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Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efficacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.

Energy management is the most prominent means of improving energy efficiency, and improved energy efficiency constitutes the cornerstone in decarbonization. For successful industrial energy management, defining accurate energy performance indicators (EnPIs) is essential. Energy-intensive industries have previously been found to have an improvement potential regarding the current monitoring of EnPIs, especially at process level. While general models for developing and implementing EnPIs exist, manufacturing industries are diverse in terms of their production processes, which is why industry-tailored models for EnPI development are needed. One major outcome of this paper is a unique model specifically tailored for kraft pulp mills. The model derives from a practice-based approach for EnPI development, building on real-life experiences from a Swedish group of companies. This paper’s developed model, and the validation of the EnPIs, further increase the understanding of the kraft pulp industry’s processes and how to apply descriptive and explanatory indicators. The developed model can potentially be generalized to other sectors. Funding: Swedish Environmental Protection Agency; Swedish Agency for Marine and Water Management [802-0082-17]

In recent years renewable and cleaner fuel for diesel engines are compulsory due to depletion of fossil fuel. Various types of bio-based fuels are investigated by the researchers. Biodiesel is anticipated as potential contenders of diesel fuel. Though it is possible to utilize pure biodiesel in diesel engines, some burdens like higher density, lower cetane number and lesser calorific value hinder it from replacing conventional diesel completely. Therefore, using blends with biofuels in diesel engines has a preference. Thus, this paper reviews two different approaches on the role of nanoparticles on biofuel production and effects of nanoparticles in biodiesel–diesel fuel blends on performance, combustion analysis and emission characteristics of diesel engines. Wide range of results from previous research studies with potential and application of nanoparticles in bioethanol production, the effect of the addition of nanoparticles into diesel fuel with different biofuels ratios are collected in this review study. There are different engine performances enhancing methods surveyed. Nanoparticles can be utilized in the production of biofuels from feedstock pre-treatment to chemical reaction as catalysts. It was observed from the overall results that by adding nanoparticles, there was a significant reduction in the brake specific fuel consumption about 20% to 23% as compared with biodiesel–diesel blends with and without alcohol as additives. Besides as nanoparticles possess high thermal conductivity, the addition of nanoparticles enhanced the process of combustion and increases the brake power about 2.5% to 4%. Emission results showed that in most reviews, NO x emission is increased by up to 55%, while HC, CO and PM are decreased significantly. It was concluded from the study that a diesel engine could be effectively run and give better performance and effective regulated emissions on the application of added nanoparticles with biodiesel and their blends as fuel in a CI engine.

Rheumatoid arthritis (RA) is a common autoimmune disease characterized by joint inflammation and immune dysfunction. Although various therapeutic approaches have been utilized for the treatment of RA in clinical applications, the low responsiveness of RA patients and undesired systemic toxicity are still unresolved problems. Targeting the resolution pathway of inflammation with pro-resolving mediators would evoke the protective actions of patient for combating the inflammation. Ac2–26, a 25-amino acid peptide derived from Annexin A (a pro-resolving mediator), has shown good efficacy in the treatment of inflammatory disorders. However, the low bioavailability of Ac2–26 peptides hinders their efficacy in vivo. In this paper, we formed PEGylated lipid nanoparticles (LDNPs) by the co-assembly of l-ascorbyl palmitate (L-AP) and N-(carbonyl methoxypolyethylene glycol-2000)-1,2-distearoyl-sn‑glycero-3-phosphoethanolamine (DSPE-PEG2k) to encapsulate and deliver Ac2–26 peptides to the arthritic rats. They showed good stability and biocompatibility. After being intravenously administrated, Ac2–26 peptide-loaded PEGylated lipid nanoparticles (ADNPs) showed the prolonged in vivo circulation time and enhanced accumulation in inflamed sites. In vivo therapeutic evaluations revealed that ADNPs could attenuate synovial inflammation and improve joint pathology. Therefore, the pro-resolving therapeutic strategy using ADNPs is effective in RA treatment., Graphical abstract The fabrication of ADNPs and their in vivo performances in arthritic rats.Image, graphical abstract

The release of high amount of nitrogen and phosphorous from agricultural, municipal and industrial sewage into natural water body is the major source of eutrophication in aquatic ecosystems. This leads to the depletion of oxygen, cyanobacteria bloom and proliferation of aquatic weed plants, and destruction of water ecosystem. Therefore, effective treatment of municipal wastewater is crucial prior to discharge into natural water bodies in order to prevent pollution, and to meet the wastewater acceptable limits set by government and Environmental Protection Agencies. The present study evaluates the performance of varying weight of S. molesta plants in phytoremediation of treated domestic wastewater samples for 14 days at 24 h retention time. Physicochemical analysis such as turbidity, phosphate, ammoniacal nitrogen and nitrate tests were conducted on the wastewater samples according to spectrophotometric methods. Also, S. molesta plants were harvested once in a week and the relative growth rate (RGR) was calculated. The outcome of the study indicated that the treated domestic wastewater from the sewage treatment plant contains pollutants that stimulates the growth of S. molesta plants. Similarly, the highest reduction efficiency of up to 97.7%, 99.7%, 99% and 90.6% was observed for turbidity, phosphate, ammoniacal nitrogen and nitrate, respectively. In addition, the outcome of the study also demonstrated that S. molesta plants with the highest weight (280 g) were more efficient in removal of the excess nutrient present in the influent samples. Furthermore, the free energy content in the wastewater provided a suitable consortium for the plant cultivation and growth. Therefore, this method can be used for lab-scale production of wastewater-based biomass for the generation of biofuels.

Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus., Graphical Abstract ga1

Combining natural materials and pretreatment processes with nanomaterials is an alternative to be evaluated for the increase in efficiency and cost reduction of water treatment processes. This work aimed to study the combination of pretreatment processes and primary treatments with bentonite impregnated with magnetic nanoparticles (Bentonite@MNPs) to reuse wastewater from the food indust|ry. Bentonite@MNPs were obtained by green synthesis using glucose as a reducing/stabilizing agent, and the nanocomposite was characterized by SEM, EDS, FTIR, VSM, and XRD analysis. The synthesized nanocomposite was used to remove nitrates and COD from wastewater from the production of guava bars that had been pretreated with coagulation-flocculation-sedimentation processes. Batch adsorption experiments were conducted, where the influence of several parameters such as contact time, adsorbent amount, and the temperature was studied. According to the results, the wastewater treated with the coagulation-flocculation-sedimentation processes was still outside the standards for agricultural reuse. After applying the treatment with Bentonite@MNPs, a reduction of COD, nitrates, chlorides, and sulfates of 82%, 75.4%, 98.18%, and 70%, respectively, was achieved. The adsorption isotherm data were best explained by the Langmuir model for COD, and Frendluich model for Nitrates and the adsorption capacity of Bentonite@MNPs for COD and Nitrates was 34.84 and 28.11 mg/g when adsorption temperature was 318 K. The results of thermodynamic studies (ΔG°, ΔH°, and ΔS°) showed the spontaneous, endothermic and randomness in the adsorption process. Desorption studies showed excellent regenerative efficiency (up to the 10th cycle) of nanocomposites using 0.01 M NaOH, so this wastewater treatment system for the food industry is promising to implement for the sake of using wastewater as a circular economy component.

