Complex Architecture Research Articles

Enhancing Interoperability in the Web of Things: A Reference Architecture Approach

The concept of the Internet of Things (IoT) has fundamentally redefined the connectivity landscape by integrating physical devices with the Internet for data exchange. The Web of Things (WoT) takes this integration a step further by embedding IoT devices within the web, thus facilitating their interoperability. However, the inherent complexity of WoT architecture requires a layer of abstraction to simplify development and integration. Patterns and reference architectures (RAs) provide such an abstraction, modeling solutions for recurrent problems within this domain. In this paper, we aim to develop an RA for the WoT ecosystem. We use architectural patterns to build the RA of the WoT. Here, we present the WoT gateway pattern, which provides a clear and adaptable framework for integrating diverse IoT devices, ensuring seamless communication and functionality within the broader web ecosystem. The purpose of this pattern is twofold: to ensure the interoperability of WoT implementations and to serve as a foundational guide for developers navigating the multifaceted challenges of WoT system design. The pattern also paves the way for building an RA for IoT environments. KEYWORDS Architectural patterns, Internet of Things, reference architecture, web development, web science, WoT

Influence of Detail Level in Acoustic Modeling of Heritage Buildings. Case of the Lonja de los Mercaderes in Valencia

Acoustic modeling of heritage buildings presents a significant challenge due to the architectural complexity of these spaces and the need to preserve their authenticity. In particular, the precision of the virtual model in historical environments largely depends on the level of detail applied during simulation. This study examines the influence of detail level in acoustic modeling applied to the Lonja de los Mercaderes in Valencia, a prominent example of Gothic civil architecture in Europe. Using ODEON software, three virtual models of the main hall were developed with varying levels of detail, from a high-precision model to a simplified representation. The results show that a highly detailed model increases computational costs, while a reduction in detail leads to greater dispersion in results, especially at low frequencies. This research highlights the importance of finding an appropriate balance in detail level, providing reliable acoustic estimates without compromising efficiency. These findings contribute to ongoing discourse on heritage conservation practices, emphasizing how simulation technologies can aid in preserving acoustic integrity while addressing practical limitations. The conclusions have implications for the application of simulation technologies in other highly complex heritage buildings.

Artificial Tertiary Lymphoid Structures: Exploring Mesenchymal Stromal Cells as a Platform for Immune Niche Formation

Constructing artificial tertiary lymphoid structures (TLSs) opens new avenues for advancing cancer immunotherapy and personalized medicine by creating controllable immune niches. Mesenchymal stromal cells (MSCs) offer an ideal stromal source for such constructs, given their potent immunomodulatory abilities and accessibility. In this study, we explored the potential of adipose-derived MSCs to adopt TLS-supportive phenotypes and facilitate lymphocyte organization. Single-cell RNA sequencing revealed a distinct subpopulation of MSCs expressing key fibroblastic reticular cell (FRC)-associated markers, including IL-7, PDPN, and IL-15, though lacking follicular dendritic cell (FDC) markers. TNF-α stimulation, but not LTα2β1, further enhanced FRC marker expression (IL-7, PDPN, and ICAM1). Notably, in 3D spheroid co-culture with lymphocytes, MSCs upregulated additional FRC markers, specifically CCL21. Upon implantation into adipose tissue, MSC-lymphocyte organoids maintained structural integrity and showed extensive T-cell infiltration and partial vascularization after 15 days in vivo, although organized B-cell follicles and FDC markers were still lacking. These findings highlight MSCs’ intrinsic ability to adopt an FRC-like phenotype that supports T-cell and HEV organization, suggesting that further optimization, including genetic modification, may be needed to achieve an FDC phenotype and replicate the full architectural and functional complexity of TLSs.

