Direct Usage Research Articles

Vulnerability to machine learning attacks of optical encryption based on diffractive imaging

In this paper, we experimentally demonstrate for the first time to our knowledge that optical encryption based on diffractive imaging is vulnerable to the attacks using learning methods. Using machine learning attack, an opponent is capable to retrieve unknown plaintexts from the given ciphertexts. The proposed method adopts end-to-end learning to extract a superior mapping relationship between the ciphertexts and the plaintexts. Without a direct retrieval or estimate of optical encryption keys, an unauthorised user can extract unknown plaintexts from the given ciphertexts by using the trained learning models. Simulations and optical experimental results demonstrate that the proposed learning method is feasible and effective to analyze the vulnerability of optical encryption schemes. The universality of the trained learning model is also illustrated, and it is verified that the machine learning model trained by using a database is robust to be used for attacking different databases. Compared with conventional cryptanalytic methods, the proposed machine learning attacks can retrieve unknown plaintexts from the given ciphertexts using the trained learning models without a direct usage of various different optical encryption keys, which provides a different strategy for the cryptanalysis of optical encryption systems. @ Elsevier, 2019.

An efficient J-aggregate based fluorescence turn-on and ratiometric sensor for heparin

Heparin is the second most widely used natural drug and an extensively employed blood anticoagulant worldwide. Construction of fluorescent sensors for Heparin has remained heavily dependent on the complex and time-consuming laborious synthetic efforts. However, reports on the direct usage of in-expensive commercially available probe molecule, for sensitive and selective fluorimetric sensing of Heparin, which can greatly simplify the synthetic efforts, has been limited and remains highly desirable. Herein, we report a very sensitive and selective fluorescence turn-on sensor for Heparin, which utilizes a commercially available organic dye molecule, Pseudoisocyanine. The supramolecular interaction between the Pseudoisocyanine and Heparin leads to the J-aggregate formation of Pseudoisocyanine on the surface of Heparin, with an exceptional fluorescence enhancement of ∼400 fold. The J-aggregates of Pseudoisocyanine also display remarkable changes in the absorption features, in the presence of Heparin, which provides a unique advantageous feature of dual sensing of Heparin, by colorimetry and fluorimetry. Pseudoisocyanine also displays high selectivity towards Heparin over its structurally related analogs Chondroitin sulphate and Hyaluronic acid, and other analytes, with a detection limit of 2 nM. More importantly, our sensor system shows good performance in the serum samples, a complex biological media. We further demonstrate that, the interaction of Heparin with its only medically approved antidote, Protamine can also be monitored using Pseudoisocyanine J-aggregates. Thus, our assay is sensitive and selective, rapid, simple, and inexpensive, which can promote the Heparin related biochemical and biomedical research.

Impact of the Opioid/Fentanyl Epidemic on the Toxicology Laboratory’s Workload: The CMCVAMC Experience

Abstract Introduction Opiates have long been used by both the population at large and the veteran population as a drug of abuse. However, recently, fentanyl—a synthetic opioid—has risen in prominence in this opioid drug abuse epidemic as a drug used by suppliers to “cut” heroin, to masquerade for another opiate, or for direct usage. As this is a recent phenomenon, the new increasing need to test for fentanyl for clinical reasons has a major impact on the toxicology laboratory’s workload. Method Quality assurance/improvement data were obtained to determine the number of fentanyl tests by gas chromatography/mass spectroscopy (GC/MS) performed by the toxicology laboratory since quarter 1 of 2011 (October-December 2010) to quarter 1 of 2018 (October-December 2017). The numbers of tests required for clinical care in each quarter were tabulated and compared in a graph. Quarters for each year begin and end in October. Results The total number of GC/MS tests for fentanyl needed for clinical care has been drastically increasing recently. From 2011 to 2015, the yearly number of tests clinically needed has ranged from 83 to 92. In 2016, the total number of clinically needed tests for fentanyl spiked to 167 and by fiscal year 2017 included 1,108 fentanyl GC/MS tests. The last examined quarter (quarter 1 of fiscal year 2018) included 527 tests, which is more than the highest number from 2017 (377 in quarter 4 of 2017). Conclusion The increasing use of fentanyl in the opioid epidemic appears to have played a role in significantly increasing the clinical need to test for fentanyl by GC/MS, increasing the volume by over 10 to 15 times. The role of fentanyl in the opioid epidemic remains a significant public health concern.

