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DNA extraction, a vital pre-requisite for most biological studies, continues to be studied extensively. According to some studies, DNA shows a certain degree of absorbability on filter paper made of plant fiber-based adsorbent material. However, the principle underlying such specific adsorption as well as plant species associated with plant fiber-based adsorbents and optimized extraction conditions have not yet been studied. This study demonstrates the tight correlation between crystallinity and hygroscopicity in plant fiber-based adsorbents used for DNA extraction and proposes the concept of DNA adsorption on plant fiber-based adsorbents, for the first time. We also explored optimal extracting and eluting conditions and developed a novel plant fiber-based DNA extraction method that was quadruple times more powerful than current approaches. Starting with the screening of various types of earthed plant fiber-based adsorbents, we went on to mine new plant fiber-based adsorbents, bamboo paper and degreased cotton, and succeeded in increasing their efficiency of DNA extraction to 4.2 times than that of current approaches. We found a very strong correlation between the crystallinity and hygroscopicity of plant fiber-based adsorbents which showed efficiency for DNA extraction, and thus propose a principle that potentially governs such specific adsorption processes, in the hope that this information may guide related multidisciplinary research studies in the future. Nanodrop, electrophoresis and PCR were selected to demonstrate the quantity, quality, integrity and utility of the extracted DNA. Furthermore, crystallinity, hygroscopicity, pore size distribution and composition of plant fiber-based adsorbents were studied to explore their correlation in an attempt to understand the principle underlying this particular type of adsorption. The findings of this study may be further extended to the extraction of other types of nucleic acids with similar biochemical properties.

Reading is considered a non-intuitive, cognitively demanding ability requiring synchronization between several neural networks supporting visual, language processing and higher-order abilities. With the involvement of technology in our everyday life, reading from a screen has become widely used. Several studies point to challenges in processing written materials from the screen due to changes in attention allocation when reading from a screen compared to reading from a printed paper. The current study examined the differences in brain activation when reading from a screen compared to reading from a printed paper focusing on spectral power related to attention in fifteen 6-8-year-old children. Using an electroencephalogram, children read two different age-appropriate texts, without illustrations, presented randomly on the screen and on a printed paper. Data were analyzed using spectral analyses in brain regions related to language, visual processing, and cognitive control, focusing on theta vs. beta waveforms. Results indicated that while reading from a printed paper was accompanied by higher energy in high-frequency bands (beta, gamma), reading from the screen was manifested by a higher power in the lower frequency bands (alpha, theta). Higher theta compared to the beta ratio, representing challenges in allocating attention to a given task, was found for the screen reading compared to the printed paper reading condition. Also, a significant negative correlation was found between differences in theta/beta ratio for screen vs paper reading and accuracy level in the age-normalized Sky-Search task measuring attention and a positive correlation with performance time. These results provide neurobiological support for the greater cognitive load and reduced focused attention during screen-based compared to print-based reading and suggest a different reliance on attention resources for the two conditions in children.

Women are under-represented among authors of scientific papers. Although the number of retractions has been rising over the past few decades, gender differences among authors of retracted papers remain poorly understood. Therefore, this study investigated gender differences in authorship of retracted papers in biomedical sciences available on RetractionWatch. Among 35,635 biomedical articles retracted between 1970 and 2022, including 20,849 first authors and 20,413 last authors, women accounted for 27.4% [26.8 to 28.0] of first authors and 23.5% [22.9 to 24.1] of last authors. The lowest representation of women was found for fraud (18.9% [17.1 to 20.9] for first authors and 13.5% [11.9 to 15.1] for last authors) and misconduct (19.5% [17.3 to 21.9] for first authors and 17.8% [15.7 to 20.3] for last authors). Women’s representation was the highest for issues related to editors and publishers (35.1% [32.2 to 38.0] for first authors and 24.8% [22.9 to 26.8] for last authors) and errors (29.5% [28.0 to 31.0] for first authors and 22.1% [20.7 to 23.4] for last authors). Most retractions (60.9%) had men as first and last authors. Gender equality could improve research integrity in biomedical sciences.

Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses.

In our study, we have empirically studied the assessment of cited papers within the framework of the anchoring-and-adjustment heuristic. We are interested in the question whether the assessment of a paper can be influenced by numerical information that act as an anchor (e.g. citation impact). We have undertaken a survey of corresponding authors with an available email address in the Web of Science database. The authors were asked to assess the quality of papers that they cited in previous papers. Some authors were assigned to three treatment groups that receive further information alongside the cited paper: citation impact information, information on the publishing journal (journal impact factor) or a numerical access code to enter the survey. The control group did not receive any further numerical information. We are interested in whether possible adjustments in the assessments can not only be produced by quality-related information (citation impact or journal impact), but also by numbers that are not related to quality, i.e. the access code. Our results show that the quality assessments of papers seem to depend on the citation impact information of single papers. The other information (anchors) such as an arbitrary number (an access code) and journal impact information did not play a (important) role in the assessments of papers. The results point to a possible anchoring bias caused by insufficient adjustment: it seems that the respondents assessed cited papers in another way when they observed paper impact values in the survey. We conclude that initiatives aiming at reducing the use of journal impact information in research evaluation either were already successful or overestimated the influence of this information.

Bisphenol A (BPA) is an industrial chemical that is widely used in various industrial applications. It has concerns in its use as a color developer in thermal paper receipts since it is identified as an endocrine disruptor and causes hormonal disturbances. In this study, thirty thermal paper receipt samples were randomly collected from various locations in Sharjah, United Arab Emirates and analyzed using high performance liquid chromatography–mass spectrometry. Sixty percent (60%) of receipt samples showed BPA levels above the acceptable limit (200 ng/mg) set by the European Union (EU) for thermal papers. On the other hand, 40% of the samples reported very low BPA levels (< 0.02 ng/mg). However, estimated weight adjusted daily intakes (EDI) ranged between 8.22 ×10−11 and 0.000812 μg/kg bw/day for the general population, and between 7.89×10−9 and 0.0681 μg/kg bw/day for the occupationally exposed cashiers. Thus, all calculated EDIs were below the European Food Safety Authority Tolerable Daily Intake (4 μg/kg·bw/day) and the provisional Health Canada Tolerable Daily Intake (25 ug/kg bw/day) under varying paper-to-skin transfer coefficients and absorption fractions. Nevertheless, due to its health effects and recent legal restrictions by EU, the occurrence of co-exposure to dietary and non-dietary sources should be considered in the health risk assessment of Bisphenol A, mainly for people with frequent occupational exposure to thermal paper, and especially with the increased use of sanitizers. The current study is a first within the UAE context in relation to BPA in thermal paper receipts, thus its significance especially with the recent EU enforcement of BPA limits in paper receipts. The study highlights that proper policies as well as education and awareness may assist in limiting transdermal BPA exposure for the general and occupationally exposed populations.

