Adaptive Architecture Research Articles

Synthesis and Additive Manufacturing of Hydrazone-Linked Covalent Organic Framework Aerogels.

Covalent Organic Frameworks (COFs) are crystalline, porous organic materials. Recent studies have demonstrated novel processing strategies for COFs to form adaptable architectures, but these have focused primarily on imine-linked COFs. This work presents a new synthesis and processing route to produce crystalline hydrazone-linked COF gels and aerogels with hierarchical porosity. The method was implemented to produce a series of hydrazone-linked COFs with different alkyl side-chain substituents, achieving control of the hydrophilicity of the final aerogel. Variation in the length of the alkyl substituents yielded materials with controllable form factors that can preferentially adsorb water or nonpolar organic solvents. Additionally, a method for additive manufacturing of hydrazone-linked COFs using hydroxymethylcellulose as a sacrificial additive is presented. This work demonstrates an effective and simple approach to the fabrication of hydrazone COF aerogels and additive manufacturing to produce hydrazone COFs of desired shape.

Distinguished biological adaptation architecture aggravated population differentiation of Tibeto-Burman-speaking people

Tibeto-Burman (TB) people have endeavored to adapt to the hypoxic, cold, and high-UV high-altitude environments in the Tibetan Plateau and complex disease exposures in lowland rainforests since the late Paleolithic period. However, the full landscape of genetic history and biological adaptation of geographically diverse TB-speaking people, as well as their interaction mechanism, remain unknown. Here, we generate a whole-genome meta-database of 500 individuals from 39 TB-speaking populations and present a comprehensive landscape of genetic diversity, admixture history, and differentiated adaptative features of geographically different TB-speaking people. We identify genetic differentiation related to geography and language among TB-speaking people, consistent with their differentiated admixture process with incoming or indigenous ancestral source populations. A robust genetic connection between the Tibetan-Yi corridor and the ancient Yellow River people supports their Northern China origin hypothesis. We finally report substructure-related differentiated biological adaptative signatures between highland Tibetans and Loloish speakers. Adaptative signatures associated with the physical pigmentation (EDAR and SLC24A5) and metabolism (ALDH9A1) are identified in Loloish people, which differed from the high-altitude adaptative genetic architecture in Tibetan. TB-related genomic resources provide new insights into the genetic basis of biological adaptation and better reference for the anthropologically informed sampling design in biomedical and genomic cohort research.

Coarse-to-fine online latent representations matching for one-stage domain adaptive semantic segmentation

Domain adaptive semantic segmentation is meaningful since collecting numerous labeled samples in different domains is expensive and time-consuming. Recent domain adaptation methods yield not so efficient performance compared with supervised learning. With the hypothesis that semantic feature can be shared across domains, this paper proposes a coarse-to-fine online matching architecture (COM) for one-stage domain adaptation. We consider subsequent learning stages progressively refining the task in the latent feature space, i.e. the finer set at each component is hierarchically derived from the coarser set of the previous components, including cross-domain global prototypes, categories and instances matching and anchor-points contrastive learning, which further achieve self-supervised learning with region-level pseudo label generated only in a single training step. Beforehand, feature refinement are performed to realize edge perception and inter-feature augmentation. Then, coarse-to-fine network fuses global and local consistency matching via specific distribution alignment between the source and target domain. Finally, the adversarial structure controls the uncertainty of generator prediction through the maximization of classification results and minimization of two classifiers discrepancy. This proposed method is evaluated in two unsupervised domain adaptation tasks, i.e. GTA5 → Cityscapes and SYNTHIA → Cityscapes. Extensive experiments verify the effectiveness of our proposed COM model and demonstrate its superiority over several state-of-the-art approaches.

Analysis of intrusion detection system in cloud computing environment using artificial neural network

This study developed a novel intrusion detection system (IDS) for cloud computing using artificial neural networks (ANNs) and machine learning techniques. The proposed IDS uses an adaptive architecture capable of detecting malicious activities within a cloud computing environment. To process and optimize the data, Adam optimization techniques were employed, and MiniMaxScaler was used to normalize the data for training. The model was designed using the TensorFlow framework for ANNs, and the LSD methodology was employed in the development. The training was conducted using the University of New Brunswick Intrusion Detection Systems dataset, which had been preprocessed. Results indicate that the proposed architecture was highly effective in detecting various attacks, with low false-positive and false-negative rates. The training and validation accuracies were 99.7% and 99.9%, respectively, using this method. This approach can automatically detect the nature of attacks, saving time and resources.

