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Periodicals

Long-Read Sequencing Emerging in Medical Genetics

The wide implementation of next-generation sequencing (NGS) technologies has revolutionized the field of medical genetics. However, the short read lengths of currently used sequencing approaches pose a limitation for identification of structural variants, sequencing repetitive regions, phasing alleles and distinguishing highly homologous genomic regions. These limitations may significantly contribute to the diagnostic gap in patients with genetic disorders who have undergone standard NGS, like whole exome or even genome sequencing. Now, the emerging long-read sequencing (LRS) technologies may offer improvements in the characterization of genetic variation and regions that are difficult to assess with the currently prevailing NGS approaches. LRS has so far mainly been used to investigate genetic disorders with previously known or strongly suspected disease loci. While these targeted approaches already show the potential of LRS, it remains to be seen whether LRS technologies can soon enable true whole genome sequencing routinely. Ultimately, this could allow the de novo assembly of individual whole genomes used as a generic test for genetic disorders. In this article, we summarize the current LRS-based research on human genetic disorders and discuss the potential of these technologies to facilitate the next major advancements in medical genetics.

Architecture & Planning• 2019• Frontiers Media
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Periodicals

An advanced deep learning models-based plant disease detection: A review of recent research

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

Architecture & Planning• 2023• Frontiers Media
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Periodicals

Medical image analysis using deep learning algorithms

In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.

Architecture & Planning• 2023• Frontiers Media
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Periodicals

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Architecture & Planning• 2016• Multidisciplinary Digital Publishing Institute
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The International Limits and Population at Risk of Plasmodium vivax Transmission in 2009

BACKGROUND: A research priority for Plasmodium vivax malaria is to improve our understanding of the spatial distribution of risk and its relationship with the burden of P. vivax disease in human populations. The aim of the research outlined in this article is to provide a contemporary evidence-based map of the global spatial extent of P. vivax malaria, together with estimates of the human population at risk (PAR) of any level of transmission in 2009. METHODOLOGY: The most recent P. vivax case-reporting data that could be obtained for all malaria endemic countries were used to classify risk into three classes: malaria free, unstable (<0.1 case per 1,000 people per annum (p.a.)) and stable (> or =0.1 case per 1,000 p.a.) P. vivax malaria transmission. Risk areas were further constrained using temperature and aridity data based upon their relationship with parasite and vector bionomics. Medical intelligence was used to refine the spatial extent of risk in specific areas where transmission was reported to be absent (e.g., large urban areas and malaria-free islands). The PAR under each level of transmission was then derived by combining the categorical risk map with a high resolution population surface adjusted to 2009. The exclusion of large Duffy negative populations in Africa from the PAR totals was achieved using independent modelling of the gene frequency of this genetic trait. It was estimated that 2.85 billion people were exposed to some risk of P. vivax transmission in 2009, with 57.1% of them living in areas of unstable transmission. The vast majority (2.59 billion, 91.0%) were located in Central and South East (CSE) Asia, whilst the remainder were located in America (0.16 billion, 5.5%) and in the Africa+ region (0.10 billion, 3.5%). Despite evidence of ubiquitous risk of P. vivax infection in Africa, the very high prevalence of Duffy negativity throughout Central and West Africa reduced the PAR estimates substantially. CONCLUSIONS: After more than a century of development and control, P. vivax remains more widely distributed than P. falciparum and is a potential cause of morbidity and mortality amongst the 2.85 billion people living at risk of infection, the majority of whom are in the tropical belt of CSE Asia. The probability of infection is reduced massively across Africa by the frequency of the Duffy negative trait, but transmission does occur on the continent and is a concern for Duffy positive locals and travellers. The final map provides the spatial limits on which the endemicity of P. vivax transmission can be mapped to support future cartographic-based burden estimations.

Architecture & Planning• 2010• Public Library of Science
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Periodicals

The RelA/SpoT Homolog (RSH) Superfamily: Distribution and Functional Evolution of ppGpp Synthetases and Hydrolases across the Tree of Life

