DESENT: Smart Decision Support System for Urban Energy and Transportation

The focus of this paper is on the DESENT project, which aims to develop, test and disseminate a comprehensive decision support system for smart energy and transport in cities. Transport energy consumption modelling, building supply and energy demand tools are combined to provide a common decision support system for appraisal of city-wide energy use. The system will be set up as planning toolbox that consists of several models and tools providing different functionalities to show actual data for energy demand and transport energy demand and furthermore allow to predict the energy demand on building and city level for future planning scenarios. Thus, the relationships between sustainability objectives, transport, spatial design of the built environment and rational use of energy are considered. The paper describes the concept and structure of the planning toolbox and its models and tools developed so far. Furthermore, it outlines methodologies developed and the data acquisition process for the city of Weiz, which serves as one of the pilot cities in the project. Keywords: energy demand prediction, smart city development, transport energy demand, uncertain supply, optimal energy distribution The focus of this paper is on the DESENT project, which aims to develop, test and disseminate a comprehensive decision support system for smart energy and transport in cities. Transport energy consumption modelling, building supply and energy demand tools are combined to provide a common decision support system for appraisal of city-wide energy use. The system will be set up as planning toolbox that consists of several models and tools providing different functionalities to show actual data for energy demand and transport energy demand and furthermore allow to predict the energy demand on building and city level for future planning scenarios. Thus, the relationships between sustainability objectives, transport, spatial design of the built environment and rational use of energy are considered. The paper describes the concept and structure of the planning toolbox and its models and tools developed so far. Furthermore, it outlines methodologies developed and the data acquisition process for the city of Weiz, which serves as one of the pilot cities in the project

Students' evacuation behavior during an emergency at schools:A systematic literature review

Disasters and emergencies frequently happen, and some of them require population evacuation. Children can be severely affected during evacuations due to their lower capability to analyze, perceive, and answer disaster risks. Although several studies attempted to address the safety of children during the evacuation, the existing literature lacks a systematic review of students' evacuation behavior during school time. Therefore, this study aims to conduct a systematic literature review to explore how students' evacuation behavior during school time has been addressed by previous scholars and identify gaps in knowledge. The review process included three steps: formulating the research question, establishing strategic search strategies, and data extraction and analysis. The studies have been identified by searching academic search engines and refined the recognized publications unbiasedly. The researchers have then thematically analyzed the objectives and findings of the selected studies resulting in the identification of seven themes in the field of students' evacuation behavior during school time. Finally, the study put forward suggestions for future research directions to efficiently address the recognized knowledge gaps.</p

Analysis of Built Environment Influence on Pedestrian route choice behavior in Dutch Design Week using GPS Data

Visitors not only have specific destinations targeting the Dutch Design Week (DDW) exhibitions distributed all over the city, but also visit the city in between exhibition activities. The mixed environment makes modeling behavior of DDW visitors more complex than shoppers and tourisms only. This research pays special attention to the influence of built environment on pedestrian route choice. The built environment includes building and transportation infrastructure. GPS tracking data and social demographic information were collected during the event. Multinomial logit model and path size logit model are used to analysis route choice behavior. The results show that some built environment factors have significant influence on route choice. Shops are more attractive for aged visitors. Females prefer shorter routes more. In big event, the alternative routes with more sharing links could increase the possibility to choose

Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation

Developing appropriate building retrofit strategies is a challenging task. This case study presents a multi-criteria decision-supporting method that suggests optimal solutions and alternative design references with a range of diversity at the early exploration stage in building retrofit. This method employs a practical two-step method to identify critical comfort and energy issues and generate optimised design options with multi-objective optimisation based on a genetic algorithm. The first step is based on a post-occupancy evaluation, which cross-refers benchmarking and correlation and integrates them with non-linear satisfaction theory to extract critical comfort factors. The second step parameterises previous outputs as objectives to conduct building simulation practice. The case study is a typical post-war highly glazed open-plan office in London. The post-occupancy evaluation result identifies direct sunlight glare, indoor temperature, and noise from other occupants as critical comfort factors. The simulation and optimisation extract the optimal retrofit strategies by analysing 480 generated Pareto fronts. The proposed method provides retrofit solutions with a criteria-based filtering method and considers the trade-off between the energy and comfort objectives. The method can be transformed into a design-supporting tool to identify the key comfort factors for built environment optimisation and create sustainability in building retrofit. Practical application : This study suggested that statistical analysis could be integrated with parametric design tools and multi-objective optimisation. It directly links users’ subjective opinions to the final design solutions, suggesting a new method for data-driven generative design. As a quantitative process, the proposed framework could be automated with a program, reducing the human effort in the optimisation process and reducing the reliance on human experience in the design question defining and analysis process. It might also avoid human mistakes, e.g. overlooking some critical factors. During the multi-objective optimisation process, large numbers of design options are generated, and many of them are optimised at the Pareto front. Exploring these options could be a less human effort-intensive process than designing completely new options, especially in the early design exploration phase. Overall, this might be a potential direction for future study in generative design, which greatly reduce the technical obstacle of sustainable design for high building performance.</p

Three Tales about Limits to Smart Cities Solutions

This editorial is the introduction to a special issue on smart cities. The concept of a smart city is not well-defined, yet expectations among urban planners and decision-makers are high. This special issue contains three papers that discuss three different manifestations of smart cities and the success—or lack of it—of the solutions discussed. The papers highlight some limitations of the concept of smart cities, but at the same time also pinpoint some potentially beneficial solutions