Empowering Preventive Care with GECA Chatbot.

Chatbots have become increasingly popular in the healthcare industry. In the area of preventive care, chatbots can provide personalized and timely solutions that aid individuals in maintaining their well-being and forestalling the development of chronic conditions. This paper presents GECA, a chatbot designed specifically for preventive care, that offers information, advice, and monitoring to patients who are undergoing home treatment, providing a cost-effective, personalized, and engaging solution. Moreover, its adaptable architecture enables extension to other diseases and conditions seamlessly. The chatbot's bilingual capabilities enhance accessibility for a wider range of users, including those with reading or writing difficulties, thereby improving the overall user experience. GECA's ability to connect with external resources offers a higher degree of personalization, which is a crucial aspect in engaging users effectively. The integration of standards and security protocols in these connections allows patient privacy, security and smooth adaptation to emerging healthcare information sources. GECA has demonstrated a remarkable level of accuracy and precision in its interactions with the diverse features, boasting an impressive 97% success rate in delivering accurate responses. Presently, preparations are underway for a pilot project at a Portuguese hospital that will conduct exhaustive testing and evaluate GECA, encompassing aspects such as its effectiveness, efficiency, quality, goal achievability, and user satisfaction.

An architecture for the dynamic adaptation of an intelligent multi-tutoring system

The articulation between machine tutoring and human tutoring remains a productive research within in the context of Intelligent Tutoring Systems (ITS), particularly in the context of e-learning where the dropout rate is high. We explore an innovative approach, the automation of tutoring as it is done in the classroom to respond to the difficulties of the learner. We propose a generic Intelligent Multi-Tutoring System (IMTS) architecture composed of two modules COMES and MAT. The Communication Entry Service (COMES) module manages communications between the IMTS and a Learning Management System (LMS). The module Multi-Agent Tutoring (MAT) is the multi-agent system developed with JADE, which allows the dynamic adaptation of tutoring (Machine, Peer, Teacher) according to the profile of the learner. We offer a configurable system to customize tutoring to the individual needs of each learner. It can be grafted onto any learning platform, making it multidisciplinary and easy to integrate into existing learning environments. The teacher will be able to devote more time to learners which need greater his intervention.The peer will develop human and relational qualities linked to their know-how, transversal skills sought by recruiters. To validate this architecture, we provide an application and results that integrate the elements of the described model. The results of the experiment prove the feasibility and reliability of our approach.

Architecture of healthcare and social inclusion in interwar Czechoslovakia: Pezinok Psychiatric Institute and the Masaryk Institute for Young People with Intellectual and Physical Disabilities in Bratislava

Abstract The formation of an independent Czechoslovak Republic created a space for the institutionalisation of health and social care as a reflection of the government’s social policy. It became crucial to modernise and expand the network of health and social facilities. Although there were medical advances in institutional care for people with mental and physical disabilities, attempts at social inclusion were rare. Few innovative institutions existed that pioneered social inclusion of clients through proper education and adaptable architecture. This topic, as reflected in the architecture of the Institute for People with Nervous and Mental Health Disorders in Pezinok and the Masaryk Institute for Young People with Intellectual and Physical Disabilities in Bratislava, is the focus of this paper. The Pezinok Institute was the first clinic in central Europe to offer treatment of children with epilepsy. It was believed that elementary education and practical skills would socialise clients, adapt them to general society, and decrease their dependence on the government and their relatives. So, in addition to effective work therapy and hippotherapy, the institute also planned to educate clients in the envisaged school. The Masaryk Institute, as the first of its kind in Slovakia, aimed not only to establish institutional health and social care of people with both intellectual and physical disabilities, but also to integrate them into the society. Its initiator, Karol Koch, was convinced that it was indispensable to adapt the architecture to the needs of the people with disabilities, while not allowing the people with disabilities to feel that their environment differs from that of the others. The innovative nature of the institute’s programme was imprinted in its progressive functionalist design. This paper aims to identify crucial problems, confront visions and reality, and to prove that, despite difficulties and minor results, even at that time, there were innovative architectural and medical reflections on the needs of people with disabilities.