How the role of dopamine differs according to the evolution of eusociality and how it is required in the flexible society of Polistes paper wasps need further clarification. In the present study, we compared the storage and usage of dopamine-related substances in brains between the castes of paper wasps. The head widths, lipid stores in the abdomen, and levels of biogenic amines in the brains were measured in newly emerged females before male emergence (workers) and after male emergence (gynes) in four Polistes species. The head widths and the lipid stores were significantly larger in gynes than workers in P. snelleni, P. rothneyi, and P. jokahamae, whereas they did not differ between castes in P. chinensis. The levels of dopamine precursors in the brains were significantly higher in gynes than workers in P. snelleni, P. chinensis, and P. rothneyi, whereas those of dopamine and its metabolites did not differ between castes in these species. In P. jokahamae, the levels of dopamine precursors and dopamine in the brains did not differ between castes, but those of a dopamine metabolite were significantly higher in gynes than workers. Thus, the caste differences in the levels of dopamine-related substances did not always match body sizes and nutritional reserves. Foundresses in P. rothneyi had significantly lower levels of dopamine precursors and higher levels of dopamine and its metabolite than newly emerged gynes. These results suggested that in several Polistes species, dopamine precursors were stored in the brain without dopamine biosynthesis at emergence, and then converted into dopamine in foundresses during colony founding. These neuroendocrinal states in Polistes species largely differed from those in eusocial bees.

Invasive paper wasps such as Polistes dominula are a major pest and problem for biodiversity around the globe. Safe and highly targeted methods for the control of these and other social wasp populations are needed. We attempted to identify potentially-lethal gene targets that could be used on adult paper wasps in a gene silencing or RNA interference (RNAi) approach. Double-stranded RNA (dsRNA) was designed to target genes for which silencing has proven lethal in other insects. dsRNA was provided either orally to foragers or directly injected into the wasps. We also provided the dsRNA unprotected or protected from degradation by gut nucleases in two different forms (lipofectamine and carbon quantum dots). The effects of oral delivery of 22 different gene targets to forager wasps was evaluated. The expression of five different genes was successfully reduced following dsRNA ingestion or injection. These gene targets included the FACT complex subunit spt16 (DRE4) and RNA-binding protein fusilli (FUSILLI), both of which have been previously shown to have potential as lethal targets for pest control in other insects. However, we found no evidence of significant increases in adult wasp mortality following ingestion or injection of dsRNA for these genes when compared with control treatments in our experiments. The methods we used to protect the dsRNA from digestive degradation altered gene expression but similarly did not influence wasp mortality. Our results indicate that while many of the same gene targets can be silenced and induce mortality in other insects, dsRNA and RNAi approaches may not be useful for paper wasp control.

Some macroalgae drift on the ocean and are stranded on coasts, and these stranded brown macroalgae are regarded to be degraded by organisms. Alginate is a major component of brown macroalgae. An uncovering of how carbon is cycled through brown macroalgae is needed to deeply understand coastal ecosystems. In this study, to gain insights into metabolism of brown macroalgae and alginate in the organisms, we initially confirmed that supralittoral talitrid amphipods (beach fleas or sandhoppers collected on the Shibagaki coast in Ishikawa Prefecture, Japan) fed on the brown macroalgae. We then isolated bacteria such as Vibrio sp. with alginate-assimilating capability from the gut of the amphipods. Metagenomic analysis of the gut of amphipods housed in several conditions (e.g. macroalgae or paper as feed, non-sterilized or sterilized environment) showed no condition-dependent compositions of bacteria and fungi, but Vibrio sp. were detected at high frequency, in good agreement with the isolation of Vibrio sp. An intervention study using antibiotics showed that amphipods fed on algae or paper at about the same rate in the presence or absence of antibiotics, and that the antibiotics had no effects on the life span. Moreover, intervention with antibiotics completely killed Vibrio sp. and some other bacteria, and had significant effects on the composition of the flora in the gut, with elimination of the variations observed in the guts of amphipods housed without antibiotics. These data suggest that microbes that were killed by antibiotics, including Vibrio sp., in the gut of talitrid amphipods are not essential for assimilation of brown macroalgae.

Nucleic acid amplification is a sensitive and powerful tool for allergen detection. However, it is limited due to the relatively cumbersome methods required to extract nucleic acids from single plant seed allergen (e.g. peanut and soybean). In view of this, an approach of extracting nucleic acid with untreated glass-fiber paper (paper-based microfluidics) was applied for nucleic acid capture and purification from plant seed allergen and commercial products. After cut by hollow cylindrical cutter, a certain size the paper chip it used to absorb DNA. And this paper-based microfluidics with DNA was directly applied for amplification by loop-mediated isothermal amplification (LAMP). To evaluate the adsorption performance of paper chip to DNA, CTAB and SDS method were used as comparisons. From amplification results, the established technique has good specificity, high repeatability (C.V. values are 4.41% and 6.17% for peanut and soybean) and favorable sensitivity (7.39 ng/μL or peanut and 6.6 ng/μL for soybean), and successfully used for commercial products (2 kinds of candy and 2 kinds of cakes containing peanut, and 2 kinds of drinks, candy and 2 kinds of biscuits containing soybean). This speed and flexible detection method makes it suit for applications in point-of-care (POC) detection at different scenario, such as custom house and import port.

BackgroundPromoting physical activity (PA) for university students is essential as PA levels decrease during the transition from secondary to higher education. Providing technology-supported university courses targeting students’ PA levels may be a viable option to combat the problem. However, it is still unclear how and what technologies should be implemented in university courses to promote PA. This study aims to create a series of design principles for technology-supported physical education courses that aim to increase university students’ PA knowledge, motivation and levels.MethodThe proposed methodology underpinning the research program is a seven-phase design-based research (DBR) approach, with the seven phases encompassed in four sequential studies. These four studies are a systematic review, a qualitative focus group study, a pilot study, and a randomised controlled trial (RCT) study. The protocol paper aims to detail the plan for conducting the four studies in a comprehensive and transparent manner, thus contributing to the methodological evidence base in this field.DiscussionDesign principles generated from this project will contribute to the growing evidence focusing on effective design and implementation features. Future practitioners can also use these to develop physical education courses that aim to promote university students’ physical activity levels, knowledge, and motivation.Ethical considerationThis project has obtained ethical approval from the University of Sydney Human Research Ethics Committee (Project No. 2021/071; Project No. 2021/935). The randomised controlled trial protocol is registered at the Australian New Zealand Clinical Trials Registry (ANZCTR).Trial registrationthe RCT registry number: ACTRN12622000712707, 18/05/2022

IntroductionLiterature is lacking on the safety of storing contaminated PPE in paper bags for reuse, potentially increasing exposure to frontline healthcare workers (HCW) and patients.ObjectivesTo evaluate the effectiveness of paper bags as a barrier for fomite transmission of SARS-CoV-2 by storing limited reusable face masks, respirators, and face shields.MethodsThis quasi-experimental study evaluated the presence of SARS-CoV-2 on the interior and exterior surfaces of paper bags containing PPE that had aerosolized exposures in clinical and simulated settings. Between May and October 2020, 30 unique PPE items were collected from critical and intermediate care COVID-19 units at two urban hospitals. Exposed PPE, worn by either an infected patient or HCW during a SARS-CoV-2 aerosolizing event, were placed into an unused brown paper bag. Samples were tested at 30-minute and 12-hour intervals.ResultsA total of 177 swabs were processed from 30 PPE samples. We found a (12/177 total) 6.8% positivity rate among all samples across both collection sites. Highest positivity rates were associated with ventilator disconnection (1/6 samples, 16.7% positivity) and exposure to respiratory droplets from coughing (2/24 samples, 8.3% positivity), compared to exposure to high-flow nasal cannula (8/129 samples, 6.2% positivity) or tracheostomy surgery (1/18 samples, 5.6% positivity). Positivity rates differed between hospital units. Total positivity rates were similar between 30-minute (6.7%) and 12-hour (6.9%) sample testing time intervals. Control samples exposed to inactivated SARS-CoV-2 droplets had higher total viral counts than samples exposed to nebulized aerosols.ConclusionsData suggests paper bags are not a significant fomite risk for SARS-CoV-2 transmission. However, controls demonstrated a risk with droplet exposure. Data can inform guidelines for storing and re-using PPE in situations of limited supplies during future pandemics.