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis

BackgroundUltra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantification. This coupling precludes the direct usage of pseudo-alignment to other expression analyses, including alternative splicing or differential gene expression analysis, without including a non-essential transcript quantification step.ResultsIn this paper, we introduce a transcriptome segmentation approach to decouple these two tasks. We propose an efficient algorithm to generate maximal disjoint segments given a transcriptome reference library on which ultra-fast pseudo-alignment can be used to produce per-sample segment counts. We show how to apply these maximally unambiguous count statistics in two specific expression analyses – alternative splicing and gene differential expression – without the need of a transcript quantification step. Our experiments based on simulated and experimental data showed that the use of segment counts, like other methods that rely on local coverage statistics, provides an advantage over approaches that rely on transcript quantification in detecting and correctly estimating local splicing in the case of incomplete transcript annotations.ConclusionsThe transcriptome segmentation approach implemented in Yanagi exploits the computational and space efficiency of pseudo-alignment approaches. It significantly expands their applicability and interpretability in a variety of RNA-seq analyses by providing the means to model and capture local coverage variation in these analyses.

Catalytic pyrolysis of cellulose with oxides: effects on physical properties and reaction pathways

Fast pyrolysis is a potential technology for converting lignocellulosic biomass into bio-oil, a potential substitute for crude oil and a source of chemicals. Nevertheless, the high amounts of acid, oxygenated compounds, and water content cause the bio-oil to be unsuitable for direct usage. Catalytic fast pyrolysis (CFP) is able to improve bio-oil properties so that downstream upgrading processes may be economically feasible. The catalytic effects of calcium oxide (CaO), magnesium oxide (MgO), and zinc oxide (ZnO) on the pyrolysis of lignocellulosic biomass were investigated and were found to be attractive. However, the reaction pathways involved have not been comprehensively compiled to our knowledge. This study aimed to study the change in physical properties of bio-oils at the simplest form upon the addition of the oxides and to provide an understanding on the catalytic reaction pathways. Such study is beneficial to further explore the potential of selected oxides in enhancing the properties of bio-oil from biomass with different lignocellulosic compositions. Experiments were carried out in a fixed-bed reactor at laboratory scale to mimic large-scale processes in a controlled environment. The catalysts exhibited strengths at different bases. CaO catalyst showed the most favorable physical effects in terms of reducing the acidity of cellulose-derived bio-oils without increasing the water content significantly and without compromising with the yield. As for deoxygenation ability, ZnO catalyst exhibited better performance.

Magnetic seeded filtration for the separation of fine polymer particles from dilute suspensions: Microplastics

Microplastics have become a highly discussed topic: Whether through the decomposition of larger plastic waste or direct usage on a small particle scale, microplastics find their way into the natural ecosystem with uncertain consequences: In particular, the ingestion and therefore the introduction of these particles into the food web (often carrying adsorbed chemicals) poses still not fully understood hazards for mankind. This work focuses on the separation of such particles from aquatic suspensions and evaluates the viability of magnetic seeded filtration in this field. In this study it could be shown that it is generally possible to separate fine polymeric particles from dilute suspensions in a highly effective manner. A broad range of process conditions were investigated with separation efficiencies reaching up to 95%. The pH and ultimately the surface charge of the particles were identified to be the crucial process relevant parameters. More exactly, the surface potentials should be either small or oppositely charged leading to a diminishing or even attractive electrostatic interaction. However, when both agglomeration partners exhibit an electrostatic repulsion, a thorough parameter optimization nevertheless yielded high separation efficiencies. In particular, the ionic strength and magnetic seed concentration proved to be determining factors in this case. Lastly, the process kinetics were investigated and a limited exponential growth character was observed which could be modeled with basic equations from agglomeration theory under simplifying assumptions. This knowledge further assists the selection of an optimal parameter set for a process engineering application.

