Multiple Subjects Research Articles

Enhancing Project-Based Learning in STEM Education with Integrated Technology and Coding

This extensive study explores the advantages and significance of incorporating technology and coding into project-based learning to improve STEM education. This integration improves students' learning experiences by encouraging computational thinking, problem-solving ability, and creativity. Collaborative coding projects promote interdisciplinary learning and a thorough comprehension of multiple subjects, while online resources and self-paced learning platforms enable students to study coding outside of the classroom. Educators can design meaningful learning experiences that match technology use with learning objectives and stimulate social connections and cooperation by embracing theories such as constructivism, TPACK, and social constructivism. Inquiry-based learning, project-based learning, and self-regulated learning are all strategies that promote student engagement and learning in integrated technology and coding. Providing resources, professional development opportunities, and technical support systems promotes in efficient implementation. Educators may develop engaging project-based learning experiences that equip students with necessary skills for the future by embracing creative techniques and incorporating technology and coding.

An EEG monitoring method based on compressed sensing for fatigue driving

In order to reduce the amount of EEG collected by the fatigue driving monitoring system, this paper proposes a new EEG monitoring method based on compressed sensing. According to the driver’s driving duration, the new proposed method adopts different sampling rate to compressive sample the EEG of the driver in different driving stages, and then realizes the accurate reconstruction of the compressed EEG data in the computer of vehicle. The experiment verifies effectiveness of the proposed method by collecting EEG signals from multiple subjects during simulated driving. The experimental results show that, compared with traditional sampling, the new method reduces the amount of sampled data by 3 orders of magnitude. And when the compression ratio is not higher than 70%, the reconstruction error is less than 0.16.

The Dilemma and Way Out of the Education of Casting Chinese National Community Consciousness in Colleges and Universities from the Perspective of Collaboration of Multiple Subjects

The theory of synergy of multiple subjects emphasizes the analysis of the relationship between various types of educational subjects from the perspective of wholeness, with a view to realizing the construction of the linkage and cooperation mechanism between the subjects. Analyzing the current situation of the education of casting Chinese national community consciousness in colleges and universities from the perspective of multi-subject synergy, it is found that the education of casting Chinese national community consciousness in colleges and universities is facing the challenges of subject dilemma, cooperation dilemma, management dilemma and so on. Colleges and universities should enhance the role of multiple subjects, strengthen the synergy between multiple subjects, and standardize the management organization of the synergy of multiple subjects, with a view to opening up the educational subjects of casting a firm sense of the Chinese national community in colleges and universities, and constructing and perfecting the synergy mechanism between the subjects, so as to continuously improve the level of education for building a firm sense of the Chinese national community.

Low-carbon production performance of agricultural green technological innovation: From multiple innovation subject perspective

Green technological innovation displays a multi-subject participation trend; however, within agriculture, literature scarcely addresses its low-carbon production performance and rarely considers citizen participation innovation. Therefore, we examine how agricultural green technological innovation impacts agricultural carbon productivity at production and performance levels, considering university and scientific institution, citizen, or enterprise participation. The results show: (1) Agricultural green technological innovation experienced rapid growth from 2000 to 2019, with enterprise participation innovation having the largest share while citizen participation innovation the lowest share. (2) Agricultural green technological innovation, with enterprises or citizens participation, can effectively enhance agricultural carbon productivity; however, universities and scientific institutions participation innovation remains relatively weak. (3) Citizen and enterprise participation innovation can enhance technical progress and economic growth performance in agriculture, ultimately promoting agricultural carbon productivity. Therefore, we should improve agricultural green technological innovation with various innovation subjects, and promote their allocation efficiency and carbon reduction performance in agricultural production.