One measure to mitigate some of the nowadays environmental problems is the generation of products from renewable resources. In this context, this study's objective is to evaluate the environmental impacts associated with the use of sugarcane and agave bagasse from Mexico as a raw material for the generation of bioenergy, applying a life cycle assessment approach. Four scenarios were compared to determine the optimal feedstock (sugarcane or agave) and processing routes (combustion or gasification) from an environmental perspective. Life cycle assessment is performed according to the cradle-to-gate approach. In the case of the two-feedstocks studied, it was observed that the feedstock processing stage has high impact values in almost all impact categories. In this sense, it was observed that the combustion scenarios have high impact values in terms of ozone depletion potential (4.73 × 10−6 and 7.59 × 10−7 kg CFC11 eq), terrestrial acidification potential (1.41 × 10−2 and 7.82 × 10−3 kg SO2 eq), and fossil fuel potential (9.30 × 10−2 and 0.12 kg oil eq) for sugar extraction and bacanora production, respectively). For the gasification scenarios, the highest impact values ​​were observed for the terrestrial acidification potential (1.27 × 10−2 kg SO2 eq) and fossil fuel potential (8.41 × 10−2 kg oil eq) for sugar production and the ozone depletion potential (6.85 × 10−7 kg CFC11 eq), human toxicity potential - non-cancer (2.05 × 10−2 kg1,4-DCB) and fossil fuel potential (0.11kg oil eq) for bacanora production. Furthermore, it was observed that the sugarcane cultivation stage generates between 2 and 6 times more impact than the agave cultivation stage for almost all impact categories. Regarding the stages related to thermochemical processes, the impact values were relatively low, except for the following categories global warming potential, photochemical oxidation formation potential - humans, photochemical oxidation formation potential - ecosystems, terrestrial acidification potential, and water consumption potential, between 21 % and 88 % for the combustion process and between 32 % and 63 % for the gasification process. The main results of the comparisons between the four scenarios showed that the best scenario from an environmental perspective is agave bagasse combustion, followed by agave bagasse gasification, sugarcane bagasse gasification, and sugarcane bagasse combustion., Una medida para mitigar algunos de los problemas ambientales actuales es la generación de productos a partir de recursos renovables. En este contexto, el objetivo de este estudio es evaluar los impactos ambientales asociados al uso del bagazo de caña de azúcar y agave de México como materia prima para la generación de bioenergía, aplicando un enfoque de evaluación del ciclo de vida. Se compararon cuatro escenarios para determinar la materia prima óptima (caña de azúcar o agave) y las rutas de procesamiento (combustión o gasificación) desde una perspectiva ambiental. La evaluación del ciclo de vida se realiza de acuerdo con el enfoque de la cuna a la puerta. En el caso de las dos materias primas estudiadas, se observó que la etapa de procesamiento de la materia prima tiene altos valores de impacto en casi todas las categorías de impacto. En este sentido,−6 y 7,59 × 10 −7 kg CFC11 eq), potencial de acidificación terrestre (1,41 × 10 −2 y 7,82 × 10 −3 kg SO 2 eq) y potencial de combustibles fósiles (9,30 × 10 −2 y 0,12 kg eq de petróleo) para la extracción de azúcar y la producción de bacanora, respectivamente). Para los escenarios de gasificación, los valores de impacto más altos se observaron para el potencial de acidificación terrestre (1,27 × 10 −2 kg SO 2 eq) y el potencial de combustibles fósiles (8,41 × 10 −2 kg eq de petróleo) para la producción de azúcar y el agotamiento de la capa de ozono. (6,85 × 10 −7 kg CFC11 eq), potencial de toxicidad humana - no cancerígeno (2,05 × 10 −2kg1,4-DCB) y potencial de combustible fósil (0.11kg eq de petróleo) para la producción de bacanora. Además, se observó que la etapa de cultivo de caña de azúcar genera entre 2 y 6 veces más impacto que la etapa de cultivo de agave para casi todas las categorías de impacto. En cuanto a las etapas relacionadas con los procesos termoquímicos, los valores de impacto fueron relativamente bajos, a excepción de las siguientes categorías potencial de calentamiento global, potencial de formación de oxidación fotoquímica - humanos, potencial de formación de oxidación fotoquímica - ecosistemas, potencial de acidificación terrestre y potencial de consumo de agua, entre 21 % y 88 % para el proceso de combustión y entre 32 % y 63 % para el proceso de gasificación. Los principales resultados de las comparaciones entre los cuatro escenarios mostraron que el mejor escenario desde una perspectiva ambiental es la combustión del bagazo de agave

In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment). Subsequently, it uses this representation to plot the data. One figure is produced for each time series. In consequence, the time series classification problem is converted into the visual pattern recognition problem. This transformation allows applying a wide range of algorithms for standard pattern recognition – in this domain, there are more options to choose from than in the domain of time series classification. In this paper, we demonstrated the high effectiveness of the new method in a series of experiments on publicly available time series. We compare our results with several state-of-the-art approaches dedicated to time series classification. The new method is robust and stable. It works for time series of differing lengths and is easy to extend and alter. Even with a baseline variant presented in an empirical study in this paper, it achieves a satisfying classification accuracy. Furthermore, the proposed conversion of raw time series into images that are subjected to feature extraction opens the possibility to apply standard clustering algorithms.

Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method.

Large-scale atmospheric circulation patterns are the primary drivers of wind power variability on power networks at timescales of hours to days. This paper proposes a methodology that allows power system operators and planners working on networks with high levels of wind generation, to conduct probabilistic power flow (PPF) analyses by defining network ‘operating scenarios’ – i.e. the probability density functions of generators, and correlations between generators representative of a future system state – based on concurrent classified atmospheric states. The most significant contribution made by this paper is in illustrating how PPF operating scenarios derived from clustering historic generation data as a function of a set of classified atmospheric states reduces simulation uncertainty within a PPF analysis. It is anticipated that the proposed methodology may provide network planners with more appropriate operating scenarios for PPF analyses when compared to an unclustered base state, and may assist network operators in converting wind power point-forecasts into probabilistic forecasts whereby the spatial correlations between generators are incorporated. This methodology is illustrated through a case study considering 11 geographically disperse wind generators on the South African transmission network.

Remote electrification remains a critical issue for the Gilgit Batistan region of Pakistan, where most of the population lives without access to the national grid and mostly relies on traditional energy resources, which undermines the huge potential of renewable energy in the region. This paper addresses the energy access issue of 1.8 million people of Gilgit-Baltistan (GB), of which 86% reside in the rural areas. To resolve the problem of energy access in the region, this paper analyzes cost-effective solutions based on renewable energy resources and proposes suitable hybrid systems for 14 independent sites. For this purpose, available energy resources and regional demands have been assessed to present the optimized hybrid energy systems for the identified sites. Hybrid optimization model for multiple energy resources is designed to carry out the techno-economic feasibility analysis. Moreover, equivalent forest absorbing CO2, glacier mass loss, and social cost have been evaluated. Results show that the combination of wind and hydro with battery backup gives the most optimum cost of energy (COE) ranging between 0.0470–0.0968 $/kWh. Similarly, 8349 acres of forest are needed to absorb the annual CO 2 emissions. As 0.58 million tons of glacier, and 0.882 million $/year social cost of carbon (SCC) can be lost due to emissions of fossil fuel-based generation. To ameliorate from such adverse effects, a community-based hybrid system may provide clean and green energy to increase energy access across Gilgit-Baltistan.

