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Injection moulding is a polymer processing method of choice for making plastic parts on industrial scale, but its traditional mould is made from tooling steel with time-consuming and costly production. Additive manufacturing technologies arise as an alternative for creating mould inserts at lower costs and shorter lead times. In this context, this study describes a series of stereolithography (SLA)-printed injection mould inserts fabricated from two photopolymer resins, utilised to mould standard tensile specimens of a commercial-grade polypropylene, aiming to evaluate effects on the polymer’s thermal and mechanical properties. Our results demonstrated that the glass fibre-filled resin inserts withstood more moulding cycles before failure, had superior mechanical properties, higher Tg and greater thermal conductivity. Calorimetric data revealed that PP thermal properties and degree of crystallinity were little affected, while mechanical testing suggests a significant effect in the elongation at break. Thus, these findings highlight the importance of adequate heat extraction during injection moulding and endorse further application of SLA mould inserts for the manufacturing of injection-moulded plastic parts in the case of prototypes or small batches, provided suitable cooling is made available, contributing to the feasibility and affordability of employing this approach for an industrial setting. This paper is an extended version of conference paper by M. Hopkins, S. Gunbay, C. Hayes, V.F. Moritz, E. Fuenmayor, J.G. Lyons, D.M. Devine “Stereolithography (SLA) utilised to print injection mould tooling in order to evaluate thermal and mechanical properties of commercial polypropylene”. Procedia Manuf. 2021, 55, 205–212. 10.1016/j.promfg.2021.10.029. yes

peer-reviewed The full text of this article will not be available in ULIR until the embargo expires on the 06/03/2021 One of the many evolved functions of photosynthetic organisms is to synthesize light harvesting nanostructures from photoactive molecules such as porphyrins. Engineering synthetic analogues with optimized molecular order necessary for the efficient capture and harvest of light energy remains challenging. Here, we address this challenge by reporting the self-assembly of zinc(II) meso-tetrakis(4-carboxyphenyl)porphyrins into films of highly ordered nanostructures. The self-assembly process takes place selectively at the interface between two immiscible liquids (water|organic solvent), with kinetically stable interfacial nanostructures formed only at pH values close to the pKa of the carboxyphenyl groups. Molecular dynamics simulations suggest that the assembly process is driven by an interplay between the hydrophobicity gradient at the interface and hydrogen bonding in the formed nanostructure. Ex situ XRD analysis and in situ UV/vis and steady state fluorescence indicates the formation of chlathrate type nanostructures that retain the emission properties of their monomeric constituents. The self-assembly method presented here avoids the use of acidic conditions, additives such as surfactants and external stimuli, offering an alternative for the realization of light-harvesting antennas in artificial photosynthesis technologies.

Despite the proliferation of e-Participation initiatives, overall efforts towards mainstreaming social media-based and citizen-led political deliberations are still limited. Consequently, there is a paucity of research on expected mutual re-shaping of deliberations on traditional e-Participation platforms and spontaneous citizen discussions on social media platforms. This mutual re- shaping phenomenon also referred to as the “duality of e-Participation” has been identified by Macintosh et al. in 2009, however up to date there is lack of methods and tools to study or enabling to harness the duality. In particular, the duality requires a specific, e-Participation-aligned, technical infrastructure to enable decision makers in government to access relevant information about ongoing citizen discussions on social media platforms. In this dissertation, we investigate the nature of the duality of e-Participation phenomenon; we construct a design inter alia of a Social Software Infrastructure (SSI), as a tool to study this duality. The design has been based upon e-Participation domain structuration and analysis leading to specific infrastructure requirements. Specifically, we combine the Structuration Theory complemented by Dynamic Capabilities Theory in an integrated model for e-Participation – an essential theoretical lens for generation of the SSI requirements. Also, we conduct a set of semi-structured interviews with politicians and decision-makers in relation to the duality. We investigate with politicians the nature of duality, we discuss possible ways of harnessing the duality and we 4 attempt to evaluate the feasibility of applying specific technologies, in the context of SSI, to enable effective social media-based e-Participation. The consolidated, structured results from the interviews inform a comprehensive analysis of the phenomenon of duality of e-Participation. From our results, we identify the essential technical capabilities that need to be obtained by governments to harness the duality. Finally, based on the knowledge acquired throughout the study, and considering all the challenges identified, we elaborate on the revised technical infrastructure implementation realising en evolved SSI design. We conclude with recommendations for governments to realise this SSI and some proposals for future work.

IntroductionSDG 4.7 mandates university contributions to the United Nations (UN) Sustainable Development Goals (SDGs) through their education provisions. Hence, universities increasingly assess their curricular alignment to the SDGs. A common approach to the assessment is to identify keywords associated with specific SDGs and to analyze for their presence in the curriculum. An inherent challenge is associating the identified keywords as used in the diverse set of curricular contexts to relevant sustainability indicators; hence, the urgent need for more systematic assessment as SDG implementation passes its mid-cycle.MethodIn this study, a more nuanced technique was evaluated with notable capabilities for: (i) computing the importance of keywords based on the term frequency-inverse document frequency (TF-IDF) method; (ii) extending this computation to the importance of courses to each SDG and; (iii) correlating such importance to a statistical categorization based on the Association for the Advancement of Sustainability in Higher Education (AASHE) criteria. Application of the technique to analyze 5,773 modules in a university's curriculum portfolio facilitated categorization of the modules/courses to be “sustainability-focused” or “sustainability-inclusive.” With the strategic objective of systematically assessing the sustainability content of taught curricula, it is critical to evaluate the precision and accuracy of the computed results, in order to attribute text with the appropriate SDGs and level of sustainability embeddedness. This paper evaluates this technique, comparing its results against a manual and labor-intensive interpretation of expert informed assessment of sustainability embeddedness on a random sample of 306 modules/courses.Results and discussionExcept for SDGs 1 and 17, the technique exhibited a reasonable degree of accuracy in predicting module/course alignment to SDGs and in categorizing them using AASHE criteria. Whilst limited to curricular contexts from a single university, this study indicates that the technique can support curricular transformation by stimulating enhancement and reframing of module/course contexts through the lens of the SDGs.

