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The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure–activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.

The vending machine is an automated device that dispenses a variety of products to customers when cash or a credit card is placed, including food, beverages, newspapers, tickets, and more. Contrary to traditional purchase methods, vending machines are more convenient and available. Today, the market for vending machines generates enormous annual revenues for developed countries like the United States, Japan, China, and some other Asian nations, including India. The goal of the paper is to create a vending machine that can dispense three different items at varying costs with the added functionality of "return change" when a coin of a higher denomination is input and "return money" when a request is cancelled. Coins of the values of five and ten are accepted by the machine. The design of vending machines uses the finite state machine (FSM) methodology. The design is accomplished by using behavioural modelling to create the Verilog code for the FSM-based machine and the Xilinx ISE tool to simulate the test-bench for three items.

With the breakneck pace of progress in today’s world, any self-sustaining business needs to ensure that they maximize their profits while also cutting down on losses, essentially described as optimization. For the same optimization in advertisements and marketing, various factors, vendors, and methods must be considered before making any significant business decisions. This paper primarily reviews Google AdWords and Meta Ads, two advertising services. Comprehensive reviews of all factors, including interest targeting an estimated reach of the advertisement to the target audience, are conducted while comparing the advantages and disadvantages of both platforms.

Sensory marketing is a rapidly advancing field of marketing that allows brands to capitalize on consumer behavior, more specifically subconscious decision-making. By understanding how the brain processes and evaluates sensorial stimuli, we can better understand how sensorial cues can lead to emotional and brand attachment with consumers. After identifying the significance of embodied cognition and how sensory awareness influences human behavior, we analyzed how sensory marketing can evoke certain bodily sensations. By conducting various case study analyses through “Neural Correlates of Behavioral Preference for Culturally Familiar Drinks” and “The Influence of In-Store Music on Wine Selections”, we’ve been able to visualize data that supports evidence of auditory stimuli' effects on in-store decision-making as well as evidence of subconscious brand preference for consumers. Furthermore, we’ve examined how the anticipation of certain brands results in increased amygdala activation, showing how sensory marketing can alter the way our brains perceive the merit of products. Finally, we’ve created a conceptual model that allows for a better understanding of the sensory marketing progression on the consumer’s end.

The generalised time–frequency transform (GTFT) is a powerful tool to analyse a large variety of frequency-modulated signals. However, it is not adequate to represent the variation of frequency over time for non-stationary signals. To solve this problem, short-time GTFT and short-time GTFT-based adaptive generalised fractional spectrogram (AGFS) are proposed. The AGFS is capable of providing a high concentration, high resolution, cross-term-free time–frequency distribution for analysing multicomponent frequency-modulated signals. It is also a generalisation of the short-time Fourier transform-based spectrogram and the short-time fractional Fourier transform-based spectrogram. The uncertainty principle for short-time GTFT is derived, and its time-bandwidth product is compared with other time–frequency distributions. With the help of simulated data examples, the effectiveness of AGFS is demonstrated in comparison with other time–frequency distributions for resolving and extracting individual components of multicomponent quadratic chirps. Robustness of AGFS is demonstrated under different input signal-to-noise ratio conditions. A local spectrogram optimisation technique is adopted for AGFS to represent simulated and real chirp signals. Finally, an application of the AGFS is presented to resolve multiple ground moving targets in synthetic aperture radar data and obtain its focused synthetic aperture radar image.

The ambiguity function (AF) is an essential time-frequency analysis tool to analyze the radar waveform properties in radar applications. It can be used effectively and reliably to analyze properties like the peak-to-side-lobe ratio, time delay resolution, Doppler resolution and tolerance characteristic. However, it fails to analyze higher-order chirp waveforms and is unable to estimate their parameters. To solve this problem, a generalized time-frequency transform-based generalized fractional AF (GFAF) and generalized fractional Wigner–Ville distribution (GFWVD) are proposed. GFAF is also a generalization of the Fourier transform-based ambiguity function and the fractional Fourier transform-based ambiguity function. The uncertainty principle for GFAF and GFWVD is derived. Examples are presented to demonstrate the effectiveness of GFAF in analyzing cubic chirp waveforms and estimating parameters of multicomponent cubic chirps. The superiority of GFAF is demonstrated by comparing the mean square error to Cramer–Rao lower bound and high-order ambiguity function under different input-signal-to-noise ratio conditions. The robustness is demonstrated by comparing the signal-to-noise ratio gain to that of the time domain-matched filtering and other ambiguity functions. Finally, fourth-order parameters of a real bat echolocation signal are estimated.