A light water reactor (LWR) is the most common reactor type in the world today, and will likely continue to be the main technology of nuclear power towards the mid-21st century. This paper analyzes the factors that made large LWRs the global mainstream and points out the root causes why only a few of other types of reactors, such as small modular reactors (SMRs) and fast neutron reactors (FRs), have been put into practical use so far. It is a universal principle common to all engineering that characteristics and weaknesses sometimes cannot be detected on paper, and it is crucially important to learn lessons from actual experiences of construction, operation and maintenance. LWRs have been selected by their customers because they use common materials such as water, concrete and stainless steel, which enabled utilities and vendors to promote the improvement of materials and operating conditions by globally sharing the experiences of many reactors including some severe accidents. If SMRs, FRs and other innovative reactors are to follow suit, not only the developer’s perspective but also the customer’s perspective, such as economics and stability of supply, is essential.

Based on binocular stereo vision measurement and wireless sensing theory, this paper studies the measurement and analysis methods of athletes' movement displacement parameters. The main purpose is to explore the relationship between sports biomechanical parameters. The analysis reveals the laws of athletes 'movements, and provides theoretical guidance on athletes' attitude recognition and physical training. Based on the principle of binocular stereo vision, a non-contact displacement measurement system was developed. The displacement measurement system uses template matching to extract the image coordinates of the measurement points and restore the spatial coordinates of the measurement points through European 3D reconstruction, which improves the practicality of the measurement system. In the cantilever beam vibration test, the traditional displacement sensor and binocular image displacement measurement system are used to simultaneously measure the cantilever beam displacement. The experimental results show that the integrated displacement measurement system in this paper has higher accuracy and better stability.

Investigations of large commercial aircraft impact effect on nuclear power plant (NPP) buildings have been drawing extensive attentions, particularly after the 9/11 event, and this paper aims to numerically assess the damage and vibrations of NPP buildings subjected to aircrafts crash. In Part I of present paper, two shots of reduce-scaled model test of aircraft impact on NPP were conducted based on the large rocket sled loading test platform. In the present part, the numerical simulations of both scaled and prototype aircraft impact on NPP buildings are further performed by adopting the commercial program LS-DYNA. Firstly, the refined finite element (FE) models of both scaled aircraft and NPP models in Part I are established, and the model impact test is numerically simulated. The validities of the adopted numerical algorithm, constitutive model and the corresponding parameters are verified based on the experimental NPP model damages and accelerations. Then, the refined simulations of prototype A380 aircraft impact on a hypothetical NPP building are further carried out. It indicates that the NPP building can totally withstand the impact of A380 at a velocity of 150 m/s, while the accompanied intensive vibrations may still lead to different levels of damage on the nuclear related equipment. Referring to the guideline NEI07-13, a maximum acceleration contour is plotted and the shock damage propagation distances under aircraft impact are assessed, which indicates that the nuclear equipment located within 11.5 m from the impact point may endure malfunction. Finally, by respectively considering the rigid and deformable impacts mainly induced by aircraft engine and fuselage, an improved Riera function is proposed to predict the impact force of aircraft A380.

Governments face increasing pressure to implement effective policy to reduce diffuse water pollution from agriculture (DWPA). Various strategies and combinations of policy mechanisms have been adopted by different countries to change farmer behaviours with varying degrees of success. This paper focuses on the use of advice delivery to farmers as a mechanism to encourage uptake of DWPA mitigation measures on farms in England. Farm advisory services in England have dramatically changed over recent years, with concerns that the sector has become fragmented due to many organisations and businesses offering advice. This paper studies the role of various farm advisors and organisations providing one-to-one advice by interviewing 81 farm advisors in three agriculturally contrasting regions of England: East Anglia, the North West and South West. Objectives were to assess: which DWPA mitigation measures are being recommended by different advisors? How do recommendations differ between sources of advice and is there any conflict? And which mechanisms do advisors use to influence uptake of advice? Results from the interviews indicate that the advice delivered and the mechanisms used to influence uptake of advice vary between organisations and some advisors do indeed have particular roles within the farm advisory sector. Policy makers therefore need to consider not only what mitigation measures should be encouraged, but also which organisations and advisors are best placed to deliver on the ground advice to farmers. There is also scope to incorporate understandings of farm advice provision into catchment management plans to aid effectiveness of future agri-environmental policy.

الملخص: عادة ما تكون المقالات ما قبل الطباعة بمثابة مقدمة أولية لأوراق تمت مراجعتها من قِبل النظراء ويتم وضعها على الفور تقريبا، باستثناء بعض الفحص السطحي، في مستودع وصول مفتوح للجمهور للسماح للمعلومات بالوصول إلى القراء بسرعة، والتغلب على الخمول المرتبط عادة بالمعالجة بطريقة المجلات التي تعتمد على مراجعة الأقران. بالنسبة للباحثين في بداية حياتهم المهنية الذين قد يكونون متحمسين للحصول على بعض التقدير لجهودهم، أو الرغبة في الحصول على مدخلات مفتوحة وعلنية حول عملهم، فإن النشر قبل الطباعة هو بالتأكيد خيار نشر مفيد. ومع ذلك، إذا لم يتم فحص البيانات والمعلومات المتعلقة بالصحة بعناية، فقد تشكل خطرا، وقد تعمل أيضا كمصدر لمعلومات مضللة عن الصحة العامة. تشير زيادة النمو والمنافسة بين خوادم ما قبل الطباعة، إلى جانب حجم هائل من المطبوعات المسبقة المتعلقة بـ كوفيد-١٩، وخاصة على خادم ما قبل الطباعة لعلم الأحياء، وخادم ما قبل الطباعة للعلوم الطبية، بالإضافة إلى تحديد الفهرسة التي يتم اختبارها الآن على موقع بوب ميد، إلى أن المطبوعات المسبقة يتم استخدامها بشكل متزايد في العلوم الطبية الحيوية. وهناك حاجة إلى سياسات أخلاقية أقوى وأكثر قوة لفحص المطبوعات المسبقة قبل نشرها للجمهور، وحتى إذا كان هذا يعني تأخيرا طفيفا في النشر، فقد يؤدي ذلك إلى زيادة ثقة الأكاديميين في هذا الشكل من المعلومات العلمية والتواصل. يجب تقديم سياسات أخلاقية واضحة وصارمة بشكل عاجل من قبل مجموعات الأخلاقيات مثل لجنة أخلاقيات النشر، واللجنة الدولية لمحرري المجلات الطبية للمجلات الأعضاء التي تسمح بنشر مقالات ما قبل الطباعة قبل مراجعة النظراء التقليدية. ستفيد الإرشادات الأخلاقية الصارمة التي تتعامل مع سوء السلوك على قدم المساواة في مقالات ما قبل الطباعة والأبحاث التي راجعها النظراء، ستفيد نزاهة النشر الأكاديمي. Abstract: Preprints are typically crude precursors of peer-reviewed papers that are placed almost immediately, save for some superficial screening, on an open-access repository to allow the information to reach readers quickly, circumventing the long-drawn process typically associated with processing in peer-reviewed journals. For early-career researchers who might be enthusiastic about obtaining some recognition for their efforts, or wanting open and public input about their work, preprints are certainly a useful publication choice. However, if health-related data and information have not been carefully scrutinised, they may pose a risk and may even serve as a source of public health misinformation. Surging growth and competition among preprint servers, coupled with a massive volume of COVID-19-related preprints, mainly on bioRxiv and medRxiv, as well as select indexing now being tested on PubMed, suggests that preprints are being increasingly used in the biomedical sciences. Stronger and more robust ethical policies are needed to screen preprints before they are released to the public, and even if this implies a slight delay in publication, it may increase academics' trust in this form of scientific information and communication. Clear and stringent ethical policies need to be urgently introduced by ethics groups such as COPE and the ICMJE, whose many member journals allow preprints to be posted before traditional peer review. Stringent ethical guidelines that treat misconduct equally in preprints and peer-reviewed papers will boost the integrity of academic publishing.

