Update Scheme Research Articles

Introduction of Artificial Intelligence Technologies in Russian Economy: A Practitioner’s View

The article presents the results of a generalization of the practical activities of companies developing and/or implementing artificial intelligence technologies (hereinafter referred to as AI), as well as companies that use these technologies: what scenarios for the use of AI technologies exist and in which industries, what kind of problems organizations implementing AI face, how members of the expert community of the artificial intelligence sphere consider solving these issues and what the state bodies offer. As to the state’s policy regarding the development of artificial intelligence, the article contains information about the factors built in the updated national AI development strategy, reflects the relations between AI technologies and state sovereignty, demonstrates the impact of artificial intelligence on the competitiveness of a company and human creativity. The article also presents the main instructions of the government of the Russian Federation on the development of artificial intelligence, some statistical data on the use of AI in economic and social sectors. It identifies measures to support developers and “implementers” of AI technologies offered by development institutions within the framework of the federal project “Artificial Intelligence” as a part of the national program “Digital Economy of the Russian Federation”. Much attention is paid to the issue of human resourcing in the AI sphere – what kind of specialists companies need, what their level of training should be and what they should be able to do, what requirements are imposed on AI teachers, what leading companies ask applicants for interviews and what the trajectory of “growing” talents in the field of artificial intelligence is. The final part of the article provides recommendations to students on how to prepare for the widespread use of artificial intelligence technologies.

FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy

Background and ObjectiveLung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. MethodsIn this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors. To be specific, we propose a dynamically updated algorithm to deal with model parameter aggregation in FSSL, which takes advantage of both the quality and quantity of client data. Moreover, to increase the accessibility of data in the federated learning (FL) network, we explore the FAIR data principle while the previous federated methods never involve. ResultThe experimental results show that the segmentation performance of our model in six centers is 0.9348, 0.8436, 0.8328, 0.7776, 0.8870 and 0.8460 respectively, which is superior to traditional deep learning methods and recent federated semi-supervised learning methods. ConclusionThe experimental results demonstrate that our method is superior to the existing FSSL methods. In addition, our proposed dynamic update strategy effectively utilizes the quality and quantity information of client data and shows efficiency in lung tumor segmentation. The source code is released on (https://github.com/GDPHMediaLab/FedDUS).

Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction

The visual prompts have provided an efficient manner in addressing visual cross-domain problems. Previous works introduce domain prompts to tackle the classification Test-Time Adaptation (TTA) problem by placing image-level prompts on the input and fine-tuning prompts for each target domain. However, since the image-level prompts mask out continuous spatial details in the prompt-allocated region, it will suffer from inaccurate contextual information and limited domain knowledge extraction, particularly when dealing with dense prediction TTA problems. To overcome these challenges, we propose a novel Sparse Visual Domain Prompts (SVDP) approach, which applies minimal trainable parameters (e.g., 0.1%) to pixels across the entire image and reserves more spatial information of the input. To better apply SVDP in extracting domain-specific knowledge, we introduce the Domain Prompt Placement (DPP) method to adaptively allocates trainable parameters of SVDP on the pixels with large distribution shifts. Furthermore, recognizing that each target domain sample exhibits a unique domain shift, we design Domain Prompt Updating (DPU) strategy to optimize prompt parameters differently for each sample, facilitating efficient adaptation to the target domain. Extensive experiments were conducted on widely-used TTA and continual TTA benchmarks, and our proposed method achieves state-of-the-art performance in both semantic segmentation and depth estimation tasks.

