Search

Selective detection and quantification of biomarkers related to human diseases are essential for preventive healthcare. Surface-enhanced Raman scattering (SERS) spectroscopy is a powerful analytical tool offering high sensitivity. However, the success of this promising analytical tool relies on the ability to effectively fabricate SERS substrate. Herein we have demonstrated a plasmonic paper-based flexible substrate (PPFS) for SERS sensing. In situ growth of silver nanostructures (AgNS) on the paper-based substrate was achieved by using a simple one-step silver mirror reaction (SMR). FESEM and TEM results depicts that the increasing silver ion content influences the morphology (growth of multifacets), as well as size of AgNS. Further, the PPFS substrate was tested with Rhodamine-6G (Rh-6G) dye and an attomole sensitivity with a LOD of 4.54 x 10-18 M was achieved. Further, two biomarkers, lactic acid (LA) and uric acid (UA) were detected on the PPFS substrate, with μM and pM sensitivity, having LOD values of 0.6 x 10-6 and 0.3 x 10-12 M respectively. Above detection levels for UA on PPFS is two orders better than reported values, whereas for LA it is comparable with reported substrates. Finally, UA, LA and their mixtures were tested on PPFS and results compared with commercial substrate. The performance of PPFS were found better in all cases, thus, multifaceted AgNS paper based PPFS offers the potential to be used as a biosensor for detection of various biomarkers from body fluids, responsible for the detection of the critical disease for preventive health care.

An electrocardiogram (ECG) uses electrodes to monitor the heart rhythm and identify minute electrical changes that occur with each beat. It is employed to investigate particular varieties of aberrant heart activity, such as arrhythmias and conduction problems. One of the most essential tools for detecting heart problems is the electrocardiogram (ECG). The majority of ECG records are still on paper. Manual ECG paper record analysis can be difficult and time-consuming. It is possible to digitally digitize these paper ECG recordings for automated analysis and diagnosis. In this paper, we proposed a system to digitize the ECG paper, automatically detecting R peaks, calculating the average heart rate, and sending SMS to the doctor via cloud in the event of detection of abnormality. The method of the system is uploading an ECG image, then dimensionality reduction, feature extraction in the form of digital signals, and saving it in a CSV file format using the MATLAB programming language. After that, the system retrieves the signals for further processing of the raw signals. We used the fast Fourier transform (FFT) algorithm to calculate R peaks and calculate the heart rate. If the heart rate is abnormal, the system sends SMS messages to doctors via a technology platform (Twilio) using the Python programming language.

Although citations are used as a quantifiable, objective metric of academic influence, references could be added to a paper solely to inflate the perceived influence of a body of research. This reference list manipulation (RLM) could take place during the peer-review process, or prior to it. Surveys have estimated how many people may have been affected by coercive RLM at one time or another, but it is not known how many authors engage in RLM, nor to what degree. By examining a subset of active, highly published authors (n = 20,803) in PubMed, we find the frequency of non-self-citations (NSC) to one author coming from a single paper approximates Zipf’s law. Author-centric deviations from it are approximately normally distributed, permitting deviations to be quantified statistically. Framed as an anomaly detection problem, statistical confidence increases when an author is an outlier by multiple metrics. Anomalies are not proof of RLM, but authors engaged in RLM will almost unavoidably create anomalies. We find the NSC Gini Index correlates highly with anomalous patterns across multiple “red flags”, each suggestive of RLM. Between 81 (0.4%, FDR

A precoding technique based on polar codes and probabilistic shaping is introduced for dirty paper coding. Two variants of the precoding use multi-level shaping and sign-bit shaping in one dimension. The decoder uses multi-stage successive-cancellation list decoding with list-passing across the bit levels. The approach achieves approximately the same frame error rates as polar codes with multi-level shaping over standard additive white Gaussian noise channels at a block length of 256 symbols and with different amplitude shift keying (ASK) constellations.

