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In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

With the development of artificial intelligence, proximity sensors show their great potential in intelligent perception. This paper aims to propose a new planar capacitive sensor for the proximity sensing of a conductor.

Different from traditional structures, the proposed sensor is characterized by sawtooth-structured electrodes. A series of numerical simulations have been carried out to study the impact of different geometrical parameters such as the width of the main trunk, the width of the sawtooth and the number of sawtooths. In addition, the impact of the lateral offset of the approaching graphite block is investigated.

It is found that sensitivity is improved with the increase of the main trunk with, sawtooth width and sawtooth number while a larger lateral offset leads to a decrease in sensitivity. The performance of the proposed planar capacitive proximity sensor is also compared with two conventional planar capacitive sensors. The results show that the proposed planar capacitive sensor is obviously more sensitive than the two conventional planar capacitive sensors.

In this paper, a new planar capacitive sensor is proposed for the proximity sensing of a conductor. The results show that the capacitive sensor with the novel structure is obviously more sensitive than the traditional structures in the detection of the proximity conductor.

To solve the problem of low sensitivity of traditional capacitive proximity sensor, this paper aims to propose a novel capacitive sensor for detection of an approaching conductor.

Five capacitive proximity sensors with different structures are designed and the performance is compared with the traditional capacitive sensor. The impacts of geometrical parameters on the performance of the proposed capacitive sensor are studied. Furthermore, the sensitivity of the proposed capacitive sensor to an approaching conductor with different sizes is discussed. Also, how the designed capacitive sensor is sensitive to the lateral placement of the approaching object is analyzed.

Several capacitive proximity sensor structures have been designed and analyzed. It is found that the capacitive sensor with the top small ring-bottom large ring structure shows stronger electric field distribution around the top electrode and higher sensitivity to the approaching conductor than other sensors. Through further analysis of the proposed sensor, the results demonstrate that proposed capacitive sensor is effective for proximity object detection.

This paper proposes a novel capacitive proximity sensor with top small ring-bottom large ring structure. Compared with the traditional capacitive sensor, the proposed capacitive sensor is more sensitive to the approaching object. This would be helpful for the accurate detection of the approaching object. Also, the top and bottom electrodes are much smaller.

China’s rapid pace of urbanization has resulted in millions of rural residents migrating from rural areas to urban areas for better job opportunities. Due to economic pressures and the nature of China’s demographic policies, many of these migrants have been forced to leave their children with relatives – typically paternal grandparents – at home in the countryside. Thus, while income for most migrant families has risen, a major unintended consequence of this labor movement has been the emergence of a potentially vulnerable sub-population of left-behind children (LBCs). The purpose of this paper is to examine the impacts of parental migration on both the academic performance and mental health of LBCs.

Longitudinal data were drawn from three waves of a panel survey that followed the same students and their families – including their migration behavior (i.e. whether both parents, one parent, no parent migrated) – between 2015 and 2016. The survey covers more than 33,000 students in one province of central China. The authors apply a student fixed-effects model that controls for both observable and unobservable confounding variables to explicate the causal effects of parental migration on the academic and mental health outcomes for LBC. The authors also employ these methods to test whether these effects differ by the type of migration or by gender of the child.

The authors found no overall impact of parental migration on either academic performance or mental health of LBCs, regardless of the type of migration behavior. The authors did find, however, that when the authors examined heterogeneous effects by gender (which was possible due to the large sample size), parental migration resulted in significantly higher anxiety levels for left-behind girls. The results suggest that parental migration affects left-behind boys and girls differently and that policymakers should take a more tailored approach to addressing the problems faced by LBCs.

The main contributions of this paper come from the large and representative sample, as well as the causal effects analysis of being left-behind on both academic performance and mental health. First, the paper uses comprehensive panel data from a representative and populous province in China, and the sample size is the largest one among LBC-related papers to the authors’ knowledge. Second, the paper separately examines the causal effects on the student outcomes of different migration strategies. Third, the paper analyzes the heterogeneous effects of different migration strategies on LBC gender. The authors believe that the paper makes a key contribution to the literature.

