Reverse Process Research Articles

Layer-by-Layer-Assembled Polyaniline/MXene Thin Film and Device for Improved Electrochromic and Energy Storage Capabilities.

Polyaniline (PANI) is an attractive electrochromic and storage material due to its reversible and sustainable electrochemical redox processes. However, the insufficient surface area and excessive charge intercalation after long-term cycling results in limited charge capacitance and unsatisfactory cycling stability. In this work, we demonstrate an innovative method to increase PANI's electrochromic and energy storage performance by incorporating MXene, to enhance electrochemical activity and reveal more active areas of ion/electron intercalation/deintercalation and charge transfer. The hydrogen bonds formed between N-H, C-H, and C-N of PANI and -OH and -O surface functional terminations of MXene further enhance the interface interaction. With substantial optical transmittance modulation and charge capacitance, excellent coloration efficiency, and outstanding durability, the PANI/MXene thin film demonstrates exceptional color-switching and energy storage characteristics. Furthermore, the sandwich device with a PANI/MXene thin film as the positive electrode and zinc foil as the negative electrode demonstrates exceptional electrochromic and Zn2+ storage capability. This work raises possibilities for next-generation intelligent energy conversion and storage technologies and offers fresh perspectives on the design of ionic devices.

Diffusion‐based Human Motion Style Transfer with Semantic Guidance

Abstract3D Human motion style transfer is a fundamental problem in computer graphic and animation processing. Existing AdaIN‐based methods necessitate datasets with balanced style distribution and content/style labels to train the clustered latent space. However, we may encounter a single unseen style example in practical scenarios, but not in sufficient quantity to constitute a style cluster for AdaIN‐based methods. Therefore, in this paper, we propose a novel two‐stage framework for few‐shot style transfer learning based on the diffusion model. Specifically, in the first stage, we pre‐train a diffusion‐based text‐to‐motion model as a generative prior so that it can cope with various content motion inputs. In the second stage, based on the single style example, we fine‐tune the pre‐trained diffusion model in a few‐shot manner to make it capable of style transfer. The key idea is regarding the reverse process of diffusion as a motion‐style translation process since the motion styles can be viewed as special motion variations. During the fine‐tuning for style transfer, a simple yet effective semantic‐guided style transfer loss coordinated with style example reconstruction loss is introduced to supervise the style transfer in CLIP semantic space. The qualitative and quantitative evaluations demonstrate that our method can achieve state‐of‐the‐art performance and has practical applications. The source code is available at https://github.com/hlcdyy/diffusion-based-motion-style-transfer.

Ecological Consequences of Changes in the Territorial and Branch Structure of Moscow Manufacturing Industry in 1990–2023

The peculiarities of the dynamics of the volume and structure of emissions from manufacturing enterprises in Moscow in 1990–2023 have been revealed. It is noted that the economic process of deindustrialization in the 1990s–2000s has been replaced in recent years by a small reverse process of reindustrialization, with an increase in the diversity of sectoral structure, and a change in the role and internal structure of machine building and chemical industry. The decrease of manufacturing industry output was noticed, while pollution from large enterprises producing products necessary for the city was preserved; at the same time, the rates of decrease in specific pollution for these enterprises have slowed down in recent years. It is concluded that during periods of crisis and recession there is a faster change in industrial structure compared to the change in pollution structure, and that during the recovery phase the pollution structure changes faster, largely due to the fact that industries are expanded initially without adequate expansion of treatment systems.

Reversible Vitrimisation of Single‐Use Plastics and their Mixtures

AbstractVitrimers are promising next‐generation materials, often exhibiting superior properties such as mechanical strength and solvent resistance compared to their thermoplastic counterparts, while still featuring recyclability due to their dynamic covalent crosslinks. The most common strategy to recycle vitrimers is via mechanical reprocessing at high temperatures, while others utilize chemical degradation to reobtain the constituent monomers. This work presents a new approach toward reprocessing by selectively breaking of the vitrimer's crosslinks, turning it back into a thermoplastic. This allows for reprocessing to be achieved in a more sustainable manner such as at lower temperatures and shorter times. After the desired shape has been obtained, facile re‐crosslinking turns the material into a vitrimer, with full restoration of thermal stability, mechanical properties, and gel fraction. To achieve this, a carbene C─H insertion strategy is utilized to introduce an imine‐based crosslinker into various thermoplastics and plastic mixtures to convert them into vitrimers. Properties typical of vitrimers such as a retained polymer network at high temperatures, and an Arrhenius‐type relationship between stress relaxation rate and temperature are observed, while its mechanical properties such as elongation at break and tensile toughness, particularly for plastic mixtures are significantly enhanced, first by crosslinking, then further enhanced up to 8 folds by the reversible crosslinking process.

