Public perceptions and attitudes to biological risks: Saudi Arabia and regional perspectives.

Saudi Arabia has experienced frequent occurrences of biological disasters due to a wide range of generator factors, including natural disasters and epidemics. A national survey (n=1,164) was conducted across 13 regions of Saudi Arabia to examine public perceptions to the risk of a biological disaster. The primary results reveal: (a) a degree of knowledge about biological threats such as SARS and H5N1 flu, despite the lack of individual experience with disasters; (b) age, gender, education and faith are positively related to the perception of biological risk; and (c) a number of important community resilience factors exist, including faith, education and willingness. This study concludes that the development of adapted resilience strategies in disaster management can be achieved through public education and training involving cooperation with official organisations and religious authorities in the country to increase public awareness, knowledge and skills in mitigating biological threats.

BIM based virtual environment for fire emergency evacuation.

Recent building emergency management research has highlighted the need for the effective utilization of dynamically changing building information. BIM (building information modelling) can play a significant role in this process due to its comprehensive and standardized data format and integrated process. This paper introduces a BIM based virtual environment supported by virtual reality (VR) and a serious game engine to address several key issues for building emergency management, for example, timely two-way information updating and better emergency awareness training. The focus of this paper lies on how to utilize BIM as a comprehensive building information provider to work with virtual reality technologies to build an adaptable immersive serious game environment to provide real-time fire evacuation guidance. The innovation lies on the seamless integration between BIM and a serious game based virtual reality (VR) environment aiming at practical problem solving by leveraging state-of-the-art computing technologies. The system has been tested for its robustness and functionality against the development requirements, and the results showed promising potential to support more effective emergency management.

Viral infection transmission and indoor air quality: A systematic review.

Respiratory disease transmission in indoor environments presents persistent challenges for health authorities, as exemplified by the recent COVID-19 pandemic. This underscores the urgent necessity to investigate the dynamics of viral infection transmission within indoor environments. This systematic review delves into the methodologies of respiratory infection transmission in indoor settings and explores how the quality of indoor air (IAQ) can be controlled to alleviate this risk while considering the imperative of sustainability. Among the 2722 articles reviewed, 178 were retained based on their focus on respiratory viral infection transmission and IAQ. Fifty eight articles delved into SARS-CoV-2 transmission, 21 papers evaluated IAQ in contexts of other pandemics, 53 papers assessed IAQ during the SARS-CoV-2 pandemic, and 46 papers examined control strategies to mitigate infectious transmission. Furthermore, of the 46 papers investigating control strategies, only nine considered energy consumption. These findings highlight clear gaps in current research, such as analyzing indoor air and surface samples for specific indoor environments, oversight of indoor and outdoor parameters (e.g., temperature, relative humidity (RH), and building orientation), neglect of occupancy schedules, and the absence of considerations for energy consumption while enhancing IAQ. This study distinctly identifies the indoor environmental conditions conducive to the thriving of each respiratory virus, offering IAQ trade-offs to mitigate the risk of dominant viruses at any given time. This study argues that future research should involve digital twins in conjunction with machine learning (ML) techniques. This approach aims to enhance IAQ by analyzing the transmission patterns of various respiratory viruses while considering energy consumption.

A European-wide exploratory study to analyse the relationship between training and energy efficiency in the construction sector.

Current evidence that supports the correlation between training and energy efficiency in the construction industry is sparse and lacks an in-depth and sector-wide analysis. Several context-specific (in terms of application, workforce segment, and scope) studies have highlighted several barriers, challenges, and gaps in the training landscape in the European construction sector. However, these do not scale up and translate to robust evidence for the entire industry. The paper aims to address this gap by adopting a quantitative and qualitative Europe-wide consultation that not only seeks to gather evidence about the relationship between training and energy efficiency but also broadens the scope of the investigation beyond this aim to understand the complexity of the training landscape in energy efficiency and to provide context to the resulting evidence, in a way that promotes generalisation of the results. A mixed-method approach is adopted involving secondary (in the form of industry studies and academic publications) and primary sources of evidence. The latter include a questionnaire (n = 52), a series of interviews (n = 28), an expert workshop, and use cases drawn across Europe providing examples of the correlation between training and energy efficiency. Five key themes emerged from the consultation, namely: (a) lack of systematic process to codify best practice into re-usable knowledge, (b) lack of industry-wide shared vision, (c) nature of the training available in the energy efficiency domain, (d) level of reliance on a trained and skilled workforce in energy efficiency, (e) efficiency of legislative frameworks, policies, and government incentives. While the analysis of the results confirms the correlation between training and energy efficiency, further efforts are needed to establish robust quantitative evidence. The research also points to several policy measures, including the need for adapted instruments to promote mutual recognition of energy skills and qualifications in the European construction sector.