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

This paper presents the optimal placement and sizing (OPAS) of active power filter (APF) considering photovoltaic distributed generation (PVDG) and nonlinear load to control the harmonics as per IEEE-519 standard limits. For the varying nature of PVDG output, the injection of harmonics into the distribution system is thoroughly investigated by incorporating hourly solar irradiance data. In this paper, a novel extended nonlinear load position based APF current injection (ENLPCI) technique is proposed to find the number of APFs and optimal buses for placement of APFs. The optimal placement for APF is calculated here for three distinct cases: a) only one state for minimum number of APFs, b) more than one state with different total harmonic distortion in voltage (THDv) with the same number of APFs and c) more than one state with the same THDv with the same number of APFs. The size and cost of APF are calculated and compared using grey wolf optimization (GWO) and its adaptive version (AGWO) algorithms for each hour. Further, the ENLPCI is also validated by GWO and AGWO algorithms for the optimal results. The proposed algorithm is tested on the IEEE-69 bus test system. The results show that the variation of solar irradiance remarkably affects the OPAS of APF. The AGWO performed better compared to GWO to find the optimal cost of APF.

Given the scarcity of safe and effective COVID-19 vaccines, a chief policy question is how to allocate them among different sociodemographic groups. This paper evaluates COVID-19 vaccine prioritization strategies proposed to date, focusing on their stated goals; the mechanisms through which the selected allocations affect the course and burden of the pandemic; and the main epidemiological, economic, logistical, and political issues that arise when setting the prioritization strategy. The paper uses a simple, age-stratified susceptible-exposed-infectious-recovered model applied to the United States to quantitatively assess the performance of alternative prioritization strategies with respect to avoided deaths, avoided infections, and life-years gained. We demonstrate that prioritizing essential workers is a viable strategy for reducing the number of cases and years of life lost, while the largest reduction in deaths is achieved by prioritizing older adults in most scenarios, even if the vaccine is effective at blocking viral transmission. Uncertainty regarding this property and potential delays in dose delivery reinforce the call for prioritizing older adults. Additionally, we investigate the strength of the equity motive that would support an allocation strategy attaching absolute priority to essential workers for a vaccine that reduces infection-fatality risk.

SARISTU was a big cooperation project granted by the European Commission, 7th Framework Programme, carried out between 2011 and 2015. It dealt with smart aeronautic structures, both morphing and sensored; its main target was to demonstrate the feasibility of designing, manufacturing and operating in representative environment, instrumented structures. Till now, it represents the major effort carried out within the European Union on the development of adaptive architectures for air systems. Inside that big activity, the realization of an Adaptive Trailing Edge Device (ATED) for wing camber adaptations aimed at compensating the weight reduction following the fuel consumption during cruise was addressed. It made the core of investigations target variable geometry aircraft components together with two other analyses concerning the development of shape-changing winglet and droop nose. ATED activities were conducted by the Italian Aerospace Research Centre (CIRA) in tight cooperation with the University of Napoli, “Federico II”, who coordinated a group of 12 different partners from 8 different nations (France, Germany, Greece, the Netherlands, Israel, Spain, Turkey, and Italy). In this paper, an integral synthesis of that work is reported, with a focus on the definition and realization of the components of the presented device. The publication is in fact meant as the first part of a series that is aimed at overviewing the whole adaptive trailing edge development, till wind tunnel tests execution. Such a concise report is a critical and harmonized review of what have been performed by many colleagues spread all over Europe, all of which are duly recalled in the reported bibliography where the reader may access more detailed information and descriptions. In detail, the paper starts with a general introduction of the concept and its aims, to move to the specs definition immediately after. Then, it deals with a short but comprehensive description of the main ATED components: structural skeleton, skin, actuation and sensing systems. It is worth remarking that the paragraph dedicated to the body frame includes some discussion about aeroelastic assessment and manufacture, seen as complementation for a complete assessment of the design constraints.

Buried metallic pipelines are used to transmit the natural gas and petroleum products to the citizens. Sometimes these pipelines are constructed in the same pathway of the overhead transmission lines (OHTLs) to save the additional right-of-way cost, and to avoid the destruction of agricultural lands. AC voltage is induced on these pipelines by the power lines electric and magnetic fields. There are many mechanisms that describe the AC voltage inducing on these pipelines; capacitive coupling describes the pipelines induced voltage by the power lines electric field, inductive coupling characterizes the induced voltage caused by the OHTLs electromagnetic fields and conductive coupling is created in buried pipelines near to OHTLs by earth fault current. In the present paper the induced voltages of pipelines created by OHTLs due to the different couplings are investigated in normal condition of operation as well as line to ground fault. The current of the coating discharging and AC corrosion are investigated. The induced overvoltage appears on the natural gas pipeline is measured in steady state condition of the transmission circuits operation. Moreover, this paper represents the mitigation of the induced voltages by using two different KOH-PCs systems.

The electric vehicle (EV) market increases due to the benefits of reducing greenhouse gas emissions using renewable energy resources. In this context, the charging scheme of electric vehicles in charging stations (CSs) is also important. Electronic devices’ charging between EV and multiple CS should consider EV’s short battery capacity, long charging time, residual energy in each CS, and time of use (ToU) for charging. In this paper, multiple CSs compete to offer electricity charging to a single EV. Based on this need, this paper proposes a deep learning-based auction which increases the charging amounts using Myerson auction while preserving truthfulness.

Facial Expression Recognition (FER) is an attractive and demanding problem in human computer interaction. Facial expressions are one of the most dominant and usual means for a human to communicate their feelings and intentions. This paper proposes the Enhanced Modified Decision Based Unsymmetric Trimmed Median Filter (EMDBUTMF) method which removes the noisy pixels from the face image. Also, this paper proposes the Local Directional Number (LDN) pattern, Dominant Gradient Local Ternary Pattern (DGLTP) descriptor for feature extraction and Support Vector Machine (SVM) classifier for classification. The histogram features are extracted from face images using the LDN and DGLTP descriptors. The experimental result shows that the proposed method gives an improved performance of 88% accuracy on two publicly available facial expression datasets JAFFE and CK.

