Complex Architecture Research Articles

Water heater storage heat pump cycle for higher operating range

Heat pumps for water heating have become increasingly attractive. However, the achievable water temperature using a vapor compression heat pump water heater (HPWH) is often constrained by the compressor operating envelope. To address this challenge, HPWHs typically employ two-stage compression with complex system architecture or rely on back-up electric heating. This work introduces an alternate storage heat pump concept to improve the operating temperature range of an R134a HPWH system. The developed system operates in two modes: Mode I functions as a typical wrap-around condenser HPWH, while Mode II splits the wrap-around coil to use the top portion as a condenser and the bottom portion as evaporator. System modeling consisting of a vapor compression cycle and water tank sub-models was conducted to study the performance. The model was validated experimentally. The storage heat pump system could achieve an additional increase of 12 °C beyond the maximum possible water temperature of 46 °C which was achieved using the baseline conventional HPWH system. In Mode II, the system exhibits a 20 % higher average heating capacity and a 15 % lower average pressure ratio compared to the conventional system operating at a 27 °C ambient temperature. Compared to usage of electric heating elements, our system showed a longer recovery time with a 50 % lower energy consumption for a similar increase in water temperature.

Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis

Alzheimer's is one of the brain syndromes that steadily affects the brain memory. The early stage of Alzheimer's disease (AD) is referred to as mild cognitive impairment (MCI), and the growth of Alzheimer's is not certain in patients with MCI. The premature detection of Alzheimer's is crucial for maintaining healthy brain function and avoiding memory loss. Different multi-neural network architectures have been proposed by researchers for efficient and accurate AD detection. The absence of improved feature extraction mechanisms and unexplored efficient optimizers in complex benchmark architectures lead to an inefficient and inaccurate AD classification. Moreover, the standard convolutional neural network (CNN)-based architectures for Alzheimer's diagnosis lack interpretability in their predictions. An interpretable, simplified, yet effective deep learning model is required for the accurate classification of AD. In this study, a generalized fractional order-based CNN classifier with explainable artificial intelligence (XAI) capabilities is proposed for accurate, efficient, and interpretable classification of AD diagnosis. The proposed study (a) classifies AD accurately by incorporating unexplored pooling technique with enhanced feature extraction mechanism, (b) provides fractional order-based optimization approach for adaptive learning and fast convergence speed, and (c) suggests an interpretable method for proving the transparency of the model. The proposed model outperforms complex benchmark architectures with regard to accuracy using standard ADNI dataset. The proposed fractional order-based CNN classifier achieves an improved accuracy of 99 % as compared to the state-of-the-art models.

Actin cytoskeleton and associated myosin motors within the renal epithelium.

This review highlights the complexity of renal epithelial cell membrane architectures and organelles through careful review of ultrastructural and physiological studies published over the past several decades. We also showcase the vital roles played by the actin cytoskeleton and actin-associated myosin motor proteins in regulating cell type-specific physiological functions within the cells of the renal epithelium. The purpose of this review is to provide a fresh conceptual framework to explain the structure-function relationships that exist between the actin cytoskeleton, organelle structure, and cargo transport within the mammalian kidney. With recent advances in technologies to visualize the actin cytoskeleton and associated proteins within intact kidneys, it has become increasingly imperative to reimagine the functional roles of these proteins in situ to provide a rationale for their unique, cell type-specific functions that are necessary to establish and maintain complex physiological processes.

The mysteries of LETM1 pleiotropy

LETM1 is a nuclear-encoded protein located in the inner mitochondrial membrane, playing a critical role in regulating mitochondrial cation and volume homeostasis. However, numerous studies on functional features, molecular interactions, and disease-associated effects of LETM1 revealed that LETM1 is also involved in other metabolic functions including glucose utilization, mitochondrial DNA and ribosome organization, cristae architecture and respiratory complex stability. Undisputedly, osmoregulatory processes are essential for mitochondrial functionality, but the pleiotropic aspects of LETM1 challenges us to understand the core function of LETM1, which still remains elusive. In this review, we provide an overview of the current knowledge and latest developments regarding the activities involving LETM1. We highlight various findings that offer different functional perspectives and ideas on the core function of LETM1. Specifically, we emphasize data supporting LETM1's role as a mitochondrial translational factor, K+/H+ exchanger, or Ca2+/H+ exchanger, along with recent findings on its interaction with ATAD3A and TMBIM5. We also present the severe clinical implications of LETM1 deficiency. Finally, we discuss emerging questions raised by the different views on LETM1, which need to be addressed to guide future research directions and ultimately resolve the function of this essential protein and develop targeted therapeutic strategies.