A highly sensitive silicon nanowire array sensor for joint detection of tumor markers CEA and AFP

Liver cancer is one of the malignant tumors with the highest fatality rate and increasing incidence, which has no effective treatment plan. Early diagnosis and early treatment of liver cancer play a vital role in prolonging the survival period of patients and improving the cure rate. Carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP) are two crucial tumor markers for liver cancer diagnosis. In this work, we firstly proposed a wafer-level, highly controlled silicon nanowire (SiNW) field-effect transistor (FET) joint detection sensor for highly sensitive and selective detection of CEA and AFP. The SiNWs-FET joint detection sensor possesses 4 sensing regions. Each sensing region consists of 120 SiNWs arranged in a 15 × 8 array. The SiNW sensor was developed by using a wafer-level and highly controllable top-down manufacturing technology to achieve the repeatability and controllability of device preparation. To identify and detect CEA/AFP, we modified the corresponding CEA antibodies/AFP antibodies to the sensing region surface after a series of surface modification processes, including O2 plasma treatment, soaking in 3-aminopropyltriethoxysilane (APTES) solution, and soaking in glutaraldehyde (GA) solution. The experimental results showed that the SiNW array sensor has superior sensitivity with a real-time ultralow detection limit of 0.1 fg ml−1 (AFP in 0.1× PBS) and 1 fg ml−1 (CEA in 0.1× PBS). Also, the logarithms of the concentration of CEA (from 1 fg ml−1 to 10 pg ml−1) and AFP (from 0.1 fg ml−1 to 100 pg ml−1) achieved conspicuously linear relationships with normalized current changes. The R2 of AFP in 0.1× PBS and R2 of CEA in 0.1× PBS were 0.99885 and 0.99677, respectively. Furthermore, the sensor could distinguish CEA/AFP from interferents at high concentrations. Importantly, even in serum samples, our sensor could successfully detect CEA/AFP. This demonstrates the promising clinical development of our sensor

A Vector Grouping Learning Brain Storm Optimization Algorithm for Global Optimization Problems

The original brain storm optimization (BSO) method does not rationally compromise global exploration and local exploitation capability, which results in the premature convergence when solving complicated optimization problems like the shifted or shifted rotated functions. To address this problem, the paper develops a vector grouping learning BSO (VGLBSO) method. In VGLBSO, the individuals&#x2019; creation based on vector grouping learning (IC-VGL) scheme is first developed to improve the population diversity and compromise the global exploration and local exploitation capability. Moreover, a hybrid individuals&#x2019; update (H-IU) scheme is established by reasonably combing two different individuals&#x2019; update schemes, which further compromises the global exploration and local exploitation capability. Finally, the random grouping (RG) scheme, instead of K-means grouping is allowed to shrink the computational cost and maintain the diversity of the information exchange between different individuals. Twenty-eight popular benchmark functions are used to compare VGLBSO with 12 BSO and nine swarm intelligence methods. Experimental results present that VGLBSO achieves the best overall performance including the global search ability, convergence speed, and scalability amongst all the compared algorithms

A supersensitive silicon nanowire array biosensor for quantitating tumor marker ctDNA

Cancer has become one of the major diseases threatening human health and life. Circulating tumor DNA (ctDNA) testing, as a practical liquid biopsy technique, is a promising method for cancer diagnosis, targeted therapy and prognosis. Here, for the first time, a field effect transistor (FET) biosensor based on uniformly sized high-response silicon nanowire (SiNW) array was studied for real-time, label-free, super-sensitive detection of PIK3CA E542K ctDNA. High-response 120-SiNWs array was fabricated on a (111) silicon-on-insulator (SOI) by the complementary metal oxide semiconductor (CMOS)-compatible microfabrication technology. To detecting ctDNA, we modified the DNA probe on the SiNWs array through silanization. The experimental results demonstrated that the as-fabricated biosensor had significant superiority in ctDNA detection, which achieved ultralow detection limit of 10 aM and had a good linearity under the ctDNA concentration range from 0.1 fM to 100 pM. This biosensor can recognize complementary target ctDNA from one/two/full-base mismatched DNA with high selectivity. Furthermore, the fabricated SiNW-array FET biosensor successfully detected target ctDNA in human serum samples, indicating a good potential in clinical applications in the future

Effects of Fuel Price Fluctuation on Individual CO2 Traffic Emissions: Empirical Findings from Pseudo Panel Data

AbstractGlobalized concerns about greenhouse gasses and increased energy consumptions have stimulated research in transportation about the relationships between fuel prices and emissions. Many researchers have found that higher fuel price can reduce fuel consumption and CO2 emissions through a number of transmission mechanisms. However, most prior studies have been based on aggregate data and therefore do not reflect individual or household CO2 adaptation behavior. Moreover, most studies have used cross-sectional data which inherently limit the study of dynamic effects. This paper therefore uses a pseudo-panel approach to estimate a dynamic model of transportation energy consumption and CO2 emission. Seemingly unrelated regression analysis is used to reveal the interrelations between several dimensions of individual travel behavior such as the number of trips conducted, CO2 emission, travel distance and fuel price. The results indicate that increasing fuel prices have negative effects on vehicle miles travelled, fuel consumption and CO2 emissions, but positive effects on travel distance by public transport and slow modes