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Nowadays, manufacturing industries still face difficulties applying traditional Work-related MusculoSkeletal Disorders (WMSDs) risk assessment methods due to the high effort required by a continuous data collection when using observational methods. An interesting solution is to adopt Inertial Measurement Units (IMUs) to automate the data collection, thus supporting occupational health professionals. In this paper, we propose a deep learning approach to predict human motion based on IMU data with the goal of preventing industrial worker problematic movements that can arise during repetitive actions. The proposed system includes an initial Madgwick filter to merge the raw inertial tri-axis sensor data into a single angle orientation time series. Then, a Machine Learning (ML) algorithm is trained with the obtained time series, allowing to build a forecasting model. The effectiveness of the developed system was validated by using an open-source dataset composed of different motions for the upper body collected in a laboratory environment, aiming to monitor the abduction/adduction angle of the arm. Firstly, distinct ML algorithms were compared for a single angle orientation time series prediction, including: three Long Short-Term Memory (LSTM) methods - a one layer, a stacked layer and a Sequence to Sequence (Seq2Seq) model; and three non deep learning methods - a Multiple Linear Regression, a Random Forest and a Support Vector Machine. The best results were provided by the Seq2Seq LSTM model, which was further evaluated for WMSD prevention by considering 11 human subject datasets and two evaluation procedures (single person and multiple person training and testing). Overall, interesting results were achieved, particularly for multiple person evaluation, where the proposed Seq2Seq LSTM has shown an excellent capability to anticipate problematic movements., This article is a result of the project STVgoDigital - Digitalization of the T&C sector (POCI-01-0247-FEDER-046086), supported by COMPETE 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional De velopment Fund (ERDF).

We live in an era where storage systems are of major importance. In this work, we focus on storing files for use in blockchain applications. A blockchain is a distributed, replicated system, that contains a ledger of operations and executes programs known as smart contracts. Although a blockchain may be considered a data storage system, public blockchains like Ethereum charge the users per each byte stored making them expensive to store large files. Therefore, applications based on blockchains often use an external file storage system, usually peer-to-peer (P2P). We propose P2C STORE , a new storage system for blockchain applications using both P2P and cloud subsystems. This way we aim to provide application developers with the flexibility of choosing the best place for their files, depending on aspects such as the trust they place in peers and clouds. We show the benefits of the approach with a blockchain application that manages education certificates. The application stores hashes of the certificates in the cloud and the certificates themselves in our storage system.

Deterministic databases (DDs) are a promising approach for replicating data across different replicas. A fundamental component of DDs is a deterministic concurrency control algorithm that, given a set of transactions in a specific order, guarantees that their execution always results in the same serial order. State-of-the-art approaches either rely on single threaded execution or on the knowledge of read- and write-sets of transactions to achieve this goal. The former yields poor performance in multi-core machines while the latter requires either manual inputs from the user — a time-consuming and error prone task — or a reconnaissance phase that increases both the latency and abort rates of transactions.In this paper, we present Prognosticator, a novel deterministic database system. Rather than relying on manual transaction classification or an expert programmer, Prognosticator employs Symbolic Execution to build fine-grained transaction profiles (at the key-level). These profiles are then used by Prognosticator’s novel deterministic concurrency control algorithm to execute transactions with a high degree of parallelism.Our experimental evaluation, based on both TPC-C and RUBiS benchmarks, shows that Prognosticator can achieve up to 5× higher throughput with respect to state-of-the-art solutions.

