Preparation of high-performance monoazo disperse dyes bearing ester groups based on benzisothiazole and their dyeing performance on polyester fabrics.

To obtain high-performance disperse dyes, a series of azo disperse dyes containing different kinds of ester groups based on benzisothiazole were synthesized by the coupling reaction of diazotization of 3-amino-5-nitro [2,1] benzisothiazole with N-substituted aniline compounds bearing different ester moieties. The structures of the synthesized dyes were evaluated using Fourier transform infrared spectroscopy (FT-IR), nuclear magnetic resonance techniques (1H-NMR), and MS analysis. UV-Vis spectrophotometry methods were applied to study absorption maxima, molar extinction coefficients, and solvatochromic behaviors of the dyes, and time-dependent density functional theory (TD-DFT) simulations were applied to reveal the nature of the absorption spectrum properties. Polyester fabrics were colored using a high-temperature dyeing method under pressure, and the dyed fabrics exhibited deep and bright intense blue hues. In addition, excellent fastness properties, including washing fastness, sublimation fastness, rubbing fastness, and light fastness, were achieved.

Self-Healing Hydrogen-Bonded Organic Frameworks for Low-Concentration Ammonia Capture.

The self-healing behavior has been extensively used in intelligent sensing systems capable of molecular recognition. However, most rigid crystalline frameworks, once collapsed under external stimuli like pressure, heat, or vacuum, could hardly recover to their crystalline phases under ambient conditions. Here, we report the self-healing of a new microporous hydrogen-bonded organic framework, FDU-HOF-3 (FDU = Fudan University), for ammonia (NH3) capture and compared it with the established mesoporous HOF-101. With the introduction of low-concentration NH3 into the pores, the HOFs became disordered but were then simply heated under a vacuum to return to their original crystalline states after NH3 removal. Close characterizations revealed that the repeatable self-healing behavior of these HOFs was achieved due to the COOH-NH3 acid-base interactions accompanied by the breaking and regeneration of complementary COOH-COOH hydrogen bonds. FDU-HOF-3 showed a record-capturing capability for low-concentration NH3 (8.13 mmol/g at 25 mbar) among all HOFs and displayed a quick photocurrent decrease after exposure to 250 ppm NH3 for less than 10 s. These self-healing HOFs were used to capture and release NH3 for over 10 cycles without any decrease in the adsorption capacities.

Multi-Objective NSGA-II Optimization for Broadband Beamforming with Spherical Harmonic Domain Assistance.

Sidelobe suppression is a major challenge in wideband beamforming for acoustic research, especially in high noise and reverberation environments. In this paper, we propose a multi-objective NSGA-II wideband beamforming method based on a spherical harmonic domain for spherical microphone arrays topology. The method takes white noise gain, directional index and maximum sidelobe level as the optimization objectives of broadband beamforming, adopts the NSGA-II optimization strategy with constraints to estimate the Pareto optimal solution, and provides three-dimensional broadband beamforming capability. Our method provides superior sidelobe suppression across different spherical harmonic orders compared to commonly used multi-constrained single-objective optimal beamforming methods. We also validate the effectiveness of our proposed method in a conference room setting. The proposed method achieves a white noise gain of 8.28 dB and a maximum sidelobe level of -23.42 dB at low frequency, while at high frequency it yields comparable directivity index results to both DolphChebyshev and SOCP methods, but outperforms them in terms of white noise gain and maximum sidelobe level, measuring 16.14 dB and -25.18 dB, respectively.

A Solution-Processable Porphyrin-Based Hydrogen-Bonded Organic Framework for Photoelectrochemical Sensing of Carbon Dioxide.

