Adjustment Module Research Articles

Multi‐Stage Optimization of Pore Size and Shape in Pore‐Space‐Partitioned Metal–Organic Frameworks for Highly Selective and Sensitive Benzene Capture

AbstractCompared to exploratory development of new structure types, pushing the limits of isoreticular synthesis on a high‐performance MOF platform may have higher probability of achieving targeted properties. Multi‐modular MOF platforms could offer even more opportunities by expanding the scope of isoreticular chemistry. However, navigating isoreticular chemistry towards best properties on a multi‐modular platform is challenging due to multiple interconnected pathways. Here on the multi‐modular pacs (partitioned acs) platform, we demonstrate accessibility to a new regime of pore geometry using two independently adjustable modules (framework‐forming module 1 and pore‐partitioning module 2). A series of new pacs materials have been made. Benzene/cyclohexane selectivity is tuned, progressively, from 4.5 to 15.6 to 195.4 and to 482.5 by pushing the boundary of the pacs platform towards the smallest modules known so far. The exceptional stability of these materials in retaining both porosity and single crystallinity enables single‐crystal diffraction studies of different crystal forms (as‐synthesized, activated, guest‐loaded) that help reveal the mechanistic aspects of adsorption in pacs materials.

Multi-Stage Optimization of Pore Size and Shape in Pore-Space-Partitioned Metal-Organic Frameworks for Highly Selective and Sensitive Benzene Capture.

Compared to exploratory development of new structure types, pushing the limits of isoreticular synthesis on a high-performance MOF platform may have higher probability of achieving targeted properties. Multi-modular MOF platforms could offer even more opportunities by expanding the scope of isoreticular chemistry. However, navigating isoreticular chemistry towards best properties on a multi-modular platform is challenging due to multiple interconnected pathways. Here on the multi-modular pacs (partitioned acs) platform, we demonstrate accessibility to a new regime of pore geometry using two independently adjustable modules (framework-forming module 1 and pore-partitioning module 2). A series of new pacs materials have been made. Benzene/cyclohexane selectivity is tuned, progressively, from 4.5 to 15.6 to 195.4 and to 482.5 by pushing the boundary of the pacs platform towards the smallest modules known so far. The exceptional stability of these materials in retaining both porosity and single crystallinity enables single-crystal diffraction studies of different crystal forms (as-synthesized, activated, guest-loaded) that help reveal the mechanistic aspects of adsorption in pacs materials.

Prussian Blue Analogue‐Based Materials: Synthesis and Their Application in Peroxymonosulfate Activation for Wastewater Treatment

Peroxymonosulfate (PMS) based advanced oxidation processes (AOPs) are effective in degrading refractory organic pollutants in water. The unique internal chemical tunability of Prussian blue analogues (PBAs), a type of metal‐organic framework materials (MOFs), makes them promising catalysts for PMS‐AOPs. However, the pristine PBA is limited in practical application due to its structural instability and easy leaching of metal ions. To this end, various methods have been developed to enhance the catalytic performance of PBAs. In this paper, the recent advances in the modification and composite strategies of PBA catalysts are systematically reviewed. PBA modification by the regulation of synthesis conditions and post‐synthesis treatment, along with composite strategies involving metal and non‐metal based materials are introduced. The structural morphology improvement, physical and chemical property adjustment, vacancy design and crystal surface modulation of PBAs induced by these modification and composite strategies are discussed in depth. In addition, the performance of the modified and composite catalysts to activate PMS for organic pollutant degradation is demonstrated. The radical pathway via SO4•‐, •OH and O2•‐, non‐radical pathway through 1O2, electron transfer process and high‐valence metal‐oxygen species, or a combination of radical and non‐radical pathways are revealed in PMS‐AOPs with PBAs and their derivatives as catalysts.

