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To determine whether living according to specific traditional Chinese cultural values was associated with satisfaction of the five needs in Maslow’s motivational hierarchy and overall life satisfaction, a mixed-method approach was employed, with an empirical questionnaire and supplemental interviews. The questionnaire assessed the hypothesized relationships that traditional Chinese values had with personal life outcomes, including health, employment, satisfaction of the five needs from Maslow’s hierarchy, and life satisfaction. The interviews examined the relationships that several demographic variables had with living by traditional Chinese values. The results of the empirical data revealed that most Chinese people today are still living according to the traditional Chinese cultural values, and that living by those traditional values are strongly associated with satisfaction of all five of the human needs in the Maslow hierarchy, as well as with overall life satisfaction. Additionally, the results of the qualitative interviews readily supported the empirical findings, and also revealed that the time during which inter-generational transmission of the Chinese cultural values occurs is when parents teach those values to their children at a very early age, that is, between 3 and 8 years old, before the children start primary school.

In this essay, we put forth a novel solution to Plantinga’s Evolutionary Argument Against Naturalism, utilizing recent work done by Duncan Pritchard on radical skepticism. Key to the success of Plantinga’s argument is the doubting of the reliability of one’s cognitive faculties. We argue (viz. Pritchard and Wittgenstein) that the reliability of one’s cognitive faculties constitutes a hinge commitment, thus is exempt from rational evaluation. In turn, the naturalist who endorses hinge epistemology can deny the key premise in Plantinga’s argument and avoid the dilemma posed on belief in the conjunction of naturalism and evolution.

Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.

Anthropogenic noise can be hazardous for the auditory system and wellbeing of animals, including humans. However, very limited information is known on how this global environmental pollutant affects auditory function and inner ear sensory receptors in early ontogeny. The zebrafish (Danio rerio) is a valuable model in hearing research, including investigations of developmental processes of the vertebrate inner ear. We tested the effects of chronic exposure to white noise in larval zebrafish on inner ear saccular sensitivity and morphology at 3 and 5 days post-fertilization (dpf), as well as on auditory-evoked swimming responses using the prepulse inhibition (PPI) paradigm at 5 dpf. Noise-exposed larvae showed a significant increase in microphonic potential thresholds at low frequencies, 100 and 200 Hz, while the PPI revealed a hypersensitization effect and a similar threshold shift at 200 Hz. Auditory sensitivity changes were accompanied by a decrease in saccular hair cell number and epithelium area. In aggregate, the results reveal noise-induced effects on inner ear structure–function in a larval fish paralleled by a decrease in auditory-evoked sensorimotor responses. More broadly, this study highlights the importance of investigating the impact of environmental noise on early development of sensory and behavioural responsiveness to acoustic stimuli.

Objective. As the preclinical stage of Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects’ educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Methodology. Firstly, four exploratory indexes (test durations, correlation degree, lengths of gaze trajectory, and drift rate) of MCI early warning are determined based on the relevant literature research and semistructured expert interview; secondly, the eye movement state is captured based on the eye tracker to realize the data extraction of four exploratory indexes. On this basis, the human-computer interactive 2.5-minute fast digital early warning paradigm for MCI is designed; thirdly, the rationality of the four early warning indexes proposed in this paper and their early warning effectiveness on MCI are verified. Results. Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70–90 in a medical institution in Hangzhou, the two indexes of “correlation degree” and “drift rate” with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion. The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.

Abstract With its large population and natural resources, Africa needs investors who can sustain its development. At the same time, foreign investors expect returns on their investments. In ...

