Periodicals
Feature-based methods for large scale dynamic programming
[no abstract]
Architecture & Planning• 1996• Springer Science+Business Media
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Periodicals
Location-Aware Communications for 5G Networks: How location information can improve scalability, latency, and robustness of 5G
Fifth-generation (5G) networks will be the first generation to benefit from location information that is sufficiently precise to be leveraged in wireless network design and optimization. We argue that location information can aid in addressing several of the key challenges in 5G, complementary to existing and planned technological developments. These challenges include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. This article gives a broad overview of the growing research area of location-aware communications across different layers of the protocol stack. We highlight several promising trends, tradeoffs, and pitfalls.
Architecture & Planning• 2014• Institute of Electrical and Electronics Engineers
Periodicals
https://journals.vgtu.lt/index.php/JCEM/article/view/5898
The paper presents risk assessment of construction projects. The assessment is based on the multi‐attribute decision‐making methods. The risk evaluation attributes are selected taking into consideration the interests and goals of the stakeholders as well as factors that have influence on the construction process efficiency and real estate value. Ranking of objects and determination of their optimality are determined by applying TOPSIS grey and COPRAS‐G methods with attributes values determined at intervals. A background and a description of the proposed model are provided and key findings of the analysis are presented. Santrauka Straipsnyje vertinama statybos projektu rizika. Vertinimas pagristas ivairiais daugiatikslio vertinimo metodais. Rizikos vertinimo rodikliai atrenkami, atsižvelgiant i suinteresuotu šaliu interesus, tikslus ir veiksnius, kurie turi itakos statybos proceso efektyvumui ir nekilnojamojo turto vertes didinimui. Projektai surikiuoti pagal naudinguma, nustatyti santykiniai ju optimalumo dydžiai. Uždavinio modeliui aprašyti ir jam išspresti taikomi TOPSIS grey ir COPRAS‐G metodai. Projektu savybes aprašomos efektyvumo rodikliu reikšmemis, apibrežiamomis intervaluose. Straipsnyje aprašomas taikomas modelis, atlikta uždavinio analize ir pateikiamos trumpos išvados.
Architecture & Planning• 2010• Taylor & Francis
Periodicals
Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging
The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model's sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network's core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.
Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
Periodicals
Sensitivity and Responses of Chloroplasts to Heat Stress in Plants
Increased temperatures caused by global warming threaten agricultural production, as warmer conditions can inhibit plant growth and development or even destroy crops in extreme circumstances. Extensive research over the past several decades has revealed that chloroplasts, the photosynthetic organelles of plants, are highly sensitive to heat stress, which affects a variety of photosynthetic processes including chlorophyll biosynthesis, photochemical reactions, electron transport and CO2 assimilation. Important mechanisms by which plant cells respond to heat stress to protect these photosynthetic organelles have been identified and analyzed. More recent studies have made it clear that chloroplasts play an important role in inducing the expression of nuclear heat-response genes during the heat stress response. In this review, we summarize these important advances in plant-based research and discuss how the sensitivity, responses and signaling roles of chloroplasts contribute to plant heat sensitivity and tolerance.
Architecture & Planning• 2020• Frontiers Media
Periodicals
Human Behavior Analysis by Means of Multimodal Context Mining
There is sufficient evidence proving the impact that negative lifestyle choices have on people's health and wellness. Changing unhealthy behaviours requires raising people's self-awareness and also providing healthcare experts with a thorough and continuous description of the user's conduct. Several monitoring techniques have been proposed in the past to track users' behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user's context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.
Architecture & Planning• 2016• Multidisciplinary Digital Publishing Institute
Periodicals
Disease Resistance Mechanisms in Plants
Plants have developed a complex defense system against diverse pests and pathogens. Once pathogens overcome mechanical barriers to infection, plant receptors initiate signaling pathways driving the expression of defense response genes. Plant immune systems rely on their ability to recognize enemy molecules, carry out signal transduction, and respond defensively through pathways involving many genes and their products. Pathogens actively attempt to evade and interfere with response pathways, selecting for a decentralized, multicomponent immune system. Recent advances in molecular techniques have greatly expanded our understanding of plant immunity, largely driven by potential application to agricultural systems. Here, we review the major plant immune system components, state of the art knowledge, and future direction of research on plant⁻pathogen interactions. In our review, we will discuss how the decentralization of plant immune systems have provided both increased evolutionary opportunity for pathogen resistance, as well as additional mechanisms for pathogen inhibition of such defense responses. We conclude that the rapid advances in bioinformatics and molecular biology are driving an explosion of information that will advance agricultural production and illustrate how complex molecular interactions evolve.
Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
Periodicals
A Review on Battery Market Trends, Second-Life Reuse, and Recycling
The rapid growth, demand, and production of batteries to meet various emerging applications, such as electric vehicles and energy storage systems, will result in waste and disposal problems in the next few years as these batteries reach end-of-life. Battery reuse and recycling are becoming urgent worldwide priorities to protect the environment and address the increasing need for critical metals. As a review article, this paper reveals the current global battery market and global battery waste status from which the main battery chemistry types and their management, including reuse and recycling status, are discussed. This review then presents details of the challenges, opportunities, and arguments on battery second-life and recycling. The recent research and industrial activities in the battery reuse domain are summarized to provide a landscape picture and valuable insight into battery reuse and recycling for industries, scientific research, and waste management.
Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
Periodicals
The Brian simulator
"Brian" is a simulator for spiking neural networks (http://www.briansimulator.org). The focus is on making the writing of simulation code as quick and easy as possible for the user, and on flexibility: new and non-standard models are no more difficult to define than standard ones. This allows scientists to spend more time on the details of their models, and less on their implementation. Neuron models are defined by writing differential equations in standard mathematical notation, facilitating scientific communication. Brian is written in the Python programming language, and uses vector-based computation to allow for efficient simulations. It is particularly useful for neuroscientific modelling at the systems level, and for teaching computational neuroscience.
Architecture & Planning• 2009• Frontiers Media
Periodicals
A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System
Traffic congestion is a perpetual problem for the sustainability of transportation development. Traffic congestion causes delays, inconvenience, and economic losses to drivers, as well as air pollution. Identification and quantification of traffic congestion are crucial for decision-makers to initiate mitigation strategies to improve the overall transportation system’s sustainability. In this paper, the currently available measures are detailed and compared by implementing them on a daily and weekly traffic historical dataset. The results showed each measure showed significant variations in congestion states while indicating a similar congestion trend. The advantages and disadvantages of each measure are identified from the data analysis. This study summarizes the current road traffic congestion measures and provides a constructive insight into the development of a sustainable and resilient traffic management system.
Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
Periodicals
Hybrid additive and subtractive machine tools – Research and industrial developments
[no abstract]
Architecture & Planning• 2015• Elsevier BV
Periodicals
In Vivo Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles
Standard clinical ultrasound (US) imaging frequencies are unable to resolve microvascular structures due to the fundamental diffraction limit of US waves. Recent demonstrations of 2-D super-resolution both in vitro and in vivo have demonstrated that fine vascular structures can be visualized using acoustic single bubble localization. Visualization of more complex and disordered 3-D vasculature, such as that of a tumor, requires an acquisition strategy which can additionally localize bubbles in the elevational plane with high precision in order to generate super-resolution in all three dimensions. Furthermore, a particular challenge lies in the need to provide this level of visualization with minimal acquisition time. In this paper, we develop a fast, coherent US imaging tool for microbubble localization in 3-D using a pair of US transducers positioned at 90°. This allowed detection of point scatterer signals in 3-D with average precisions equal to [Formula: see text] in axial and elevational planes, and [Formula: see text] in the lateral plane, compared to the diffraction limited point spread function full-widths at half-maximum of 488, 1188, and [Formula: see text] of the original imaging system with a single transducer. Visualization and velocity mapping of 3-D in vitro structures was demonstrated far beyond the diffraction limit. The capability to measure the complete flow pattern of blood vessels associated with disease at depth would ultimately enable analysis of in vivo microvascular morphology, blood flow dynamics, and occlusions resulting from disease states.
Architecture & Planning• 2014• Institute of Electrical and Electronics Engineers
Periodicals
The Emergence of National Electronic Health Record Architectures in the United States and Australia: Models, Costs, and Questions
Emerging electronic health record models present numerous challenges to health care systems, physicians, and regulators. This article provides explanation of some of the reasons driving the development of the electronic health record, describes two national electronic health record models (currently developing in the United States and Australia) and one distributed, personal model. The US and Australian models are contrasted in their different architectures ("pull" versus "push") and their different approaches to patient autonomy, privacy, and confidentiality. The article also discusses some of the professional, practical, and legal challenges that health care providers potentially face both during and after electronic health record implementation.
Architecture & Planning• 2005• JMIR Publications
Periodicals
Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression
This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.
Architecture & Planning• 2016• Frontiers Media
Periodicals
State of Industry 5.0—Analysis and Identification of Current Research Trends
The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing the various themes and research trends of what Industry 5.0 is using text mining tools and techniques. Toward this, the abstracts of 196 published papers based on the keyword “Industry 5.0” search in IEEE, science direct and MDPI data bases were extracted. Data cleaning and preprocessing were performed for further analysis to apply text mining techniques of key terms extraction and frequency analysis. Further topic mining i.e., unsupervised machine learning method was used for exploring the data. It is observed that the terms artificial intelligence (AI), big data, supply chain, digital transformation, machine learning, internet of things (IoT), are among the most often used and among several enablers that have been identified by researchers to drive Industry 5.0. Five major themes of Industry 5.0 addressing, supply chain evaluation and optimization, enterprise innovation and digitization, smart and sustainable manufacturing, transformation driven by IoT, AI, and Big Data, and Human-machine connectivity were classified among the published literature, highlighting the research themes that can be further explored. It is observed that the theme of Industry 5.0 as a gateway towards human machine connectivity and co-existence is gaining more interest among the research community in the recent years.
