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2022 ◽  
Vol 3 ◽  
Author(s):  
Thomas J. Bannan ◽  
James Evans ◽  
Jack S. Benton ◽  
Pete Edwards ◽  
Sebastian Diez ◽  
...  

Cities must address many challenges including air quality, climate change and the health and wellbeing of communities. Public authorities and developers increasingly look to improve these through the implementation of interventions and innovations, such as low traffic neighbourhoods, deep housing retrofits and green infrastructure. Monitoring the impacts of interventions is essential to determine the success of such projects and to build evidence for broader urban transformation. In this paper we present a mixed-method cross-disciplinary approach that brings together cutting edge atmospheric and data science, measurements of activity in public spaces and novel methods to assess wellbeing-promoting behaviours. The Manchester Urban Observatory focuses on living areas that have a high density of inter-related systems, which require observation, understanding and intervention at multiple levels. This must be completed in line with urban planning goals as well as a clear and succinct data solution that allows robust scientific conclusions to be made and viewed in real time. Delivery of such a monitoring strategy is not trivial and is time, resource and expertise heavy. This paper discusses the methods employed by the Manchester Urban Observatory to monitor the effectiveness off interventions implemented within cities and effective communication strategies with local communities.


2022 ◽  
Vol 7 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Shakib Zohrehvandi ◽  
Roya Soltani

In the project management, buffers are considered to handle uncertainties that lead to changes in project scheduling which in turn causes project delivery delay. The purpose of this survey is to discuss the state of the art on models and methods for project buffer management and time optimization of construction projects and manufacturing industries. There are not literally any surveys which review the literature of project buffer management and time optimization. This research adds to the previous literature surveys and focuses mainly on papers after 2014 but with a quick review on previous works. This research investigates the literature from project buffer sizing, project buffer consumption monitoring and project time/resource optimization perspectives.


2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller

Abstract Walkability is an important measure with strong ties to our health. However, there are existing gaps in the literature. Our previous work proposed new approaches to address existing limitations. This paper explores new ways of applying transferability using transfer-learning. Road networks, POIs, and road-related characteristics grow/change over time. Moreover, calculating walkability for all locations in all cities is very time-consuming. Transferability enables reuse of already-learned knowledge for continued learning, reduce training time, resource consumption, training labels and improve prediction accuracy. We propose ALF-Score++, that reuses trained models to generate transferable models capable of predicting walkability score for cities not seen in the process. We trained transfer-learned models for St. John's NL and Montréal QC and used them to predict walkability scores for Kingston ON and Vancouver BC. MAE error of 13.87 units (ranging 0-100) was achieved for transfer-learning using MLP and 4.56 units for direct-training (random forest) on personalized clusters.


2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller

Walkability is an important measure with strong ties to our health. However, there are existing gaps in the literature. Our previous work proposed new approaches to address existing limitations. This paper explores new ways of applying transferability using transfer-learning. Road networks, POIs, and road-related characteristics grow/change over time. Moreover, calculating walkability for all locations in all cities is very time-consuming. Transferability enables reuse of already-learned knowledge for continued learning, reduce training time, resource consumption, training labels and improve prediction accuracy. We propose ALF-Score++, that reuses trained models to generate transferable models capable of predicting walkability score for cities not seen in the process. We trained transfer-learned models for St. John's NL and Montréal QC and used them to predict walkability scores for Kingston ON and Vancouver BC. MAE error of 13.87 units (ranging 0-100) was achieved for transfer-learning using MLP and 4.56 units for direct-training (random forest) on personalized clusters.


2021 ◽  
Vol 4 (1) ◽  
pp. 7-15
Author(s):  
Sekamwa Umar ◽  
Semwogerere Twaibu ◽  
Gilbert Gilibrays Ocen ◽  
Lusiba Badru ◽  
Alunyu Andrew ◽  
...  