This paper examines how the level of low-carbon awareness (LCA) affects the remanufacturing strategy in a supply chain with an original equipment manufacturer (OEM) and an independent remanufacturer (IR) competing with each other.

Game theory and operations optimization.

The studies analytically characterize the threshold levels of the LCA in response to which the OEM and the IR will change their remanufacturing strategies from no remanufacturing to partial remanufacturing and then to full remanufacturing. In addition, the studies reveal that as compared with the OEM, the IR has more flexibility in terms of the market entry to remanufacturing with the level of LCA increasing. With the extended studies, it is exhibited that the above findings are robust to a good extent.

It can provide decision support for remanufacturing enterprises.

For one thing, despite the fact that it is popular to research the minimization of the power losses in power systems, the optimization of single objective seems insufficient to fully improve the performance of power systems. Multi-objective VAR Dispatch (MVARD) generally minimizes two objectives simultaneously: power losses and voltage deviation. The purpose of this paper is to propose Multi-Objective Enhanced PSO (MOEPSO) algorithm that achieves a good performance when applied to solve MVARD problem. Thus, the new algorithm is worthwhile to be known by the public.

Motivated by differential evolution algorithm, cross-over operator is introduced to increase particle diversity and reinforce global searching capacity in conventional PSO. In addition to that, a constraint-handling approach considering Constrain-prior Pareto-Dominance (CPD) is presented to handle the inequality constraints on dependent variables. Constrain-prior Nondominated Sorting (CNS) and crowding distance methods are considered to maintain well-distributed Pareto optimal solutions. The method combining CPD approach, CNS technique, and cross-over operator is called the MOEPSO method.

The IEEE 30 node and IEEE 57 node on power systems have been used to examine and test the presented method. The simulation results show the MOEPSO method can achieve lower power losses, smaller voltage deviation, and better-distributed Pareto optimal solutions comparing with the Multi-Objective PSO approach.

The most original parts include: the presented MOEPSO algorithm, the CPD approach that is used to handle constraints on dependent variables, and the CNS method which is considered to maintain a well-distributed Pareto optimal solutions. The performance of the proposed algorithm successfully reflects the value of this paper.

Self-organization has been regarded as a tool for the synthesis of well-defined organic nanostructures. Heterocyclic annulated perylene diimides are the subjects of considerable current research studies. The purpose of this study is to reveal the photophysical property, electronic structure and solid-state packing of O-heterocyclic annulated perylene diimide.

Asymmetrically five-membered O-heterocyclic annulated perylene diimide (OAPDI) was synthesized. Structure and purity of OAPDI were confirmed by ¹H NMR, ¹³C NMR, IR and mass spectral techniques. Photophysical properties of OAPDI were studied using UV–vis absorption and fluorescence in both solution (CHCl₃) and solid state. Scanning electron microscopic and atomic force microscopy were used to characterize the surface morphology of OAPDI. Conducting properties of the OAPDI were evaluated by current–voltage measurements. The compounds geometries were also optimized at 6-31G* using density functional theory.

The UV–vis absorption and fluorescence spectra of OAPDI in solution are blue-shifted in comparison with that of unsubstituted perylene bisimide. Solid-state UV–vis measurements of OAPDI indicate that it is capable of forming highly ordered structure. The non-covalent interactions, electrostatic attraction and p-p stacking moieties of OAPDI synergistically guide assembly and domain growth while maintaining the interpenetrating network of nanofibers in the solid film. The OAPDI gave higher current at −2.0 V (0.68 µA) and 4.0 V (1.0 µA).

This study will be helpful for exploring feasible routes to acquire soluble perylene diimides and well-defined organic nanostructures. Furthermore, such molecular tailoring approach would be helpful for designing and synthesizing novel organic semiconductive materials with excellent charge-transporting and light-emitting capabilities.

