With the rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in coronavirus disease 2019 (COVID-19), corporate entities, federal, state, county and city governments, universities, school districts, places of worship, prisons, health care facilities, assisted living organizations, daycares, homeowners, and other building owners and occupants have an opportunity to reduce the potential for transmission through built environment (BE) mediated pathways. Over the last decade, substantial research into the presence, abundance, diversity, function, and transmission of microbes in the BE has taken place and revealed common pathogen exchange pathways and mechanisms. In this paper, we synthesize this microbiology of the BE research and the known information about SARS-CoV-2 to provide actionable and achievable guidance to BE decision makers, building operators, and all indoor occupants attempting to minimize infectious disease transmission through environmentally mediated pathways. We believe this information is useful to corporate and public administrators and individuals responsible for building operations and environmental services in their decision-making process about the degree and duration of social-distancing measures during viral epidemics and pandemics.

Lung nodule segmentation in CT images and its subsequent volume analysis can help determinethe malignancy status of a lung nodule. While several efficient segmentation schemes have beenproposed, only a few studies evaluated the segmentation’s performance for large nodules. In thisresearch, we contribute a semi-automatic system which is capable of performing robust 3-D segmen-tations on both small and large nodules with good accuracy. The target CT volume is de-noisedwith an anisotropic diffusion filter and a region of interest is selected around the target nodule ona reference slice. The proposed model performs nodule segmentation by incorporating a mean in-tensity based threshold in Geodesic Active Contour model in level sets. We also devise an adaptivetechnique using image intensity histogram to estimate the desired mean intensity of the nodule.The proposed system is validated on both lung nodules and phantoms collected from publicly avail-able diverse databases. Quantitative and visual comparative analysis of the proposed work withthe Chan-Vese algorithm and statistic active contour model of 3D Slicer platform is also presented.The resulting mean spatial overlap between segmented nodules and reference nodules is 0.855, themean volume bias is 0.10±0.2 ml and the algorithm repeatability is 0.060 ml. The achieved resultssuggest that the proposed method can be used for volume estimations of small as well as large-sizednodules.

Salinity intrusion is one of the most serious consequences of climate change coupled with rising sea level that significantly affects agricultural activities in many parts of the world. This phenomenon has increasingly become more serious and frequently occurred in the Mekong Delta of Vietnam. As a result, Vietnam has been ranked among top five countries where have been devastatingly impacted by climate change, in particular, its Tra Vinh Province characterized by coastal plain and alluvial deposit. In addition, this area is of the tropical monsoon zone of long rainy season with source of salt brought from the sea by the tides and sea level rise. Regions that are contaminated by salt are located in lowland and often suffer from floods linking to tidal effects with salty water from river systems and channels. Soil salinity evaluation is critical for coastal protection, restoration, and agricultural planning since it can be considered as an agricultural indicator to evaluate quality of soil. Here, we attempt to estimate the soil salinity in Tra Vinh Province, in the Mekong Delta of Vietnam. Landsat 8 OLI images are utilized to derive indices for soil salinity evaluation including single bands, Vegetation Soil Salinity Index (VSSI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Salinity Index (NDSI). Subsequently, satistical analysis between soil salinity, electrical conductivity (EC, dS/m), and environmental indices derived from Landsat 8 OLI image is performed. Results indicate that spectral value of Near Infrared (NIR) band and VSSI are highly correlated with EC (R2 = 0.7779 and R2 = 0.6957, respectively) in comparison with the other indices. Comparative results show that soil salinity derived from Landsat 8 is consistent with in situ data. Findings of this study demonstrate that Landsat 8 OLI images reveal a high potential for spatiotemporally monitoring the magnitude of soil salinity at the top soil layer. Outcomes of this study are useful for agricultural activities, planners, and farmers by providing the base map of soil salinity contamination for better selection of accomodating crop types to reduce economical lost in the context of climate change. Our proposed method that estimates soil salinity using satellite-derived variables can be applied in the other regions.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

The social criteria of sustainable development have remained underexplored. Moreover, a large number of green building assessment tool and social sustainability documentations have been developed which, has had a direct impact on social criteria issues, but there seems to be a substantial gap in the study of social criteria in green building assessment tools. In examining the problem facing social sustainability, taking into consideration social sustainability in sustainable development reviews and green building assessment tool towards social aspects. This paper through analysis identified a centripetal conceptual framework composed of seven key components equity, education, participation & control, social cohesion, health & safety, accessibility & satisfaction, and cultural values. The interpretation of the social sustainability in green building assessment tool would impact building practitioners towards implementing social criteria in GBAT. The aim was to identify social categories as well as consider a starting point for the development of an effective social criteria assessment tool for green building.

The number of green buildings has increased to address the global environmental crisis. However, green buildings face risks resulting from new materials and methods. In addition, these buildings are expected to perform at higher levels than traditional ones. The objectives of this study are to identify the possible risk factors for architects developing green building projects in South Korea and to assess risk mitigation measures. To attain this goal, fourteen risk factors and twelve mitigation measures were identified from a comprehensive literature review. A questionnaire survey was administered to architects practicing green building design. Findings revealed the ‘adoption of new technology and processes’ was the largest difference between green and traditional building projects. This study identified ‘financial risk,’ ‘design changes,’ and ‘client’s goal uncertainty’ as the top three risk factors in green building design. Additionally, the survey proposed the four most effective risk mitigation measures for green building projects: (1) ‘contract indicating each party’s roles, liabilities and limitations clearly’; (2) ‘utilizing integrated design process’; (3) ‘understanding client’s goal in green building projects’; and (4) ‘improving communication and coordination among stakeholders.’ There are a few studies focusing on the architects’ perceived risk concerning green building projects; however, this study expands the knowledge and fills the literature gap. Additionally, this study provides a comprehensive understanding of critical risks and mitigation measures that can benefit South Korea’s green building design practice through better risk management.

Building information modeling (BIM) is the digital representation of physical and functional characteristics (such as geometry, spatial relationship, and geographic information) of a facility to support decisions during its life cycle. BIM has been extended beyond 3D geometrical representations in recent years, and now includes time as a fourth dimension and cost as a fifth dimension, as well as such other applications as virtual reality and augmented reality. The Internet of Things (IoT) has been increasingly applied in various products (smart homes, wearables) to enhance work productivity, living comfort, and entertainment. However, research addressing the integration of these two technologies (BIM and IoT) is still very limited, and has focused exclusively on the automatic transmission of sensor information to BIM models. This paper describes an attempt to represent and visualize sensor data in BIM with multiple perspectives in order to support complex decisions requiring interdisciplinary information. The study uses a university campus as an example and includes several scenarios, such as an auditorium with a dispersed audience and energy saving options for rooms with different functions (mechanical/electric equipment, classrooms, and laboratory). This paper also discusses the design of a common platform allowing communication among sensors with different protocols (Arduino, Raspberry Pi), the use of Dynamo to accept sensor data as input and automatically redraw visualized information in BIM, and how visualization may help in making energy-saving management decisions.

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

Employees’ organizational commitment is considered to be a critical issue in higher educational setting for the success of its visions and goals, as well as to keep its employees motivation granted for achieving better work performance. This subject has therefore, been studied so as to draw attention to enhance effectiveness of higher educational institution in Ethiopia, particularly in reference to Haramaya University. The major objective of study was to find out the level of academic staffs’ commitment. The study assessed whether significant difference exist in academic staffs’ commitment level in terms of their gender and level of education. Researchers used cross-sectional research design and both primary and secondary data sources were used for the study; 242 participants were selected using stratified sampling technique. Questionnaire, focus group discussion and document review were used to collect data. The quantitative data were analyzed using descriptive and inferential statistics; and the qualitative data were also analyzed using narration methods of analysis. Study result showed there was moderate level of in overall commitment and in specific commitment dimensions among academic staffs. The study further indicated that there is no significance difference in commitment of academic staffs with reference to gender; however, a significant difference in commitment was existed in reference to level of education.

Building Information Modelling (BIM) has been more popular in the construction industry as a consequence of the many advantages it offers, such as increased productivity, improved project outcomes, and reduced expenses. However, there are many obstacles that must be overcome before BIM can be widely used. This research uses a poll of construction industry experts to investigate the challenges associated with using BIM in projects. Cost of implementation, inadequate standards and procedures, and a lack of knowledge with BIM were some of the hurdles that were measured in the study. Statistical techniques were used to examine the data, and the findings indicate that the high cost of implementation and the absence of sufficient standards and procedures are the two main obstacles to the widespread adoption of BIM. Problems with supply chain management and a lack of experience with BIM are also mentioned as obstacles. The report also suggests solutions, such as a more focused teaching effort on BIM-related issues and the creation of a specialised BIM curriculum. Professionals in the construction industry may utilise the study’s results to their advantage by expanding their knowledge of the obstacles standing in the way of widespread adoption of BIM.

Unmanned Aerial Vehicles (UAVs) are now filling in the gaps between spaceborne and ground-based observations and enhancing the spatial resolution and temporal coverage of data acquisition. In the realm of hydrological observations, UAVs have a key role to quantitatively characterize the surface flow allowing for remotely accessing the water body of interest. In this paper we propose a technology which uses a sensing platform encompassing a drone and a camera to determine the water level. The images acquired my means of the sensing platform are then analyzed using the Canny method to detect the edges of water level and of Ground Control Points (GCPs) used as reference points. The water level is then retrieved from images and compared to a benchmark value obtained by a traditional device. The method is tested at four locations in an artificial lake in central Italy. Results are encouraging as the overall mean error between estimated and true water level values is around 0.02 m. This technology is well suited to improve hydraulic modeling and thus provide a reliable support to flood mitigation strategies also in uneasy-to-access environments.

Identification of costs drivers and their influence level on building development costs play a key role in the development of construction models and improve the efficiency and effectiveness of any project. Forty-five indicators influencing building development costs in New Zealand are explored by literature review and pilot interviews. These indicators are grouped into seven categories. The determination and ranking of the cost drivers are carried out by a questionnaire survey distributed to key professionals working in New Zealand’s construction industry. Structural equation modeling (SEM) software was employed for analysis of the collected data. One of the key advantages of this powerful software is to provide the p-value according to the structure of the research model. Findings of this study indicate that the property market and construction industry factor, statutory and regulatory factor, and socio-economic factor are major factors affecting building development costs in New Zealand.

The significance of virtual reality (AR/VR/MR) technology stands out because it can be used in various construction fields such as urban design, construction review, maintenance and education, etc. As to prove this, conglomerates such as Facebook, Google, Sony, Microsoft, and Samsung are increasing their investments to preoccupy the virtual reality market and are competing to dominate the contents and platform market. Virtual reality technology has a concept that collectively refers to VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality), but technically, the two technologies are strictly separated. While VR technology is an immersive virtual environment using computer graphic technology, AR technology is a more advanced technology that combines real data and VR data, and MR technology is a technology that combines both AR and VR. In Korea, Republic of, the use of BIM (Building Information Modeling), a 3D information model, becomes mandatory and based on this, the demand for a new market where VR/AR/MR technologies and advanced sensing equipment are combined increases. This study implemented a building maintenance platform using AR based on 5G, and developed matching technology between BIM of the building maintenance platform and AR equipment. Besides this study implemented the technology to enhance the matching rate of the matching technology and carried out the process to enhance the matching rate empirically through continuous tests. As a result, the initial target of 90% matching rate could be improved to 96%. Based on this, it is expected that effective EV (Optimum Life Cycle Cost) could be achieved at a lower cost compared to the previous one by using it for maintenance monitoring after construction and completion of the building, and can be used as an effective solution in the aspect of building maintenance as well.