Fault is a common geological structure that has been revealed in the process of underground coal excavation and mining. The nature of its discontinuous structure controls the deformation, damage, and mechanics of the coal or rock mass. The interaction between this discontinuous structure and mining activities is a key factor that dominates fault reactivation and the coal burst it can induce. This paper first summarizes investigations into the relationships between coal mining layouts and fault occurrences, along with relevant conceptual models for fault reactivation. Subsequently, it proposes mechanisms of fault reactivation and its induced coal burst based on the superposition of static and dynamic stresses, which include two kinds of fault reactivations from: mining-induced quasi-static stress (FRMSS)-dominated and seismic-based dynamic stress (FRSDS)-dominated. These two kinds of fault reactivations are then validated by the results of experimental investigations, numerical modeling, and in situ microseismic monitoring. On this basis, monitoring methods and prevention strategies for fault-induced coal burst are discussed and recommended. The results show that fault-induced coal burst is triggered by the superposition of high static stress in the fault pillar and dynamic stress from fault reactivation. High static stress comes from the interaction of the fault and the roof structure, and dynamic stress can be ascribed to FRMSS and FRSDS. The results in this paper could be of great significance in guiding the monitoring and prevention of fault-induced coal bursts.

Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE) are two of the essential tools to design and develop new products. CAD systems help to design a new product efficiently, whereas CAE systems are used to predict and assess the future functionalities (durability, structural strength, etc.) of a product. Usually, these two systems work independently; however, their mutual dependency is a topic of interest for the researchers. This paper discusses the contemporary research efforts in the domain of CAD to CAE integration and later find the research gap. Based on the research gap this paper develop a methodology to integrate a CAD to CAE systems where scientific data format HDF (Hierarchical Data Format) acts as a bridge to link the gap between a CAD and CAE system. The virtual prototype of a product is getting accurate with the advancement of CAD systems; hence, the transferability of those models to a CAE system has become more sophisticated using traditional (e.g., .step, .iges files) methodologies. Since HDF is often used to handle massive and complex datasets for various research domains; hence, in this research effort HDF acts as a data repository neutral system to interact with the CAD and CAE systems. Based on the comprehensive literature review, this research effort aims to develop an uninterrupted integrated platform to handle complex design and analysis models using a big open-source data handling platform HDF (Hierarchical Data Format). MATLAB is used to bridge from the CAD system to HDF primarily and later from the HDF to CAE. A simple case study of analyzing a classic fluid mixing elbow demonstrates the preliminary implementation effort towards the proposed HDF-based integrated platform.

It is important to manage operational disruptions to ensure the success of supply chain operations. To achieve this aim, researchers have developed techniques that determine the occurrence of operational risk events which assists supply chain operational risk managers develop plans to manage them by detection/monitoring, mitigation/management, or optimization techniques. Various artificial intelligence (AI) approaches have been used to develop such techniques in the broad activities of operational risk management. However, all of these techniques are black box in their working nature. This means that the chosen technique cannot explain why it has given that output and whether it is correct and free from bias. To address this, researchers argue the need for supply chain management professionals to move towards using explainable AI methods for operational risk management. In this paper, we conduct a systematic literature review on the techniques used to determine operational risks and analyse whether they satisfy the requirement of them being explainable. The findings highlight the shortcomings and inspires directions for future research. From a managerial perspective, the paper encourages risk managers to choose techniques for supply chain operational risk management that can be auditable as this will ensure that the risk managers know why they should take a particular risk management action rather than just what they should do to manage the operational risks.

Analyses of the future for manufacturing and heavy industries in a climate constrained world many times focus on technological innovations in the early stages of the value chain, assuming few significant changes are plausible, wanted, or necessary throughout the rest of the value chain. Complex questions about competing interests, different ways of organising resource management, production, consumption, and integrating value chains are thus closed down to ones about efficiencies, pay-back times, and primary processing technologies. In this analysis, we move beyond this to identify archetypal pathways that span across value chains in four emissions intensive industries: plastics, steel, pulp and paper, and meat and dairy. The pathways as presented in the present paper were inductively identified in a multi-stage process throughout a four-year European research project. The identified archetypal pathways are i) production and end-use optimisation, ii) electrification with CCU, iii) CCS, iv) circular material flows, and v) diversification of bio-feedstock use. The pathways are at different stages of maturity and furthermore their maturity vary across sectors. The pathways show that decarbonisation is likely to force value chains to cross over traditional boundaries. This implies that an integrated industrial and climate policy must handle both sectoral specificities and commonalities for decarbonised industrial development.

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: • A Bayesian model assessment which compares two sets of values using nested sampling. This approach is new in NF grouping. • A Arsinh-Ordered Weighted Average model (Arsinh-OWA) which applies the arsinh transformation to the distance between two NFs, and then rescales the result to the arsinh of a biologically relevant threshold before grouping using OWA based distance. This approach is new in NF grouping. • An x-fold comparison as used in the ECETOC NanoApp. • Euclidean distance, which is a highly established distance metric. The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.

During the last decade, the Integrated Circuit industry has paid special attention to the security of products. Hardware-based vulnerabilities, in particular Hardware Trojans, are becoming a serious threat, pushing the research community to provide highly sophisticated techniques to detect them. Despite the considerable effort that has been invested in this area, the growing complexity of modern devices always calls for sharper detection methodologies. This paper illustrates a pre-silicon simulation-based technique to detect hardware trojans. The technique exploits well-established machine learning algorithms. The paper introduces all the background concepts and presents the methodology. The validity of the approach has been demonstrated on the AutoSoC CPU, an industrial-grade, safety-oriented, automotive benchmark suite. Experimental results demonstrate the applicability and effectiveness of the approach: the proposed technique is highly accurate in pinpointing suspicious code sections. None of the hardware trojans from the set has been left undetected.

Digital inspection twin (DIT) acting as a mirror between the physical and virtual measuring world. In this paper the measurement system with the CMMs ZEISS UMM 500 and DEA-IOTA-2203 was used as a physical twin, and a virtual machine, generated after modelling and configuring in PTC Creo software. Virtual measurement system is developed for inspection of the standard types of tolerances (concentricity, perpendicularity i.e.) and basic inspection features (IF) (plane, cylinder, truncated cone i.e.). The result of this paper is a new approach of the conceptual development of the digital inspection twin, based on CMMs - towards off-line DIT based on CMM by virtue of the control data list (CDL) and .ncl (DMIS) file.

This paper presents the results of experimental testing on fifteen reinforced concrete (RC) columns repaired or strengthened using Near Surface Mounted-Carbon Fiber Reinforced Polymer (NSM-CFRP) under concentric load, eccentric load with 125 mm eccentricity (balanced state), and pure bending moment load, while considering the effect of several configurations on the interaction diagram. Furthermore, this paper investigates the efficiency of these new configurations in the capacity of the RC columns by varying the direction of CFRP strips to be used as reinforcement. The specimens of RC columns, having cross-sectional dimensions 1200 mm × 200 mm × 200 mm (length × width × height) with 150 mm corbel at both ends (in the plane of bending moment) and 1.13 % steel reinforcement ratio, were used. The specimens were divided into five groups: control group, preloaded until 80 % transversely strengthened group, preloaded until 80 % longitudinally strengthened group, preloaded until 80 % transversely and longitudinally strengthened group, and un-loaded transversely and longitudinally strengthened group. Each group included three specimens with a different type of loading as described above. The overall response of the specimens was investigated in terms of axial load carrying capacity, axial displacement, toughness, and failure modes. Furthermore, the interaction diagram for each group was developed and investigated. The strengthened and retrofitted specimens showed a significant increase in axial capacity and significant enhancement in the interaction diagram for all groups. The study showed that the most improvement in the interaction diagram, for both tension and compression control region, occurred when specimens were strengthened in both directions (transversely and longitudinally). In addition, the interaction diagrams were modified to provide a direct solution for the NSM-CFRP ratio. The modified design model was found to be capable of predicting the axial load capacity of NSM-CFRP strips columns.

