Similarity Analysis Method Research Articles

Evolution of structural safety for tunnel arch crown linings under the coupled influence of void and insufficient thickness

The existence of voids behind tunnel linings constitutes a recognized inherent defect in tunnel engineering. Void and insufficient thickness can significantly compromise structural safety, especially at the tunnel arch crown. In this study, we employed similar model tests and numerical analysis methods to examine how the coupling of voids and insufficient thickness affects the structural performance of arch crown linings. Key findings reveal that the coupling of arch crown voids and insufficient thickness causes notable shifts in displacement patterns and internal force distribution. The displacement pattern of the arch shifts from the inner side to the outer side of the tunnel, resulting in the concrete stress state on the inner side of the arch crown changing to compression. Spatial distribution characteristics of voids and insufficient thickness defects were analyzed to assess their impact on structural degradation, with quantitative analysis using the safety factor index. Geometric parameters of voids and insufficient thickness (longitudinal length, thickness, circumferential range) were chosen based on analysis under various conditions to predict the structural safety factor using a Wide Neural Network regression model with 98 % accuracy. This method offers a new approach for rapidly predicting the safety status of lining structures. These findings are crucial for comprehending the effects of arch crown voids and insufficient thickness defects and for devising effective strategies to address tunnel lining defects.

SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion

- Accurate state of health (SOH) of batteries is a crucial prerequisite for ensuring the safety and stable operation of electric vehicles. However, existing conventional prediction methods do not consider the influence of early-stage capacity variations on full-cycle SOH with extensive test data. This paper develops a SOH early prediction method of lithium-ion batteries based on voltage interval selection and features fusion. To identify the battery SOH curves with high similarity under identical charge-discharge conditions, a double correlation based early-stage SOH similarity analysis method is presented. To minimize the amount of voltage training data collected during feature extraction, a voltage interval selection method considering sampling time and features correlation is introduced. Meanwhile, a feature fusion method combining entropy weight and correlation factor is used to reduce the impact of redundant health feature information. To address the problems of low accuracy and computational inefficiency using the least squares support vector machine (LSSVM) model, a grey wolf optimizer-LSSVM-adaptive boosting model is developed for battery SOH prediction. The experimental results show that the coefficients of determination remain above 0.98, indicating high SOH prediction accuracy using the developed method. Compared to other methods, the mean absolute errors based on the developed method are maintained below 1.5 %.

Improved similarity analysis of industrial alarm flood sequences by considering alarm correlations

Alarm floods are leading issues that compromise the efficiency of industrial alarm systems and are identified as major causes of many industrial accidents. As an advanced technique to handle alarm floods, sequence alignment based similarity analysis has been developed to match alarm flood sequences, and thus can help with further root cause identification and early warning of alarm floods. However, existing methods based on biological sequence alignment algorithms ignore the relations between alarm occurrences, and thus may cause incorrect matches or mismatches of alarms when comparing two flood sequences. Accordingly, this paper proposes a new alarm flood similarity analysis method based on global vectors and Move–Split–Merge (MSM) distance. The contributions are mainly twofold: (1) An alarm encoding model based on modified global vectors is devised to convert alarm sequences into numerical vectors that reflect the correlations of alarms; (2) a similarity analysis method based on the modified MSM distance is proposed for comparison of encoded alarm flood sequences of unequal lengths. The effectiveness of the proposed method is demonstrated through a case study with a publicly accessible industrial model for Vinyl Acetate Monomer.

Code Clones: A Novel Approach to Detecting Plagiarism in Binary Decomposition of C Programs

Abstract The escalation of code plagiarism in computer science education has necessitated the development of more sophisticated detection methods, particularly for binary files which pose a unique challenge. This study introduces a novel approach to plagiarism detection through binary decomposition, which decompiles binary files back into high-level code to reveal similarities that may be hidden at the binary level. The methodology is crucial for scenarios where students submit compiled programs, enabling the application of traditional code similarity analysis methods to detect instances of plagiarism. Statistical data on code similarity among student submissions in a C programming course are presented, highlighting the urgent need for robust detection strategies. This paper explores the ethical implications of plagiarism, the technical challenges of binary file analysis, and the potential for applying this technique across various programming languages and educational contexts.

Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction

Currently, building energy consumption prediction models are usually based on a large amount of historical operational data in high demands of building operating hours and monitoring systems. However, many buildings may lack operational data due to relatively limited monitoring systems, causing the failure to use data-driven methods to characterize the energy profile. In this context, transfer learning is a promising method to establish the knowledge transfer between many high-quality building operation datasets and a small amount of target building data, and to help predict energy consumption in the target building. This paper studies the possibility of employing transfer learning to achieve both short and long-term building energy consumption prediction. Firstly, a similarity analysis method, based on variable modal decomposition and dynamic time warping, is proposed for identifying the source buildings with the most similar energy features to the target building. Then, transfer learning for long-term prediction air-conditioning energy consumption is developed with weather parameters generated by the Morphing method as inputs. For the short-term single-step prediction, the proposed model CV-RMSE improves 81.3% (AEC) and 77.4% (EEC), respectively, compared to the prediction model that does not implement the transfer learning strategy and directly uses the target BEC data. As for the short-term multi-step prediction, the proposed model CV-RMSE improves 62.0% (AEC) and 65.5% (EEC), respectively. For the long-term prediction, the average CV-RMSE for the whole year is 6.62% and 11.15% for the proposed and directly target domain-based model, respectively. The proposed method explores the practicality of transfer learning in building energy forecasting, contributing to the use of existing building operation data for energy management at different timespan.

Similarity visualization of soundscapes in ecology and music

The concept of a soundscape is found in both ecology and music studies. Nature soundscapes and soundscape compositions are analyzed by both disciplines, respectively, to understand their biological diversity and ecosystem functioning and to interpret their compositional structure. A major challenge for both disciplines is visualizing the information embedded in a large variety of soundscapes and to share it with different audiences, from non-professionals to experts. To analyze soundscapes, both disciplines have independently developed similarity visualizations. However, no attempt has been made yet to combine these two fields of research to improve our ecological and musical perception of environmental sounds through shared similarity analysis methods. In this paper, we introduce a new visualization tool, the soundscape chord diagram (SCD), a circular similarity representation method that can be applied to any type of soundscape, either in ecoacoustics or electroacoustic studies. Our approach consists of visualizing spectral similarities between predefined sound segments based on the computation of a β-diversity acoustic index and on automatic clustering. SCDs were tested on two ecoacoustic forest databases and two electroacoustic soundscape compositions. SCDs were performant for the identification of specific acoustic events and highlighted known diel periods for nature soundscapes and written parts for soundscape compositions. This new visualization tool allows us to easily decipher the structure of musical and ecological acoustic data. SCDs could be applied to a large variety of soundscapes and promote their knowledge and preservation. This study opens a new way of investigating soundscapes at the interface between ecology and music, bringing together science and the arts.

QuadGridSIM: A quadrilateral grid‐based method for high‐performance and robust trajectory similarity analysis

AbstractMeasuring trajectory similarity is a fundamental algorithm in trajectory data mining, playing a key role in trajectory clustering, pattern mining, and classification, for instance. However, existing trajectory similarity measures based on vector representation have challenges in achieving both fast and accurate similarity measurements. On one hand, most existing methods have a high computational complexity of O(n × m), resulting in low efficiency. On the other hand, many of them are sensitive to trajectory sampling rates and lack of accuracy. This article proposes QuadGridSIM, a quadrilateral grid‐based method for trajectory similarity analysis, which enables high‐performance trajectory similarity measure without the cost of low effectiveness. Specifically, we first realize the multiscale coding representation of trajectory data based on quadrilateral discrete grids. Then, a novel trajectory similarity measure is defined to reduce the computational complexity of O(n). Several effectiveness properties of QuadGridSIM are further optimized, including the spatial overlap, directionality, symmetry, and robustness to sampling rate variations. Experimental results based on real‐world and simulated taxi trajectory data indicate that QuadGridSIM outperforms most of the other tested algorithms developed previously in terms of effectiveness, particularly in its robustness regarding trajectory sampling rates. Furthermore, QuadGridSIM exhibits superior performance and is approximately one order of magnitude faster than previous methods in the literature. QuadGridSIM provides a solution to the low‐efficiency problem of massive trajectory similarity analysis and can be applied in many application scenarios, such as route recommendation and suspect detection.