Spot the bot: the inverse problems of NLP

This article concerns the problem of distinguishing human-written and bot-generated texts. In contrast to the classical problem formulation, in which the focus falls on one type of bot only, we consider the problem of distinguishing texts written by any person from those generated by any bot; this involves analysing the large-scale, coarse-grained structure of the language semantic space. To construct the training and test datasets, we propose to separate not the texts of bots, but bots themselves, so the test sample contains the texts of those bots (and people) that were not in the training sample. We aim to find efficient and versatile features, rather than a complex classification model architecture that only deals with a particular type of bots. In the study we derive features for human-written and bot generated texts, using clustering (Wishart and K-Means, as well as fuzzy variations) and nonlinear dynamic techniques (entropy-complexity measures). We then deliberately use the simplest of classifiers (support vector machine, decision tree, random forest) and the derived characteristics to identify whether the text is human-written or not. The large-scale simulation shows good classification results (a classification quality of over 96%), although varying for languages of different language families.

Enhancing the Effectiveness of STLs for GPUs via Bounded Model Checking

Graphics Processing Units (GPUs) are becoming widespread, even in safety-critical applications. In that case, it is imperative to guarantee that the probability of producing critical failures due to hardware faults is lower than a given threshold. To detect possible permanent hardware faults as soon as they appear during the operational phase (e.g., due to aging), Software Test Libraries (STLs) have gained significant traction as a widely adopted test solution due to their effectiveness in terms of fault detection capabilities, test application time, and flexibility. However, a major drawback of this solution is the lack of automation in the STL generation phase. As a result, high manual labor is required for their generation. This becomes even more arduous in complex architectures that require in-depth knowledge to cover hard-to-test faults. In this paper, we introduce a methodology based on Bounded Model Checking to support the generation and improvement of stuck-at-oriented STLs for hard-to-test units in GPUs, showing that we can enhance the test coverage achieved by pre-existing STLs while also identifying a set of functionally untestable faults. To experimentally validate the proposed method’s effectiveness, we use the FlexGripPlus GPU model to target two hard-to-test units, one medium to low complexity sub-unit and one high complexity sub-unit, as study cases. For both units, we had pre-existing STLs written for the stuck-at model. Resorting to the proposed method, the STLs’ test coverage was increased by 9.57% and 2.19%, respectively. In addition, the method also identified a significant number of functionally untestable faults.

HAIPO: Hybrid AI Algorithm-Based Post-Fabrication Optimization for Modern 3D NAND Flash Memory

To successfully meet the various requirements of modern storage systems, NAND flash memory should be highly optimized by precisely tuning a huge number of internal operating parameters. Although 3D NAND flash memory succeeds in increasing the capacity of storage systems, its complex architecture and unique error behavior make such optimization a more difficult and time-consuming process during NAND manufacturing. In this paper, we introduce HAIPO, a novel methodology for post-fabrication optimization of NAND flash memory, which is an essential step in the manufacturing process of modern 3D NAND flash memory to simultaneously meet various requirements on reliability, performance, yield, etc. HAIPO is based on simple machine-learning approaches that consist of (i) a lightweight deep-learning (DL) model to generate initial device parameters and (ii) an evolutionary algorithm (EA) to explore device parameters automatically. To more effectively explore device parameters, we introduce three key guidelines for each generation in the EA: (1) domain-specific rules, (2) recent optimization results, and (3) online Bayesian simulation, respectively, to enable quick optimization for a huge number of device parameters within the limited product turnaround time (TAT). In addition, we integrate two optimization modules with HAIPO to improve optimization efficiency even in environments with severe process variation. We demonstrate the feasibility and effectiveness of HAIPO using real 320 3D TLC/QLC NAND flash chips, showing significant performance and reliability improvements by up to 8.8% and 12% on average, respectively, within a quite limited optimization TAT.

Copper‐Mediated C−H Functionalization of Amines, Imines, and Amides: Recent Developments and Synthetic Applications

AbstractCopper‐mediated C−H functionalization has emerged as a powerful strategy for the selective modification of nitrogen‐containing molecules, offering significant advancements in organic synthesis. This review provides a comprehensive overview of recent developments in copper‐catalyzed C−H functionalization reactions of amines, imines, and amides, from 2013 to present, emphasizing their synthetic applications and mechanistic insights. It also discusses the impact of these methodologies on the construction of complex molecular architectures, including pharmaceuticals and natural products. This review aims to provide valuable guidance for researchers seeking to leverage copper‐mediated strategies in their synthetic endeavors by creating current knowledge and identifying future directions. We have highlighted the new bonds formed through C−H functionalizations for the convenience of readers.