RelA/SpoT Homologue (RSH) proteins, named for their sequence similarity to the RelA and SpoT enzymes of Escherichia coli, comprise a superfamily of enzymes that synthesize and/or hydrolyze the alarmone ppGpp, activator of the "stringent" response and regulator of cellular metabolism. The classical "long" RSHs Rel, RelA and SpoT with the ppGpp hydrolase, synthetase, TGS and ACT domain architecture have been found across diverse bacteria and plant chloroplasts, while dedicated single domain ppGpp-synthesizing and -hydrolyzing RSHs have also been discovered in disparate bacteria and animals respectively. However, there is considerable confusion in terms of nomenclature and no comprehensive phylogenetic and sequence analyses have previously been carried out to classify RSHs on a genomic scale. We have performed high-throughput sensitive sequence searching of over 1000 genomes from across the tree of life, in combination with phylogenetic analyses to consolidate previous ad hoc identification of diverse RSHs in different organisms and provide a much-needed unifying terminology for the field. We classify RSHs into 30 subgroups comprising three groups: long RSHs, small alarmone synthetases (SASs), and small alarmone hydrolases (SAHs). Members of nineteen previously unidentified RSH subgroups can now be studied experimentally, including previously unknown RSHs in archaea, expanding the "stringent response" to this domain of life. We have analyzed possible combinations of RSH proteins and their domains in bacterial genomes and compared RSH content with available RSH knock-out data for various organisms to determine the rules of combining RSHs. Through comparative sequence analysis of long and small RSHs, we find exposed sites limited in conservation to the long RSHs that we propose are involved in transmitting regulatory signals. Such signals may be transmitted via NTD to CTD intra-molecular interactions, or inter-molecular interactions either among individual RSH molecules or among long RSHs and other binding partners such as the ribosome.

Architecture & Planning• 2011• Public Library of Science
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Periodicals

Estimation uncertainty of direct monetary flood damage to buildings

Abstract. Traditional flood design methods are increasingly supplemented or replaced by risk-oriented methods which are based on comprehensive risk analyses. Besides meteorological, hydrological and hydraulic investigations such analyses require the estimation of flood impacts. Flood impact assessments mainly focus on direct economic losses using damage functions which relate property damage to damage-causing factors. Although the flood damage of a building is influenced by many factors, usually only inundation depth and building use are considered as damage-causing factors. In this paper a data set of approximately 4000 damage records is analysed. Each record represents the direct monetary damage to an inundated building. The data set covers nine flood events in Germany from 1978 to 1994. It is shown that the damage data follow a Lognormal distribution with a large variability, even when stratified according to the building use and to water depth categories. Absolute depth-damage functions which relate the total damage to the water depth are not very helpful in explaining the variability of the damage data, because damage is determined by various parameters besides the water depth. Because of this limitation it has to be expected that flood damage assessments are associated with large uncertainties. It is shown that the uncertainty of damage estimates depends on the number of flooded buildings and on the distribution of building use within the flooded area. The results are exemplified by a damage assessment for a rural area in southwest Germany, for which damage estimates and uncertainty bounds are quantified for a 100-year flood event. The estimates are compared to reported flood damages of a severe flood in 1993. Given the enormous uncertainty of flood damage estimates the refinement of flood damage data collection and modelling are major issues for further empirical and methodological improvements.

Architecture & Planning• 2004• Copernicus Publications
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Periodicals

Educational design and networked learning: Patterns, pattern languages and design practice

&lt;span&gt;There is a growing demand for advice about effective, time efficient ways of using ICT to support student learning in higher education. This paper uses one such area of activity - networked learning - as a context in which to outline a novel approach to educational design. The paper makes two main contributions. It provides a high level view of the educational design problem space. It then introduces the patterns based approach to educational design. While other professional communities, particularly in software engineering, have been developing patterns based approaches to sharing and re-using design experience, this paper goes back to the original conceptions of participatory design that informed Christopher Alexander's early work on patterns and pattern languages. In particular, it makes connections between the technicalities of design and the central place of values. A patterns based approach can help with encoding, sharing and using knowledge for educational design. But it is also a powerful way of connecting educational values and vision to the details of the tasks, tools and resources we offer our students.&lt;/span&gt;

Architecture & Planning• 2005• Australasian Society for Computers in Learning in Tertiary Education
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Periodicals

Mathematical Modeling of Release Kinetics from Supramolecular Drug Delivery Systems

