Efficient Processing Scheme Research Articles

How does varying the number of personas affect user perceptions and behavior? Challenging the ‘small personas’ hypothesis!

Studies in human-computer interaction recommend creating fewer than ten personas, based on stakeholders’ limitations to cognitively process and use personas. However, no existing studies offer empirical support for having fewer rather than more personas. Investigating this matter, thirty-seven participants interacted with five and fifteen personas using an interactive persona system, choosing one persona to design for. Our study results from eye-tracking and survey data suggest that when using interactive persona systems, the number of personas can be increased from the conventionally suggested ‘less than ten’, without significant negative effects on user perceptions or task performance, and with the positive effects of increasing engagement with the personas, having a more diverse representation of the end-user population, as well as users accessing personas from more varied demographic groups for a design task. Using the interactive persona system, users adjusted their information processing style by spending less time on each persona when presented with fifteen personas, while still absorbing a similar amount of information than with five personas, implying that more efficient information processing strategies are applied with more personas. The results highlight the importance of designing interactive persona systems to support users’ browsing of more personas.

Reaction-extraction platforms towards CO2-derived cyclic carbonates catalyzed by ionic liquids

This work systematically monitors the keys to understand the applicability of two close-cycle processes recently proposed that enable the regeneration of homogeneous catalysts based on ionic liquids by liquid-liquid extraction. It aims at looking for more efficient CO2 conversion platforms to prevent global warming. The universal character of these two platforms is here demonstrated for a substrate scope that accounts the 7 most representative cyclic carbonates of the literature and 17 ionic liquids that play the role of efficient homogeneous catalysts. Water-based platform proves to be more appropriate for separating hydrophilic IL catalysts from less polar and hydrophobic carbonates, improving the energy consumption when compared with distillation as benchmark separation approach in the literature. On the contrary, hydrophobic ionic liquids enhance the process combined with fatty alcohols to produce hydrophilic cyclic carbonates and drastically reduce energy consumption at reasonable lower product specifications when compared with benchmark process. Overall, liquid-liquid extraction stands out as a more efficient process scheme to separate cyclic carbonates from homogeneous catalysts based on ionic liquids.

An Efficient Data Acquisition and Processing Scheme for Wireless Multimedia Sensor Networks.

This study studies the problem of efficient multimedia data acquisition and decreasing whole energy expenditure of wireless multimedia sensor networks and proposes a three-step multimedia data acquisition and wireless energy supplement strategy. Firstly, for network partition, this study proposes a network partition scheme based on vicinity likeness and distance of sensor nodes (VLD), which divides the whole sensor network into multiple regions. The physical links inside the region are dense and concentrated, while the link connections between regions are sparse. Disconnecting the connections between regions hardly affects the data transmission of sensor nodes. Secondly, this study proposes an efficient data acquisition and processing scheme for wireless multimedia sensor network ASS. Compared with other anchor selection schemes, this scheme has obvious performance advantages. Then, the problem of minimizing network energy expenditure is defined, and the optimal sensor node data perception rate and network link transmission rate of the optimization function are obtained by dual decomposition and sub-gradient method. Finally, in the case of a given network energy threshold, the performance of the overall strategy in this study is verified by comparing the amount of data collected by the base station.

Zinc oxide-coated carboxyl-riched graphene oxide efficiently degrades microcystin-leucine arginine and antibiotics under visible light