Background Advances in medicine and public health mean that people are living longer; however, a significant proportion of that increased lifespan is spent in a prolonged state of declining health and wellbeing which places increasing pressure on medical, health and social services. There is a social and economic need to develop strategies to prevent or delay age-related disease and maintain lifelong health. Several studies have suggested links between the gut microbiome and age-related disease, which if confirmed would present a modifiable target for intervention development. The MOTION study aims to determine whether and how changes in the gut microbiome are associated with physical and mental capacity. A comprehensive longitudinal multiparameter study such as this has not been previously undertaken. Methods MOTION is a longitudinal prospective cohort study with a focus on gut health and cognitive function. 360 healthy individuals aged 60 years and older, living in East Anglia, UK will be recruited to the study, stratified into one of three risk groups (cohorts) for developing dementia based on their cognitive function. Participants will attend study appointments every six months over four years, providing stool and blood samples and a health questionnaire. Participants will also undergo physical measurements and cognitive tests at alternating appointments, and undergo Optical Coherence Tomography scans at 3 timepoints. Two subgroups of participants in the study will provide colonic tissue biopsies (n = ≥30 from each cohort), and brain imaging (n = 30) at two timepoints. Discussion This study will provide new insights into the gut-(microbiota)-brain axis and the relationship between age-associated changes in gut microbe populations and cognitive health. Such insights could help develop new microbe-based strategies to improve lifelong health and wellbeing. Trial registration This study is registered in the ClinicalTrials.gov Database with ID: NCT04199195 Registered: May 14, 2019.

We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model.

The highly contagious nature of SARS-CoV-2 has led to several studies on the transmission of the virus. A little studied potential fomite of great concern in the community is currency, which has been shown to harbor microbial pathogens in several studies. Since the onset of the COVID-19 pandemic, many businesses in the United States have limited the use of banknotes in favor of credit cards. However, SARS-CoV-2 has shown greater stability on plastic in several studies. Herein, the stability of SARS-CoV-2 at room temperature on banknotes, money cards and coins was investigated. In vitro studies with live virus suggested SARS-CoV-2 was highly unstable on banknotes, showing an initial rapid reduction in viable virus and no viral detection by 24 hours. In contrast, SARS-CoV-2 displayed increased stability on money cards with live virus detected after 48 hours. Environmental swabbing of currency and money cards on and near the campus of Brigham Young University supported these results, with no detection of SARS-CoV-2 RNA on banknotes, and a low level on money cards. However, no viable virus was detected on either. These preliminary results suggest that the use of money cards over banknotes in order to slow the spread of this virus may be ill-advised. These findings should be investigated further through larger environmental studies involving more locations.

Based on the panel data of 30 provinces in China from 2010 to 2019, this paper measured the coupling coordination development of energy-economy-carbon emissions and investigated its regional differences and spatial convergence. The research methods in this paper include entropy weight technique method for order preference by similarity to an ideal solution, coupling coordination degree model, Dagum Gini coefficient and decomposition method, Moran’s I index, σ convergence model and β convergence model. The study found that the coupling coordination degree of energy-economy-carbon emissions in China has been continuously improved and has obvious regional and stage characteristics, but it is still on the verge of imminent disorder; the overall difference in the coupling coordination degree of energy-economy-carbon emissions shows a decreasing and then increasing trend, the main source of which is inter-regional differences; the coupling coordination degree of energy-economy-carbon emissions has a positive spatial correlation; except for the Southern Coastal Economic Zone and the Middle Yangtze River Economic Zone, there is no significant σ-convergence and β-convergence in the coupling coordination degree of energy-economy-carbon emissions system in other economic zones; the coupling coordination degree of energy-economy-carbon emissions changes fastest in the Middle Yangtze River Economic Zone. The innovation of this paper is to measure the coupling coordination degree of energy-economy-carbon emissions and to analyse its regional differences and spatial effects. It is of great practical significance to promote the coupling coordination development and regional balanced development of energy-economy-carbon emissions in China under the background of "dual carbon".

Ranked set sampling (RSS) has created a broad interest among researchers and it is still a unique research topic. It has at long last begun to find its way into practical applications beyond its initial horticultural based birth in the fundamental paper by McIntyre in the nineteenth century. One of the extensions of RSS is median ranked set sampling (MRSS). MRSS is a sampling procedure normally utilized when measuring the variable of interest is troublesome or expensive, whereas it might be easy to rank the units using an inexpensive sorting criterion. Several researchers introduced ratio, regression, exponential, and difference type estimators for mean estimation under the MRSS design. In this paper, we propose three new mean estimators under the MRSS scheme. Our idea is based on three-fold utilization of supplementary information. Specifically, we utilize the ranks and second raw moments of the supplementary information and the original values of the supplementary variable. The appropriateness of the proposed group of estimators is demonstrated in light of both real and artificial data sets based on the Monte-Carlo simulation. Additionally, the performance comparison is also conducted regarding the reviewed families of estimators. The results are empowered and the predominant execution of the proposed group of estimators is seen throughout the paper.

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into a one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. Our proposed 1D-CNN model, which is trained on the digitized ECG signals, allows identifying individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42%, 95.63%, and 98.50% for classifying COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19., Accepted with minor revision by Plos One

Currently, JavaScript malicious code detection methods are becoming more and more effective. Still, the existing methods based on deep learning are poor at detecting too long or too short JavaScript code. Based on this, this paper proposes an adaptive code length deep learning network JACLNet, composed of convolutional block RDCNet, BiLSTM and Transfrom, to capture the association features of the variable distance between codes. Firstly, an abstract syntax tree recombination algorithm is designed to provide rich syntax information for feature extraction. Secondly, a deep residual convolution block network (RDCNet) is designed to capture short-distance association features between codes. Finally, this paper proposes a JACLNet network for JavaScript malicious code detection. To verify that the model presented in this paper can effectively detect variable JavaScript code, we divide the datasets used in this paper into long text dataset DB_Long; short text dataset DB_Short, original dataset DB_Or and enhanced dataset DB_Re. In DB_Long, our method’s F1 − score is 98.87%, higher than that of JSContana by 2.52%. In DB_Short, our method’s F1-score is 97.32%, higher than that of JSContana by 7.79%. To verify that the abstract syntax tree recombination algorithm proposed in this paper can provide rich syntax information for subsequent models, we conduct comparative experiments on DB_Or and DB_Re. In DPCNN+BiLSTM, F1-score with abstract syntax tree recombination increased by 1.72%, and in JSContana, F1-score with abstract syntax tree recombination increased by 1.50%. F1-score with abstract syntax tree recombination in JACNet improved by 1.00% otherwise unused.

