Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications.

Biomedical sensors are the key units of medical and healthcare systems [...].

Recent Advances in Sensor Technology for Healthcare and Biomedical Applications (Volume II).

With remarkable progress being witnessed in recent years in the development of sensors, these advances in sensor technology provide unprecedented opportunities for (1) the early diagnosis and prevention of human diseases by detecting critical biomarkers; (2) health assessments by monitoring and analyzing human physiological signals in healthcare and biomedical applications; and (3) the efficient evaluation of human-health-relevant environmental factors by monitoring and measuring environmental determinants [...].

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods.

This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples were firstly prepared with different adulterant ratios ranging from 1% to 90% at varying intervals, and their spectra were collected by an in-house built experimental platform. Next, the spectra were preprocessed using Savitzky-Golay (SG)-Continuous Wavelet Transform (CWT) and the feature wavelengths were extracted using four different algorithms. Finally, Support Vector Regression (SVR) and Random Forest (RF) models were developed to rapidly predict adulteration content. The results indicated that SG-CWT with decomposition scale of 25 and the Iterative Variable Subset Optimization (IVSO) algorithm can effectively improve the accuracy of the models. Furthermore, the SVR model performed best for predicting adulteration of camellia oil with soybean oil, while the RF models were optimal for camellia oil adulterated with rapeseed, corn, or peanut oil. Additionally, we verified the models' robustness by examining the correlation between the absorbance and adulteration content at certain feature wavelengths screened by IVSO. This study demonstrates the feasibility of using low-cost UV-Vis-NIR spectroscopy for the authentication of Camellia oil.

Low-Dose CT Image Post-Processing Based on Learn-Type Sparse Transform.

As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This study exploits the ability of sparse representation to learn sparse transformations of information and combines it with image decomposition theory. The structural information of low-dose CT images is separated from noise and artifact information, and the sparse expression of sparse transformation is used to improve the imaging effect. In this paper, two different learned sparse transformations are used. The first covers more organizational information about the scanned object. The other can cover more noise artifacts. Both methods can improve the ability to learn sparse transformations to express various image information. Experimental results show that the algorithm is effective.

Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation.

A bilateral neural network adaptive controller is designed for a class of teleoperation systems with constant time delay, external disturbance and internal friction. The stability of the teleoperation force feedback system with constant communication channel delay and nonlinear, complex, and uncertain constant time delay is guaranteed, and its tracking performance is improved. In the controller design process, the neural network method is used to approximate the system model, and the unknown internal friction and external disturbance of the system are estimated by the adaptive method, so as to avoid the influence of nonlinear uncertainties on the system.

Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface.

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.

Sparse-view CBCT reconstruction via weighted Schatten p-norm minimization.

A novel iterative algorithm is proposed for sparse-view cone beam computed tomography (CBCT) reconstruction based on the weighted Schatten p-norm minimization (WSNM). By using the half quadratic splitting, the sparse-view CBCT reconstruction task is decomposed into two sub-problems that can be solved through alternating iteration: simple reconstruction and image denoising. The WSNM that fits well with the low-rank hypothesis of CBCT data is introduced to improve the denoising sub-problem as a regularization term. The experimental results based on the digital brain phantom and clinical CT data indicated the advantages of the proposed algorithm in both structural information preservation and artifacts suppression, which performs better than the classical algorithms in quantitative and qualitative evaluations.

Promoting or Prohibiting? Investigating How Time Pressure Influences Innovative Behavior under Stress-Mindset Conditions.

The existing empirical evidence on the relationship between time pressure and innovative behavior is paradoxical. An intriguing yet unresolved question is "When does time pressure promote or prohibit innovative behavior, and how?" We theorize that the paradoxical effect of time pressure on innovative behavior can be elucidated by the moderating role of stress mindset, and we also explore the mediating role of thriving at work. Our research involved a field study of 390 research and development personnel from eight enterprises and research institutes in China to test our proposed model. Results indicated that the stress-is-debilitating mindset negatively moderated the association between time pressure and thriving at work, while the stress-is-enhancing mindset positively moderated the link between time pressure and thriving at work. Furthermore, the findings demonstrated that the stress-is-debilitating mindset negatively moderated the indirect impact of time pressure on employees' innovative behavior through thriving at work, while the stress-is-enhancing mindset positively moderated the indirect effect of time pressure on employees' innovative behavior through thriving at work. The theoretical and practical implications of these findings are also discussed.

Flexible, ultrathin and integrated nanopaper supercapacitor based on cationic bacterial cellulose.