PPO-Clip Attains Global Optimality: Towards Deeper Understandings of Clipping

Proximal Policy Optimization algorithm employing a clipped surrogate objective (PPO-Clip) is a prominent exemplar of the policy optimization methods. However, despite its remarkable empirical success, PPO-Clip lacks theoretical substantiation to date. In this paper, we contribute to the field by establishing the first global convergence results of a PPO-Clip variant in both tabular and neural function approximation settings. Our findings highlight the O(1/√T ) min-iterate convergence rate specifically in the context of neural function approximation. We tackle the inherent challenges in analyzing PPO-Clip through three central concepts: (i) We introduce a generalized version of the PPO-Clip objective, illuminated by its connection with the hinge loss. (ii) Employing entropic mirror descent, we establish asymptotic convergence for tabular PPO-Clip with direct policy parameterization. (iii) Inspired by the tabular analysis, we streamline convergence analysis by introducing a two-step policy improvement approach. This decouples policy search from complex neural policy parameterization using a regression-based update scheme. Furthermore, we gain deeper insights into the efficacy of PPO-Clip by interpreting these generalized objectives. Our theoretical findings also mark the first characterization of the influence of the clipping mechanism on PPO-Clip convergence. Importantly, the clipping range affects only the pre-constant of the convergence rate.

An active learning Kriging model with approximating parallel strategy for structural reliability analysis

With the ever-increasing complexity of engineering problems, active learning functions fused with Kriging models are receiving significant attention and are extensively applied in various reliability analysis methods. It is argued unfavorably that the existing fusion-type methods usually obtain only a single optimal sample point during model updating process. Meanwhile, the information of the used sample points is not sufficiently utilized. To these gaps, this study proposes an adaptive Kriging reliability analysis method based on an approximate parallel computing strategy, namely AP-AK for short. To start with, a new Kriging model update strategy is proposed, where the first selected sample points help to introduce the intermediate model and further search for optimal points closer to the limit state surface. An improved learning function is then proposed to limit the locations of points based on the information of used sample points. The new function is characterized by setting the acceptance and rejection domains of the sample points, which prevents computational waste incurred by local sample point aggregation. Superior performance of the AP-AK method is demonstrated against other Kriging-based methods through five numerical cases and one solid attitude orbit control engine case. The comparison results show that the proposed method exhibits high efficiency and robustness for solving complex reliability analysis problems.

Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction

Asynchronous federated learning (AFL) is a distributed machine learning technique that allows multiple devices to collaboratively train deep learning models without sharing local data. However, AFL suffers from low efficiency due to poor client model training quality and slow server model convergence speed, which are a result of the heterogeneous nature of both data and devices. To address these issues, we propose Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction (FedAC). Our framework consists of three key components. The first component is client weight evaluation based on temporal gradient, which evaluates the client weight based on the similarity between the client and server update directions. The second component is adaptive server update with prospective weighted momentum, which uses an asynchronous buffered update strategy and a prospective weighted momentum with adaptive learning rate to update the global model in server. The last component is client update with fine-grained gradient correction, which introduces a fine-grained gradient correction term to mitigate the client drift and correct the client stochastic gradient. We conduct experiments on real and synthetic datasets, and compare with existing federated learning methods. Experimental results demonstrate effective improvements in model training efficiency and AFL performance by our framework.

GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework

Particle-based Variational Inference (ParVI) methods approximate the target distribution by iteratively evolving finite weighted particle systems. Recent advances of ParVI methods reveal the benefits of accelerated position update strategies and dynamic weight adjustment approaches. In this paper, we propose the first ParVI framework that possesses both accelerated position update and dynamical weight adjustment simultaneously, named the General Accelerated Dynamic-Weight Particle-based Variational Inference (GAD-PVI) framework. Generally, GAD-PVI simulates the semi-Hamiltonian gradient flow on a novel Information-Fisher-Rao space, which yields an additional decrease on the local functional dissipation. GAD-PVI is compatible with different dissimilarity functionals and associated smoothing approaches under three information metrics. Experiments on both synthetic and real-world data demonstrate the faster convergence and reduced approximation error of GAD-PVI methods over the state-of-the-art.