Intelligent System for Artillery Mount Autonomous Guidance And Firing Adjustment

The article presents mathematical models, numerical algorithms and a software package for an intelligent system for artillery mount autonomous guidance and adjustment of firing. The software package includes the following components: a module for solving the direct problem of external ballistics, a neural network training module, and a firing guidance and adjustment module. The first module is designed to form a database of computational experiments by repeatedly solving the direct problem of external ballistics under varying firing conditions. In the second module, a neural network is trained to solve the inverse problem of external ballistics using the database generated from computational experiments. The third module implements algorithms for calculating the artillery mount launching angles on a target using a trained neural network and calculating corrections to launching angles based on data on the projectile impact point deviation from the target. Having received data on the artillery mount readiness to fire, the coordinates of the target position and projectile’s impact point received from a digital observer, the software package calculates the necessary launching angles or corrections to them. Launching angles are transferred via text files to the control system of the artillery mount drives, then a shot is fired and the conditions for hitting the target are assessed. The algorithm is then repeated for the next shot or new target. Testing of the developed algorithms for autonomous guidance and shooting correction was carried out using modeling of external ballistics processes in the software package, as well as carrying out experimental testing on a working simulation device of an artillery mount. The developed software package allows visualization of the obtained results and saving the history of the performed operations.

A Joint Network for Low-Light Image Enhancement Based on Retinex

Methods based on the physical Retinex model are effective in enhancing low-light images, adeptly handling the challenges posed by low signal-to-noise ratios and high noise in images captured under weak lighting conditions. However, traditional models based on manually designed Retinex priors do not adapt well to complex and varying degradation environments. DEANet (Jiang et al., Tsinghua Sci Technol. 2023;28(4):743–53 2023) combines frequency and Retinex to address the interference of high-frequency noise in low-light image restoration. Nonetheless, low-frequency noise still significantly impacts the restoration of low-light images. To overcome this issue, this paper integrates the physical Retinex model with deep learning to propose a joint network model, DEANet++, for enhancing low-light images. The model is divided into three modules: decomposition, enhancement, and adjustment. The decomposition module employs a data-driven approach based on Retinex theory to split the image; the enhancement module restores degradation and adjusts brightness in the decomposed images; and the adjustment module restores details and adjusts complex features in the enhanced images. Trained on the publicly available LOL dataset, DEANet++ not only surpasses the control group in both visual and quantitative aspects but also achieves superior results compared to other Retinex-based enhancement methods. Ablation studies and additional experiments highlight the importance of each component in this method.

An Adaptive Vehicle Detection Model for Traffic Surveillance of Highway Tunnels Considering Luminance Intensity.

Vehicle detection is essential for road traffic surveillance and active safety management. Deep learning methods have recently shown robust feature extraction capabilities and achieved improved detection results. However, vehicle detection models often perform poorly under abnormal lighting conditions, especially in highway tunnels. We proposed an adaptive vehicle detection model that accounts for varying luminance intensities to address this issue. The model categorizes the image data into abnormal and normal luminance scenarios. We employ an improved CycleGAN with edge loss as the adaptive luminance adjustment module for abnormal luminance scenarios. This module adjusts the brightness of the images to a normal level through a generative network. Finally, YOLOv7 is utilized for vehicle detection. The experimental results demonstrate that our adaptive vehicle detection model effectively detects vehicles under abnormal luminance scenarios in highway tunnels. The improved CycleGAN can effectively mitigate edge generation distortion. Under abnormal luminance scenarios, our model achieved a 16.3% improvement in precision, a 1.7% improvement in recall, and a 9.8% improvement in mAP_0.5 compared to the original YOLOv7. Additionally, our adaptive luminance adjustment module is transferable and can enhance the detection accuracy of other vehicle detection models.

SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation

Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual perception algorithms due to their effectiveness in analyzing local features and global context. In this paper, we propose a hybrid transformer architecture that consists of a CNN-based encoder and transformer-based decoder. We propose a feature adjustment module that refines the multiscale feature maps extracted from an EfficientNet backbone network. The adjusted feature maps are integrated into the transformer-based decoder to perform the semantic segmentation of the remote sensing images. This paper refers to the proposed encoder–decoder architecture as a semantic feature adjustment network (SFA-Net). To demonstrate the effectiveness of the SFA-Net, experiments were thoroughly conducted with four public benchmark datasets, including the UAVid, ISPRS Potsdam, ISPRS Vaihingen, and LoveDA datasets. The proposed model achieved state-of-the-art accuracy on the UAVid, ISPRS Vaihingen, and LoveDA datasets for the segmentation of the remote sensing images. On the ISPRS Potsdam dataset, our method achieved comparable accuracy to the latest model while reducing the number of trainable parameters from 113.8 M to 10.7 M.

Thermadapt shape memory AB-copolymer networks exhibiting tunable properties and kinetics of topological rearrangement

Polycaprolactone (PCL) based thermadapt shape memory polymers (SMPs) have exhibited superiority over traditional analogues by allowing shape-editing to reconfigure permanent shapes into more complex geometrical shapes, driven by the pursuit of advanced functionalities for high-value applications. Nevertheless, although transesterification-based PCL networks exhibit impressive shape reconfigurability as prototypical thermadapt SMPs, there are still constraints in terms of the need for significant flexibility in architectural adjustment and property modulation without compromising reconfigurability, which would be relevant for practical applications. Here, we present a simple yet efficient strategy to create PCL-based thermadapt SMPs that possess a highly tunable structure and properties, as well as exceptional reconfigurability. The family of SMPs, called thermadapt shape memory AB copolymer networks (AB-CPNs), was synthesized by UV-initiated free radical polymerization between PCL diacrylate as crosslinker and 4-hydroxybutyl acrylate (HBA) as comonomer. The thermadapt AB-CPNs allow for significant structural alterations with 0 wt% to 70 wt% HBA while maintaining excellent shape memory and shape reconfigurability effects. The insertion of the polymer segment containing free hydroxyls not only promises tunable material properties but also makes network rearrangement easier since dynamic hydroxy-ester bonds are more active than dynamic ester-ester bonds. It is envisioned that the thermadapt shape memory AB-CPNs with widely tunable macroscopic properties will drive progress in the real-world applications of SMP devices with complicated geometric structures.

DBAD: Dual branch reconstruction for industrial anomaly detection

AbstractReconstruction‐based methods are commonly used in industrial visual anomaly detection. They rely on a well‐reconstructed normal mode of the model. However, it is difficult to manage the boundary of generalization. The strength of the model's generalization capability can directly affect the fidelity of the reconstruction, resulting in the occurrence of false positives. To address the above challenges, a novel dual branch reconstruction anomaly detection approach is proposed to control the model generalization capability at two dimensions. It reconstructs abnormal images into normal ones by resolution recovery and denoising branches. Detection results are generated from their comparison. In addition, an innovative channel adjustment module is introduced to improve information exchange between branches. It uses multiple dilated convolutions for interactions over different scales. Simulation experiments demonstrate that the method outperforms most inspection methods on the MVTec AD and MVTec 3D‐AD datasets. It also shows good results on the self‐generated automotive paint scratches dataset of this study.

Energy-Efficient Smart Window Based on a Thermochromic Hydrogel with Adjustable Critical Response Temperature and High Solar Modulation Ability.