With the existence of several programming languages such as C/C++, Java, C#, LISP, Prolog, Python, Simula, F#, Go, Haskell, Scala, Ruby, Dart, Swift, Groovy etc. and diverse paradigms like structured, object-oriented, list, aspect-oriented, service-oriented, web, mobile and logic programming, there is a need to perform an exhaustive comparative analysis of diverse compilers and environments before making a choice of implementation technology in software engineering. Optimization of compilers helps to reduce execution time by making use of high speed processor registers, thereby, eliminating redundant computation. This paper reports some series of performance analysis done with some popular programming languages including Java, C++, Python and PHP. Programs involving recursive and iterative functions like factorial of large numbers and binary search of large arrays were run on the various platforms with the execution time recorded in milliseconds and represented in a chart. This can aid in making a selection of the appropriate language to use for a given application domain.

The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product which was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère). This SMOS-INRA-CESBIO (SMOS-IC) product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated to the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary data sets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A 6 year (2010-2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and of the northern mid-latitudes.

Talking about sustainable development refers mainly to the environmental sphere, but the concept is much broader and also takes into account the social and economic conditions. The concept of sustainability, in this sense, is linked to the compatibility between the development of economic activities, the related social phenomena, and the protection of the environment. Therefore, the ability to balance social, economic and environmental sustainability is the very meaning of the concept of sustainable development. Firms that choose to develop policies and strategies to enhance and pursue sustainable development in the medium to long term have the burden of having to quantitatively document the improvements in production processes with the aim of sustainable development. As a result, one of the biggest challenges for European industry is to introduce sustainability principles into business models leading to competitive advantage. This is particularly important in raw material and energy intensive manufacturing sectors such as the ceramic industry. The present state of knowledge lacks a comprehensive operational tool for industry to support decision-making processes geared towards sustainability. In the ceramic sector, the economic and social dimensions of the product and processes have not yet been given sufficient importance. Moreover, the traditional research on industrial districts lacks an analysis of the relations between firms and the territory with a view to sustainability. Finally, the attention of scholars in the field of economic and social sustainability, has not yet turned to the analysis of the Sassuolo district. Therefore, in this paper we introduce the Life Cycle Sustainability Assessment (LCSA), as a method that can be a suitable tool to fill this gap, because through a mathematical model it is possible to obtain the information useful for decision makers to integrate the principles of sustainability both at the microeconomic level in enterprises, and at the meso-economic level for the definition of economic policies and territorial governance. Environmental and socio-economic analysis was performed from the extraction of raw materials to the packaging of the product on different product categories manufactured by the Italian ceramic industries of the Sassuolo district (northern Italy). For the first time the LCSA model, usually applied to unitary processes, is extended to the economic and industrial activities of the entire district, extending the prospect of investigation from the enterprise and its value chain to the integrated network of district enterprises.

Background: The prevalence of atopic dermatitis in domestic animals is one of the problems of modern veterinary. Treating with standard techniques using chemotherapeutic agents not always leads to a positive result of therapy; moreover, many drugs produce adverse side effects. Methods: Low level laser therapy, in particular, intravenous laser blood illumination (ILBI) has a pronounced and long-lasting impact on the immune system of animals. The combined technique including ILBI-635 (635 nm, 2 mW, 5 min) and LUVBI^® (365 nm, 2 mW, 3 min) every other day provides a positive change in clinical status of cats with allergic dermatitis after the 3rd-4th treatment session. Results: The increased level of erythrocytes and hemoglobin was identified in the course of treatment, and it indirectly indicates increased blood transport activity, which improves trophic provision and microcirculation. A double reduction of leukocytes and a significant decrease of neutrophil cells indicate the immunomodulatory effect of LILI (low-intensity laser illumination). The increase in the percentage of lymphocytes and the decrease of eosinophils and monocytes against the background of basophil concentrations deviations within physiological concentration result in the reduction of inflammatory mediators expression that induce itching. The reduction of total IgE concentration 32 times against control on the 7th day of treatment correlates with the decrease in the quantitative content of peripheral blood eosinophils, indicating the decrease in severity of an allergic process. Conclusion: LLLT is recommended against the background of standard drug therapy to achieve quick clinical outcome together with a long-lasting prolonged effect.

Fractal array antennas are multiband arrays having ultra wide band and space filling facility. But Side lobe levels and large number of antenna elements are the major designing challenges of these arrays. In this paper, design and analysis of octagonal fractal array antenna is investigated with Morse-Thue fractal density tapering technique (MTFDT). Due to the proposed technique, a remarkable improvement has observed in Side lobe levels and thinning of the elements can also be attained at the various iterations of octagonal fractal array antenna. These arrays are analyzed and simulated by MATLAB-15 programming.

Bisphenol A production plant possesses considerable potential risks in the top of the methanol separation column, as pressurized acetone, methanol and water are processed at an elevated temperature, especially in the event of an abnormal pressure increase due to sudden power outage. This study assesses the potential risks in the methanol separation column through hazard and operability assessment and evaluates the damages in the case of fire and explosion accident scenarios. The study chooses three leakage scenarios: a 5-mm puncture on the methanol separation column, a 50-mm diameter fracture of a discharge pipe and a catastrophic rupture, and simulates using Phast (Ver. 6.531) the concentration distribution of scattered methanol, thermal radiation distribution of fires and overpressure distribution of vapor cloud explosions. Implementation of safety instrumented system equipped with two-out-of-three voting as a safety measure can detect overpressure at the top of the column and shut down the main control valve and the emergency shutoff valve simultaneously, all at the same time. By applying safety integrity level of three, the maximal release volume of the safety relief valve can be reduced and therefore, the design capacity of the flare stack can also be reduced. Such integration will lead to improved safety at a reduced cost.

The building envelope has the most significant contribution in the reduction of the building energy consumption. Application of new, alternative and improved materials and systems has an important impact on the buildings performances. This paper is focused on the thermal transmittance, as an indicator of the thermal conductance of the construction element. It includes comparisons of the U-values, calculated by software, with those measured in situ on three representative façade walls. The walls have been constructed with the new wall system Fragmat NZ-1, a new product in Macedonian buildings. This research provides basic information on the thermal transmittance of the system. The results of the analysis show that the in situ measuring is a useful tool in validation of the precision of analytically calculated values, since it was difficult to obtain precise results from the analytical calculations only, especially when the layers are with non-uniform thickness.

A new method for wide-area urban roof assessment of suitability for solar photovoltaics is introduced and validated. Knowledge of roof geometry and physical features is essential for evaluation of the impact of multiple rooftop solar photovoltaic (PV) system installations on local electricity networks. This paper begins by reviewing and testing a range of existing techniques for identifying roof characteristics. It was found that no current method is capable of delivering accurate results with publicly available input data. Hence a different approach is developed, based on slope and aspect using LIDAR data, building footprint data, GIS tools and aerial photographs. It assesses each roof’s suitability for PV installation. That is, its properties should allow the installation of at least a minimum size photovoltaic system. In this way the minimum potential solar yield for region or city may be obtained. The accuracy of the new method is then established, by ground-truthing against a database of 886 household systems. This is the largest validation of a rooftop assessment method to date. The method is flexible with few prior assumptions. It is based on separate consideration of buildings and can therefore generate data for various PV scenarios and future analyses.

Phenotype variation is produced through a complex of interactions between genotype and environment. Phenotype, genotype, and environment are addresses the relationship between architecture and identity. The term genotype biology and phenotype have been adopted into architecture in the late twentieth century. Genotypes are abstract relational models that govern the arrangement of space, and the principle of organizing space and phenotypes is the real realization of genotypes in the physical environment. The genotype is a reflection that is not only about the spatial organization but also the nature of social and cultural patterns. Then this study purpose to an understanding of the connectedness variant phenotype from a genotype and environment. The repetition pattern being stable structure in variation phenotype uses as a database to finding an identity in architecture. The method used in this research was Levi Strauss's structuralism and multi-layer of a biological system. This research samples traditional Malay houses in West Borneo, Indonesia. These houses have a unique site and existing environment. The houses can be found mainly along the river. The results found from the phenotype, genotype, and environment have value and meaning as a traditional Malay house rule in West Borneo which was always handed down from generation to generation.

Infertility affects approximately 15% of couples worldwide, an estimated 30% of which is related to male factor infertility. Application of low level laser therapy (LLLT) to improve fertility status is a rapidly growing discipline in medicine. Laser therapy triggers a variety of biological processes through interaction with primary cellular photoacceptors. The present review aims at evaluating the literature available in the MEDLINE/ PubMed on cellular and molecular mechanisms of photobiomodulation in the domains of reproductive and veterinary medicine. We primarily focused on the clinical application of laser treatment on seminal quality, in particular its role in promoting spermatozoa motility, as well as the role of phototherapy in modulating assisted reproduction (ART). Additionally, we investigated the strength of evidence in support of the positive impact of photobiomodulation on facilitating ART.

When extract building from high resolution remote sensing image with meter/sub-meter accuracy, the shade of trees and interference of roads are the main factors of reducing the extraction accuracy. Proposed a Bayesian Convolutional Neural Networks(BCNET) model base on standard fully convolutional networks(FCN) to solve these problems. First take building with no shade or artificial removal of shade as Sample-A, woodland as Sample-B, road as Sample-C. Set up 3 sample libraries. Learn these sample libraries respectively, get their own set of feature vector; Mixture Gauss model these feature vector set, evaluate the conditional probability density function of mixture of noise object and roofs; Improve the standard FCN from the 2 aspect:(1) Introduce atrous convolution. (2) Take conditional probability density function as the activation function of the last convolution. Carry out experiment using unmanned aerial vehicle(UVA) image, the results show that BCNET model can effectively eliminate the influence of trees and roads, the building extraction accuracy can reach 97%.

Building envelope can play a crucial role in building improvement efficiency and the adoption of Phase Change Materials (PCMs) coupled with transparent elements may (i) allow a better control of the heat flows from/to the outdoor environment, (ii) increase the exploitation of solar energy at building scale and (iii) modulate the light transmission to avoid glare effects. Starting from a literature review of experimental works, this research identifies the main possible integration of PCM in transparent/translucent building envelope components (in glazing, in shutters and in multilayer façade system) drawing a global picture potentials and limitations of these technologies. Transparent envelopes with PCMs have been classified from the simplest “zero” technology, which integrates the PCM into the double glass unit (DGU), to more complex solutions – with different numbers of cavities of the glass (TGU), different position of PCM layer (internal/external shutter), and in combination with other materials (TIM, aerogel, prismatic solar reflector, PCM curtain controlled by an electric pump). The results of the analysis are resumed in a SWOT (strengths, weakness, opportunities and threats) analysis table to underline strengths and weaknesses of transparent building envelope components with PCMs, and to indicate opportunities and threats for future research and building application.

Abstract The article discusses about the current situation of Novel Corona Virus also called as the COVID-19 that hinder for all human’s life including the education. Rapidly escalating COVID-19, has caused havoc in quality education and every educational institution are closed. As the UNESCO report it showed that 1.6 billion children being affected due to the close of institution across 191 countries. With the alternative method every education institution started blended learning virtual classes in order to continue learning environment in students. The articles investigate COVID-19 impact on student’s quality education in Nepal and social work implication. The findings of the study shows that the COVID-19 has seriously effects on the students learning environment. It showed the huge gap between getting the good education in Nepal. However, Nepal has also made some policies to provide equal quality education to all the children through the ICT and also encourage social work to actively participate on providing education to all the majority of group children in Nepal. Whereas social work applied the micro, messo, and macro level of implication in practice to provide the education for children in remote area of Nepal.