In this study, we develop a framework for an intelligent and self-supervised industrial pick-and-place operation for cluttered environments. Our target is to have the agent learn to perform prehensile and non-prehensile robotic manipulations to improve the efficiency and throughput of the pick-and-place task. To achieve this target, we specify the problem as a Markov decision process (MDP) and deploy a deep reinforcement learning (RL) temporal difference model-free algorithm known as the deep Q-network (DQN). We consider three actions in our MDP; one is ‘grasping’ from the prehensile manipulation category and the other two are ‘left-slide’ and ‘right-slide’ from the non-prehensile manipulation category. Our DQN is composed of three fully convolutional networks (FCN) based on the memory-efficient architecture of DenseNet-121 which are trained together without causing any bottleneck situations. Each FCN corresponds to each discrete action and outputs a pixel-wise map of affordances for the relevant action. Rewards are allocated after every forward pass and backpropagation is carried out for weight tuning in the corresponding FCN. In this manner, non-prehensile manipulations are learnt which can, in turn, lead to possible successful prehensile manipulations in the near future and vice versa, thus increasing the efficiency and throughput of the pick-and-place task. The Results section shows performance comparisons of our approach to a baseline deep learning approach and a ResNet architecture-based approach, along with very promising test results at varying clutter densities across a range of complex scenario test cases.

Significant advances have been made in recent years in the materials development of liquid-based 4D printing. Nevertheless, employing additive materials such as nanoparticles for enhancing printability and shape memory characteristics is still challenging. Herein, we provide an overview of recent developments in liquid-based 4D printing and highlights of novel 4D-printable polymeric resins and their nanocomposite components. Recent advances in additive manufacturing technologies that utilise liquid resins, such as stereolithography, digital light processing, material jetting and direct ink writing, are considered in this review. The effects of nanoparticle inclusion within liquid-based resins on the shape memory and mechanical characteristics of 3D-printed nanocomposite components are comprehensively discussed. Employing various filler-modified mixture resins, such as nanosilica, nanoclay and nanographene, as well as fibrous materials to support various properties of 3D printing components is considered. Overall, this review paper provides an outline of liquid-based 4D-printed nanocomposites in terms of cutting-edge research, including shape memory and mechanical properties.

—Exactly-once data processing/delivery can be guaranteed in traditional big data processing systems, e.g. Apache Flink. Checkpoint is commonly used as the solution. Each operator in these systems can restart from the last successfully saved state whenever a failure happens. It is not necessary to restore the logical job graph onto the same device(s) in traditional datacentre scenarios with powerful servers close to each other. However, the datacentre oriented solutions are not suitable for IoT collaborative edge computing scenarios. The logical job graph is tightly coupled to the physical topology in IoT networks. Data processing task(s) cannot be placed at a random edge device to recover from a network failure as it needs to evaluate the benefits of transmitting data versus processing/aggregating the data. To address the above challenges, this paper proposes an Information Centric Networking based solution and correspondent protocols to provide Exactly-oncecomputation for the Collaborative Edge in IoT (ECE). It contains a job execution scheme to deliver IoT jobs with exactly once data computation guarantee and a recovery procedure to dynamically change the IoT job execution graph while experiencing link failures. The protocol also provides a checking procedure on data state (received/un-received and computed/un-computed) to prevent any data loss or duplicated data processing due to the updated job graph. A data identification approach based on the job graph is devised to support the ECE functionality. A testbed has been developed on ndnSIM and the simulation results have verified the feasibility and scalability of ECE design. It also evaluates the overhead incurred by the ECE protocol to guarantee exactly once data computation. yes

Refined optimization of complex curve–linear-shaped radiators, such as traveling-wave Vivaldi antennas, can be achieved by considering simulated near fields to interpret in detail the structural influences of a design. The relationships between the space and time distributions of electromagnetic (EM) energy clusters and the geometric features are revealed with appropriate use of impulse response analysis combined with the multiple signal classification (MUSIC) algorithm. This article reports a deeper approach when applied to the adjustment of the geometric features of a traveling-wave antenna based on an analysis of near-field propagation features.

Various complex biological effects occur when ultrasonic compression waves travel through biological material. The myriad of biological outcomes instigated by ultrasound are evident when viewed through the lens of the hallmarks of cancer. Herein, we summarise the therapeutic potential of ultrasound, enhanced by microbubbles, for the treatment of cancer.

Content replication and name-based routing lead to a natural multi-source and multipath transmission paradigm in NDN. Due to the unique connectionless characteristic of NDN, current end-to-end multipath congestion control schemes (e.g. MPTCP) cannot be used directly on NDN. This paper proposes a Network Utility Maximization (NUM) model to formulate multi-source and multipath transmission in NDN with in-network caches. From this model, a family of receiver-driven transmission solutions can be derived, named as path-specified congestion control. The path-specified congestion control enables content consumers to separate the traffic control on each path, which consequently facilitates fair and efficient bandwidth sharing amongst all consumers. As a specific instance, a Delay-based Path-specified Congestion Control Protocol (DPCCP) is presented, which utilizes queuing delays as signals to measure and control congestion levels of different bottlenecks. In addition, a set of high-performance congestion control laws are designed to accelerate bandwidth and fairness convergence towards the optimum defined by the NUM model. Finally, DPCCP is compared with state-of-the-art solutions. The experimental evaluations show that DPCCP outperforms existing solutions in terms of bandwidth utilization, convergence time and packet loss. yes

Poor durability of reinforced concrete structures can lead to serious structural failures. An accurate model to observe the effects of aggressive agents like carbonation, sulfate ingress, and seawater solutions on the solid phase assemblage will help designers and specifiers better understand how cement behaves in these environments. This paper presents the first steps in developing such a model using the PHREEQC geochemical software by accounting for alkali binding and dissolution. It also presents the use of discrete solid phases (DSPs) to account for the solid-solution behaviour of siliceous hydrogarnet and magnesium silicate hydrate (M-S-H). A new thermodynamic description of the vaterite phase has also been developed for this work using the cemdata18 thermodynamic database. The predicted phase assemblages of cements in these environments here agree with previously published findings using a different thermodynamic model supported with experimental data.

