In recent years, digital microfluidics (DMF) has emerged as a powerful tool for the manipulation over individual droplets in integrated microfluidic devices. In DMF, discrete droplets at low volume can be individually controlled to merge, mix, split, and dispense from reservoirs through an array of electrodes. Apart from possessing the advantages of conventional microfluidics, including minimized reagent use and reaction time, prevention of cross-contamination as well as a high level of integration, DMF is also capable of maintaining precise control over unit droplets without the use of propulsion devices such as pumps or mechanical mixers, which cannot be made possible in traditional channel-based microfluidics. The flexibility of DMF also makes it an ideal platform for integration with biosensors, which allows for in-line detection in a fully integrated system. These unique properties have allowed DMF to be used for multiplexed detection of biological and chemical reactions at microscale or nanoscale. In fact, it has been exploited in a large variety of application scenarios, including cell-based applications, DNA-based applications, and protein-based applications. DMF devices have also opened many new possibilities in point-of-care diagnostics. In this chapter, we first introduce the basic theory of DMF devices and analyze the mechanism of droplet actuation. Two common configurations (parallel plate and single plate) of DMF devices as well as their fabrication methods have also been described. Furthermore, a detailed discussion focusing on the integration of biosensors into the DMF systems is given for biological applications, including both electrochemical biosensors and optical biosensors. Various application scenarios based on these devices have also been summarized. Finally, we summarize the advantages of DMF devices and challenges facing their development in the future.
Over the past few decades, fiber-optic devices have shown remarkable potential for cell analysis. Recently emerged fiber-optic technologies have been successfully applied to sense, manipulate, and image cells in vitro and in vivo. These new technologies not only offer great opportunities to perform sensitive and distributed/multiplexed analysis on cells but also provide a novel and minimally invasive approach to investigate the intercellular and intracellular dynamic events. In this chapter, we focus on the review of fiber-optic-based compact devices and their applications in cell analysis. We first introduce the basic principles of fiber-optic sensing and trapping technologies, followed by a detailed discussion of their applications in bulk-cell analysis, single-cell analysis, and intravital-cell analysis. We then conclude the chapter with a brief summary and outlook.
The 316L stainless steels, often used in turbine blades for naval and marine applications, usually suffer from localized pitting corrosion after long exposure to chlorinated environments. The aluminum-zirconium coatings deposited by magnetron sputtering technique can be used to ensure cathodic protection for steels. In this work, we study the influence of atomic layer deposited (ALD) Al2O3, ZnO, and TiO2 thin films on the structural, mechanical, and electrochemical properties of Al-Zr (4 at.% Zr) magnetron sputtered coatings. The morphology, preferred orientation growth, mechanical properties, wettability, and corrosion resistance were investigated. The change in the sputtered Al-Zr morphology is mainly due to the insertion of the ALD layer. The Al-Zr layer deposited on ZnO and TiO2 layers presented a distinctive morphology. The agglomerate particles of AlZr/Al2O3/AlZr, AlZr/ZnO/AlZr and AlZr/TiO2/AlZr coatings exhibited a cauliflower shape. For ALD/PVD coatings, the insertion of an ALD oxide layer promoted the intensity of the peaks corresponding to the (111) crystallographic orientation. The nanoindentation measurements confirmed the enhancement in the mechanical properties, where the hardness increased by about 75%. The ALD oxide layers promoted the hydrophobicity of the coatings. The electrochemical characterization in a 3.5 wt.% NaCl solution also confirmed the role of the ALD oxides layers in delaying the pitting corrosion of the Al-Zr coating by widening the passive region and enhancing the protective efficiency of the passive film.
New services with low-latency (LL) requirements are one of the major challenges for the envisioned Internet. Many optimizations targeting the latency reduction have been proposed, and among them, jointly re-architecting congestion control and active queue management (AQM) has been particularly considered. In this effort, the Low Latency, Low Loss and Scalable Throughput (L4S) proposal aims at allowing both Classic and LL traffic to cohabit within a single node architecture. Although this architecture sounds promising for latency improvement, it can be exploited by an attacker to perform malicious actions whose purposes are to defeat its LL feature and consequently make their supported applications unusable. In this paper, we exploit different vulnerabilities of L4S which are the root of possible attacks and we show that application-layer protocols such as QUIC can easily be hacked in order to exploit the over-sensitivity of those new services to network variations. By implementing such undesirable flows in a real testbed and characterizing how they impact the proper delivery of LL flows, we demonstrate their reality and give insights for research directions on their detection.