Molecular characteristics and antimicrobial susceptibility profiles of bovine mastitis agents in western Türkiye.

Identifying bovine mastitis agents using molecular methods to reveal their phylogenetic relationships and antimicrobial resistance profiles is essential for developing up-to-date databases in mastitis cases that cause severe economic losses. This study examined bacterial mastitis agents in cows with clinical and subclinical mastitis observed in various dairy cattle farms to reveal their phylogenetic relationships and antibiotic resistance properties. Sixty-two clinical and subclinical bovine mastitis milk samples were collected from 15 dairy farms. The polymerase chain reaction (PCR) was used to amplify the 16S rRNA gene regions of the bacteria. The 16S rRNA gene sequences obtained from sequencing include the V4-V6 regions. The strains were compared using a similarity analysis method that produced phylogenetic trees using the Molecular Evolutionary Genetics Analysis 11 program. Antibiotic susceptibilities were determined using the Kirby-Bauer disk diffusion method. Sixty-three bacteria were isolated and identified in this study. The most isolated bacteria from all mastitis cases were Staphylococcus spp. (30.2%), Escherichia coli (25.4%), Streptococcus spp. (14.3%), and Aerococcus spp. (7.9%), respectively. The phylogenetic trees were drawn from the 16S rRNA sequences. Some of these bacteria showed resistance to different types of antibiotics at varying rates. The bacteria isolated in this study originated from environmental sources. Regular cleaning of barns and proper hygiene practices are essential. Regular screenings for mastitis should be conducted in herds instead of the random or empirical use of antibiotics.

Electrophysiological representations of multivariate human emotion experience

ABSTRACT Despite the fact that human daily emotions are co-occurring by nature, most neuroscience studies have primarily adopted a univariate approach to identify the neural representation of emotion (emotion experience within a single emotion category) without adequate consideration of the co-occurrence of different emotions (emotion experience across different emotion categories simultaneously). To investigate the neural representations of multivariate emotion experience, this study employed the inter-situation representational similarity analysis (RSA) method. Researchers used an EEG dataset of 78 participants who watched 28 video clips and rated their experience on eight emotion categories. The EEG-based electrophysiological representation was extracted as the power spectral density (PSD) feature per channel in the five frequency bands. The inter-situation RSA method revealed significant correlations between the multivariate emotion experience ratings and PSD features in the Alpha and Beta bands, primarily over the frontal and parietal-occipital brain regions. The study found the identified EEG representations to be reliable with sufficient situations and participants. Moreover, through a series of ablation analyses, the inter-situation RSA further demonstrated the stability and specificity of the EEG representations for multivariate emotion experience. These findings highlight the importance of adopting a multivariate perspective for a comprehensive understanding of the neural representation of human emotion experience.

An algorithm for extracting similar segments of moving target trajectories based on shape matching

Trajectory similarity analysis of moving target is the foundation for mining high-value and regular behavioral information such as motion preferences, activity hotspots and frequent paths. Unlike most trajectory similarity analysis methods aimed at discovering correlations of target activities in time, space or spatio-temporal domains, this paper focuses on the shape matching of target trajectories. If some specific shapes frequently appear in historical trajectories, extracting these local shapes would be beneficial for analyzing the target motion templates and behavior modes. Trajectory segments with similar shapes may not have spatio-temporal correlation, and the shapes also have geometric transformation characteristics such as rotation, scaling and translation. Since the existing trajectory similarity analysis methods cannot be directly applied, an algorithm for extracting similar segments based on shape matching is proposed. First, a new shape descriptor based on signed barycenter distance (SBD) is established. It describes a trajectory as a one-dimensional shape feature sequence, which has the advantage of low computational complexity. Then, the distributed nearest neighbor search strategy is used in the particle swarm optimization (PSO) method, which aims to accelerate the retrieval of trajectory segments with similar shapes and improve the matching accuracy. Experiments on MPEG-7, handwritten character and maneuvering target simulation trajectory data sets show that compared with the existing typical shape descriptors, SBD shape descriptor has advantages in accuracy and noise insensitivity, and the improved PSO method can efficiently and accurately obtain the local shape matching results.