Thermochromic smart windows realize an intelligent response to changes in environmental temperature through reversible physical phase transitions. They complete a real-time adjustment of solar transmittance, create a livable indoor temperature for humans, and reduce the energy consumption of buildings. Nevertheless, conventional materials that are used to prepare thermochromic smart windows face challenges, including fixed transition temperatures, limited solar modulation capabilities, and inadequate mechanical properties. In this study, a novel thermochromic hydrogel was synthesized from 2-hydroxy-3-butoxypropyl hydroxyethyl celluloses (HBPEC) and poly(N-isopropylacrylamide) (PNIPAM) by using a simple one-step low-temperature polymerization method. The HBPEC/PNIPAM hydrogel demonstrates a wide response temperature (24.1-33.2 °C), high light transmittance (Tlum = 87.5%), excellent solar modulation (ΔTsol = 71.2%), and robust mechanical properties. HBPEC is a functional material that can be used to adjust the lower critical solution temperature (LCST) of the smart window over a wide range by changing the degree of substitution (DS) of the butoxy group in its structure. In addition, the use of HBPEC effectively improves the light transmittance and mechanical properties of the hydrogels. After 100 heating and cooling cycles, the hydrogel still has excellent stability. Furthermore, indoor simulation experiments show that HBPEC/PNIPAM hydrogel smart windows have better indoor temperature regulation capabilities than traditional windows, making these smart windows potential candidates for energy-saving building materials.

Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network

Most of the existing low-light enhancement research focuses on global illumination enhancement while ignoring the issues of brightness unevenness and color distortion. To address this dilemma, we propose a low-light image enhancement method that can achieve good performance in luminance adjustment and color correction simultaneously. Specifically, the Luminance Adjustment Module is designed to model the global luminance adjustment parameters while taking into account the relationship between global and local illumination features, in order to prevent overexposure or underexposure. Furthermore, we design a Color Correction Module based on the attention mechanism, which utilizes the attention mechanism to capture global color features and correct the color deviation in the illumination-enhanced image. Additionally, we design a color loss function based on a 14-dimensional statistical feature vector related to color, enabling further restoration of the image’s true color. We conduct empirical studies on multiple public low-light datasets, demonstrating that the proposed method outperforms other representative state-of-the-art models regarding illumination enhancement and color correction.

A learning-based framework for topology-preserving segmentation using quasiconformal mappings

We propose the Topology-Preserving Segmentation Network, a deformation-based model that can extract objects in an image while maintaining their topological properties. This network generates segmentation masks that have the same topology as the template mask, even when trained with limited data. The network consists of two components: the Deformation Estimation Network, which produces a deformation map that warps the template mask to enclose the region of interest, and the Beltrami Adjustment Module, which ensures the bijectivity of the deformation map by truncating the associated Beltrami coefficient based on Quasiconformal theories. The proposed network can also be trained in an unsupervised manner, eliminating the need for labeled training data. This is achieved by incorporating an unsupervised segmentation loss. Our experimental results on various image datasets show that TPSN achieves better segmentation accuracy than state-of-the-art models with correct topology. Furthermore, we demonstrate TPSN’s ability to handle multiple object segmentation.

Engineering Modular DNA Reaction Networks for Signal Processing.

Diversified molecular information-processing methods have significant implications for nanoscale manipulation and control, monitoring and disease diagnosis of organisms, and direct intervention in biological activities. However, as an effective approach for implementing multifunctional molecular information processing, DNA reaction networks (DRNs) with numerous functionally specialized molecular structures have challenged them on scale design, leading to increased network complexity, further causing problems such as signal leakage, attenuation, and cross-talk in network reactions. Our study developed a strategy for performing various signal-processing tasks through engineering modular DRNs. This strategy is based on a universal core unit with signal selection capability, and a time-adjustable signal self-resetting module is achieved by combing the core unit and self-resetting unit, which improves the time controllability of modular DRNs. In addition, multi-input and -output signal cross-catalytic and continuously adjustable signal delay modules were realized by combining core and threshold units, providing a flexible, precise method for modular DRNs to process the signal. The strategy simplifies the design of DRNs, helps generate design ideas for large-scale integrated DRNs with multiple functions, and provides prospects in biocomputing, gene regulation, and biosensing.