Multiple approaches have been used to investigate how musical cues are used to shape different emotions in music. The most prominent approach is a perception study, where musical stimuli varying in cue levels are assessed by participants in terms of their conveyed emotion. However, this approach limits the number of cues and combinations simultaneously investigated, since each variation produces another musical piece to be evaluated. Another less used approach is a production approach, where participants use cues to change the emotion conveyed in music, allowing participants to explore a larger number of cue combinations than the former approach. These approaches provide different levels of accuracy and economy for identifying how cues are used to convey different emotions in music. However, do these approaches provide converging results? This paper’s aims are two-fold. The role of seven musical cues (tempo, pitch, dynamics, brightness, articulation, mode, and instrumentation) in communicating seven emotions (sadness, joy, calmness, anger, fear, power, and surprise) in music is investigated. Additionally, this paper explores whether the two approaches will yield similar findings on how the cues are used to shape different emotions in music. The first experiment utilises a production approach where participants adjust the cues in real-time to convey target emotions. The second experiment uses a perception approach where participants rate pre-rendered systematic variations of the stimuli for all emotions. Overall, the cues operated similarly in the majority (32/49) of cue-emotion combinations across both experiments, with the most variance produced by the dynamics and instrumentation cues. A comparison of the prediction accuracy rates of cue combinations representing the intended emotions found that prediction rates in Experiment 1 were higher than the ones obtained in Experiment 2, suggesting that a production approach may be a more efficient method to explore how cues are used to shape different emotions in music.

As an important part of a modern economic system, a modern industrial system is the key to promoting high-quality economic development. China’s modern industrial system construction focuses on industrial restructuring. At present, in order to strengthen the support and leading role of transportation in the modern economic system, China is actively promoting the construction of a competitive transportation power. Therefore, it is necessary to study whether large-scale investment in transportation infrastructure can promote industrial structure transformation and upgrade. This paper takes China as the research background. Firstly, a RAM model was employed to evaluate the unified economic and environmental efficiency of transportation infrastructure that measures the level of transportation infrastructure investment. Secondly, a PVAR model was built to evaluate the dynamic effects of transportation infrastructure investment on industrial structure transformation and upgrade. Finally, from the perspective of rational flow and optimal allocation of resource factors, the paper points out that transportation infrastructure investment can indirectly promote industrial structure transformation and upgrade through three paths, namely expanding market demand, reducing resource misallocation and increasing technological innovation, and the first half of these paths are positively regulated by policies. Then, an empirical test was done with the moderated mediation model. Research findings suggest that: first, improvements in the unified economic and environmental efficiency of transportation infrastructure can only promote industrial structure supererogation in a short time, but have no significant effect on promoting industrial structure rationalization in the short or long term. Second, in actual situation, transportation infrastructure investment can promote industrial structure transformation and upgrade only by expanding market demand and technological innovation, but not by reducing resource misallocation. Third, the first half of these paths through which transportation infrastructure investment promotes industrial structure transformation and upgrade are positively regulated by policies. This paper provided some theoretical reference for promoting industrial structure transformation and upgrade by virtue of the sustainable development of transportation.

With the rapid development of electric vehicle (EV) technology under the background of double carbon, a reasonable evaluation of the network planning of EV charging stations is conducive to the long-term stable development of the EV industry. Therefore, this paper proposes a comprehensive evaluation system and method for EV charging networks under the traffic network and power grid coupling. First, an EV travel model is developed based on the travel probability matrix to analyze the spatial and temporal characteristics of EVs. Secondly, the coupling relationship among users, charging network, road network, and the power grid is analyzed, four criteria of user feedback, charging network operation, road network operation impact, and power grid operation impact are proposed, and corresponding evaluation indexes are constructed under each criterion to form a comprehensive evaluation index system. Furthermore, the Analytic Hierarchy Process (AHP) and entropy weighting method are used to assign weights to the index layer and the criterion layer respectively, and the improved TOPSIS evaluation method is used to quantitatively analyze the impact of charging networks on different subjects for comprehensive evaluation. Finally, through the comprehensive evaluation of different charging network planning schemes, the effectiveness of the comprehensive evaluation method and model proposed in this paper is verified, which can provide a reference basis for the planning and improvement of EV charging station networks.

With the development of smart mobile devices and global positioning technology, people’s daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents’ car-hailing trips. Using online data on car-hailing trajectories, points of interest (POIs) data and other auxiliary data, the paper explores how the built environment impacts online car-hailing passengers. Within a 200 x 200m research grid, the unique spatiotemporal patterns of weekday car-hailing trips during a one-week period are analyzed, using statistics on pick-ups and drop-offs at different time of the day. By combining these data with built environment variables and various economic and traffic indicators, a multi-scale geographically weighted regression (MGWR) model is developed for different time scales. The MGWR model outperforms the classical geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model in terms of goodness of fit and all other aspects. More importantly, this study finds a high degree of temporal and spatial heterogeneity in the impact of built environment factors on local car-hailing trips across different regions, and the paper analyzes the business residence coefficient in detail. The study provides valuable insights to help improve the level of urban transportation services, as well as urban transportation planning and construction.

In order to study the impact of inclusive finance on agricultural green development, This paper uses both static panel and dynamic panel (system GMM model) estimation methods to make empirical analysis of the impact of inclusive financial development on agricultural green development. The results both find that there is a significant positive correlation between the level of inclusive financial development, real GDP per capita, the proportion of the added value of agriculture, forestry, animal husbandry and fishery in GDP and agricultural green development. This paper puts forward some countermeasures and suggestions to promote agricultural green development, including vigorously developing inclusive finance, promoting economic growth, promoting the development of agriculture, forestry, animal husbandry and fishery, and increasing environmental protection expenditures.

Acute coronary syndrome (ACS) is a serious cardiovascular disease that can lead to cardiac arrest if not diagnosed promptly. However, in the actual diagnosis and treatment of ACS, there will be a large number of redundant related features that interfere with the judgment of professionals. Further, existing methods have difficulty identifying high-quality ACS features from these data, and the interpretability work is insufficient. In response to this problem, this paper uses a hybrid feature selection method based on gradient boosting trees and recursive feature elimination with cross-validation (RFECV) to reduce ACS feature redundancy and uses interpretable feature learning for feature selection to retain the most discriminative features. While reducing the feature set search space, this method can balance model simplicity and learning performance to select the best feature subset. We leverage the interpretability of gradient boosting trees to aid in understanding key features of ACS, linking the eigenvalue meaning of instances to model risk predictions to provide interpretability for the classifier. The data set used in this paper is patient records after percutaneous coronary intervention (PCI) in a tertiary hospital in Fujian Province, China from 2016 to 2021. In this paper, we experimentally explored the impact of our method on ACS risk prediction. We extracted 25 key variables from 430 complex ACS medical features, with a feature reduction rate of 94.19%, and identified 5 key ACS factors. Compared with different baseline methods (Logistic Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and 1D Convolutional Networks), the results show that our method achieves the highest Accuracy of 98.8%.