Most European cities are characterized by very large areas, often formed by buildings with low quality, from a series of point of view. The possibility of renovating them is strategic to improve both the quality of life and to the possibility of economic recovery for building companies. In the last decades, the attention of the scientific community has been addressed to the energy renovation, thanks to the strong activities of the European Community in this field. However, since a relevant part of the EC territory is at risk of earthquake, the possibility to combine both energy and seismic renovation actions may be strategic for many countries. In particular, Italy and Romania are linked by a common social tradition that springs from the Roman Empire. Nowadays, this link is stronger, thanks to common interests in social, cultural and business fields. Therefore, the investigation of possible synergies for seismic and energy renovation strategies may be really interesting for both countries. This paper represents the first step in this direction. After an overview of regulations and common practices for buildings with reinforced concrete structures, in both states, some key combined renovation interventions will be described and discussed, as well as advantages and perspectives of integrated renovation approaches.

Lower cutoff levels in screening programs have led to an increase in the proportion of detected cases with transient hypothyroidism, leading to increase of the overall incidence of primary congenital hypothyroidism (CH) in several countries. We have performed retrospective evaluation on the data from 251,008 (96.72%) neonates screened for thyroid-stimulating hormone (TSH) level in dried blood spot specimens taken 48 hours after birth, between 2002 and 2015, using DELFIA method. A TSH value of 15 mIU/L was used as the cutoff point until 2010 and 10 mIU/L thereafter. Primary CH was detected in 127 newborns (1/1976) of which 81.1% had permanent and 18.9% had transient CH. The incidence of primary CH was increased from 1/2489 until to 2010 to 1/1585 thereafter (p=0.131). However, the incidence of permanent CH was slightly increased (p=0.922), while the transient CH incidence had 8-fold increasing after lowering the TSH cutoff level (p<0.001). In cases with permanent CH, we observed lower frequency for thyroid dysgenesis (82.7 vs. 66.7%) and higher frequency for normal in-situ thyroid gland (17.3 vs. 33.3%), for the period with reduced TSH cutoff value. Our findings support the impact of lower TSH cutoff on the increasing incidence of congenital hypothyroidism.

Nizatidine is a gastroprotective drug with a short biological half-life and narrow absorption window. This study aimed at developing floating tablets of nizatidine using various HPMC viscosity grades, namely K4M, E4M, K15 and K200M. Directly compressed tablets revealed an excellent uniformity in hardness, thickness and weight and nizatidine was evenly distributed within the matrix floating tablets. Buoyancy study revealed floating lag time as low as 18–38 s, and tablets remain buoyant for upto 24 h. However, the later depended upon viscosity grade of HPMC and that the higher the viscosity, the less was the total floating time. In vitro dissolution indicated viscosity dependent nizatidine release from the floating tablets. HPMC K4M and E4M based floating tablets released almost 100% drug in 12 h, whilst higher viscosity polymers such as K15 and K200M only released 81.88% and 75.81% drug, respectively. The drug release followed non-Fickian diffusion from tablets formulated with K4M, K15 and K200M, whilst super case II transport was observed with E4M based tablets. More interestingly, K4M and E4M polymers have similar viscosity yet exhibited different drug release mechanism. This was attributed to the difference in degree of substitution of methoxyl- and hydroxypropoxyl- groups on polymer backbone.

Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or ‘multi‐level convergent evolution’. To investigate multi‐level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long‐standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi‐level convergent evolution.

This paper presents a device for measuring and maintaining the constant water level in a tank. The device uses a capacity sensor to measure the water level. The sensor has a DC voltage output proportional to the water level in the tank. This voltage can be used in other automation too. The water level, in percentages, is displayed on a vu meter with 8 LEDs. The circuit for maintaining the constant level consists in a comparator with hysteresis. The level of the water is adjustable, using a reference voltage from a potentiometer (marked in percentages). The level of the water is compared with the prescribed level and the difference between them commands the start/stop of the water pump which supplies the tank through a relay. The device is powered by 220 V and is isolated from the environment.

Mercury has a strong environmental impact since both its organic and inorganic forms are toxic and it represents a pollutant of global concern. Liquid Hg is highly volatile and it can be released during natural and anthropogenic processes in the hydrosphere, biosphere and atmosphere. In this study the distribution of Gaseous Elemental Mercury (GEM) and the total and leached mercury concentrations on paints, plasters, roof tiles, concretes, metals, dust and wood structures were determined in the main buildings and structures of the former Hg-mining area of Abbadia San Salvatore (Siena, Central Italy). The mining complex (divided into 7 units) covers a surface of about 65 ha and contains mining structures and managers and workers buildings. In this work, nine surveys of GEM measurements were carried out from July 2011 to August 2015 for the buildings and structures located in the units 2, 3 and 6. Moreover, detailed measurements were performed in February, April, July, September and December 2016 in the edifices and mining structures of Unit 6. GEM concentrations showed a strong variability in terms of space and time mostly depending on the distance from the building hosting driers, furnaces and condensers and ambient temperature, respectively. In the Unit 2 surveys carried out in the hotter period (from June to September) showed GEM concentrations up to 27,500 ng m⁻³, while in the Unit 6 they were on average much higher and occasionally they saturated the GEM measurement device (>50,000 ng m⁻³). Concentrations of total (in mg kg⁻¹) and leached (in μg L⁻¹) mercury measured in different building materials (up to 46,580 mg kg⁻¹ and 4,470 mg L⁻¹ for total and leached mercury, respectively) showed for the same type of material highly variable values in dependence on the edifice or mining structure from which they were collected. The results obtained in this study are of relevant interest for the operational cleanings to be carried out during the reclamation activities.

Autonomous and cooperative vehicle systems represent a key priority in the automotive realm. Networked driving simulation can be utilized as a safe, cost-effective experimental replica of real traffic environments in order to support and accelerate the development of such systems. In networked driving simulation, different independent systems collaborate to achieve a common task: multi-driver traffic scenario simulation. Yet distinct system complexity levels are necessary to fulfill the requirements of various application scenarios, such as development of vehicle systems, analysis of driving behavior, and training of drivers. With myriad alternatives of available systems and components, developers of networked driving simulation are typically confronted with high design complexity. There are no systematic approaches to date for the design of networked driving simulation in accordance with the specific requirements of the concerned application scenarios. This paper presents a novel design method for networked driving simulation. The method consists mainly of a procedure model that is accompanied by a configuration software. The procedure model includes the necessary phases for the systematic design of application-oriented platforms for networked driving simulation. The configuration software embeds supportive decision-making processes that enable developers to apply the design method and easily create different system models. The design method was validated by generating system models and developing platforms of networked driving simulation for three different application scenarios.

The sea level rise, a consequence of climate change, is one of the biggest challenges that countries and regions with coastal lowland areas will face in the medium term. This study proposes a methodology for assessing the vulnerability to sea level rise (SLR) on the Atlantic coast of Portugal mainland. Some scenarios of extreme sea level for different return periods and extreme flooding events were estimated for 2050 and 2100, as proposed by the European Union Directive 2007/60/EC. A set of physical parameters are considered for the multi-attribute analysis technique implemented by the Analytic Hierarchy Process, in order to define a Physical Vulnerability Index fundamental to assess coastal vulnerability. For each SLR scenario, coastal vulnerability maps, with spatial resolution of 20 m, are produced at national scale to identify areas most at risk of SLR, constituting key documents for triggering adaptation plans for such vulnerable regions. For 2050 and 2100, it is estimated 903 km² and 1146 km² of vulnerable area, respectively, being the district of Lisbon the most vulnerable district in both scenarios. Results are available through a Web Map Service, for Portuguese public entities, and through a web map viewer for public and communities in general.

Fibrin rich of platelets (PRF^®) of Choukroun represents a new step in the therapeutic concept of platelet gel with a simplified processing and biochemical changes little artificial. A valid method of preparation of the PRF must effectively separate the plates by erythrocytes and concentrate without damaging or lysing the plates themselves. In this study the experimental design is to standardize the production of L-PRF in horse directing it to human production. Our hypothesis is that the L-PRF is easy to produce in the horse, without modifications of the human protocol, thus allowing a better standardization of the human protocol. A new device for the preparation and the standardization of L-PRF clots and membranes is the L-PRF Wound Box^®. The optical microscopy, most cell bodies were highlighted concentrated in the proximal portion of each membrane, the last 1/4 was observed at the center; the distal part had only residual traces of cell bodies. The L-PRF will form constantly when the phases described above are strictly adhered to. The success of the art L-PRF depends entirely on the speed of blood collection and transfer in centrifuge within a minute and by a temperature of centrifugation and compression is higher than 21°C (between 21 and 30°C). Our experiments on the horse will no doubt be able to improve our understanding on wound healing, in particularly in chronic skin lesions therapy.

We present observations from evaluation of internal environmental quality of industrial halls with priority on daylighting in combination with the integral lighting. The physical parameters related to indoor lighting in large industrial halls in winter and summer periods were analyzed using in situ measurements and computational methods. These are part of a comprehensive research on indoor environmental quality of industrial halls with the aims of saving energy and providing a comfortable environment for the workers while improving the productivity. The results showed that the procedures used for evaluation of residential or office buildings may not be used for industrial buildings. We also observed that the criteria of occupants’ comforts for indoor industrial buildings may differ from those of other kinds of buildings. Based on these results, an adequate attention is required for designing the industrial buildings. For this reason, appropriate evaluation methods and criteria should be created. We found the measured values of daylight factor very close to the skylight component of the total illumination. The skylight component was observed on average 30% that of the measured daylight factor values. Although the daylight is not emphasized when designing the industrial buildings and its contribution is small, but it is very important for the workers psychology and physiology. The workers must feel a connection with the exterior environment; otherwise, their productivity decreases.

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill-merge-spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level-set method based on graph theory. The proposed level-set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level-set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine-resolution LiDAR-derived DEMs show that the raster-based level-set algorithm is much more efficient (~150 times faster) than the vector-based contour tree method. The proposed level-set algorithm has great potential for being applied to large-scale ecohydrological analysis and watershed modeling.

Community-level wastewater monitoring for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA has demonstrated useful correlation with both coronavirus disease 2019 (COVID-19) case numbers and clinical testing positivity. Wastewater monitoring on college campuses has demonstrated promising predictive capacity for the presence and absence of COVID-19 cases. However, to date, such monitoring has largely relied upon composite or grab samples and reverse transcription quantitative PCR (RT-qPCR) techniques, which limits the accessibility and scalability of wastewater monitoring. In this study, we piloted a workflow that uses tampons as passive swabs for collection and reverse transcription loop-mediated isothermal amplification (RT-LAMP) to detect SARS-CoV-2 RNA in wastewater. Results for the developed workflow were available same day, with a time to result following tampon swab collection of approximately three hours. The RT-LAMP 95% limit of detection (76 gene copies reaction-1) was greater than RT-droplet digital PCR (ddPCR; 3.3 gene copies reaction-1). Nonetheless, during a building-level wastewater monitoring campaign conducted in the midst of weekly clinical testing of all students, the workflow demonstrated a same-day positive predictive value (PPV) of 33% and negative predictive value (NPV) of 80% for incident COVID-19 cases. The NPV is comparable to that reported by wastewater monitoring using RT-qPCR. These observations suggest that even with lower analytical sensitivity the tampon swab and RT-LAMP workflow offers a cost-effective and rapid approach that could be leveraged for scalable same-day building-level wastewater monitoring for COVID-19.