Complex Genomic Landscape of Inversion Polymorphism in Europe's Most Destructive Forest Pest.

In many species, polymorphic genomic inversions underlie complex phenotypic polymorphisms and facilitate local adaptation in the face of gene flow. Multiple polymorphic inversions can co-occur in a genome, but the prevalence, evolutionary significance, and limits to complexity of genomic inversion landscapes remain poorly understood. Here, we examine genome-wide genetic variation in one of Europe's most destructive forest pests, the spruce bark beetle Ips typographus, scan for polymorphic inversions, and test whether inversions are associated with key traits in this species. We analyzed 240 individuals from 18 populations across the species' European range and, using a whole-genome resequencing approach, identified 27 polymorphic inversions covering ∼28% of the genome. The inversions vary in size and in levels of intra-inversion recombination, are highly polymorphic across the species range, and often overlap, forming a complex genomic architecture. We found no support for mechanisms such as directional selection, overdominance, and associative overdominance that are often invoked to explain the presence of large inversion polymorphisms in the genome. This suggests that inversions are either neutral or maintained by the combined action of multiple evolutionary forces. We also found that inversions are enriched in odorant receptor genes encoding elements of recognition pathways for host plants, mates, and symbiotic fungi. Our results indicate that the genome of this major forest pest of growing social, political, and economic importance harbors one of the most complex inversion landscapes described to date and raise questions about the limits of intraspecific genomic architecture complexity.

ENHANCING DEEP LEARNING PERFORMANCE THROUGH A GENETIC ALGORITHM-ENHANCED APPROACH: FOCUSING ON LSTM

Deep learning has shown remarkable success in various applications, such as image classification, natural language processing, and speech recognition. However, training deep neural networks is challenging due to their complex architecture and the number of parameters required. Genetic algorithms have been proposed as an alternative optimization technique for deep learning, offering an efficient alternative way to find an optimal set of network parameters that minimize the objective function. In this paper, we propose a novel approach integrating genetic algorithms with deep learning, specifically LSTM models, to enhance performance. Our method optimizes crucial hyper-parameters including learning rate, batch size, neuron count per layer, and layer depth through genetic algorithms. Additionally, we conduct a comprehensive analysis of how genetic algorithm parameters influence the optimization process and illustrate their significant impact on improving LSTM model performance. Overall, the presented method provides a powerful mechanism for improving the performance of deep neural networks, and; thus, we believe that it has significant potential for future applications in the artificial intelligence discipline.

An Iterative Benchmark Configuration Method forQuantifying Multi-core Interference

Interference within a multi-core architecture may have several origins. Understanding where interference comes from is mandatory for verification and certification purposes. Unfortunately, the complexity of current architectures makes it difficult to quantify such interference. In this article, a new approach is introduced that enables benchmark configurations to isolate and quantify interference. An experiment with DMA interference is presented and shows a WCET overhead of up to 0.26% at 25 Mbit/s. This experiment was also able to discover and identify interference related to DMA, such as interruptive flow overhead, around 3% for 25 Mbit/s, or packet transmission memory access overhead, around 9% for 25 Mbit/s.

DABCO‐Mediated Photoelectrochemical Three‐Component Sulfonocyclization of 3‐Aza‐1,5‐dienes

Comprehensive SummaryHerein, we report a rare example of three‐component net‐oxidative sulfonylation of a SO2 surrogate with an oxidatively activated radical precursor under mild and metal‐ and external‐oxidant‐free conditions. The mildness and sustainability of the reaction are enabled by photoelectrocatalysis, and 3‐aza‐1,5‐dienes, organotrifluoroborates and 1,4‐diazabicyclo[2.2.2]octane bis(sulfur dioxide) adduct (DABSO) undergo sulfonylative cyclization to afford sulfono 4‐pyrrolin‐2‐ones in an atom‐economical manner with a broad substrate scope and good functional‐group tolerance. The protocol is amenable to the late‐stage diversification of complex molecular architectures as well as the gram‐scale synthesis. Sunlight could be used as the light source, and the reaction could be conducted in an all‐solar mode using a commercially available photovoltaic panel to generate electricity in situ. Mechanistic studies reveal that the in situ generated 1,4‐diazabicyclo[2.2.2]octane (DABCO), which was generally innocent in previous reactions, functions as an electron shuttle between the photocatalytic cycle and the reactants.