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives.

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings' performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings' management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings' performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

Reduction of PAH and soot precursors in benzene flames by addition of ethanol.

A one-dimensional premixed flame model (PREMIX) and schemes resulting from the merging of validated kinetic schemes for the oxidation of the components of the present mixtures (benzene and ethanol) were used to investigate the effect of oxygenated additives on aromatic species, which are known to be soot precursors, in fuel-rich benzene combustion. The specific flames were low-pressure (45 mbar), laminar, premixed flames at an equivalence ratio of 2.0. The blended fuels were formed by incrementally adding 4% wt of oxygen (ethanol) to the neat benzene flame and by keeping the inert mole fraction (argon) and the equivalence ratio constants. Special emphasis was directed toward the causes for the concentration-dependent influence of the blends on the amount of polycyclic aromatic hydrocarbons (PAHs) formed. The effects of oxygenate addition to the benzene base flame were seen to result in interesting differences, especially regarding trends to form PAH. The modeling results indicated that the concentration of acetylene and propargyl radicals, the main PAH precursors, as well as the PAH amounts were lower in the flame of the ethanol-benzene fuel mixture than in the pure benzene flame and that all of the formed PAHs were issued from the phenyl radical. Finally, the modeling results provided evidence that the PAH reduction was a result of simply replacing "sooting" benzene with "nonsooting" ethanol without influencing the combustion chemistry of the benzene.

Impact of ventilation and avoidance measures on SARS-CoV-2 risk of infection in public indoor environments.

BACKGROUND: The literature includes many studies which individually assess the efficacy of protective measures against the spread of the SARS-CoV-2 virus. This study considers the high infection risk in public buildings and models the quality of the indoor environment, related safety measures, and their efficacy in preventing the spread of the SARS-CoV-2 virus. METHODS: Simulations are created that consider protective factors such as hand hygiene, face covering and engagement with Covid-19 vaccination programs in reducing the risk of infection in a university foyer. Furthermore, a computational fluid dynamics model is developed to simulate and analyse the university foyer under three ventilation regimes. The probability of transmission was measured across different scenarios. FINDINGS: Estimates suggest that the Delta variant requires the air change rate to be increased >1000 times compared to the original strain, which is practically not feasible. Consequently, appropriate hygiene practices, such as wearing masks, are essential to reducing secondary infections. A comparison of different protective factors in simulations found the overall burden of infections resulting from indoor contact depends on (i) face mask adherence, (ii) quality of the ventilation system, and (iii) other hygiene practices. INTERPRETATION: Relying on ventilation, whether natural, mechanical, or mixed, is not sufficient alone to mitigate the risk of aerosol infections. This is due to the internal configuration of the indoor space in terms of (i) size and number of windows, their location and opening frequency, as well as the position of the air extraction and supply inlets, which often induce hotspots with stagnating air, (ii) the excessive required air change rate. Hence, strict reliance on proper hygiene practices, namely adherence to face coverings and hand sanitising, are essential. Consequently, face mask adherence should be emphasized and promoted by policymakers for public health applications. Similar research may need to be conducted using a similar approach on the Omicron (B.1.1.529) variant.

Predictive assembling model reveals the self-adaptive elastic properties of lamellipodial actin networks for cell migration.

Branched actin network supports cell migration through extracellular microenvironments. However, it is unknown how intracellular proteins adapt the elastic properties of the network to the highly varying extracellular resistance. Here we develop a three-dimensional assembling model to simulate the realistic self-assembling process of the network by encompassing intracellular proteins and their dynamic interactions. Combining this multiscale model with finite element method, we reveal that the network can not only sense the variation of extracellular resistance but also self-adapt its elastic properties through remodeling with intracellular proteins. Such resistance-adaptive elastic behaviours are versatile and essential in supporting cell migration through varying extracellular microenvironments. The bending deformation mechanism and anisotropic Poisson's ratios determine why lamellipodia persistently evolve into sheet-like structures. Our predictions are confirmed by published experiments. The revealed self-adaptive elastic properties of the networks are also applicable to the endocytosis, phagocytosis, vesicle trafficking, intracellular pathogen transport and dendritic spine formation.

User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization.

This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system's intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings.

A systematic mixed-integer differential evolution approach for water network operational optimization.