Detecting CO2 in complex gas mixtures is challenging due to the presence of competitive gases in the ambient atmosphere. Photoelectrochemical (PEC) techniques offer a solution, but material selection and specificity remain limiting. Here, we constructed a hydrogen-bonded organic framework material based on a porphyrin tecton decorated with diaminotriazine (DAT) moieties. The DAT moieties on the porphyrin molecules not only facilitate the formation of complementary hydrogen bonds between the tectons but also function as recognition sites in the resulting porous HOF materials for the selective adsorption of CO2 . In addition, the in-plane growth of FDU-HOF-2 into anisotropic molecular sheets with large areas of up to 23000 µm2 and controllable thickness between 0.298 and 2.407 µm were realized in yields of over 89 % by a simple solution-processing method. The FDU-HOF-2 can be directly grown and deposited onto different substrates including silica, carbon, and metal oxides by self-assembly in situ in formic acid. As a proof of concept, a screen-printing electrode deposited with FDU-HOF-2 was fabricate as a label-free photoelectrochemical (PEC) sensor for CO2 detection. Such a signal-off PEC sensor exhibits low detection limit for CO2 (2.3 ppm), reusability (at least 30 cycles), and long-term working stability (at least 30 days).

Reticular Chemistry in Pore Engineering of a Y-Based Metal-Organic Framework for Xenon/Krypton Separation.

The fine-tuning of metal-organic framework (MOF) pore structures is of critical importance in developing energy-efficient xenon/krypton (Xe/Kr) separation techniques. Capitalizing on reticular chemistry, we constructed a robust Y-based MOF (NU-1801) that is isoreticular to NPF-500 with a shortened organic ligand and a larger metal radius while maintaining the 4,8-connected flu topology, giving rise to a narrowed pore structure for the efficient separation of a Xe/Kr mixture. At 298 K and 1 bar, NU-1801 possessed a moderate Xe uptake of 2.79 mmol/g but exhibited a high Xe/Kr selectivity of 8.2 and an exceptional Xe/Kr uptake ratio of about 400%. NU-1801 could efficiently separate a Xe/Kr mixture (20:80, v/v), as validated by breakthrough experiments, due to the outstanding discrimination in van der Waals interactions of Xe and Kr toward the framework confirmed by grand canonical Monte Carlo simulations. This work highlights the importance of reticular chemistry in designing structure-specific MOFs for gas separation.

Thermally Crosslinked Hydrogen-Bonded Organic Framework Membranes for Highly Selective Ion Separation.

The weak bonding energy and flexibility of hydrogen bonds can hinder the long-term use of hydrogen-bonded organic framework (HOF) materials under harsh conditions. Here we invented a thermal-crosslinking method to form polymer materials based on a diamino triazine (DAT) HOF (FDU-HOF-1), containing high-density hydrogen bonding of N-H⋯N. With the increase of temperature to 648 K, the formation of -NH- bonds between neighboring HOF tectons by releasing NH3 was observed based on the disappearance of the characteristic peaks of amino groups on FDU-HOF-1 in the Fourier transform infrared (FTIR) and solid-state nuclear magnetic resonance (ss-NMR). The variable temperature PXRD indicated the formation of a new peak at 13.2° in addition to the preservation of the original diffraction peaks of FDU-HOF-1. The water adsorption, acid-base stability (12 M HCl to 20 M NaOH) and solubility experiments concluded that the thermally crosslinked HOFs (TC-HOFs) are highly stable. The membranes fabricated by TC-HOF demonstrate the permeation rate of K+ ions as high as 270 mmol m-2 h-1 as well as high selectivity of K+/Mg2+ (50) and Na+/Mg2+ (40), which was comparable to Nafion membranes. This study provides guidance for the future design of highly stable crystalline polymer materials based on HOFs.

Associations between liver function parameters and poor clinical outcomes in peritoneal dialysis patients.

Patients with end-stage renal disease (ESRD) have significantly lower survival rates compared with the general population of the same age. Peritoneal dialysis (PD) is an effective treatment for patients with ESRD, but the clinical outcome of PD patients is still not promising. The survival of PD patients is associated with various clinical factors, and exploring some valid risk predictors may be beneficial for this population. In this review, by integrating the latest research, we summarized the association of some common and novel liver function parameters (ALT, AST, ALP, GGT, serum bilirubin, pre-albumin, albumin, albumin-globulin ratio [AGR], serum ferritin, and hyaluronic acid) with clinical outcomes in PD patients. It may contribute to a better understanding of potential risk factors and help to develop strategies to prevent the disease progression.

Corrigendum to "A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets".

[This corrects the article DOI: 10.1155/2022/4752450.].

Design Rules of Hydrogen-Bonded Organic Frameworks with High Chemical and Thermal Stabilities.