Herein is presented a novel and efficient portable paper-based sensing platform using paper-incorporated histidine stabilized gold nanoclusters (His-AuNCs), for the sensitive and selective detection of Fe ions from low-volume real water samples based on photoluminescence (PL) quenching. Highly photoluminescent colloidal His-AuNCs are obtained via a novel microwave-assisted method. The His-AuNCs-based sensor reveals a limit of detection (LOD) as low as 0.2 μM and a good selectivity towards Fe ions, in solution. Further, the fabricated portable sensing device based on paper impregnated with His-AuNCs proves to be suitable for the easy detection of hazardous Fe levels from real water samples, under UV light exposure, through evaluating the level of PL quenching on paper. Photographic images are thereafter captured with a smartphone camera and the average blue intensity ratio (I/I0) of the His-AuNCs-paper spots is plotted against [Fe2+] revealing a LOD of 3.2 μM. Moreover, selectivity and competitivity assays performed on paper-based sensor prove that the proposed platform presents high selectivity and accuracy for the detection of Fe ions from water samples. To validate the platform, sensing assays are performed on real water samples from local sources, spiked with 35 μM Fe ions (i.e., Fe2+). The obtained recoveries prove the high sensitivity and accuracy of the proposed His-AuNCs-paper-based sensor pointing towards its applicability as an easy-to-use, fast, quantitative and qualitative sensor suitable for on-site detection of toxic levels of Fe ions in low-volume real water samples.

To improve the storage and transport of clinical specimens for the diagnosis of Neisseria meningitidis (Nm) infections in resource-limited settings, we have evaluated the performance of dried blood spot (DBS) and dried cerebrospinal fluid spot (DCS) assays. DBS and DCS were prepared on filter paper from liquid specimens previously tested for Nm in the United Kingdom. Nm was detected and genogrouped by real-time PCR performed on crude genomic DNA extracted from the DBS (n = 226) and DCS (n = 226) specimens. Targeted whole-genome sequencing was performed on a subset of specimens, DBS (n = 4) and DCS (n = 6). The overall agreement between the analysis of liquid and dried specimens was (94.2%; 95% CI 90.8–96.7) for blood and (96.4%; 95% CI 93.5–98.0) for cerebrospinal fluid. Relative to liquid specimens as the reference, the DBS and DCS assays had sensitivities of (89.1%; 95% CI 82.7–93.8) and (94.2%; 95% CI 88.9–97.5), respectively, and both assays had specificities above 98%. A genogroup was identified by dried specimen analysis for 81.9% of the confirmed meningococcal infections. Near full-length Nm genome sequences (>86%) were obtained for all ten specimens tested which allowed determination of the sequence type, clonal complex, presence of antimicrobial resistance and other meningococcal genotyping. Dried blood and CSF filter spot assays offer a practical alternative to liquid specimens for the molecular and genomic characterisation of invasive meningococcal diseases in low-resource settings.

Using a qualitative historical research approach, this paper examines the South African (SA) e-Education (referred to as White paper 7 (WP7)) policy formulation’s contextual influences and their possible impacts on the policy implementation. A critical discourse analysis (CDA) was conducted on both the policy itself and data collected through semi-structured interviews (SSI) with purposefully selected participants. Findings of the study reveal tacit contradictions and tensions that depict a policy that is in contradiction with itself. That is from its textual, discourse and process perspectives. The study reveals that, from its textual perspective, WP7’s formulation was influenced by the post-apartheid’s government desire to make use of Education and Information and Communication Technology (ICT) as quick fixers in response to the dire socioeconomic situation of the majority of citizens. Yet, its discourse perspective points to the fact that its formulation’s motive was merely to coordinate donations in hardware, software and funding from interest groups (IGs). While the donations in question were premised by IGs’ ultimate aim of maximizing profit through an anticipated government funded massive rollout of ICT resources to schools. This resulted in the formulation of WP7 being excessively influenced by contextual factors that had little to do with the policy’s vision of transforming teaching and learning (TL) through ICT. This situation may have contributed to the policy’s failure to reach its set objectives by 2013 as envisaged, at least from the point of view of policy as text.

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the need for versatile diagnostic assays that can discriminate among emerging variants of the virus. Here we report the development and performance benchmarking of an inexpensive (approximately US$0.30 per test) assay for the rapid (sample-to-answer time within 30 min) colorimetric detection of SARS-CoV-2 variants. The assay, which we integrated into foldable paper strips, leverages nucleic acid strand-displacement reactions, the thermodynamic energy penalty associated with single-base-pair mismatches and the metal-ion-controlled enzymatic cleavage of urea to amplify the recognition of viral RNAs for the colorimetric readout of changes in pH via a smartphone. For 50 throat swab samples, the assay simultaneously detected the presence of SARS-CoV-2 and mutations specific to the SARS-CoV-2 variants Alpha, Beta and Gamma, with 100% concordance with real-time quantitative polymerase chain reaction and RNA sequencing. Customizable and inexpensive paper-based assays for the detection of viruses and their variants may facilitate viral surveillance.

