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in Architecture & Planning
Periodicals

Circular Business Models: Defining a Concept and Framing an Emerging Research Field

To aid companies in transitioning towards a circular economy and adopting strategies such as reuse, repair, and remanufacturing, the concept of circular business models has been developed. Although the concept draws on contributions from various academic disciplines, and despite its increasingly frequent use, few scholars clearly define what a circular business model is. Understanding about what makes a business model circular is diverse, hampering the theoretical development and practical application of circular business models. This study aims to help frame the field of circular business model research, by clarifying the fundamentals of the concept from the perspectives of resource efficiency and business model innovation. Expanding on these findings, a review of how the concept is used in recent academic literature is provided. It shows that a coherent view is lacking on which resource efficiency strategies classify a business model as circular. This study clarifies which resource efficiency strategies can be deemed as relevant key strategies for circular business models, and suggests a new definition of the concept. With the definition grounded in analysis of the fundamentals in terms of resource efficiency and business models, the study contributes to theoretical advancement and effective implementation of circular business models.

Architecture & Planning• 2017• Multidisciplinary Digital Publishing Institute
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Psychedelics, Meditation, and Self-Consciousness

In recent years, the scientific study of meditation and psychedelic drugs has seen remarkable developments. The increased focus on meditation in cognitive neuroscience has led to a cross-cultural classification of standard meditation styles validated by functional and structural neuroanatomical data. Meanwhile, the renaissance of psychedelic research has shed light on the neurophysiology of altered states of consciousness induced by classical psychedelics, such as psilocybin and LSD, whose effects are mainly mediated by agonism of serotonin receptors. Few attempts have been made at bridging these two domains of inquiry, despite intriguing evidence of overlap between the phenomenology and neurophysiology of meditation practice and psychedelic states. In particular, many contemplative traditions explicitly aim at dissolving the sense of self by eliciting altered states of consciousness through meditation, while classical psychedelics are known to produce significant disruptions of self-consciousness, a phenomenon known as drug-induced ego dissolution. In this article, we discuss available evidence regarding convergences and differences between phenomenological and neurophysiological data on meditation practice and psychedelic drug-induced states, with a particular emphasis on alterations of self-experience. While both meditation and psychedelics may disrupt self-consciousness and underlying neural processes, we emphasize that neither meditation nor psychedelic states can be conceived as simple, uniform categories. Moreover, we suggest that there are important phenomenological differences even between conscious states described as experiences of self-loss. As a result, we propose that self-consciousness may be best construed as a multidimensional construct, and that "self-loss," far from being an unequivocal phenomenon, can take several forms. Indeed, various aspects of self-consciousness, including narrative aspects linked to autobiographical memory, self-related thoughts and mental time travel, and embodied aspects rooted in multisensory processes, may be differently affected by psychedelics and meditation practices. Finally, we consider long-term outcomes of experiences of self-loss induced by meditation and psychedelics on individual traits and prosocial behavior. We call for caution regarding the problematic conflation of temporary states of self-loss with "selflessness" as a behavioral or social trait, although there is preliminary evidence that correlations between short-term experiences of self-loss and long-term trait alterations may exist.

Architecture & Planning• 2018• Frontiers Media
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Periodicals

Biopolymer-Based Sustainable Food Packaging Materials: Challenges, Solutions, and Applications

Biopolymer-based packaging materials have become of greater interest to the world due to their biodegradability, renewability, and biocompatibility. In recent years, numerous biopolymers-such as starch, chitosan, carrageenan, polylactic acid, etc.-have been investigated for their potential application in food packaging. Reinforcement agents such as nanofillers and active agents improve the properties of the biopolymers, making them suitable for active and intelligent packaging. Some of the packaging materials, e.g., cellulose, starch, polylactic acid, and polybutylene adipate terephthalate, are currently used in the packaging industry. The trend of using biopolymers in the packaging industry has increased immensely; therefore, many legislations have been approved by various organizations. This review article describes various challenges and possible solutions associated with food packaging materials. It covers a wide range of biopolymers used in food packaging and the limitations of using them in their pure form. Finally, a SWOT analysis is presented for biopolymers, and the future trends are discussed. Biopolymers are eco-friendly, biodegradable, nontoxic, renewable, and biocompatible alternatives to synthetic packaging materials. Research shows that biopolymer-based packaging materials are of great essence in combined form, and further studies are needed for them to be used as an alternative packaging material.

Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
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Periodicals

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.

Architecture & Planning• 2019• Nature Portfolio
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Periodicals

Developments in International Video Coding Standardization After AVC, With an Overview of Versatile Video Coding (VVC)

In the last 17 years, since the finalization of the first version of the now-dominant H.264/Moving Picture Experts Group-4 (MPEG-4) Advanced Video Coding (AVC) standard in 2003, two major new generations of video coding standards have been developed. These include the standards known as High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC). HEVC was finalized in 2013, repeating the ten-year cycle time set by its predecessor and providing about 50% bit-rate reduction over AVC. The cycle was shortened by three years for the VVC project, which was finalized in July 2020, yet again achieving about a 50% bit-rate reduction over its predecessor (HEVC). This article summarizes these developments in video coding standardization after AVC. It especially focuses on providing an overview of the first version of VVC, including comparisons against HEVC. Besides further advances in hybrid video compression, as in previous development cycles, the broad versatility of the application domain that is highlighted in the title of VVC is explained. Included in VVC is the support for a wide range of applications beyond the typical standard- and high-definition camera-captured content codings, including features to support computer-generated/screen content, high dynamic range content, multilayer and multiview coding, and support for immersive media such as 360° video.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
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Periodicals

K-nearest neighbour technique for the effective prediction of refrigeration parameter compatible for automobile

Manufacturing simulation is an encouraging research area in resent decade. Creation or development of better simulation tool or technique is one of the major intension in manufacturing simulation. In resent research most of the manufacturing processes are simulated successfully. But some processes are not yet simulated effectively, especially automatic air conditioning (AC) system or refrigeration system. The automatic AC system for the passenger vehicle are not yet effectively simulated. Hence in this paper a machine learning technique is adopted for the effective prediction of parameter of automatic AC system. The proposed system uses k-nearest neighbour technique for the prediction of parameter will less error and high accuracy. The proposed system is implemented using MATLAB and its performance is compared with the support vector machine and ANN in terms of mean square error and accuracy. The proposed technique out-performs the conventional technique and suggest that the k-nearest neighbour become the most suitable technique for the modelling and performance analysis of automatic AC system.

Architecture & Planning• 2019• Vinča Institute of Nuclear Sciences
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Periodicals

Resource Management in Fog/Edge Computing

Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013 and 2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

Architecture & Planning• 2019• Association for Computing Machinery
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Periodicals

The HMO Research Network Virtual Data Warehouse: A Public Data Model to Support Collaboration

The HMO Research Network (HMORN) Virtual Data Warehouse (VDW) is a public, non-proprietary, research-focused data model implemented at 17 health care systems across the United States. The HMORN has created a governance structure and specified policies concerning the VDW's content, development, implementation, and quality assurance. Data extracted from the VDW have been used by thousands of studies published in peer-reviewed journal articles. Advances in software supporting care delivery and claims processing and the availability of new data sources have greatly expanded the data available for research, but substantially increased the complexity of data management. The VDW data model incorporates software and data advances to ensure that comprehensive, up-to-date data of known quality are available for research. VDW governance works to accommodate new data and system complexities. This article highlights the HMORN VDW data model, its governance principles, data content, and quality assurance procedures. Our goal is to share the VDW data model and its operations to those wishing to implement a distributed interoperable health care data system.