Distributed systems are increasingly important in modern society, often operating on a global scale with stringent dependability requirements. Despite the vast amount of research and the development of techniques to build dependable systems, faults are inevitable as one can witness from regular failures of major providers of IT services. It is therefore fundamental to evaluate distributed systems under different fault patterns and adversarial conditions to assess their high-level behaviour and minimize the occurrence of failures. However, succinctly capturing the system configuration, environment, fault patterns and other variables affecting an experiment is very hard, leading to a reproducibility crisis. In this paper we propose the FaultSee toolkit. The two components of FaultSee are (1) the simple and descriptive FDSL language that captures the system, environment, workload and fault pattern characteristics; and (2) an easy-to-use platform to deploy and run the experiments described by the language. FaultSee allows to precisely describe and reproduce experiments and leads to a better assessment the impact of faults in distributed systems. We showcase the key features of FaultSee by studying the impact of faults with real deployments of Apache Cassandra and BFT-Smart.

Given the large adoption and economical impact of permissionless blockchains, the complexity of the underlying systems and the adversarial environment in which they operate, it is fundamental to properly study and understand the emergent behavior and properties of these systems. We describe our experience on a detailed, one-month study of the Ethereum network from several geographically dispersed observation points. We leverage multiple geographic vantage points to assess the key pillars of Ethereum, namely geographical dispersion, network efficiency, blockchain efficiency and security, and the impact of mining pools. Among other new findings, we identify previously undocumented forms of selfish behavior and show that the prevalence of powerful mining pools exacerbates the geographical impact on block propagation delays. Furthermore, we provide a set of open measurement and processing tools, as well as the data set of the collected measurements, in order to promote further research on understanding permissionless blockchains.

Network dynamics, such as sudden changes in latency or available bandwidth, have a significant impact on the performance of distributed systems. While such dynamics are common, especially in WAN deployments, existing tools lack the capabilities to systematically evaluate the impact of such changes in real systems. We present THUNDERSTORM, a tool to evaluate the impact of dynamic network topologies on the performance of large-scale distributed systems. THUNDERSTORM is a fully functional tool that integrates with Kubernetes and can be used to evaluate off-the-shelf applications. THUNDERSTORM defines an easy-to-use language to describe arbitrarily complex network topologies and dynamic events used to enrich the default container composition descriptors. Our evaluation, using micro-and macro-benchmarks, as well as off-the-shelf unmodified systems (e.g., Apache Cassandra, MariaDB) shows that THUNDERSTORM is easy to use, accurate in reproducing dynamic behaviours and that it can help researchers uncover unexpected behaviours otherwise very costly to reproduce in real deployments typically captured only during malfunctioning periods.

Mobile performance marketing is a growing industry due to the massive adoption of smartphones and tablets. In this paper, we explore Deep Multilayer Perceptrons (MLP) to predict the Conversion Rate (CVR) of mobile users that are redirected to ad campaigns (i.e., if there will be a sale). We analyze recent real-world big data provided by a global mobile marketing company. Using a realistic rolling window validation, we conducted several experiments with different datasets (two sampling and two data traffic modes), in which we measure both the predictive binary classification performance and the computational effort. The modeling experiments include: two data preprocessing methods, the popular one-hot encoding and a proposed Percentage Categorical Pruning (PCP); and two MLP learning modes, offline (reset) and online (reuse). Overall, competitive classification results were achieved by the PCP transform and the two MLP learning modes, producing real-time predictions and comparing favorably against a Convolutional Neural Network and a Logistic Regression., This article is a result of the project NORTE-01-0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by FCT Fundac¸ao para a Ciência e Tecnologia within the Project ˆScope: UID/CEC/00319/2019.

In this paper, we perform an exploratory study of user Conversion Rate (CVR) prediction using recent big data from a global mobile marketing company. We design a stream processing engine to collect sampled mobile marketing data. Then, we execute a large set of CVR prediction tests, under a two-stage experimental procedure that considers a rolling window evaluation. First, several preprocessing and machine learning combinations are analyzed using preliminary data. Next, the se- lected combinations are tested on a larger set of unseen datasets. Interesting classification performances were achieved, with some learning models (e.g., XGboost, Logistic Regression) requiring a reduced computational effort, thus showing a potential value for user CVR prediction in this domain., This article is a result of the project NORTE-01-0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundação para a Ciência e Tecnologia within the Project ˆScope: UID/CEC/00319/2013.

