to speculate the posterior behavior of multiple threads. DSMT employs simple mechanisms based on state bits that keep track of inter-thread dependencies in registers and memory, synchronize thread execution, and control recovery from misspeculation. Moreover, DSMT utilizes a novel greedy policy for choosing those sections of code which provide the highest performance based on their past execution history. The DSMT architecture was simulated with a new cycle-accurate, execution-driven simulator. Our simulation results show that DSMT has very good potential to improve SMT performance, even when only a single program is available. However, we found that dynamic thread behavior together with fre-quent misspeculation may also produce diminishing re-turns in performance. Therefore, the challenge is to max-imize the amount of thread-level parallelism that DSMT is capable of exploiting and at the same time reduce the fre-quency of misspeculations.
Rodrigo Moreira, Flavio de Oliveira Silva, Tereza Cristina Melo de Brito Carvalho
et al.
Network slicing is a crucial enabler and a trend for the Next Generation Mobile Network (NGMN) and various other new systems like the Internet of Vehicles (IoV) and Industrial IoT (IIoT). Orchestration and machine learning are key elements with a crucial role in the network-slicing processes since the NS process needs to orchestrate resources and functionalities, and machine learning can potentially optimize the orchestration process. However, existing network-slicing architectures lack the ability to define intelligent approaches to orchestrate features and resources in the slicing process. This paper discusses machine learning-based orchestration of features and capabilities in network slicing architectures. Initially, the slice resource orchestration and allocation in the slicing planning, configuration, commissioning, and operation phases are analyzed. In sequence, we highlight the need for optimized architectural feature orchestration and recommend using ML-embed agents, federated learning intrinsic mechanisms for knowledge acquisition, and a data-driven approach embedded in the network slicing architecture. We further develop an architectural features orchestration case embedded in the SFI2 network slicing architecture. An attack prevention security mechanism is developed for the SFI2 architecture using distributed embedded and cooperating ML agents. The case presented illustrates the architectural feature's orchestration process and benefits, highlighting its importance for the network slicing process.
Sobhi Alfayoumi, Joan Melia-Segui, Xavier Vilajosana
This article proposes O-LoRaWAN, an adaptation of the LoRaWAN architecture into a modular network architecture based on the Open RAN (O-RAN) principles. In our vision, standardization of the network components and interfaces will enable the reuse of network functions, and thus, foster an accelerated tailoring of the network functions to the changing application demands. LoRaWAN shares similarities to cellular networks and becomes an interesting candidate for a transformation to the O-RAN standard. In the article we draw several transition strategies, these include the reorganization of the LoRa gateway functions into Radio and Distributed Units; enhancing network performance with RAN Intelligent Controllers exploiting the network data; and the standardization of the management and orchestration of network components. Key for that adaptation are the O-RAN interfaces. Along the article, we analyze them and suggest protocol extensions or adjustments for compatibility and interoperability between network components, advocating for the design of extensible protocols
Accidental architecture is an imperfect architecture due to the contingent reality of the world. An accidental architecture abandons the futile binaries of form and function and the telos(end goal) of Pure Formalism, Pure Functionalism and absolute accuracy in form-function relations in favour of the poetic interplay and collisions(metaphors) of things—any entities—to create objects through accident. This change of approach from teleological to accidental architecture allows for a new form of aesthetics, an accidental aesthetic that is more than just the subsequent product of form-function relations, an aesthetic that has the same hierarchy with the notions of form and function. This thesis explores accident as a mode of design to create an imperfect architectural object, an accidental architectural object.
Kyeong-Tae Lee, Garance Perrois, Hyun-Sung Yang
et al.
This study was carried out to determine the levels of resistance and resilience of kelp forests to large-scale physical disturbances. Our study site, Seongsan, Jeju Island, was impacted by super typhoon ‘Hinnamnor’. Before the typhoon, Seongsan had shown high ecosystem stability. Our results indicated that the ecological stability of a kelp forest facing a severe typhoon is strongly linked to the prevailing environmental conditions. Although typhoon impact resulted in a significant loss of brown macroalgae canopy, <i>Ecklonia cava</i> remained dominant within the kelp forest community. Resistance and resilience levels strongly depended on water temperature and movement and presence of turf-forming algae. Hence, hydrodynamic and biological factors strongly influence the overall stability of a kelp forest. We also report the first occurrences of a scleractinian coral species (i.e., <i>Montipora millepora</i>) at Seongsan, which became visible after canopy loss following the typhoon. Our findings provide valuable ecological information about the benthic community of kelp-dominated ecosystems and are essential to mitigate the impacts of expected climate change-driven rises in seawater temperature and the frequency of super typhoons.