A wise irrigation to contribute to integrated water resource management

Irrigated agriculture accounts for about 20% of cultivated lands worldwide while currently generating an estimated 40% of crop production, and two-thirds of future gains in crop production are expected to come from irrigated lands. Therefore, irrigation is strategic to ensure food for the world’s increasing population and slow down the pace of deforestation. Irrigated agriculture also accounts for more than 70% of global water withdrawals, and the way agriculture uses freshwater is crucial for ensuring availability for other activities and preserving water-related ecosystems. To give an agri-environmental interpretation of the water fluxes involved in irrigation, the total amount of water withdrawn from a source is called white water (W) and subdivided as follows: gold water (G) is the amount that is actually used by the crops. This fraction represents the ultimate goal of irrigation, which is to increase crop productivity and food availability. We can also consider this amount as the irrigation water for crops. The gold color means an income for the farm; emerald water (E) is the amount of water withdrawn for irrigation that does not reach the crops but provides ecosystem services, i.e., the benefits that people get from ecosystems. This amount of water is useful for the community and can be identified as irrigation of the territory; red water (R) is losses, i.e., the fraction of water that has to be reduced as much as possible if not eliminated. It is an economic, social and environmental cost; the red color is associated with the red traffic light that means STOP! The quantification of W has to be preceded by a careful evaluation of the benefits achievable with irrigation in relation to the environmental characteristics of the cultivation area. Strategic issues at this stage are the choice between full or deficit irrigation and the individuation of opportunities to exploration of opportunities to increase the availability of water resources by using non-conventional sources. The quantification of G and E fluxes is necessary to reduce losses, while strategies/techniques useful to reduce irrigation needs at the field level are key factors to allocate irrigation water within the framework of an integrated and sustainable management of water aimed at turning user conflicts into synergies. This implies multiple subjects and actors in a multi-disciplinary approach.

Exploring Imagined Temporalities in Resettlement Workers’ Narratives: Renegotiating Temporal and Emotional Boundaries in Post-Brexit Britain

This paper develops the concept of ‘imagined temporalities’ to explore multiple temporal subjectivities, time cultures, ‘myths’, and realities evident in interviews with resettlement workers who were part of the Syrian Vulnerable Persons Resettlement Scheme (VPRS) in Merseyside, United Kingdom (UK). Conducted in 2019, the interviews took place as the triggering of Article 50 signalled the withdrawal of the UK from the European Union (EU). This period of unprecedented social, economic, and political changes formed a crucial backdrop framing our interviewees’ narratives. The views of resettlement workers have been little explored and are employed here to complement the insights provided by work undertaken by others with refugees and asylum seekers. This research provides important insights into their perceptions of the interplay of factors that affect belonging and access to supports for refugees and asylum seekers, revealing wider, largely underreported, concerns.1 These include, their own personal experiences working in support services and system changes, driven by growing socio-political pressures that impact on community-building among refugees during their resettlement. Significantly, debates about “Brexit” and the UK’s political future, as well as heated public discussions of the historical legacies of colonialism which underpin the present treatment of migrants, are reflected in these resettlement workers’ views as well. Subsequently, this paper employs the concept of ‘imagined temporalities’ to explore how support workers understand the treatment of migrants by social and political systems—and their own personal struggles and hopes,—against this wider, divisive post-Brexit backdrop. Overall, the paper underlines the highly politicised space the resettlement workers operate in, where they balance the needs of service users in the midst of constraints imposed by overly rigid time regimes.

Governance mechanism of quality and safety of imported agricultural products in China based on grounded theory

With the continuous improvement of economic development and people’s living standards, Chinese consumers are placing higher demands on the quality of imported agricultural products. The study adopts Grounded Theory to derive a theoretical model of the governance mechanism of quality and safety of imported agricultural products from the perspective of multiple subjects involved in governance and uses structural equation modeling to empirically validate the hypothesis. The results show that government regulation has a positive influence on the quality and safety of imported agricultural products. Importer regulation and consumer supervision can indirectly affect the quality and safety of imported agricultural products through government regulation. Consumer supervision positively affects the level of importer and government regulation and indirectly influences the quality and safety of imported agricultural products through active monitoring of the government and importers. Therefore, the government, importers, and consumers are the three parties who need to optimize their respective governance paths and enhance the quality and safety of imported agricultural products through collaborative governance.

Predatory journals in dermatology: a bibliometric review

PurposeBy distorting the peer review process, predatory journals lure researchers and collect article processing charges (APCs) to earn income, thereby threatening clinical decisions. This study aims to identifying the characteristics of predatory publishing in the dermatology literature.Design/methodology/approachThe authors used Kscien's list to detect dermatology-related predatory journals. Bibliometric parameters were analyzed at the level of journals, publishers, documents and authors.FindingsSixty-one potential predatory dermatology publishers published 4,164 articles in 57 journals from 2000 to 2020, with most publishers claiming to be located in the United States. Most journals were 1–5 years old. Six journals were indexed in PubMed, two in Scopus and 43 in Google Scholar (GS). The average APC was 1,049 USD. Skin, patient, cutaneous, psoriasis, dermatitis and acne were the most frequently used keywords in the article's title. A total of 1,146 articles in GS received 4,725 citations. More than half of the journals had <10 citations. Also, 318 articles in Web of Science were contaminated by the most cited articles and 4.49% of the articles had reported their funding source. The average number of authors per article was 3.7. India, the United States and Japan had the most articles from 119 involved countries. Asia, Europe and North America had the most contributed authors; 5.2% of articles were written through international collaboration. A majority of authors were from high- and low-middle-income countries. Women contributed 43.57% and 39.66% as the first and corresponding authors, respectively.Research limitations/implicationsThe study had limitations, including heavy reliance on Kscien's list, potential for human error in manual data extraction and nonseparation of types of articles. Journals that only published dermatology articles were reviewed, so those occasionally publishing dermatology articles were missed. Predatory journals covering multiple subjects (Petrisor, 2016) may have resulted in overlooking some dermatology papers. This study did not claim to have covered all articles in predatory dermatology journals (PDJs) but evaluated many of them. The authors accept the claim that Kscien's list may have made a mistake in including journals.Originality/valueThe wide dispersion of authors involved in PDJs highlights the need to increase awareness among these authors.