Recirculating Aquaculture Systems (RAS) present an innovative, clean and practical way of producing fish intensively. Stress caused by high concentrations of chemical species such as nitrite and un-ionized ammonia, affects fish health and growth and therefore the sustainability of RAS would require an online monitoring for those chemical stressors. This work reveals a study on the suitability of Aliivibrio fischeri as a toxicity sensor for un-ionized ammonia and nitrite. Temperature variation effects were also considered. An EC50 of 0.17 mg/L was found for nitrite and 0.57 mg/L for un-ionized ammonia. It was concluded that Allivibrio fischeri is suitable as an indicator for nitrite in aquaculture at optimal salinity and temperature conditions.

The use of Aliivibrio fischeri as a water quality sensor for monitoring aquaculture water is of great interest. Here, a Uniform Ray design was employed to study the mixture of five important toxicants present in recirculating aquaculture systems (RAS) water. The results were compared with the Concentration Addition and the Independent Action models. None of the models could accurately predict the behavior of the mixture, possibly due to synergism and antagonism interactions. The first was considered adequate to be used as a predictive model for a new water quality sensor for RAS. This demonstrate the need of non-additive water quality sensors.

Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) is arguably the most popular method for free binding energy prediction since it is considerably more accurate than docking or scoring methods, but considerably less demanding than Free Energy Perturbation (FEP). In this review, the most important challenges and opportunities associated to this method were addressed and evaluated. The following conclusions were obtained: accuracy of MM-PBSA calculations still strongly depends on the settings used for the prediction, the conditions in which Molecular Dynamics simulations (MD) are made, the conditions from which the experimental data are obtained, it depends on the presence of the complex structure and this method has also shown to be unable to provide accurate results for charged molecules. On the other hand, it presents many opportunities since many improvements were based on MM-PBSA calculations such as Single Amino Acid Mutation based change in Binding free Energy (SAAMBE), bootstrap, dampened Molecular Mechanics Poisson-Boltzmann Surface Area (dMM-PBSA), a high throughput method called g_mmpbsa, a method based on both MM-PBSA calculations and a scoring function, force fields for inhomogeneous protein specific environments and many other new ways of improving the accuracy of this promising method. This study intends to clarify what are the major trends in this field and thus contribute to increase MM-PBSA's applicability and efficiency.

Cloud infrastructures provide database services as cost-efficient and scalable solutions for storing and processing large amounts of data. To maximize performance, these services require users to trust sensitive information to the cloud provider, which raises privacy and legal concerns. This represents a major obstacle to the adoption of the cloud computing paradigm. Recent work addressed this issue by extending databases to compute over encrypted data. However, these approaches usually support a single and strict combination of cryptographic techniques invariably making them application specific. To assess and broaden the applicability of cryptographic techniques in secure cloud storage and processing, these techniques need to be thoroughly evaluated in a modular and configurable database environment. This is even more noticeable for NoSQL data stores where data privacy is still mostly overlooked. In this paper, we present a generic NoSQL framework and a set of libraries supporting data processing cryptographic techniques that can be used with existing NoSQL engines and composed to meet the privacy and performance requirements of different applications. This is achieved through a modular and extensible design that enables data processing over multiple cryptographic techniques applied on the same database. For each technique, we provide an overview of its security model, along with an extensive set of experiments. The framework is evaluated with the YCSB benchmark, where we assess the practicality and performance tradeoffs for different combinations of cryptographic techniques. The results for a set of macro experiments show that the average overhead in NoSQL operations performance is below 15%, when comparing our system with a baseline database without privacy guarantees.