Generalizable Fourier Augmentation for Unsupervised Video Object Segmentation

The performance of existing unsupervised video object segmentation methods typically suffers from severe performance degradation on test videos when tested in out-of-distribution scenarios. The primary reason is that the test data in real- world may not follow the independent and identically distribution (i.i.d.) assumption, leading to domain shift. In this paper, we propose a generalizable fourier augmentation method during training to improve the generalization ability of the model. To achieve this, we perform Fast Fourier Transform (FFT) over the intermediate spatial domain features in each layer to yield corresponding frequency representations, including amplitude components (encoding scene-aware styles such as texture, color, contrast of the scene) and phase components (encoding rich semantics). We produce a variety of style features via Gaussian sampling to augment the training data, thereby improving the generalization capability of the model. To further improve the cross-domain generalization performance of the model, we design a phase feature update strategy via exponential moving average using phase features from past frames in an online update manner, which could help the model to learn cross-domain-invariant features. Extensive experiments show that our proposed method achieves the state-of-the-art performance on popular benchmarks.

ODTrack: Online Dense Temporal Token Learning for Visual Tracking

Online contextual reasoning and association across consecutive video frames are critical to perceive instances in visual tracking. However, most current top-performing trackers persistently lean on sparse temporal relationships between reference and search frames via an offline mode. Consequently, they can only interact independently within each image-pair and establish limited temporal correlations. To alleviate the above problem, we propose a simple, flexible and effective video-level tracking pipeline, named ODTrack, which densely associates the contextual relationships of video frames in an online token propagation manner. ODTrack receives video frames of arbitrary length to capture the spatio-temporal trajectory relationships of an instance, and compresses the discrimination features (localization information) of a target into a token sequence to achieve frame-to-frame association. This new solution brings the following benefits: 1) the purified token sequences can serve as prompts for the inference in the next video frame, whereby past information is leveraged to guide future inference; 2) the complex online update strategies are effectively avoided by the iterative propagation of token sequences, and thus we can achieve more efficient model representation and computation. ODTrack achieves a new SOTA performance on seven benchmarks, while running at real-time speed. Code and models are available at https://github.com/GXNU-ZhongLab/ODTrack.

Abstract 5515: Low body mass index (BMI) induces neuronal NPY and promotes brain metastasis of lung cancer

Abstract Brain metastases (BrMs) are a common occurrence in lung cancer with a dismal outcome. To develop a more effective therapeutic strategy, it is crucial to understand the underlying pathological mechanism of brain metastasis. Recent clinical data has unveiled a significant association between low body mass index (BMI) and an increased risk of brain metastasis in lung cancer patients, leading to reduced survival rates compared to other primary tumors. However, the molecular mechanism underlying this paradoxical relationship, often referred to as the "obesity paradox," in the context of lung cancer mortality presents a complex and intriguing puzzle and warrants in-depth investigation. Here, we examined biological mechanism of how low BMI promotes lung cancer brain metastasis. In this study, we conducted a retrospective pan-analysis of 5,516 patient cohorts with brain metastasis originating from lung, breast, melanoma, and renal cancers with their BMI status. We found that lung cancer patients with low BMI have significantly higher brain metastatic incidence compared to the high BMI patients, in contrast to breast, melanoma, and renal cancers. Furthermore, we found that low BMI plays a pivotal role in mediating neuron activation within the brain and that the secretion of neuronal neuropeptide Y (NPY), a protein involved in appetite and energy homeostasis via GHSR-receptor manner, promotes metabolic switching through NPY/NPY5R axis in the tumor cells in the brain thereby enabling metastasis. Elevated levels of neuronal NPY in the brains of cancer-free subjects with low BMI suggest its potential utility as a promising prognostic biomarker for identifying the increased risk of metastatic disease in lung cancer patients with low BMI. We also demonstrated that reversing low BMI or blocking the NPY/Y5R interactions effectively abrogated brain metastasis in our animal model. Our findings suggest a novel pro-metastatic role of low BMI-induced NPY in the progression of brain metastasis. We emphasize the importance of elucidating the clinical implications of this relationship to develop an updated intervention strategy for the clinical care of patients with lung cancer and low BMI. Citation Format: Abhishek Tyagi, Shih-Ying Wu, Ravindra Deshpande, Kerui Wu, Liang Liu, Kounosuke Watabe. Low body mass index (BMI) induces neuronal NPY and promotes brain metastasis of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5515.