The operational management of potable water distribution networks presents a great challenge to water utilities, as reflected by the complex interplay of a wide range of multidimensional and nonlinear factors across the water value chain including the network physical structure and characteristics, operational requirements, water consumption profiles and the structure of energy tariffs. Nevertheless, both continuous and discrete actuation variables can be involved in governing the water network, which makes optimizing such networks a mixed-integer and highly constrained decision-making problem. As such, there is a need to situate the problem holistically, factoring in multidimensional considerations, with a goal of minimizing water operational costs. This paper, therefore, proposes a systematic optimization methodology for (near) real-time operation of water networks, where the operational strategy can be dynamically updated using a model-based predictive control scheme with little human intervention. The hydraulic model of the network of interest is thereby integrated and successively simulated with different trial strategies as part of the optimization process. A novel adapted mixed-integer differential evolution (DE) algorithm is particularly designed to deal with the discrete-continuous actuation variables involved in the network. Simulation results on a pilot water network confirm the effectiveness of the proposed methodology and the superiority of the proposed mixed-integer DE in comparison with genetic algorithms. It also suggests that 23.69% cost savings can be achieved compared with the water utility's current operational strategy, if adaptive pricing is adopted for all the pumping stations.

Efficient least angle regression for identification of linear-in-the-parameters models.

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm.

Progress in ambient assisted systems for independent living by the elderly.

One of the challenges of the ageing population in many countries is the efficient delivery of health and care services, which is further complicated by the increase in neurological conditions among the elderly due to rising life expectancy. Personal care of the elderly is of concern to their relatives, in case they are alone in their homes and unforeseen circumstances occur, affecting their wellbeing. The alternative; i.e. care in nursing homes or hospitals is costly and increases further if specialized care is mobilized to patients' place of residence. Enabling technologies for independent living by the elderly such as the ambient assisted living systems (AALS) are seen as essential to enhancing care in a cost-effective manner. In light of significant advances in telecommunication, computing and sensor miniaturization, as well as the ubiquity of mobile and connected devices embodying the concept of the Internet of Things (IoT), end-to-end solutions for ambient assisted living have become a reality. The premise of such applications is the continuous and most often real-time monitoring of the environment and occupant behavior using an event-driven intelligent system, thereby providing a facility for monitoring and assessment, and triggering assistance as and when needed. As a growing area of research, it is essential to investigate the approaches for developing AALS in literature to identify current practices and directions for future research. This paper is, therefore, aimed at a comprehensive and critical review of the frameworks and sensor systems used in various ambient assisted living systems, as well as their objectives and relationships with care and clinical systems. Findings from our work suggest that most frameworks focused on activity monitoring for assessing immediate risks, while the opportunities for integrating environmental factors for analytics and decision-making, in particular for the long-term care were often overlooked. The potential for wearable devices and sensors, as well as distributed storage and access (e.g. cloud) are yet to be fully appreciated. There is a distinct lack of strong supporting clinical evidence from the implemented technologies. Socio-cultural aspects such as divergence among groups, acceptability and usability of AALS were also overlooked. Future systems need to look into the issues of privacy and cyber security.

Experimental and numerical investigation of the effect of liquid temperature on the sonolytic degradation of some organic dyes in water.

This paper presents a comprehensive experimental and numerical investigation of the effects of liquid temperature on the sonochemical degradation of three organic dyes, Rhodamine B (RhB), Acid orange 7 (AO7) and Malachite green (MG), largely used in the textile industry. The experiments have been carried out for an ultrasonic frequency of 300 kHz. The obtained experimental results were discussed using a new approach combining the results of single-bubble event and the number of active bubbles. The single-bubble event was predicted using a model that combines the bubble dynamics with chemical kinetics occurring inside a bubble during the strong collapse. The number of active bubbles was predicted using a method developed in our previous work. The experiments showed that the degradation rate of the three dyes increased significantly with increasing liquid temperature in the range 25-55°C. It was predicted that the main pathway of pollutants degradation is the attack by OH radicals. The simulations showed that there exists an optimum liquid temperature of about 35°C for the production of OH inside a bubble whereas the number of active bubbles increased sharply with the rise of the liquid temperature. It was predicted that the overall production rate of OH increased with increasing liquid temperature in the range 25-55°C. Finally, it was concluded that the effect of liquid temperature on the sonochemical degradation of the three dyes in aqueous phase was controlled by the number of active bubbles in the range 35-55°C and by both the number of bubbles and the single bubble yield in the range 25-35°C.

Sensitivity of free radicals production in acoustically driven bubble to the ultrasonic frequency and nature of dissolved gases.

