Public Perception of Vernacular Architecture in the Arabian Peninsula: The Case of Rawshan

This research extends Hasan Fathy’s (1986) principle of vernacular architecture by focusing on the Rawshan through an investigation of two criteria: aesthetics and energy efficiency. The paper discusses the views of both the Saudi public and key decision-makers on reviving vernacular architecture in the context of Saudi Arabia’s rapidly developing economy, characterized by relatively high rates of energy consumption and CO2 emissions. This research explores (a) the interaction in domestic buildings of Saudi occupants with their windows, and how these are perceived as an interface with the external environment; (b) awareness and knowledge of the use of shading elements (such as Rawshans) to reduce the use of artificial lighting while maintaining indoor privacy; (c) Saudi awareness of, and familiarity with, the Rawshan as a vernacular element and a secular architectural tradition; and (d) Saudi views on the revival of traditional architectural elements with a focus on the Rawshan. An online survey (n = 812) was conducted across Saudi Arabia complemented by interviews with expert decision-makers (n = 23) to (a) assess criteria such as privacy, aesthetics, daylight, ventilation, and energy consumption in Saudi residences and (b) investigate the level of acceptance of an optimized retrofitted Rawshan design

Open complete dislocation of trapezium with a vertically split fracture: a case report

Open complete dislocation of the trapezium is an extraordinarily rare injury with only a few cases reported so far in literature. The association of a vertically split fracture makes this injury even rare and hence worth reporting. A 14 year old Kashmiri boy presented to us with a history of massive trauma to the non dominant left hand sustained as a result of a blow from a heavy hammer. The thenar area was burst out and the trapezium was vertically split apart into two halves which were dislocated from the articular surfaces of the scaphoid as well as the first metacarpal. The mechanism of injury as in other such reported cases was a massive direct force localized over the carpal bone which causes its enucleation and fracture. Although some authors have recommended excision of the dislocated trapezium, open reduction of the fracture dislocation and fixation with K wires was carried out under General anesthesia. At the end of one year although there was some functional deficit in the affected thumb, especially in opposition, the patient was quite satisfied with the outcome as this was the non dominant hand

Expanding the set of rhodococcal Baeyer–Villiger monooxygenases by high-throughput cloning, expression and substrate screening

To expand the available set of Baeyer–Villiger monooxygenases (BVMOs), we have created expression constructs for producing 22 Type I BVMOs that are present in the genome of Rhodococcus jostii RHA1. Each BVMO has been probed with a large panel of potential substrates. Except for testing their substrate acceptance, also the enantioselectivity of some selected BVMOs was studied. The results provide insight into the biocatalytic potential of this collection of BVMOs and expand the biocatalytic repertoire known for BVMOs. This study also sheds light on the catalytic capacity of this large set of BVMOs that is present in this specific actinomycete. Furthermore, a comparative sequence analysis revealed a new BVMO-typifying sequence motif. This motif represents a useful tool for effective future genome mining efforts.

Deep highway networks and tree-based ensemble for predicting short-term building energy consumption

Predictive analytics play a significant role in ensuring optimal and secure operation of power systems, reducing energy consumption, detecting fault and diagnosis, and improving grid resilience. However, due to system nonlinearities, delay, and complexity of the problem because of many influencing factors (e.g., climate, occupants’ behaviour, occupancy pattern, building type), it is a challenging task to get accurate energy consumption prediction. This paper investigates the accuracy and generalisation capabilities of deep highway networks (DHN) and extremely randomized trees (ET) for predicting hourly heating, ventilation and air conditioning (HVAC) energy consumption of a hotel building. Their performance was compared with support vector regression (SVR), a most widely used supervised machine learning algorithm. Results showed that both ET and DHN models marginally outperform the SVR algorithm. The paper also details the impact of increasing the deep highway network’s complexity on its performance. The paper concludes that all developed models are equally applicable for predicting hourly HVAC energy consumption. Possible reasons for the minimum impact of DHN complexity and future research work are also highlighted in the paper