Making Soil Particle Size Analysis by Laser Diffraction Compatible with Standard Soil Texture Determination Methods

The standard sieving, pipette, and hydrometer methods for soil particle size analysis (PSA) have three main drawbacks: (i) the procedures are tedious, (ii) the procedures are time consuming, (iii) and the results are protocol dependent. Laser diffraction PSA delivers rapid results using standardized procedures, but so far it has been difficult to reconcile results with those from standard sedimentation methods. The objective of this study was to develop a protocol that would permit direct usage of laser diffraction PSA and render results compatible with current methods. The protocol was developed using 54 standard soil samples from different textural classes. Regression of the laser diffraction PSA against the hydrometer/pipette method yielded R2 values of 0.92/0.9, 0.92/0.94, and 0.99/0.99 and RMSE values of 0.04/0.05, 0.07/0.06 and 0.05/0.03 for clay, silt, and sand, respectively. These statistics are comparable to those obtained by regressing results of the hydrometer against the sieve and pipette methods. A key factor in securing accurate and precise results was limiting the particle size range of the samples by wet sieving the sand fraction. This created representative samples and stable soil dispersed suspensions, allowing accurate estimations of particle size distribution for clay and silt fractions without empirical transformations. Results obtained with the proposed protocol matched those of standard sedimentation analyses for a wide range of soils, encouraging further adoption of laser diffraction for soil PSA.Core Ideas Laser diffraction particle size analysis can produce results compatible with standard pipette and hydrometer methods. A key step is to wet‐sieve the sand fraction after suspending the soil sample in the dispersant solution. The proposed protocol is faster, uses smaller samples, and provides more detail than standard sedimentation methods.

Improving pedestrian safety using combined HOG and Haar partial detection in mobile systems

Objective: The objective of this study is to develop a novel algorithm on a mobile system that can warn drivers about the possibility of a collision with a pedestrian. The constraints of the algorithm are near-real-time detection speed and a good detection rate.Method: Histogram of gradients (HOG)-based detection is widely used in pedestrian safety applications; however, it has low detection speed for real-time systems. Hence, it has no direct usage for mobile systems. In order to achieve near-real-time detection speed, partial Haar transform predetections are applied to an image before HOG detection. The partial and HOG detections are merged and a score-based confidence level is defined for the final detection phase. In this way, the outcome is prioritized and different warning levels can be issued to warn the driver before a possible pedestrian collision.Results: The proposed algorithm provides an increase in detection speed (from 46 to 76 fps) and detection rate (from 80 to 91%) with respect to HOG-based pedestrian detection. It also improves confidence of the results by multidetection merging and score assignment to detections.Conclusions: Performance improvement of the algorithm is compared with respect to state-of-the-art detectors/algorithms. Based on the detection rate and detection speed performance, it can be concluded that the proposed algorithm is suitable to be used for mobile systems to warn drivers about the possibility of collision with a pedestrian.

Effect of oxide catalysts on the properties of bio-oil from in-situ catalytic pyrolysis of palm empty fruit bunch fiber

Fast pyrolysis is a potential technology for converting lignocellulosic biomass into bio-oil. Nevertheless, the high amounts of acid, oxygenated compounds, and water content diminish the energy density of the bio-oil and cause it to be unsuitable for direct usage. Catalytic fast pyrolysis (CFP) is able to improve bio-oil properties so that downstream upgrading processes can be economically feasible. Here, calcium oxide (CaO), magnesium oxide (MgO), and zinc oxide (ZnO) were employed due to their potential in enhancing bio-oil properties. The results showed that overall, all three catalysts positively impacted the empty fruit bunch fibre-derived bio-oil properties. Among the catalysts, CaO showed the most favorable effects in terms of reducing the acidity of the bio-oil and anhydrosugar. Thermal stability of bio-oils produced in the presence of CaO was studied as well.

Online parameter estimation considering input errors for synchronous generation systems

Parameter estimation of synchronous generation systems helps system operators develop control schemes to enhance system stability and reliability. However, the input signals used in the online estimation are measured with errors and their direct usage may produce biased parameters. To handle this problem, an online parameter estimation method considering input errors is proposed. A Lagrange multiplier-based input variable selection method is further proposed to reduce the scales of estimation problem and improve the computational efficiency. In order to avoid ill-conditioned problem resulted from input error over-fitting, a Tikhonov regularisation approach is applied and the optimal regularisation parameter is determined via cross-validation. In the case study, the numerical experiments and real-world results validate the effectiveness and advantages of the proposed method.

Innovative industrial Cu(In,Ga)Se2 thin film solar cell with high characterization using nanoparticles structure

This paper presents new design of ZnO/CdS/CIGS/Mo nanocomposites thin film solar cell based on individual and multiple nanocomposites absorber layer. A theoretical analysis is presented for enhancing the efficiency and characterization of individual and multiple nanocomposites CdS/Cu (In, Ga) Se2 thin film solar cell. Moreover, this paper is achieving high efficiency with a thinner CIGS layer for reducing the direct materials usage and there by the materials costs by adding various metallic nanoparticles in CIGS absorber layer .