Robformer: A robust decomposition transformer for long-term time series forecasting

Transformer-based forecasting methods have been widely applied to forecast long-term multivariate time series, which achieves significant improvements on extending the forecasting time. However, their performance can degenerate terribly when abrupt trend shift and seasonal fluctuation arise in long-term time series. Hence, we identify two bottlenecks of previous Transformers architecture: (1) the robustless decomposition module and (2) trend shifting problem. These result in a different distribution between the trend prediction and ground truth in the long-term multivariate series forecasting. Towards these bottlenecks, we design Robformer as a novel decomposition-based Transformer, which consists of three new inner module to enhance the predictability of Transformers. Concretely, we renew the decomposition module and add a seasonal component adjustment module to tackle the unstationarized series. Further, we propose a novel inner trend forecasting architecture inspired by polynomial fitting method, which outperforms previous design in accuracy and robustness. Our empirical studies show that Robformer can achieve 17% and 10% relative improvements than state-of-the-art Autoformer and FEDformer baselines under the fair long-term multivariate setting on six benchmarks, covering five mainstream time series forecasting applications: energy, economics, traffic, weather, and disease. The code will be released upon publication.

Efficient complete denture metal base design via a dental feature-driven segmentation network

Background and objectiveComplete denture is a common restorative treatment in dental patients and the design of the core components (major connector and retentive mesh) of complete denture metal base (CDMB) is the basis of successful restoration. However, the automated design process of CDMB has become a challenging task primarily due to the complexity of manual interaction, low personalization, and low design accuracy. MethodsTo solve the existing problems, we develop a computer-aided Segmentation Network-driven CDMB design framework, called CDMB-SegNet, to automatically generate personalized digital design boundaries for complete dentures of edentulous patients. Specifically, CDMB-SegNet consists of a novel upright-orientation adjustment module (UO-AM), a dental feature-driven segmentation network, and a specific boundary-optimization design module (BO-DM). UO-AM automatically identifies key points for locating spatial attitude of the three-dimensional dental model with arbitrary posture, while BO-DM can result in smoother and more personalized designs for complete denture. In addition, to achieve efficient and accurate feature extraction and segmentation of 3D edentulous models with irregular gingival tissues, the light-weight backbone network is also incorporated into CDMB-SegNet. ResultsExperimental results on a large clinical dataset showed that CDMB-SegNet can achieve superior performance over the state-of-the-art methods. Quantitative evaluation (major connector/retentive mesh) showed improved Accuracy (98.54 ± 0.58 %/97.73 ± 0.92 %) and IoU (87.42 ± 5.48 %/70.42 ± 7.95 %), and reduced Maximum Symmetric Surface Distance (4.54 ± 2.06 mm/4.62 ± 1.68 mm), Average Symmetric Surface Distance (1.45 ± 0.63mm/1.28 ± 0.54 mm), Roughness Rate (6.17 ± 1.40 %/6.80 ± 1.23 %) and Vertices Number (23.22 ± 1.85/43.15 ± 2.72). Moreover, CDMB-SegNet shortened the overall design time to around 4 min, which is one tenth of the comparison methods. ConclusionsCDMB-SegNet is the first intelligent neural network for automatic CDMB design driven by oral big data and dental features. The designed CDMB is able to couple with patient's personalized dental anatomical morphology, providing higher clinical applicability compared with the state-of-the-art methods.

A novel membrane-aerated biofilm reactor using flexible and adjustable plate membrane modules: Treatment of wastewater containing anionic surfactants

Membrane-aerated biofilm reactors (MABRs) are a promising biofilm technology for wastewater treatment; however, achieving high pollutant removal in MABR systems with a low specific membrane surface area, low air-based aeration pressure and flexible aeration control remains a substantial challenge. In this study, a flexible and adjustable plate membrane module was developed and applied in an air-based plate MABR (PMABR) for treating anionic surfactant wastewater. The plate membrane module achieved high oxygen mass transfer under a low air-based membrane aeration pressure. When the influent linear alkylbenzene sulfonate (LAS) load was 0.559±0.010 kg/m3·d and a gradient-pressure membrane aeration scheme of (8,7,6) kPa was adopted, the LAS removal rate reached 99.2±0.1%, and the LAS removal load per unit membrane area reached 14.08±0.25 g/m2·d. The bacteria participating in the degradation of LAS were mainly Parvibaculum, Pseudomonas, Desulfovibrio, Flavobacterium and Holophagaceae. The dominant microbial communities of different biofilms in the PMABR played an important role in ω-oxidation, β-oxidation, benzene ring breaking and sulfonic acid group shedding in the degradation of LAS. The developed plate membrane module and PMABR provide a clean, energy-saving and flexible technical solution for MABR applications in wastewater treatment.