Recommendation algorithms save a lot of valuable time for people to get the information they are interested in. However, the feature calculation and extraction process of each machine learning or deep learning recommendation algorithm are different, so how to obtain various features with different dimensions, i.e., how to integrate the advantages of each model and improve the model inference efficiency, becomes the focus of this paper. In this paper, a better deep learning model is obtained by integrating several cutting-edge deep learning models. Meanwhile, to make the integrated learning model converge better and faster, the parameters of the integrated module are initialized, constraints are imposed, and a new activation function is designed for better integration of the sub-models. Finally, the integrated large model is distilled for knowledge distillation, which greatly reduces the number of model parameters and improves the model inference efficiency.

Resource allocation problem is one of key issues in the field of revenue management. The traditional models usually rely on some restrictive assumptions about demand information or arrival process, which is sometimes out of line with reality. To overcome this shortcoming, the method of competitive analysis of online algorithms, which eliminates the need for the assumptions on demand and arrivals, is adopted to deal with the quantity-based revenue management problem. The current model in this paper considers two downgrade compatible levels of resources. Given the capacities and fares of both levels of resources, the objective is to accept appropriate customers and assign them to appropriate resources so as to maximize revenues. Compared with the existing literature, this paper generalizes the concerned resource allocation problem by considering multiple fares for each level of resources. From the perspective of online algorithms and competitive analysis, both an upper bound and an optimal online strategy are derived in this paper.

Although both the analysis of regional culture and urbanisation are long-standing preoccupations in geography, few studies have considered the relationship between the two, the former traditionally being a topic in cultural geography, while the latter is usually interpreted and analysed as a process in economic geography. Taking evidence from the 1851–1911 censuses of England and Wales, this article analyses individual migration paths to identify stable regions of human interaction by applying a sophisticated community-detection algorithm. By accurately mapping the regions within which the majority of migration occurred between 1851 and 1911 and arguing that the stability of these geographies is evidence of more than just mutable communities but rather of persistent regional cultures, this article responds to previous studies that have sought to identify the cultural provinces of England and Wales. Indeed, by demonstrating that the regions bear a striking resemblance to those that have long been hypothesised as being distinct cultural provinces of England and Wales, this article empirically corroborates their existence. In order to further demonstrate that the regions constitute cultural provinces, this paper incorporates these boundaries into a spatial interaction model (SIM). The results of the SIM not only shows that the boundaries between the regions limited the number of migrants that crossed them–over and above that explained by control variables–and therefore represented the boundaries of cultural provinces, demarcating discrete regions of human interaction–but that such boundaries disproportionately restricted rural-urban migrants, thereby slowing the pace at which England and Wales urbanised. This paper therefore demonstrates that urbanisation should not only be interpreted as only an economic phenomenon, but a cultural one also, and that if urbanisation is to be fully understood, individuals’ attachment to place as a component of their identity, ought to be formally incorporated into models of migration.

Based on the panel data of 30 provinces in China from 2011 to 2019, this paper uses a two-stage DEA model to measure regional innovation efficiency, then non-parametric test is used to examine the impact of innovation network structure and government R&D investment on regional innovation efficiency. The results show that, at the provincial level, innovation efficiency of regional R&D is not necessarily in direct proportion to the innovation efficiency in the commercialization stage. Commercialization efficiency is not necessarily high in provinces with high technical R&D efficiency. At the national level, the innovation efficiency gap between our country’s R&D and commercialization stage is small, indicating that the development of the national innovation efficiency is more and more balanced. Innovation network structure can promote the R&D efficiency, but has no significant effect on the commercialization efficiency. Government R&D investment helps to improve the R&D efficiency, but it is not conducive to the improvement of commercialization efficiency. The interaction between innovation network structure and government R&D investment will have compound effects on regional innovation efficiency; the region with underdeveloped innovation network structure can increase the government R&D investment to make it have a higher level of R&D. This paper provides insights into how to improve innovation efficiency in different social networks and policy environments.

Sustainable development is the theme of world economic development in the 21st century. As a key part of sustainable development, sustainable land use (SLU) encompasses economic development and environmentally friendly and social progress. In recent decades, China has formulated many environmental regulatory policies to achieve sustainable development and "carbon peaking and carbon neutrality (double-carbon)" goals, among which the carbon emission trading scheme (CETS) is the most representative and provides valuable research. In this paper, we aimed to reflect the spatio-temporal evolution of SLU in China under the influence of environmental regulatory policies through an indicator measurement strategy based on the DID estimation method. The study conclusions are as follows: (1) The CETS can effectively improve SLU from the perspectives of economic development and environmentally friendly progress, and the impact has primarily been in the pilot areas. And, its effectiveness is closely linked to local locational factors. (2) With respect to the dimension of economic development, the CETS has not changed the provincial distribution patterns of SLU; rather, it continues to remain "high to low, east to west". However, regarding the environmentally friendly progress dimension, the CETS has significantly changed the provincial distribution patterns of SLU, which are characterized by spatial agglomeration with urban agglomerations such as the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) as the core. (3) The screening results of the SLU indicators based on economic development showed that the CETS primarily improved the innovation capacities of pilot regions, and the impacts on economic levels were relatively small. Similarly, the screening results of the SLU indicators based on environmentally friendly progress showed that the CETS had primarily acted on reducing pollution emission intensity and strengthening greening construction, revealing only short-term effects on improving energy use efficiency. Based on the above, this paper explored the meaning and role of the CETS in more detail, with a view to providing insight into the implementation and formulation of environmental regulation policies.

The grey prediction is a common method in the prediction. Studies show that general grey models have high modeling precision when the time sequence varies slowly, but some grey models show low modeling precision for the high-growth sequence. The paper researches the grey modeling for the high-growth sequence using the extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α). To improve the nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α)’s prediction precision and make data have better adaptability to the model, the paper makes improvements in the following three aspects: (1) the paper improves the accumulated generating sequence of original time sequence, i.e. making a new transformation of traditional accumulated generating sequence; (2) the paper improves the model’s structure, extends the grey action and builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α); (3) the paper improves the model’s background value and uses the value of cubic spline function to approximate the background value. Because the parameters of the new accumulated generating sequence transformed, the nonlinear grey Bernoulli model’s time response equation and the background value are optimized simultaneously, the prediction precision increases greatly. The paper builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃2,α) using the method proposed and seven comparison models for China’s express delivery volume per capita. Comparison results show that the extended nonlinear grey Bernoulli model built with the method proposed has high simulation and prediction precision and shows the precision superior to that of seven comparison models.