Microcystin-leucine arginine (Mc-LR) and antibiotic residual in water bodies threaten human health and necessitate the development of efficient processing scheme. Photocatalytic composites are promising alternatives for eliminating these hazardous substances. Herein, we prepared a nanohybrid composed of zinc oxide (ZnO) and carboxyl-riched graphene oxide (cGO). The sustainable material termed ZnO@cGO rapidly degraded Mc-LR (k = 0.68 min−1, t1/2 = 1.02 min, pH 7) under visible light. Mc-LR degradation products were nontoxic to human L0–2 cells. Animal experiments revealed that oral administration of Mc-LR impaired the hepatic function and caused mice death, which could be inspiringly relieved once the toxin was processed by ZnO@cGO. Furthermore, ZnO@cGO efficiently degraded three quinolone antibiotics, ciprofloxacin (k = 2.30 min−1, t1/2 = 0.30 min), ofloxacin (k = 2.11 min−1, t1/2 = 0.33 min), and norfloxacin (k = 0.94 min−1, t1/2 = 0.74 min) at pH 7 under visible light. Owing to the antibiotic removal, ZnO@cGO dramatically suppressed the development of drug resistance of sensitive bacteria. This study characterizes an easy-to-produce photocatalysed nanohybrid to eliminate Mc-LR and antibiotics in water that enriches the existing measures for water detoxication and benefits environmental improvement.

Lignin as Green Filler in Polymer Composites: Development Methods, Characteristics, and Potential Applications

After cellulose, lignin is the most commonly used natural polymer in green biomaterials. Pulp and paper mills and emerging cellulosic biorefineries are the main sources of technical lignin. However, only 2–5% of lignin has been converted into biomaterials. Making lignin-based polymer biocomposites to replace petroleum-based composites has piqued the interest of many researchers worldwide due to the positive environmental impact of traditional composites over time. In composite development, lignin is being used as a filler in commercial polymers to improve biodegradability and possibly lower production costs. As a natural polymer, lignin may have different properties depending on the isolation method and source, affecting polymer-based composites. The application has been affected by the characteristics of lignin and the uniform distribution of lignin in polymers. The review’s goal was to provide an overview of technical lignin extraction, properties, and its potential appropriate utilization. It was also planned to revisit the lignin-based composites’ preparation procedure as well as their composite characteristics. Solvent casting and extrusion methods are used to fabricate lignin from polymeric matrices such as polypropylene, epoxy, polyvinyl alcohol, polylactic acid, starch, wood fiber, natural rubber, and chitosan. Packaging, biomedical materials, automotive, advanced biocomposites, flame retardant, and other applications for lignin-based composites has existed. As a result, the technology is still being refined to increase the performance of lignin-based biocomposites in several applications. This review could assist explain lignin’s position as a composite additive, which could lead to more efficient processing and application strategies.

Federated Learning Driven Secure Internet of Medical Things

With the outbreak of COVID-19, people are experiencing increasing physical and mental health issues. Therefore, personal daily healthcare and monitoring become vital for our physical and mental well being. As a combination of the Internet of Things (IoT) and healthcare services, the Internet of Medical Things (IoMT) has emerged to provide intelligent medical services. However, privacy and security concerns have deterred its wide adoption. In this article, we propose a Federated Learning Driven IoMT (FLDIoMT) framework, which aims to support flexible deployment of IoMT services and address the privacy and security issues at the same time. Also, a systematic workflow of IoMT services is proposed to show an efficient data processing and analysis scheme for specific medical applications. Moreover, we demonstrate the feasibility of the proposed FLDIoMT framework by implementing a novel sleep monitoring system called iSmile.

Efficient NC process scheme generation method based on reusable macro and micro process fusion

Process reuse technology has been widely studied and applied in the manufacturing industry. However, the current NC machining process reuse generally assumes that the micro process is compatible with the macro process but, in fact, reusable processes from similar local structures from multiple parts are usually incompatible with each other when under the overall manufacturing requirements of the target parts. As a result, a large number of user interactions are still required for modification and adjustment in practical engineering applications and process reuse do not provide a significant improvement to the design efficiency. Therefore, an efficient NC process scheme generation method based on reusable macro and micro process fusion is proposed in this paper. Firstly, according to the calculation of semantic distance of process design intention, the micro process is mapped to the macro process to realize fusion of the two processes, and a compatibility credibility evaluation model is established to evaluate the fusion results. Then, when the fusion result is credible, the machining areas corresponding to the process scheme are adjusted and optimized at the geometric level. The adjustment and optimization of machining areas provides the integration of machining areas and the optimization of the machining sequence. Finally, the effectiveness and feasibility of the proposed method are verified through a test of example parts.