The impact of “Internet plus” enterprises on municipal solid waste classification and social welfare: An example from China

Following rapid economic development and urbanization, the classification of municipal solid waste (MSW) has become an urgent problem. This study analyzes the multiple subjects, including government, residents, and “Internet plus” MSW classification enterprises (IPMCE), and proposes a dynamic evolutionary collaborative framework for the coproduction of public goods to address the government and market failure during the supply of MSW classification as a public good. Using China as an example, this study constructs a multi-agent equilibrium simulation model to analyze this theoretical framework and investigate how to improve the related policies. The simulation results reveal that the collaborative and coproduction of public goods facilitated by IPMCE can improve classification rates and social welfare. Moreover, government subsidies for the research and development (R&D) expenditures of IPMCE are shown to further enhance this effect. In addition, the social welfare growth rate exceeds the subsidy growth rate, indicating that the subsidy has a leverage effect. Although this study has some limitations, such as the fact that the simulation model setting cannot fully reproduce the real situation, it provides policy recommendations on how to better utilize IPMCE in MSW classification as well as strategies for solving similar problems in other regions or public goods.

Analysis on the Influence and Approaches of Children's Self-Identity Construction

Children's self-identity plays a very important role in children's character formation, the establishment of three views and life development. Therefore, it is crucial to focus on the factors that form children's self-identity. This paper analyzes the important role of self-identity in children's development and reveals the key factors affecting the construction of self-identity. This paper argues that factors such as social and cultural environment, parental concepts, cultural background, and social development will affect the formation of children's self-identity. Therefore, this paper suggests that in order to pay attention to the formation of children's self-identity, it is necessary to work together from multiple subjects and aspects. First of all, in a multicultural society, it is necessary to understand different cultures with an open and inclusive attitude, so that children can be exposed to more perspectives and enrich their cognition. Second, parents should give children support and correct guidance. Schools and other educational departments should strengthen the education and guidance of children.

The Causes, Manifestations, and Effects of Benevolent Sexism and How It Contributes to the Perpetuation of Gender Discrimination

Benevolent sexism is a subtle form of gender discrimination that manifests itself in seemingly friendly attitudes and behaviors towards women. This research synthesizes existing literature, incorporating research and analysis from multiple subjects including psychology, sociology, and gender studies to explore various forms of benevolent sexism, dissect the underlying ideologies that support its existence, and investigate its impact on women in society. In addition, this paper includes case studies of real-life situations to illustrate the practical implications of benevolent sexism. According to analysis, it can be concluded that benevolent sexism manifests itself in various ways, such as an expression of kindness and appreciation, traditional gender roles and stereotypes, denial of womens capabilities due to deep-rooted prejudices, and paternalistic and chivalrous behavior. From the perspectives of historical foundation, a lack of awareness, and social environment, benevolent sexism exists and contributes to the perpetuation of gender discrimination by reinforcing the stereotype about women, hindering women's advancement in professional and academic fields, gradually normalizing gender inequality within societies and communities, and lacking recognition of women's achievements, contributions, and abilities.

The everyday work of farm advisors as interface bureaucrats in greening French agricultural policies

For the last three decades, public agricultural policies in France and in Europe have progressively integrated environmental objectives through a wide range of instruments. Whereas a large body of research has examined the extent to which the greening of agricultural policies has shaped farmers' decisions and practices, few studies have considered how these normative policy orientations may trigger tensions within the agricultural bureaucratic apparatus, and notably among local farm advisors who play the role of interface bureaucrats. In this article, we have explored how farm advisors from different types of advisory structures develop multiple subjectivities and deal with the tensions that they face when navigating between policy goals, professional norms, and their personal beliefs and values. Drawing on plural qualitative methods, we focus on the implementation of two national public agroecological schemes in southwest France that support the emergence and facilitation of farmers' groups engaged towards agroecology. We find that organizational strategies shape the type of tensions advisors face and their ability to manage these. The type of policy instrument we considered also matters in the forms of creative bricolage that farm advisors develop: in the case of organizational instruments based on farmers' participation, maintaining relationships with farmers is critical to give meaning to their work. If such instruments enlarge the creative space of farm advisors, the schemes we examined also hold in-built limitations in empowering farm advisors as they fail to address the structural constraints that shape agroecological transitions.