Significant statistical model of heat transfer rate in radiative Carreau tri-hybrid nanofluid with entropy analysis using response surface methodology used in solar aircraft

The recent advancement of technologies focuses on the use of solar-based heat radiation and nanotechnology. The primary source of heat is solar energy, which is obtained by absorbing sunlight. In addition, solar thermal planes, photovoltaic cells, and sun-based hybrid nanofluids all help to harness solar energy. Therefore, main concern of the study is to explored the two-dimensional Engine oil-based Carreau tri-hybrid nanofluid (SiO2 (silica dioxide), MOS2 (Molybdenum disulfide), Fe3O4(black iron oxide)) flow via a stretching sheet within solar wings. The flow phenomena of the non-Newtonian fluid enrich for the effects of radiant energy and the external heat source. By adopting similarity transfiguration, the dimensional partial main flow equations are changed into nonlinear dimensionless ordinary differential equations. The most effective and powerful tool of the semi-analytical method ADM (Adomain Decomposition Method) is utilized to solve the nanofluid flow problem. The uniqueness of the proposed study arises due to the analysis of entropy generation obtained by various irreversibility processes. The graphical illustration of outcome of various governing parameters on velocity, temperature, entropy terms and engineering quantities have been presented and theoretically analyzed. The findings reveal that The Carreau ternary hybrid nanofluids enhance the system's thermal conductivity for optimal temperature regulation in solar aircraft and better heat dissipation. The use of Carreau ternary hybrid nanofluids significantly reduces entropy generation, which implies a more reversible process, enhancing the overall efficiency of the energy conversion systems in solar aircraft. The heat flow of the system is accurately reflected by the chosen variables and their interactions, as demonstrated by the constructed model's strong statistical significance. For real-world applications, this improves the model's predictability and dependability.

DM-HAP: Diffusion model for accurate hand pose prediction

Forecasting hand poses is a challenging task due to inherent uncertainties, occlusions, and inaccuracies in 3D pose estimation. Diffusion models provide a promising direction for predicting precise 3D hand poses under noisy conditions. In this work, we introduce Dual-diffusion, an innovative framework for the precise prediction of future hand poses. Our approach leverages the strengths of diffusion models by framing hand pose forecasting as a reverse diffusion process, effectively addressing the complexities of noisy hand joint movements and their subtleties. To enhance the learning of hand pose representations, we propose a unique neural architecture that simultaneously captures both local and global features. This is achieved through the deployment of Global and Local Diffusion (GLD) blocks within our network, which facilitate the exchange of information between local and global features. This diffusion-based interaction enables the integration of global hand actions and local finger actions, leading to a more powerful representation learning approach. We evaluate the effectiveness of our Dual-diffusion method on three public 3D hand pose estimation datasets (MSRA, F-PHAB, and BigHand2.2M), and our approach outperforms previous methods.

Mitigating Adversarial Attacks in Object Detection through Conditional Diffusion Models

The field of object detection has witnessed significant advancements in recent years, thanks to the remarkable progress in artificial intelligence and deep learning. These breakthroughs have significantly enhanced the accuracy and efficiency of detecting and categorizing objects in digital images. Nonetheless, contemporary object detection technologies have certain limitations, such as their inability to counter white-box attacks, insufficient denoising, suboptimal reconstruction, and gradient confusion. To overcome these hurdles, this study proposes an innovative approach that uses conditional diffusion models to perturb adversarial examples. The process begins with the application of a random chessboard mask to the adversarial example, followed by the addition of a slight noise to fill the masked area during the forward process. The adversarial image is then restored to its original form through a reverse generative process that only considers the masked pixels, not the entire image. Next, we use the complement of the initial mask as the mask for the second stage to reconstruct the image once more. This two-stage masking process allows for the complete removal of global disturbances and aids in image reconstruction. In particular, we employ a conditional diffusion model based on a class-conditional U-Net architecture, with the source image further conditioned through concatenation. Our method outperforms the recently introduced HARP method by 5% and 6.5% in mAP on the COCO2017 and PASCAL VOC datasets, respectively, under non-APT PGD attacks. Comprehensive experimental results confirm that our method can effectively restore adversarial examples, demonstrating its practical utility.

