Adaptive Architecture Research Articles

Flywheel resistance training promotes unique muscle architectural and performance-related adaptations in young adults.

The purpose of this study was to examine the skeletal muscle hypertrophic, architectural, and performance-related adaptations in response to volume-matched, total-body flywheel versus traditional resistance training in a randomized, non-exercise controlled study in physically active young adults. Thirty-one healthy young adults (24±3y) were randomized to 10weeks of traditional resistance training (TRT; n=7F/5M), flywheel training (FWRT; n=7F/4M), or a habitual activity control (CON; n=5F/3M). Maximal voluntary isometric torque (MVIT), one repetition-maximum (1RM) for the free weight squat and bench press, three repetition work maximum (3Wmax) for the flywheel squat and bench press, countermovement jump height, and broad jump distance, as well as site-specific muscle hypertrophy, fascicle length (FL), and pennation angle, were measured. Both TRT and FWRT increased MVIT (p≤0.021) and FFM (p≤0.032) compared to CON. However, TRT promoted superior improvements in free weight squat and bench 1RM (p<0.001), and FWRT improved flywheel 3Wmax squat and bench (p<0.001). FWRT increased the FL and cross-sectional area of the distal VL, countermovement jump height, and broad jump distance (p≤0.048), whereas TRT increased the pennation angle and cross-sectional area of the proximal VL. Therefore, 10weeks of volume-matched, total-body traditional, and flywheel resistance training similarly increased maximal isometric strength and fat-free mass. However, FWRT promoted unique skeletal muscle architectural adaptations that likely contributed to region-specific VL hypertrophy and jump performance improvements. Thus, FWRT provides a novel training stimulus that promotes architectural adaptations that support improved athletic performance in a manner that is not provided by traditional resistance exercise training.

A Theoretical Design Framework of Contemporary Vernacular Architecture Based on a Scoping Review of the Best Practices Worldwide

This paper reviews the development of vernacular architecture design and practices worldwide and then focuses on Chinese rural development. It explores the evolution of vernacular architecture and related concepts from a longitudinal perspective, and indicates the dynamic adaptability of vernacular architecture. A scoping review based on archival materials and secondary source literature of the last 10 years is conducted. As a result, 41 design factors have been collected. With a cross-comparative study with related publications, five intangible design factors and ten key tangible design factors are discussed to build a theoretical design framework, which finally provides a foundation for related design practices and further research.

Increasing Renewable Energy Penetration on Low-Voltage Networks: An Expert Knowledge Approach

While South Africa is deemed one of the countries with the highest irradiation levels, it still utilises coal as its primary energy source due to its abundance. Due to the world-wide drive towards carbon neutrality, residential, commercial, agricultural, and industrial consumers are considering small-scale embedded generation systems. The National Rationalised Specifications 097-2-3 document specifies the scale of the embedded generation capacity a consumer is allowed to install. However, specifications do not yet make the required provisions for the addition of energy storage. The effective collective management of the grouped small-scale embedded generation systems could provide a high level of energy security and increase the percentage of renewable energy generation in the total energy mix. Potential challenges come into play when considering the stochastic nature of photovoltaic generation and its effect on the storage capacity and the dispersion in load profiles of the residential units typically present on a low-voltage network. This paper contributes by investigating the utilisation of photovoltaic generation in conjunction with storage as the basis for virtual power plant control, with the aim to safely increase renewable energy penetration and improve energy security, all while remaining within the South African low-voltage regulatory limits. A two-level virtual power plant controller is proposed with the dispersed energy storage units as the primary controllable resources and the dispersed photovoltaic generation as the secondary controllable resources. The objective of the controller is to achieve nodal energy management, energy sharing, and ancillary service provision and finally to increase renewable energy penetration. A representative single-feeder low-voltage network is simulated, and test cases of 50% and 75% renewable energy penetration are investigated as the basis for evaluation. The proposed controller architecture proved to maintain network integrity for both test cases. The adaptability of the controller architecture was also confirmed for a changed feeder topology; in this case, it was a multi-feeder topology. Future work is warranted to inform policy on the allowed levels of renewable energy penetration to be based not only on demand but also on the level of energy storage present in a network.