A tensor based varying-coefficient model for multi-modal neuroimaging data analysis.

All neuroimaging modalities have their own strengths and limitations. A current trend is toward interdisciplinary approaches that use multiple imaging methods to overcome limitations of each method in isolation. At the same time neuroimaging data is increasingly being combined with other non-imaging modalities, such as behavioral and genetic data. The data structure of many of these modalities can be expressed as time-varying multidimensional arrays (tensors), collected at different time-points on multiple subjects. Here, we consider a new approach for the study of neural correlates in the presence of tensor-valued brain images and tensor-valued covariates, where both data types are collected over the same set of time points. We propose a time-varying tensor regression model with an inherent structural composition of responses and covariates. Regression coefficients are expressed using the B-spline technique, and the basis function coefficients are estimated using CP-decomposition by minimizing a penalized loss function. We develop a varying-coefficient model for the tensor-valued regression model, where both covariates and responses are modeled as tensors. This development is a non-trivial extension of function-on-function concurrent linear models for complex and large structural data, where the inherent structures are preserved. In addition to the methodological and theoretical development, the efficacy of the proposed method based on both simulated and real data analysis (e.g., the combination of eye-tracking data and functional magnetic resonance imaging (fMRI) data) is also discussed.

OVAR-BPnet: A General Pulse Wave Deep Learning Approach for Cuffless Blood Pressure Measurement.

Pulse wave analysis, a non-invasive and cuff-less approach, holds promise for blood pressure (BP) measurement in precision medicine. In recent years, pulse wave learning for BP estimation has undergone extensive scrutiny. However, prevailing methods still encounter challenges in grasping comprehensive features from pulse waves and generalizing these insights for precise BP estimation. In this study, we propose a general pulse wave deep learning (PWDL) approach for BP estimation, introduc-ing the OVAR-BPnet model to powerfully capture intricate pulse wave features and showcasing its effectiveness on multiple types of pulse waves. The approach involves constructing population pulse waves and employing a model comprising an omni-scale convolution subnet, a Vision Transformer subnet, and a multilayer perceptron subnet. This design enables the learning of both single-period and multi-period waveform features from multiple subjects. Additionally, the approach employs a data augmentation strategy to enhance the morphological features of pulse waves and devise a label sequence regularization strategy to strengthen the intrinsic relationship of the subnets' output. Notably, this is the first study to validate the performance of the deep learning approach of BP estimation on three types of pulse waves: photoplethysmography, forehead imaging photoplethysmography, and radial artery pulse pressure waveform. Experiments show that the OVAR-BPnet model has achieved advanced levels in both evaluation indicators and international evaluation criteria, demonstrating its excellent competitiveness and generalizability. The PWDL approach has the potential for widespread application in convenient and continuous BP monitoring systems.

Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition.

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) Multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) An improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) Invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.

Population-based estimation for PDE system – Applications in electroporation of tumor spheroids

The estimation of partial differential systems (PDE) – in particular, the identification of their parameters – is fundamental in many applications to combine modeling and available measurements. However, it is well known that parameter prior values must be chosen appropriately to balance our distrust of measurements, especially when data are sparse or corrupted by noise. A classic strategy to compensate for this weakness is to use repeated measurements collected in configurations with common priors, such as multiple subjects in a clinical trial. In the mixed-effects approach, all subjects are pooled and a global distribution of model parameters in the population is estimated. However, due to the high computational cost, this strategy is often not applicable in practice for PDE. In this paper, we propose an estimation strategy to overcome this challenge. This sophisticated method is based on two important existing methodological strategies: (1) a population-based Kalman filter and, (2) a joint state-parameter estimation. More precisely, the errors coming from the initial conditions are controlled by a Luenberger observer and the parameters are estimated using a population-based reduced-order Kalman filter restricted to the parameter space. The performance of the algorithm is evaluated using synthetic and real data for tumor spheroid electroporation.

Actual state of the diurnal masseteric electromyogram: Differences between awareness and non-awareness of awake bruxism.