Trends in Temperatures in Latin America: A Time-Series Perspective Based on Fractional Integration

Abstract In this article, we examine the time-series properties of the temperatures in Latin America. We look at the presence of time trends in the context of potential long-memory processes, looking at the average, maximum, and minimum values from 1901 to 2021. Our results indicate that when looking at the average data, there is a tendency to return to the mean value in all cases. However, it is noted that in the cases of Guatemala, Mexico, and Brazil, which are the countries with the highest degree of integration, the process of reversion could take longer than in the remaining countries. We also point out that the time trend coefficient is significantly positive in practically all cases, especially in temperatures in the Caribbean islands such as Antigua and Barbuda, Aruba, and the British Virgin Islands. When analyzing the maximum and minimum temperatures, the highest degrees of integration are observed in the minimum values, and the highest values are obtained again in Brazil, Guatemala, and Mexico. The time trend coefficients are significantly positive in almost all cases, with the only two exceptions being Bolivia and Paraguay. Looking at the range (i.e., the difference between maximum and minimum temperatures), evidence of orders of integration above 0.5 is found in nine countries (Aruba, Brazil, Colombia, Cuba, Ecuador, Haiti, Panama, the Turks and Caicos Islands, and Venezuela), implying that shocks in the range will take longer to disappear than in the rest of the countries.

Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion.

Recently, diffusion models have shown considerable promise for MRI reconstruction. However, extensive experimentation has revealed that these models are prone to generating artifacts due to the inherent randomness involved in generating images from pure noise. To achieve more controlled image reconstruction, we reexamine the concept of interpolatable physical priors in k-space data, focusing specifically on the interpolation of high-frequency (HF) k-space data from low-frequency (LF) k-space data. Broadly, this insight drives a shift in the generation paradigm from random noise to a more deterministic approach grounded in the existing LF k-space data. Building on this, we first establish a relationship between the interpolation of HF k-space data from LF k-space data and the reverse heat diffusion process, providing a fundamental framework for designing diffusion models that generate missing HF data. To further improve reconstruction accuracy, we integrate a traditional physics-informed k-space interpolation model into our diffusion framework as a data fidelity term. Experimental validation using publicly available datasets demonstrates that our approach significantly surpasses traditional k-space interpolation methods, deep learning-based k-space interpolation techniques, and conventional diffusion models, particularly in HF regions. Finally, we assess the generalization performance of our model across various out-of-distribution datasets. Our code are available at https://github.com/ZhuoxuCui/Heat-Diffusion.

Research and Experiment on Efficient Fracturing Technology of Tight Gas in Qaidam Basin

Abstract The lithology of the Huangguamao block reservoir in the Qaidam Basin is complex and dense. The difference between gas and water is not obvious. The gas reservoir has the characteristics of low porosity, low permeability, low saturation, and coexistence of gas and water. The problem of water output after fracturing and short stable production period has put forward higher requirements for improving production and efficiency in tight gas horizontal wells. Based on successful cases of tight gas reservoir fracturing both domestically and internationally, as well as the experience of unconventional reservoir volume transformation in the Qaidam Basin, this study conducts a historical analysis of reservoir characteristics and measures transformation in the Huangguamao block, evaluates the difficulties of measures transformation, optimizes the formation of two process ideas: high viscosity liquid controlled sand lifting fracturing and reverse composite fracture network fracturing, and prepares fracturing programme for MP2 and MP3 wells. After on-site fracturing, two processes were evaluated and analyzed, and the results showed that the reverse composite fracturing process, which takes into account both crack length and complexity, can effectively increase the transformation volume, provide more sufficient formation energy, increase production, and have stronger adaptability.