Host immunity to infection with SARS-CoV-2 is highly variable, dictating diverse clinical outcomes ranging from asymptomatic to severe disease and death. We previously reported reduced type I interferon in severe COVID-19 patients preceded clinical worsening. Further studies identified genetic mutations in loci of the TLR3- or TLR7-dependent interferon-I pathways, or neutralizing interferon-I autoantibodies as risk factors for development of COVID-19 pneumonia. Here we show in patient cohorts with different severities of COVID-19, that baseline plasma interferon α measures differ according to the immunoassay used, timing of sampling, the interferon α subtype measured, and the presence of autoantibodies. We also show a consistently reduced induction of interferon-I proteins in hospitalized COVID-19 patients upon immune stimulation, that is not associated with detectable neutralizing autoantibodies against interferon α or interferon ω. Intracellular proteomic analysis shows increased monocyte numbers in hospitalized COVID-19 patients but impaired interferon-I response after stimulation. We confirm this by ex vivo whole blood stimulation with interferon-I which induces transcriptomic responses associated with inflammation in hospitalized COVID-19 patients, that is not seen in controls or non-hospitalized moderate cases. These results may explain the dichotomy of the poor clinical response to interferon-I based treatments in late stage COVID-19, despite the importance of interferon-I in early acute infection and may guide alternative therapeutic strategies.

In hydrating Portland cements, more than one of the AFm family of calcium aluminates may exist. Depending on the amount of carbonate and sulfate present in the cement, the most common phase to precipitate is monosulfate, monocarbonate and/or hemicarbonate. It has been reported in the literature that hemicarbonate often appears in measurements such as XRD but not predicted to form/equilibrate in thermodynamic models. With the ongoing use of commercial cements such as CEM I and CEM II containing more and more limestone, it is important to understand which hydrate solids physically precipitate and numerically predict over time. Using 27 cement samples with three w/c ratios analysed at 1, 3 and 28 days, this paper shows that although hemicarbonate was observed in a hydrating commercial Portland cement, as well as being predicted based on its carbonate (CO2/Al2O3) and sulfate (SO3/Al2O3) ratios, thermodynamic analysis did not predict it to equilibrate and form as a solid hydrate. Regardless of the w/c ratio, thermodynamic analysis did predict hemicarbonate to form for calcite contents < 2 wt.%. It appears that the dominant stability of monocarbonate in thermodynamic models leads to it precipitating and remaining as a persistent phase.

The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

Because of the thermoelectric (TE) effect (or Seebeck effect), a difference of potential is generated as a consequence of a temperature gradient across a sample. The TE effect has been mostly studied and engineered in semiconducting materials and it already finds several commercial applications. Only recently the TE effect in cement-based materials has been demonstrated and there is a growing interest in its potential. For instance, a temperature gradient across the external walls of a building can be used to generate electricity. By the inverse of the TE effect (or Peltier effect), one can also seek to control the indoor temperature of a building by biasing TE elements embedded in its external walls. In designing possible applications, the TE properties of cement-based materials must be determined as a function of their chemical composition. For instance, the TE properties of cement paste can be enhanced by the addition of metal oxide (e.g., Fe2O3) powder. In this paper, a single thermoelectric leg is studied using the finite element method. Metal oxide additives in the cement paste are modelled as spherical inhomogeneities. The thermoelectric properties of the single components are based on experimental data, while the overall thermoelectric properties of the composites are obtained from the numerical model. The results of this numerical study are interpreted according to the effective medium theory (EMT) and its generalisation (GEMT).

The thermoelectric building envelope (TBE) integrates thermoelectric materials with the building envelope for active space heating and cooling. The advantage of TBE heating and cooling includes its significantly low-profile design and no refrigerant use. Although there are existing studies evaluating TBE performance, they were based on limited operating conditions. The study aims to experimentally evaluate the heating and cooling performance of a TBE prototype under various operating conditions. The TBE prototype was installed between two psychrometric chambers, which simulated indoor and outdoor conditions. The prototype was tested at an indoor temperature of around 22.35–23.58 degrees C and outdoor temperatures from about 7.35 degrees C and 16.99 degrees C for heating and from 28.36 degrees C to 40.95 degrees C for cooling, with varied power inputs and fan conditions. The maximum coefficient of performance (COP) of TBE in heating mode is 3.2. The average heating COP of TBE with a current of 1.5 A in four winter scenarios is 1.37. The average heating COP of TBE operating with the current of 0.3–1.5 A at an outdoor temperature of 12 degrees C is 2.27. The TBE system demonstrates a better heating efficiency than an auxiliary electric heater for the heat pump system. The experimental results and evaluation obtained provide critical guidance for the deployment of TBE applications.

Host immunity to infection with SARS-CoV-2 is highly variable, dictating diverse clinical outcomes ranging from asymptomatic to severe disease and death. We previously reported that reduced blood type I interferon (IFN-I) in severe COVID-19 patients preceded clinical worsening. These results were supported by studies which identified genetic mutations in loci of the TLR3- or TLR7-dependent IFN-I pathways, or autoantibodies neutralizing IFNα or IFNω, as major risk factors for development of severe and critical COVID-19 pneumonia. Here, we analyzed a range of IFN-I associated responses in patient cohorts with different severities of COVID-19, showing that baseline plasma IFNα measures differed significantly according to the immunoassay used, as well as timing of sampling, the IFNα subtype measured, and the presence of autoantibodies. We then compared immune responses induced by ex vivo stimulation between non-hospitalized moderate cases (n=27) and hospitalized (n=17) adult patients that required oxygen supplementation. This showed a consistently reduced induction of IFN-I proteins in hospitalized COVID-19 patients upon stimulation, that was not associated with detectable neutralizing autoantibodies against IFNα or IFNω. We confirmed the poor induction of IFN-I in an independent patient cohort (n=33), and showed it was more pronounced with severe disease. Intracellular proteomic analysis showed that while monocyte numbers were increased in hospitalized COVID-19 patients, they did not secrete IFN-I in response to stimulation. This was further confirmed by ex vivo whole blood stimulation with IFN-I which induced a transcriptomic response associated with inflammation in hospitalized COVID-19 patients, that was not seen in controls or non-hospitalized moderate cases. These results may explain the dichotomy of the poor clinical response to IFN-I based treatments in late stage COVID-19, despite the critical importance of IFN-I in early acute infection. An improved understanding of such variable responses to treatment may help to identify potential alternative therapeutic strategies.