Organic Photo Detectors (OPD) present an attractive detection system to be incorporated in Point of Care (POC) disposable diagnostic devices due to their low cost fabrication and flexible device structure. Indium Tin Oxide (ITO) is the most commonly used material for the fabrication of transparent anode for these devices. The fabrication of ITO is an expensive process and the rarity of the materials required itself makes this the cost driving factor in realizing cheap and hence disposable detection system for the POC devices. Furthermore ITO has a spiked morphology that results in a shorter device lifetime. All these disadvantages associated with ITO have made the search for an alternative material for ITO which has comparable characteristics an important research field. In this study we explore the possibility of utilizing a commercially available high conductivity grade solution of PEDOT:PSS and further enhance its characteristics by addition of gold nano particles and a MEMS structure to enhance the photo absorption of the OPD by increased surface area. The reported results show comparable sheet resistance values (18.2 Ω/sq for the fabricated electrode) and optical characteristics to the ITO electrode and a much better surface morphology characteristics for the new fabricated electrode.

A distributed system is often built on top of an overlay network. Overlay networks enable network topology transparency while, at the same time, can be designed to provide efficient data dissemination, load balancing, and even fault tolerance. They are constructed by defining logical links between nodes creating a node graph. In practice, this is materialized by a Peer Sampling Service (PSS) that provides references to other nodes to communicate with. Depending on the configuration of the PSS, the characteristics of the overlay can be adjusted to cope with application requirements and performance concerns. Unfortunately, overlay efficiency comes at the expense of dependability. To overcome this, one often deploys an application overlay focused on efficiency, along with a safety-net overlay to ensure dependability. However, this approach results in significant resource waste since safety-net overlays are seldom used. In this paper, we focus on safety-net overlay networks and propose an adaptable mechanism to minimize resource usage while maintaining dependability guarantees. In detail, we consider a random overlay network, known to be highly dependable, and propose BuzzPSS, a new Peer Sampling Service that is able to autonomously fine-tune its resource consumption usage according to the observed system stability. When the system is stable and connectivity is not at risk, BuzzPSS autonomously changes its behavior to save resources. Alongside, it is also able to detect system instability and act accordingly to guarantee that the overlay remains operational. Through an experimental evaluation, we show that BuzzPSS is able to autonomously adapt to the system stability levels, consuming up to 6x less resources than a static approach.

Domestic environments are particularly challenging for distant speech recognition: reverberation, background noise and interfering sources, as well as the propagation of acoustic events across adjacent rooms, critically degrade the performance of standard speech processing algorithms. In this application scenario, a crucial task is the detection and localization of speech events generated by users within the various rooms. A specific challenge of multi-room environments is the inter-room interference that negatively affects speech activity detectors. In this paper, we present and compare different solutions for the multi-room speech activity detection task. The combination of a model-based room-independent speech activity detection module with a room-dependent inside/outside classification stage, based on specific features, provides satisfactory performance. The proposed methods are evaluated on a multi-room, multi-channel corpus, where spoken commands and other typical acoustic events occur in different rooms.

An integrated capillary-flow driven opto-microfluidic device for on-site, parallel detection of bio-analytes has been demonstrated in this work. An array of capillary-induced flow microchannels and chambers were arranged on a microfluidic chip made of poly(methyl methacrylate). This poly(methyl methacrylate) chip was further integrated with an array of eight ring-shaped organic photodiodes for measuring chemiluminescence of pathogen immunoassays. Highly sensitive detection of chemiluminescence was ensured by the use of gold nanoparticles, which were attached to the chambers of the microfluidic chip. The developed device demonstrated a detection limit in the range of ∼104 cells/mL for bacteria detection in water. Furthermore, the device revealed feasibility to perform parallel detection of two types of bacteria and two types of viruses quantitatively. The presented concept may offer a promising route to realize truly in-the-field devices for medical and/or environmental measurements.