In Pursuit of Beauty: Aesthetic-Aware and Context-Adaptive Photo Selection in Crowdsensing

The pervasive view of the mobile crowd bridges various real-world scenes and people's perceptions with the gathering of distributed crowdsensing photos. To elaborate informative visuals for viewers, existing techniques introduce photo selection as an essential step in crowdsensing. Yet, the aesthetic preference of viewers, at the very heart of their experiences under various crowdsensing contexts (e.g., travel planning), is seldom considered and hardly guaranteed. We propose CrowdPicker, a novel photo selection framework with adaptive aesthetic awareness for crowdsensing. With the observations on aesthetic uncertainty and bias in different crowdsensing contexts, we exploit a joint effort of mobile crowdsourcing and domain adaptation to actively learn contextual knowledge for dynamically tailoring the aesthetic predictor. Concretely, an aesthetic utility measure is invented based on the probabilistic balance formalization to quantify the benefit of photos in improving the adaptation performance. We prove the NP-hardness of sampling the best-utility photos for crowdsourcing annotation and present a (1-1/e) approximate solution. Furthermore, a two-stage distillation-based adaptation architecture is designed based on fusing contextual and common aesthetic preferences. Extensive experiments on three datasets and four raw models demonstrate the performance superiority of CrowdPicker over four photo selection baselines and four typical sampling strategies. Cross-dataset evaluation illustrates the impacts of aesthetic bias on selection.

Persistence Initialization: a novel adaptation of the Transformer architecture for time series forecasting

AbstractTime series forecasting is an important problem, with many real world applications. Transformer models have been successfully applied to natural language processing tasks, but have received relatively little attention for time series forecasting. Motivated by the differences between classification tasks and forecasting, we propose PI-Transformer, an adaptation of the Transformer architecture designed for time series forecasting, consisting of three parts: First, we propose a novel initialization method called Persistence Initialization, with the goal of increasing training stability of forecasting models by ensuring that the initial outputs of an untrained model are identical to the outputs of a simple baseline model. Second, we use ReZero normalization instead of Layer Normalization, in order to further tackle issues related to training stability. Third, we use Rotary positional encodings to provide a better inductive bias for forecasting. Multiple ablation studies show that the PI-Transformer is more accurate, learns faster, and scales better than regular Transformer models. Finally, PI-Transformer achieves competitive performance on the challenging M4 dataset, both when compared to the current state of the art, and to recently proposed Transformer models for time series forecasting.

Mutation and Selection Induce Correlations between Selection Coefficients and Mutation Rates.

AbstractThe joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) the "nominal" distribution of possible changes, prior to mutation or selection; (2) the "de novo" distribution of realized mutations; and (3) the "fixed" distribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong-selection/weak-mutation (SSWM) regime. The de novo distribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resulting de novo and fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.

PF-BERxiT: Early exiting for BERT with parameter-efficient fine-tuning and flexible early exiting strategy

The industrial usage of huge pre-training language models such as BERT and ALBERT are limited by the computational probability problem in the fine-tuning process and overthinking problem in the inference process. PF-BERxiT has been proposed to optimize the pre-trained languages with a novel parameter-efficient fine-tuning method and a flexible early exiting strategy. Significantly, the new parameter-efficient fine-tuning model integrates a bottleneck adapter architecture parallel to the transformer architecture, and only the adapter's parameters are adjusted. In addition, we integrate an extra sub-learning module to learn the samples' characteristics, improving the accuracy and efficiency simultaneously. The flexible exiting strategy allows the model to exit early if the similarity score of adjacent layers is less than the threshold for pre-defined times. It is more flexible than previous early exiting methods, for it can simultaneously adjust the similarity score thresholds and patience parameters according to the request traffic. Extensive experiments are conducted on the GLUE benchmark, demonstrating that: (1) PF-BERxiT outperforms conventional training and parameter-efficient strategies with only a few parameters fine-tuned. (2) PF-BERxiT strikes a better balance between model performances and speedup ratios than previous state-of-the-art (SOTA) early exiting methods such as PABEE and BERxiT. (3) Ablation studies in the fine-tuning process demonstrate that the best bottleneck dimension r of the adapters is 32, and the adapters placed parallel to the feed-forward module are more efficient. (4) Ablation studies in the inference process demonstrate that for variants of PF-BERxiT with different similarity scores, PF-BERxiT-kl and PF-BERxiT-bikl attain better speedup-accuracy trade-offs than PF-BERxiT-rekl. Our PF-BERxiT helps attain a better trade-off between performance and efficiency, providing a reference for the efficient application of neural computing.