Thermoelectric materials enable the direct conversion of thermal to electrical energy. One application of this is ambient heat energy harvesting where relatively stable temperature gradients existing between the inside and outside of a building could be utilized to produce electricity. Buildings can thus change from energy consumers to energy generators. This could ultimately help reduce the surface temperatures and energy consumption of buildings, especially in urban areas. In this paper, research work carried out on developing and characterizing a cement-based thermoelectric material is presented. Cement-based samples are doped with different metal oxides (Bi2O3 and Fe2O3) to enhance their thermoelectric properties, which are defined through their Seebeck coefficient, electrical conductivity and thermal conductivity. The study also discusses the positive impact of moisture content on the electrical conductivity

Phosphoester containing polymers are promising materials in biomedical applications due to their biocompatibility and biodegradability. Utilising thiol-ene chemistry, the synthesis of two novel structural polymer motives combining polyesters and phophoester groups was explored. The first polymer was obtained by coupling ene-functional poly(thioether-phosphoester) with thiol functional poly(pentadecalactone). While the coupling reaction was successful, yields remained low presumably due to inadequate endgroup stoichiometry. The second polymer comprised phosphoester side groups conjugated to unsaturated poly(globalide). Double bond conversions up to 84% were achieved depending of the type of phosphoester thiol and relative reactant ratios. The resulting polymers transitioned from solid semicrystaline to liquid amorphous with increasing degree of phosphoester conjugation. Electrospun fibres from polymers with 14% phosphoester conjugation allowed attachment and survival of human dermal fibroblasts, indicating their biocompatibility. These polymers represent a new class of easily accessible biocompatible polyester-phosphoester hybrid materials as potential building blocks for tunable biomaterials.

The photocatalytic behaviours of semiconductive ceramic nanoparticles such as TiO2, ZnO, Fe2O3, and Fe3O4, have been extensively studied in photocatalysis and photopolymerization, due to their ability to produce radical species under ultraviolet–visible light, and even in dark conditions. In addition, in the form of microparticles, TiO2 and its Magnéli phases are capable of neutralizing radical species, and a heterogeneous catalytic process has been suggested to explain this property, as it is well known as scavenging activity. Thus, in this study, we demonstrate that these ceramic powders, in the form of microparticles, could be used as photoinitiators in UV polymerization in order to synthesize a hydrogel matrix. Them, embedded ceramic powders could be able to neutralize radical species of physiological media once implanted. The hydrogel matrix would regulate the exchange of free radicals in any media, while the ceramic particles would neutralize the reactive species. Therefore, in this work, the scavenger activities of TiO2, ZnO, Fe2O3, and Fe3O4 microparticles, along with their photoinitiation yield, were evaluated. After photopolymerization, the gel fraction and swelling behaviour were evaluated for each hydrogel produced with different ceramic initiators. Gel fractions were higher than 60%, exhibiting variation in their scavenging activity. Therefore, we demonstrate that ceramic photoinitiators of TiO2, ZnO, Fe2O3, and Fe3O4 can be used to fabricate implantable devices with scavenger properties in order to neutralize radical species involved in inflammatory processes and degenerative diseases.

Ultrasound is a sound wave with frequencies ranging between 20 kHz and 20 MHz. Ultrasound is able to temporarily and repeatedly open the BBB safely and enhance chemotherapeutic delivery without adverse effects. This novel technique in drug delivery benefits from the powerful ability of ultrasound to produce cavitation activity. Cavitation is the generation and activity of gas-filled bubbles in a medium exposed to ultrasound. As the pressure wave passes through the media, gas bubbles expand at low pressure and contract at high pressure. This leads to oscillation which produces a circulating fluid flow known as microstreaming around the bubble with velocities and shear rates proportional to the amplitude of the oscillation. At high amplitudes the associated shear forces can cut open liposomes

The aim of this study was to determine the effects of a pin‐to‐plate cold atmospheric plasma (CAP) on U‐251 MG three‐dimensional (3D) glioblastoma spheroids under different conditions. 3D tumorspheres showed higher resistance to the CAP treatment compared to 2D monolayer cells. A single CAP treatment was able to induce cytotoxicity, while multiple CAP treatments augmented this effect. CAP was also able to induce cytotoxicity throughout the tumoursphere, and we identified that reactive oxygen species(ROS) plays a major role, while H2O2plays a partial role in CAP‐induced cytotoxicity in tumour-spheres. We conclude that ROS‐dependent cytotoxicity is induced uniformly throughout glioblastoma and epidermoid tumourspheres by direct CAP treatment.

Ever since its discovery, the applications of Shape Memory Alloys (SMA) can be found across a range of application domains, from structural design to medical technology. This is based upon the unique and inherent characteristics such as thermal Shape Memory Effect (SME) and Superelasticity (or Pseudoelasticity). While thermal SME is used for shape morphing applications wherein temperature change can govern the shape and dimension of the SMA, Superelasticity allows the alloy to withstand a comparatively very high magnitude of loads without undergoing plastic deformation at higher temperatures. These unique properties in wearables have revolutionized the field, and from fabrics to exoskeletons, SMA has found its place in robotics and cobotics. This review article focuses on the most recent research work in the field of SMA-based smart wearables paired with robotic applications for human-robot interaction. The literature is categorized based on SMA property incorporated and on actuator or sensor-based concept. Further, use-cases or conceptual frameworks for SMA fiber in fabric for ‘Smart Jacket’ and SMA springs in the shoe soles for ‘Smart Shoes’ are proposed. The conceptual frameworks are built upon existing technologies; however, their utility in a smart factory concept is emphasized, and algorithms to achieve the same are proposed. The integration of the two concepts with the Industrial Internet of Things (IIoT) is discussed, specifically regarding minimizing hazards for the worker/user in Industry 5.0. The article aims to propel a discussion regarding the multi-faceted applications of SMAs in human-robot interaction and Industry 5.0. Furthermore, the challenges and the limitations of the smart alloy and the technological barriers restricting the growth of SMA applications in the field of smart wearables are observed and elaborated.