Short paper, The Smart Grid vision needs to address hard challenges such as interoperability, reliability and scalability before it can become fulfilled. The need to provide full interoperability between current and future energy and non-energy systems and its disparate technologies along with the problem of seamless discovery, configuration, and communication of a large variety of networked devices ranging from the resource constrained sensing devices to the large machines inside a data center requires an agnostic Service Oriented Architecture. Moreover, the sheer scale of the Smart Grid and the criticality of the communication among its subsystems for proper management, demands a scalable and reliable communication framework able to work in an heterogeneous and dynamic environment. In this position paper, we propose a generic framework, based on Web Services for interoperability, and epidemic or gossip based communication protocols for reliability and scalability, that can serve a general management substrate where several Smart Grid problems can be solved. We illustrate the flexibility of the proposed framework by showing how it can be used in two specific scenarios., Important challenges in interoperability, reliability, and scalability need to be addressed before the Smart Grid vision can be fulfilled. The sheer scale of the electric grid and the criticality of the communication among its subsystems for proper management, demands a scalable and reliable communication framework able to work in an heterogeneous and dynamic environment. Moreover, the need to provide full interoperability between diverse current and future energy and non-energy systems, along with seamless discovery and configuration of a large variety of networked devices, ranging from the resource constrained sensing devices to servers in data centers, requires an implementation-agnostic Service Oriented Architecture. In this position paper we propose that this challenge can be addressed with a generic framework that reconciles the reliability and scalability of Peer-to-Peer systems, with the industrial standard interoperability of Web Services. We illustrate the flexibility of the proposed framework by showing how it can be used in two specific scenarios.

This work reports on integrated passive-flow optical microfluidic devices to detect waterborne pathogens in the field. Ring-shaped organic photodiodes were integrated to a capillary-induced flow microfluidic channel for monitoring chemiluminescent sandwich immunoassays enhanced by gold nanoparticles. The integrated device yielded a resolution of 4×10(4) cells/mL for the detection of Legionella pneumophila, which represented a 25-fold improvement over chemiluminescence detection devices employing no gold-nanoparticle enhancement. This work demonstrates the feasibility of a low-cost but highly sensitive lab-on-a-chip device amenable for point-of-use applications.

It is reported a high-throughput microfluidic system integrating organic photodetectors based on poly(2,7-carbazole) semiconducting polymer. The system reported here showed detection limits of 105-106 cells/mL for the chemiluminescence detection of two bacterial cells. The detection of viral particles as low as 10-4 μg/mL was also shown. High specificity and ability to perform multiplexed analysis in an integrated lab-on-a-chip device was demonstrated.

A novel photodetector integrated microfluidic system for chemiluminescence (CL) detection is reported. The system incorporates a polycarbazole/fullerene photodiode whose optical characteristics (i.e. dark current, external quantum efficiency and photosensitivity) are described here. Using a CL immunoassay for detecting the stress hormone cortisol, the integrated photodetector achieved a detection sensitivity of 1.775 pA × nM(-1) and a detection limit of less than 0.28 nM. The device would be a powerful low-cost alternative to silicon photodiode and photomultiplier tube for bioanalytical assays, with potentially wide-ranging applications within point-of-care diagnostics.

It is reported the development of a polycarbazole-based organic photodetector for chemiluminescent immunoassays. The optical detector comprised a 1∶4 blend by weight of poly [N-9'-heptadecanyl-2,7-carbazole-alt-5,5-(4',7'-di-2-thienyl-2',1',3'-benzothiadiazole)] (PCDTBT) and [6,6]-phenyl C71-butyric acid methyl ester (PC70BM). Optimization of the photodetector design was conducted aiming to maximize photosensitivity and reduce the background level. Quantitation of recombinant human thyroid stimulating hormone indicated good linearity and yielded a detection sensitivity of ∼3.7 nA × nM(-1) and a detection limit of 80 pg/ml.