The influence of climatic factors on the sublimation and mechanical properties of paper fiber reinforced ice as building envelope

The climatic adaptability of ice-shell architecture under long-term natural influence remains underexplored. This study investigates the weatherability of paper fiber reinforced ice (PFRI) as envelopes, including the changes in the sublimation mechanism and mechanical properties changes. The PFRI specimens were layered poured and exposed to nature. The sublimation rates, climatic parameters, and stress–strain data were recorded. The results suggest that PFRI sublimation is a dynamic process with cumulative effects. A calculation model is proposed to predict sublimation in engineering. Considering fibre escape, PFRI with 2% or 3% fibre content has better weatherability, with a 30% decrease in elastic modulus on the sunny side and a 15% decrease on the shaded side.

A state-of-the-art digital factory integrating digital twin for laser additive and subtractive manufacturing processes

PurposeThis study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.Design/methodology/approachThis study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.FindingsA comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.Originality/valueBased on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Behaviour of bio-inspired grouped battered minipiles under lateral loading in clay

The field of bio-inspired geotechnics has been growing in response to the demand for foundations that are sustainable and yet have improved load-bearing capacities. This study aims to address the gap in a specialised adaptation of root system architecture for designing resilient foundations. The lateral load behaviour of one such novel grouped battered minipile configuration is evaluated in this study based on full-scale field testing and numerical modelling to report the unknown increase of load capacity caused by shape modification. First, three single minipiles battered at 0° and 25° were subjected to static lateral loading in fine-grained soil. The strain profiles along the individual minipile shafts were obtained using optic fibre sensors. Consecutively, full-scale lateral load tests on two types of minipile groups were also performed; one group had a configuration of two 25° battered minipiles perpendicular to the direction of loading mimicking a tree-root system, and another conventional group had two positive and negative battered minipiles. A numerical model was developed to investigate the effect of pile spacing and obtain soil pressures, bending moments and axial forces of the battered minipile groups. Results show that increased bearing area and higher engagement of soil volume for the novel minipile group with two perpendicular battered minipiles were larger than the conventional minipile group; thus, the former offered higher lateral resistance. The deflection pattern, bending moment and p-y curves showed a shadowing effect in stiff clay for battered minipile groups at a pile head spacing of three times the minipile diameter.

A theory of oligogenic adaptation of a quantitative trait.

Rapid phenotypic adaptation is widespread in nature, but the underlying genetic dynamics remain controversial. Whereas population genetics envisages sequential beneficial substitutions, quantitative genetics assumes a collective response through subtle shifts in allele frequencies. This dichotomy of a monogenic and a highly polygenic view of adaptation raises the question of a middle ground, as well as the factors controlling the transition. Here, we consider an additive quantitative trait with equal locus effects under Gaussian stabilizing selection that adapts to a new trait optimum after an environmental change. We present an analytical framework based on Yule branching processes to describe how phenotypic adaptation is achieved by collective changes in allele frequencies at the underlying loci. In particular, we derive an approximation for the joint allele-frequency distribution conditioned on the trait mean as a comprehensive descriptor of the adaptive architecture. Depending on the model parameters, this architecture reproduces the well-known patterns of sequential, monogenic sweeps, or of subtle, polygenic frequency shifts. Between these endpoints, we observe oligogenic architecture types that exhibit characteristic patterns of partial sweeps. We find that a single compound parameter, the population-scaled background mutation rate Θbg, is the most important predictor of the type of adaptation, while selection strength, the number of loci in the genetic basis, and linkage only play a minor role.

Unified Architecture Adaptation for Compressed Domain Semantic Inference.

Advances in both lossy image compression and semantic content understanding have been greatly fueled by deep learning techniques, yet these two tasks have been developed separately for the past decades. In this work, we address the problem of directly executing semantic inference from quantized latent features in the deep compressed domain without pixel reconstruction. Although different methods have been proposed for this problem setting, they either are restrictive to a specific architecture, or are sub-optimal in terms of compressed domain task accuracy. In contrast, we propose a lightweight, plug-and-play solution which is generally compliant with popular learned image coders and deep vision models, making it attractive to vast applications. Our method adapts prevalent pixel domain neural models that are deployed for various vision tasks to directly accept quantized latent features (other than pixels). We further suggest training the compressed domain model by transferring knowledge from its corresponding pixel domain counterpart. Experiments show that our method is compliant with popular learned image coders and vision task models. Under fair comparison, our approach outperforms a baseline method by a) more than 3% top-1 accuracy for compressed domain classification, and b) more than 7% mIoU for compressed domain semantic segmentation, at various data rates.

Honey bees and social wasps reach convergent architectural solutions to nest-building problems.