In this article, we have reported a one-step scalable synthesis of MgCo2O4 nanostructures as efficient anode material for Li-ion batteries and investigated the role of post-synthesis calcination temperature (400, 600 and 800 °C) on its physiochemical properties and electrochemical performances. The XRD pattern of the calcinated sample at 400 °C (MC 400) indicates a pure phase of MgCo2O4. However, on increasing the calcination temperature to 600 °C (MC 600), an additional phase corresponding to MgO was detected and the corresponding XRD peak intensity further increased on increasing the calcination temperature to 800 °C (MC 800 °C). This was accompanied by a morphological transformation from flake and rod-like nanostructures, to an agglomerated dense flake-like morphology. Electrochemical studies revealed that the calcination temperature plays an important role in determining the electrochemical performance of the MgCo2O4 as anode material. In a half cell, the MC 600 showed the best electrochemical performance with high discharge capacity of 980 mA h g−1 (2nd discharge at 60 mA g−1) and a reversible discharge capacity of 886 mA h g−1 at the end of 50 cycles with high coulombic efficiency of 98%. Long term stability was carried out at 0.5C which showed a capacity retention of 358 mA h g−1 at the end of 500 cycles. The superior electrochemical performance of the MC600 can be attributed to the presence of the small amount of MgO, which is believed to provide the anode materials better structural stability during cycling. The claim was further supported by ex-situ TEM analysis of the anode material of a cycled cell (50 cycles).

We report a simple and scalable synthesis of MnCo2O4/MWCNT nanocomposite by a simple precipitation method and its application as a high capacity anode material for Li-ion batteries. X-ray diffraction analysis showed a pure phase of MnCo2O4 and morphological analysis revealed a uniform decoration of MnCo2O4 nanoparticles along MWCNT backbone. As an electrode material, MnCo2O4 showed a high initial discharge capacity of 1431 mA h g−1 with a capacity retention of 444 mA h g−1 at the end of 30 cycles. With a small addition of MWCNT to MnCo2O4, the stability of the composite increased with a reversible capacity of 871 mA h g−1 at the end of 30 cycles. The inclusion of MWCNT as a conductive matrix resulted in a significant improvement in rate capability and cyclic stability.

It has recently been shown that periodicity in discrete-time data can be analyzed using Ramanujan sums and associated dictionaries. This paper explores the role of dictionary learning methods in the context of period estimation and periodic signal representation using dictionaries. It is shown that a well-known dictionary learning algorithm, namely K-SVD, is able to learn Ramanujan and Farey periodicity dictionaries from the noisy, sparse coefficient data generated from them without imposing any periodicity structure in the learning stage. This similarity between the learned dictionary and the underlying original periodicity dictionary reaffirms the power of the K-SVD in predicting the right dictionary from data without explicit application-specific constraints. The paper also examines how the choice of different parameter values affect the similarity of the learned dictionary to the underlying dictionary. Two versions of K-SVD along with different initializations are analyzed for their effect on representation and denoising error for the data.

Ramanujan filter banks have been used for identifying periodicity structure in streaming data. This letter studies the locations of zeros of Ramanujan filters. All the zeros of Ramanujan filters are shown to lie on or inside the unit circle in the $z$ -plane. A convenient factorization appears as a corollary of this result, which is useful to identify common factors between different Ramanujan filters in a filter bank. For certain families of Ramanujan filters, further structure is identified in the locations of zeros of those filters. It is shown that increasing the number of periods of Ramanujan sums in the filter definition only increases zeros on the unit circle in $z$ -plane. A potential application of these results is that by identifying common factors between Ramanujan filters, one can obtain efficient implementations of Ramanujan filter banks (RFB) as demonstrated here.