This paper highlights the problems associated with daylight use in industrial facilities. In a case study of a multi-story textile factory, we report how to evaluate daylight (as part of integral light) in the production halls marked F and G. This study follows the article in the Buildings journal, where Hall E was evaluated (unilateral daylight). These two additional halls have large areas that are 54 × 54 meters and are more than 5 meters high. The daylight is only on the side through the attached windows in envelope structures in the vertical position. In this paper, we want to present two case studies of these two production halls in a textile factory in the eastern part of Slovakia. These are halls that are illuminated by daylight from two sides through exterior peripheral walls that are against or next to each other. The results of the case studies can be applied in similar production halls illuminated by a ‘double-sided’ (bilateral) daylight system. This means that they are illuminated by natural illumination through windows on two sides in a vertical position. Such a situation is typical for multi-storied industrial buildings. The proposed approximate calculation method for the daylight factor can be used to predict the daylight in similar spaces in other similar buildings.

Smart buildings concept aims at the use of the smart technology to reduce energy consumption as well as improvement of the comfort conditions and users’ satisfaction. It is based on the use of smart sensors to follow both outdoor and indoor conditions as well as software for the control of comfort and security devices. The optimal control of the energy devices requires software for indoor temperature forecasting. This paper presents an ANN – based model for the indoor temperature forecasting. The model is developed using data recoded in an old building of the engineering school Polytech’Lille. Data covered both indoor and outdoor conditions. Analysis of the relevance of the input parameters allowed to develop a simplified forecasting model of the indoor temperature that uses only the outdoor temperature as well as the history of the façade temperature as input parameters. The paper presents successively, data collection, the ANN concept used in the temperature forecasting, and finally the ANN model developed for the façade and indoor forecasting. It shows that an ANN-based model using outdoor and façade temperature sensors provides a good forecasting of the indoor temperature. This model could be used for the optimal control of buildings energy devices.

Ventilation in cities is crucial for the well being of their inhabitants. Therefore, local governments require air ventilation assessments (AVAs) prior to the construction of new buildings. In a standard AVA, however, only neutral stratification is considered, although diabatic and particularly unstable conditions may be observed more frequently in nature. The results presented here indicate significant changes in ventilation within most of the area of Kowloon City, Hong Kong, included in the study. A new definition for calculating ventilation was introduced, and used to compare the influence of buildings on ventilation under conditions of neutral and unstable stratification. The overall ventilation increased due to enhanced vertical mixing. In the vicinity of exposed buildings, however, ventilation was weaker for unstable stratification than for neutral stratification. The influence on ventilation by building parameters, such as the plan area index, was altered when unstable stratification was considered. Consequently, differences in stratification were shown to have marked effects on ventilation estimates, which should be taken into consideration in future AVAs.

Estimates of extreme water level return periods in river systems are crucial for hydraulic engineering design and planning. Recorded historical water level data of Taiwan’s rivers are not long enough for traditional frequency analyses when predicting extreme water levels for different return periods. In this study, the integration of a one-dimensional flash flood routing hydrodynamic model with the Monte Carlo simulation was developed to predict extreme water levels in the Danshuei River system of northern Taiwan. The numerical model was calibrated and verified with observed water levels using four typhoon events. The results indicated a reasonable agreement between the model simulation and observation data. Seven parameters, including the astronomical tide and surge height at the mouth of the Danshuei River and the river discharge at five gauge stations, were adopted to calculate the joint probability and generate stochastic scenarios via the Monte Carlo simulation. The validated hydrodynamic model driven by the stochastic scenarios was then used to simulate extreme water levels for further frequency analysis. The design water level was estimated using different probability distributions in the frequency analysis at five stations. The design high-water levels for a 200-year return period at Guandu Bridge, Taipei Bridge, Hsin-Hai Bridge, Da-Zhi Bridge, and Chung-Cheng Bridge were 2.90 m, 5.13 m, 6.38 m, 6.05 m, and 9.94 m, respectively. The estimated design water levels plus the freeboard are proposed and recommended for further engineering design and planning.

Thresholding is considered as a statistical-decision making theory which can lessen the average error incurred in allocating pixels to two or more groups. The traditional Bayes decision rule can be applied with the prior knowledge of the Probability Density Function (PDF) of each class. It is surmised that a threshold resulting in the best class separation is the optimal one. In this paper, Otsu’s thresholding for image segmentation has been implemented. The well-known Otsu’s method is to learn a threshold that can maximize the between-class variance or equivalently make light of the within-class variance of the entire image. At first, a color image of a tree is taken. After that, the image is transformed into a grayscale image. Then in the first part, two-level thresholding is conducted, and later on, three-level thresholding is also applied. Again, two-level thresholding, as well as three level thresholding, are also applied to some other images. Finally, the comparison is made between two level thresholding and three level thresholding

Recent technological improvements have made abundant changes in construction industry. In specific, some technical applications, such as Building Information Modeling (BIM) opens up many possibilities. Some studies have articulated the use of BIM and its advantages in construction, but most of them are theoretical, not practical. This study is to provide an insight to such obstacles in BIM research. By investigating a real project that could utilize BIM in planning and construction phases, the authors try to investigate a possible outline of advantages in BIM implementation. The study area was set to a railway construction site in South Korea. The site covers a multiple railway tracks, stations, telecommunication facilities, infrastructure facilities, railway structures, and so numerous. In the site, the authors have identified 12 errors in 7 projects that could be prevented if BIM was utilized before the construction. The total upfront costs required to provide a BIM for 7 projects were $116,348. On the other hand, the total costs required to fix the errors in 7 projects were $166,486. This can be regarded as the benefits of using BIM because if BIM was implemented then the associated errors are easily replaced. Therefore, the benefit-cost ratio can be estimated as 1.32 for one-month delay and 1.36 for a three-month delay.

Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed n-level phase shift keying (n-PSK) optical fiber communication systems, involving a one-tap normalized least-mean-square (LMS) algorithm, a block-wise average algorithm, and a Viterbi-Viterbi algorithm. The analytical expressions for evaluating the estimated carrier phase and for predicting the bit-error-rate (BER) performance (such as the BER floors) have been presented and discussed in the n-PSK coherent optical transmission systems by considering both the laser phase noise and the equalization enhanced phase noise. The results indicate that the Viterbi-Viterbi carrier phase recovery algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS algorithm is more sensitive to the level of modulation formats.

This paper deals with a non-contact method to identify the aerodynamic propeller parameters of the Parrot AR.Drone quadrotor. The experimental set consists in a camera recording the vehicle flights, the audio signal is extracted and is used a spectrogram analysis to estimates the propeller velocity. First, the aerial vehicle takes off and starts a hovering maneuver. The experiment is repeated with different additional masses attached to its rigid body. If the weight over the UAV increases/decreases, then the propeller must rotates faster/slower to produce a higher/lower thrust, and consequently, the sound frequency increases/decreases. Finally, this proposition is validated experimentally, and the estimated velocity is used to identify the quadrotor thrust parameters.

Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environments under rapid urban expansion. Current MODIS data is, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data can’t explore temporally the refined analysis due to the low temporal resolution. In order to resolve this situation, we used MODIS and Landsat data to generate “Landsat-like” data by using the flexible spatiotemporal data fusion method (FSDAF), and then studied spatiotemporal variation of land surface temperature (LST) and its driving factors. The results showed that 1) The estimated “Landsat-like” data have high precision; 2) By comparing 2013 and 2016 datasets, LST increases ranging from 1.8°C to 4°C were measurable in areas where the impervious surface area (ISA) increased, while LST decreases ranging from -3.52°C to -0.70°C were detected in areas where ISA decreased; 3) LST has a strongly negative relationship with the Normalized Difference Vegetation Index (NDVI), and a strongly positive relationship with Normalized Difference Built Index (NDBI) in summer; and 4) LST is well correlated with Building density (BD), in a complex conic mode, and LST may increase by 0.460°C to 0.786°C when BD increases by 0.1. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

At present European buildings typically consume two to five times more energy than predicted at the design stage. An important cause of this performance gap is the discrepancies between the design specification and the As-Built condition. Such discrepancies are mainly due to the gaps in knowhow between design, production and construction professionals. Design is more and more contained into a virtual environment and loses touch with the physical production and construction sites. As the construction sector enters the Industry 4.0 era, Building Information Modelling (BIM) based Mixed Reality can intertwine virtual and real worlds to bridge the knowhow gaps.

This study aims to understand the current state of research in urban resilience and to open a discussion about multi-level perspectives for this concept. Starting with the history of the concept of resilience, we identify three main stages in resilience concept’s evolution: conceptualization, contextualization and operationalization. Confusion occurs between sustainability and resilience, therefore we clearly separate these two concepts by creating conceptual maps. Such maps also underline the specificities of urban and regional resilience discourses. We illustrate that urban resilience research, operating within intra-urban processes, is oriented towards natural disasters, while regional resilience research, operating mostly within inter-urban processes, is oriented towards economic shocks. We show that these two approaches to resilience – urban and regional – are complementary, and we propose to integrate them into a multi-level perspective. By combining these two discourses, we propose a multi-level approach to urban resilience that takes into account both top-down and bottom-up resistance processes. In the discussion section, we propose to take the panarchy perspective as a theoretical framework for multi-level urban resilience, that explains the interactions between different levels through adaptive cycles, relationships between which can help to explain urban resilience.

In networked driving simulation, two or more human drivers participate interactively within a shared virtual environment. Thereby, typical applications of driving simulation can be extended to consider multi-driver scenarios, where a much closer approximation of reality with its unpredictability is achieved. However, the utilized network is typically prone to a considerable amount of message traffic. In addition to restricted system scalability, the resulting degradation of network performance leads to invalid simulation outcomes or unacceptable system behavior. High-level architecture (HLA) is an IEEE standard for networked simulation. Data distribution management (DDM) is one of the service groups provided by HLA standard. The aim of the DDM service is to reduce network traffic and to save effort required to process unnecessary received data. However, different approaches for current DDM implementations show major drawbacks in terms of utilization complexity, inefficiency, and yet added network overhead. This paper presents a concept of an interest manager that takes over the DDM functionality and avoids these drawbacks. Simulation data is exchanged between the participating driving simulators only when it is necessary according to the driving situations. The concept is validated by analyzing the network load of two driving maneuvers with and without the interest manager.

Coastal inundation is an increasing problem. Sea-level rise will greatly increase the frequency and depth of inundation, forcing vulnerable communities to adapt. Communities will need to decide when and how to adapt. The process of decision-making along adaptive pathways is now being used internationally to plan for adaptation over time by anticipating decision points in the future however it unfolds. This process requires risk and uncertainty considerations to be transparent in the scenarios used in such planning. We outline a framework for uncertainty identification and management within coastal hazard assessments which recognizes different types of decision and identifies the types of uncertainty that must be accounted for, such as statistical, scenario and deep uncertainty types. We show how coastal-inundation hazard can be mapped and presented in a way that clearly separates sources of uncertainty, so that they are transparent within a dynamic adaptive pathways planning process. Traditional coastal inundation maps show inundated area only. We present maps of inundation depth and frequency which clearly show the degree of exposure, where that exposure occurs, and how much sea-level rise can be tolerated. The new uncertainty framework and mapping techniques can better identify decision points and their expected time range, which provides more useful input to the adaptation process than traditional coastal inundation assessments.