Microplastics (MPs) (size < 5 mm) marine pollution have been investigated and monitored by many researchers and found in many coasts around the world. These toxic chemicals make their way into human diet through food chain when aquatic organisms ingest MPs. Attenuated Total Reflection Fourier transform infrared spectroscopy (ATR-FTIR) is a very effective method to detect MPs. To provide the automatic detecting method for MPs, Numerous studies have proposed Machine Learning (ML) based methods, such as Support Vector Machines, K Nearest Neighbours, and Random Forests, for identification and classification of MPs through using the ATR FTIR data. The evaluations of these ML based methods primarily focus on the average scores across all types of MPs. However, the existing FTIR datasets are normally imbalanced. Furthermore, some MPs contain the identical functional group, and some MPs may be fouled or contaminated, which will reduce the quality of FTIR data samples (e.g. lacking of peaks or creating noises). These factors will interfere the ML classification algo rithms and cause the algorithms to perform differently while identifying different MPs. Hence, this work pro poses an ensemble learning algorithm to exploit the advantage of different ML algorithms based on a systematic evaluation of the existing ML based MP identification approaches. A neural network is employed to fuse the outputs of chosen ML algorithms to improve the overall metrics. The evaluation results show that the proposed algorithm outperforms existing single ML based approaches. no

To provide energy-efficient space heating and cooling, a thermoelectric building envelope (TBE) embeds thermoelectric devices in building walls. The thermoelectric device in the building envelope can provide active heating and cooling without requiring refrigerant use and energy transport among subsystems. Thus, the TBE system is energy and environmentally friendly. A few studies experimentally investigated the TBE under limited operating conditions, and only simplified models for the commercial thermoelectric module (TEM) were developed to quantify its performance. A holistic approach to optimum system performance is needed for the optimal system design and operation. The study developed a holistic TBE-building system model in Modelica for system simulation and performance analysis. A theoretical model for a single TEM was first established based on energy conversion and thermoelectric principles. Subsequently, a TBE prototype model combining the TEM model was constructed. The prototype model employing a feedback controller was used in a whole building system simulation for a residential house. The system model computed the overall building energy efficiency and dynamic indoor conditions under varying operating conditions. Simulation results indicate the studied TBE system can meet a heating demand to maintain the desired room temperature at 20 °C when the lowest outdoor temperature is at -26.3 degrees C, with a seasonal heating COP near 1.1, demonstrating a better heating performance than electric heaters. It suggests a potential energy-efficient alternative to the traditional natural gas furnaces and electric heaters for space heating.

International audience; IRE1 alpha cleaves several mRNAs upon accumulation of misfolded proteins. Here the authors show that active IRE1 alpha cleaves DGAT2 mRNA encoding the rate-limiting enzyme in the synthesis of triacylglycerols, suggesting a role of IRE1 alpha in reprogramming lipid metabolism in cancer cells. IRE1 alpha is constitutively active in several cancers and can contribute to cancer progression. Activated IRE1 alpha cleaves XBP1 mRNA, a key step in production of the transcription factor XBP1s. In addition, IRE1 alpha cleaves select mRNAs through regulated IRE1 alpha-dependent decay (RIDD). Accumulating evidence implicates IRE1 alpha in the regulation of lipid metabolism. However, the roles of XBP1s and RIDD in this process remain ill-defined. In this study, transcriptome and lipidome profiling of triple negative breast cancer cells subjected to pharmacological inhibition of IRE1 alpha reveals changes in lipid metabolism genes associated with accumulation of triacylglycerols (TAGs). We identify DGAT2 mRNA, encoding the rate-limiting enzyme in TAG biosynthesis, as a RIDD target. Inhibition of IRE1 alpha, leads to DGAT2-dependent accumulation of TAGs in lipid droplets and sensitizes cells to nutritional stress, which is rescued by treatment with the DGAT2 inhibitor PF-06424439. Our results highlight the importance of IRE1 alpha RIDD activity in reprograming cellular lipid metabolism.

Currently, microservices are trending as the most popular software application design architecture. Software organisations are also being targeted by more cyber-attacks every day and newer security measures are in high demand. One available measure is the application of anomaly detection, which is defined as the discovery of irregular or unusual activity that occurs to a greater or lesser degree than normal occurrences in a data series. In this paper, we continue existing work where various real-world cyber-attacks are executed against a running microservices application, and the application traffic is logged and returned in the form of distributed traces. A Diffusion Convolutional Recurrent Neural Network is used to model the set of distributed traces and learn the spatial and temporal dependencies of the application traffic. Subsequently, the model is used to make predictions for ongoing microservice activity and threshold-based anomaly detection is applied to detect irregular microservice activity indicating the presence of seeded cyber security attacks, or anomalies. The cyber-attacks used to evaluate this approach include a brute force attack, a batch registration of bot accounts and a distributed denial of service attack. yes

International audience; Rifting of the Red Sea resulted in the formation of one of the highest escarpments on our planet: the Great Escarpment of southwestern Saudi Arabia. Published low-temperature geochronology ages are around the Paleogene-Neogene transition, but geomorphic features like the height, the steepness and the preservation of the escarpment may indicate a younger aspect to the exhumation history. Here we use apatite fission track (AFT) and (U-Th-Sm)/He (AHe) cooling ages in combination with a geomorphic analysis of the region from Jeddah to Jizan to test this idea. Pooled AFT ages range from to Ma (1σ). The base of the AFT partial annealing zone prior to rock uplift is at ∼200 m elevation in most of the studied area, but track length data show that the amount of exhumation is insufficient to affect all coastal plain samples. Consistent with earlier estimates in the literature, the total amount of rock uplift is estimated to be ∼4 km. Single grain AHe ages range from to Ma (1σ) and do not have a clear trend with distance from the escarpment. Overall, the distribution of data appears to indicate widespread downwearing of the elevated topography around the rift flank. In contrast, geomorphic analysis that indicates escarpment retreat including a flexural uplift response. We suggest that the conflict between geomorphological and geochronological data can be resolved by invoking a two-stage erosion model. The first stage involved dome-shaped rift flank uplift since initiation of the Afar plume, downwearing of this topography and cooling recorded by the AFT ages. The second stage involved the massive erosion that excavated the present day Saudi escarpment and reset the AHe ages. We suggest that this second stage is related to the formation of oceanic lithosphere since 13 Ma in the Red Sea, associated downwarping of the margin and flexural updoming of the eastern rift flank. The consequential orographic precipitation initiated asymmetric erosion and formation of a retreating escarpment. This model is consistent with the large age difference between Miocene AFT and Pliocene AHe ages and the geomorphic metrics.