OCIAD1 and prohibitins regulate the stability of the TIM23 protein translocase

Mitochondrial proteins are transported and sorted to the matrix or inner mitochondrial membrane by the presequence translocase TIM23. In yeast, this essential and highly conserved machinery is composed of the core subunits Tim23 and Tim17. The architecture, assembly, and regulation of the human TIM23 complex are poorly characterized. The human genome encodes two paralogs, TIMM17A and TIMM17B. Here, we describe an unexpected role of the ovarian cancer immunoreactive antigen domain-containing protein 1 (OCIAD1) and the prohibitin complex in the biogenesis of human TIM23. Prohibitins were required to stabilize both the TIMM17A- and TIMM17B-containing variants of the translocase. Interestingly, OCIAD1 assembled with the prohibitin complex to protect the TIMM17A variant from degradation by the YME1L protease. The expression of OCIAD1 was in turn regulated by the status of the TIM23 complex. We postulate that OCIAD1 together with prohibitins constitute a regulatory axis that differentially regulates variants of humanTIM23.

Analysis of structural architecture in western Saudi Neom City area, northwestern Arabian Plate: Field investigation

Saudi Neom City is being constructed as an urban mega-project in the northwestern part of the Arabian plate at the southeastern part of the NNE-oriented sinistral Dead Sea continental transform fault that links the NNW-oriented Red Sea Rift to the Zagros collision through the NE-oriented sinistral East Anatolian Fault. The present study aims to detect and analyze the different structural elements in the western Neom city. It also attempts to provide valuable data to help decision-makers for better achievement of such vital projects. From field investigation, the study area mainly comprises Neogene sedimentary sequence and exhibits a complex structural architecture represented by extensional- and wrench-style deformations. Different fault orientations (NNW–SSE striking extensional faults, WNW–ESE and ENE–WSW striking oblique-slip faults, and NNE–SSW and NNW–SSE striking strike-slip faults) dissect the western Neom area. Individual NNW-oriented Red Sea Rift-related extensional faults and fault blocks are antithetically tilted to the northeast. The sinistral movement onset along the Dead Sea Transform Fault postdates the Lower Miocene Burqan Formation deposition. Furthermore, the sinistral strike-slip regime of the western Neom area involves extensional structures, including normal faults, contractional structures (folds and reverse faults), and structures representing horizontal shear on near-vertical planes. NNW-oriented negative flower structures and forced folding occur along a synthetic NNW-oriented sinistral strike-slip fault set. Contractional structures are expressed by sets of NE-oriented left-handed en echelon-arranged folds in the Middle Miocene deposits. A proposed strain ellipse reveals these structures are associated with the NNE-oriented sinistral Dead Sea Transform Fault. The complexity of the structural architecture of the western Neom area can be attributed to its geologic setting under the influence of the Red Sea extensional rifting followed by the tectonic activity along the Dead Sea transform faulting. It is recommended that the structural attributes investigated, especially the geographic distribution of brittle structures (faults and fractures), be considered during the construction of Neom City.

An Honest Reckoning With the Amygdala and Mental Illness.

Anxiety disorders are a leading source of human misery, morbidity, and premature mortality. Existing treatments are far from curative for many, underscoring the need to clarify the underlying neural mechanisms. Although many brain regions contribute, the amygdala has received the most intense scientific attention. Over the past several decades, this scrutiny has yielded a detailed understanding of amygdala function, but it has failed to produce new clinical assays, biomarkers, or cures. Rising to this urgent public health challenge demands an honest reckoning with the functional-neuroanatomical complexity of the amygdala and a shift from theories anchored on "the amygdala" to models centered on specific amygdala nuclei and cell types. This review begins by examining evidence from studies of rodents, monkeys, and humans for the "canonical model," the idea that the amygdala plays a central role in fear- and anxiety-related states, traits, and disorders. Next, the authors selectively highlight work indicating that the canonical model, while true, is overly simplistic and fails to adequately capture the actual state of the evidentiary record, the breadth of amygdala-associated functions and illnesses, or the complexity of the amygdala's functional architecture. The authors describe the implications of these facts for basic and clinical neuroimaging research. The review concludes with some general recommendations for grappling with the complexity of the amygdala and accelerating efforts to understand and more effectively treat amygdala-related psychopathology.