Topographic mapping using stereo plotting is not effective because it takes much time and labor-intensive. Thus, this research was conducted to find the effective way to extract building footprint for mapping acceleration. Building extraction method in this process comprises four steps: ground / non-ground filtering, building classification, segmentation, and building extraction. Non-ground points from filtering process were classified as building with the algorithm based on multi-scale local dimensionality to separate points at the maximum separability plane. Segmentation using segment growing was used to separate each building, so edge detection could be conducted for each segment to create boundary of each building. Lastly, building extraction was conducted through three steps: edge points detection, building delineation, and building regularization. With 10 samples and step 0.5, classification resulted quality and miss factor of 0.597 and 0.524, respectively. The quality was improved by segmentation process to 0.604, while miss factor was getting worse to 0.561. Meanwhile, on average shape index value from extracted building had 0.02 difference and the number of errors was 30% for line segment comparison. Regarding positional accuracy using centroid accuracy assessment, this method could produce RMSE of 1.169 meters.

The grid nudging technique is often used in regional climate dynamical downscaling to make the simulated large-scale fields consistent with the driving fields. In this study, we focused on two specific questions about grid nudging: (1) which nudged variable had a larger impact on the downscaling results and (2) what was the “optimal” grid nudging strategy for each nudged variable to achieve better downscaling result during summer over the Chinese mainland. To solve this queries, 41 3-month long simulations for the summer of 2009 and 2010 were performed using the Weather Research and Forecasting model (WRF) to downscale National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data to a 30-km horizontal resolution. The results showed that nudging horizontal wind or temperature had significant influence on the simulation of almost all conventional meteorological elements; nudging water vapor mainly affected the precipitation, humidity, and 500 hPa temperature. Moreover, the optimum nudging scheme varied with simulated regions and layers. As a whole, the optimal nudging time was one hour or three hours for nudging wind, three hours for nudging temperature, and one hour for nudging water vapor. The optimal nudged level was above the planetary boundary layer for almost every nudged variable.

Cloud computing has become a prominent technology due to its important utility service; this service concentrates on outsourcing data to organizations and individual consumers. Cloud computing has considerably changed the manner in which individuals or organizations store, retrieve, and organize their personal information. Despite the manifest development in cloud computing, there are still some concerns regarding the level of security and issues related to adopting cloud computing that prevent users from fully trusting this useful technology. Hence, for the sake of reinforcing the trust between Cloud Clients (CC) and Cloud Service Providers (CSP), as well as safeguarding the CC’s data in the cloud, several security paradigms of cloud computing based on a Third-Party Auditor (TPA) have been introduced. The TPA, as a trusted party, is responsible for checking the integrity of the CC’s data and all the critical information associated with it. However, the TPA could become an adversary and could aim to deteriorate the privacy of the CC’s data by playing a malicious role. In this paper, we present the state-of-art of cloud computing’s privacy-preserving models (PPM) based on a TPA. Three TPA factors of paramount significance have been discussed: TPA involvement, security requirements, and security threats caused by vulnerabilities. Moreover, TPA’s privacy preserving models have been comprehensively analyzed and categorized into different classes with an emphasis on their dynamicity. Finally, we discuss the limitations of the models and present our recommendations for their improvement.

The present article is dealing with the state of question about building-energy monitoring systems used for data collection to estimate the Heat Lost Coefficient (HLC) with existing methods and so determinate buildings’ Envelope Energy Performance (EEP). In addition, the data requirements of HLC estimation methods are related with commonly used methods for fault detection, calibration and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests since 1978, a qualitative and quantitative analysis of Monitoring and Controlling System (MCS) specifications has been carried out. Although most actual Buildings Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply the HLC estimation methods.

As is known, the construction industry has one of the highest occupational accidents incidence rates among all economic sectors. Currently, Building Information Modeling (BIM) appears as a tool that addresses occupational safety issues throughout the construction life cycle projects, avoiding hazards and risks and, consequently, increasing safety. This work investigates BIM methodology and related technologies application for building safety planning in construction and demonstrates the potential of this technology for the integrated implementation of safety measures during the design phase and the construction site management. The first step consisted of a literature review on the application of BIM in safety in the design and planning phases. Following, to show the potentialities of construction simulation, a case study based on BIM 4D to prevent falls from height was developed. With BIM 4D, it is possible to follow the construction process over time, giving the construction safety technicians’, designers, supervisors and managers the capability to analyse, in each phase, the potential risks and which safety measures should be implemented. BIM can effectively integrate safety measures from the design phase to the construction and use phase and do integrated safety planning within construction planning, leading to reliable safety management throughout the construction process.

This study evaluated the effect of low-level laser irradiation induces by diode laser on the speed of orthodontic tooth movement of canines submitted to initial retraction. Twenty-four mandibular canines were retracted by using NiTi spring (force of 150 g/side). Thirteen of those were irradiated with a diode laser 980-nm diode laser (Wiser Laser Doctor Smile, Lambda) operating at an 810-nm wavelength (1 W of output power, continuous wave of 66.7 J/cm2) that was equipped with a 0.6-mm optical fiber in continuous-wave mode. The canine retraction was accomplished by using prefabricated coil springs. The right of the mandible was chosen to be irradiated with the laser, whereas the left side was considered the control without laser irradiation. The laser was applied with 0-, 3-, 7-, and 14-day intervals. The amount of canine retraction was measured with a digital electronic caliper while the pain level was prompted by a patient questionnaire. The speed of tooth movement was significantly greater in the laser group than in the control group. The pain intensity was also at a lower level in the laser group. Our findings suggest that diode laser therapy can highly accelerate tooth movement during orthodontic treatment and can also effectively reduce pain level.

In this research paper we describe the development phase of a low-cost LoRa IoT solution applied to a kindergarten school with three years results. A set of sensors solution was developed in a LoRa communication board, battery powered, providing a simplified setup process. These sensors were used in order to measure temperature, humidity, luminosity, air quality and presence. Also, energy monitor solutions were integrated. The acquired data is transmitted and analysed for knowledge extraction, identifying savings and other related KPIs. From data, automatic saving actions were performed towards heating and cooling systems, lighting and a set of if-then actions were developed for automatic cost-saving actions, based on infrared signals to heating/cooling systems using some procedure of external command devices. This approach avoids the usage of proprietary vendor solutions in a flexible approach that can easily be deployed to any building facility. This is an important achievement since most of the building consumption is based on heating and cooling systems. In a three years test of the solution, the total energy consumption savings surpassed 20%

Electrification coverage in Sarawak is the lowest at 78.74%, compared to Peninsular Malaysia at 99.62% and Sabah at 82.51%. Kapit, Sarawak with its 88.4% populations located in rural areas and mostly situated along the main riverbanks has great potential to generate electrical energy by hydrokinetic system. Yearly water velocity data is the most significant parameter to perform hydrokinetic analysis study. Nevertheless, the data retrieved from local river databases are inadequate for river energy analysis, thus hindering its progression. Instead, flow rates and rainfall data had been utilised to estimate the water velocity data. This signifies no estimation of water velocity in an unregulated river by using water level data had been made. Therefore, a novel technique of estimating the daily average water velocity data in unregulated rivers is proposed. The modelling of regression equation for water velocity estimation was performed and two regression model equations were generated to estimate both water level and water velocity on-site and proven to be valid as the coefficient of determination values had been R² = 87.4% and R²=87.9%, respectively. The combination of both regression model equations can be used to estimate long-term time series water velocity data for type-C unregulated river in remote areas.

Many reported sea level records are often not a single measurement. They are then a composition of different records from several tide gauges. Sometimes, they are from the same tide gauge, but the tide gauge stability has been affected by earthquakes. This is the case of Guam as discussed in the present manuscript. The claimed sea level acceleration of Guam is only the result of two earthquakes that have compromised the stability of the tide gauge.

The conservation of coastal wetland ecosystems, like mangrove forests and salt marshes, represents a critical strategy for mitigating atmospheric emissions and climate change in the 21st century. Yet the existence of these environments is threatened by human-induced disturbances, namely deforestation and accelerated sea-level rise. Coastal systems maintain surface elevation in response to sea-level rise through a combination of physical and biological processes both above and below the ground surface. The quantification and relative contribution of belowground process controls (e.g., seasonal water content, organic matter decomposition) on surface elevation change is largely unexplored but crucial for informing coastal ecosystem sustainability. To address this knowledge deficit, we integrated measurements of surface elevation change of the live root zone (0.5 to 1 m depth) with geotechnical data from co-located sediment cores in the Sundarbans mangrove forest (SMF) of southwest Bangladesh. Core data reveal that the primary belowground controls on surface elevation change include seasonal fluctuations in pore-water content and the relative abundance of fine-grained sediments capable of volumetric expansion and contraction, supporting an elevation gain of ~2.42 ± 0.26 cm yr⁻¹. In contrast to many mangrove environments, the soils of the SMF contain little organic matter and are dominantly composed (>90%) of inorganic clastic sediments. The mineral-rich soil texture likely leads to less compaction-induced subsidence as compared to organic-rich substrates and facilitates surface equilibrium in response to sea level rise. Despite a relatively high soil bulk density, soil carbon (C) density of the SMF is very low owing to the dearth of preserved organic content. However, rates of C accumulation are balanced out by locally high accretion rates, rendering the SMF a greater sink of terrestrial C than the worldwide mangrove average. The findings of this study demonstrate that C accumulation in the SMF, and possibly other alluvial mangrove forests, is highly dependent on the continued delivery of sediment to the mangrove platform and associated settings.

The main objective of this paper is to find a close link between the adaptive level of significance, presented here, and the sample size. We, statisticians, know of the inconsistency, or paradox, in the current classical tests of significance that are based on p-value statistics that is compared to the canonical significance levels (10%, 5% and 1%): "Raise the sample to reject the null hypothesis" is the recommendation of some ill-advised scientists! This paper will show that it is possible to eliminate this problem of significance tests. The Bayesian Lindley's paradox – "increase the sample to accept the hypothesis" – also disappears. Obviously, we present here only the beginning of a possible prominent research. The intention is to extend its use to more complex applications such as survival analysis, reliability tests and other areas. The main tools used here are the Bayes Factor and the extended Neyman-Pearson Lemma.

High-Rise buildings with their particular features can affects on surrounding environment and makes new microclimates. In the windy conditions, the spaces that are between building blocks changes to passages and affects on the wind velocity, intensity and it’s other parameters.The importance of this effect is different in each level of building height. The Pedestrian-Level is the lowest and one of important areas. Markets, playgrounds and pedestrian access had located in this area and any unwanted microclimate changes like high velocity and turbulence in this level can makes discomfort and dangerous condition for residents. So this research tries to consider the pedestrian- level wind comfort in some High-Rise building complexes arrangement that had located in Tehran district 22 with Computational Fluid Dynamics (CFD) modeling and reaching to a suitable arrangement pattern. It had collected the required data through field study and librarian databases and then compared them with standard guidelines and analyzed them by comparative comparison method. As a result a linear arrangement that placed crossover to wind direction for providing wind comfort and preventing wind danger is been suggested in this region.

The paper proposes TOPSIS method for solving multi-objective multi-level programming problem (MO-MLPP) with fuzzy parameters via fuzzy goal programming (FGP). At first, - cut method is used to transform the fuzzily described MO-MLPP into deterministic MO-MLPP. Then, for specific , we construct the membership functions of distance functions from positive ideal solution (PIS) and negative ideal solution (NIS) of all level decision makers (DMs). Thereafter, FGP based multi-objective decision model is established for each level DM for obtaining individual optimal solution. A possible relaxation on decisions for all DMs is taken into account for satisfactory solution. Subsequently, two FGP models are developed and compromise optimal solutions are found by minimizing the sum of negative deviational variables. To recognize the better compromise optimal solution, the concept of distance functions is utilized. Finally, a novel algorithm for MO-MLPP involving fuzzy parameters is provided and an illustrative example is solved to verify the proposed procedure.