The recently explored inactive Tianzuo hydrothermal field, in the amagmatic segment of the ultraslow-spreading Southwest Indian Ridge (SWIR), is closely associated with detachment faults. In this site, sulfide minerals are hosted by serpentine-bearing ultramafic rocks and include high-temperature (isocubanite, sphalerite, and minor pyrrhotite) and low-temperature (pyrite I, marcasite, pyrite II, and covellite) phases. In this study, trace-element concentrations of isocubanite and pyrite II were used to elucidate mineralization processes in ultramafic rocks hosting sulfides. Results show that isocubanite is enriched in metals such as Cu, Co, Sn, Te, Zn, Se, Pb, Bi, Cd, Ag, In, and Mn, and pyrite II is enriched in Mo and Tl. The marked enrichment in Te, Cu, Co, and In in isocubanite (compared with Se, Zn, Ni, and Sn, respectively) is most likely due to the contribution of magmatic fluids from gabbroic intrusions beneath the hydrothermal field. The intrusion of gabbroic magmas would have enhanced serpentinization reactions and provided a relatively oxidizing environment through the dissolution of anhydrite precipitated previously in the reaction zone, within high temperature and low pH conditions. This might have facilitated the extraction of metals by initial hydrothermal fluids, leading to the general enrichment of most metals in isocubanite. Metals in pyrite II have compositions similar to those of isocubanite, except for strong depletion in magmatically derived Te, Cu, Co, and In. This means that serpentinization processes had a dominating role in pyrite II precipitation as well. The enrichment of pyrite II in Mo and Tl is also indicative of seawater contribution in its composition. The study concludes that serpentinization reactions contribute effectively both to high- and low-temperature sulfide mineralization at Tianzuo hydrothermal field, with gabbroic intrusions further promoting high-temperature sulfide mineralization, providing additional metals, fluids and heat. In contrast, low-temperature sulfide mineralization occurred during the cooling of gabbroic intrusions, with decreasing rates of serpentinization reactions and a significant influence of seawater.

Various materials, objects, and sensors have been explored earlier for creating tangible user interfaces (TUIs). However, there is little work on 3D-printed TUIs based on visual markers for smartphone-based extended reality (XR) experiences. The combination of visual markers and smartphones results in cheap, accessible XR systems within reach of many people. Combined with 3D printing, it could foster do-it-yourself (DIY) projects for XR experiences, which may further expand and open-up possibilities for accessible and tangible interaction. This work explores the design space of modular 3D-printed tangibles for smartphone-based XR. The authors report the design exploration process, provide several interactive 3D-printed markers, and reflect on the resulting possibilities.

Macao inhabit a population of 683,100. The birth rate has been dropping while the death rate has risen compared to two years ago. Cemeteries are becoming crowded, and burial spots are demanding. In this case, video calls and social media can be the solution. How about our beloved ancestors? Can we video call them on their memorial days? This paper presents a VR experience of immersing oneself in the 3D VR of the Chapel of St. Michael of Macao to create a peaceful atmosphere for grave mourning. The chapel is also a personal space where we can be truly isolated in serenity. It is a retreat to pray, disconnect, and reconnect to the beloved deaths that may not be buried in an easily accessible location. The authors propose a possible future of mourning our loved ones through virtual reality and telepresence: an immersive experience connected with Macao's extraordinary and cultural unicity.

In this essay we argue that, based on current scientific data, the most prudential course of future actions that an American conservative can take, is one that assumes what we call climate change alarmism. In order to establish this thesis, we first provide a basic overview of the relevant climate change science, as well as give an analysis of the alarmist and lukewarming dialectic (the two primary interpretations of the data). We then move to develop our environmental wager. Finally, following Roger Scruton, we end this work by proposing what sort of policies conservatives should endorse going further.

Launonen and Mullins argue that if Classical Theism is true, human cognition is likely not theism-tracking, at least, given what we know from cognitive science of religion. In this essay, we develop a model for how classical theists can make sense of the findings from cognitive science, without abandoning their Classical Theist commitments. We also provide an argument for how our model aligns well with the Christian doctrine of general revelation.

With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.

The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high-risk pregnancies based on IoT sensors, feature extraction based on data analytics, and an intelligent diagnostic aid system based on a 1-D convolutional neural network (CNN) classifier. The fetal heart rate and a group of maternal clinical indicators, such as the uterine tonus activity, blood pressure, heart rate, temperature, and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different formats and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal, and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.

As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home.

Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.