Ambiguous AI: Lossy Compression, Model Collapse, and Intercultural Sensitivity

This paper examines the intersections of artificial intelligence (AI), cultural representation, and intercultural sensitivity, critiquing the impact of lossy compression and model collapse on cultural expression in AI systems. The study introduces “intentional patiency” as an alternative framework, conceptualizing AI not merely as an active agent but as a cultural receptor, and in turn, producer. AI systems often rely on compression algorithms that strip away cultural complexities, rendering nuanced traditions as simplified, stereotypical artifacts. Drawing on frameworks from Ted Chiang’s metaphor of lossy compression to Wendy Chun’s critique of noise, the essay explores how AI’s data optimization selectively filters cultural diversity, perpetuating dominant narratives while silencing marginalized voices. Model collapse in generative systems demonstrates while also exacertabting the pitfalls of homogenous data to reduce cultural representations to monolithic “single stories,” echoing concerns raised by Chimamanda Ngozi Adichie. To address this issue, this essay advocates for an interculturally sensitive AI development paradigm, incorporating cultural expansion and ethical safeguards to mitigate cultural erasure. Through the concept of “ambiguous AI,” a paradigm rooted in probabilistic engagement and intentional patiency, the essay seeks to reframe AI’s role as a nuanced participant in cultural discourse, promoting culturally adaptive architectures.

Multi-agent distributed control of integrated process networks using an adaptive community detection approach

This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency.

Avian-inspired embodied perception in biohybrid flapping-wing robotics

Avian feather intricate adaptable architecture to wing deformations has catalyzed interest in feathered flapping-wing aircraft with high maneuverability, agility, and stealth. Yet, to mimic avian integrated somatic sensation within stringent weight constraints, remains challenging. Here, we propose an avian-inspired embodied perception approach for biohybrid flapping-wing robots. Our feather-piezoelectric mechanoreceptor leverages feather-based vibration structures and flexible piezoelectric materials to refine and augment mechanoreception via coupled oscillator interactions and robust microstructure adhesion. Utilizing convolutional neural networks with the grey wolf optimizer, we develop tactile perception of airflow velocity and wing flapping frequency proprioception. This method also senses pitch angle via airflow direction and detects wing morphology through feather collisions. Our low-weight, accurate perception of flapping-wing robot flight states is validated by motion capture. This investigation constructs a biomechanically integrated embodied perception system in flapping-wing robots, which holds significant promise in reflex-based control of complex flight maneuvers and natural bird flight surveillance.

ARQUITETURA BIOMIMÉTICA: COMO A NATUREZA É EXPLORADA NOS PROJETOS ARQUITETÔNICOS

The present work aims to analyze how biomimetic architecture draws inspiration from nature and how this contemporary approach has been applied to architectural projects worldwide. Through an exploratory research study, with bibliographic research procedures and a qualitative approach, the purpose of the investigation was achieved. Based on the information gathered throughout the research, the results obtained include: understanding how biomimetic architecture adapts architectural designs, the strategies and knowledge that transform the concept of architecture and nature, as well as the correlation between nature and biomimetic architecture in the contemporary world. Additionally, an analysis of projects that employed the biomimetic concept was conducted to understand how this methodology is applied in practice.

Mutation potentiates migration swamping in polygenic local adaptation.

Locally adapted traits can exhibit a wide range of genetic architectures, from pronounced divergence at a few loci to small frequency divergence at many loci. The type of architecture that evolves depends strongly on the migration rate, as weakly selected loci experience swamping and do not make lasting contributions to divergence. Simulations from previous studies showed that even when mutations are strongly selected and should resist migration swamping, the architecture of adaptation can collapse and become transient at high mutation rates. Here, we use an analytical two-population model to study how this transition in genetic architecture depends upon population size, strength of selection, and parameters describing the mutation process. To do this, we develop a mathematical theory based on the diffusion approximation to predict the threshold mutation rate above which the transition occurs. We find that this performs well across a wide range of parameter space, based on comparisons with individual-based simulations. The threshold mutation rate depends most strongly on the average effect size of mutations, weakly on the strength of selection, and marginally on the population size. Across a wide range of the parameter space, we observe that the transition to a transient architecture occurs when the trait-wide mutation rate is 10-3-10-2, suggesting that this phenomenon is potentially relevant to complex traits with a large mutational target. On the other hand, based on the apparent stability of genetic architecture in many classic examples of local adaptation, our theory suggests that per-trait mutation rates are often relatively low.

Quadriceps Architectural Adaptations in Team Sports Players: A Meta-analysis.