The hexagonal cells built by honey bees and social wasps are an example of adaptive architecture; hexagons minimize material use, while maximizing storage space and structural stability. Hexagon building evolved independently in the bees and wasps, but in some species of both groups, the hexagonal cells are size dimorphic-small worker cells and large reproductive cells-which forces the builders to join differently sized hexagons together. This inherent tiling problem creates a unique opportunity to investigate how similar architectural challenges are solved across independent evolutionary origins. We investigated how 5 honey bee and 5 wasp species solved this problem by extracting per-cell metrics from 22,745 cells. Here, we show that all species used the same building techniques: intermediate-sized cells and pairs of non-hexagonal cells, which increase in frequency with increasing size dimorphism. We then derive a simple geometric model that explains and predicts the observed pairing of non-hexagonal cells and their rate of occurrence. Our results show that despite different building materials, comb configurations, and 179 million years of independent evolution, honey bees and social wasps have converged on the same solutions for the same architectural problems, thereby revealing fundamental building properties and evolutionary convergence in construction behavior.

A capacitively coupled digital isolator using multiple-pulse-coding architecture with CMTI of 200kV/μs and static current of 60μA

This paper presents a capacitively coupled digital isolator with low power consumption and superior Common Mode Transient Immunity (CMTI). It proposes a multiple-pulse-coding architecture and a receiver with an adaptive architecture, which improve the transmission accuracy, eliminate the CMT and achieve low power consumption. The multiple-pulse-coding characterizes edge signal with multi-pulses. The receiver consists of an adaptive pre-amplifier and an adaptive comparator. Fabricated in a 0.18 μm CMOS process, the chip achieves 200 kV/μs CMTI, 60 μA static current, 250 μA dynamic current and 14 kV isolation breakdown voltage with the area of the isolation capacitance of 2×104 μm2.

THE INFLUENTIAL FACTORS OF THE HERITAGE SHOPHOUSES’ ARCHITECTURAL DESIGN IN URBAN HISTORIC DISTRICTS IN MALAYSIA: A REVIEW

Heritage shophouses are the primary architectural legacy that has greatly characterised the place identity of the urban historic district in Malaysia since the 17th century through its distinctive architectural design character. In contrast to the modern building design that is seen as contextually isolated from the place identity of the urban historic district, the architectural identity of heritage shophouses is seen as successful in fitting into the place identity due to its significantly responsive architectural identity to contextual factors. This paper aims to systematically review the influential factors on the Malaysian heritage shophouse design which constitutes its architectural identity through content analysis. This paper extracts scholars’ studies on heritage shophouse architectural design features, heritage shophouse morpho-typologies, and the factors that influence its architectural identity development. Heritage shophouses in Malaysia are morphologically categorized into Early Heritage Shophouse styles, Eclectic Heritage Shophouses, and Non-Cultural Heritage Shophouse styles. Regardless of their architectural styles, all heritage shophouses are characterized by the façade articulation, the interior design including the spatial organization system, and the building construction materials. Every element is primarily influenced by the tropical topographical climates and material sources as a basic architectural adaptation, the building development standards set by the colonial regimes, cultural assimilation, and the advancement of building construction and materials. The findings of this study can help architects how to prioritize design considerations in developing their new architectural intervention design ideas in the urban historic district to preserve its valuable historical characteristics.

Effect of risk perception and vulnerability on adaptive architecture in flood resilience: a case from Ratnapura, Sri Lanka

In the last few decades, the frequency and severity of floods in South Asia have vastly increased due to the increase in global surface temperature and change in rainfall patterns. The lack of risk perception, high vulnerability and lack of proper architectural adaptations have added to the negative consequences of disasters. This study discusses the effect of risk perception and vulnerability on flood resilient architecture. The research was conducted with 35 participants selected from the Mudduwa area in Rathnapura, Sri Lanka where the annual flood frequency and damage is very high. The collected data was analyzed using statistical software to calculate a composite mean score to each theoretical factor risk perception, vulnerability, and architectural adaptation. With assumptions, a non-parametric correlation test was carried out to identify the relationship between the three theoretical factors and their subsequent variables. The research findings concluded that risk perception has a positive correlation with architectural adaptation, but vulnerability demonstrates a negative correlation. According to the study, vulnerability acts as a resistance to the correlation between risk perception and architectural adaptation. In conclusion this study elaborates on the importance of a proper system of adaptation for vulnerable people living in flood prone areas.