Cellulose composite nanopaper is extensively employed in flexible energy storage systems owing to their light weight, good flexibility and high specific surface area. Nevertheless, achieving flexible and ultrathin nanopaper supercapacitors with excellent electrochemical performance remains a challenge. Herein, surface cationization of bacterial cellulose (BC) nanofibers was conducted using 2,3-epoxypropyltrimethylammonium chloride (EPTMAC). Anion-doped polypyrrole (PPy) was incorporated onto the surface of the cationic bacterial cellulose (BCE) nanofibers by an interfacial electrostatic self-assembly process. The obtained PPy@BCE electrode exhibited excellent electrochemical performance, including an areal capacitance of 3988 mF cm-2 at 1.0 mA cm-2 and a capacitance retention of 97 % after 10,000 cycles. A laminated paper-forming strategy was adopted to design and fabricate all-in-one integrated flexible supercapacitors (IFSCs) using PPy@BCE nanopaper as electrodes and BC nanopaper as a separator. The IFSCs showed superior areal capacitance (3669 mF cm-2 at 1 mA cm-2), high energy density (193.7 µWh cm-2 at a power density of 827.3 µW cm-2), and outstanding mechanical flexibility (with no significant capacitance attenuation after repeatedly bending for 1000 times). The present strategy paves a way for the large-scale production of paper-based energy storage devices.

Meaning matters: linking proactive vitality management to subjective well-being.

Prior research has indicated that positive affect, energy, and vitality are positively related to subjective well-being. Unfortunately, most scholars have overlooked the possibility that individuals may proactively manage their energetic, affective, and cognitive resources to boost their subjective well-being. Grounded in social cognitive theory, the current research focuses on explaining why students' proactive vitality management (PVM) leads to positive outcomes (i.e., meaning in life, subjective well-being) and considers how school support climate moderates these effects. One experimental study (Study 1) and a three-wave lagged survey (Study 2) were conducted to examine the benefits of PVM. The results demonstrated that PVM was positively related to students' meaning in life, further promoting their subjective well-being. Moreover, school support climate accentuated PVM's effect on meaning in life and its indirect effect on subjective well-being via meaning in life. Implications for research and practice are also discussed, along with study limitations and future research directions.

Calculation of carbon emissions in wastewater treatment and its neutralization measures: A review.

As the pursuit of "carbon neutrality" gains momentum, the emphasis on low-carbon solutions, emphasizing energy conservation and resource reuse, has introduced fresh challenges to conventional wastewater treatment approaches. Precisely evaluating carbon emissions in urban water supply and drainage systems, wastewater treatment plants, and establishing carbon-neutral operating models has become a pivotal concern in the future of wastewater treatment. Regrettably, limited research has been devoted to carbon accounting and the development of carbon-neutral strategies for wastewater treatment. In this review, to facilitate comprehensive carbon accounting, we initially recognizes direct and indirect carbon emission sources in the wastewater treatment process. We then provide an overview of several major carbon accounting methods and propose a carbon accounting framework. Furthermore, we advocate for a systemic perspective, highlighting that achieving carbon neutrality in wastewater treatment extends beyond the boundaries of wastewater treatment plants. We assess current technical measures both within and outside the plants that contribute to achieving carbon-neutral operations. Encouraging the application of intelligent algorithms for the multifaceted monitoring and control of wastewater treatment processes is paramount. Supporting resource and energy recycling is also essential, as is recognizing the benefits of synergistic wastewater treatment technologies. We advocate a systematic, multi-level planning approach that takes into account a wide range of factors. Our goal is to offer valuable insights and support for the practical implementation of water environment management within the framework of carbon neutrality, and to advance sustainable socio-economic development and contribute to a more environmentally responsible future.

Comparative metabolic profiling of different pakchoi cultivars reveals nutritional diversity via widely targeted metabolomics.

Pakchoi (Brassica rapa ssp. chinensis) is cultivated for its high nutritional value; however, the nutritional diversity of different pakchoi cultivars is rarely investigated. Herein, we performed widely targeted metabolic profiling analyses of five popular pakchois. A total of 670 metabolites were detected, which could be divided into 13 categories. The accumulation patterns of main nutritional metabolites among the five pakchois were significantly different and complementary. Moreover, the pakchoi cultivar 'QYC' showed quite different metabolomic profiles compared with other pakchois. The Venn diagram showed that the 75 differential metabolites were shared among the comparison groups ('QYC' vs. 'MET'/ 'NBC'/ 'PPQ'/ 'XQC'), of which 52 metabolites were upregulated in 'QYC'. The phenolic acids had the largest variations between 'QYC' and the other pakchoi cultivars. These findings expand metabolomic information on different pakchoi cultivars and further provide new insights into the selection and breeding of excellent pakchoi cultivars.