Adjustable patch and feature prior token-based transformer for weakly supervised semantic segmentation

Weakly supervised semantic segmentation is a challenging task, utilizing only low-cost weak supervision to produce pixel-level predictions. Existing transformer-based methods for weakly supervised semantic segmentation have some limitations: (1) Fixing patch size might destroy the structured semantics, which is unfriendly to objects of different scales, and (2) Ignoring the prior features when using multi-head attention mechanisms might lead to inaccurate segmentation localization. To tackle these issues, we proposed an effective transformer framework coupled with the adjustable patch and prior feature tokens, termed as APFPformer, in which an adjustable patch module is developed to split the image according to the area of the salient object for preserving the structured semantics in patches. A prior feature token module is devised to exploit the edge and texture information as prior tokens, ensuring the gain of discriminative representation. Additionally, a single-stage scheme is applied to reduce the computation and rapid segmentation process. Our experiments demonstrate the superiority of our approach over early methods, gaining competitive mean Intersection-over-Union scores of 67.9% on the PASCAL VOC2012 dataset and 40.2% on the MS COCO dataset.

Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification

Recently, a number of Few-Shot Fine-Grained Image Classification (FS-FGIC) methods have been proposed, but they primarily focus on better fine-grained feature extraction while overlooking two important issues. The first one is how to extract discriminative features for Fine-Grained Image Classification tasks while reducing trivial and non-generalizable sample level noise introduced in this procedure, to overcome the over-fitting problem under the setting of Few-Shot Learning. The second one is how to achieve satisfying feature matching between limited support and query samples with variable spatial positions and angles. To address these issues, we propose a novel Cross-layer and Cross-sample feature optimization Network for FS-FGIC, C2-Net for short. The proposed method consists of two main modules: Cross-Layer Feature Refinement (CLFR) module and Cross-Sample Feature Adjustment (CSFA) module. The CLFR module further refines the extracted features while integrating outputs from multiple layers to suppress sample-level feature noise interference. Additionally, the CSFA module addresses the feature mismatch between query and support samples through both channel activation and position matching operations. Extensive experiments have been conducted on five fine-grained benchmark datasets, and the results show that the C2-Net outperforms other state-of-the-art methods by a significant margin in most cases. Our code is available at: https://github.com/zenith0923/C2-Net.

A low-light image enhancement method for personnel safety monitoring in underground coal mines

Intelligent monitoring technology plays an important role in promoting the development of coal mine safety management. Low illumination in the coal mine underground leads to difficult recognition of monitoring images and poor personnel detection accuracy. To alleviate this problem, a low illuminance image enhancement method proposed for personnel safety monitoring in underground coal mines. Specifically, the local enhancement module maps low illumination to normal illumination at pixel level preserving image details as much as possible. The transformer-based global adjustment module is applied to the locally enhanced images to avoid over-enhancement of bright areas and under-illumination of dark areas, and to prevent possible color deviations in the enhancement process. In addition, a feature similarity loss is proposed to constrain the similarity of target features to avoid the possible detrimental effect of enhancement on detection. Experimental results show that the proposed method improves the detection accuracy by 7.1% on the coal mine underground personal dataset, obtaining the highest accuracy compared to several other methods. The proposed method effectively improves the visualization and detection performance of low-light images, which contributes to the personnel safety monitoring in underground coal mines.

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.