Hydrogen-bonded organic frameworks (HOFs), self-assembled from strategically pre-designed molecular tectons with complementary hydrogen-bonding patterns, are rapidly evolving into a novel and important class of porous materials. In addition to their common features shared with other functionalized porous materials constructed from modular building blocks, the intrinsically flexible and reversible H-bonding connections endow HOFs with straightforward purification procedures, high crystallinity, solution processability, and recyclability. These unique advantages of HOFs have attracted considerable attention across a broad range of fields, including gas adsorption and separation, catalysis, chemical sensing, and electrical and optical materials. However, the relatively weak H-bonding interactions within HOFs can potentially limit their stability and potential use in further applications. To that end, this Perspective highlights recent advances in the development of chemically and thermally robust HOF materials and systematically discusses relevant design rules and synthesis strategies to access highly stable HOFs.

Reconfiguration of cortical brain network from searching to spotting for dynamic visual targets.

BACKGROUND: Detecting dynamic targets from complex visual scenes is an important problem in real world. However, the cognitive mechanism accounting for dynamic visual target detection remains unclear. NEW METHOD: Herein, we aim to explore the cognitive process of dynamic visual target detection from searching to spotting and provide more concrete evidence for cognitive studies related to target detection. Cortical source responses with high spatiotemporal resolution were reconstructed from scalp EEG signals. Then, time-varying cortical networks were built using adaptive directed transfer function to explore the cognitive processes while detecting the dynamic visual target. RESULTS: The experimental results demonstrated that the dynamic visual target detection enhanced the activation in both the visual and attention networks. Specially, the information flow from the middle occipital gyrus (MOG) mainly contributed to the position function, whereas the activation of the prefrontal cortex (PFC) reflected spatial attention maintenance. CONCLUSION: The left "frontal-central-parietal" network played as a leading information source in dynamic target detection tasks. These findings provide new insights into cognitive processes of dynamic visual target detection.

A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets.

The superiority of collaborative brain-computer interface (cBCI) in performance enhancement makes it an effective way to break through the performance bottleneck of the BCI-based dynamic visual target detection. However, the existing cBCIs focus on multi-mind information fusion with a static and unidirectional mode, lacking the information interaction and learning guidance among multiple agents. Here, we propose a novel cBCI framework to enhance the group detection performance of dynamic visual targets. Specifically, a mutual learning domain adaptation network (MLDANet) with information interaction, dynamic learning, and individual transferring abilities is developed as the core of the cBCI framework. MLDANet takes P3-sSDA network as individual network unit, introduces mutual learning strategy, and establishes a dynamic interactive learning mechanism between individual networks and collaborative decision-making at the neural decision level. The results indicate that the proposed MLDANet-cBCI framework can achieve the best group detection performance, and the mutual learning strategy can improve the detection ability of individual networks. In MLDANet-cBCI, the F1 scores of collaborative detection and individual network are 0.12 and 0.19 higher than those in the multi-classifier cBCI, respectively, when three minds collaborate. Thus, the proposed framework breaks through the traditional multi-mind collaborative mode and exhibits a superior group detection performance of dynamic visual targets, which is also of great significance for the practical application of multi-mind collaboration.

P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection.

Single-trial electroencephalogram detection has been widely applied in brain-computer interface (BCI) systems. Moreover, an individual generalized model is significant for applying the dynamic visual target detection BCI system in real life because of the time jitter of the detection latency, the dynamics and complexity of visual background. Hence, we developed an unsupervised multi-source domain adaptation network (P3-MSDA) for dynamic visual target detection. In this network, a P3 map-clustering method was proposed for source domain selection. The adversarial domain adaptation was conducted for domain alignment to eliminate individual differences, and prediction probabilities were ranked and returned to guide the input of target samples for imbalanced data classification. The results showed that individuals with a strong P3 map selected by the proposed P3 map-clustering method perform best on the source domain. Compared with existing schemes, the proposed P3-MSDA network achieved the highest classification accuracy and F1 score using five labeled individuals with a strong P3 map as the source domain. These findings can have a significant meaning in building an individual generalized model for dynamic visual target detection.

Neural mechanism for dynamic distractor processing during video target detection: Insights from time-varying networks in the cerebral cortex.