Background Speech and language therapy involves the identification, assessment, and treatment of children and adults who have difficulties with communication, eating, drinking, and swallowing. Globally, pressing needs outstrip the availability of qualified practitioners who, of necessity, focus on individuals with advanced needs. The potential of voice-assisted technology (VAT) to assist people with speech impairments is an emerging area of research but empirical work exploring its professional adoption is limited. Objective This study aims to explore the professional experiences of speech and language therapists (SaLTs) using VAT with their clients to identify the potential applications and barriers to VAT adoption and thereby inform future directions of research. Methods A 23-question survey was distributed to the SaLTs from the United Kingdom using a web-based platform, eliciting both checkbox and free-text responses, to questions on perceptions and any use experiences of VAT. Data were analyzed descriptively with content analysis of free text, providing context to their specific experiences of using VAT in practice, including barriers and opportunities for future use. Results A total of 230 UK-based professionals fully completed the survey; most were technologically competent and were aware of commercial VATs (such as Alexa and Google Assistant). However, only 49 (21.3%) SaLTs had used VAT with their clients and described 57 use cases. They reported using VAT with 10 different client groups, such as people with dysarthria and users of augmentative and alternative communication technologies. Of these, almost half (28/57, 49%) used the technology to assist their clients with day-to-day tasks, such as web browsing, setting up reminders, sending messages, and playing music. Many respondents (21/57, 37%) also reported using the technology to improve client speech, to facilitate speech practice at home, and to enhance articulation and volume. Most reported a positive impact of VAT use, stating improved independence (22/57, 39%), accessibility (6/57, 10%), and confidence (5/57, 8%). Some respondents reported increased client communication (5/57, 9%) and sociability (3/57, 5%). Reasons given for not using VAT in practice included lack of opportunity (131/181, 72.4%) and training (63/181, 34.8%). Most respondents (154/181, 85.1%) indicated that they would like to try VAT in the future, stating that it could have a positive impact on their clients’ speech, independence, and confidence. Conclusions VAT is used by some UK-based SaLTs to enable communication tasks at home with their clients. However, its wider adoption may be limited by a lack of professional opportunity. Looking forward, additional benefits are promised, as the data show a level of engagement, empowerment, and the possibility of achieving therapeutic outcomes in communication impairment. The disparate responses suggest that this area is ripe for the development of evidence-based clinical practice, starting with a clear definition, outcome measurement, and professional standardization.

While Next-Generation Sequencing (NGS) can now be considered an established analysis technology for research applications across the life sciences, the analysis workflows still require substantial bioinformatics expertise. Typical challenges include the appropriate selection of analytical software tools, the speedup of the overall procedure using HPC parallelization and acceleration technology, the development of automation strategies, data storage solutions and finally the development of methods for full exploitation of the analysis results across multiple experimental conditions. Recently, NGS has begun to expand into clinical environments, where it facilitates diagnostics enabling personalized therapeutic approaches, but is also accompanied by new technological, legal and ethical challenges. There are probably as many overall concepts for the analysis of the data as there are academic research institutions. Among these concepts are, for instance, complex IT architectures developed in-house, ready-to-use technologies installed on-site as well as comprehensive Everything as a Service (XaaS) solutions. In this mini-review, we summarize the key points to consider in the setup of the analysis architectures, mostly for scientific rather than diagnostic purposes, and provide an overview of the current state of the art and challenges of the field.

The widely used conventional touch-based fingerprint identification system has drawbacks like the elastic deformation due to nonuniform pressure, fingerprints collection time and hygiene. To overcome these drawbacks, recently the touchless fingerprint technology is gaining popularity and various touchless fingerprint acquisition solutions have been proposed. Nowadays due to the wide use of the smartphone in various biometric applications, smartphone-based touchless fingerprint systems using an embedded camera have been proposed in the literature. These touchless fingerprint images are very different from conventional ink-based and live-scan fingerprints. Due to varying contrast, illumination and magnification, the existing touch-based fingerprint matchers do not perform well while extracting reliable minutiae features. A touchless fingerprint recognition system using a smartphone is proposed in this paper, which incorporates a novel monogenic-wavelet-based algorithm for enhancement of touchless fingerprints using phase congruency features. For the comparative performance analysis of our system, we created a new touchless fingerprint database using the developed android app and this is publicly made available along with its corresponding live-scan images for further research. The experimental results in both verification and identification mode on this database are obtained using three widely used touch-based fingerprint matchers. The results show a significant improvement in Rank-1 accuracy and equal error rate (EER) achieved using the proposed system and the results are comparable to that of the touch-based system.