SIMTSeg: A self-supervised multivariate time series segmentation method with periodic subspace projection and reverse diffusion for industrial process

Subsequences with varied regimes in the industrial multivariate time series (MTS) are closely associated with the dynamic status of the multi-phased industrial process. Time series segmentation (TSS) provides insights into the underlying behavior of industrial systems. However, the complexity of industrial data poses significant challenges to the conventional TSS methods. Motivated by this, a Self-supervised Industrial Multivariate Time-series Segmentation method (SIMTSeg) is presented in this work. An MTS folding module based on Ramanujan periodic subspace projection is first proposed, where the MTS is reshaped into the 3D feature map to realize the compact representation of the intricate data dependencies. Subsequently, a self-supervised module based on the encoder-decoder architecture is adopted to address the problem of deficient and task-specific annotations in industrial data. The folded feature map is denoised step by step following the reverse diffusion process, and finally turns into the segmentation mask without redundant details. The proposed SIMTSeg has been validated by a popular industrial benchmark, the Tennessee Eastman Process, and outperforms the unsupervised data-driven baselines in terms of various performance metrics. SIMTSeg has no prerequisite on the number of segmentation points or regime types, and is capable of giving more meaningful segmentation results that are in line with the high-level semantics.

Innovative methods in the construction of digital twin factory

The digital twin factory is the digital twin model of the physical factory. It can accurately reflect the physical world. In the early stage of building an intelligent factory, it is very meaningful to design a digital twin model of a physical factory through process planning, logistics optimization, layout planning, and so on, to shorten the time of building and debugging a physical factory. This paper takes the construction of an intelligent factory for laptop assembly as an example. Firstly, construct the model of the laptop disassembly/assembly graph. Because assembly is the reverse process of disassembly, the algorithm is used to plan the assembly sequence of the laptop. After that, the digital twin model of the laptop assembly line according to the laptop assembly process sequence. Subsequently, time series optimization was used to optimize the bottleneck process of the model, resulting in a 424% increase in output. Lastly, we explain the modules and steps used to build the physical laptop factory. This paper innovatively takes the laptop assembly process sequence as the main line of factory construction and takes the bottleneck process time chain optimization as the breakthrough of capacity improvement, which verifies the important role and significance of digital twin factories in constructing intelligent factories. It provides a basis for the promotion and application of digital twin technology in constructing intelligent factories.

Droplet Shape Representation Using Fourier Series and Autoencoders

The shape of liquid droplets in air plays an important role in the aerodynamic behavior and combustion dynamics of miniaturized propulsion systems such as microsatellites and small drones. Their precise manipulation can yield optimal efficiency in such systems. It is desired to have a minimal representation of droplet shapes using as few parameters as possible to automate shape manipulation using self-learning algorithms, such as reinforcement learning. In this paper, we use a neural compression algorithm to represent, with only two parameters, elliptical and bullet-shaped droplets initially represented with 200 points (400 real numbers) at the droplet boundary. The mapping of many to two points is achieved in two stages. Initially, a Fourier series is formulated to approximate the contour of the droplet. Subsequently, the coefficients of this Fourier series are condensed to lower dimensions utilizing a neural network with a bottleneck architecture. Finally, 5000 synthetically generated droplet shapes were used to train the neural network. With a two-real-number representation, the recovered droplet shapes had excellent overlap with the original ones, with a mean square error of ∼10−3. Hence, this method compresses the droplet contour to merely two numerical parameters via a fully reversible process, a crucial feature for rendering learning algorithms computationally tractable.

A Hybrid System Based on Three Levels to Hide Information using JPEG Color Images

Nowadays, due to the circulation of information among people, information security has become an important requirement to protect information from hacking. Many techniques have been discovered to protect this information and maintain its confidentiality from hacking, the most prominent of which are cryptography and steganography techniques. In this paper, a hybrid system based on three levels to hide information using Joint Photographic Experts Group (JPEG) color images, where this system works in both processes sender and receiver. The sender process at the first level uses a mixing of the Advanced Encryption Standard (AES) and Rivest Shamir Adleman (RSA), while at the second level uses a Fuzzy Stream Algorithm (FSA) adds a higher complexity and eliminates the non-linearity of the encrypted information. The third level is to hide encrypted information using the Least Significant Bit (LSB) technique, while the receiver process, performs the reverse process of three levels. This system provides high information embedding capability and the inability to perceive hidden information, the system was evaluated based on criteria like Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), Structure Similarity (SSIM), and correlation, which were characterized by high and good rates. The study was compared with modern steganography techniques.