Supermarkets are large retail stores operated on a self-service basis. They sell a range of goods from agricultural produce to electronics with tagged prices. They are coupled with numerous advantages like supporting advanced means of payment like cheques, credit cards, smart store electronic cards and mobile money, offering transportation incentives and discounts. The study aimed at coming up with an RFID-Based billing system through automation. The methods and materials used included document reviews, observational experiments, system design, implementation and testing based on current situations in the supermarket business. Findings showed that there are several weaknesses with the existing systems and the new system could ably uphold the time resource, efficiency improvement of both workers and customers, and it is secure, cost-effective, and time-saving especially from queues. The widely implemented system can improve the revenue gap and possibly rejuvenate the national or international economy to a large extent.


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7902
Author(s):  
Deok-Won Yun ◽  
Won-Cheol Lee

Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we propose the implementation of intelligent dynamic real-time spectrum resource management through the application of data mining and case-based reasoning, which reduces the complexity of existing intelligent dynamic spectrum resource management and enables efficient real-time resource allocation. In this case, data mining and case-based reasoning analyze the activity patterns of incumbent users using vast amounts of sensing data from industrial IoT and enable rapid resource allocation, making use of case DB classified by case. In this study, we confirmed a number of optimization engine operations and spectrum resource management capabilities (spectrum handoff, handoff latency, energy consumption, and link maintenance) to prove the effectiveness of the proposed intelligent dynamic real-time spectrum resource management. These indicators prove that it is possible to minimize the complexity of existing intelligent dynamic spectrum resource management and maintain efficient real-time resource allocation and reliable communication; also, the above findings confirm that our method can achieve a superior performance to that of existing spectrum resource management techniques.


Separations ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 221
Author(s):  
Fabio Vaiano ◽  
Elisabetta Bertol ◽  
Maria Mineo ◽  
Laura Pietrosemoli ◽  
Jolanda Rubicondo ◽  
...  

In the last few years, liquid chromatography coupled with mass spectrometry (LC/MS) has been increasingly used for screening purposes in forensic toxicology. These techniques have the advantages of low time/resource-consuming and high versatility and have been applied in numerous new multi-analytes methods. The new psychoactive substance (NPS) phenomenon provided a great impulse to this wide-range approach, but it is also important to keep the attention on “classical” psychoactive substances, such as benzodiazepines (BDZ). In this paper, a fully validated screening method in blood for the simultaneous detection of 163 substances (120 NPS and 43 other drugs) by a dynamic multiple reaction monitoring analysis through LC-MS/MS is described. The method consists of a deproteinization of 200 µL of blood with acetonitrile. The LC separation is achieved with a 100 mm long C18 column in 35 min. The method was very sensitive, with limits of quantification from 0.02 to 1.5 ng/mL. Matrix effects did not negatively affect the analytical sensitivity. This method proved to be reliable and was successfully applied to our routinary analytical activity in several forensic caseworks, allowing the identification and quantification of many BDZs and 3,4-methylenedioxypyrovalerone (MDPV).


2021 ◽  
pp. 616-629
Author(s):  
Berkay Çataltuğ ◽  
Helin Su Çorapcı ◽  
Levent Kandiller ◽  
Fatih Kağan Keremit ◽  
Giray Bingöl Kırbaş ◽  
...  

2021 ◽  
Vol 2042 (1) ◽  
pp. 012140
Author(s):  
A S Bahaj ◽  
P Turner ◽  
M Mahdy ◽  
S Leggett ◽  
N Wise ◽  
...  

Abstract The UK was the first major economy to pass a Climate Change Act in 2008, which was revised in 2019 to achieve net zero emissions by 2050. In 2019, Southampton City Council (SCC) declared a climate emergency setting ambitious targets for the city to become carbon neutral under the banner Green City Charter (GCC), which was signed by 70 city-based organisations. There is, however, no specific methodology to quantify progress towards the targets. Here we present the outcomes from developing the GCC Tracker in collaboration with local authorities and stakeholders. The approach is based on the Analytical Hierarchy Process, with expertise agreed weights to measure the success or otherwise of carbon environmental commitments. The outcome is the Green City Tracker encompassing an assessment matrix that provides ratings and quantifies annual progress for achieving committed targets. The Tracker was applied to 10 institutions and the results show their ratings as a function of each sub-criteria and as an overarching rating. The approach highlighted the importance of generating a universally applicable and time/resource efficient processes in order to incentivise organisation participation. The Tracker was widely accepted by regional local authorities with a plan to widely adapt it to other cities declared targets.


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