Optimization for cost-effective design of water distribution networks: a comprehensive learning approach

The Comprehensive Learning Gravitational Search Algorithm (CLGSA) has demonstrated its effectiveness in solving continuous optimization problems. In this research, we extended the CLGSA to tackle NP-hard combinatorial problems and introduced the Discrete Comprehensive Learning Gravitational Search Algorithm (D-CLGSA). The D-CLGSA framework incorporated a refined position and velocity update scheme tailored for discrete problems. To evaluate the algorithm's efficiency, we conducted two sets of experiments. Firstly, we assessed its performance on a diverse range of 24 benchmarks encompassing unimodal, multimodal, composite, and special discrete functions. Secondly, we applied the D-CLGSA to a practical optimization problem involving water distribution network planning and management. The D-CLGSA model was coupled with the hydraulic simulation solver EPANET to identify the optimal design for the water distribution network, aiming for cost-effectiveness. We evaluated the model's performance on six distribution networks, namely Two-loop network, Hanoi network, New-York City network, GoYang network, BakRyun network, and Balerma network. The results of our study were promising, surpassing previous studies in the field. Consequently, the D-CLGSA model holds great potential as an optimizer for economically and reliably planning and managing water networks.

Diversity-guided particle swarm optimization with multi-level learning strategy

Particle swarm optimization (termed as PSO) is a metaheuristic algorithm inspired by the swarm intelligence. Since its advent, PSO has been successfully applied to tackle various issues that are hard to optimize. Unfortunately, similar to other evolutionary computation, PSO is also difficult to get rid of the bad luck of premature convergence and local optimization, especially when dealing with complex multimodal issues. Hence, this work proposes a diversity-guided PSO with multi-level learning strategy (DPSO-MLS). To begin with, chaotic opposition-based learning (OBL) is used to generate well-distributed initial particles to speed up convergence process of DPSO-MLS. Subsequently, according to the current swarm diversity, the high-layer learning mechanism enables PSO to explore the entire search space from a global perspective via attractive and repulsive strategies, respectively. Next, according to average fitness value of entire swarm, the low-layer learning strategy fine-tunes the particles search from a local perspective to sustain its diversity. To be specific, in the context of the repulsion phase, the regular varying function (RVF) embedded update strategy is used to block potential local optima so as to continue exploring the potential search space when the particle's fitness is less than the average of swarm, otherwise an alternative mutation scheme is utilized to enrich the swarm diversity. Accordingly, the slowly varying function (SVF) embedded update strategy is applied when the particle's fitness is greater than the average of swarm, otherwise the worst-best example learning based mechanism is exploited to update the worst particle to improve the quality of swarm in the context of the attraction phase. To verify the effectiveness of DPSO-MLS, extensive experiments are conducted and the results show that our proposal is superior or highly competitive to several existing PSOs according to the robustness, convergence rate and solution accuracy.

An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles

Due to the complex variations in slope within deep-sea mining areas, effective path planning for mining vehicle operations is crucial for minimizing energy consumption. However, traditional ant colony algorithms (ACO) neglect the effect of a terrain slope in mining areas. Additionally, these algorithms exhibit limitations such as slow convergence and susceptibility to local optima. To address these issues, this study proposes an enhanced ant colony algorithm, called DYACO, for mining vehicle path optimization. This algorithm dynamically adjusts heuristic information, pheromone volatilization factor, pheromone update strategy, and state transition probability during the iterative process to enhance traditional ACO. Simulation experiments were conducted to comprehensively assess the proposed model, revealing that DYACO not only generates optimal solutions but also demonstrates significant advantages in terms of convergence speed and turning times. Furthermore, DYACO converts the time required for mining vehicles to traverse different slope regions into distances, then incorporating slope effects to path planning for deep-sea mining vehicles. In comparison to ACO, DYACO achieves a 15.3% reduction in the length of an optimal path and a 70.0% decrease in the number of turn times.