Enhanced long short-term memory architectures for chaotic systems modeling: An extensive study on the Lorenz system.

Despite recent advancements in machine learning algorithms, well-established models like the Long Short-Term Memory (LSTM) are still widely used for modeling tasks. This paper introduces an enhanced LSTM variant and explores its capabilities in multiple input single output chaotic system modeling, offering a large-scale analysis that focuses on LSTM gate-level architecture, the effects of noise, non-stationary and dynamic behavior modeling, system parameter drifts, and short- and long-term forecasting. The experimental evaluation is performed on datasets generated using MATLAB, where the Lorenz and Rössler system equations are implemented and simulated in various scenarios. The extended analysis reveals that a simplified, less complex LSTM-based architecture can be successfully employed for accurate chaotic system modeling without the need for complex deep learning methodologies. This new proposed model includes only three of the four standard LSTM gates, with other feedback modifications.

The Integrated Development of Wireless Networks and AI

This paper combines the current research status of wireless networks and artificial intelligence, delving into the state of wireless network research and its impacts, and highlights the importance and necessity of the integrated development of wireless networks and AI. The aim is to provide readers with an understanding of the current situation and challenges of wireless networks. With the increasing number of mobile communication users, spectrum resources are becoming increasingly scarce, network security issues are more prominent, and the complexity of network architecture and business processes has significantly increased, making it difficult for traditional operations and maintenance methods to meet these demands. Therefore, leveraging AI to build a new mode of communication network operation has become an inevitable choice. The intelligent evolution of wireless networks is an unstoppable trend. In the future, full intelligence will be achieved through digital infrastructure, knowledge introduction, and digital twins, driving the efficient development of wireless networks.

Fault-tolerant hierarchical energy management system for an electrical power system on more-electric aircraft

The concept of More-Electric Aircraft (MEA) has the potential to improve the environmental, economic, and reliability performance in the energy and transportation sectors. To achieve this potential, it has become a tendency to develop complex architectures of Electrical Power Systems (EPSs) for MEA to supply increasing electrical power demands. Moreover, a reliable and intelligent Energy Management System (EMS) is critical to coordinate the various EPS subsystems to ensure safe and efficient flight, following the real-time EPS operating requirements and safety criteria, while reducing the operating costs for all flight stages. This paper presents a fault-tolerant hierarchical EMS, for an innovative multi-converter-based aircraft EPS, to configure the system, ensure power distribution, and manage energy storage in multiple faulty scenarios over different time scales. There are two levels in this EMS: The High Level (HL) is based on Model Predictive Control (MPC), formulated by Mixed-Integer-Linear-Programming (MILP), to optimise the long-term EPS performance while considering future predictions; The Low Level (LL) adopts deterministic rules to cope with load changes and fault occurrences over the short term, during the HL sample intervals, with a faster clock. In particular, the LL controller contains four modes: to either cooperate with the HL online MPC or to operate independently, in either EPS normal or faulty conditions. The proposed EMS is evaluated in two cases, firstly considering load deviations in a normal operating scenario, and then considering behaviour in fault scenarios. The results indicate that the proposed EMS successfully reduces the EPS operational costs while ensuring quick responses to dynamic changes with either EPS component faults or EMS internal faults.