Research on academic paper ranking has received great attention in recent years, and many algorithms have been proposed to automatically assess a large number of papers for this purpose. How to evaluate or analyse the performance of these ranking algorithms becomes an open research question. Theoretically, evaluation of an algorithm requires to compare its ranking result against a ground truth paper list. However, such ground truth does not exist in the field of scholarly ranking due to the fact that there does not and will not exist an absolutely unbiased, objective, and unified standard to formulate the impact of papers. Therefore, in practice researchers evaluate or analyse their proposed ranking algorithms by different methods, such as using domain expert decisions (test data) and comparing against predefined ranking benchmarks. The question is whether using different methods leads to different analysis results, and if so, how should we analyse the performance of the ranking algorithms? To answer these questions, this study compares among test data and different citation-based benchmarks by examining their relationships and assessing the effect of the method choices on their analysis results. The results of our experiments show that there does exist difference in analysis results when employing test data and different benchmarks, and relying exclusively on one benchmark or test data may bring inadequate analysis results. In addition, a guideline on how to conduct a comprehensive analysis using multiple benchmarks from different perspectives is summarised, which can help provide a systematic understanding and profile of the analysed algorithms.

Charantin is a mixture of β-sitosterol and stigmastadienol glucosides, which effectively lowers high blood glucose. Novel molecularly imprinted polymers coated magnetic nanoparticles (Fe3O4@MIPs) and filter paper (paper@MIPs) were synthesized by sol-gel polymerization to selectively extract charantin. β-sitosterol glucoside was selected as a template for imprinting a specific recognition owing to its larger molecular surface area than that of 5,25-stigmastadienol glucoside. Factorial designs were used to examine the effects of the types of porogenic solvents and cross-linkers on the extraction efficiency and imprinting factor before investigating other factors (for example, amounts of template and coated MIPs, and types of substrates for MIP immobilization). Compared to traditional liquid–liquid extraction, the optimal Fe3O4@MIP-based dispersive micro-solid phase extraction and paper@MIP extraction provided excellent extraction efficiency (87.5 ± 2.1% and 85.0 ± 2.9%, respectively) and selectivity. Charantin was well separated, and a new unidentified sterol glucoside was observed using the developed high-performance liquid chromatography with diode-array detection (Rs ≥ 2.0, n > 16,400). The developed methods were successfully utilized to extract and quantify charantin from M. charantia fruit powder and herbal products. Moreover, these methods are rapid (

A growing research interest currently exists in the use of paper as a building material. This work aims to present the results of a measurement campaign developed on innovative waste paper-based building components. The research was carried out in Southern Italy and used some local by-product aggregates. Three different mixture designs were developed in the laboratory by adding three kinds of biomass to a pulp paper blend: fava bean residues (FB), sawdust powder (SP) and coffee grains (CG) extracted from exhausted chaffs. A physical characterization was carried out measuring the bulk density and bulk porosity. Scanning Electron Microscopy (SEM) analysis of the single aggregates was followed by a microstructure analysis of the final components. Bulk density evaluation showed a range between 200 and 348 kg·m−3. Furthermore, thermal performances were measured; the thermal conductivity of the experimented samples ranged from 0.071 to 0.093 W·m−1·K−1, thus it is possible to classify the tested materials as thermal insulators. Moreover, the acoustic properties were evaluated and tested. The normal incidence sound absorption coefficient was measured by the impedance tube on cylindrical specimens. In general, a different behavior was observed between the upper and lower base of each specimen due to the manufacturing process and the shrinkage caused by the different interactions occurring between the aggregates and the pulp paper waste; for example, the presence of sawdust reduced shrinkage in the final specimens, with consequent smaller physical variations among the two faces. The correlation existing between the manufacturing process and the microstructural properties was also investigated by the estimation of the non-acoustical parameters using the inverse method and taking into account the JCA (Johnson, Champoux and Allard) model as a reference.

Cloud manufacturing is a service-oriented networked manufacturing model that aims to provide manufacturing resources as services in an on-demand manner. Scheduling is one of the key techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is a critical problem in cloud manufacturing. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to address the issue, which, however, either are incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) as the combination of deep learning (DL) and reinforcement learning (RL) provides an effective technique in this regard. In view of this, we employ a typical DRL algorithm—Deep Q-network (DQN)—and propose a DQN-based approach for multitask scheduling in cloud manufacturing. Three different task arrival modes—arriving at the same time, arriving in random batches, and arriving one by one sequentially—are considered. Four baseline methods including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time (min-time) scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is effective and performs best among all approaches in addressing the multitask scheduling problem in cloud manufacturing.