Embedding of active substances in supramolecular systems has as the main goal to ensure the controlled release of the active ingredients. Whatever the final architecture or entrapment mechanism, modeling of release is challenging due to the moving boundary conditions and complex initial conditions. Despite huge diversity of formulations, diffusion phenomena are involved in practically all release processes. The approach in this paper starts, therefore, from mathematical methods for solving the diffusion equation in initial and boundary conditions, which are further connected with phenomenological conditions, simplified and idealized in order to lead to problems which can be analytically solved. Consequently, the release models are classified starting from the geometry of diffusion domain, initial conditions, and conditions on frontiers. Taking into account that practically all solutions of the models use the separation of variables method and integral transformation method, two specific applications of these methods are included. This paper suggests that "good modeling practice" of release kinetics consists essentially of identifying the most appropriate mathematical conditions corresponding to implied physicochemical phenomena. However, in most of the cases, models can be written but analytical solutions for these models cannot be obtained. Consequently, empiric models remain the first choice, and they receive an important place in the review.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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Periodicals

The relationship between obesity and cognitive health and decline

The relationship between obesity and cognitive impairment is important given the globally ageing population in whom cognitive decline and neurodegenerative disorders will carry grave individual, societal and financial burdens. This review examines the evidence for the link between obesity and cognitive function in terms of both the immediate effects on cognitive performance, and effects on the trajectory of cognitive ageing and likelihood of dementia. In mid-life, there is a strong association between obesity and impaired cognitive function. Anthropometric measures of obesity are also associated with reduced neural integrity (e.g. grey and white matter atrophy). Increasing age coupled with the negative metabolic consequences of obesity (e.g. type 2 diabetes mellitus) are likely to significantly contribute to cognitive decline and incidence of dementia. Stress is identified as a potential risk factor promoting abdominal obesity and contributing to impaired cognitive function. However, the potentially protective effects of obesity against cognitive decline in older age require further examination. Finally, surgical and whole diet interventions, which address obesity may improve cognitive capacity and confer some protection against later cognitive decline. In conclusion, obesity and its comorbidities are associated with impaired cognitive performance, accelerated cognitive decline and neurodegenerative pathologies such as dementia in later life. Interventions targeting mid-life obesity may prove beneficial in reducing the cognitive risks associated with obesity.

Architecture & Planning• 2017• Cambridge University Press
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Periodicals

Benchmarks for models of short-term and working memory.

Any mature field of research in psychology-such as short-term/working memory-is characterized by a wealth of empirical findings. It is currently unrealistic to expect a theory to explain them all; theorists must satisfice with explaining a subset of findings. The aim of the present article is to make the choice of that subset less arbitrary and idiosyncratic than is current practice. We propose criteria for identifying benchmark findings that every theory in a field should be able to explain: Benchmarks should be reproducible, generalize across materials and methodological variations, and be theoretically informative. We propose a set of benchmarks for theories and computational models of short-term and working memory. The benchmarks are described in as theory-neutral a way as possible, so that they can serve as empirical common ground for competing theoretical approaches. Benchmarks are rated on three levels according to their priority for explanation. Selection and ratings of the benchmarks is based on consensus among the authors, who jointly represent a broad range of theoretical perspectives on working memory, and they are supported by a survey among other experts on working memory. The article is accompanied by a web page providing an open forum for discussion and for submitting proposals for new benchmarks; and a repository for reference data sets for each benchmark. (PsycINFO Database Record

Architecture & Planning• 2018• American Psychological Association
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Periodicals

Digital Business Model, Digital Transformation, Digital Entrepreneurship: Is There A Sustainable “Digital”?

Digitalization plays a major role in contributing towards the United Nations Sustainable Development Goals. Without transformation of existing businesses, both economic and environmental challenges of the future cannot be solved sustainably. However, there is much confusion on interrelationships and terms dealing with digitization or digitalization: Digital business model, digital transformation, digital entrepreneurship. How do these terms interrelate with and to digitalization, and how do they support firms to grow sustainably? To answer this question, we identified seven core digital-related terms based on a structured literature search within the management and economics domain, namely: Digital, Business Model, Digital Business Model, Digital Technology, Digital Innovation, Digital Transformation, and Digital Entrepreneurship. Thereafter, we analyzed prior literature for deriving a common understanding and definition as a basis for interrelations within a conceptual framework. Definitions were presented in a case study setup with twelve innovation and research and development (R&amp;D) managers from various business units of a German high-tech company. Based on these insights, we propose a conceptual framework on how Digital Readiness, Digital Technology, and Digital Business Models might sustainably relate to Innovation, moderated by a Digital Transformation Process. With this approach, we aim to equip practitioners and researchers alike in handling and addressing change through digitalization sustainably.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Periodicals

Sustainable Manufacturing and Design: Concepts, Practices and Needs

An investigation is reported on the importance of integrating sustainability with manufacturing and design, along with other objectives such as function, competitiveness, profitability and productivity. The need of utilizing appropriate tools like design for environment, life cycle assessment and other environmentally sound practices that are cognizant of the entire life cycle of a process or product is highlighted. It is likely that sustainability and environmental stewardship will be increasingly important considerations in manufacturing and design in the future and are likely to influence the main priorities for advancing manufacturing operations and technologies. Designers and manufacturing decision makers who adopt a sustainability focus and establish a sustainability culture within companies are more likely to be successful in enhancing design and manufacturing. It is concluded that more extensive research and collaboration is needed to improve understanding of sustainability in manufacturing and design, and to enhance technology transfer and applications of sustainability.