Architecture & Planning• 2022• Multidisciplinary Digital Publishing Institute
Periodicals
A Review of Last Mile Logistics Innovations in an Externalities Cost Reduction Vision
In this paper, a review of the recent scientific literature contributions on innovative strategies for last mile logistics, focusing on externalities cost reduction, is presented. Transport is causing problems in urban areas, in particular in freight transport: modern cities need solutions to reduce externalities costs such as congestion, pollution and others, which have increased in the last few years, especially due to the growth of goods delivery. Online sales and globalization lead to new trends in freight transport, and moreover, a larger quantity of goods is expected to be delivered in the next future. In this context, most of the delivered goods end up in the city centers. Last mile logistics is the least efficient stage of the supply chain and comprises up to 28% of the total delivery cost. Therefore, the improvement of last mile logistics and a significant externalities reduction are very important challenges for researchers. New technologies and transport means, innovative techniques and organizational strategies allow handling in a more effective way the last mile delivery in urban areas. Based on the Systematic Literature Review (SLR) method, recent papers that significantly contributed, with original proposals, to the reduction of externalities in urban logistics are identified and analyzed in this work. Furthermore, a classification of the papers dealing with the externality reduction problem is presented. It is consistent with a general formulation proposed to evaluate external costs in urban area. The innovative contributions are classified into five main categories: innovative vehicles, proximity stations or points, collaborative and cooperative urban logistics, optimization of transport management and routing, innovations in public policies and infrastructures. The new paradigm of smart logistics is based on the combination of these concepts and on the proposed innovations.
Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
Periodicals
Development of the ASHRAE Global Thermal Comfort Database II
[no abstract]
Architecture & Planning• 2018• Elsevier BV
Periodicals
Innovation: Exploring the knowledge base
[no abstract]
Architecture & Planning• 2012• Elsevier BV
Periodicals
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
[no abstract]
Architecture & Planning• 2020• Elsevier BV
Periodicals
The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review
Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.
Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
Periodicals
Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the Unified Theory of Acceptance and Use of Technology
[no abstract]
Architecture & Planning• 2018• Elsevier BV
Periodicals
Context-oriented Programming.
Context-dependent behavior is becoming increasingly important for a wide range of application domains, from pervasive computing to common business applications. Unfortunately, mainstream programming languages do not provide mechanisms that enable software entities to adapt their behavior dynamically to the current execution context. This leads developers to adopt convoluted designs to achieve the necessary runtime flexibility. We propose a new programming technique called Context-oriented Programming (COP) which addresses this problem. COP treats context explicitly, and provides mechanisms to dynamically adapt behavior in reaction to changes in context, even after system deployment at runtime. In this paper, we lay the foundations of COP, show how dynamic layer activation enables multi-dimensional dispatch, illustrate the application of COP by examples in several language extensions, and demonstrate that COP is largely independent of other commitments to programming style.
Architecture & Planning• 2008• EtH Zurich
Periodicals
Nonviral delivery of synthetic siRNAs in vivo
Sequence-specific gene silencing using small interfering RNA (siRNA) is a Nobel prize-winning technology that is now being evaluated in clinical trials as a potentially novel therapeutic strategy. This article provides an overview of the major pharmaceutical challenges facing siRNA therapeutics, focusing on the delivery strategies for synthetic siRNA duplexes in vivo, as this remains one of the most important issues to be resolved. This article also highlights the importance of understanding the genocompatibility/toxicogenomics of siRNA delivery reagents in terms of their impact on gene-silencing activity and specificity. Collectively, this information is essential for the selection of optimally acting siRNA delivery system combinations for the many proposed applications of RNA interference.
Architecture & Planning• 2007• American Society for Clinical Investigation
Periodicals
Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows
The spatial arrangement of urban hubs and centers and how individuals interact with these centers is a crucial problem with many applications ranging from urban planning to epidemiology. We utilize here in an unprecedented manner the large scale, real-time 'Oyster' card database of individual person movements in the London subway to reveal the structure and organization of the city. We show that patterns of intraurban movement are strongly heterogeneous in terms of volume, but not in terms of distance travelled, and that there is a polycentric structure composed of large flows organized around a limited number of activity centers. For smaller flows, the pattern of connections becomes richer and more complex and is not strictly hierarchical since it mixes different levels consisting of different orders of magnitude. This new understanding can shed light on the impact of new urban projects on the evolution of the polycentric configuration of a city and the dense structure of its centers and it provides an initial approach to modeling flows in an urban system.
Architecture & Planning• 2011• Public Library of Science