International audience; Phosphorus (P) removal from the domestic wastewater is required to counter the eutrophication in receiving water bodies and is mandated by the regulatory frameworks in several countries with discharge limits within 1-2mgPL−1. Operating at higher sludge retention time (SRT) and higher biomass concentration than the conventional activated sludge process (CASP), membrane bioreactors (MBRs) are able to remove 70–98% phosphorus without addition of coagulant. In full-scale facilities, enhanced biological phosphorus removal (EBPR) is assisted by the addition of metal coagulant to ensure >95% P-removal. MBRs are successfully used for super-large-scale wastewater treatment facilities (capacity >100,000 m3d−1). This paper documents the knowledge of P-removal modeling from lab to full-scale submerged MBRs and assesses the existing mathematical models for P-removal from domestic wastewater. There are still limited studies involving integrated modeling of the MBRs (full/super large-scale), considering the complex interactions among biology, chemical addition, filtration, and fouling. This paper analyses the design configurations and the parameters affecting the biological and chemical P-removal in MBRs to understand the P-removal process sensitivity and their implications for the modeling studies. Furthermore, it thoroughly reviews the applications of bio-kinetic and chemical precipitation models to MBRs for assessing their effectiveness with default stoichiometric and kinetic parameters and the extent to which these parameters have been calibrated/adjusted to simulate the P-removal successfully. It also presents a brief overview and comparison of seven (7) chemical precipitation models, along with a quick comparison of commercially available simulators. In addition to advantages associated with chemical precipitation for P-removal, its role in changing the relative abundance of the microbial community responsible for P-removal and denitrification and the controversial role in fouling mitigation/increase are discussed. Lastly, it encompasses several coagulant dosing control systems and their applications in the pilot to full-scale facilities to save coagulants and optimize the P-removal performance.

The rapid development of technology is leading to the emergence of smart factories where the Artificial Intelligence paradigm of deep learning plays a significant role in processing data streams from machines. This paper presents the application of Augmented Attention Blocks embedded in a deep convolutional neural network for the purposes of estimating the state of remote machines using remotely collected acoustic data. An Android application was developed for the purposes of transferring audio data from a remote machine to a base station. At the base station, we propose and developed a deep convolutional neural network called MAABL (MobileNetv2 with Augmented Attention Block). The structure of the neural network is constructed by combining an inverted residual block of MobileNetv2 with an augmented attention mechanism block. Attention Mechanism is an attempt to selectively concentrate on a few relevant things, while ignoring others in deep neural networks. Due to the presence of audio frames containing silent features not relevant to the task at hand, an Attention Mechanism is particularly important when processing audio data. The MAABL network proposed in this paper obtains the state of the art results on the accuracy and parameters of three different acoustic data sets. On a relatively large-scale acoustic dataset regarding machine faults, the method proposed in this paper achieves 98% accuracy on the test set. Moreover, after using transfer learning, the model achieved the state of the art accuracy with less training time and fewer training samples.

This paper explores the rise of short-term rental (STR) management companies and reveals the transition from a sharing economy activity to the consolidation of a professional industry hinging on what we call ‘corporate hosts’. By relying on interviews with companies operating in Lisbon and Porto, Portugal, we found: first, that a phenomenon of market concentration occurred in which individual hosts have outsourced the management of their properties to corporate hosts; second, that through the use of digital technology and vertical integration, corporate hosts are able to enhance the profitability of large portfolios of STRs; and, third, that corporate hosts imitate practices from the hotel industry, leading to the formation of a hybrid product in which the lines between hotels and STRs have blurred. We argue that corporate hosts constitute a new layer of intermediation that challenges the way we understand the STR industry and the overall functioning of this market., This research received financial support from the Portuguese Science and Technology Research Council (FCT) within the scope of the SMARTOUR project (Ref: PTDC/GES-URB/30551/2017) and grant numbers: UIDB/GEO/00295/2020; UIDP/GEO/00295/2020. We would like to thank the two anonymous reviewers for their constructive feedback. We are grateful to Ynnon Rother for his comments on an early version of the paper and to Gloria Dominguez for her help during fieldwork.

Recent developments in Information and Communication Technology (ICT) and ongoing digitalisation processes play key roles in the energy transition. It is often argued that digital technology has the potential to empower citizens and communities and thereby contribute not only to a more sustainable but also to a more democratic and fairer energy system. It is however unclear how the increasing number of frontrunner energy communities that enter the field of smart grids can benefit from these novel ICT solutions and meaningfully contribute to the sustainable energy transition. This paper explores how energy communities can mobilise ICT to enhance their agency in the energy transition. As part of a holistic multiple-case study approach, two energy communities were closely followed over a period of three years. Data were collected during project meetings and through semi-structured interviews. This paper observes that energy communities can mobilise ICT to change the way technology operates, strengthen collaboration to increase collective agency and, support their efforts in creating, disrupting, or maintaining institutions. The studied energy communities adopted a ‘fit and transform’ strategy in which they mobilised ICT to fit in the incumbent energy system in the short term, while aiming for transformation in the long term. ICT however also creates new challenges in the form of interoperability issues. This paper calls for more attention on the role of ICT when studying agency in unfolding sustainability transitions, especially in fields in which digital technology is believed to play a major role in the transformation.

Existing RC framed buildings lack significant ductility, especially when they have been built with pre-code criteria. Improving their ductile capacity can help to prevent them from the brittle collapse mechanism and to reduce the seismic damage expected. This paper aims to investigate the enhancement of the ductile response behaviour of RC framed buildings considering different non-invasive retrofitting techniques. To do so, a pre-code RC framed school located in the Spanish province of Huelva has been selected as a case study. Five non-invasive retrofitting techniques have been tested: FRP wraps and steel jackets in columns, steel beams and plates under RC beams and single steel braces. They have been selected so that they can be easily implemented in the building. Some of them have been studied in detail in previous works and others have been included for further research in this paper. In order to compare the results obtained, the most typical technique in the seismic retrofitting of RC framed buildings, the addition of X-bracings in bays, has also been tested. Most previous studies on the seismic retrofitting of RC buildings are focused on validating a method based on artificial models. This paper compares the different techniques in terms of the capacity improvement and the damage reduction, performing analyses in detail and adding them in an existing RC building. A sensitivity analysis has been carried out to determine the influence of each technique in the building’s ductile capacity considering the finite element method. Nonlinear static analyses have been performed to obtain the capacity, the displacement ductility factor (μ) and the behaviour factor (q) of each model defined. The damage expected has been determined considering the ductile and fragile failure of the elements according to the Eurocode-8 (EC8) requirements. To analyse the suitability and the efficiency of each solution, a benefit/cost ratio has been obtained taking into account the ductility improvement and the damage reduction with regards to the retrofitting costs. The results have shown that the best benefit has been obtained with the addition of steel braces. However, the optimal solutions have been single braces and steel jackets due to their combination between benefit and cost. It has been observed that the solutions that increase the stiffness of the joints have had a higher improvement due to the key role that joints have in the resistant capacity of RC structurers. Also, it has been obtained that the values of the fundamental periods have been reduced, when adding the retrofitting elements and materials, up to 30% owing to the increase of the stiffness of the system. Finally, it must be highlighted that a detailed analysis of the behaviour of the whole building must be conducted in order to avoid additional rotation effects and shear forces that could worsen the building’s seismic behaviour.