We consider a system modeled by a semi-Markov process where we include geometric renewal process for sojourn times. Pérez-Ocón and Torres-Castro first study this system . In our work here we consider an extended state space for up and down times separately. This allows us to use the standard theory for semi-Markov processes in order to obtain all reliability related measurements as reliability, availability (point and steady-state), mean times and rate of mortality of the system with general initial law. We proceed with a convolution algebra, which allows us to obtain final closed form formulas for the above measurements. Moreover, we present numerical examples.
Integrated single photon sources are essential elements in quantum computing, simulation, communication, and photonic neural networks. The directional radiation and scattering of single‐photon sources play a crucial role in light manipulation and rely on electric and magnetic dipole moments. Although clever physical insights and designer intuition strategies have been successfully applied in the development of integrated sources, inverse strategies could enhance and maximize its performance. Recently, topology‐optimized couplers for on‐chip single‐photon sources are designed to efficiently couple a guided mode and an electric dipole. However, the superposition of orthogonal electric and magnetic dipoles can also be harnessed due to the additional degrees of freedom via their interference. Here, the authors have extended the strategy to couplers that enhance spin‐direction coupling of circularly polarized, Huygens, and Janus dipoles. The authors demonstrate that optimization not only increases the coupling to a desire mode but also enhances the electric and magnetic local density of states (LDOS) while maintaining the amplitude and phase relation between the orthogonal dipoles for unidirectional coupling. Currently, coupling efficiency and enhanced LDOS of up to 88%, 94%, and 93% and up to 9.4, 18.6, and 14.9 are obtained for these dipoles. The topology optimization improves the performances of 3D integrated photonic devices. Integrated couplers that enhance spin‐direction coupling between circular polarized electric and magnetic dipoles and photonic modes are demonstrated. The topology‐optimized nanophotonic devices not only increase the light coupling to a desire mode but also enhance the electric and magnetic local density of states. Currently, coupling efficiency of more than 90% is obtained for the circular polarized, Huygens, and Janus dipoles.
Over the past 30 years, the demand for cooling energy has tripled in the Middle East (ME). This is provoked by the need to ensure thermal comfort in a very hot climate, as indoor thermal conditions are important for maintaining occupants' comfort. In a subtropical region, it becomes more critical in terms of energy consumption. This paper investigates the thermal comfort in buildings located in the city of Byblos, Lebanon. Thermal comfort was evaluated using subjective methods. The thermal perceptions of respondents were determined by questionnaires. Indoor environmental conditions such as relative humidity, air temperature, and air velocity were measured using a sensor during winter and summer seasons. While the results show a strong correlation between thermal comfort and architectural parameters, this study specifically focuses on building height and orientation. Findings highlight the need to propose an adapted suitably strategy to evaluate thermal comfort.