Resistance training is the most effective strategy to modify muscle architecture, enhancing sport performance and reducing injury risk. The aim of this study was to compare the effects of high loads (HL) versus lower loads (LL), maximal versus submaximal efforts, and high frequency (HF) versus low frequency (LF) on quadriceps architectural adaptations in team sports players. Five databases were searched. Vastus lateralis thickness, fascicle length and pennation angle, and rectus femoris thickness were analyzed as main outcomes. Overall, resistance training significantly improved muscle thickness and pennation angle, but not fascicle length. LL led to greater fascicle length adaptations in the vastus lateralis compared to HL (p=0.01), while no substantial differences were found for other load comparisons. Degree of effort and training frequency did not show meaningful differences (p>0.05). In conclusion, LL lengthen the fascicle to a greater extent than HL, and training with LL twice a week could maximize architectural adaptations, whereas the degree of effort does not appear to be a determinant variable on quadriceps architectural adaptations.

Karakteristik Fisik Arsitektur Islam pada Bangunan Masjid di Kecamatan Curup Tengah Kabupaten Rejang Lebong

This study explores the characteristics of Islamic architecture in Curup Tengah, an area with geographic, cultural, and traditional contexts distinct from the origins of Islamic architecture in Arabia. This study focuses on the adaptation of Islamic architecture to local contexts and community needs, as well as its influence on the appication of Islamic architectural elements in mosques. This study aimed to analyze and describe the characteristics and implementation of Islamic architectural elements, including domes, minarets, calligraphy, geometry, floral motifs, prayer halls, mihrabs, pulpits, porticos, ablution areas, study spaces, ventilation and natural lighting. The methodology employed is qualitative research with a descriptive comparative approach, utilizing data collection through observations, interviews, questionnaires, and relevant literature reviews. The finding indicate that mosques in Curup Tengah generally lack minarets and instead optimize doors and windows for natural ventilation, avoiding the use of artificial cooling systems. The application of Islamic architectural elements in these mosques remains limited, with only eight to twelve of the total thirteen elements being implemented. This research enhances the understanding of how Islamic architectural elements are applied and offers valuable insights for future mosque design in Curup.

Identity Symbolism: Cultural Significance in Tobong Monument and Balairung Hotel

The romantic perception of vernacular architecture, often rooted in nostalgia, significantly influences its use as a symbol of cultural identity. Referring to the works of Paul Oliver and Robert Redfield on vernacular architecture, exploring romanticism and attitudes towards vernacular buildings as well as the differences between them. Tobong, a tower structure for brick burning, and Hotel Balairung in Jakarta are examples of two buildings related to vernacular architecture that can symbolize culture and society and the romance of the past. Rooted in local production traditions, Tobong transcends its functional purpose to become a symbol of cultural heritage and the significance of vernacular architecture to this day. Similarly, Hotel Balairung represents the adaptation of the vernacular architecture and culture of West Sumatra, which can symbolize regional pride and economic development. How vernacular structures like Tobong being made into monuments and Hotel Balairung represent the cultural significance and adaptability of vernacular architecture, my argument is that both vernacular architecture buildings, Tobong being made into a monument in Godean Yogyakarta and West Sumatra architecture being used as a hotel in Jakarta, show variations of the significance and adaptability of vernacular architecture in the present era. Through a comparison method of two buildings that elevate vernacular architecture, this article shows the connection between vernacular buildings and their cultural identity, as well as their ability to be used in various contexts of adaptability, and significance in shaping communitiesKeywords: sentiment, vernacular architecture, monument, culture, identity

WGS-YOLO: A real-time object detector based on YOLO framework for autonomous driving

The safety and reliability of autonomous driving depends on the precision and efficiency of object detection systems. In this paper, a refined adaptation of the YOLO architecture (WGS-YOLO) is developed to improve the detection of pedestrians and vehicles. Specifically, its information fusion is enhanced by incorporating the Weighted Efficient Layer Aggregation Network (W-ELAN) module, an innovative dynamic weighted feature fusion module using channel shuffling. Meanwhile, the computational demands and parameters of the proposed WGS-YOLO are significantly reduced by employing the Space-to-Depth Convolution (SPD-Conv) and the Grouped Spatial Pyramid Pooling (GSPP) modules that have been strategically designed. The performance of our model is evaluated with the BDD100k and DAIR-V2X-V datasets. In terms of mean Average Precision (mAP0.5), the proposed model outperforms the baseline Yolov7 by 12%. Furthermore, extensive experiments are conducted to verify our analysis and the model’s robustness across diverse scenarios.