Unipolar voltage electroanatomic mapping detects structural atrial remodeling identified by LGE-MRI

BackgroundIn atrial fibrillation (AF) management, understanding left atrial (LA) substrate is crucial. While both electroanatomic mapping (EAM) and late gadolinium enhancement magnetic resonance imaging (LGE-MRI) are accepted methods for assessing the atrial substrate and are associated with ablation outcome, recent findings have highlighted discrepancies between low-voltage areas (LVAs) in EAM and LGE areas. ObjectiveThe purpose of this study was to explore the relationship between LGE regions and unipolar and bipolar LVAs using multipolar high-density mapping. MethodsTwenty patients scheduled for AF ablation underwent preablation LGE-MRI. LA segmentation was conducted using a deep learning approach, which subsequently generated a 3-dimensional mesh integrating the LGE data. High-density EAM was performed in sinus rhythm for each patient. The electroanatomic map and LGE-MRI mesh were coregistered. LVAs were defined using cutoffs of 0.5 mV for bipolar voltage and 2.5 mV for unipolar voltage. The correspondence between LGE areas and LVAs in the LA was analyzed using confusion matrices and performance metrics. ResultsA considerable 87.3% of LGE regions overlapped with unipolar LVAs, compared with only 16.2% overlap observed with bipolar LVAs. Across all performance metrics, unipolar LVAs outperformed bipolar LVAs in identifying LGE areas (precision: 78.6% vs 61.1%; sensitivity: 87.3% vs 16.2%; F1 score: 81.3% vs 26.0%; accuracy: 74.0% vs 35.3%). ConclusionOur findings demonstrate that unipolar LVAs strongly correlate with LGE regions. These findings support the integration of unipolar mapping alongside bipolar mapping into clinical practice. This would offer a nuanced approach to diagnose and manage AF by revealing critical insights into the complex architecture of the atrial substrate.

Domestic Space Structure and Abandonment Behavior: The Ye Family Complex at Baomei, Tong An District (Fujian, P.R. China)

Anthropological archaeology strives to recover, understand and explain past social dynamics. It relies on the uncovered material record to highlight past behavior patterns. For such a research agenda to be successful, one needs to fully understand all aspects of the processes involved in the formation of the archaeological record. It is undisputed that “Abandonment” is the condition _sine qua none_ for the formation of archaeological sites. Fieldwork conducted at the Ye Family high ranking Qing Dynasty office-holder housing complex, outlines the “use-history” of the investigated architectural complex and its adjustment to China recent contemporary history. The housing complex shifted from an initial prestigious imperial office-holder family to a “commune” housing for disadvantaged families during the cultural revolution, to be finally either progressively abandoned and/or re-cycled as storage complex. Combining informant input and field archaeology methodologies, the study of an abandoned elite traditional vernacular house provides the opportunity to decipher abandonment processes and contribute to the theoretical agenda of anthropological archaeology.