Effects of compound sodium nitrophenol on microspore embryogenesis and plantlet regeneration in flowering Chinese cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee).

Isolated microspore culture has been implemented in breeding programs to produce doubled haploid (DH) lines and thus accelerates the breeding process. However, low microspore embryogenesis frequency in flowering Chinese cabbage remains a key obstacle to the practical application of this technique. This study aimed to establish an efficient microspore culture protocol for flowering Chinese cabbage that would be applied for heterosis breeding. Microspores of five genotypes, 19AY05, 19AY06, 19AY10, 19AY12, and 19AY15, were successfully induced to produce embryos in NLN-13 medium. Microspores of two genotypes, 19AY05 and 19AY15, were cultivated in NLN-13 medium supplemented with different concentrations (0, 0.01, 0.05, 0.1, or 0.2 mg·L-1) of compound sodium nitrophenol (sodium nitrophenol, 5-nitrophenol) to enhance microspore embryogenesis and plant regeneration without an intervening callus phase. The results showed that 0.05 ~ 0.1 mg· L-1 sodium nitrophenol and 0.01 ~ 0.2 mg· L-1 of 5-nitrophenol significantly promoted the induction of microspore embryogenesis of two genotypes, and the best concentrations required for different genotypes are different. Moreover, 0.1 mg· L-1 sodium nitrophenol can significantly increase the plant regeneration rate of the two genetypes. The 5-nitrophenol at 0.01 mg·L-1 significantly increased rate of embryos directly convert to plant in 19AY15. In addition, the average doubled haploid rates in the five genotypes were close to 63%. Horticultural traits of DH lines from 19AY05 were identified and all of them were self-incompatible lines. They showed a high uniformity and consistency that can be directly used for hybrid breeding. Furthermore, the hybrid combination was prepared with the selected DH lines and the Guangdong nucleus genic sterile line GMS019 to screen the excellent hybrid combination for the flowering Chinese cabbage breeding program. This method accelerates the application of microspore culture in hybrid breeding of flowering Chinese cabbage.

The multi-modal fusion in visual question answering: a review of attention mechanisms.

Visual Question Answering (VQA) is a significant cross-disciplinary issue in the fields of computer vision and natural language processing that requires a computer to output a natural language answer based on pictures and questions posed based on the pictures. This requires simultaneous processing of multimodal fusion of text features and visual features, and the key task that can ensure its success is the attention mechanism. Bringing in attention mechanisms makes it better to integrate text features and image features into a compact multi-modal representation. Therefore, it is necessary to clarify the development status of attention mechanism, understand the most advanced attention mechanism methods, and look forward to its future development direction. In this article, we first conduct a bibliometric analysis of the correlation through CiteSpace, then we find and reasonably speculate that the attention mechanism has great development potential in cross-modal retrieval. Secondly, we discuss the classification and application of existing attention mechanisms in VQA tasks, analysis their shortcomings, and summarize current improvement methods. Finally, through the continuous exploration of attention mechanisms, we believe that VQA will evolve in a smarter and more human direction.

Compressible polyaniline-coated sodium alginate-cattail fiber foam for efficient and salt-resistant solar steam generation.

Solar steam generation has attracted widespread attention because of its ability to produce clean water through desalination and wastewater treatment without conventional energy consumption. In this work, a polyaniline (PANI)-coated sodium alginate (SA)/cattail fiber (CF) foam for photothermal evaporator is prepared via directional freezing and oxidative polymerization. The SA/CF foam displays desirable water pumping capability because of the lamellar sandwich structure interconnected by porous networks. More importantly, the directional porous network architecture ameliorates the mechanical and salt-resistant performances of the SA/CF foam. The as-prepared PANI@SA/CF foam shows inferior heat conductivity of 0.047 W m-1 K-1 and outstanding light absorption over 96% in solar window. A vapor evaporation rate of 2.04 kg m-2 h-1 under 1 sun illumination is achieved for the PANI@SA/CF evaporator. Furthermore, the PANI@SA/CF foam could be employed in solar-driven freshwater generation from seawater and wastewater with high ion and dye removal rates. The combination of water evaporation and cleaning capabilities of the PANI@SA/CF foam as photothermal materials provide a framework for the exploration of next-generation evaporators in seawater desalination and wastewater treatment applications.

Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network.

The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. This paper combines the multi-layer semantic representation network with the deep fusion matching network to solve the limitations of only considering a sentence representation module or a reasoning model. It proposes a joint optimization method based on multi-layer semantics called the Semantic Fusion Deep Matching Network (SCF-DMN) to explore the influence of sentence representation and reasoning models on reasoning performance. Experiments on text entailment recognition tasks show that the joint optimization representation reasoning method performs better than the existing methods. The sentence representation optimization module and the improved optimization reasoning model can promote reasoning performance when used individually. However, the optimization of the reasoning model has a more significant impact on the final reasoning results. Furthermore, after comparing each module's performance, there is a mutual constraint between the sentence representation module and the reasoning model. This condition restricts overall performance, resulting in no linear superposition of reasoning performance. Overall, by comparing the proposed methods with other existed methods that are tested using the same database, the proposed method solves the lack of in-depth interactive information and interpretability in the model design which would be inspirational for future improving and studying of natural language reasoning.

Reconstruct Dynamic Soft-Tissue With Stereo Endoscope Based on a Single-Layer Network.

In dynamic minimally invasive surgery environments, 3D reconstruction of deformable soft-tissue surfaces with stereo endoscopic images is very challenging. A simple self-supervised stereo reconstruction framework is proposed to address this issue, which bridges the traditional geometric deformable models and the newly revived neural networks. The equivalence between the classical thin plate spline (TPS) model and a single-layer fully-connected or convolutional network is studied. By alternating training of two TPS equivalent networks within the self-supervised framework, disparity priors are learnt from the past stereo frames of target tissues to form an optimized disparity basis, on which disparity maps of subsequent frames can be estimated more accurately without sacrificing computational efficiency and robustness. The proposed method was verified on stereo-endoscopic videos recorded by the da Vinci® surgical robots.

Control of Time Delay Force Feedback Teleoperation System With Finite Time Convergence.

In order to make the teleoperation system more practical, it is necessary to effectively control the tracking error convergence time of the teleoperation system. By combining the terminal sliding mode control method with the neural network adaptive control method, a bilateral continuous finite time adaptive terminal sliding mode control method is designed for the combined teleoperation system. The Lyapunov theory is used to analyze the stability of the closed-loop system, and the position tracking error is able to effectively converge in time. Finally, the effectiveness of the proposed control scheme is verified by MATLAB Simulink numerical simulation, and the numerical analysis of the results shows that the method has better system performance. Compared with the traditional two-sided control method (TPDC) of PD time-delay teleoperation system, the control method in this paper has good performance, improves stability, and makes steady-state errors smaller and better tracking.

Polypyrrole/SnCl2 modified bacterial cellulose electrodes with high areal capacitance for flexible supercapacitors.

Polypyrrole (PPy)/bacterial cellulose (BC) composite membranes are a promising kind of lightweight and flexible electrodes for supercapacitors. Herein, we explored a facile and efficient electrostatic self-assembly approach to uniformly depositing anion-doped PPy onto positively charged SnCl2-modifed BC (SBC). The obtained PPy@SBC electrode exhibited a high areal capacitance of 5718 mF cm-2 at a current density of 0.5 mA cm-2, a desirable capacitance retention of 83.1% at 5.0 mA cm-2 and excellent cycling stability (a capacitance retention of 86.8% after 10,000 cycles at 10 mA cm-2). A symmetric flexible supercapacitor was further assembled with the PPy@SBC electrodes, which delivered outstanding mechanical flexibility with negligible capacitance decay under different bent states. This study shows impressive potential in fabricating high-performance electrodes for flexible supercapacitors.

Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching.

Endoscopic imaging plays a very important role in the diagnosis and treatment of lesions. However, the imaging range of endoscopes is small, which may affect the doctors' judgment on the scope and details of lesions. Image mosaic technology can solve the problem well. In this paper, an improved feature-point pair purification algorithm based on SIFT (Scale invariant feature transform) is proposed. Firstly, the K-nearest neighbor-based feature point matching algorithm is used for rough matching. Then RANSAC (Random Sample Consensus) method is used for robustness tests to eliminate mismatched point pairs. The mismatching rate is greatly reduced by combining the two methods. Then, the image transformation matrix is estimated, and the image is determined. The seamless mosaic of endoscopic images is completed by matching the relationship. Finally, the proposed algorithm is verified by real endoscopic image and has a good effect.