In dynamic video target detection tasks, distractors may suddenly appear due to the dynamicity of the visual scene and the uncertainty of the visual information, strongly influencing participants' attention and target detection performance. Moreover, the neural mechanism that accounts for dynamic distractor processing remains unknown, which makes it difficult to compensate for in EEG-based video target detection. Here, cortical activities with high spatiotemporal resolution were reconstructed using the source localization method. The time-varying networks among important brain regions in different cognitive phases, including information integration, decision-making, and execution, were identified to investigate the neural mechanism of dynamic distractor processing. The experimental results indicated that dynamic distractors could induce a P3-like component. In addition, there was obvious asymmetry between the two hemispheres during video target detection. Specifically, the brain responses induced by dynamic distractors were weak and more concentrated in the left hemisphere during the information integration phase; left superior frontal gyrus activity related to preparation for the presence of distractors was critical, while the attention network and primary visual network, especially in the left visual pathway, were more active for dynamic targets during the decision-making phase. These findings provide guidance for designing an effective EEG-based model for dynamic video target detection.

Asynchronous Video Target Detection Based on Single-Trial EEG Signals.

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems to detect sensitive targets. However, asynchronous BCI systems based on video-target-evoked ERPs can pose a challenge in real-world applications due to the absence of an explicit target onset time and the time jitter of the detection latency. To address this challenge, we developed an asynchronous detection framework for video target detection. In this framework, an ERP alignment method based on the principle of iterative minimum distance square error (MDSE) was proposed for constructing an ERP template and aligning signals on the same base to compensate for possible time jitter. Using this method, ERP response characteristics induced by video targets were estimated. Online video target detection results indicated that alignment methods reduced the false alarm more effectively than non-alignment methods. The false alarm of the proposed Aligned-MDSE method was one-third lower than that of existing alignment methods under the same right hit level using limited individual samples. Furthermore, cross-subject results indicated that untrained subjects could directly perform online detection tasks and achieve excellent performance by a general model trained from more than 10 subjects. The proposed asynchronous video target detection framework can thus have a significant impact on real-world BCI applications.

Wireless Sensor Networks for Noise Measurement and Acoustic Event Recognitions in Urban Environments.

Nowadays, urban noise emerges as a distinct threat to people's physiological and psychological health. Previous works mainly focus on the measurement and mapping of the noise by using Wireless Acoustic Sensor Networks (WASNs) and further propose some methods that can effectively reduce the noise pollution in urban environments. In addition, the research on the combination of environmental noise measurement and acoustic events recognition are rapidly progressing. In a real-life application, there still exists the challenges on the hardware design with enough computational capacity, the reduction of data amount with a reasonable method, the acoustic recognition with CNNs, and the deployment for the long-term outdoor monitoring. In this paper, we develop a novel system that utilizes the WASNs to monitor the urban noise and recognize acoustic events with a high performance. Specifically, the proposed system mainly includes the following three stages: (1) We used multiple sensor nodes that are equipped with various hardware devices and performed with assorted signal processing methods to capture noise levels and audio data; (2) the Convolutional Neural Networks (CNNs) take such captured data as inputs and classify them into different labels such as car horn, shout, crash, explosion; (3) we design a monitoring platform to visualize noise maps, acoustic event information, and noise statistics. Most importantly, we consider how to design effective sensor nodes in terms of cost, data transmission, and outdoor deployment. Experimental results demonstrate that the proposed system can measure the urban noise and recognize acoustic events with a high performance in real-life scenarios.

Indoor Pedestrian Self-Positioning Based on Image Acoustic Source Impulse Using a Sensor-Rich Smartphone.

The ubiquity of sensor-rich smartphones provides opportunities for a low-cost method to track indoor pedestrians. In this situation, pedestrian dead reckoning (PDR) is a widely used technology; however, its cumulative error seriously affects its accuracy. This paper presents a method of combining infrastructure-free indoor acoustic self-positioning with PDR self-positioning, which verifies the rationality of PDR results through the acoustic constraint between a sound source and its image sources. We further determine the first-order echo delay measurements, thus obtaining the mobile user position. We verify that the proposed method can achieve a continuous self-positioning median error of 0.19 m, and the error probability below 0.12 m is 54.46%, which indicates its ability to eliminate PDR error, as well as its adaptability to environmental disturbances.