Formation of property gradient in coarse-grained niobium using a wedge tool: Experiment and analysis

We introduce a novel severe plastic deformation process for coarse-grained niobium, which employs a tool with an inclined (wedge) surface for deforming the material by a reverse shear scheme. The process increases the intensity of shear deformations and the depth of plastic deformation in the body of the workpiece when a wedge tool acts on its surface. The essence of the process is in the repeated displacement of the workpiece material in opposite directions during the asymmetrical introduction of a wedge tool until the required degree of deformation is accumulated in the tool-affected volume. This deformation scheme applies a 15° angle wedge tool to a 21-mm high workpiece. After nine cycles of plastic deformation, a gradient of the accumulated degree of deformation in the range of true strain e = 0.3–4.5 was created. At maximum deformation, the microhardness of the workpieces increased by 1.86 times and the tensile strength by 1.6 times. Fractograms show a significant influence of the accumulated degree of deformation on the nature of the fracture. The finite element method simulation of the deformation process showed that creating a uniformly strengthened layer requires at least five deforming operations. For example, the proposed reverse shear process with a wedge tool can be applied to improve the structure of the surface layers of niobium ingots for subsequent forming. Due to the creation of a significant gradient of properties, the reverse shear process can be used as an express method for determining the mechanical characteristics of different materials in a wide range of accumulated degree of deformation.

Metabolic regulation of cytoskeleton functions by HDAC6-catalyzed α-tubulin lactylation.

Posttranslational modifications (PTMs) of tubulin, termed the "tubulin code", play important roles in regulating microtubule functions within subcellular compartments for specialized cellular activities. While numerous tubulin PTMs have been identified, a comprehensive understanding of the complete repertoire is still underway. In this study, we report that α-tubulin lactylation is catalyzed by HDAC6 by using lactate to increase microtubule dynamics in neurons. We identify lactylation on lysine 40 of α-tubulin in the soluble tubulin dimers. Notably, lactylated α-tubulin enhances microtubule dynamics and facilitates neurite outgrowth and branching in cultured hippocampal neurons. Moreover, we discover an unexpected function of HDAC6, acting as the primary lactyltransferase to catalyze α-tubulin lactylation. HDAC6-catalyzed lactylation is a reversible process, dependent on lactate concentrations. Intracellular lactate concentration triggers HDAC6 to lactylate α-tubulin, a process dependent on its deacetylase activity. Additionally, the lactyltransferase activity may be conserved in HDAC family proteins. Our study reveals the primary role of HDAC6 in regulating α-tubulin lactylation, establishing a link between cell metabolism and cytoskeleton functions.

Occurrence and Ecological Risk Assessment of Highly Toxic Halogenated Byproducts during Chlorination Decolorization of Textile Printing and Dyeing Wastewater.

Textile printing and dyeing wastewater is a substantial source of highly toxic halogenated pollutants because of the chlorination decolorization. However, information on the occurrence and fate of the highly toxic halogenated byproducts, which are produced by chlorination decolorization of the textile printing and dyeing wastewater, is very limited. In this study, the occurrence of six categories of halogenated byproducts (haloacetic acids (HAAs), haloacetonitriles (HANs), N-nitrosamines (NAs), trihalomethanes, halogenated ketones, and halonitromethanes) was investigated along the full-scale treatment processes of textile printing and dyeing wastewater treatment plants. Furthermore, the ecological risk of the halogenated byproducts was evaluated. The results showed that the total concentration of halogenated byproducts increased significantly after chlorination. Large amounts of HAAs (average 122.1 μg/L), HANs (average 80.9 μg/L), THMs (average 48.3 μg/L), and NAs (average 2314.3 ng/L) were found in the chlorinated textile wastewater, and the results showed that the generations of HANs and NAs were positively correlated with the BIX and β/α index, indicating that the HANs and NAs might form from the microbial metabolites. In addition, HAAs and HANs exhibited high ecological risk quotients (>1), suggesting their high potential ecological risk. The results also demonstrated that most halogenated byproducts could be effectively removed by reverse osmosis treatment processes except NAs, with a lower removal rate of 18%. This study is believed to provide an important theoretical basis for controlling and reducing the ecological risks of halogenated byproducts in textile printing and dyeing wastewater effluents.