Picosecond pulsed laser machining of hardened martensitic stainless steel drive shaft

A fundamental method for estimating the laser parameters and for predicting the achievable ablation rate is demonstrated for hardened martensitic stainless steel X46Cr13. The laser ablation rate is evaluated for a range of pulse energies. The effective threshold fluence for ablation and the effective penetration depth are determined for a low and a high energy regime applying the Neuenschwander model. These parameters are used for numerical prediction of the ablated geometry. A very time efficient processing strategy and parameter set is selected and applied for laser machining of micro channels into a high precision drive shaft to increase friction for torque transmission leading to a processing time of 30 s per part, which corresponds to a structuring rate of 0.88 mms-1 . The deviation between the simulation and the ablated depth in the experiment are measured to be below 7 % at a total ablation depth of 25 µm. This demonstrates that the Neuenschwander model is suitable to determine the parameters needed for the prediction of the geometry resulting from an ablation process on martensitic stainless steel.

Pilot-scale modification of polyethersulfone membrane with a size and charge selective nanocellulose layer

The utilisation of plant-derived nanoscale cellulosic materials (cellulose nanofibrils, CNF) in tailoring water purification membranes is constantly gaining interest in the context of green-functionalised membrane solutions. However, most of the existing approaches based on renewable and biobased materials suffer from the lack of efficient and scalable processing strategies. Here, we introduce a roll-to-roll membrane modification approach based on thin submicron nanocellulose coatings (400–800 nm) to manufacture anti-biofouling membranes with size and charge dependent selectivity using unit operations compatible with existing industrial lines. We turned a commercial polymeric polyethersulfone (PES) microfiltration membrane into highly hydrophilic and tight membrane structure by applying thin and water-durable cellulose nanofibril layers using cast or spray coating methods. Nanocellulose coated membranes exhibited water permeance values of 80 – 100 LMH/MPa with the highest rejection levels of > 90% for Cytochrome C. Furthermore, the nanocellulose layers were able to withstand relatively high filtration pressure levels of 1 MPa, indicating that the selected procedures to improve mechanical integrity i.e. polyethylene imine-based anchoring and acid induced CNF cross-linking were successful. The coated membranes with the thinnest nanocellulose layer exhibited a molecular weight cut-off (MWCO) of 2 kDa for negatively charged polystyrene sulfonate and 14 kDa for neutral dextrane indicating charge selective behaviour. It can be concluded that our nanocellulose coated PES membranes represent nanofiltration membranes and lower boundary of ultrafiltration membranes with clear anti-biofouling performance directly evidenced via systematic bovine serum albumin (BSA) adsorption investigations. Our approach paves the way towards tunable and sustainable water treatment technologies simultaneously opening space for novel biobased solutions in membrane sector.

Performance and Cost Analysis of Natural Gas Combined Cycle Plants with Chemical Looping Combustion.

The natural gas combined cycle (NGCC) is the most popular and efficient fossil fuel power plant; however, integrating a carbon capture system reduces its performance efficiency. The demand to reduce the carbon capture cost and improve eco-friendliness drives the development of alternatives. In this study, four alternative NGCC-based process schemes were designed: NGCC with amine carbon capture as a base configuration and NGCCs with three different chemical looping combustion (CLC) configurations. Detailed heat and material balances were evaluated for all four cases using the PRO/II simulation package. A comparative analysis of the gross and net power, plant efficiency, and carbon capture efficiency, which are imperative to optimizing the process configuration, was conducted for all of the proposed cases. All NGCC-CLC processes could produce higher net power than NGCC-MEA because the amine regenerator consumes a high amount of power in its operation. In the condition using an equal amount of natural gas supply, NGCC-CLC configurations using excess air could produce a net power of 510.1 MW with a plant efficiency of 44.35%. The excess air fed in both cases enabled the turbine to generate more power. NGCC-CLC using excess air with steam turbine integration has an investment cost of 132.9 $/net MWh, an operating cost of 56.7 $/net MWh year, and a levelized cost of electricity of 90.9 $/MWh. In addition, NGCC-CLC with excess air resulted in a carbon capture efficiency of 99.93% under 59.2 $/ton of CO2, which was higher than that of NGCC-MEA with a carbon efficiency of 95.1%. NGCC-CLC using excess air with steam turbine integration is considered as the most efficient process scheme for generating power from natural gas with regard to efficiency, cost, and environmental impact.