Influence of updated isoprene oxidation mechanisms on the formation of intermediate and secondary products in MCM v3.3.1

In this study, we developed an observation based photochemical box model with the measurement data collected at a suburban site (i.e., the Xianghe, XH site) of the Beijing-Tianjin-Hebei (BTH) region and the latest version of the Master Chemical Mechanism (MCM v3.3.1), which newly incorporated 498 reaction for the additional 155 species with the oxidation of isoprene by the OH radical. The influence of updated isoprene degradation on the radical budgets, formation of intermediate and secondary products by comparing the simulation results in the original version (MCM v3.2) was firstly analyzed to estimate whether the updates of precursors could influence the model performance on its evolution pathways and related products. The results demonstrated that though the input of isoprene could significantly decrease or increase the concentrations of OH and HO2 radicals in the model simulation, respectively, the difference in the simulated concentrations of these radicals with isoprene oxidation before and after the updates was not statistically significant. For intermediate products, the simulated concentrations of formaldehyde and methacrolein (MACR) did not significantly alter before and after the updates, while the abundance of methyl vinyl ketone (MVK) reduced obviously (the maximum reduction of 31%) due to lower reaction coefficient between ISOPBO2 and NO and the lower branching ratios caused by the newly added pathways involving CISOPA and TISOPA in the updated scheme. On the other hand, the simulation concentrations of acrolein (ACR), glyoxal (Gly) and methylglyoxal (Mgly) increased significantly after the model updates. The dominant pathway for the increment of ACR was the decomposition of CH2CHCH2O produced by PE4E2CO, while those of Gly and Mgly were associated with radicals of C537O and PACLOOA that were both from the new intermediate radical of CISOPC. In addition, 39 isoprene oxidation products among the 155 new added species were identified to partition in the formation of secondary organic aerosol (SOA), which were coupled in the gas-particle partitioning module to optimize the formation of SOA, contributing 1.15 μg m−3 to the increase of SOA mean concentration. The updates of isoprene degradation further resulted in the significant change for its sensitivity in SOA formation, making it as the secondary important SOA precursor at the XH site. Last but not the least, the influence on photochemical ozone formation related to updates of isoprene degradation was ignorable. Overall, the above results confirmed the insufficient description of the degradation of precursors indeed limited the simulation of the intermediate and secondary products, highlighting the needs of exploring and parameterizing the precursor degradation processes for better understanding of their behaviors and SOA formation in the atmosphere.

Data-driven ensemble optimal compensation control for partially known delayed and persistently disturbed nonlinear systems

In almost all industrial processes, the development of an accurate mathematical model that comprehensively characterizes the regulated system poses a substantial obstacle. Diverse factors, such as unidentified system components and time-varying external disturbances, contribute to this phenomenon. This can result in a disparity between the model and the plant and thereby compromising the performance of the designed controller. In this study, a novel data-driven ensemble optimal compensation control framework is proposed for partially known nonlinear systems with state delays and structural disturbances. Initially, the estimated system model is leveraged to construct a robust fuzzy model predictive controller (RFMPC) based on the Lyapunov–Krasovskii functional (LKF), ensuring the input-to-state stability (ISS) of the plant. Then, a reinforcement learning (RL)-based compensator is assembled to gradually minimize the model-plant mismatch across the entire state space. To streamline the training process of the compensator and facilitate its stability, a dynamic network update scheme is devised, incorporating a moving average gradient calculation method and a learning rate tuning strategy. Furthermore, comparative discussions with some prevalent methods are demonstrated. With the established control framework, the performance of the RFMPC can be securely enhanced, and convergence is ensured within a narrow proximity to equilibrium, despite time-varying perturbation dynamics. By integrating this approach with deep learning (DL) methods, the efficacy of numerous industrial processes could be significantly improved. A numerical simulation and a discretized continuous-time stirring tank reactor (CSTR) example validate the effectiveness and benefits of the proposed control strategy.