Architecture & Planning• 2014• Ubiquity Press
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Periodicals

Genome Sequence of Babesia bovis and Comparative Analysis of Apicomplexan Hemoprotozoa

Babesia bovis is an apicomplexan tick-transmitted pathogen of cattle imposing a global risk and severe constraints to livestock health and economic development. The complete genome sequence was undertaken to facilitate vaccine antigen discovery, and to allow for comparative analysis with the related apicomplexan hemoprotozoa Theileria parva and Plasmodium falciparum. At 8.2 Mbp, the B. bovis genome is similar in size to that of Theileria spp. Structural features of the B. bovis and T. parva genomes are remarkably similar, and extensive synteny is present despite several chromosomal rearrangements. In contrast, B. bovis and P. falciparum, which have similar clinical and pathological features, have major differences in genome size, chromosome number, and gene complement. Chromosomal synteny with P. falciparum is limited to microregions. The B. bovis genome sequence has allowed wide scale analyses of the polymorphic variant erythrocyte surface antigen protein (ves1 gene) family that, similar to the P. falciparum var genes, is postulated to play a role in cytoadhesion, sequestration, and immune evasion. The approximately 150 ves1 genes are found in clusters that are distributed throughout each chromosome, with an increased concentration adjacent to a physical gap on chromosome 1 that contains multiple ves1-like sequences. ves1 clusters are frequently linked to a novel family of variant genes termed smorfs that may themselves contribute to immune evasion, may play a role in variant erythrocyte surface antigen protein biology, or both. Initial expression analysis of ves1 and smorf genes indicates coincident transcription of multiple variants. B. bovis displays a limited metabolic potential, with numerous missing pathways, including two pathways previously described for the P. falciparum apicoplast. This reduced metabolic potential is reflected in the B. bovis apicoplast, which appears to have fewer nuclear genes targeted to it than other apicoplast containing organisms. Finally, comparative analyses have identified several novel vaccine candidates including a positional homolog of p67 and SPAG-1, Theileria sporozoite antigens targeted for vaccine development. The genome sequence provides a greater understanding of B. bovis metabolism and potential avenues for drug therapies and vaccine development.

Architecture & Planning• 2007• Public Library of Science
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Periodicals

Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Compositional texture engineering for highly stable wide-bandgap perovskite solar cells

The development of highly stable and efficient wide-bandgap (WBG) perovskite solar cells (PSCs) based on bromine-iodine (Br–I) mixed-halide perovskite (with Br greater than 20%) is critical to create tandem solar cells. However, issues with Br–I phase segregation under solar cell operational conditions (such as light and heat) limit the device voltage and operational stability. This challenge is often exacerbated by the ready defect formation associated with the rapid crystallization of Br-rich perovskite chemistry with antisolvent processes. We combined the rapid Br crystallization with a gentle gas-quench method to prepare highly textured columnar 1.75–electron volt Br–I mixed WBG perovskite films with reduced defect density. With this approach, we obtained 1.75–electron volt WBG PSCs with greater than 20% power conversion efficiency, approximately 1.33-volt open-circuit voltage ( V oc ), and excellent operational stability (less than 5% degradation over 1100 hours of operation under 1.2 sun at 65°C). When further integrated with 1.25–electron volt narrow-bandgap PSC, we obtained a 27.1% efficient, all-perovskite, two-terminal tandem device with a high V oc of 2.2 volts.

Architecture & Planning• 2022• American Association for the Advancement of Science
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Periodicals

A Modified YOLOv8 Detection Network for UAV Aerial Image Recognition

UAV multitarget detection plays a pivotal role in civil and military fields. Although deep learning methods provide a more effective solution to this task, changes in target size, shape change, occlusion, and lighting conditions from the perspective of drones still bring great challenges to research in this field. Based on the above problems, this paper proposes an aerial image detection model with excellent performance and strong robustness. First, in view of the common problem that small targets in aerial images are prone to misdetection and missed detection, the idea of Bi-PAN-FPN is introduced to improve the neck part in YOLOv8-s. By fully considering and reusing multiscale features, a more advanced and complete feature fusion process is achieved while maintaining the parameter cost as much as possible. Second, the GhostblockV2 structure is used in the backbone of the benchmark model to replace part of the C2f module, which suppresses information loss during long-distance feature transmission while significantly reducing the number of model parameters; finally, WiseIoU loss is used as bounding box regression loss, combined with a dynamic nonmonotonic focusing mechanism, and the quality of anchor boxes is evaluated by using “outlier” so that the detector takes into account different quality anchor boxes to improve the overall performance of the detection task. The algorithm’s performance is compared and evaluated on the VisDrone2019 dataset, which is widely used worldwide, and a detailed ablation experiment, contrast experiment, interpretability experiment, and self-built dataset experiment are designed to verify the effectiveness and feasibility of the proposed model. The results show that the proposed aerial image detection model has achieved obvious results and advantages in various experiments, which provides a new idea for the deployment of deep learning in the field of UAV multitarget detection.

Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
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Periodicals

Progress and Challenges in Intelligent Remote Sensing Satellite Systems

Due to advances in remote sensing satellite imaging and image processing technologies and their wide applications, intelligent remote sensing satellites are facing an opportunity for rapid development. The key technologies, standards, and laws of intelligent remote sensing satellites are also experiencing a series of new challenges. Novel concepts and key technologies in the intelligent hyperspectral remote sensing satellite system have been proposed since 2011. The aim of these intelligent remote sensing satellites is to provide real-time, accurate, and personalized remote sensing information services. This article reviews the current developments in new-generation intelligent remote sensing satellite systems, with a focus on intelligent remote sensing satellite platforms, imaging payloads, onboard processing systems, and other key technological chains. The technological breakthroughs and current defects of intelligence-oriented designs are also analyzed. Intelligent remote sensing satellites collect personalized remote sensing data and information, with real-time data features and information interaction between remote sensing satellites or between satellites and the ground. Such developments will expand the use of remote sensing applications beyond government departments and industrial users to a massive number of individual users. However, this extension faces challenges regarding privacy protection, societal values, and laws regarding the sharing and distribution of data and information.

Architecture & Planning• 2022• Institute of Electrical and Electronics Engineers
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Physics-Informed Neural Networks for Cardiac Activation Mapping

A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points recorded inside the atria; they neither include prior knowledge of the underlying physics nor uncertainty of these recordings. Here we propose a physics-informed neural network for cardiac activation mapping that accounts for the underlying wave propagation dynamics and we quantify the epistemic uncertainty associated with these predictions. These uncertainty estimates not only allow us to quantify the predictive error of the neural network, but also help to reduce it by judiciously selecting new informative measurement locations via active learning. We illustrate the potential of our approach using a synthetic benchmark problem and a personalized electrophysiology model of the left atrium. We show that our new method outperforms linear interpolation and Gaussian process regression for the benchmark problem and linear interpolation at clinical densities for the left atrium. In both cases, the active learning algorithm achieves lower error levels than random allocation. Our findings open the door towards physics-based electro-anatomic mapping with the ultimate goals to reduce procedural time and improve diagnostic predictability for patients affected by atrial fibrillation. Open source code is available at https://github.com/fsahli/EikonalNet.

Architecture & Planning• 2020• Frontiers Media
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Periodicals

MIRD Pamphlet No. 23: Quantitative SPECT for Patient-Specific 3-Dimensional Dosimetry in Internal Radionuclide Therapy

In internal radionuclide therapy, a growing interest in voxel-level estimates of tissue-absorbed dose has been driven by the desire to report radiobiologic quantities that account for the biologic consequences of both spatial and temporal nonuniformities in these dose estimates. This report presents an overview of 3-dimensional SPECT methods and requirements for internal dosimetry at both regional and voxel levels. Combined SPECT/CT image-based methods are emphasized, because the CT-derived anatomic information allows one to address multiple technical factors that affect SPECT quantification while facilitating the patient-specific voxel-level dosimetry calculation itself. SPECT imaging and reconstruction techniques for quantification in radionuclide therapy are not necessarily the same as those designed to optimize diagnostic imaging quality. The current overview is intended as an introduction to an upcoming series of MIRD pamphlets with detailed radionuclide-specific recommendations intended to provide best-practice SPECT quantification-based guidance for radionuclide dosimetry.