Central events of ultrasonic action are the bubbles of cavitation that can be considered as powered microreactors within which high-energy chemistry occurs. This work presents the results of a comprehensive numerical assessment of frequency and saturating gases effects on single bubble sonochemistry. Computer simulations of chemical reactions occurring inside a bubble oscillating in liquid water irradiated by an ultrasonic wave have been performed for a wide range of ultrasonic frequencies (213-1100kHz) under different saturating gases (O2, air, N2 and H2). For O2 and H2 bubbles, reactions mechanism consisting in 25 reversible chemical reactions were proposed for studying the internal bubble-chemistry whereas 73 reversible reactions were taken into account for air and N2 bubbles. The numerical simulations have indicated that radicals such as OH, H, HO2 and O are created in the bubble during the strong collapse. In all cases, hydroxyl radical (OH) is the main oxidant created in the bubble. The production rate of the oxidants decreases as the driving ultrasonic frequency increases. The production rate of OH radical followed the order O2>air>N2>H2 and the order becomes more remarkable at higher ultrasonic frequencies. The effect of ultrasonic frequency on single bubble sonochemistry was attributed to its significant impact on the cavitation process whereas the effects of gases were attributed to the nature of the chemistry produced in the bubble at the strong collapse. It was concluded that, in addition to the gas solubility, the nature of the internal bubble chemistry is another parameter of a paramount importance that controls the overall sonochemical activity in aqueous solutions.

A method for predicting the number of active bubbles in sonochemical reactors.

Knowledge of the number of active bubbles in acoustic cavitation field is very important for the prediction of the performance of ultrasonic reactors toward most chemical processes induced by ultrasound. The literature in this field is scarce, probably due to the complicated nature of the phenomena. We introduce here a relatively simple semi-empirical method for predicting the number of active bubbles in an acoustic cavitation field. By coupling the bubble dynamics in an acoustical field with chemical kinetics occurring in the bubble during oscillation, the amount of the radical species OH and HO2 and molecular H2O2 released by a single bubble was estimated. Knowing that the H2O2 measured experimentally during sonication of water comes from the recombination of hydroxyl (OH) and perhydroxyl (HO2) radicals in the liquid phase and assuming that in sonochemistry applications, the cavitation is transient and the bubble fragments at the first collapse, the number of bubbles formed per unit time per unit volume is then easily determined using material balances for H2O2, OH and HO2 in the liquid phase. The effect of ultrasonic frequency on the number of active bubbles was examined. It was shown that increasing ultrasonic frequency leads to a substantial increase in the number of bubbles formed in the reactor.

New interpretation of the effects of argon-saturating gas toward sonochemical reactions.

A number of literature reports showed that argon provides a more sonochemical activity than polyatomic gases because of its higher polytropic ratio; whereas several recent studies showed that polyatomic gases, such as O2, can compensate the lower bubble temperature by the self decomposition in the bubble. In this work, we show for the first time a numerical interpretation of these controversial reported effects. Computer simulations of chemical reactions inside a collapsing acoustic bubble in water saturated by different gases (Ar, O2, air and N2) have been performed for different frequencies (213-1100 kHz). In all cases, OH radical is the main powerful oxidant created in the bubble. Unexpectedly, the order of saturating gases toward the production rate of OH radical was strongly frequency dependent. The rate of production decreases in the order of Ar>O2>air>N2 for frequencies above 515 kHz, and Ar starts to lose progressively its first order to the following gases with a gradually decreasing of frequency below 515 kHz up to a final order of O2>air∼N2>Ar at 213 kHz. The analysis of chemical kinetic results showed a surprising aspect: in some cases, there exists an optimum bubble temperature during collapse at which the chemical yield is much higher than that of the maximum bubble temperature achieved in the bubble. On the basis of this, we have concluded that the lower sonochemical activity induced by Ar for frequencies below 515 kHz is mainly due to the forte consumption of radicals inside a bubble prior the complete collapse being reached.

Comprehensive experimental and numerical investigations of the effect of frequency and acoustic intensity on the sonolytic degradation of naphthol blue black in water.

In the present work, comprehensive experimental and numerical investigations of the effects of frequency and acoustic intensity on the sonochemical degradation of naphthol blue black (NBB) in water have been carried out. The experiments have been examined at three frequencies (585, 860 and 1140 kHz) and over a wide range of acoustic intensities. The observed experimental results have been discussed using a more realistic approach that combines the single bubble sonochemistry and the number of active bubbles. The single bubble yield has been predicted using a model that combines the bubble dynamics with chemical kinetics consisting of series of chemical reactions (73 reversible reactions) occurring inside an air bubble during the strong collapse. The experimental results showed that the sonochemical degradation rate of NBB increased substantially with increasing acoustic intensity and decreased with increasing ultrasound frequency. The numerical simulations revealed that NBB degraded mainly through the reaction with hydroxyl radical (OH), which is the dominant oxidant detected in the bubble during collapse. The production rate of OH radical inside a single bubble followed the same trend as that of NBB degradation rate. It increased with increasing acoustic intensity and decreased with increasing frequency. The enhancing effect of acoustic intensity toward the degradation of NBB was attributed to the rise of both the individual chemical bubble yield and the number of active bubbles with increasing acoustic intensity. The reducing effect of frequency was attributed to the sharp decrease in the chemical bubble yield with increasing frequency, which would not compensated by the rise of the number of active bubbles with the increase in ultrasound frequency.