Innovative industrial Cu(In,Ga)Se2 thin film solar cell with high characterization using nanoparticles structure

This paper presents new design of ZnO/CdS/CIGS/Mo nanocomposites thin film solar cell based on individual and multiple nanocomposites absorber layer. A theoretical analysis is presented for enhancing the efficiency and characterization of individual and multiple nanocomposites CdS/Cu (In, Ga) Se2 thin film solar cell. Moreover, this paper is achieving high efficiency with a thinner CIGS layer for reducing the direct materials usage and there by the materials costs by adding various metallic nanoparticles in CIGS absorber layer .

A review of quality characteristics of solar dried food crop producst

Sun drying is perhaps one of the oldest methods of food preservation that has been practiced for centuries. The direct usage of solar radiation which is renewable and abundant favours farmers that harvest and process at small quantity. As technology advances, an alternative to sun drying evolves to maximize the potential of solar radiation and this technology is known as solar drying. Solar drying has several inherent advantages over sun drying namely faster drying rate, better protection of products, reduce risk of prolonged drying, lesser risk of product spoilage and improvement in product quality. Various studies have reported the application of solar drying for fruits, vegetables, grains, seeds, beans, herbs, spices and medicinal plants. Product quality improvement is definitely associated with solar dried products as compared to sun dried and to some extent oven/hot air dried products. However, uptake of this technology especially among farmers in developing countries are still low despite the many years of research and technology advancement. Nonetheless, some successful application of solar drying have been reported in countries such as Indonesia, Laos, Zimbabwe, Tanzania, Brazil, Uganda, Kenya and Senegal.

ILearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.

Linux device driver for Radial Neutron Camera in view of ITER long pulses with variable data throughput

The ITER Radial Neutron Camera (RNC) Data Acquisition (DAQ) prototype is based on the PCIe protocol as the interface to be used between the I/O unit and the host computer, allowing the scalability of the final RNC DAQ system. The prototype architecture comprises two digitizer modules with two channels in each, installed in the host computer and the maximum produced data throughput is up to 1.6 GB/s per board, allowing a sustainable 2 MHz peak event to cope with the long plasma discharges, up to half an hour.The Linux Device Driver provides the interface between the hardware and the host applications running on a high-performance computer, which receives the acquired data through the Direct Memory Access (DMA) channels.The preliminary tests show that the Linux kernel miss some hardware interrupts when the time between interrupts is in the microsecond scale, which implies data loss if a traditional read technique based on interrupt handling is implemented. The direct usage of the polling mechanisms to retrieve the data is not suitable as the variable event rate over the same discharge does not allow to define an optimal fixed-time to retrieve data and identify missing data packets.This contribution presents the architecture, implementation and test of a different device driver approach using the polling mechanism which improves the performance and reliability. This allows the device driver to automatically check and transfer missing data blocks, recovering the data losses transparently for the host applications. The performance results for tests with different event data rates and duration up to an hour are also presented.

Cost-Effectiveness of a Team-Based Integrative Medicine Approach to the Treatment of Back Pain.

To report the results of health economic analyses comparing two treatment approaches for chronic low back pain (CLBP). Observational prospective cohort study comparing effectiveness and cost-effectiveness of CLBP care provided at an integrative care clinic with that provided in other clinics within the same hospital. CLBP-related medical utilization, function, quality of life, and days of work incapacity were self-reported at baseline, 3, 6, and 12 months. Osher Clinical Center (OCC) based at a tertiary academic hospital (Brigham and Women's Hospital [BWH]) and other clinics at BWH. CLBP patients seeking care at OCC or non-OCC BWH clinics. Integrative or conventional care for CLBP as prescribed by the treating clinician(s). Quality-adjusted life years (QALYs) were estimated per treatment approach based on the SF-12. Cost per QALY gained was evaluated using an incremental cost-effectiveness ratio (ICER). ICERs based on CLBP-specific effectiveness measures (Roland Disability Questionnaire [RDQ] and bothersomeness of pain [BOP]) were exploratory outcomes. Total adjusted annual CLBP-related costs per patient were greater in the OCC versus non-OCC group ($11,526.73 vs. $6,810.63). Between group differences in QALYs were small and ICER estimate of cost per QALY gained was high ($436,676). However, unadjusted mean direct costs per patient decreased over time in the OCC group. Savings in direct costs of $391 (95% confidence interval: -1,078 to 1,861) were observed in the OCC group for the 6- to 12-month period, driven primarily by reduced medication usage. ICERs based on adjusted RDQ and BOP group differences showed cost of $2,073 and $4,203 for a one-point reduction per respective scale. When adjusted for baseline differences, self-reported costs were higher in the OCC group with only small effects on QALYs. However, trends toward decreased direct expenditures and medication usage over time warrant further investigation. Future studies evaluating potential benefits of integrative care models for the management of CLBP should employ randomized designs, longer observational periods, and explore multiple metrics of cost-effectiveness.