Using the data of the 2018 China Family Panel Studies (CFPS), this paper empirically tests the impact of the "Big Five" personality characteristics on household charitable donation behavior. The benchmark regression results show that after controlling the individual characteristics and family characteristics of the household heads, the conscientiousness and openness of the household heads have a significant positive impact on the social donation behavior of the family. On this basis, this paper takes the openness personality as an example, selects the identification strategy of processing effect, and tests the robustness of the causal effect of personality on household donation behavior. The openness personality has a significant positive impact on household external donation behavior. In the further study, it is found that with the improvement of the level of household charitable donation, the positive effect of the household head ’s openness personality on household charitable donation behavior is gradually weakening; The influence of openness personality on household charitable donation has the nonlinear characteristics of "marginal effect" increasing and obvious life cycle characteristics.

As a major energy sources province in China, Shaanxi Province ranks top three in terms of raw coal production in China and undertakes the important task of ensuring national energy supply and security. Affected by the endowment of energy resources, fossil energy accounts for a large proportion of the energy consumption structure in Shaanxi Province, and it will face huge challenges under the severe carbon emission situation in future. In order to analyze the relationship between energy consumption structure, energy efficiency and carbon emissions, the paper introduces the concept of biodiversity into the energy industry. Taking Shaanxi Province as an example, the paper calculates the energy consumption structure diversity index and analyzes the impact of energy consumption structure diversity on energy efficiency and carbon emissions in Shaanxi Province. The results shows that the diversity index and equilibrium index of energy consumption structure in Shaanxi exhibits a slow upward trend in general. In most years, energy consumption structure diversity index in Shaanxi is higher than 0.8, and the equilibrium index is higher than 0.6. The carbon emissions of energy consumption in Shaanxi generally show increasing trend, and the carbon emissions have increased from 5,064.6 tons to 21899.67 tons from 2000 to 2020. The paper also shows that Shaanxi H index is negatively correlated with total factor energy utilization efficiency in Shaanxi, and positively correlated with carbon emissions in Shaanxi. The main reason is the internal substitution of fossil energy, and the proportion of primary electricity and other energy sources is still relatively low, which leads to a higher level of carbon emissions.

The Fenwei Plain is listed as one of the most serious air pollution regions in China, along with Beijing-Tianjin-Hebei and Yangtze River Delta regions. This paper proposed a functional data analysis method to study the environmental pollution problem in the Fenwei Plain of China. Functional spatial autoregressive combined (FSAC) model with spatial autocorrelation of both the response variable and error term is developed. The model takes the SO2 concentration of Fenwei Plain as the dependent variable and the dew point temperature as the independent variable and realizes the maximum likelihood estimation using functional principal component analysis to obtain the asymptotic properties of parameter estimation and the confidence interval of the slope function. According to the findings of the empirical analysis of the Fenwei Plain, the SO2 concentration has significant seasonal characteristics and has decreased year over year for three years in a row. Winter is the season with the highest concentration on the Fenwei Plain, followed by spring and autumn, while summer is the season with the lowest concentration. Winter also has a high spatial autocorrelation. The FSAC model is more effective at fitting the concentration and dew point temperature of the Fenwei Plain in China because its mean square error (MSE) is significantly lower than that of the other models. As a result, this paper can more thoroughly study the pollution problem on the Fenwei Plain and offer guidance for prevention and control.

BackgroundMalnutrition imposes enormous costs resulting from lost investments in human capital and increased healthcare expenditures. There is a dearth of research focusing on the prediction of women’s body mass index (BMI), and the malnutrition outcomes (underweight, overweight and obesity) in developing countries. This paper attempts to fill out this knowledge gap by predicting the BMI and the risks of malnutrition outcomes for Bangladeshi women of childbearing age from their economic, health, and demographic features.MethodsData from the 2017-18 Bangladesh Demographic and Health Survey and a series of supervised machine learning (SML) techniques are used. Additionally, this study circumvents the imbalanced distribution problem in obesity classification by utilizing an oversampling approach.ResultsStudy findings demonstrate that support vector machine and k-nearest neighbor are the two best-performing methods in BMI prediction based on coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The combined predictor algorithms consistently yield top specificity, Cohen’s kappa, F1-score, and AUC in classifying the malnutrition status, and their performance is robust to alternative standards. The feature importance ranking based on several nonparametric and combined predictors indicates that socioeconomic status, women’s age, and breastfeeding status are the most important features in predicting women’s nutritional outcomes. Furthermore, the conditional inference trees corroborate that those three features along with the partner’s educational attainment and employment significantly predict malnutrition risks.ConclusionTo the best of our knowledge, this is the first study that predicts BMI and one of the pioneer studies to classify all three malnutrition outcomes for women of childbearing age in Bangladesh, let alone in any lower-middle income country, using SML techniques. Moreover, in the context of Bangladesh, this paper is the first to identify and rank features that are critical in predicting nutritional outcomes using several feature selection algorithms. The estimators from this study predict the outcomes of interest most accurately and efficiently compared to other existing studies in the relevant literature. Therefore, study findings can aid policymakers in designing policy and programmatic approaches to address the double burden of malnutrition among Bangladeshi women, thereby reducing the country’s economic burden.

During pandemics like COVID-19, both the quality and quantity of services offered by businesses and organizations have been severely impacted. They often have applied a hybrid home office setup to overcome this problem, although in some situations, working from home lowers employee productivity. So, increasing the rate of presence in the office is frequently desired from the manager’s standpoint. On the other hand, as the virus spreads through interpersonal contact, the risk of infection increases when workplace occupancy rises. Motivated by this trade-off, in this paper, we model this problem as a bi-objective optimization problem and propose a practical approach to find the trade-off solutions. We present a new probabilistic framework to compute the expected number of infected employees for a setting of the influential parameters, such as the incidence level in the neighborhood of the company, transmission rate of the virus, number of employees, rate of vaccination, testing frequency, and rate of contacts among the employees. The results show a wide range of trade-offs between the expected number of infections and productivity, for example, from 1 to 6 weekly infections in 100 employees and a productivity level of 65% to 85%. This depends on the configuration of influential parameters and the occupancy level. We implement the model and the algorithm and perform several experiments with different settings of the parameters. Moreover, we developed an online application based on the result in this paper which can be used as a recommender for the optimal rate of occupancy in companies/workplaces.

In recent years, deep learning has seen remarkable progress in many fields, especially with many excellent pre-training models emerged in Natural Language Processing(NLP). However, these pre-training models can not be used directly in music generation tasks due to the different representations between music symbols and text. Compared with the traditional presentation method of music melody that only includes the pitch relationship between single notes, the text-like representation method proposed in this paper contains more melody information, including pitch, rhythm and pauses, which expresses the melody in a form similar to text and makes it possible to use existing pre-training models in symbolic melody generation. In this paper, based on the generative pre-training-2(GPT-2) text generation model and transfer learning we propose MT-GPT-2(music textual GPT-2) model that is used in music melody generation. Then, a symbolic music evaluation method(MEM) is proposed through the combination of mathematical statistics, music theory knowledge and signal processing methods, which is more objective than the manual evaluation method. Based on this evaluation method and music theories, the music generation model in this paper are compared with other models (such as long short-term memory (LSTM) model,Leak-GAN model and Music SketchNet). The results show that the melody generated by the proposed model is closer to real music.