Large-scale distributed systems appear as the major in- frastructures for supporting planet-scale services. These systems call for appropriate management mechanisms and protocols. Slicing is an example of an autonomous, fully decentral- ized protocol suitable for large-scale environments. It aims at organizing the system into groups of nodes, called slices, according to an application-specific criteria where the size of each slice is relative to the size of the full system. This al- lows assigning a certain fraction of nodes to different task, according to their capabilities. Although useful, current slicing techniques lack some features of considerable practical importance. This pa- per proposes a slicing protocol, that builds on existing so- lutions, and addresses some of their frailties. We present novel solutions to deal with non-uniform slices and to per- form online and dynamic slices schema reconfiguration. Moreover, we describe how to provision a slice-local Peer Sampling Service for upper protocol layers and how to en- hance slicing protocols with the capability of slicing over more than one attribute. Slicing is presented as a complete, dependable and inte- grated distributed systems primitive for large-scale systems., (undefined)

Topic-based publish/subscribe is at the core of many distributed systems, ranging from application integration middleware to news dissemination. Therefore, much research was dedicated to publish/subscribe architectures and protocols, and in particular to the design of overlay networks for decentralized topic-based routing and efficient message dissemination. Nonetheless, existing systems fail to take full advantage of shared interests when disseminating information, hence suffering from high maintenance and traffic costs, or construct overlays that cope poorly with the scale and dynamism of large networks. In this paper, we present StaN, a decentralized protocol that optimizes the properties of gossip-based overlay networks for topic-based publish/subscribe by sharing a large number of physical connections without disrupting its logical properties. StaN relies only on local knowledge and operates by leveraging common interests among participants to improve global resource usage and promote topic and event scalability. The experimental evaluation under two real workloads, both via a real deployment and through simulation, shows that StaN provides an attractive infrastructure for scalable topic-based publish/subscribe.

Recently, tuple-stores have become pivotal struc- tures in many information systems. Their ability to handle large datasets makes them important in an era with unprecedented amounts of data being produced and exchanged. However, these tuple-stores typically rely on structured peer-to-peer protocols which assume moderately stable environments. Such assumption does not always hold for very large scale systems sized in the scale of thousands of machines. In this paper we present a novel approach to the design of a tuple-store. Our approach follows a stratified design based on an unstructured substrate. We focus on this substrate and how the use of epidemic protocols allow reaching high dependability and scalability., (undefined)

This study presents the Biomedical Micro-Refining (BMR) system, as a novel approach for the continuous enrichment of Giardia lamblia cysts in large-volume source water samples. The BMR system was comprised by two refining levels with a variable number of counter-flow micro-concentration units. Concentration of 10-L and 100-L water to hundreds of milliliters was achieved employing no recirculation. Using spiked surface water, the system was able to recover 89.2% cysts without significant clogging. The recovery was consistent for a wide range of cyst concentrations and turbidity levels, as well as different sample volumes.

In this paper, it is presented the counter-flow refinery (CFR) system, which is a novel approach of continuous pathogen enrichment device for large-volume raw water. The device is comprised by two refining levels, in each of which the counter-flow microconcentrator units are arranged in series. The designed CFR module is able to concentrate 100-L water samples by a concentrating factor >500. By spiking Cryptosporidium Parvum oocysts in surface water, the system achieved the recovery efficiency as high as 89.2% without clogging for a processing time of

There is an increasing demand for efficient and robust systems able to cope with today's global needs for intensive data dissemination, e.g., media content or news feeds. Unfortunately, traditional approaches tend to focus on one end of the efficiency/robustness design spectrum, by either leveraging rigid structures such as trees to achieve efficient distribution, or using loosely-coupled epidemic protocols to obtain robustness. In this paper we present BRISA, a hybrid approach combining the robustness of epidemic-based dissemination with the effi- ciency of tree-based structured approaches. This is achieved by having dissemination structures such as trees implicitly emerge from an underlying epidemic substrate by a judicious selection of links. These links are chosen with local knowledge only and in such a way that the completeness of data dissemination is not compromised, i.e., the resulting structure covers all nodes. Failures are treated as an integral part of the system as the dissemination structures can be promptly compensated and repaired thanks to the underlying epidemic substrate. Besides presenting the protocol design, we conduct an extensive evaluation in a real environment, analyzing the effectiveness of the structure creation mechanism and its robustness under faults and churn. Results confirm BRISA as an efficient and robust approach to data dissemination in the large scale., This work was supported in part by the Swiss National Foundation under agreement number 200021-127271/1 and by the Portuguese Science Foundation (FCT) grants SFRH/BD/62380/2009 and PTDC/EIA-CCO/115570/2009