A proximal method for solving nonlinear minmax location problems with perturbed minimal time functions via conjugate duality

We investigate via a conjugate duality approach general nonlinear minmax location problems formulated by means of an extended perturbed minimal time function, necessary and sufficient optimality conditions being delivered together with characterizations of the optimal solutions in some particular instances. A parallel splitting proximal point method is employed in order to numerically solve such problems and their duals. We present the computational results obtained in matlab on concrete examples, successfully comparing these, where possible, with earlier similar methods from the literature. Moreover, the dual employment of the proximal method turns out to deliver the optimal solution to the considered primal problem faster than the direct usage on the latter. Since our technique successfully solves location optimization problems with large data sets in high dimensions, we envision its future usage on big data problems arising in machine learning.

An integrated approach to sustainable heat recovery in a sedimentary geothermal reservoir considering surface energy demands

Direct usage of low-to-medium enthalpy geothermal energy can provide clean and renewable energy for heating purposes. Optimum geothermal extraction strategies improve the efficiency and effectiveness supplying heat energy, which calls for a comprehensive and integrated study on geothermal reservoir development. In this paper, an integrated modeling workflow considering surface energy demands is presented to analyze the heat extraction process in a sedimentary geothermal reservoir. Effects of surface demands, well positioning, heat production rates, and number of geothermal wells on heat extraction efficiency are thoroughly studied. Results indicate that surface energy demands, well positioning, and heat production rates jointly decide the heat extraction effectiveness and the number of geothermal wells required. Neglecting surface energy demands decreases the effectiveness of heat extraction strategy planning. For the 27 surface energy demand cases studied in this work, the geothermal project can sustainably meet the surface demands in certain cases for up to 30 years, while in some cases, the project can only meet the surface demands for 4 years. In addition, the well spacing of 300m can maintain the heat production temperature at its initial level regardless of the heat production rates.

Influence of pentanol and dimethyl ether blending with diesel on the combustion performance and emission characteristics in a compression ignition engine under low temperature combustion mode

Dimethyl ether (DME) and n-pentanol can be derived from non-food based biomass feedstock without unsettling food supplies and thus attract increasing attention as promising alternative fuels, yet some of their unique fuel properties different from diesel may significantly affect engine operation and thus limit their direct usage in diesel engines. In this study, the influence of n-pentanol, DME and diesel blends on the combustion performance and emission characteristics of a diesel engine under low-temperature combustion (LTC) mode was evaluated at various engine loads (0.2–0.8 MPa BMEP) and two Exhaust Gas Recirculation (EGR) levels (15% and 30%). Three test blends were prepared by adding different proportions of DME and n-pentanol in baseline diesel and termed as D85DM15, D65P35, and D60DM20P20 respectively. The results showed that particulate matter (PM) mass and size-resolved PM number concentration were lower for D85DM15 and D65P35 and the least for D60DM20P20 compared with neat diesel. D60DM20P20 turned out to generate the lowest NOx emissions among the test blends at high engine load, and it further reduced by approximately 56% and 32% at low and medium loads respectively. It was found that the combination of medium EGR (15%) level and D60DM20P20 blend could generate the lowest NOx and PM emissions among the tested oxygenated blends with a slight decrease in engine performance. THC and CO emissions were higher for oxygenated blends than baseline diesel and the addition of EGR further exacerbated these gaseous emissions. This study demonstrated a great potential of n-pentanol, DME and diesel (D60DM20P20) blend in compression ignition engines with optimum combustion and emission characteristics under low temperature combustion mode, yet long term durability and commercial viability have not been considered.

A New Approach to Prediction of Responses in Closed Loop Systems Based on the Direct Usage of One-shot Experimental Data

A New Approach to Prediction of Responses in Closed Loop Systems Based on the Direct Usage of One-shot Experimental Data