Music-emotion EEG coupling effects based on representational similarity

BackgroundMusic can evoke intense emotions and music emotion is a complex cognitive process. However, we know little about the cognitive mechanisms underlying these processes, and there are significant individual differences in the emotional responses to the same musical stimuli. New MethodWe used the inter-subject representational similarity analysis (IS-RSA) method to investigate the shared music emotion responses across multiple participants. In addition, we extended IS-RSA to estimate the group cross-frequency coupling effects of music emotion. Based on the cross-frequency coupling IS-RSA, we analyzed the differences in cross-frequency coupling patterns under different music emotions using MI. Comparison of existing methods: most current IS-RSA analyses focus on within-frequency band analysis. However, the cognitive processing of music emotion involves not only activation and brain network connections differences within frequency bands but also information communication between frequency bands. ResultsThe results of the within-frequency band IS-RSA analysis showed that the theta and gamma frequency bands play important roles in the inter-participant consistency of music emotion. The inter-frequency band IS-RSA analysis showed that the theta-beta coupling pattern exhibited stronger inter-participant consistency compared to the theta-gamma coupling pattern, and the theta-beta coupling had significant consistent representation across various music conditions. Through the significant regions of cross-frequency coupling representation similarity analysis, we performed phase-amplitude coupling analysis on FC4-C6 and FC4-Pz connections. For the theta-beta coupling pattern, we found that the MI of these two connections exhibited different coupling patterns under different music conditions, and they showed a significant decrease compared to the baseline period.

Similarity and vibration signal prediction of planetary gear transmission system based on hierarchy analysis

Large high-power gear transmission systems, especially complex gear systems, have problems such as high processing costs, long manufacturing cycles, and difficult testing, resulting in the inability to implement performance evaluation and structural optimization of gear systems. To solve this problem, the similarity and vibration signal prediction method of planetary gear transmission system (PGTS) is studied based on hierarchical analysis. The similarity relationship analysis is completed based on the similarity theory of complex systems. According to the hierarchical idea, the PGTS can be divided into subsystems at different levels from top to bottom, which can be divided into the component layer at least. The concepts of functional similarity and structural similarity are proposed, and the similarity conditions of each subsystem are formulated. Based on the equation analysis method, the dynamic similarity relationship of the PGTS is deduced. Furthermore, the parameter design and working condition design of the scale model is completed. The scale model can predict the time and frequency domain curves of each vibration signal of the prototype. Under different working conditions, the scale model can accurately predict the vibration signal and floating amount of the prototype. The similarity analysis method applicable to PGTSs has been verified.

Multitask fatigue crack recognition network based on task similarity analysis

For fatigue cracks in steel box girder bridges, comprehensive perception is urgent and practical since it provides diverse damage information. Multitask learning networks, which learn multiple tasks jointly, could realize various detection tasks simultaneously. However, multitask networks are hard to avoid task conflicts, thus limiting recognition performance. In this study, the representational similarity analysis method was introduced to quantify task similarity, which helps decouple the parameters of unrelated tasks. Considering task correlation, a multitask network called CrackFormer was developed to recognize the initiation location, repair scene, and region morphology of fatigue cracks. From the perspective of learning difficulty, multiple tasks were trained and coordinated dynamically. Finally, the trained model was evaluated by comparative tests. Results showed that task similarity analysis was verified to be reasonable by transfer learning test. Compared with isolated tasks, CrackFormer demonstrated superior accuracy, efficiency, and robustness by mutual complementation of task knowledge. After optimizing the parameter sharing design, detection performance was further improved.