Since the pandemic organizations have been required to build agility to manage risks, stakeholder engagement, improve capabilities and maturity levels to deliver on strategy. Not only is there a requirement to improve performance, a focus on employee engagement and increased use of technology have surfaced as important factors to remain competitive in the new world. Consideration of the strategic horizon, strategic foresight and support structures is required to manage critical factors for the formulation, execution and transformation of strategy. Strategic foresight and Artificial Intelligence modelling are ways to predict an organizations future agility and potential through modelling of attributes, characteristics, practices, support structures, maturity levels and other aspects of future change. The application of this can support the development of required new competencies, skills and capabilities, use of tools and develop a culture of adaptation to improve engagement and performance to successfully deliver on strategy. In this paper we apply an Artificial Intelligence model to predict an organizations level of future agility that can be used to proactively make changes to support improving the level of agility. We also explore the barriers and benefits of improved organizational agility. The research data was collected from 44 respondents in public and private Australian industry sectors. These research findings together with findings from previous studies identify practices and characteristics that contribute to organizational agility for success. This paper contributes to the ongoing discourse of these principles, practices, attributes and characteristics that will help overcome some of the barriers for organizations with limited resources to build a framework and culture of agility to deliver on strategy in a changing world.

Information on the living conditions of widows in Vietnam is limited. Prior studies of gender gaps have identified widows as vulnerable to the risks of poverty. However, widows are only included as a sub-group in broader studies of women’s well-being. Large gaps remain in the knowledge based on the factors affecting both widows’ susceptibility to poverty and the conditions or circumstances that might minimize these risks. This paper attempts to help redress these knowledge gaps by contributing an analysis of data from the 2018 Vietnam Household Living Standard Surveys. The paper compares the likelihood of poverty for widowed and other households using probit regression. It also assesses whether widows who head up their households face different risks of poverty from those who live in other households. Finally, the paper examines the effects on the risk of poverty of a range of social, demographic and locational characteristics of widow households in Vietnam. Our results indicate that widowed households have experienced a higher probability of falling into poverty. Moreover, widow-headed households have faced more vulnerability to fresh water and housing, especially among widowed households. Policy implications have emerged based on the findings of this paper.

For the problem of direction-of-arrival (DOA) estimation using a coprime array, there are high spatial spectrum outputs of false alarms caused by the overlap of main and grating lobes from subarrays. In this paper, a DOA estimation method of more than two co-frequency sources for a coprime vector hydrophone array is proposed. The method is based on vector cross terms (VCTs), making full use of the directivity of channel combinations for vector hydrophones. Based on VCTs, the characteristic data point identification method is conducted and ensures that the bearing data with the characteristic can be preserved. For further interference rejection, the paper designs Queue Selection (QS) method based on inverse beamforming. The influence of grating lobes can be weakened with the QS, further improving the accuracy of direction extraction. The algorithm in this work does not require decoherence processing, and the simulation work shows that it achieves stable DOA estimation with a low signal-to-noise ratio (SNR).

This paper presents a novel strategy for computing mathematical functions with molecular reactions, based on theory from the realm of digital design. It demonstrates how to design chemical reaction networks based on truth tables that specify analog functions, computed by stochastic logic. The theory of stochastic logic entails the use of random streams of zeros and ones to represent probabilistic values. A link is made between the representation of random variables with stochastic logic on the one hand, and the representation of variables in molecular systems as the concentration of molecular species, on the other. Research in stochastic logic has demonstrated that many mathematical functions of interest can be computed with simple circuits built with logic gates. This paper presents a general and efficient methodology for translating mathematical functions computed by stochastic logic circuits into chemical reaction networks. Simulations show that the computation performed by the reaction networks is accurate and robust to variations in the reaction rates, within a log-order constraint. Reaction networks are given that compute functions for applications such as image and signal processing, as well as machine learning: arctan, exponential, Bessel, and sinc. An implementation is proposed with a specific experimental chassis: DNA strand displacement with units called DNA “concatemers”.

Music performance action generation can be applied in multiple real-world scenarios as a research hotspot in computer vision and cross-sequence analysis. However, the current generation methods of music performance actions have consistently ignored the connection between music and performance actions, resulting in a strong sense of separation between visual and auditory content. This paper first analyzes the attention mechanism, Recurrent Neural Network (RNN), and long and short-term RNN. The long and short-term RNN is suitable for sequence data with a strong temporal correlation. Based on this, the current learning method is improved. A new model that combines attention mechanisms and long and short-term RNN is proposed, which can generate performance actions based on music beat sequences. In addition, image description generative models with attention mechanisms are adopted technically. Combined with the RNN abstract structure that does not consider recursion, the abstract network structure of RNN-Long Short-Term Memory (LSTM) is optimized. Through music beat recognition and dance movement extraction technology, data resources are allocated and adjusted in the edge server architecture. The metric for experimental results and evaluation is the model loss function value. The superiority of the proposed model is mainly reflected in the high accuracy and low consumption rate of dance movement recognition. The experimental results show that the result of the loss function of the model is at least 0.00026, and the video effect is the best when the number of layers of the LSTM module in the model is 3, the node value is 256, and the Lookback value is 15. The new model can generate harmonious and prosperous performance action sequences based on ensuring the stability of performance action generation compared with the other three models of cross-domain sequence analysis. The new model has an excellent performance in combining music and performance actions. This paper has practical reference value for promoting the application of edge computing technology in intelligent auxiliary systems for music performance.

Ternary Optical Computer (TOC) is more advanced than traditional computer systems in parallel computing, which is characterized by huge amounts of repeated computations. However, the application of the TOC is still limited because of lack of key theories and technologies. In order to make the TOC applicable and advantageous, this paper systematically elaborates the key theories and technologies of parallel computing for the TOC through a programming platform, including reconfigurability and groupable usability of optical processor bits, parallel carry-free optical adder and the TOC’s application characteristics, communication file to express user’s needs and data organization method of the TOC. Finally, experiments are carried out to show the effectiveness of the present theories and technologies for parallel computing, as well as the feasibility of the implementation method of the programming platform. For a special instance, it is shown that the clock cycle on the TOC is only 0.26% of on a traditional computer, and the computing resource spent on the TOC is 25% of that on a traditional computer. Based on the study of the TOC in this paper, more complex parallel computing can be realized in the future.