The human gut microbiome, of which the genus Bifidobacterium is a prevalent and abundant member, is thought to sustain and enhance human health. Several surface-exposed structures, including so-called sortase-dependent pili, represent important bifidobacterial gut colonization factors. Here we show that expression of two sortase-dependent pilus clusters of the prototype Bifidobacterium breve UCC2003 depends on replication slippage at an intragenic G-tract, equivalents of which are present in various members of the Bifidobacterium genus. The nature and extent of this slippage is modulated by the host environment. Involvement of such sortase-dependent pilus clusters in microbe-host interactions, including bacterial attachment to the gut epithelial cells, has been shown previously and is corroborated here for one case. Using a Maximum Depth Sequencing strategy aimed at excluding PCR and sequencing errors introduced by DNA polymerase reagents, specific G-tract sequences in B. breve UCC2003 reveal a range of G-tract lengths whose plasticity within the population is functionally utilized. Interestingly, replication slippage is shown to be modulated under in vivo conditions in a murine model. This in vivo modulation causes an enrichment of a G-tract length which appears to allow biosynthesis of these sortase-dependent pili. This work provides the first example of productive replication slippage influenced by in vivo conditions. It highlights the potential for microdiversity generation in “beneficial” gut commensals.

International audience; Pinned and mobile ferroelastic domain walls are detected in response to mechanical stress in a Mn3+ complex with two-step thermal switching between the spin triplet and spin quintet forms. Single-crystal X-ray diffraction and resonant ultrasound spectroscopy on [Mn-III(3,5-diCl-sal(2)(323))]BPh4 reveal three distinct symmetry-breaking phase transitions in the polar space group series Cc -> Pc -> P1 -> P1((1/2)). The transition mechanisms involve coupling between structural and spin state order parameters, and the three transitions are Landau tricritical, first order, and first order, respectively. The two first-order phase transitions also show changes in magnetic properties and spin state ordering in the Jahn-Teller-active Mn3+ complex. On the basis of the change in symmetry from that of the parent structure, Cc, the triclinic phases are also ferroelastic, which has been confirmed by resonant ultrasound spectroscopy. Measurements of magnetoelectric coupling revealed significant changes in electric polarization at both the Pc -> P1 and P1 -> P1((1/2)) transitions, with opposite signs. All these phases are polar, while P1 is also chiral. Remanent electric polarization was detected when applying a pulsed magnetic field of 60 T in the P1 -> P1((1/2)) region of bistability at 90 K. Thus, we showcase here a rare example of multifunctionality in a spin crossover material where the strain and polarization tensors and structural and spin state order parameters are strongly coupled.

Background Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a complex antibody response that varies by orders of magnitude between individuals and over time. Methods We developed a multiplex serological test for measuring antibodies to 5 SARS-CoV-2 antigens and the spike proteins of seasonal coronaviruses. We measured antibody responses in cohorts of hospitalized patients and healthcare workers followed for up to 11 months after symptoms. A mathematical model of antibody kinetics was used to quantify the duration of antibody responses. Antibody response data were used to train algorithms for estimating time since infection. Results One year after symptoms, we estimate that 36% (95% range, 11%–94%) of anti-Spike immunoglobulin G (IgG) remains, 31% (95% range, 9%–89%) anti-RBD IgG remains, and 7% (1%–31%) of anti-nucleocapsid IgG remains. The multiplex assay classified previous infections into time intervals of 0–3 months, 3–6 months, and 6–12 months. This method was validated using data from a seroprevalence survey in France, demonstrating that historical SARS-CoV-2 transmission can be reconstructed using samples from a single survey. Conclusions In addition to diagnosing previous SARS-CoV-2 infection, multiplex serological assays can estimate the time since infection, which can be used to reconstruct past epidemics., Longitudinal follow-up of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reveals differences in the duration of antibodies to multiple antigens. Mathematical models of antibody waning and statistical algorithms can estimate an individual’s time since SARS-CoV-2 infection, and previous transmission waves in a population.

International audience; The interleukin-12 (IL-12) family comprises the only heterodimeric cytokines mediating diverse functional effects. We previously reported a striking bimodal IL-12p70 response to lipopolysaccharide (LPS) stimulation in healthy donors. Herein, we demonstrate that interferon β (IFNβ) is a major upstream determinant of IL-12p70 production, which is also associated with numbers and activation of circulating monocytes. Integrative modeling of proteomic, genetic, epigenomic, and cellular data confirms IFNβ as key for LPS-induced IL-12p70 and allowed us to compare the relative effects of each of these parameters on variable cytokine responses. Clinical relevance of our findings is supported by reduced IFNβ-IL-12p70 responses in patients hospitalized with acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or chronically infected with hepatitis C (HCV). Importantly, these responses are resolved after viral clearance. Our systems immunology approach defines a better understanding of IL-12p70 and IFNβ in healthy and infected persons, providing insights into how common genetic and epigenetic variation may impact immune responses to bacterial infection.

This paper presents a cement hydration model over time using the cemdata07 thermodynamic database and a series of derived discrete solid phases (DSPs) to represent calcium silicate hydrate (C-S-H) as a binary solid solution with two end-members. C-S-H in cement is amorphous and poorly crystalline with a range of molar Ca/Si ratios from 0.6 to 1.7. It displays strongly incongruent dissolution behaviour, where the release of calcium into solution is several orders of magnitude greater than silicon. It is, therefore, important that any cement hydration model provides a credible account of this behaviour. C-S-H has been described in the cemdata07 thermodynamic database as a number of solid solutions using different end-members with differing levels of complexity. While solid solutions can be included in most modern geochemical software programs, they often lead to a significant increase in computation time. This paper presents how an incongruent solid solution between two C-S-H end-members may be represented as a number of DSPs to model cement hydration over time using the PHREEQC geochemical software. By using DSPs rather than modelling C-S-H as a nonideal solid solution, this gives the user full control of the input for the model, reducing the computational demand and analysis time with no loss in accuracy in predicting stable-phase assemblages and their associated pore chemistry over time.