Continuous Trajectory Similarity Search for Online Outlier Detection

In this paper, we study a new variant of trajectory similarity search from the context of continuous query processing. Given a moving object from <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> , following a reference route <inline-formula><tex-math notation="LaTeX">$T_r$</tex-math></inline-formula> , we monitor the trajectory similarity between the reference route and the current partial route at each timestamp for online detour detection. Since existing trajectory distance measures fail to adequately capture the deviation between a partial route and a complete route, we propose a partial trajectory similarity measure to bridge the gap. In particular, we enumerate all the possible routes extended from the partial route to reach the destination <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> and calculate their minimum distance to <inline-formula><tex-math notation="LaTeX">$T_r$</tex-math></inline-formula> . We consider deviation calculation in both euclidean space and road networks. In euclidean space, we can directly infer the optimal future path with the minimum trajectory distance. In road networks, we propose an efficient expansion algorithm with a suite of pruning rules. Furthermore, we propose efficient incremental processing strategies to facilitate continuous query processing for moving objects. Our experiments are conducted on multiple real datasets and the experimental results verify the efficiency of our query processing algorithms.

High-Throughput Tailoring of Nanocellulose Films: From Complex Bio-Based Materials to Defined Multifunctional Architectures.

This paper demonstrates a high-throughput approach to fabricate nanocellulose films with multifunctional performance using conventionally existing unit operations. The approach comprises cast-coating and direct interfacial atmospheric plasma-assisted gas-phase modification along with the microscale patterning technique (nanoimprint lithography, NIL), all applied in roll-to-roll mode, to introduce organic functionalities in conjunction with structural manipulation. Our strategy results in multifunctional cellulose nanofibrils (CNF) films in which the high optical transmittance (∼90%) is retained while the haze can be adjusted (2–35%). Concomitantly, the hydrophobic/hydrophilic balance can be tuned (50–21 mJ/m2 with the water contact angle ranging from ∼20 up to ∼120°), while intrinsic hygroscopicity of CNF films is not significantly compromised. Therefore, a challenge to produce multifunctional bio-based materials with properties defined by various high-performance applications conjoined to the lack of efficient processing strategies is elucidated. Overall, economically and ecologically viable strategy, which was realized by facile and upscalable unit operations using the R2R technology, is introduced to expand the properties’ spaces and thus offer a vast variety of interesting applications for CNF films.

An efficient pipeline processing scheme for programming Protocol-independent Packet Processors

OpenFlow is unable to provide customized flow tables, resulting in memory explosions and high switch retirement rates. This is the bottleneck for the development of SDN. Recently, P4 (Programming Protocol-independent Packet Processors) attracts much attentions from both academia and industry. It provides customized networking services by offering flow-level control. P4 can “produce” various forwarding tables according to packets. P4 increases the speed of custom ASICs. However, with the prevalence of P4, the multiple forwarding tables could explode when used in large scale networks. The explosion problem can slow down the lookup speed, which causes congestions and packet losses. In addition, the pipelined structure of forwarding tables brings additional processing delay. In this study, we will improve the lookup performance by optimizing the forwarding tables of P4. Intuitively, we will install the rules according to their popularity, i.e., the popular rules will appear earlier than others. Thus, the packets can hit the matched rule sooner. In this paper, we formalize the optimization problem, and prove that the problem is NP-hard. To solve the problem, we propose a heuristic algorithm called EPSP (Efficient Pipeline Processing Scheme for P4), which can largely reduce the lookup time while keeping the forwarding actions the same. Because running the optimization algorithm frequently brings additional processing burdens, wedesign an incremental update algorithm to alleviate this problem. To evaluate the proposed algorithms, we set up the simulation environments based on ns-3. The simulation results show that the algorithm greatly reduces both the lookup time and the number of memory accesses. The incremental algorithm largely reduces the processing burdens while the lookup time remains almost the same with the non-incremental algorithm. We also implemented a prototype using floodlight and mininet. The results show that our algorithm brings acceptable burder, and performs better than traditional algorithm.

Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

Most of the existing energy-consumption models of machine tools are related to specific machine components and hence cannot be applied to other machine tools with different specifications. In order to help operators optimize machining parameters for improving energy efficiency, the tool tip cutting specific energy prediction model based on machining parameters and tool wear in milling is developed, which is independent of the standby power of machine tools and the spindle no-load power. Then, the prediction accuracy of the proposed model is verified with dry milling AISI 1045 steel experiments. Finally, the influence of machining parameters and tool wear on tool tip cutting specific energy is studied. The developed model is independent of machine components, so it can reveal the influence of machining parameters and tool wear on tool tip cutting specific energy. The tool tip cutting specific energy reduces with the increase in the cutting depth, side cutting depth, feed rate, and cutting speed, while increases linearly as the tool wears gradually. The research results are helpful to formulate efficient and energy-saving processing schemes on various milling machines.

Distributed Detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios

In this letter, we consider the detection of sparse stochastic signals with sensor networks (SNs), where the fusion center (FC) collects 1-bit data from the local sensors and then performs global detection. For this problem, a newly developed 1-bit locally most powerful test (LMPT) detector requires 3.3Q sensors to asymptotically achieve the same detection performance as the centralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector is based on 1-bit quantized observations without any additional processing at the local sensors. However, direct quantization of observations is not the most efficient processing strategy at the sensors since it incurs unnecessary information loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bit LMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) instead of directly quantized local observations. In addition, we design the quantization thresholds at the local sensors to ensure asymptotically optimal detection performance of the proposed detector. It is shown theoretically and numerically that, with the designed quantization thresholds, the proposed Im-1-bit LMPT detector for the detection of sparse signals requires less number of sensor nodes to compensate for the performance loss caused by 1-bit quantization.

Thermodynamic analysis and fuel processing strategies for propane-fueled solid oxide fuel cell

Propane is a particularly attractive fuel for solid oxide fuel cell (SOFC) because it is cheap, widely available and easily stored with high energy density, while carbon deposition may be a serious problem in direct propane-fueled SOFC, thus efficient propane processing strategy is favored by SOFC. The fuel compositions, efficiency and stability of SOFC may be affected by propane processing strategies apparently. In this paper, the fuel compositions and cell efficiency are investigated for different propane pre-treating techniques. For steam reforming, CO2 reforming and anode off-gas reforming of propane, the decreasing rates of real fuel utilization and cell efficiency are apparently different with the change of equivalent fuel utilization. At high equivalent fuel utilization, the real fuel utilization and cell efficiency decrease slowly with the increase of oxygen to carbon (O/C) ratio. While at low equivalent fuel utilization, the real fuel utilization and cell efficiency decrease rapidly with the increase of O/C ratio. For catalytic partial oxidation of propane, the real fuel utilization and cell efficiency decrease rapidly and the decreasing rate is same at different equivalent fuel utilization with the increase of O/C ratio. Suitable proportions of steam, carbon dioxide and/or oxygen in the feed and efficient propane processing strategy can depress the carbon deposition and improve the cell efficiency.