Web-платформа для організації хмарних обчислень у нейрофізіологічних дослідженнях на основі даних айтрекінгу

The purpose of the work is to develop the architecture and web version of the software complex based on the proposed new concept of cloud computing organization, which allows to expand the diagnostic capabilities of the tools of model-oriented information technology for assessing the neurophysiological state of a person using methods of nonlinear dynamic identification of the oculomotor system based on eye tracking data. The concept of cloud computing is proposed, which is based on the combination of PaaS and SaaS services as part of the developed software complex, due to which the cross-platform nature of cloud computing is ensured, the productivity and efficiency of scientific research increases. The developed architecture allows you to easily expand the functionality of the software complex and adapt it to different application conditions. The key feature of the complex is its undemanding hardware on the client side thanks to the use of cloud computing and its modular structure, which allows for easy scaling of the complex, as well as the isolation of the script code execution process in the cloud computing environment to increase the level of security when interpreting the script code on the server . Compared to other similar services, the complex has several advantages: it provides effective work in research and educational applications, supports Python and JavaScript programming languages, and also allows the use of software-implemented identification methods through specially developed GUI interfaces. In addition, the complex offers social opportunities and a high level of abstraction, which allows to optimize the research process.

Microtubule choreography: spindle self-organization during cell division

AbstractDuring cell division, the network of microtubules undergoes massive rearrangement to self-organize into the spindle, a bipolar structure essential for accurate chromosome segregation. This structure ensures the stable transmission of the genome from the mother cell to two daughter cells, yet the process by which the ordered architecture emerges from a collection of protein “parts” remains a mystery. In this review, we focus on several key spindle proteins, describing how they move, crosslink, and grow microtubules in vitro and contribute to the spindle’s structural organization. We categorize these proteins into groups, such as transporters, bundlers, and nucleators, to highlight their functional roles. We also present an advanced perspective on the spindle’s complex polymer architecture and its temporal assembly order in cellular contexts. This in situ level information should guide the minimal reconstitution of the spindle, helping to elucidate the biophysical principles underlying essential cytoskeletal self-organization.

Сохранить, нельзя разрушить: памятники истории и культуры горной Ингушетии

The article examines the architectural monuments of mountainous Ingushetia from the point of view of their physical preservation and preservation of historical and cultural value. These monu-ments have undergone active restoration in recent years. Patrons are involved in financing these works. It is established that there is concern for preserving historical and cultural value during these works. The reason for this is the inability to determine the original appearance in detail of most dilapidated monuments, as well as the fact that the number of restored or restored tower set-tlements over the past couple of years has exceeded a dozen, and the materials on mandatory ar-chaeological excavations are few indicating irreparable damage that can be caused to historical and cultural monuments. Also, the absence of the need for practical use of restored premises of cultural heritage sites is an indicator of the inexpediency of using such an approach. In world prac-tice, restoration is used in exceptional cases, in particular when there is an ongoing historical cul-tural process. To solve this problem, it is necessary to attract the attention of state bodies, institu-tions, commercial organizations engaged in the preservation of cultural heritage sites, the public to the need to preserve the historical and cultural value of historical and cultural monuments as tower and castle complexes, as well as religious and burial structures concentrated on the territory of modern mountainous Ingushetia, when carrying out work on their preservation, as well as the priority of their conservation over other types of work. It is concluded that a monument of history and culture is valuable insofar as it contains reliable information about human activity in previous eras, and principles for the preservation of architectural (tower and castle) complexes of moun-tainous Ingushetia have been developed, adherence to which will solve the problems identified in this study in matters of conservation work architectural monuments of mountainous Ingushetia.

Sensitivity Analysis of Word Importance using GPT Model: A Ranking XAI Approach with Attention Weights and KL Divergence

This paper delves into the intricate realm of generative Artificial Intelligence (AI) models, specifically focusing on transformers like GPT (Generative Pre-trained Transformer). Despite their remarkable capabilities, these models pose challenges in terms of interpretability and accountability, owing to their complex architectures and vast training data. This paper employs a model to investigate the importance of words within the corpus, employing sensitivity analysis techniques. Specifically, attention weights are used to measure the impact of individual words on the model's predictions. The paper proposes a novel approach to rank the importance of words by leveraging attention weights and conducting sensitivity analysis across the dataset. To quantify the discrepancies between model-generated outputs and ground truth, the Kullback-Leibler (KL) divergence is employed. This divergence measure aids in evaluating how well the model captures the underlying distribution of words in the corpus. By integrating KL divergence into the sensitivity analysis, the study aims to provide a more comprehensive understanding of word importance.