Open architecture of archaea MCM and dsDNA complexes resolved using monodispersed streptavidin affinity CryoEM

The cryo-electron microscopy (cryoEM) method has enabled high-resolution structure determination of numerous biomolecules and complexes. Nevertheless, cryoEM sample preparation of challenging proteins and complexes, especially those with low abundance or with preferential orientation, remains a major hurdle. We developed an affinity-grid method employing monodispersed single particle streptavidin on a lipid monolayer to enhance particle absorption on the grid surface and alleviate sample exposure to the air-water interface. Using this approach, we successfully enriched the Thermococcus kodakarensis mini-chromosome maintenance complex 3 (MCM3) on cryoEM grids through biotinylation and resolved its structure. We further utilized this affinity method to tether the biotin-tagged dsDNA to selectively enrich a stable MCM3-ATP-dsDNA complex for cryoEM structure determination. Intriguingly, both MCM3 apo and dsDNA bound structures exhibit left-handed open spiral conformations, distinct from other reported MCM structures. The large open gate is sufficient to accommodate a dsDNA which could potentially be melted. The value of mspSA affinity method was further demonstrated by mitigating the issue of preferential angular distribution of HIV-1 capsid protein hexamer and RNA polymerase II elongation complex from Saccharomyces cerevisiae.

Quantitative Unraveling of Exsolved Heteroboundaries for High-Temperature Electrocatalysis.

The application of perovskite oxide for high-temperature electrocatalysis is hindered by its limited activity. Exsolution is a smart strategy that allows the enrichment of the perovskite's surface with highly reactive phases, yielding heteroboundaries. However, the identification of the exact catalytic role of this complex architecture is still elusive. Here we presented a quantitative analysis of the CO2 electroreduction reactivity of a series of perovskite thin film platforms (La0.4Ca0.4Ti0.94Ni0.06O3, LCTN) boosted by exsolved heteropical nanoparticles (particularly for Ni and NiO). The cross-scale electrochemical characterizations, together with density functional theory (DFT) modelings, have shown clear evidence that the boundary length of the NP/perovskite interface is strictly correlated with the CO2RR activity. The intrinsic reaction rate per active site at the NiO/LCTN boundary demonstrates a highest turnover frequency (TOF) of 7.05 ± 0.75 × 104 s-1 at 800 °C, which is 2.5 times and 4 orders of magnitude better than that of Ni/LCTN and LCTN, respectively. The ab initio molecular dynamics (AIMD) proves that the CO2 absorption at the NiO/LCTN boundary leverages a bidentate carbonate modality with a reduced dissociation energy barrier. Moreover, a multifold enhancement in oxygen exchange rate was confirmed, which correlated to the facilitated oxygen ion hopping between adjacent TiO6 octahedrons. Further near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) during CO2 electrolysis on model electrolyzers reveals the crucial role of the NiO/LCTN boundary in stabilizing oxidized carbon intermediates, raising the onset potential threshold of adventitious carbon as well as preventing its build-up.

Genetics architecture of spontaneous coronary artery dissection in an Italian cohort.

Spontaneous coronary artery dissection (SCAD) is a relevant non-atherosclerotic cause of acute coronary syndrome with a complex genetic architecture. Recent discoveries have highlighted the potential role of miRNAs and protein-coding genes involved in the processing of small RNAs in the pathogenesis of SCAD. Furthermore, there may be a connection between SCAD and the increased cardiovascular risk observed in fragile X premutation carriers as well as a correlation with pathogenetic variants in genes encoding for collagen and extracellular matrix, which are related to connective tissue disorders (CTDs). In our cohort of 15 Italian SCAD patients, a total of 37 rare variants were identified in 34 genes using whole exome sequencing (WES) and TRIO-WES analysis when both parents were available. Three likely pathogenic/pathogenetic variants were found in genes previously associated with SCAD and CTDs (COL3A1, COL1A2, and SMAD3) and 26 variants of uncertain significance in genes previously associated with SCAD and CTDs. TRIO-WES analysis revealed 7 de novo variants, 1 of which was found in a potential novel candidate gene (DROSHA). In addition, a premutation allele of 55 ± 2 CGG repeats in the promoter of the FMR1 gene was identified in two related SCAD patients by test for CGG-repeat expansions in the 5'-UTR of the FMR1 gene. Our findings suggest various potential mechanisms such as mRNA toxicity, miRNA regulation, alteration of collagen, and the extracellular matrix architecture, all of which could disrupt vascular homeostasis, and finally, WES and TRIO-WES have proven to be the most powerful approaches for characterizing the genetic background of SCAD.