Over the past two decades, the wind turbine industry has grown rapidly. As a result, thousands of tons of composite materials from these end-of-life (EoL) wind turbine blades (WTBs) are discarded every year. Due to their three-dimensional structure, which consists of a thermoset matrix and mainly glass fibers (GF), their recovery is a challenge. The objective of this study is to compare several recycling technologies for composite materials using landfill as a baseline scenario. Several aspects can influence the performance of plastic composite recycling, but one of the most important is the efficiency of recycling technologies in terms of the recovered glass fiber rate. To evaluate this amount of fiber annually, a material flow analysis (MFA) was performed using 2023 as the study year. A correlation with other aspects was established in order to perform a multi-criteria approach based on maturity level, technical, economic and environmental aspects. These criteria are the Technology Readiness (TRL), cost, energy demand and retained tensile strength
Priority substances likely to pollute water can be characterized by mid-infrared spectroscopy based on their specific absorption spectral signature. In this work, the detection of volatile aromatic molecules in the aqueous phase by evanescent-wave spectroscopy has been optimized to improve the detection efficiency of future in situ optical sensors based on chalcogenide waveguides. To this end, a hydrophobic polymer was deposited on the surface of a zinc selenide prism using drop and spin-coating methods. To ensure that the water absorption bands will be properly attenuated for the selenide waveguides, two polymers were selected and compared: polyisobutylene and ethylene/propylene copolymer coating. The system was tested with benzene, toluene, and ortho-, meta-, and para-xylenes at concentrations ranging from 10 ppb to 40 ppm, and the measured detection limit was determined to be equal to 250 ppb under these analytical conditions using ATR-FTIR. The polyisobutylene membrane is promising for pollutant detection in real waters due to the reproducibility of its deposition on selenide materials, the ease of regeneration, the short response time, and the low ppb detection limit, which could be achieved with the infrared photonic microsensor based on chalcogenide materials. To improve the sensitivity of future infrared microsensors, the use of metallic nanostructures on the surface of chalcogenide waveguides appears to be a relevant way, thanks to the plasmon resonance phenomena. Thus, in addition to preliminary surface-enhanced infrared absorption tests using these materials and a functionalization via a self-assembled monolayer of 4-nitrothiophenol, heterostructures combining gold nanoparticles/chalcogenide waveguides have been successfully fabricated with the aim of proposing a SEIRA microsensor device.
Plastics are used widely, and modern civilization would have to behave differently without them. However, plastics pose a threat to sustainable life. This paper focuses on some of the provisions being made for sustainable production to date and focuses on one key sector-plastic manufacturing-where sustainable production patterns are urgently needed. The paper describes the latest trends related to plastic production, its environmental impacts, and how this sector is adjusting its processes in order to meet the current and forthcoming legal requirements and consumer demands. The methodological approach of the study has focused on both a literature review on the one hand and the consumers’ perspective obtained via a survey on the other. These two approaches were then crosschecked in order to assess current trends in plastic manufacturing and to understand how consumers see these trends as being consistent with the aims of the UN Sustainable Development Goal 12. The results obtained suggest that a greater engagement of consumers is needed in supporting the efforts to manage plastic more sustainably. Based on its findings, the paper provides useful insights linked to principles and tools for sustainable plastic production and design, and it demonstrates the usefulness and urgency of a sound materials management in order to tackle plastic pollution, one of today´s major environmental problems.
Nowadays, energy saving is one of the main concerns of companies. Therefore, different studies have developed and integrated methods to measure the energy consumption of machine tools. In this paper, an integrated economic production quantity (EPQ) model that considers energy consumption is analyzed. The concept of specific energy consumption (SEC) is used to evaluate the energy consumption during the production time of the machine. Three types of SEC, depending on the production rate, were considered. By minimizing the total cost, the status of the machine in the non-production phase, the optimal production rate, the optimal cycle time, and the influence of these different methods of measuring energy consumption are defined. The results of the study show that the difference in using the SEC -types model does not affect the state selection of the machine during the non-production phase but can change the optimal solutions of the overall system. From the manager’s point of view, the choice of different models to represent the energy consumption of the machine leads to different optimal decisions. Numerical analysis are performed, and results are discussed.
Surface plasmon resonance devices typically rely on the use of gold-coated surfaces, but the use of more abundant metals is desirable for the long-term development of plasmonic biochips. As a substitute for gold, thin copper films have been deposited on glass coverslips by thermal evaporation. As expected, these films immersed in a water solution initially exhibit an intense plasmonic resonance comparable to gold. However, without protection, an angle-resolved optical analysis shows a rapid degradation of the copper, characterized by a continuous angular shift of the plasmonic resonance curve. We show that copper films protected with a thin layer of aluminum oxide of a few nanometers can limit the oxidation rate for a sufficient time to perform some standard measurements. As the process is simple and compatible with the current biochip production technique, such an approach could pave the way for the production of alternative and more sustainable biochips.