Molecular architecture of the mammalian 2-oxoglutarate dehydrogenase complex

The 2-oxoglutarate dehydrogenase complex (OGDHc) orchestrates a critical reaction regulating the TCA cycle. Although the structure of each OGDHc subunit has been solved, the architecture of the intact complex and inter-subunit interactions still remain unknown. Here we report the assembly of native, intact OGDHc from Sus scrofa heart tissue using cryo-electron microscopy (cryo-EM), cryo-electron tomography (cryo-ET), and subtomogram averaging (STA) to discern native structures of the whole complex and each subunit. Our cryo-EM analyses revealed the E2o cubic core structure comprising eight homotrimers at 3.3-Å resolution. More importantly, the numbers, positions and orientations of each OGDHc subunit were determined by cryo-ET and the STA structures of the core were resolved at 7.9-Å with the peripheral subunits reaching nanometer resolution. Although the distribution of the peripheral subunits E1o and E3 vary among complexes, they demonstrate a certain regularity within the position and orientation. Moreover, we analyzed and validated the interactions between each subunit, and determined the flexible binding mode for E1o, E2o and E3, resulting in a proposed model of Sus scrofa OGDHc. Together, our results reveal distinctive factors driving the architecture of the intact, native OGDHc.

Exploiting Spatial Ionic Dynamics in Solid-State Organic Electrochemical Transistors for Multi-Tactile Sensing and Processing.

The human nervous system inspires the next generation of sensory and communication systems for robotics, human-machine interfaces (HMIs), biomedical applications, and artificial intelligence. Neuromorphic approaches address processing challenges; however, the vast number of sensors and their large-scale distribution complicate analog data manipulation. Conventional digital multiplexers are limited by complex circuit architecture and high supply voltage. Large sensory arrays further complicate wiring. An 'in-electrolyte computing' platform is presented by integrating organic electrochemical transistors (OECTs) with a solid-state polymer electrolyte. These devices use synapse-like signal transport and spatially dependent bulk ionic doping, achieving over 400 times modulation in channel conductance, allowing discrimination of locally random-access events without peripheral circuitry or address assignment. It demonstrates information processing from 12 tactile sensors with a single OECT output, showing clear advantages in circuit simplicity over existing all-electronic, all-digital implementations. This self-multiplexer platform offers exciting prospects for circuit-free integration with sensory arrays for high-quality, large-volume analog signal processing.

Defect-Free SiGe/Si Multistacks for Next-Generation CFET Architectures

The advancement of CMOS technology nodes demands increasingly complex device architectures. In this frame, the epitaxial deposition of complicated SiGe/Si multistacks presents a significant technology challenge required to enable such cutting-edge devices. This work presents the characterization of defect-free multistacks featuring SiGe layers with two possible Ge concentrations and incorporating either two or six stacked Si nanosheets. Our comprehensive analysis reveals good matching with the nominal structures and demonstrates remarkable repeatability of the Si channels. Furthermore, the multistacks exhibit ideal surface morphology, crucial for subsequent processing steps. Notably, the study also highlights optimal wafer-to-wafer repeatability, a key factor in ensuring consistent performance across large-scale production.

SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data.

Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.

Identifying defects and varieties of Malting Barley Kernels

This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models to complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach’s advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.

Software tools for organizing cloud computing in psychophysiological research based on eyetracking data

The architecture and web version of the software complex have been developed, which significantly expands the diagnostic capabilities of model-oriented information technologies for the assessment of the neurophysiological state. The complex provides cross-platform cloud computing, increases the productivity and efficiency of scientific research, using methods of non-parametric identification of the oculomotor system based on eyetracking data, which is achieved thanks to a new concept of cloud computing organization. Cloud computing technology has been further developed thanks to the proposed concept that combines the principles of PaaS (Platform as a Service) and SaaS (Software as a Service). The key feature of this software complex is its undemanding hardware on the client side thanks to the use of cloud computing, and its modular structure, which allows it to be easily scaled. Compared to other similar services, the complex has several advantages: it provides effective work in research and educational areas, supports several programming languages for improving algorithms, and also allows the use of ready-made identification methods through specially developed GUI interfaces. In addition, it offers social capabilities and a high level of abstraction that optimizes the research process.