Hydraulic Turbines are being used since the ancient times to harness the energy stored in flowing streams, rivers and lakes. The oldest and the simplest form of a hydraulic turbine was the waterwheel used for grinding grains. The basic idea of a Pelton Wheel Turbine is derived from this ancient waterwheel. Pelton wheel is the only hydraulic turbine of the impulse type in common use. It is named after the American engineer Laster A. Pelton, who contributed much to its development around the year 1880. Therefore, this machine is known as Pelton Turbine or Pelton Wheel. Pelton Wheels are the preferred turbine for hydro-power when the available water source has relatively high hydraulic head at low flow rates. In the present study, a complete analysis of the Pelton Wheel Test Rig with brake drum loading made available on college campus is carried out. The major input and output parameters along with their working ranges are identified. Stochastic analysis using Monte Carlo’s simulation is carried out by randomly generating a design matrix and thus calculating the responses using predetermined equations. Response Surface Methodology is adapted to identify the optimal set of inputs and also generate inputoutput relations. Furthermore, a design matrix is generated by taking practical readings on the Test Rig keeping speed constant and the respective outputs are calculated. Similar to the stochastic study, Response Surface Methodology is adapted by inputting the design matrix in a design expert software. Input-output relations are generated and also the optimal set of input parameters are determined using this software.

Background Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits. Results We have developed the Cancer Systems Biology Database (CancerSysDB), a resource for highly flexible queries and analysis of cancer-related data across multiple data types and multiple studies. The CancerSysDB can be adopted by any center for the organization of their locally acquired data and its integration with publicly available data from multiple studies. A publicly available main instance of the CancerSysDB can be used to obtain highly flexible queries across multiple data types as shown by highly relevant use cases. In addition, we demonstrate how the CancerSysDB can be used for predictive cancer classification based on whole-exome data from 9091 patients in The Cancer Genome Atlas (TCGA) research network. Conclusions Our database bears the potential to be used for large-scale integrative queries and predictive analytics of clinically relevant traits. Electronic supplementary material The online version of this article (10.1186/s12859-018-2157-7) contains supplementary material, which is available to authorized users.

Parallel programming represents the next turning point in how software engineers write software. Today, low-cost multi-core processors are widely available for both desktop computers and laptops. As a result, applications will increasingly need to be paralleled to fully exploit the multi-core-processor throughput gains that are becoming available. Unfortunately, writing parallel code is more complex than writing serial code. This is where the OpenMP programming model enters the parallel computing picture. OpenMP helps developers create multi-threaded applications more easily while retaining the look and feel of serial programming. The term algorithm performance is a systematic and quantitative approach for constructing software systems to meet the performance objectives such as response time, throughput, scalability and resource utilization. The performances (speedup) of parallel algorithms on multi-core system have been presented in this paper. The experimental results on a multi-core processor show that the proposed parallel algorithms achieves good performance compared to the sequential.