Extracting random forest features with improved adaptive particle swarm optimization for industrial robot fault diagnosis

Feature extraction is a vital step for the fault diagnosis of industrial robots, while large-scale measured signals produce redundant features impairing the diagnosis performance. To address this problem, an improved adaptive particle swarm optimization (IAPSO) is suggested to extract effective features for random forest (RF) diagnosis. Raw data collected under different kinds of complex conditions are first represented by statistical parameters of its wavelet coefficients. A relative permutation order based scaling method with analytic hierarchy process is then used for selecting suitable updated strategies. RF is finally used to measure classification performance of each particle. The proposed method was evaluated by experiments on an industrial robot. Feature set was reduced 52 % from the initial size by using IAPSO, still achieving a superior classification precision over 96 %. The proposed method performs better than other peer methods and exhibits an essential improvement potential for the fault diagnosis of industrial robots.

A novel automatic detection and classification algorithm for elderly cognitive impairment using CSVM

Alzheimer’s disease (AD) is a degenerative disease with insidious onset and chronic nervous system, which can be roughly classified into three stages: preclinical AD, mild cognitive impairment (MCI), and clinical AD. Given that timely treatment of MCI can delay the course of AD, identifying what stage of the disease is at the time of early diagnosis is critical in clinical practice. Based on the survey data of elderly residents in Changsha community, this paper constructs the cognitive impairment data set and explores the application of the emerging classification algorithm Convolutional Support Vector Machine(CSVM) in identifying elderly cognitive impairment, a small data classification problem. CSVM is an improved Support Vector Machine classification(SVC) algorithm, which uses the convolution filtering idea of the Convolutional Neural Networks(CNN) algorithm to preprocess the nonlinearly separable sample set to improve the classification ability of SVM. In addition, the optimal convolution filter (solution) is found by the Simplified Swarm Optimization(SSO) algorithm and orthogonal array experimental design. A variable update amplitude method is also proposed to optimize the SSO update strategy further. Aiming at the problem of an unbalanced sample set, a stratified sampling method is used to divide and cross-validate the sample data, and a variety of evaluation indicators are used to evaluate the performance of the model. Experimental results show that the data set processed by a specific convolution filter can significantly improve the classification performance of linear SVM. The accurate differentiation of normal cognition(NC), MCI, and AD helps intervene in adjuvant treatment for potential patients timely.

DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies

MotivationThe prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy.ResultsAccurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

Consensus statement on the diagnosis and treatment of sclerosing diseases of the skin, Part 2: Scleromyxoedema and scleroedema.

The term 'sclerosing diseases of the skin' comprises specific dermatological entities, which have fibrotic changes of the skin in common. These diseases mostly manifest in different clinical subtypes according to cutaneous and extracutaneous involvement and can sometimes be difficult to distinguish from each other. The present consensus provides an update to the 2017 European Dermatology Forum Guidelines, focusing on characteristic clinical and histopathological features, diagnostic scores and the serum autoantibodies most useful for differential diagnosis. In addition, updated strategies for the first- and advanced-line therapy of sclerosing skin diseases are addressed in detail. Part 2 of this consensus provides clinicians with an overview of the diagnosis and treatment of scleromyxoedema and scleroedema (of Buschke).

Consensus statement on the diagnosis and treatment of sclerosing diseases of the skin, Part 1: Localized scleroderma, systemic sclerosis and overlap syndromes.

The term 'sclerosing diseases of the skin' comprises specific dermatological entities, which have fibrotic changes of the skin in common. These diseases mostly manifest in different clinical subtypes according to cutaneous and extracutaneous involvement and can sometimes be difficult to distinguish from each other. The present consensus provides an update to the 2017 European Dermatology Forum Guidelines, focusing on characteristic clinical and histopathological features, diagnostic scores and the serum autoantibodies most useful for differential diagnosis. In addition, updated strategies for the first- and advanced-line therapy of sclerosing skin diseases are addressed in detail. Part 1 of this consensus provides clinicians with an overview of the diagnosis and treatment of localized scleroderma (morphea), and systemic sclerosis including overlap syndromes.