Adaptation of the REINVENT neural network architecture to generate potential HIV-1 entry inhibitors

Objectives. The main purpose of this work is to adapt the architecture of the REINVENT neural network to generate potential inhibitors of the HIV-1 envelope protein gp120 using in the learning process with reinforcement of molecular docking on GPUs.Methods. To modify the initial network model, molecular docking on GPUs implemented in the learning process with reinforcement was used, and an algorithm was developed that allows converting the representations of connections generated by the SMILES network into the PDBQT format necessary for docking. To accelerate the learning of the neural network in the modified version of the REINVENT model, the AutoDock-Vina-GPU-2.1 docking program was used, and to clarify the results of its work, the procedure for revaluing the affinity of compounds to the target using the RFScore-4 evaluation function was used.Results. Using a modified version of the REINVENT model, more than 60,000 compounds were obtained, of which about 52,000 molecules have a binding energy value to the HIV-1 gp120 protein comparable to the value calculated for the HIV-1 inhibitor NBD-14204, used in calculations as a positive control. Of the 52,000 compounds selected, about 34,000 molecules satisfy the restrictions imposed on a potential drug to ensure its bioavailability when taken orally.Conclusion. The results obtained allow us to demonstrate the effectiveness of an adapted neural network by the example of designing new potential inhibitors of the gp120 HIV-1 protein capable of blocking the CD4- binding site of the gp120 virus envelope protein and preventing its penetration into host cells.

ACA-Net: adaptive context-aware network for basketball action recognition.

The advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball players' actions using artificial intelligence technology can provide valuable assistance and guidance to athletes, coaches, and analysts, and can help referees make fairer decisions during games. However, unlike action recognition in simpler scenarios, the background in basketball is similar and complex, the differences between various actions are subtle, and lighting conditions are inconsistent, making action recognition in basketball a challenging task. To address this problem, an Adaptive Context-Aware Network (ACA-Net) for basketball player action recognition is proposed in this paper. It contains a Long Short-term Adaptive (LSTA) module and a Triplet Spatial-Channel Interaction (TSCI) module to extract effective features at the temporal, spatial, and channel levels. The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. We conducted extensive experiments on the popular basketball action recognition datasets SpaceJam and Basketball-51. The results show that ACA-Net outperforms the current mainstream methods, achieving 89.26% and 92.05% in terms of classification accuracy on the two datasets, respectively. ACA-Net's adaptable architecture also holds potential for real-world applications in autonomous robotics, where accurate recognition of complex human actions in unstructured environments is crucial for tasks such as automated game analysis, player performance evaluation, and enhanced interactive broadcasting experiences.

Examining Facade Adaptability: A Study of Adaptive Facade Classification Criteria

In this research, the facade classification of three different buildings located in Europe, Asia and Australia is carried out by using a classification model developed by utilising the criteria of some adaptive facade classification models in the literature. The control parameters affecting the facade design, physical environment criteria, facade adaptation method, control system and technique, visibility of adaptation and time scale criteria in the model are discussed in detail. Besides, the historical development, contemporary trends and basic parameters on architecture are analysed comprehensively. The emergence of adaptive architecture is discussed, as well as efforts to demonstrate changes in the design of elements sensitive to dynamic environmental and user needs. Furthermore, how adaptability is integrated with the building envelope and facade, the historical evolution of adaptive facades, and the development of key components are also examined. Thus, this study aims to make an important contribution to understanding adaptive architecture and adaptive facades while categorising examples of adaptive facades across three continents, including cultural and geographical differences.

Conceptual Scenarios of Architectural Adaptation of Novosibirsk Power Plant

This year marks the 100th anniversary of the foundation of the First Large Power Plant in Novosibirsk, a significant industrial building. Today, Novosibirsk is facing the problem of losing its integral cultural and historical environment due to the large number of unused dilapidated historical and architectural monuments. In order to prevent this trend and save the architectural heritage, it is necessary to develop modern strategies for reuse of these buildings.In this study, conceptual scenarios of architectural adaptation of M. I. Kalinin power plant, the object of cultural heritage of regional significance, are put forward through the methods of contextual, retrospective, ideographic analysis. These design solutions are classified by the original image of the building, focused on the transformation of the former industrial building in order to reveal the socio-historical side of the power plant as a symbol of the dynamic rise of the economy and energy industry in Novosibirsk.As a result, among the scenarios developed for the main building of the city's first major power plant, the most appropriate one is chosen in the context of the historical development of Novosibirsk.The conceptual principle of Nuanced combination with the historical context is the most effective among the described scenarios, assuming preservation of the general architectural image of the historical monument with reversible changes in the architectural solutions of the building.