Conflicting claims are made about the extent to which Travellers in Britain remain outsiders in urban society (McGarry 2017; Sibley 1981). This paper sheds light on this question by addressing a neglected aspect of Traveller mobility, namely the day-to-day traversing of urban spaces beyond their (typically segregated) trailer sites. Focusing on public transport, it draws on eighteen months of ethnographic research with young people from Traveller backgrounds from a London-based youth group to understand the nature of the encounters they have as they go out and about in the city. The paper is interested in the place of affect in these encounters, and more generally, affect's role in shaping – or failing to shape – young Travellers' behaviour. The paper considers discomfort experienced through the mobility of young Travellers; it argues that young Travellers are encouraged by their elders to discipline and regulate emotional responses in encounters with non-Travellers and that this constitutes evidence of their continuing outsider status.

The scientific community is hardly working to propose reliable methodologies of analysis and non-invasive technologies of investigation to assess the current state of conservation of historic buildings to verify their ability to resist future threats. These structures, mostly made of masonry, are difficult to assess due to the heterogeneity of materials and their mechanical behavior, but it is vital to preserve this invaluable cultural heritage by suitable structural assessment techniques. A great deal of research attention has been paid to monitoring their structural health; in many recent publications new advanced technological methods have been provided such as cheaper sensors, wireless connections, non-contact surveys and continuous monitoring. A bibliometric study has shown that more than half of the papers on Structural Health Monitoring (SHM) and Nondestructive Testing (NDT) on masonry have been published between 2018 and 2020, and 30% of those published in 2020 were on ‘slender’ elements like towers, chimneys or minarets. This paper presents a wide-ranging review of static and dynamic studies published on SHM and NDT of slender masonry structures summarizing and discussing the different experimental techniques used. With respect to the dynamic testing, Operational Modal Analysis (OMA) by accelerometers is the mostly frequent used technique by scholars, but other promising methods such as radar interferometry are also reported. This overall discussion is concluded with a short review of some examples on numerical structural health assessment and signal processing tools. An inclusive list of papers is provided describing the most important slender masonry structures characteristics, natural frequencies, experimental and numerical techniques employed and reference values. This paper, set on a practical perspective, is expected to be of interest to those researchers and practitioners who require an extensive and up-to-date review of this topic.

The circular economy is a widely discussed topic in the field of Eco-industrial initiatives and environmentally responsive economies. The current economic and industrial model which is termed as the produce-use-dispose model is a linear model in which the resources are lost forever after their utilization; a waste of resources as well as money. In addition to economic impact, it creates immense pressure on the environment while disposing of the waste products. For this reason, scholars are trying to find an effective solution to this problem by ensuring the re-utilization of resources. The economic concept of the circular economy ensures the recycling and utilization of resources and closes the resource loop. In a world of reducing and often scarce natural resources, the recycling and utilization of resources increase the opportunities for economic growth– this is especially important given the rising demand for natural resources in emerging economies – exacerbated by the low-carbon transition. This study encompasses these ideas and explores the barriers, drivers and triggers of the circular economy transition for the mining industry. In this paper, authors review several circular economy initiatives taken by mining industries. The paper aims to demonstrate some common themes across three big mining companies with the help of content analysis; and evaluates the identified barriers, drivers and triggers of these circular economy initiatives. The authors argue that the mining industry might capitalize on the learning of other industries in relation to CE, enabling timely advancement of circular economy initiatives.

Decision makers, practitioners and community members need to assess the disaster resilience of their communities and to understand better the risks they face from natural hazards. There is a lack of consensus on what resilience means and how it can be measured as each stakeholder potentially brings a different perspective to understanding community disaster resilience. The paper will identify the key features and characteristics of Community Disaster Resilience (CDR) frameworks from the literature to develop a resilience framework that can be adapted and customised according to stakeholder needs. The paper used a 5-step process to develop an adaptable CDR framework. First, a review of 36 resilience frameworks was conducted to identify key features and characteristics of resilience frameworks. In Steps 2 and 3, a matrix of indicators and measures was populated by resilience dimensions covered in the current CDR literature reviewed. Subsequently, the indicators were sorted for similarities and duplicates were removed. Finally, they were clustered by six critical resilience dimensions (i.e. Physical, Health, Economic, Environmental, Social and Governance) into a library of 86 resilience indicators (composed of 360 measures) that can be used to operationalize a CDR framework according to the needs of the stakeholders. The review indicated that majority of the articles selected use objective approaches to measure resilience showing a gap for more frameworks using subjective, or participatory, approaches to measuring community resilience. An adaptable CDR framework may make resilience assessment more grounded in local stakeholder perspectives and lead to a better understanding of community resilience.

The Solar collectors and cogeneration units have gained increasing attention in research recently, while being developed and deployed rapidly. However, due to the instability of the solar energy, the heat flux input of solar collectors is unstable potentially causing an adverse effect on the cogeneration unit. This paper presents the mathematical models for two cogeneration units: (a) the thermal series U-shaped segmented unidirectional heat pipe array cogeneration unit with thermoelectric generator (TEG-TSUSUHP) and (b) the thermal parallel U-shaped segmented unidirectional heat pipe array cogeneration unit with thermoelectric generator (TEG-TPUSUHP). The proposed models have been validated by a series of experiments. Further, the cogeneration unit is then optimized using the proposed models. The results show that the heat loss rate of the TEG-TSUSUHP cogeneration unit is lower than that of the TEG-TPUSUHP cogeneration unit, and the lowest heat loss rate is 15.56%. Moreover, the cogeneration unit designed in this work has good thermal insulation performance with a high thermal storage efficiency of up to 80.53%. Therefore, the U-shape segmented unidirectional heat pipe array cogeneration units with thermoelectric generator (TEG-USUHP) proposed in this paper can promote the development of solar cogeneration units significantly.

[EN] Off grid electrification is key to achieving universal electricity access. Despite the potential of biomass gasifiers as a clean technology to ensure reliable systems, they are not as well looked upon as their advantages suggest. In particular, the software HOMER, which researchers and technicians use the most to simulate and design Microgrids, does not include biomass gasification among its simulation technologies. Although some authors did simulate biomass gasifiers in HOMER, the necessary parameters and procedures to calculate and include them in the tool remain unclear, non-replicable, and leave open research questions as regards modelling. Using the inbuilt Biogas power plant modules in HOMER, this paper presents a set of steps to include the technical and economic parameters to simulate fuelling an electric generator through the syngas produced in a downdraft biomass gasification plant. Two case studies of isolated rural communities in Honduras and Zambia show the viability of the procedure. These case studies also confirm the technical and economic viability of islanded biomass-photovoltaic hybrid renewable energy microgrids. In both cases, the energy demand supplied and distributed by the microgrid had a Levelized Cost of Energy lower than the alternative of extending the electric grid to the communities., We do appreciate the work of MSc Josefine Axelsson, who conducted part of the study of Mumbeji, Zambia. This is an extended and updated version of a paper originally presented at the 15th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES 2020) held in Cologne, Germany over the period 1st to September 5, 2020 (denoted then as paper SDEWES2019.0458 - Modelling a gasifier with HOMER for the simulation of off-grid hybrid renewable energy microgrids). This work was supported in part by the Spanish public administration under the grant FPU2016/00962, by the regional public administration of Valencia under the grant ACIF/2018/106, by the Food and Agricuture Organisation under the Letter of Agreement (PO number: 332412). And finally, by the Catedra de Transicion Energetica Urbana (Las Naves-UPV).