The popular motor drive systems with a single two-level voltage source inverter (VSI) have one main problem that is the occurrence of the common-mode voltage (CMV), which is an effect of the electromagnetic interference, shaft voltage, bearing currents, leakage current. These cause the high stress, increasung temperature and early mechanical failure in machine. To overcome this problem, the technology of the dual two-level VSI fed open-end three-phase ac loads is now available to eliminate the CMV at the ac/induction motor load with the 120-degree modulation technique for controlling each inverter. In this paper, the discontinuous space vector modulation (DSVM) schemes are proposed and applied for the dual two-level VSI fed open-end load. It is based on the 120-degree modulation technique by using only 12 active voltage vectors and the 10 zero voltage vectors from the total 64 voltage vectors along with the different five-segment swicthing sequence designs with centralizing pulse width modulation technqiue in order to not only cancel the CMV in the ac load, but also reduce the switching number/switching loss of the conversion system. Among the various DSVM schemes, their performances are compared in this paper, such as the number of the switching, the step and peak value of the CMV in each inverter, and the quality of the output waveform, etc. The details of the verfication and comparison are carried out by simulation using Matlab/Simulink software.

The building sector has a big potential to reduce the material resource demand needed for building construction and therefore, greenhouse gas (GHG) emissions. Digitalisation can help to make use of this potential and improve sustainability throughout the entire building’s life cycle. One way to address this potential is through the integration of Life-Cycle Assessment (LCA) into the building process by employing Building Information Modelling (BIM). BIM can reduce the effort needed to carry out an LCA and therefore facilitate the integration into the building process. A review of current industry practice and scientific literature shows two main approaches to address BIM-LCA integration. Either the LCA is performed in a simplified way at the beginning of the building process, or it is done at the very end when all the needed information is available, but it is too late for decision-making. One reason for this is the lack of methods, workflows and tools to implement BIM-LCA integration over the entire building process. Therefore, the main objective of this study is to develop an integrated BIM-LCA workflow implemented into a method for the whole building process using an existing structure for cost estimation. A tool is created and used in a case study in Switzerland to test the developed approach. The results of this study show that LCA can be performed continuously in each building phase over the entire building process using existing BIM modelling techniques. The main benefit of this approach is that the re-work caused by the need for re-entering data and the usage of many different software tools that characterise most of the current LCA practices is minimised. Furthermore, decision-making, both at the element and building levels, is supported.

This paper develops a novel energy universal service bus system (EUSBS) based on emerging energy Internet (E-net) technologies. This EUSBS is a unified identification and plug-and-play interface platform to which high penetration distributed energy and equipment (DEE), including photovoltaic (PV), fans, electric vehicle charging stations (EVCSs), energy storage equipment (ESE), and commercial and residential users (CRUs), can access in a coordinated control and optimized utilization mode. First, the functions design, overall framework and topology architecture design of the EUSBS are expounded, among which the EUSBS is mainly composed of a hardware system and a software platform. Moreover, several future application scenarios are presented. Then, the hardware part of EUSBS is designed and developed, including the framework design of this hardware subsystem, and development of the hardware equipment for PV access, fans access, EVCS access, ESE access, and CRU access. The hardware subsystem consists of smart socket, and household/floor/building concentrators. Based on this, the prototypes development of EUSBS hardware equipment is completely demonstrated. Third, the software part of the EUSBS is developed as a cloud service platform for electricity use data analysis of DEE. This software subsystem contains the power quality & energy efficiency analysis module, optimization control module, information and service module, and data monitoring and electricity behavior analysis module. Based on this design, the software interfaces are developed. Finally, an application study on energy management and optimization of a smart commercial building is conducted to evaluate the functions and practicality of this EUSBS. The EUSBS developed in this paper is able to overcome difficulties in big data collection and utilization on sides of distribution network and electricity utilization, and eventually implement a deep information-energy fusion and a friendly supply-demand interaction between the grid and users. This contribution presents a detailed and systematic development scheme of the EUSBS, and moreover, the laboratory prototypes of the hardware and software subsystems have been developed based on E-net technologies. This paper can provide some thoughts and suggestions for the research of active distribution network and comprehensive energy management and optimization in power systems, as well as references and guidance for researchers to carry out research regarding energy management, optimization and coordinated control of the smart buildings.

Ninety percent of the existing building stock in Europe was built before 1990. These buildings are in urgent need for a significant improvement of energy-efficiency through renovation. Regrettably, so far only five percent of renovation projects have been able to yield energy-saving at deep renovation level. State-of-the-art renovation solutions are available, but costly and lengthy renovation processes and incomprehensible technical complexities hinder the achievement of a wide impact at a European scale. This paper presents research on Plug-and-Play (PnP) technologies supported by Building Information Modelling (BIM) to provide affordable, interchangeable and quick-installation solutions to overcome the main barriers of building deep renovation.

Backward electromagnetic waves are extraordinary waves with contra-directed phase velocity and energy flux. Unusual properties of the coherent nonlinear optical coupling of the phase-matched ordinary and backward electromagnetic waves with contra-directed energy fluxes are described which enable greatly-enhanced frequency and propagation direction conversion, parametrical amplification, as well as control of shape of the light pulses. Extraordinary transient processes that emerge in such metamaterials in pulsed regimes are described. The results of the numerical simulation of particular plasmonic metamaterials with hyperbolic dispersion are presented, which prove the possibility to match phases of such coupled guided ordinary and backward electromagnetic waves. Particular properties of the outlined processes in the proposed metamaterial are demonstrated through numerical simulations. Potential applications include ultra-miniature amplifiers, frequency changing reflectors, modulators, pulse shapers, and remotely actuated sensors.

The incorporation of renewable energy into power systems poses serious challenges to the transmission and distribution power system operators (TSOs and DSOs). To fully leverage these resources there is a need for a new market design with improved coordination between TSOs and DSOs. In this paper we propose two coordination schemes between TSOs and DSOs: one centralised and another decentralised that facilitate the integration of distributed based generation; minimise operational cost; relieve congestion; and promote a sustainable system. To this end, we approximate the power equations with linearised equations so that the resulting optimal power flows (OPFs) in both the TSO and DSO become convex optimisation problems. In the resulting decentralised scheme, the TSO and DSO collaborate to optimally allocate all resources in the system. In particular, we propose an iterative bi-level optimisation technique where the upper level is the TSO that solves its own OPF and determines the locational marginal prices at substations. We demonstrate numerically that the algorithm converges to a near optimal solution. We study the interaction of TSOs and DSOs and the existence of any conflicting objectives with the centralised scheme. More specifically, we approximate the Pareto front of the multi-objective optimal power flow problem where the entire system, i.e., transmission and distribution systems, is modelled. The proposed ideas are illustrated through a five bus transmission system connected with distribution systems, represented by the IEEE 33 and 69 bus feeders.

This study aims to suggest a basis for the selection of technologies for developing high-performance buildings to reduce energy consumptions and greenhouse gas emissions. Energy-saving technologies comprising of 15 cases were categorized into passive, active, and renewable energy systems. EnergyPlus v8.8 was used to analyze the contribution of each technology in reducing the primary energy consumptions and CO₂ emissions in the Korean climate. The primary energy consumptions of the base model were 464.1 and 485.1 kWh/m²a in the Incheon and Jeju, respectively, and the CO₂ emissions were 83.4 and 87.4 kgCO₂/m²a, respectively. Each technology (cases 1–15) provided different energy-saving contributions in the Korean climate depending on their characteristics. The heating, cooling, and other energy-saving contributions of each technology indicate that their saving rates can be used when selecting suitable technologies during the cooling and heating seasons. Case 15 (active chilled beam with dedicated outdoor air system + ground source heat pump) showed the highest energy saving rate. In case 15, the Incheon and Jeju models were reduced by 189.4 (59.2%) and 206.2 kWh/m²a (57.4%) compared to the base case, respectively, and the CO₂ emissions were reduced by up to 32.7 (60.8%) and 35.6 kgCO₂/m²a (59.3%), respectively.

A plenty of theories on the origin of genetic codes have been proposed so far, yet all ignored the energetic driving force, its relation to the biochemical system, and most importantly, the missing “matchmaker” between proteins and nucleic acids. Here, a new hypothesis is proposed, according to which ATP is at the origin of the primordial genetic code by driving the coevolution of the genetic code with the pristine biochemical system. This hypothesis aims to show how the genetic code was produced e.g. by photochemical reactions in a protocell that derived from a lipid vesicle enclosing various life’s building blocks (e.g. nucleotides and peptides). At extant cell, ATP is the only energetic product of photosynthesis, and is at the energetic heart of the biochemical systems. ATP could energetically form and elongate chains of both polynucleotides and polypeptides, thus acting a “matchmaker” between these two bio-polymers and eventually mediating precellular biochemical innovation from energy transformation to informatization. ATP was not the only one that could drive the formation of polynucleotides and polypeptides, but favored by precellular selection. The protocell innovated a photosynthesis system to produce ATP efficiently and regularly with the aids of proteins and RNA/DNA. The completion of permanently recording the genetic information by DNA marked the dawn of cellular life operated by Darwinian evolution. The ATP hypothesis assumes or supports the photochemical origin of life, shedding light on the origins of both photosynthetic and biochemical systems, which remains largely unknown thus far. Based on ATP hypothesis, virus (like the new coronavirus) could not be the earliest life on Earth, as it has neither biochemical systems nor lipid bilayer membrane that provided relatively isolated environment for the development of protobiochemical reactions, although it owns the genetic code of a cellular life. Virus could not be a bridge between life and non-life, but is an anti-life substance, as it depletes cellular material for its own replication, and then spreads by destroying the host cells. It can be imagined that if cellular life are completely wiped out by the virus, the complete destruction of life on Earth would be inevitable.

This paper describes a case study of ventilation as well as measured and perceived indoor air quality (IAQ) in a Finnish comprehensive school with a hybrid ventilation system and reported IAQ problems. An operational error was found when investigating the ventilation system that prevented air from coming into classrooms, except for short periods of high carbon dioxide (CO₂) concentrations. However, results indicated that hybrid ventilation system was able to provide adequate ventilation and sufficient IAQ once properly designed and maintained. After ventilation operation was improved, occupants reported less unpleasant odors and stuffy air. The amount of total volatile organic compounds (TVOC) and some single volatile organic compounds (VOCs) decreased. Indoor mycobiota was observed in settled dust in the classrooms, from which ventilation improvement eliminated the dominant, opportunistic human pathogen species Trichoderma citrinoviride found before improvement.

Information regarding the cost of a construction project is available to the investor and project participants in order to determine the subsequent success of a project, given that the information they collect has an impact on the decisions they make. Cost calculations, especially in the initial phase of a project, often generate large errors. This paper presents the new approach based on a combination of the Case Based Reasoning method (CBR) with the originally selected criteria for the description of a construction project (as a result of Pearson correlation coefficient and Spearman's rank correlation coefficient) and Building Information Modeling (BIM) technology. The CBR method fulfils expectations for a simple and fast system supporting the cost estimation process. It does not require any specialist knowledge, so it will be comprehensible to cost estimation practitioners. The BIM-based model gives the opportunity for the calculation of quantity take-offs and enables the use of the information contained in the BIM model in the cost estimation process. In order to prepare the model an appropriate relational database had to be developed. With extensive research, a database of 173 construction projects, including the construction of a sports field, was obtained. There were 14 variables defined originally by authors; however, only 10 (as a result of the correlation analysis) were used for the calculation. Data related to the project were collected in the BIM model. Results estimating the project’s unit price, using the CBR method, were presented and discussed. The Mean Absolute Estimate Error was used to evaluate the model.