This study aimed to elucidate the relationship between diurnal masseter muscle activity and awareness of diurnal awake bruxism (d-AB) by conducting a comparative analysis of electromyographic (EMG) data from individuals with and without awareness of diurnal awake bruxism (d-AB), utilizing EMG data gathered from multiple subjects. Unilateral masseter electromyography (EMG) recordings were performed during the daytime using an ultraminiature wearable EMG device. A total of 119 participants (59 with awareness of diurnal tooth clenching [d-TC] and 60 without awareness of d-TC) were included. Waveforms longer than 0.25 s with the two amplitude conditions, exceeding twice the baseline and >5% of maximum voluntary clenching, were extracted. In addition, the number of bursts and episodes (groups of bursts), burst duration, and burst peak amplitude were calculated for each participant. There were no significant differences in the EMG parameters between the groups with and without awareness of d-TC. Additionally, the frequency distribution of the number of EMG waveforms exhibited wide ranges and substantial overlap between the two groups. The variability in the number of bursts and episodes, burst peak amplitude, and burst duration among subjects suggests the need for an objective classification of d-AB severity based on EMG values. The absence of significant differences and large overlap in frequency distributions between the groups with and without awareness of d-TC indicate difficulty in predicting muscle activity solely based on awareness of d-AB.

How to promote GBTs innovation through multi-agent collaboration: An evolutionary game system simulation approach

Facing the serious pollution and carbon emission problem, China actively develops and applies GBTs (green building technologies) to realize the dual-carbon target, while the innovation of GBTs is deeply influenced by multiple subjects and factors. This study constructs a tripartite evolutionary game model of GACEs-ARIs, analyzes the inner mechanism of the collaborative innovation process of GBTs through simulation. It is found that: (1) GA is the most crucial subject in the process of collaborative innovation, which plays the role of coordinating the decision-making of the rest of subjects; (2) The trust degree and the benefit distribution coefficient are the influencing factors that play an important role in determining the start and continuation of collaborative innovation; (3) To actively promote the collaborative innovation of GBTs, GA should play a good role as an intermediary to ensure that the trust between the two sides is at a high level, and negotiate a reasonable distribution ratio, actively mobilizing the cooperation of CEs and ARIs by incentives and penalties.

Subject-Independent Deep Architecture for EEG-Based Motor Imagery Classification.

Motor imagery (MI) classification based on electroencephalogram (EEG) is a widely-used technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings suffer from heterogeneity across subjects and labeled data insufficiency, designing a classifier that performs the MI independently from the subject with limited labeled samples would be desirable. To overcome these limitations, we propose a novel subject-independent semi-supervised deep architecture (SSDA). The proposed SSDA consists of two parts: an unsupervised and a supervised element. The training set contains both labeled and unlabeled data samples from multiple subjects. First, the unsupervised part, known as the columnar spatiotemporal auto-encoder (CST-AE), extracts latent features from all the training samples by maximizing the similarity between the original and reconstructed data. A dimensional scaling approach is employed to reduce the dimensionality of the representations while preserving their discriminability. Second, a supervised part learns a classifier based on the labeled training samples using the latent features acquired in the unsupervised part. Moreover, we employ center loss in the supervised part to minimize the embedding space distance of each point in a class to its center. The model optimizes both parts of the network in an end-to-end fashion. The performance of the proposed SSDA is evaluated on test subjects who were not seen by the model during the training phase. To assess the performance, we use two benchmark EEG-based MI task datasets. The results demonstrate that SSDA outperforms state-of-the-art methods and that a small number of labeled training samples can be sufficient for strong classification performance.

Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli.

Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from, e.g., electroencephalography (EEG) signals. In this context, generalized canonical correlation analysis (GCCA) is often used as a group analysis technique, which allows the extraction of correlated signal components from the neural activity of multiple subjects attending to the same stimulus. GCCA can be used to improve the signal-to-noise ratio of the stimulus-following neural responses relative to all other irrelevant ( non-)neural activity, or to quantify the correlated neural activity across multiple subjects in a group- wise coherence metric. However, the traditional GCCA technique is stimulus-unaware: no information about the stimulus is used to estimate the correlated components from the neural data of several subjects. Therefore, the GCCA technique might fail to extract relevant correlated signal components in practical situations where the amount of information is limited, for example, because of a limited amount of training data or group size. This motivates a new stimulus-informed GCCA (SI-GCCA) framework that allows taking the stimulus into account to extract the correlated components. We show that SI-GCCA outperforms GCCA in various practical settings, for both auditory and visual stimuli. Moreover, we showcase how SI-GCCA can be used to steer the estimation of the components towards the stimulus. As such, SI-GCCA substantially improves upon GCCA for various purposes, ranging from preprocessing to quantifying attention.