Forecasting CPI inflation under economic policy and geopolitical uncertainties

Forecasting consumer price index (CPI) inflation is of paramount importance for both academics and policymakers at central banks. This study introduces the filtered ensemble wavelet neural network (FEWNet) to forecast CPI inflation, tested in BRIC countries. FEWNet decomposes inflation data into high- and low-frequency components using wavelet transforms, and incorporates additional economic factors, such as economic policy uncertainty and geopolitical risk, to enhance forecast accuracy. These wavelet-transformed series and filtered exogenous variables are input into downstream autoregressive neural networks, producing the final ensemble forecast. Theoretically, we demonstrate that FEWNet reduces empirical risk compared to fully connected autoregressive neural networks. Empirically, FEWNet outperforms other forecasting methods and effectively estimates prediction uncertainty, due to its ability to capture non-linearities and long-range dependencies through its adaptable architecture. Consequently, FEWNet emerges as a valuable tool for central banks to manage inflation and enhance monetary policy decisions.

Tracing the Origins of the Wind Catcher: A Comparative Study of Iran, Egypt, and its Influence on Kuwaiti Architecture

This paper explores the architectural significance and evolution of wind towers and wind catchers, examining their various types and tracing their origins across ancient civilizations. Special emphasis is placed on the dispute between Iran and Egypt regarding the invention of the wind catcher. Furthermore, the study delves into how these architectural elements were introduced to Kuwait and adapted to suit the region’s hot arid climate. By analyzing historical records and architectural adaptations, the research aims to provide insights into the cultural and environmental factors that contributed to the development and diffusion of this sustainable cooling technology in the Gulf region

Building Scalable Data Warehouses: Best Practices and Case Studies

In today's data-driven world, the ability to manage, store, and analyze large volumes of data is crucial for business success. The demand for scalable data warehouses has risen dramatically as organizations seek to handle the explosion of data generated by modern applications and digital transactions. "Building Scalable Data Warehouses: Best Practices and Case Studies" explores the key strategies, methodologies, and technologies involved in designing and implementing scalable data warehouses that meet the demands of today and the future. The paper highlights the importance of architecture choices, data modeling techniques, and performance optimization in creating data warehouses that can grow with an organization’s needs. Additionally, it provides case studies that demonstrate the real-world application of these principles in various industries, showing how scalable data warehouses have enabled companies to maintain high performance, reduce costs, and enhance decision-making capabilities. The paper begins by defining what constitutes a scalable data warehouse, emphasizing the importance of a flexible and adaptive architecture that can accommodate growing data volumes and changing business requirements. It explores different architectural approaches, including the benefits and challenges of traditional on-premises data warehouses versus cloud-based solutions. The paper also discusses the role of data modeling, including dimensional and normalized approaches, in ensuring that the data warehouse can handle increasing complexity without compromising performance.

The accuracy of predicting maladaptation to new environments with genomic data.

Rapid environmental change poses unprecedented challenges to species persistence. To understand the extent that continued change could have, genomic offset methods have been used to forecast maladaptation of natural populations to future environmental change. However, while their use has become increasingly common, little is known regarding their predictive performance across a wide array of realistic and challenging scenarios. Here, we evaluate the performance of currently available offset methods (gradientForest, the Risk-Of-Non-Adaptedness, redundancy analysis with and without structure correction and LFMM2) using an extensive set of simulated data sets that vary demography, adaptive architecture and the number and spatial patterns of adaptive environments. For each data set, we train models using either all, adaptive or neutral marker sets and evaluate performance using in silico common gardens by correlating known fitness with projected offset. Using over 4,849,600 of such evaluations, we find that (1) method performance is largely due to the degree of local adaptation across the metapopulation (LA), (2) adaptive marker sets provide minimal performance advantages, (3) performance within the species range is variable across gardens and declines when offset models are trained using additional non-adaptive environments and (4) despite (1) performance declines more rapidly in globally novel climates (i.e. a climate without an analogue within the species range) for metapopulations with greater LA than lesser LA. We discuss the implications of these results for management, assisted gene flow and assisted migration.