Architecture & Planning• 2012• Multidisciplinary Digital Publishing Institute
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A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Artificial Intelligence and Marketing: Pitfalls and Opportunities

This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations.

Architecture & Planning• 2020• SAGE Publishing
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Review of Battery Management Systems (BMS) Development and Industrial Standards

The evolving global landscape for electrical distribution and use created a need area for energy storage systems (ESS), making them among the fastest growing electrical power system products. A key element in any energy storage system is the capability to monitor, control, and optimize performance of an individual or multiple battery modules in an energy storage system and the ability to control the disconnection of the module(s) from the system in the event of abnormal conditions. This management scheme is known as “battery management system (BMS)”, which is one of the essential units in electrical equipment. BMS reacts with external events, as well with as an internal event. It is used to improve the battery performance with proper safety measures within a system. Therefore, a safe BMS is the prerequisite for operating an electrical system. This report analyzes the details of BMS for electric transportation and large-scale (stationary) energy storage. The analysis includes different aspects of BMS covering testing, component, functionalities, topology, operation, architecture, and BMS safety aspects. Additionally, current related standards and codes related to BMS are also reviewed. The report investigates BMS safety aspects, battery technology, regulation needs, and offer recommendations. It further studies current gaps in respect to the safety requirements and performance requirements of BMS by focusing mainly on the electric transportation and stationary application. The report further provides a framework for developing a new standard on BMS, especially on BMS safety and operational risk. In conclusion, four main areas of (1) BMS construction, (2) Operation Parameters, (3) BMS Integration, and (4) Installation for improvement of BMS safety and performance are identified, and detailed recommendations were provided for each area. It is recommended that a technical review of the BMS be performed for transportation electrification and large-scale (stationary) applications. A comprehensive evaluation of the components, architectures, and safety risks applicable to BMS operation is also presented.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
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Sii-Mobility: An IoT/IoE Architecture to Enhance Smart City Mobility and Transportation Services

The new Internet of Things/Everything (IoT/IoE) paradigm and architecture allows one to rethink the way Smart City infrastructures are designed and managed, but on the other hand, a number of problems have to be solved. In terms of mobility the cities that embrace the sensoring era can take advantage of this disruptive technology to improve the quality of life of their citizens, also thanks to the rationalization in the use of their resources. In Sii-Mobility, a national smart city project on mobility and transportation, a flexible platform has been designed and here, in this paper, is presented. It permits one to set up heterogeneous and complex scenarios that integrate sensors/actuators as IoT/IoE in an overall Big Data, Machine Learning and Data Analytics scenario. A detailed and complex case-study has been presented to validate the solution in the context of a system that dynamically reverse the traveling direction of a road segment, with all the safety conditions in place. This case study composes several building blocks of the IoT platform, which demonstrate that a flexible and dynamic set-up is possible, supporting security, safety, local, cloud and mixed solutions.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
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After Liberal Hegemony: The Advent of a Multiplex World Order

While the West woke up to the threat to the liberal international order when Donald Trump was elected U.S. president, its decline was apparent even at the height of the Obama-Clinton era. What follows the end of the U.S.-dominated world order is not a return to multipolarity as many pundits assume. The twenty-first-century world—politically and culturally diverse but economically and institutionally interlinked—is vastly different from the multipolar world that existed prior to World War II. China and India are major powers now; and globalization will not end, but will take on a new form, driven more by the East than the West and more by South-South linkages than North-North ones. The system of global governance will fragment, with new actors and institutions chipping away at the old UN-based system. Liberal values and institutions will not disappear, but will have to coexist and enmesh with the ideas and institutions of others, especially those initiated by China. This “multiplex world” carries both risks and opportunities for managing international stability. Instead of bemoaning the passing of the old liberal order, the West should accept the new realities and search for new ways to ensure peace and stability in partnership with the rising powers.