A series of field sampling and controlled laboratory experiments were undertaken to quantify the role of trace metals found in ambient fine particulate matter and metal-rich primary sources in the heterogenous catalytic conversion of SO2 gas into sulfate particulate matter (PM) in the atmosphere. Analysis produced source profiles of three primary source materials, fluidized-bed catalytic cracking catalyst, coal-fired combustion fly ash, and paved road dust, featuring 33 elements including rare earth metals, which are not commonly reported in the literature. Subsequently three sets of experiments were conducted exposing 1) source materials, 2) ambient PM, and 3) ambient PM augmented with approximately an equal amount of source material to SO2 gas and measuring sulfate formation. Source material experiments revealed that the greatest extent of reaction was on the surface of coal fly ash with sulfate formation of 19 ± 5 mg sulfate g−1 material. Ambient fine particulate matter (PM) experiments showed sulfate formation ranging from negligible amounts to 180 ± 10 mg sulfate g−1 PM. It was much more difficult to quantify the sulfate formation on ambient filters augmented with the source materials. In these experiments, sulfate formation ranged from negligible amounts to 40 ± 8 mg sulfate g−1 of particles (ambient + augmented material). These three sets of experiments shows that heterogenous sulfate formation is often negligible but, under some conditions can contribute 10% or more to the total sulfate concentrations when exposed to high SO2 concentrations such as those found in plumes. Factor analysis of the source material experiments grouped metals into two categories, crustal components and anthropogenically emitted metals representative of catalyst material, with the former showing the strongest correlation with sulfate formation. Subsequent analysis of data collected from the ambient PM experiments showed a much weaker correlation of sulfate formation with the crustal components, including iron and titanium, remaining clustered with sulfate formation. Independent research has been previously reported in the literature establishing mechanisms for the iron and titanium catalyzed conversion of S(IV) to S(VI) suggesting there may be other metals within these crustal type metal components that behave similarly. Additional experiments spanning a wider range of variables including more sources, SO2 concentrations and exposure times, ambient PM locations, as well as more individual samples may be necessary to obtain more conclusive evidence into the role of various metals in catalyzing the conversion of S(IV) to S(VI).

In the absence of a certified reference material, a robust microwave-assisted acid digestion procedure followed by inductively coupled plasma - mass spectrometry (ICP-MS) was developed to quantify rare earth elements (REEs) in fluidized-bed catalytic cracking (FCC) catalysts and atmospheric fine particulate matter (PM2.5). High temperature (200 C), high pressure (200 psig), acid digestion (HNO3, HF, and H3BO3) with 20 minute dwell time effectively solubilized REEs from six fresh catalysts, a spent catalyst, and PM2.5. This method was also employed to measure 27 non-REEs including Na, Mg, Al, Si, K, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Zr, Mo, Cd, Cs, Ba, Pb, and U. Complete extraction of several REEs (Y, La, Ce, Pr, Nd, Tb, Dy, and Er) required HF indicating that they were closely associated with the aluminosilicate structure of the zeolite FCC catalysts. Internal standardization using 115In quantitatively corrected non-spectral interferences in the catalyst digestate matrix. Inter-laboratory comparison using ICP-optical emission spectroscopy (ICP-OES) and instrumental neutron activation analysis (INAA) demonstrated the applicability of the newly developed analytical method for accurate analysis of REEs in FCC catalysts. The method developed for FCC catalysts was also successfully implemented to measure trace to ultra-trace concentrations of La, Ce, Pr, Nd, Sm, Gd, Eu, and Dy in ambient PM2.5 in an industrial area of Houston, TX.

A study was conducted in Houston, TX focusing on rare earth elements (REEs) in atmospheric fine particles and their sources. PM2.5 samples were collected from an ambient air quality monitoring site (HRM3) located in the proximity of a large number of oil refineries and petrochemical industries to estimate the potential contributions of emissions from fluidized-bed catalytic cracking operations to ambient fine particulate matter. The elemental composition of ambient PM2.5, several commercially available zeolite catalysts, and local soil was measured after microwave assisted acid digestion using inductively coupled plasma—mass spectrometry. Source identification and apportionment was performed by principal component factor analysis (PCFA) in combination with multiple linear regression. REE relative abundance sequence, ratios of La to light REEs (Ce, Pr, Nd, and Sm), and enrichment factor analysis indicated that refining and petrochemical cat cracking operations were predominantly responsible for REE enrichment in ambient fine particles. PCFA yielded five physically meaningful PM2.5 sources: cat cracking operations, a source predominantly comprised of crustal material, industrial high temperature operations, oil combustion, and sea spray. These five sources accounted for 82% of the total mass of atmospheric fine particles (less carbon and sulfate). Factor analysis confirmed that emissions from cat cracking operations primarily contributed to REE enrichment in PM2.5 even though they comprised only 2.0% of the apportioned mass. Results from this study demonstrate the need to characterize catalysts employed in the vicinity of the sampling stations to accurately determine local sources of atmospheric REEs.