Study on the Mechanism and Control of Strong Rock Pressure in Thick Coal Seam Mining under the Goaf of Very Close Multiple Coal Seams

With the increasing proportion of close-distance coal seam mining in China, the problem of strong mining pressure during the mining of close-distance coal seams is becoming more and more severe. This article focuses on the complex stress environment and severe mining pressure encountered in the mining of thick coal seams under the multi-coal-seam goaf of Zhunnan Coal Mine. By using research methods, such as similar material simulation, theoretical analysis, and numerical simulation, it studies in depth the instability characteristics of the overlying rock structure of the W1701 working face, the inducing factors and mechanisms of strong mining pressure during the mining process, and control measures. The results show that the roof structure of the W1701 working face can be divided into “high-level key layer (hard rock)–giant thick soft and weak rock group–low-level key layer (hard rock)”, and the law of mining pressure manifestation presents a small cycle formed by the instability of “masonry beam” structure and a main large cycle formed by the periodic penetration and step-down of the giant thick soft and weak rock group, with the load on the support during the large cycle up to 5.4 times the rated working resistance. In addition, this article proposes the strategy of using layered mining to control the manifestation of strong mining pressure under the “hard sandwiched soft” overlying rock condition of the Zhunnan Coal Mine, optimizes the thickness of the layered mining of the thick coal seam, and finally, determines the upper layer thickness of 2.8 m and the lower layer thickness of 4 m, inducing the giant thick soft and weak rock formation to undergo incremental damage and releasing the fracture energy incrementally, effectively controlling the manifestation threat of strong mining pressure in the mining of thick coal seams under the close-distance coal seam goaf. As the proportion of close-range coal seam mining increases in China, the problem of strong mining pressure during the mining of close-range coal seams becomes more severe. This article focuses on the complex stress environment and severe mining pressure in the mining of thick coal seams under multiple mined-out areas in the Zhunnan coal mine. Similar material simulation, theoretical analysis, and numerical simulation methods were used to conduct in-depth research on the unstable characteristics of the overlying rock structure of the W1701 working face, the causes and mechanisms of strong mining pressure during the mining process, and control measures. The results show that the roof structure of the W1701 working face can be divided into “high-level key layer (hard rock)–thick soft weak rock group–low-level key layer (hard rock).” The law of mining pressure manifestation presents small cycles of instability formed by “block beams” and main cycles of pressure formed by vertically cracked periodic penetration and step sinking of the thick soft weak rock group. Moreover, during the main cycle of pressure, the load-bearing capacity of the support is up to 5.4 times the rated working resistance. Furthermore, it is proposed to use hierarchical mining to control the manifestation of strong mining pressure in the “hard-inlaid soft” overlying rock condition of the Zhunnan coal mine and optimize the thickness of layered mining of thick coal seams. Ultimately, the upper layer thickness was determined as 2.8 m; the lower layer thickness was determined as 4 m, and the layered mining induced the thick soft weak rock group to undergo gradual damage and energy release, effectively controlling the threat of severe mining pressure during the mining of thick coal seams under the close-range coal seam mining.

A Novel Fast Approach for Protein Classification and Evolutionary Analysis

In this paper, we propose a new fast alignment-free method for protein sequence similarity and evolutionary analysis. First 20 natural amino acids are clustered into 6 groups based on their physicochemical properties, then a 12-dimensional vector is constructed based on the frequency and the average position of occurrence of amino acids in each reduced amino acid sequences. Finally, the Euclidean distance is used to measure the similarity and evolutionary distance between protein sequences. The test on three datasets shows that our method can cluster each protein sequence accurately, which illustrates the effective of our method.

Performance evaluation of deep learning architectures for load and temperature forecasting under dataset size constraints and seasonality

Buildings and their energy systems have unique characteristics and temporally changing dynamics. Additionally, building data have strong seasonality. Therefore, a deep learning model with good adaptability (i.e., high performance with little data) is necessary for model predictive control of buildings. Considering the specificity of the building-energy field, we benchmarked six deep learning architectures: the multilayer perceptron, simple recurrent neural network (RNN), long short-term memory, gated recurrent unit (GRU), dilated convolutional neural network, and transformer. Moreover, the data similarity analysis method was developed to analyze the effect of data seasonality on forecasting performance. A publicly accessible data generator and the open-source Python library DeepTimeSeries were used to design a reproducible benchmark. The zone temperatures and thermal loads over a future 24-h period were the forecasting targets. The benchmark results, with varying training dataset sizes from 0.3 to 0.9 y, demonstrated that the transformer architecture performs best, especially on small training datasets. The GRU and RNN were the second- and third-best performers, whereas the rank of other architectures varied depending on the training dataset size and forecasting targets. The similarity analysis revealed that simply increasing the dataset size does not necessarily improve performance, indicating the importance of training with highly similar data for the forecasting period.