With a low rate of new building construction and insufficient rate of existing building renovation, there is the need for stepping up the pace of building renovation with ambitious performance targets to achieve EU climate change policies. However, effective technologies alone cannot solve the low renovation rate of existing buildings in Europe that is hindering the reaching of EU-wide targets. A workshop was held at the Sustainable Place Conference 2018 to present successful experiences with an integrative approach from H2020 innovation actions (4RinEU, P2ENDURE, Pro-GET-OnE, MORE-CONNECT) aiming at improving building energy performance through deep renovation. This article presents the outcomes of the joint workshop and interactive discussion, by focusing on the different technical, financial and social added values, barriers and challenges in building renovation as well as on the identification of open questions to address future innovation opportunities.

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. We propose a method for automated detection and localisation of key building defects from images using deep learning and convolution neural networks. The proposed model is based on a pre-trained VGG-16 classifier with Class Activation Mapping (CAM) for object localisation. The model has proven to be robust and able to accurately detect and localise mould growth, stains, and paint deterioration defects arising from dampness in buildings. The approach is being developed with potentials to scale-up to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is raising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. To solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, Freesurfer MRI segmentation data, and psychological data. For Alzheimer’s disease vs cognitively normal prediction, the random forest classifier provides 100% accuracy. Furthermore, Alzheimer’s disease and non-Alzheimer’s dementia should be classified properly because their symptoms are similar. To the best of our knowledge, we are the first to present a three-class classification on Alzheimer’s disease vs cognitively normal vs non-Alzheimer’s dementia and achieved 99.86% accuracy using an ensemble model. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work..

Published data or available literature on sustainable building plan-design, construction, performance, and renovation criteria have covered some stages or some parts of each stage. These data usually have been published partially in many different papers―there have not been any papers that published these data together. Hence, this paper aims to collectively review these data and publish them together. The collection and review of these data were carried out by our twenty-five team members who specialized in sustainable urban, architectural, and civil engineering and construction management. The gathered and reviewed outputs were combined and validated based on a general group consensus. This consensus decision-making proceeded through two major group meetings with several follow-up meetings. The first major meeting was to combine and improve the gathered reviewed sustainable building criteria for Cambodia. The second major meeting was to validate the improved reviewed sustainable building criteria for Cambodia. The several follow-up meetings were to discuss the relevance and importance of the validated data “criteria and their classifications and descriptions” in all stages and more focused on their importance and applicability to Cambodia. The collective reviewed data in this paper would be useful to researchers in the fields. They could also be useful collective knowledge and information for policymakers from governmental agencies and development partners, particularly for sustainable building and construction companies.

The residential built form, including open space, provides the physical environment for social interaction. Understanding urban open space, including semi-public and public domains, through the lens of physical accessibility and visual permeability can potentially facilitate the building of a sense of community contributing to a better quality of life. Using an inner-city suburb in Perth, Western Australia as a case study, this research explores the importance of physical accessibility patterns and visual permeability for socialising in semi-public and public domains, such as the front yard and the residential streets. It argues that maintaining a balance between public and private inter-relationship in inner city residential neighbourhoods is important for creating and maintaining a sense of community.

Accurate forest reference and emission level (FRL, FREL) with related policies and regulations are the key determinants in establishing sustainable forest ecosystem management programmes (e.g. REDD+). This fundamental is for promoting and sustaining climate smart agricultural practices in a changing climate. With the aim to deliver better knowledge to the scientific community and policy makers on regulations and existing tools for more rigorous scientific communication when it comes to FRL and FREL accountability and policies. Thus, this review investigates forest in the changing climate and policies and underlines the performance of land use transition and intensity analysis towards deforestation with some key examples and achievements (e.g. Togo). Simply put, (i) forest as break of greenhouse gas (GHGs) and ecosystem regulator, (ii) policies and REDD+ actions, (iii) potential drivers and (iv) transition and intensity analysis approach for their accountability are discussed. In sum, impressive studies, policies and regulations are under initiations and implementations regarding the role, place and evaluation of forest losses and its ecosystem functions and services. However, there are still some gaps when it comes to: the choice of the evaluation methods in the real context of a specific ecosystem as well as the firm implementations of formulating policies in developing countries. This paper concludes with some policy measures for forest sustainability, carbon enhancement and accountability.

The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e. areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by ~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in 1) radar altimetry, 2) laser (lidar) altimetry, 3) gravimetry, 4) multispectral optical imagery and, 5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features.

There has been rampant fold-ups, merger and acquisitions occurring in the Ghanaian banking industry. Then, the questions arise: Is the Ghanaian Financial System in Crisis? This study was conducted to find answers to these problems unsolved with prior literature. A sample of seventy customers of the Royal Bank, 8 employees of the Royal Bank and 2 managers of the Royal Bank were selected for a case-survey. The study also monitored the Trend of the Ghanaian Financial System through the reading and monitoring of daily news on the Financial System and reports of banks. The data from the field and the secondary data from news and reports were analysed symmetrically. The study drew on Minsky’s Financial Crisis Theory to explain the phenomenon in the Ghanaian economy and to draw predictions of what would happen in other developing economies. The study found out that: (1) The Ghanaian financial system is fragile and it holds true for most developing economies; (2) The financial system suffers greatly when the confidence level of customers falls significantly; (3) Management information systems raises the confidence level of customers (borrowers and lenders) such that there is a greater fall and impact in times of instability in the economy; (4) The higher the level of MIS adoption in an unstable economy, the more fragile the Financial System becomes and (5) A higher adoption of Management Information Systems in a Fragile Financial System indirectly contributes to Financial Crisis of the Financial System.

This paper presents a novel approach for semantic segmentation of building roof in dense urban environment with Deep Convolution Neural Network (DCNN) using imagery acquired by a Chinese Very High Resolution (VHR) satellite mission, i.e. GaoFen-2 (GF-2). To provide an operational end-to-end work flow for accurate build roof mapping with feature extraction as well as image segmentation, a fully convolutional DCNN with both convolutional and deconvolutional layers is designed to perform the VHR image analysis for labeling pixels. Since the diverse urban patterns and building styles in large areas, sample image data sets of building roof and non-building roof are collected over different metropolitan regions in China. We selected typical cities with dense urban environment in each metropolitan region as study areas for collecting training and test samples. High performance cluster with GPU-mounted workstations is employed to perform the model training and optimization. With the building roof samples collected over different cities, the predictive model with multiple NN layers is developed for building roof labeling. The validation of the building roof map shows that the overall accuracy(OA) and the mean Intersection Over Union( mIOU) of DCNN based segmentation are 94.67%, 0.85 respectively, while CRF-refined segmentation achieved OA of 94.69% and mIOU of 0.83. The results suggest that the proposed approach is a promising solution for building roof mapping with VHR images over large areas across different urban and building patterns. With the operational acquisition of GF2 VHR imagery, it is expected to develop an automated pipeline for operational built-up area monitoring and timely update of building roof map over large areas.

In the recent decade, Building Information Modelling (BIM) has widely been adopted in the Architecture, Engineering and Construction (AEC) industry and completely upended the way we build. While BIM continues gain momentum in the industry, it has also attracted increasing attentions from researchers. However, most of the current study focuses on reviewing BIM for management, BIM for green building, BIM for infrastructure and BIM for Facilities Management (FM). There are few studies about Global BIM review and to discuss their complex inter-connections. In this study, we adopted a scientometric analysis method to review global BIM research from 2004-2019. A total of 1455 scholarly bibliographic records obtained from Web of Science Core Collection databased were established for the analysis. This study has identified the top productive and influential researchers, research institutes, regions/countries, subject categories and journals in the BIM field. In addition, 11 clusters of Global BIM research were also identified including construction project, green BIM, construction safety planning, automated IFC-based workflow and so on. Authors distinguished 11 clusters of global BIM research into 3 stages, namely formulating stage, accelerating stage and transforming stage. Furthermore, authors reviewed the BIM policy of Singapore and observed there is a co-production relationship between evolution of BIM policy and global BIM research. These findings provide valuable information for researchers, practitioners and policy makers by visualizing the current progress in the research field of BIM and highlighting future research needs.

Although the existing paradigm states that CMV reactivation is under control of cellular immune response, the role of humoral counterpart is underestimated. Anti-CMV positive woman after conditioning with Bu-Flu-ATG underwent stem cell transplantation from fully matched, seronegative sibling donor. In immunodeficient recipient fast and significant decrease of specific immune response was observed but reconstitution of marrow-derived B and NK cells was prior thymic origin T cells. The lowest CMV-IgG(89 U/ml) was observed just before CMV viremia. Noteworthy, the sole and exclusive factor of CMV-specific immune response is a residual recipient antibody class IgG. The CMV-quantiferon increase was observed later, but in the first phase immune reconstitution of the PHA-induced IFNγ release was significantly lower than that CMV-induced. It corresponds with NK cells increase at the top of lymphocyte reconstitution and undetected CMV-specific CD8 cells by pentamer technique. Most of NK cells are CD16+, thus are stimulated by residual IgG. In immunocompetent donor the cellular and humoral immune response increases in parallel manner but symptoms of CMV mononucleosis were observed till the increase of specific IgG. Our observations shed light on a unique host-CMV interaction: indicate that significant decrease of CMV-IgG is a good predictor for CMV reactivation during secondary immunodeficiency.

Existing methods for building extraction from remotely sensed images strongly rely on aerial or satellite-based images with very high resolution, which are usually limited by spatiotemporally accessibility and cost. In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction. In this paper, based on image super-resolution and segmentation techniques, we propose a two-stage framework (SRBuildingSeg) for achieving super-resolution (SR) building extraction using relatively low-resolution remotely sensed images. SRBuildingSeg can fully utilize inherent information from the given low-resolution images to achieve high-resolution building extraction. In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware super-resolution module (EASR) to improve the perceptional features, following the dual-encoder building segmentation module (DES). Both qualitative and quantitative experimental results demonstrate that our proposed approach is capable of achieving high-resolution (e.g. 0.5 m) building extraction results at 2×, 4× and 8× SR. Our approach outperforms 8 other methods with respect to the extraction result of mean Intersection over Union (mIoU) values by a ratio of 9.38%, 8.20% and 7.89% with SR ratio factors of 2, 4, and 8, respectively. The results indicate that the edges and borders reconstructed in super-resolved images serve a pivotal role in subsequent building extraction and reveal the potential of the proposed approach to achieve super-resolution building extraction. Our code is available at https://github.com/xian1234/SRBuildSeg.

The devastating COVID-19 pandemic forced academia to go virtual. Educational institutions around the world have stressed online learning programs in the aftermath of the pandemic. However, because of insufficient access to ICT, a substantial number of students failed to harness the opportunity of online learning. This study explores the latent digital divide exhibited during the COVID-19 pandemic while online learning activities are emphasized among Bangladeshi students. It also investigates the digital divide exposure and the significant underlying drivers of the divide. A cross-sectional survey was employed to collect quantitative data mixed with open-ended questions to collect qualitative information from the student community. The findings revealed that despite the majority of students have physical access to ICT but only 32.5% of students could attend online classes seamlessly, 34.1% of the students reported the data prices as the critical barrier, and 39.8% of students identified the poor network infrastructure is the significant barrier for them to participate in online learning activities. Although most students possess physical access to the device and the Internet, they face the first-level digital divide due to the quality of access and maintaining subscriptions. Consequently, they fail to take advantage of physical access, resulting in the third-level digital divide (Utility Gap) and submerging them into a digital divide cycle. This paper aimed to explore the underlying issues of the digital divide among Bangladeshi students to assist relevant stakeholders (e.g., the Bangladesh government, Educational Institutions, Researchers) in providing the necessary insights and theoretical understanding to arrange adequate support for students to undertake conducive online learning activities.