Urban remnant mountains (URMs) are precious natural green habitat patches that can provide a series of ecosystem services for multi-mountainous cities. The increase in ecological sensitivity and degradation of ecosystem services affected by urban expansion and climate change have led to an increasing vulnerability of urban remnant mountain ecosystems (URMEs). To explore the vulnerability of URMEs, taking the central urban built-up area of the Guiyang city as the study area and URMs as the research object, the vulnerability of URMEs under natural factors and human disturbance was analyzed based on the Pressure-State-Response (PSR) model. The results showed that: (1) Karst rocky desertification, human disturbance, and road density within the buffer zones around URMs were the most important factors affecting the vulnerability of URMEs. Karst rocky desertification was the most likely eco-environmental problem of URMs, and carbon storage was the most important ecosystem service of URMEs. (2) Characteristics of fragile karst habitats in URMs and unreasonable human activities led to high ecological vulnerability, mainly with moderate and severe vulnerability predominating, and the low vulnerability of URMEs when they had moderate park utilization. (3) The ecological vulnerability of small URMs and those distributed in the urban center is higher, and the invulnerable URMEs and the slightly vulnerable URMEs are mainly distributed in the urban edge. The results of this study could provide references for ecological restoration and protection of URMs, and offer a basis for improving the resilience of multi-mountainous cities.
The geographical nature of the Hashemite Kingdom of Jordan is distinguished by its environmental diversity and its climatic conditions, in addition to the richness and diversity of cultural legacies that appear clearly in the spaces interior design of the traditional Jordanian houses.This architectural heritage, is considered one of the most important elements in preserving the special characteristics of peoples and nations and the distinctive cultural differences. And with the growing interest in the environmental design elements, it was found that many of the old designs carry a lot of elements and treatments that exploited or employed the available environmental elements to provide the highest levels of comfort in houses design. This is because it works to enhance the internal environment by employing the available environmental elements to reduce and limit the negative impacts on the environment and construction, in addition to using the natural environment to enhance the internal environment, which works to provide the highest levels of comfort in designing, reducing energy consumption and preserving the environment.The study will examine the green design, the concept of green architecture, the features of green buildings and the foundations on which the green design is based, in order to identify the suitability of the interior design of these buildings with green architecture.The research depends on monitoring and analyzing the similarities and differences between the heritage houses in some areas of the Hashemite Kingdom of Jordan and its internal spaces, to reach the most important points that must be restored and preserved. thus, listing them and developing proposals for ways to restore and preserve them.
Control of the disturbed displacement of adjacent tunnel during excavation is a significant issue for design and construction. Based on the multi-objective optimization method, the multi-type monitoring data in the excavation of the excavation are integrated, the key soil parameters are inverted and identified, and the time effect of the tunnel displacement is quantified and corrected. A dynamic multi-objective optimization method with adaptive infill criterion (DMO-AIC) is proposed to improve the updating efficiency of dynamic surrogate models. The proposed method takes into account the computational redundancy of dynamic surrogate models in engineering optimization, and designs an adaptive point-adding discrimination strategy, which can autonomously identify invalid updates of surrogate models on the optimization path. The results show that the proposed DMO-AIC significantly reduces the invocations of the black-box model during optimization while ensuring the good search performance and the convergence speed of the algorithm. The improved computational efficiency of DMO-AIC is helpful for the application of dynamic surrogate models in engineering optimization. The results of the virtual numerical example show that DMO-AIC can predict and update multiple model responses during excavation, such as wall deflections and tunnel displacements. The engineering practice of Shanghai Bund 596 excavation indicates that the time effect is properly updated, and the staged vertical displacements of the adjacent tunnel are accurately predicted.
Engineering (General). Civil engineering (General), Chemical engineering
Luis F. Bobadilla, Lola Azancot, Ligia A. Luque-Álvarez
et al.