Raman spectroscopy, an ideal tool for studying the physical properties and applications of metal-organic frameworks (MOFs).

Metal-organic frameworks (MOFs) are a unique family of materials constructed by coordinating metal ions or clusters to bridging organic ligands. Many of these materials are well known for their intricate structures, and exceptional gas adsorption properties, and have potential applications in the separation of alkanes, catalysis, energy storage, surface-enhanced Raman spectroscopy (SERS) based detections, and diagnostics. In situ or in operando Raman spectroscopic studies provide real-time information about the different processes and associated structural changes in MOFs. In the last few decades, there has been phenomenal growth in the publications on MOFs containing insights from Raman spectroscopy. Such studies have helped the research community in identifying the adsorption sites, defect sites, structural or spin transitions, reaction centers, intermediates, etc. In this review, we present the current research status of Raman spectroscopy in probing the structure, guest adsorption, catalytic activity, and reaction mechanisms of MOFs, and their application in energy storage and SERS detection. We highlight the advancements in the Raman spectroscopy technique that have facilitated in situ studies in atmosphere as well as various chemical environments. We briefly discuss the relevance of computational studies in understanding phonon modes and predicting the stability of MOFs. Although this review is particularly focussed on works related to Raman spectroscopy of MOFs, we do discuss infrared studies on MOFs, where such results or analyses are missing from the Raman studies. These discussions have been provided with the intent to develop similar analysis techniques or methods in Raman spectroscopy research.

Emergency management decision of urban rainstorm and flood disasters based on similar cases analysis

Under the background of global climate change and rapid development of urbanization, urban extreme occurs more frequently. In consideration of the existing problems in the emergency management decision-making of urban rainstorm and flood disasters in China, this study put forward the decision-making method of urban rainstorm and flood disaster emergency management based on similar cases analysis method. Based on the evolution process of urban waterlogging disaster, the problem attribute system of urban rainstorm and flood disasters was established. The case-based reasoning (CBR) method was used to calculate the global similarity to determine the best historical case. The case-based decision theory (CBDT) method was used to calculate the comprehensive utility value of alternative cases to determine the optimal alternative cases. The empirical analysis was took in Xi'an city, one of the national center cities of China. Results show that the CBR method was used to obtain the highest similarity of historical case P3, which is 0.481. The CBDT method was used to calculate the high similarity of historical cases P3 and P4 to form the similar case set, with the similarity of 0.820 and 0.851, respectively. Combined with the event development, the comprehensive utility values were calculated. When the decision-maker pays the same attention to the emergency effect and response cost, the comprehensive utility value of P3 and P4 is 0.922 and 0.900, respectively, and P3 is the best reference scheme. By comparison, the latter is more suitable. The results can provide scientific basis for emergency management of urban rainstorm and flood disasters.

Similarity Analysis Between SAR Target Images Based on Siamese Network

Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.

Green synthesis of chitosan-based membrane modified with uniformly micro-sizing selenium particles decorated graphene oxide for antibacterial application

In this study, selenium microparticles (SeMPs) were green-synthesized by utilizing the Terminalia catappa leaves extract as an effective reducing agent. SeMPs were then decorated onto graphene oxide (GO) with the assistance of ultrasound using the ex-situ technique to obtain the SeMPs-GO composite. SeMPs and SeMPs-GO were thoroughly characterized with modern analytical methods, whereas the antibacterial performance of the composites was evaluated via the optical density method. Particularly, SeMPs-GO held up an inhibition of 99 % against both Gram-positive and Gram-negative bacteria strains as well as restrained 50 % of fungal activity. SeMPs-GO was additionally incorporated onto chitosan (CTS) to collect the SeMPs-GO/CTS membrane which was characterized by similar advanced analysis methods. The antibacterial property of the membrane was determined by the inhibition zone diameter. Furthermore, the membrane exhibited good thermal and mechanical characteristics, showing no sign of degradation at a temperature below 260 °C, and a tensile strength of 38 N/mm2. The swelling degree reached 148 % after 6 h of immersion in water, which was stable after 72 h (153 %). The obtained membrane can potentially be utilized for medical and food applications.