On-Chip Thermoelectric Devices Based on Standard Silicon Processing.

The strong reduction of thermal conductivity with respect to bulk silicon makes nanostructured silicon one of the best materials for highly efficient direct conversion of heat into electrical power and vice-versa. The widespread technologies for the integration of silicon devices can be used to define on-chip micro thermoelectric generators (scavengers); similar structures could also be used for precise and well-localized cooling through the reverse process of heat pumping. However, the road to the fabrication of integrated thermal energy scavengers or cooler, based on silicon, is still very long. In this work, the design and the fabrication process of on-chip thermoelectric devices based on a large number of interconnected monocrystalline silicon nanobeams, very tall (>1 µm) and thin (less than 200 nanometers), arranged in large areas combs is shown. The small width of the nanobeams gives a reduced thermal conductivity, and the height perpendicular to the substrate allows the definition of a highly dense collection of nanostructures. The total cross-sectionis far broader than that of other nanostructures, a characteristic that guarantees both mechanical stability and larger deliverable power per unit area.

Enhanced temperature sensing in lead-free Cs3Bi2Cl9 perovskite co-doping Yb3+/Er3+ for optical thermometry application

A simple and rapid grinding method for preparing Er3+-doped and Er3+/Yb3+co-doped Cs3Bi2Cl9 upconversion phosphors was developed. We avoid to employ strong acids as reaction solvents, aligning with the principles of green chemistry. By controlling the thermal treatment temperature and adding BiCl3, a reversible phase transformation process between Cs3Bi2Cl9 and Cs3BiCl6 perovskite phosphors was achieved. The Er3+/Yb3+ co-doped Cs3Bi2Cl9 phosphors emit green light under 980 nm excitation, whereas Cs3BiCl6 phosphors emit red light. Their temperature sensing performance was tested through variable-temperature processes. Compared to Er3+-doped Cs3Bi2Cl9 phosphors, the co-doped Er3+/Yb3+ phosphors have a higher relative sensitivity of 1.60 % K−1. Compared to previously reported materials, Cs3Bi2Cl9 phosphors show enhanced sensitivity in temperature sensing. The study demonstrates that lead-free halide perovskites’ unique color-tunability not only underscores the innovativeness of this synthetic strategy but also provides a significant technological foundation for temperature sensing applications. Given their environmental friendliness and facile preparation process, these phosphors possess notable advantages for industrial production and hold promise as potential candidates for next-generation high-performance phosphor materials.

High recovery design of reverse osmosis process with high permeate water quality and low wastewater discharge for ultra-pure water production

Ultra-pure water (UPW) for electronic industry is produced via many unit processes including reverse osmosis (RO), pretreatments, membrane degasifier, and ion exchange processes. Generally, 2-pass RO system is adopted in UPW production process to remove ionic and organic matters to meet water quality requirements of post-treatments. Since RO concentrate occupies most of wastewater discharged from UPW production process, high recovery design is beneficial to reduce wastewater discharge. According to the solution-diffusion model, permeate concentrations increase at higher feed concentrations, which may limit to increase RO recovery. Interestingly, the bench-scale 2-pass RO test using five commercial membrane modules revealed permeate concentrations were not affected by feed concentrations below a critical value (35 mg/L as NaCl in this work). This means the first-pass RO recovery can be increased without decreasing permeate water quality as far as second-pass RO feed concentrations are below the critical values. Various 2-pass UPW-RO system designs (250 mg/L as NaCl of feed concentration) were analyzed using an industrial RO system simulator to investigate the effect of the first- and second-pass recovery. The simulation works found that increasing the first- and second-pass recovery decreased wastewater discharge, energy consumption, and the number of membrane modules without degrading permeate water quality.