Action recognition is an important research topic in video analysis that remains very challenging. Effective recognition relies on learning a good representation of both spatial information (for appearance) and temporal information (for motion). These two kinds of information are highly correlated but have quite different properties, leading to unsatisfying results of both connecting independent models (e.g., CNN-LSTM) and direct unbiased co-modeling (e.g., 3DCNN). Besides, a long-lasting tradition on this task with deep learning models is to just use 8 or 16 consecutive frames as input, making it hard to extract discriminative motion features. In this work, we propose a novel network structure called ResLNet (Deep Residual LSTM network), which can take longer inputs (e.g., of 64 frames) and have convolutions collaborate with LSTM more effectively under the residual structure to learn better spatial-temporal representations than ever without the cost of extra computations with the proposed embedded variable stride convolution. The superiority of this proposal and its ablation study are shown on the three most popular benchmark datasets: Kinetics, HMDB51, and UCF101. The proposed network could be adopted for various features, such as RGB and optical flow. Due to the limitation of the computation power of our experiment equipment and the real-time requirement, the proposed network is tested on the RGB only and shows great performance.
Rapid advancements in communication technology have made vehicular networks a reality with numerous applications. However, vehicular network security is still an open research problem. Artificial intelligence (AI) techniques have emerged to address these issues. AI and its variants are becoming more popular for detecting attacks and dealing with many types of security issues in vehicular networks. This paper presents a comprehensive survey of AI-based techniques for security issues in vehicular networks. We first give a background on vehicular networks and their vulnerabilities. In addition, assess AI fundamentals with their impact on vehicular security. Furthermore, we classify and compare the AI-based solutions related to security for vehicular networks by proposing a new taxonomy. Finally, we present an analysis of the works included in this survey.
Gradient features including stress-strain response, yield strength, and hardening behaviour of a shot-peened structure is studied under uniaxial tension by modelling residual stress field and residual work hardening (RWH). Three representative constitutive models are used in predicting the tensile behaviour of the shot-peened structure, with the RWH being considered in different ways. The experimentally observed early yielding and strengthening effect of the shot-peened structure can be well predicted by the reconstructed RWH gradient coupled with a developed combined isotropic/kinematic hardening (Developed-CIK) model. Analyses show that the shot peening does not obviously increase yield strength of the material in each layer, but improves the resistance to plastic deformation through increasing the kinematic hardening level. This is contradictory to the general opinion that the yield strength can be improved due to shot peening. Further discussion indicates that an accurate characterization of the RWH and a thorough consideration in an appropriate constitutive model are crucial to precisely predict mechanical properties of a shot-peened structure and residual stress release during a certain loading.
The durability improvement of mechanical systems produced by Additive Manufacturing has become a key challenge in the industry, especially with architectured materials which present enhanced physical properties. Additive Manufacturing has also made it possible to develop architectured materials which are cellular or distributed materials in which the topological allocation is controlled and optimized for specific functions or properties, such as auxetic materials. Auxetics are structures that have a negative Poisson’s ratio which becomes thicker perpendicular to the applied tensile force. Moreover, Generative Design is a design exploration process to create highly optimized design making additive technology one of the best ways to access some new design space. This work aims to combine Generative Design and Integrated Design as part of a simultaneous approach to evaluate the capabilities of auxetic materials made by Fused Filament Fabrication in 3D printing. Three main aspects are considered in this approach: the parametric design of a pattern geometry to generate a structured part considering fabrication constraints through design guidelines, the additive manufacturing of the generated part and the evaluation of the structured material’s mechanical behavior. Three-point bending tests are also carried out to validate the analytical approach as well as to study auxetic structures properties. This parametric design methodology allowed for the fabrication of auxetic bending specimens. Outcomes from mechanical tests were used to elaborate an equivalent simplified beam model to predict the elastic behavior of auxetic materials before manufacturing. Then, the mechanical tests have shown also that the pattern’s compression around stress application aims to reinforce the structure. The results show the potential of the implemented global and simultaneous approach to develop auxetic materials through a Generative Design for Additive Manufacturing methodology. Hence, this research work provides a basis for further work to improve the numerical analysis aspect and extend the approach to develop architectured and functionalized materials. The results show multiple auxetic design iterations of bending specimens based on geometric, material, and additive technology parameters in order to verify their mechanical behaviors.
Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart’s rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.
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