The research reported in this paper was partially supported the COPKIT project from the 8th Programme Framework (H2020) research and innovation programme (grant agreement No 786687) and from the BIGDATAMED projects with references B-TIC-145-UGR18 and P18-RT-2947., The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building., COPKIT project from the 8th Programme Framework (H2020) research and innovation programme 786687, BIGDATAMED projects B-TIC-145-UGR18 P18-RT-2947

The use of ultrasonic guided waves to probe the materials/structures for damage continues to increase in popularity for non-destructive evaluation (NDE) and structural health monitoring (SHM). The use of high-frequency waves such as these offers an advantage over low-frequency methods from their ability to detect damage on a smaller scale. However, in order to assess damage in a structure, and implement any NDE or SHM tool, knowledge of the behaviour of a guided wave throughout the material/structure is important (especially when designing sensor placement for SHM systems). Determining this behaviour is extremely diffcult in complex materials, such as fibre-matrix composites, where unique phenomena such as continuous mode conversion takes place. This paper introduces a novel method for modelling the feature-space of guided waves in a composite material. This technique is based on a data-driven model, where prior physical knowledge can be used to create structured machine learning tools; where constraints are applied to provide said structure. The method shown makes use of Gaussian processes, a full Bayesian analysis tool, and in this paper it is shown how physical knowledge of the guided waves can be utilised in modelling using an ML tool. This paper shows that through careful consideration when applying machine learning techniques, more robust models can be generated which offer advantages such as extrapolation ability and physical interpretation., Comment: 33 pages, 11 figures

Silica aerogel is the ultimate thermal insulator thanks to its record-breaking low thermal conductivity (λ), open porosity and hydrophobicity. Silica aerogel's thermal conductivity is lowest at intermediate densities (ρ ≈ 0.11 g/cm ) and, because of the strong, power-law dependence of the E modulus on density, this rather low density so far led to low E moduli. Even with polymer reinforcement, increasing stiffness is only possible at higher density, thus higher conductivity. This paper explores the synthesis of silica aerogel granules using ambient pressure drying to provide enhanced mechanical stiffness whilst maintaining thermal conductivities well below 20 mW m K . The aging and drying conditions affect the interplay between mechanical and thermal properties, and are varied to optimize the physical properties. The dense (ρ≤0.29 g/cm ), but superinsulating (λ≈15 mW m K ) silica aerogels presented in this paper challenge the community's understanding of heat transport in aerogels, and do not rely on polymer reinforcement. The underlying microscopic structural parameters affecting the mechanical and thermal transport properties are investigated by modelling and simulation of the aerogel back-bone. Short aging times reduce the cross-section of, and heat transport through, inter-particle necks, leading to an overall decrease in thermal conductivity through the solid skeleton (λ ). In addition, short-aged gels undergo a partial pore collapse during ambient pressure drying of the pore fluid due to less aged, hence weaker network structures. The resulting denser structure contains additional point contacts that increase stiffness, by up to an order of magnitude. However, heat transport through these newly formed point-contacts is limited and the gas phase conduction (λ ) is further suppressed due to the even smaller pore sizes. Strong and superinsulating particles are ideal fillers for aerogel composites, concrete and renders. The optimized APD aerogels, available as granules, are finally compiled in a composite thermal insulation board with an effective thermal conductivity down to 20 mW m K with improved strength: a 2-fold increase for E, compared to a board produced from classical silica aerogel granulate. The possibility to improve mechanical properties of pure silica aerogels can help aerogels to break into new high-strength, superinsulating structural applications needed to reduce carbon emissions of the built environment., Acta Materialia, 213, ISSN:1359-6454

Despite growing interest in the relationship between human mobility and environmental variability and change in recent years, there is relatively little understanding of the role of human agency within this nexus. This paper helps to address this knowledge gap by illuminating the role of perception, action and decision-making in the everyday. Using an innovative walking methodology, it presents an empirical case study of regularised farmers’ movements in and out of a floodplain during the rainy season in central Mozambique to show how people’s day-to-day routes are continuously reproduced through meaningful encounters and engagements with physical obstacles and other people. The paper demonstrates how a concern with everyday mobility highlights people’s day-to-day capacities to respond to environmental variability and change while also drawing attention to the challenges associated with the gradual accumulation of risk in mobile, rural livelihoods.

Copyright © 2021 The Author(s). The VECMA toolkit enables automated Verification, Validation and Uncertainty Quantification (VVUQ) for complex applications that can be deployed on emerging exascale platforms and provides support for software applications for any domain of interest. The toolkit has four main components including EasyVVUQ for VVUQ workflows, FabSim3 for automation and tool integration, MUSCLE3 for coupling multiscale models and QCG tools to execute application workflows on high performance computing (HPC). A more recent addition to the VECMAtk is EasySurrogate for various types of surrogate methods. In this paper, we present five tutorials from different application domains that apply these VECMAtk components to perform uncertainty quantification analysis, use surrogate models, couple multiscale models and execute sensitivity analysis on HPC. This paper aims to provide hands-on experience for practitioners aiming to test and contrast with their own applications. This work was supported by the VECMA project, which has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 800925. The development of MUSCLE3 and its respective description was supported by the Netherlands eScience Center and NWO under the e-MUSC project. The development of ISR3D was supported by the InSilc project and the In Silico World (ISW) project (European Union Horizon 2020 research and innovation programme grant agreements #777119 and #101016503 respectively). The calculations were performed in the Poznan Supercomputing and Networking Center.

International audience; Thermal spray is a high energy-based process, like additive manufacturing and powder atomization, leading to fine particle emission. But there is little information about these nanoparticles and their characteristics: workers’ exposure, filtration efficiency, dustiness. This paper aims to update the knowledge of the thermal spray community, to focus on thermal spraying and to a lesser degree on powder atomization and additive manufacturing technology, regarding emissions due to the high energy-based process. The three processes are presented before introducing an updated survey of thermal spray activities in France. The main thermal spray equipment is wire arc and plasma spraying. The main metal used is Al and Ni. The paper also highlights the lack of knowledge among workers on thermal spray emissions. Most workers are not made aware of collective protection (emission filtration equipment used) and personal protection (PPE). A complete bibliographical review is presented on the problem of ultrafine particle emissions mainly for the thermal spray process. Ultrafine particles are always characterized inside a working environment. The objective of this article is to list all publications related to ultrafine particle emissions around the working environment of a thermal spray process to raise awareness of the academic and industrial communities about the actual risks associated with this exposure. Finally, the authors are looking at the list of dust filtration equipment by presenting their advantages and disadvantages for particle filtration.