Nowadays, global climate change is likely the most compelling problem mankind is facing. In this scenario, decarbonisation of the chemical industry is one of the global challenges that the scientific community needs to address in the immediate future. Catalysis and catalytic processes are called to play a decisive role in the transition to a more sustainable and low-carbon future. This critical review analyses the unique advantages of structured reactors (isothermicity, a wide range of residence times availability, complex geometries) with the multifunctional design of efficient catalysts to synthesise chemicals using CO<sub>2</sub> and renewable H<sub>2</sub> in a Power-to-X (PTX) strategy. Fine-chemistry synthetic methods and advanced in situ/operando techniques are essential to elucidate the changes of the catalysts during the studied reaction, thus gathering fundamental information about the active species and reaction mechanisms. Such information becomes crucial to refine the catalyst’s formulation and boost the reaction’s performance. On the other hand, reactors architecture allows flow pattern and temperature control, the management of strong thermal effects and the incorporation of specifically designed materials as catalytically active phases are expected to significantly contribute to the advance in the valorisation of CO<sub>2</sub> in the form of high added-value products. From a general perspective, this paper aims to update the state of the art in Carbon Capture and Utilisation (CCU) and PTX concepts with emphasis on processes involving the transformation of CO<sub>2</sub> into targeted fuels and platform chemicals, combining innovation from the point of view of both structured reactor design and multifunctional catalysts development.
Olusola T. Odeyomi, Olubiyi O. Akintade, Temitayo O. Olowu
et al.
The convergence of 5G architecture and deep learning has gained a lot of research interests in both the fields of wireless communication and artificial intelligence. This is because deep learning technologies have been identified to be the potential driver of the 5G technologies, that make up the 5G architecture. Hence, there have been extensive surveys on the convergence of 5G architecture and deep learning. However, most of the existing survey papers mainly focused on how deep learning can converge with a specific 5G technology, thus, not covering the full spectrum of the 5G architecture. Although there is a recent survey paper that appears to be robust, a review of that paper shows that it is not well structured to specifically cover the convergence of deep learning and the 5G technologies. Hence, this paper provides a robust overview of the convergence of the key 5G technologies and deep learning. The challenges faced by such convergence are discussed. In addition, a brief overview of the future 6G architecture, and how it can converge with deep learning is also discussed.
We conducted the feasibility analysis of utilizing a highly available multi-stage architecture for TSN switches used for sending high priority, mission-critical traffic within a bounded latency instead of traditional single-stage architectures. To verify the TSN functionality, we implemented the 'strict priority' feature. We evaluated the performance of both architectures on multiple parameters such as fault tolerance, packet latency, throughput, reliability, path length effectiveness, and cost per unit. The fault tolerance analysis demonstrated that the multi-stage architecture fairs better than the single-stage counterpart. The average latency and throughput performance of multi-stage architectures, although low, can be considered comparable with single-stage counterparts. However, the multi-stage architecture fails to meet the performance of single-stage architectures on parameters such as reliability, path length effectiveness, and cost-effectiveness. The improved fault tolerance comes at the cost of increased hardware resources, cost, and complexity. However, with the advent of cost-effective technologies in hardware design and efficient architecture designs, the multi-stage switching architecture-based TSN switches can be made reasonably comparable to single-stage switching TSN switches. This work gives initial confidence that the multi-stage architecture can be pursued further for safety-critical systems that require determinism and reliability in the communication of critical messages.
Zachariah Threet, Christos Papadopoulos, William Lambert
et al.
As in-vehicle communication becomes more complex, the automotive community is exploring various architectural options such as centralized and zonal architectures for their numerous benefits. Zonal architecture reduces the wiring cost by physically locating related operations and ECUs near their intended functions and the number of physical ECUs through function consolidation. Centralized architectures consolidate the number of ECUs into few, powerful compute units. Common characteristics of these architectures include the need for high-bandwidth communication and security, which have been elusive with standard automotive architectures. Further, as automotive communication technologies evolve, it is also likely that multiple link-layer technologies such as CAN and Automotive Ethernet will co-exist. These alternative architectures promise to integrate these diverse sets of technologies. However, architectures that allow such co-existence have not been adequately explored. In this work we explore a new network architecture called Named Data Networking (NDN) to achieve multiple goals: provide a foundational security infrastructure and bridge different link layer protocols such as CAN, LIN, and automotive Ethernet into a unified communication system. We created a proof-of-concept bench-top testbed using CAN HATS and Raspberry PIs that replay real traffic over CAN and Ethernet to demonstrate how NDN can provide a secure, high-speed bridge between different automotive link layers. We also show how NDN can support communication between centralized or zonal high-power compute components. Security is achieved through digitally signing all Data packets between these components, preventing unauthorized ECUs from injecting arbitrary data into the network. We also demonstrate NDN's ability to prevent DoS and replay attacks between different network segments connected through NDN.