International audience; The complexity of karst groundwater flow modelling is reflected by the amount of simulation approaches. The goal of the Karst Modelling Challenge (KMC) is comparing different approaches on one single system using the same data set. Thirteen teams with different computational models for simulating discharge variations at karst springs have applied their respective models on one single data set coming from the Milandre Karst Hydrogeological System (MKHS). The approaches include neural networks, reservoir models, semi-distributed models and fully distributed groundwater models. Four and a half years of hourly or daily meteorological input and hourly discharge data were provided for model calibration. The validation comprised forecasting one year of discharge, without the observed discharge data. The model performance was evaluated using the volume conservation, Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) applied on the total discharge and individual flow components. As a result, the comparison of model performances is a challenging task due to the differences in the model architecture but also required time steps: some of the models require aggregated daily steps while others could be run using hourly data, which provided some interesting differences depending on how the data was transformed. The use of instantaneous data (e.g. value at noon) produces less bias that averaging hourly data over one day. The transformation of hourly into daily data produces a decrease of Nash and KGE of 0.05 to 0.08 (i.e. from 1 to ~0.93). The resulting simulations (forecasted values for year 2016) produced KGEs ranging between 0.83 and 0.37 (0.83 to −0.24 for NSE). Although the simulations matched the monitored flows reasonably well, most models struggled to simulate baseflow conditions accurately. In general, the models that performed the best for this exercise were the global ones (Gardenia and Varkarst), with a limited number of parameters, which can be calibrated using automatic calibration procedures. The neural network models also showed a fair potential, with one providing reasonable results despite the relatively short dataset available for warming-up (4.5 years). Semi-and fully distributed models also suggested that with some more effort they could perform well. The accuracy of model predictions does not seem to increase by using models with more than 9–12 calibration parameters. An evaluation of the relative errors between the forecasted and the observed values revealed that for most models, 50% of the forecasted values contained more than 50% of difference against the observed discharge rate, with 25% having a difference larger than 100%. A significant part of the poorly forecasted values corresponded to base-flow which was surprising given that as base-flow is generally much easier to predict than peak flow. Hence, this shows that modelling approaches and criteria for the calibration are too oriented towards peak-flow sections of the hydrographs, and that improvements could be gained by more focus on the base-flow.

Data center networks (DCNs) are the backbone of many cloud and Internet services. They are vulnerable to link failures, that occur on a daily basis, with a high frequency. Service disruption due to link failure may incur financial losses, compliance breaches and reputation damage. Performance metrics such as packet loss and routing flaps are negatively affected by these failure events. We propose a new Bayesian learning approach towards adaptive path allocation that aims to improve DCN performance by reducing both packet loss and routing flaps ratios. The proposed approach incorporates historical information about link failure and usage probabilities into its allocation procedure, and updates this information on-the-fly during DCN operational time. We evaluate the proposed framework using an experimental platform built with the POX controller and the Mininet emulator. Compared with a benchmark shortest path algorithm, the results show that the proposed methods perform better in terms of reducing the packet loss and routing flaps.

The Internet of Things (IoT) is revolutionising nearly every aspect of modern life, playing an ever greater role in both industrial and domestic sectors. The increasing frequency of cyber-incidents is a consequence of the pervasiveness of IoT. Threats are becoming more sophisticated, with attackers using new attacks or modifying existing ones. Security teams must deal with a diverse and complex threat landscape that is constantly evolving. Traditional security solutions cannot protect such sys- tems adequately and so researchers have begun to use Machine Learning algorithms to discover effective defence systems. In this paper, we investigate how one approach from the domain of evolutionary computation - grammatical evolution - can be used to identify cyberattacks in IoT environments. The experiments were conducted on up-to-date datasets and compared with state- of-the-art algorithms. The potential application of evolutionary computation-based approaches to detect unknown attacks is also examined and discussed

COVID-19 has made eHealth an imperative. The pandemic has been a true catalyst for remote eHealth solutions such as teleHealth. Telehealth facilitates care, diagnoses, and treatment remotely, making them more efficient, accessible, and economical. However, they have a centralized identity management system that restricts the interoperability of patient and healthcare provider identification. Thus, creating silos of users that are unable to authenticate themselves beyond their eHealth application’s domain. Furthermore, the consumers of remote eHealth applications are forced to trust their service providers completely. They cannot check whether their eHealth service providers adhere to the regulations to ensure the security and privacy of their identity information. Therefore, we present a blockchain-based decentralized identity management system that allows patients and healthcare providers to identify and authenticate themselves transparently and securely across different eHealth domains. Patients and healthcare providers are uniquely identified by their health identifiers (healthIDs). The identity attributes are attested by a healthcare regulator, indexed on the blockchain, and stored by the identity owner. We implemented smart contracts on an Ethereum consortium blockchain to facilities identification and authentication procedures. We further analyze the performance using different metrics, including transaction gas cost, transaction per second, number of blocks lost, and block propagation time. Parameters including block-time, gas-limit, and sealers are adjusted to achieve the optimal performance of our consortium blockchain.

The open-source event-driven ECG dataset is available at https://github.com/jedaiproject/Open-Source-Event-Driven-ECG-Dataset; International audience; In this article, non-uniformly sampled electrocardiogram (ECG) signals obtained from level-crossing analog-to-digital converters (LC-ADCs) are analyzed for event-driven classification and compression performance. The signal compression results show that it is important to assess the distortion in event-driven signals when simulating LC-ADC models, especially at lower resolutions and larger quantization steps. The effects of varying the LC-ADC parameters for the application of cardiac arrhythmia classifiers are also assessed using an artificial neural network (ANN) and the MIT-BIH Arrhythmia Database. In comparison with uniformly-sampled data, it is possible to achieve comparable classification accuracy at a much lower complexity with event-driven ECG signals. The results show the best event-driven model achieves over 97% accuracy with 79% reduction in ANN complexity with signal-to-distortion ratio (S/D)≥21dB. For S/D

3D cell culture is a technique that is used to grow cells in vitro that will mimic an in vivo environment. 3D cell models are a helpful learning tool for researchers to better understand disease mechanisms and to explore different therapeutic properties of drugs. 3D cell cultures can be developed using patient derived cancer cells. Once they have been grown, these 3D cells can be used to screen for small molecule drugs or for genetic modification in for analysis of disease pathways or to predict drug treatments toxicity or efficacy. 3D cell cultures are a big step towards the more ethical testing of drug toxicity and efficacy as they decrease the need to use animals in research as well as providing more reliable results as the cells used are of human physiology. Cellusponge are 3D porous hydroxipropylcellulose scaffolds that are designed for use with cells that do not require specific ligands. As well as the standard non-coated cellusponge, there are two more of the same type of scaffold available for use that are made with two different coatings to allow for improved adaptation of different cell types, these are called Cellusponge-Gal and Cellusponge-Col. Cellusponge is a no-coating approach that is intended for use in the development of general soft tissue 3D culture. It has been used as soft matrix for 3D cell culture and 3D tumour model.