In-street wind direction variability in the vicinity of a busy intersection in central London

We present results from fast-response wind measurements within and above a busy intersection between two street canyons (Marylebone Road and Gloucester Place) in Westminster, London taken as part of the DAPPLE (Dispersion of Air Pollution and Penetration into the Local Environment; www.dapple.org.uk) 2007 field campaign. The data reported here were collected using ultrasonic anemometers on the roof-top of a building adjacent to the intersection and at two heights on a pair of lamp-posts on opposite sides of the intersection. Site characteristics, data analysis and the variation of intersection flow with the above-roof wind direction (θref) are discussed. Evidence of both flow channelling and recirculation was identified within the canyon, only a few metres from the intersection for along-street and across-street roof-top winds respectively. Results also indicate that for oblique rooftop flows, the intersection flow is a complex combination of bifurcated channelled flows, recirculation and corner vortices. Asymmetries in local building geometry around the intersection and small changes in the background wind direction (changes in 15-min mean θref of 5–10 degrees) were also observed to have profound influences on the behaviour of intersection flow patterns. Consequently, short time-scale variability in the background flow direction can lead to highly scattered in-street mean flow angles masking the true multi-modal features of the flow and thus further complicating modelling challenges

A framework to identify and prioritise the key sustainability indicators: Assessment of heating systems in the built environment

Sustainability indicators (SIs) are important instruments to quantify, analyse, and communicate complex sustainability information, with a history of application in energy research. It is critical to identify an effective set of indicators which can holistically evaluate the energy systems encompassing the three facets of sustainability: environment, economy, and society. However, the literature has been lacking in either proportionally representing the sustainability dimensions or reflecting the stakeholders’ preferences. This paper develops a framework to identify and prioritise a set of SIs, critically reviewed to ensure reflection of a wide array of factors and conceptions of what sustainability entails. The developed framework utilises a series of methods within three phases: identification, refinement, and prioritisation. Applying the proposed framework to building heating technologies, a set of 22 SIs consisting of 4 economic, 8 environmental, and 10 social indicators were identified. According to the results, the economic indicators of Operation & Maintenance Cost and Net Present Value were found to be the most impactful factors, while environmental SIs contribute the most to the overall sustainability weight. The identified indicators apply to the assessment of heating systems and policies, and the proposed framework could more broadly support analysis of key sustainability criteria in various fields

Planning energy interventions in buildings and tackling fuel poverty: Can two birds be fed with one scone?

Energy retrofitting and renovations are an inseparable part of decarbonisation strategies in the building sector. These measures are often tied up with several social factors that can potentially impact the wellbeing of households and the community if the end-user requirements are not carefully considered. Fuel poverty is one of these social factors that is an essential consideration for designing effective, just, and user-centred interventions, but it is often overlooked in engineering processes. Therefore, this article seeks to re-connect the notion of fuel poverty to practice by bringing it forward from the post-intervention assessments to the design and decision-making stages. To do so, a new indicator, Potential Fuel Poverty Index (PFPI), is developed to obtain the likelihood of fuel poverty that future interventions can pose to the households. The PFPI presents a more targeted analysis of fuel poverty by reflecting the socio-spatial characterisation of the households. Using the PFPI, fuel poverty can be observed as a design/decision factor at the early stages of sketching interventions, in conjunction with other economic, environmental, and technical factors. Finally, the utility of the developed method is demonstrated using a real case study in the UK, assessing the impact of heat decarbonisation through heat pumps on fuel poverty

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins.

Funder: Mahidol UniversityFunder: College of Arts, Media and Technology, Chiang Mai UniversityFunder: Chiang Mai UniversityFunder: Information Technology Service Center (ITSC) of Chiang Mai UniversityFast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository ( https://github.com/saeed344/SCORPION )