The regulatory landscape surrounding international construction projects presents significant challenges, and contractors are still struggling to pay a painful price for their performance in the project. While existing research has identified various causes of contractor compliance, the intricate interplay of these factors and their impact on compliance remain largely elusive. The motivation-opportunity-ability (MOA) framework may hold the key to determining what factors can foster induced contractor compliance in international projects.

This study collected 124 valid data samples from practitioners involved in large-scale international contracting projects through expert interviews and questionnaire surveys. Fuzzy-set qualitative comparative analysis (fsQCA) was employed to analyze the diverse combinations of contractor compliance factors.

The study identifies seven key factors that contribute to compliance behavior among international construction contractors: economic motivation, social motivation, normative motivation, legal completeness, deterrent sanctions, organizational learning and compliance management ability. The interplay of these factors promotes compliance in the following ways: When international construction contractors are influenced by both social and normative motivations, they exhibit a higher level of compliance. In situations where regulatory systems are relatively weak, the ability to manage compliance becomes the primary driver of compliance behavior for businesses. A comprehensive legal framework creates a conducive environment for contractors to improve their compliance through organizational learning.

The findings offer guidance for international construction contractors in enhancing compliance by considering factors such as motivations, legal frameworks, organizational learning and compliance management. This can lead to improved risk management and performance in international projects.

This research enhances fair and ethical practices in international construction by identifying compliance drivers, fostering positive social impact, mitigating negative consequences and empowering local communities. It informs legal and regulatory reform, encourages improved business practices and contributes to knowledge advancement in the field. Overall, the findings have the potential to positively impact the social fabric of international construction projects.

This study has made an important contribution to the field of compliance theory by integrating theories from multiple disciplinary domains and constructing a new theoretical framework from the perspectives of motivation, opportunity and capability. By elucidating how these factors interact and influence compliance behavior among international construction contractors, this research aids in understanding the complex dynamics of contractor compliance behavior and provides theoretical reference for compliance governance within the construction industry.

This study aims to look into consumers' green consumption intention by incorporating social media exposure, environmental concern and habit into theory of planned behavior (TPB) in the context of China advocating green consumption.

Based on the data collected from online survey, this study use partial least squares structural equation modeling (PLS-SEM) to analysis the measurement model and structure model.

The results show attitude, social media exposure, environmental concern and subjective norms are important determinants of consumers' intention to purchase green products.

This study explores the new associations between the major constructs of the TPB model in green consumption. This study also expanded the TPB model by adding three extra factors, namely social media exposure, habit and environmental concern. In addition, this study attempts to bridge the gap between attitude and intention from a new perspective by using social media exposure as a moderating variable.

The purpose of this study is to develop luminescence sensors for the detection of nitroaromatic compounds (NACs) and metal ions to protect human health and prevent environmental pollution.

The composition and morphology of Eu-metal-organic frameworks (MOF) (1) were well characterized by powder X-ray diffraction, elemental analyses, Fourier-transform infrared spectroscopy, thermogravimetric analysis, X-ray photoelectron spectroscopy and scanning electron microscopy. The emission spectrum displays that 1 has significant characteristic emission bands of Eu(III) ions. The authors further investigated the fluorescence sensing performances of 1 to NACs and metal ions.

The results show that Eu-MOF (1) exhibits significant fluorescence quenching effect toward p-nitroaniline and Fe³⁺ ions with good stability and recyclability. This means that 1 can be used as a multifunctional sensing material for the detection of p-nitroaniline and Fe³⁺ ions.

The authors have successfully synthesized a fluorescence Eu-based sensing material under hydrothermal conditions. In addition, the fluorescence property and sensing performances for detecting NACs and metal ions were studied. The results suggest that 1 has highly selective fluorescence quenching toward p-nitroaniline and Fe³⁺ ions with not only high sensitivity and selectivity but also excellent stability and recyclability. Furthermore, this study has confirmed that the multifunctional MOF material is very useful in environment pollutants’ detection and monitoring.