Ultra-sensitive Bio-MEMS devices can automatically perform multiple laboratory functions on chips and measure traces of molecules. This research work demonstrates that the novel lab-on-a-chip system can quantify hormones in a blood sample at subfemtomolar levels with high specificity and development potential to a high-throughput tool. The notable advantage of the Lab-on-a-chip (LOC) system is the complete compatibility with the microplate reader in a biological laboratory, which can be operated easily by end users. The prototype chip contains 48 reaction chambers and can be used to quantify the concentrations of up to 6 kinds of hormones in the sample at the same time. The LOC device was designed based on IMRAMP (Immuno-real-time-amplification) assay, which is similar to a sandwich antibody-mediated immuno-PCR assay. In this assay, the barcode DNA containing the T7 RNA polymerase promoter was designed to label the antibody and utilized as the template in the following Nucleic Acid Sequence-based Amplification (NASBA) assay. Based on the result of preliminary tests, this IMRAMP chip is intended as an ultra-sensitive biomedical tool to monitor hormones in serums, which can also be used to detect water-borne disease or aquatic environmental biomarkers.

This paper presents a novel equipment with an improved structure of the magnetron sputtering for thin film fabrication. By using this equipment, a vanadium oxide thin film with a better TCR and uniformity can be fabricated. In this equipment, two magnets are aligned with the wafer table eccentrically so that the coupling effect is rendered. This changes the distribution of magnetic field lines for each single sputtering target; as a result, a magnetic field gradient in the z-directionis generated, which acts on Ar together with the alternating current (AC) electric field. Consequently, the goal of using relatively small sputtering target material to generate large uniform film on the substrate is achieved. Besides, there being no sedimentary insulation on the anode as the traditional structure, it can avoid the periodical purification for the equipment to ensure the continuous work.

In the present work, the microfabricated impedance sensing array (MISA) unit, based on interdigitated microelectrodes (IDMEs) coated with nickel hexacyanoferrate (NiHCF) film, is presented as an alternative solution to detect pathogens. The NiHCF film, which provide a [Fe(CN) 6 ]3-/4- probe-free redox reaction, was functionalized with hexahistine tags (6×His) antibodies with which they have the high-affinity. Variation in the interfacial impedance caused by bio-activated beads was measured to detect targeted Infectious Salmon Anaemia Virus (ISAV) antigen. A detection limit of 10pg/ml was demonstrated, which indicates that the MISA unit is promising to detect pathogens at early-stage of the infection.

Microfluidic devices can automatically perform multiple laboratory functions on a single, compact and integrated chip. This study demonstrates that a new concept lab-on-a-chip system is able to quantify soluble biomarkers in water environment with femtomolar sensitivity, high specificity and ability for middle throughput monitoring. This system is based on a new assay named ‘real-time immuno-nucleic acid sequence-based amplification’ (immuno-NASBA), which general scheme is similar to a sandwich antibody-mediated immuno-PCR assay. However, the barcode DNA linked with antibodies in the immunoassay contains a T7 RNA polymerase promoter is utilized as the template in the following nucleic acid sequence-based amplification (NASBA) assay. Compared with PCR, NASBA is an isothermal amplification assay which can greatly simplify microfluidic devices with the similar ultra-sensitivity. Thanks to the simplification of NASBA chip, the prototype microfluidic device introduced in this paper contains 48 reaction chambers that can be used to measure the concentrations of multiple biomarkers at the same time. The immuno-NASBA chip provides a novel tool of water environmental protection to monitor water quality in both lakes and rivers, which can also be used to detect water-borne disease or early warning of water environment pollution.