Architecture & Planning• 2012• Society of Nuclear Medicine and Molecular Imaging
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AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.

Architecture & Planning• 2021• IEEE Computer Society
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Recent Advances in Electrochemical Sensors for Detecting Toxic Gases: NO2, SO2 and H2S

, H₂S and other S-containing gases, cause numerous harmful effects on human health even at very low gas concentrations. Reliable detection of various gases in low concentration is mandatory in the fields such as industrial plants, environmental monitoring, air quality assurance, automotive technologies and so on. In this paper, the recent advances in electrochemical sensors for toxic gas detections were reviewed and summarized with a focus on NO₂, SO₂ and H₂S gas sensors. The recent progress of the detection of each of these toxic gases was categorized by the highly explored sensing materials over the past few decades. The important sensing performance parameters like sensitivity/response, response and recovery times at certain gas concentration and operating temperature for different sensor materials and structures have been summarized and tabulated to provide a thorough performance comparison. A novel metric, sensitivity per ppm/response time ratio has been calculated for each sensor in order to compare the overall sensing performance on the same reference. It is found that hybrid materials-based sensors exhibit the highest average ratio for NO₂ gas sensing, whereas GaN and metal-oxide based sensors possess the highest ratio for SO₂ and H₂S gas sensing, respectively. Recently, significant research efforts have been made exploring new sensor materials, such as graphene and its derivatives, transition metal dichalcogenides (TMDs), GaN, metal-metal oxide nanostructures, solid electrolytes and organic materials to detect the above-mentioned toxic gases. In addition, the contemporary progress in SO₂ gas sensors based on zeolite and paper and H₂S gas sensors based on colorimetric and metal-organic framework (MOF) structures have also been reviewed. Finally, this work reviewed the recent first principle studies on the interaction between gas molecules and novel promising materials like arsenene, borophene, blue phosphorene, GeSe monolayer and germanene. The goal is to understand the surface interaction mechanism.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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The Ethics of Realism in Virtual and Augmented Reality

In this paper we consider ethical issues that could emerge with the widespread adoption ofvirtual and augmented reality (XR) as a consumer product. Our major focus is on a likelyfuture where, with technological advances, virtual and augmented reality may becomeindistinguishable from reality thanks to the improvements in sensory feedback, in the abilityto interact with virtual features, and in other elements that enhance the realism of the virtualexperience—what we refer to as superrealism. In the introductory section we first considermore general ethical issues related to XR use, and in later sections we consider issues specificto superrealism in XR. Finally, we propose approaches towards remedies and outline a seriesof scientific questions targeted at the research community. This work is the result of a groupmeeting under the auspices of Digital Catapult in London, and reports findings of theirdiscussions. The intention of this paper is to highlight important areas for consideration bythe wider XR community, and provoke the discussion and further research needed toinvestigate and address these important topics.

Architecture & Planning• 2020• Frontiers Media
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Self-assembling dipeptide antibacterial nanostructures with membrane disrupting activity

Peptide-based supramolecular assemblies are a promising class of nanomaterials with important biomedical applications, specifically in drug delivery and tissue regeneration. However, the intrinsic antibacterial capabilities of these assemblies have been largely overlooked. The recent identification of common characteristics shared by antibacterial and self-assembling peptides provides a paradigm shift towards development of antibacterial agents. Here we present the antibacterial activity of self-assembled diphenylalanine, which emerges as the minimal model for antibacterial supramolecular polymers. The diphenylalanine nano-assemblies completely inhibit bacterial growth, trigger upregulation of stress-response regulons, induce substantial disruption to bacterial morphology, and cause membrane permeation and depolarization. We demonstrate the specificity of these membrane interactions and the development of antibacterial materials by integration of the peptide assemblies into tissue scaffolds. This study provides important insights into the significance of the interplay between self-assembly and antimicrobial activity and establishes innovative design principles toward the development of antimicrobial agents and materials.

Architecture & Planning• 2017• Nature Portfolio
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