Objectives Breast cancer is a major health problem with high mortality rates. Early detection of breast cancer will promote treatment. A technology that determines whether a tumor is benign desirable. This article introduces a new method in which deep learning is used to classify breast cancer. Methods A new computer-aided detection (CAD) system is presented to classify benign and malignant masses in breast tumor cell samples. In the CAD system, (1) for the pathological data of unbalanced tumors, the training results are biased towards the side with the larger number of samples. This paper uses a Conditional Deep Convolution Generative Adversarial Network (CDCGAN) method to generate small samples by orientation data set to solve the imbalance problem of collected data. (2) For the high-dimensional data redundancy problem, this paper proposes an integrated dimension reduction convolutional neural network (IDRCNN) model, which solves the high-dimensional data dimension reduction problem of breast cancer and extracts effective features. The subsequent classifier found that by using the IDRCNN model proposed in this paper, the accuracy of the model was improved. Results Experimental results show that IDRCNN combined with the model of CDCGAN model has superior classification performance than existing methods, as revealed by sensitivity, area under the curve (AUC), ROC curve and accuracy, recall, sensitivity, specificity, precision,PPV,NPV and f-values analysis. Conclusion This paper proposes a Conditional Deep Convolution Generative Adversarial Network (CDCGAN) which can solve the imbalance problem of manually collected data by directionally generating small sample data sets. And an integrated dimension reduction convolutional neural network (IDRCNN) model, which solves the high-dimensional data dimension reduction problem of breast cancer and extracts effective features.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

The problem of dust pollution in the open-pit coal mine significantly impacts the health of staff, the regular operation of mining work, and the surrounding environment. At the same time, the open-pit road is the largest dust source. Therefore, it analyzes the influencing factors of road dust concentration in the open-pit coal mine. It is of practical significance to establish a prediction model for scientific and effective prediction of road dust concentration in the open pit coal mine. The prediction model helps reduce dust hazards. This paper uses the hourly air quality and meteorological data of an open-pit coal mine in Tongliao City, Inner Mongolia Autonomous Region, from January 1, 2020, to December 31, 2021. Create a CNN-BiLSTM-Attention multivariate hybrid model consisting of a Convolutional Neural Network (CNN), a bidirectional long short-term memory neural network (BiLSTM), and an attention mechanism, Prediction of PM2.5 concentration in the next 24h. Establish prediction models of parallel and serial structures, and carry out many experiments according to the change period of the data to determine the optimal configuration and the input and output size. Then, a comparison of the proposed model and Lasso regression, SVR, XGBoost, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM models for short-term prediction (24h) and long-term prediction (48h, 72h, 96h, and 120h). The results show that the CNN-BiLSTM-Attention multivariate mixed model proposed in this paper has the best prediction performance. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) of the short-term forecast (24h) are 6.957, 8.985, and 0.914, respectively. Evaluation indicators of long-term forecasts (48h, 72h, 96h, and 120h) are also superior to contrast models. Finally, we used field-measured data to verify, and the obtained evaluation indexes MAE, RMSE, and R2 are 3.127, 3.989, and 0.951, respectively. The model-fitting effect was good.

An estimated 30–70% of available medications in low-income countries and conflict states are of low quality or counterfeit. Reasons for this vary but most are rooted in regulatory agencies being poorly equipped to oversee quality of pharmaceutical stocks. This paper presents the development and validation of a method for point-of-care drug stock quality testing in these environs. The method is termed Baseline Spectral Fingerprinting and Sorting (BSF-S). BSF-S leverages the phenomena that all compounds in solution have nearly unique spectral profiles in the UV spectrum. Further, BSF-S recognizes that variations in sample concentrations are introduced when preparing samples in the field. BSF-S compensates for this variability by incorporating the ELECTRE-TRI-B sorting algorithm, which contains parameters that are trained in the laboratory using authentic, proxy low quality and counterfeit samples. The method was validated in a case study using fifty samples that include factually authentic Praziquantel and inauthentic samples prepared in solution by an independent pharmacist. Study researchers were blinded to which solution contained the authentic samples. Each sample was tested by the BSF-S method described in this paper and sorted to authentic or low quality/counterfeit categories with high levels of specificity and sensitivity. In combination with a companion device under development using ultraviolet light emitting diodes, the BSF-S method is intended to be a portable and low-cost method for testing medications for authenticity at or near the point-of-care in low income countries and conflict states.

This paper presents a finite-time approach for tracking control of a quadrotor system subjected to external disturbances and model uncertainties. The proposed approach offers a preassigned performance guarantee. Firstly, integral terminal sliding manifolds and nonsingular terminal sliding manifolds are considered to produce the new hyperplane sliding variables for both position and attitude of a quadrotor. The designed hyperplane sliding variables guaranteed a finite-time convergence. The objective is to develop a finite-time control scheme for a disturbed quadrotor to follow a predefined trajectory based on a nonlinear sliding mode controller. The main contribution of this paper is to design a hyperplane-based nonlinear sliding mode control strategy for a quadrotor subjected to disturbances. A concept of robust controllers for a quadrotor is presented based on Lyapunov theory, which proves finite-time stability of the proposed control technique. Numerical simulations with two different scenarios verify the accuracy of the proposed hyperplane-based sliding mode control approach. The simulations study also included a comparison with another nonlinear controller. Results demonstrated overperformance of the proposed control strategy.

Background Conflict affected populations, in particular women in such settings, face an increased risk of developing mental health disorders as well as well as economic vulnerability and reduced productivity. However, the link between the two has rarely been studied. Data and methods The data in this paper come from a cross-sectional dataset (n = 1053) and a panel dataset of (n = 499) women suffering from post-traumatic stress disorder (PTSD) in eastern Democratic Republic of the Congo. This paper investigates the association between mental health disorders (PTSD, depression and/or anxiety) and employment for women in a conflict-affected setting. Results The study finds that worsened local functioning is associated with reduced likelihood of working, earnings, and engagement in paid work. Reduction in probable depression and/or anxiety and PTSD are both associated with increased likelihood of engaging in paid work compared to unpaid work. Reduction in probable depression and/or anxiety is also associated with engaging in a secondary economic activity, as well as with higher productivity. However, when controlling for daily (local) functioning impairment, the primary pathway through which mental health may impact working, we detect a positive relationship between work or working hours and increased symptoms of PTSD and depression and/or anxiety. Working women with worse PTSD and depression and/or anxiety symptoms are also less likely to be self-employed, especially in an off-farm setting, and more likely to be engaged in farming. Conclusion A complex relationship between working and mental health emerges. Our findings also suggest that in this population farming, particularly farm-based wage work, is positively associated with worse mental health even after accounting for wealth and other relevant socio-demographic factors. These findings highlight the importance of paying close attention to the mental health of beneficiaries of livelihood support projects in post-conflict settings, where the relationship between mental health and employment is not straightforward.

The current evaluation of M&A performance lacks consideration of M&A motives. In this paper, we theoretically analyse and empirically test the effect of network synergy generated by M&A on the degree of realization of corporate M&A motives and the mechanism of its effect by constructing an equity network between a listed company and its subsidiaries within the company. The results show that the greater the variation of internal network node degree and strength, the more beneficial it is to promote the degree of realization of corporate M&A motivation; the results of further mechanism of action tests show that the variation of network node degree and strength has significant effects on economies of scale, economies of scope, and transaction costs; Furthermore, the heterogeneity test finds that the effect of variation of network node degree and strength to promote the degree of realization of corporate M&A motivation is more significant in the case of non-cash payment method and related M&A. This paper extends the study of complex networks to the field of M&A and uniquely explains the paradox of the "high failure rate" of M&A and the increasing activity of M&A activities from the perspective of network synergy, which helps to rationalize the M&A behavior of enterprises and further regulate the M&A behavior of listed companies by regulatory authorities.