This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach

Despite recent developments in monitoring and visualizing construction progress, the data exchange between construction jobsite and office lacks automation and real-time recording. To address this issue, a near real-time construction work inspection system called iVR is proposed; this system integrates 3D scanning, extended reality, and visual programming to visualize interactive onsite inspection for indoor activities and provide numeric data. iVR comprises five modules: iVR-location finder (finding laser scanner located in the construction site) iVR-scan (capture point cloud data of jobsite indoor activity), iVR-prepare (processes and convert 3D scan data into a 3D model), iVR-inspect (conduct immersive visual reality inspection in construction office), and iVR-feedback (visualize inspection feedback from jobsite using augmented reality). An experimental lab test is conducted to validate the applicability of iVR process; it successfully exchanges required information between construction jobsite and office in a specific time. This system is expected to assist Engineers and workers in quality assessment, progress assessments, and decision-making which can realize a productive and practical communication platform, unlike conventional monitoring or data capturing, processing, and storage methods, which involve storage, compatibility, and time-consumption issues.

Internet of Things (IoT) provides a huge business value for customers, organizations, and governments due to the developments of so many applications in different sectors like energy and healthcare. Nevertheless, as a new emerging technology, IoT faces several security concerns that are more challenging than conventional Internet because of its limited resources as well as its complex ecosystem. Toward this end, we first highlight IoT security challenges and briefly discuss its security goals like confidentiality and integrity. Second, we discuss the most common attacks against IoT, along with their violated security goals. We also review the existing frameworks of security and privacy guidelines for IoT and illustrate their shortcomings. Third, we propose a novel framework for securing IoT objects, the key objective of which is to assign different Security Level Certificates (SLCs) for IoT objects based on their hardware capabilities and protection measures. Objects with SLCs, therefore, will be able to communicate with each other or with the Internet in a secure manner. The proposed framework is composed of five main phases. In phase 1, we classify IoT assets into four components: (i) physical objects, (ii) protocols, (iii) data at rest, and (iv) software, which includes operating systems, middlewares, and applications. We also classify IoT objects into five categories based on their hardware capabilities. In phase 2, we propose security and privacy guidelines for previously mentioned IoT assets, along with their protection measures. In phase 3, we classify protection measures into five SLCs, and then we assign different SLCs for IoT objects. In phase 4: we develop a communication plan between objects based on their SLCs. In phase 5, we propose a four-step method to seamlessly integrate our objects with legacy objects ( objects are not developed according to our framework). Fourth, the feasibility and application of this framework are illustrated using smart homes as a case study. Finally, we investigate how our framework would lessen several attacks and threats against IoT like routing attacks and physical damage. We also provide qualitative arguments to show that this framework could be utilized to solve some of IoT security challenges such as tight resource constrains. Moreover, we discuss the shortcomings of our suggested framework.

In recent years, farmers and policymakers have faced ample challenges and have struggled to support the sustainability of the agricultural sector. Sustainable agriculture encompasses multiple concepts, and its performance produces extensive debate about data requirements, appropriate indicators, evaluation methods, and tools. Under the European Union (EU) financed project FLINT (Farm Level Indicators for New Topics in policy evaluation), detailed data have been collected at the farm level to provide broader coverage of sustainability indicators on a wide range of relevant topics to facilitate the assessment of sustainability performance. The approach has been applied in a pilot network of representative farms at the EU level, considering the heterogeneity of the EU farming sector to provide data infrastructure with up to date information for sustainability indicators. This study aims to assess sustainability performance at the farm level in Greece. Representative and dominant agricultural systems, such as permanent crops, olive trees, arable crops, and livestock (sheep) farms, comprise the Greek sample. It uses the analytical hierarchy process (AHP) methodology and attempts to gain insights into the sustainability performance of agricultural systems. The outcome of the sustainability assessment reveals knowledge and develops support for strategic farm choices in order to support both farmers and policymakers towards more sustainable development plans. The results indicate that three typical Mediterranean farming systems, like permanent crops, olive trees, and extensive livestock systems (sheep farms), are more sustainable in contrast to intensive and arable crop farms.

Along with density and mass variations of the oceans driven by global warming, Glacial Isostatic Adjustment (GIA) in response to the last deglaciation still contributes significantly to present-day sea-level change. Indeed, in order to reveal the impacts of climate change, long term observations at tide gauges and recent absolute altimetry data need to be decontaminated from the effects of GIA. This is now realized by means of global models constrained by the observed evolution of the paleo-shorelines since the Last Glacial Maximum, which account for the complex interactions between the solid Earth, the cryosphere and the oceans. In the recent literature, past and present-day effects of GIA are often expressed in terms of fingerprints describing the spatial variations of several geodetic quantities like crustal deformation, the harmonic components of the Earth's gravity field, relative and absolute sea level. However, since it is driven by the sluggish readjustment occurring within the viscous mantle, GIA shall taint the pattern of sea-level variability also during the forthcoming centuries. The shapes of the GIA fingerprints reflect inextricable deformational, gravitational, and rotational interactions occurring within the Earth system. Using up-to-date numerical modeling tools, our purpose is to revisit and to explore some of the physical and geometrical features of the fingerprints, their symmetries and intercorrelations, also illustrating how they stem from the fundamental equation that governs GIA, i.e., the Sea Level Equation.

Noise pollution is an environmental problem due to its effect on hearing and other related health issues. The elevated noise levels above standard limits cause hearing loss and other attendant problems. In Nigeria, viewing centers where football matches are watched via satellite are known as ‘Mini Stadium’. In this study, the noise pollution of a viewing center was assessed. To do this, five top teams each from the English Premier League (EPL), La Liga, Bundesliga, League 1, and Serie A were selected for the assessment. A sound level meter (GB: 2266204, made in China) was used for the monitoring. The range of noise levels in dBA showed as follow: EPL (56-108); La Liga (46-106); Bundesliga (54-102); Serie A (49-101); and Ligue 1 (54-101). The results when compared with the standard limits, it was observed that part of the results exceeded the limits, this means that the viewers and others within the viewing center may be prone to the hearing problem. It is recommended that acoustic insulators and hearing aids should be used to reduce the attendant problems associated with noise.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

Universitas Gadjah Mada (UGM) is implementing the concept of green campus by various means including through elements in its sustainable campus development plan. With regards to several factors related to Energy and Climate Change indicators set by UI GreenMetric, this research examined the existing condition of the area of UGM which belongs to academic zone in order to construct a proposal based on a building mass reconfiguration. This proposal was aimed to improve the implementation of energy-specific green campus concept within the Forestry cluster area as a sample case study. This selection was based on the average value of the Building Coverage Ratio (BCR) values in all of the clusters; thus, the scenario was relevant for generalization and could be replicated in other clusters in the university. The study was done in three stages of simulation, and was based on a series of digital simulation of sunlight hour and solar radiation run in Grasshopper using Lady Bug environmental analysis plug-in for a period of five summer months during the building office hours. Following the reconfiguration, results had shown a similar downtrend between the amount of sunlight hour and solar radiation in the area (up to 49% and 45% respectively on building envelopes, and up to 44% and 42% respectively on landscaping surfaces). This reduction indicated a potential of energy efficiency by applying selective building mass reconfiguration as a passive design strategy that goes hand in hand with the campus’s development policy to optimize the use of BCR for a greener UGM campus through its many undergoing and upcoming redevelopment projects.

The concept of transit-oriented development (TOD) has been widely recognized in recent years for its role in reducing car traffic, improving public transportation, and enhancing traffic sustainability. This paper conducts empirical research on a developed rail transit network, using Shanghai as a case study. In addition to traditional TOD features, other factors based on urban rail transit are introduced, including multi-level modeling (MLM), which is used to analyze the possible factors influencing rail patronage. To avoid the bias of research results led by the correlation between independent variables, factors are divided into two levels. The first level includes three groups of variables: the built environment, station characteristics, and socioeconomic and demographic characteristics. The second level includes a set of variables which are regional characteristics. Results show that the most significant impact on train patronage is station location in the business district area. Other factors that have a positive effect on promoting rail transit travel include the number of service facilities around the station, degree of employment around the station, economic level, intensity of residential development, if the station is a transfer station, the operating period of the station, and the size of the large transportation hub around the station.

The objective of the study was to investigate the gap between data and evidence-based decisions among healthcare professionals considering the enormous amount of individual and aggregate data collected. Our study assessed the capacity, skills, and knowledge of the Ministry of Health leadership staff to understand data management, analysis, utilization, and dissemination. Three key components were assessed: 1) Knowledge through true/false questions, 2) Level of Skill (and Competency) using a Likert scale, and 3) Understanding of Key Concepts and Tools based on a Likert scale. The 183 study respondents were diverse healthcare professionals from Kenya, Tanzania, and Rwanda. Majority of respondents had not received any training on data management, analysis, interpretation, and utilization techniques, further there was a significant difference between those who had received training versus those who had not(p=0.005). The respondents were competent in work-related experiences but lacked skills and knowledge on: data concepts and tools, study designs, and types of data analysis. These findings explain the gap between data management, analysis, utilization, and dissemination among health professional’s cadre. To enhance service delivery and optimal provision of health care, it is imperative to have all health care professionals receive a well-designed training on data management, analysis, interpretation, and utilization.

Spent nuclear fuel and high-level radioactive waste can be disposed in deep horizontal drillholes in sedimentary, metamorphic, or igneous rocks. Horizontal drillhole disposal has safety, operational, and economic benefits: The repository is deep in the brine-saturated zone far below aquifers in a reducing environment of formations that can be shown to have been isolated from the surface for millions of years; its depth provides safety against inadvertent intrusion, earth¬quakes, and near-surface perturbations; it can be placed close to the reactors and interim storage facilities, minimizing transportation; disposal costs per ton of waste can be kept substantially lower than for mined repositories by its smaller size, reduced infrastructure needs, and staged imple¬mentation; and, if desired, the waste could be retrieved using “fishing” technology. In the proposed disposal concept, corrosion-resistant canisters containing unmodified fuel assemblies from commercial reactors would be placed end-to-end in up to 50 cm diameter horizontal drill¬holes, a configuration that reduces mechanical stresses and keeps the temperatures below the boiling point of the brine. Other high-level wastes, such as capsules containing 137Cs and 90Sr, can be disposed in small-diameter drillholes. We provide an overview of this novel disposal concept and its technology, discuss some of its safety aspects, and compare it to mined repositories and the deep vertical borehole disposal concept.

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

District heating networks have become widespread due to their ability to distribute thermal energy efficiently, which leads to reduced carbon emissions and improved air quality. The characteristics of these networks vary remarkably depending on the urban layout and system amplitude. Moreover, extensive data about the energy distribution and thermal capacity of different areas are seldom available. Design, optimization and control of these systems are enabled by the availability of fast and scalable models of district heating networks. This work addresses this issue by proposing a novel method to develop a scale-free model of large-scale district heating networks. Starting from coarse data available at the main substations, a physics-based model of the system aggregated regions is developed by identifying the heat capacity and heat loss coefficients. The model validation on the network of Västerås, Sweden, shows compatibility with literature data and can therefore be exploited for system design, optimization and control-oriented applications. In particular, the possibility to estimate the heat storage potential of network regions allows new smart management strategies to be investigated.