Metallothermic Al-Sc Co-Reduction by Vacuum Induction Melting Using Ca

Due to its enhancing properties in high-tech material applications, the rare earth element Scandium (Sc) is continuously gaining interest from researchers and material developers. The aim of this research is to establish an energy and resource efficient process scheme for an in situ extraction of Al-Sc master alloys, which offers usable products for the metallurgical industry. An AlSc20 alloy is targeted with an oxyfluoridic slag as a usable by-product. The thermochemical baseline is presented by modelling using the software tool FactSage; the experimental metal extraction is conducted in a vacuum induction furnace with various parameters, whereas kinetic aspects are investigated by thermogravimetric analysis. The Sc-containing products are analyzed by ICP-OES/IC concerning their chemical composition. Optimum parameters are derived from a statistical evaluation of the Sc content in the obtained slag phase. The material obtained was high in Ta due to the crucible material and remarkably low in Al and F; a comparison between the modelled and the obtained phases indicates kinetic effects inhibiting the accomplishment of equilibrium conditions. The formation of a Sc-rich Al-Sc phase (32.5 wt.-% Sc) is detected by SEM-EDS analysis of the metal phase. An in situ extraction of Al from Ca with subsequent metallothermic reduction of ScF 3 as a process controlling mechanism is presumed.

Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non-invasive BCI

Electroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.

Characterization of flexible Poly(vinyl butyral)/wood flour composites

The purpose of this study is to deeply characterize a flexible poly(vinyl butyral) (PVB)/wood flour (WF) recycled composite produced by an efficient and simplified processing strategy. The mixing of the PVB/WF composite (25, 40 and 55 wt%) was performed in a single screw extruder equipped with a Maillefer screw with mixer block type pineapple in the metering zone, with the screw operating in starved feed condition in its feeding zone. Thermogravimetric analysis showed that the thermostability of PVB is the limiting factor for the composite processing window and that prior drying of wood flour and PVB is important to avoid hydrolytic degradation. Although SEM analysis has shown the presence of WF aggregates when a high content of reinforcement is added, the extrusion process disperses the wood flour relatively well. The composites have a good interphase adhesion even without a coupling agent. Relative and true density linearly increased with wood flour incorporation, as predicted by the simple rule of mixture. The hardness of pure PVB is 18 Shore D and the composite with 55% WF has 60 Shore D hardness, an increase of 230%. The incorporation of the WF reduced the PVB damping effect but did not change significantly the glass transition temperature, kept around 30 °C. This study is very positive when the objective is the production of a flexible and natural composite from recycled materials in a simple and economically attractive way.

Unsupervised speech recognition through spike-timing-dependent plasticity in a convolutional spiking neural network.

Speech recognition (SR) has been improved significantly by artificial neural networks (ANNs), but ANNs have the drawbacks of biologically implausibility and excessive power consumption because of the nonlocal transfer of real-valued errors and weights. While spiking neural networks (SNNs) have the potential to solve these drawbacks of ANNs due to their efficient spike communication and their natural way to utilize kinds of synaptic plasticity rules found in brain for weight modification. However, existing SNN models for SR either had bad performance, or were trained in biologically implausible ways. In this paper, we present a biologically inspired convolutional SNN model for SR. The network adopts the time-to-first-spike coding scheme for fast and efficient information processing. A biological learning rule, spike-timing-dependent plasticity (STDP), is used to adjust the synaptic weights of convolutional neurons to form receptive fields in an unsupervised way. In the convolutional structure, the strategy of local weight sharing is introduced and could lead to better feature extraction of speech signals than global weight sharing. We first evaluated the SNN model with a linear support vector machine (SVM) on the TIDIGITS dataset and it got the performance of 97.5%, comparable to the best results of ANNs. Deep analysis on network outputs showed that, not only are the output data more linearly separable, but they also have fewer dimensions and become sparse. To further confirm the validity of our model, we trained it on a more difficult recognition task based on the TIMIT dataset, and it got a high performance of 93.8%. Moreover, a linear spike-based classifier—tempotron—can also achieve high accuracies very close to that of SVM on both the two tasks. These demonstrate that an STDP-based convolutional SNN model equipped with local weight sharing and temporal coding is capable of solving the SR task accurately and efficiently.