The use of 3D cell culture has been a major step in developing cellular models that can mimic physiological tissues. Traditional 2D cell cultures are often unable to accurately represent the cellular functions and responses that are present in tissues, as a result, research findings based on 2D cultures tend to be skewed with limited predictive capability. 3D cell cultures can be grown from cells obtained from cancer organoids in patients. These models are useful for understanding disease mechanisms and exploring drug therapeutics in areas such as toxicity and efficacy. In order to gather more physiologically relevant data, a variety of 3D cell culture techniques have been developed to mimic the in vivo characteristics of physiological tissues. This protocol describes in vitro generation of U-251MG spheroids using the hanging drop method. Advantages of using hanging drop plate method are, able to produce uniform size spheroids, low cost, comfortable to handling and suitable for short term culture. The main downside of this method is medium change, different drug treatment at different time points are impossible and labor intensive. This method uses the Perfecta3D hanging drop plate, a novel cell culture device that simplifies the process of spheroid formation, testing and analysis. Rather than having to invert the plates which often results in spillage or detachment, these plates are designed to create hanging drops using a plateau structure at the bottom of the plate.

3D cell culture is a process used to grow cells in vitro to mimic an in vivo environment. 3D cell models are very useful for understanding disease mechanisms and exploring drug therapeutics. 3D cultures can be grown from cells taken from cancer organoids in patients. Once grown, they can be used to screen for small molecule drugs or they can be genetically modified in order to analyse disease pathways or predict the toxicity or efficacy of a drug treatment. These cultures decrease the need to use animals in research and provides more reliable results as it uses human physiology. This protocol describes the in vitro generation of spheroids using the low attachment plate method. This method uses low-adhesion plates that are coated with hydrophilic polymer to allow cells to cluster together, forming their own extracellular matrix, rather than sticking to the plate surface. The scaffold-free 3D cell culture models produced can more accurately reflect an in vivo microenvironment making them useful in the study of oncology, hepatotoxicity, neurology, nephrology and stem cell biology.

International audience; We investigate giant magnetoelectric coupling at a Mn spin crossover in [MnL]BPh (L = (3,5-diBr-sal)323) with a field-induced permanent switching of the structural, electric, and magnetic properties. An applied magnetic field induces a first-order phase transition from a high spin/low spin (HS-LS) ordered phase to a HS-only phase at 87.5 K that remains after the field is removed. We observe this unusual effect for DC magnetic fields as low as 8.7 T. The spin-state switching driven by the magnetic field in the bistable molecular material is accompanied by a change in electric polarization amplitude and direction due to a symmetry-breaking phase transition between polar space groups. The magnetoelectric coupling occurs due to a γη coupling between the order parameter γ related to the spin-state bistability and the symmetry-breaking order parameter η responsible for the change of symmetry between polar structural phases. We also observe conductivity occurring during the spin crossover and evaluate the possibility that it results from conducting phase boundaries. We perform ab initio calculations to understand the origin of the electric polarization change as well as the conductivity during the spin crossover. Thus, we demonstrate a giant magnetoelectric effect with a field-induced electric polarization change that is 1/10 of the record for any material.

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

The general context of this work is the feedback control of an infinite-dimensional system so that the closed-loop system satisfies a fading-memory property and achieves the setpoint tracking of a given reference signal. More specifically, this paper is concerned with the Proportional Integral (PI) regulation control of the left Neumann trace of a one-dimensional reaction-diffusion equation with a delayed right Dirichlet boundary control. In this setting, the studied reaction-diffusion equation might be either open-loop stable or unstable. The proposed control strategy goes as follows. First, a finite-dimensional truncated model that captures the unstable dynamics of the original infinite-dimensional system is obtained via spectral decomposition. The truncated model is then augmented by an integral component on the tracking error of the left Neumann trace. After resorting to the Artstein transformation to handle the control input delay, the PI controller is designed by pole shifting. Stability of the resulting closed-loop infinite-dimensional system, consisting of the original reaction-diffusion equation with the PI controller, is then established thanks to an adequate Lyapunov function. In the case of a time-varying reference input and a time-varying distributed disturbance, our stability result takes the form of an exponential Input-to-State Stability (ISS) estimate with fading memory. Finally, another exponential ISS estimate with fading memory is established for the tracking performance of the reference signal by the system output. In particular, these results assess the setpoint regulation of the left Neumann trace in the presence of distributed perturbations that converge to a steady-state value and with a time-derivative that converges to zero. Numerical simulations are carried out to illustrate the efficiency of our control strategy., Comment: Preprint

Currently, drones represent a promising technology for combating Coronavirus disease 2019 (COVID-19) due to the transport of goods, medical supplies to a given target location in the quarantine areas experiencing an epidemic outbreak. Drone missions will increasingly rely on drone collaboration, which requires the drones to reduce communication complexity and be controlled in a decentralized fashion. Blockchain technology becomes a must in industrial applications because it provides decentralized data, accessibility, immutability, and irreversibility. Therefore, Blockchain makes data public for all drones and enables drones to log information concerning world states, time, location, resources, delivery data, and drone relation to all neighbors drones. This paper introduces decentralized independent multi-drones to accomplish the task collaboratively. Improving blockchain with a consensus algorithm can improve network partitioning and scalability in order to combat COVID-19. The multi-drones task is to combat COVID-19 via monitoring and detecting, social distancing, sanitization, data analysis, delivering goods and medical supplies, and announcement while avoiding collisions with one another. We discuss End to End (E2E) delivery application of combination blockchain and multi-drone in combating COVID-19 and beyond future pandemics. Furthermore, the challenges and opportunities of our proposed framework are highlighted. yes

The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.