Catalytic Intermolecular [4+2]-Cycloaddition toward the Stereo­selective C2–C3 Annulation of Indoles

AbstractCatalytic dearomative cycloaddition involving the C2–C3 bond of indoles is a powerful strategy for the synthesis of fused indoline scaffolds. Through dearomative cycloaddition/annulation, planar indole substrates can be readily transformed into rigid, three-dimensional polycyclic complex structures in one step. Molecules with architectural complexity are generally considered to have drug-like properties. Hence, annulation products have tremendous potential for discovering therapeutic properties, and this strategy has become an important part of the medicinal chemistry toolbox. Using appropriate catalyst control, desirable stereoselectivity can be achieved. Previous literature reports reveal that [3+2]-cycloadditions of indoles have been extensively studied. In contrast, the catalytic [4+2]-cycloaddition/dearomatization of indoles has been much less investigated. In this short review, we focus specifically on six-membered ring annulations via [4+2]-cycloaddition with the C2–C3 bond of indoles and discuss the various catalytic methods that have been developed toward this objective.1 Introduction2 [4+2]-Cycloaddition/Annulation of Indoles2.1 Electron-Rich Indoles2.1.1 Transition-Metal Catalysis2.1.2 Organocatalysis2.2 Electron-Deficient Indoles2.2.1 Transition-Metal Catalysis2.2.2 Organocatalysis3 Summary and Outlook

Polyhedral oligomeric silsesquioxane-modulated mesoporous amorphous bimetallic organic frameworks for the efficient isolation of immunoglobulin G

The efficient and accurate separation of immunoglobulin G (IgG) plays a vital role for disease diagnosis and therapy, but it is always hampered by the huge geometric size and complex structure of IgG. In this work, an amorphous Fe/Co bimetallic organic framework (denoted as PMOF-Fe/Co) is fabricated for IgG separation, with octa-carboxyl polyhedral oligomeric silsesquioxane (OCPOSS) as modulator for the first time. Benefiting from the rigid nanostructure and competitive coordination of OCPOSS, the aperture of PMOF-Fe/Co is enlarged to ∼20 nm along with the generation of enormous structural defects, which enables the accommodation of protein species with high molecular weights and large sizes. OCPOSS is also found exerting a positive impact on mediating the specific recognition and adsorption ability of PMOF-Fe/Co towards IgG through metal affinity, hydrophilic and hydrophobic interactions. Consequently, the multimode and multivalent affinity of PMOF-Fe/Co gives rise to an extraordinary adsorption capacity (2691.7 mg g−1) and satisfactory practical application performance. This study is convinced to provide a simple avenue for the efficient separation of specific large-sized proteins, as well as the engineering of abiotic affinity reagents with compositional and architectural complexity.

The architectural complex of modern Yiyang Garden in Guangzhou, China

The architectural complex of modern Yiyang Garden in Guangzhou, China

Promises and challenges of crop translational genomics.

Crop translational genomics applies breeding techniques based on genomic datasets to improve crops. Technological breakthroughs in the past ten years have made it possible to sequence the genomes of increasing numbers of crop varieties and have assisted in the genetic dissection of crop performance. However, translating research findings to breeding applications remains challenging. Here we review recent progress and future prospects for crop translational genomics in bringing results from the laboratory to the field. Genetic mapping, genomic selection and sequence-assisted characterization and deployment of plant genetic resources utilize rapid genotyping of large populations. These approaches have all had an impact on breeding for qualitative traits, where single genes with large phenotypic effects exert their influence. Characterization of the complex genetic architectures that underlie quantitative traits such as yield and flowering time, especially in newly domesticated crops, will require further basic research, including research into regulation and interactions of genes and the integration of genomic approaches and high-throughput phenotyping, before targeted interventions can be designed. Future priorities for translation include supporting genomics-assisted breeding in low-income countries and adaptation of crops to changing environments.