Architecture & Planning• 2017• Cambridge University Press
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What, If Anything, Is Rodent Prefrontal Cortex?

Prefrontal cortex (PFC) means different things to different people. In recent years, there has been a major increase in publications on the PFC, especially using mice. However, inconsistencies in the nomenclature and anatomical boundaries of PFC areas has made it difficult for researchers to compare data and interpret findings across species. We conducted a meta-analysis of publications on the PFC of humans and rodents and found dramatic differences in the focus of research on these species. In addition, we compared anatomical terms and criteria across several common rodent brain atlases and found inconsistencies among, and even within, leading atlases. To assess the impact of these issues on the research community, we conducted a survey of established PFC researchers on their use of anatomical terms and found little consensus. We report on the results of the survey and propose an alternative scheme for interpreting data from rodent studies, based on structural analysis of the corpus callosum and nomenclature used in research on the anterior cingulate cortex (ACC) of primates.

Architecture & Planning• 2018• Society for Neuroscience
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Wood modification technologies - a review

The market for new durable products of modified wood has increased substantially during the last few years, especially in Europe. This increased interest depends partly on the restricted use of toxic preservatives due to increased environmental concern, as well as the need for reduced maintenance for wood products that are mainly for exterior use. Furthermore, as sustainability becomes a greater concern, the environmental impact of construction and interior materials should be included in planning by considering the entire life cycle and embodied energy of the materials used. As a result, wood modification has been implemented to improve the intrinsic properties of wood, widen the range of sawn timber applications, and acquire the form and functionality desired by engineers, without bringing environmental friendliness into question. The different wood modification processes are at various stages of development, and the challenges that must be overcome to expand to industrial applications differ amongst them. In this paper, three groups of wood modification processes are discussed and exemplified with modified wood products that have been newly introduced to the market: (i) chemical processing (acetylation, furfurylation, resin impregnation etc.); (ii) thermo-hydro processing (thermal treatment); and (iii) thermo-hydro-mechanical processing (surface densification). Building on these examples, the paper will discuss the environmental impact assessment of modification processes and further development needs.

Architecture & Planning• 2017
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Report on the development of the Advanced Encryption Standard (AES)

In 1997, the National Institute of Standards and Technology (NIST) initiated a process to select a symmetric-key encryption algorithm to be used to protect sensitive (unclassified) Federal information in furtherance of NIST's statutory responsibilities. In 1998, NIST announced the acceptance of 15 candidate algorithms and requested the assistance of the cryptographic research community in analyzing the candidates. This analysis included an initial examination of the security and efficiency characteristics for each algorithm. NIST reviewed the results of this preliminary research and selected MARS, RC™, Rijndael, Serpent and Twofish as finalists. Having reviewed further public analysis of the finalists, NIST has decided to propose Rijndael as the Advanced Encryption Standard (AES). The research results and rationale for this selection are documented in this report.

Architecture & Planning• 2001• The National Institute of Standards and Technology
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The default mode network and social understanding of others: what do brain connectivity studies tell us

The Default Mode Network (DMN) has been found to be involved in various domains of cognitive and social processing. The present article will review brain connectivity results related to the DMN in the fields of social understanding of others: emotion perception, empathy, theory of mind, and morality. Most of the reviewed studies focused on healthy subjects with no neurological and psychiatric disease, but some studies on patients with autism and psychopathy will also be discussed. Common results show that the medial prefrontal cortex (MPFC) plays a key role in the social understanding of others, and the subregions of the MPFC contribute differently to this function according to their roles in different subsystems of the DMN. At the bottom, the ventral MPFC in the medial temporal lobe (MTL) subsystem and its connections with emotion regions are mainly associated with emotion engagement during social interactions. Above, the anterior MPFC (aMPFC) in the cortical midline structures (CMS) and its connections with posterior and anterior cingulate cortex contribute mostly to making self-other distinctions. At the top, the dorsal MPFC (dMPFC) in the dMPFC subsystem and its connection with the temporo-parietal junction (TPJ) are primarily related to the understanding of other's mental states. As behaviors become more complex, the related regions in frontal cortex are located higher. This reflects the transfer of information processing from automatic to cognitive processes with the increase of the complexity of social interaction. Besides the MPFC and TPJ, the connectivities of posterior cingulate cortex (PCC) also show some changes during tasks from the four social fields. These results indicate that the DMN is indispensable in the social understanding of others.

Architecture & Planning• 2014• Frontiers Media
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