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Decrypting and inspecting encrypted malicious communications may assist crime detection and prevention. Access to client or server memory enables the discovery of artefacts required for decrypting secure communications. This paper develops the MemDecrypt framework to investigate the discovery of encrypted artefacts in memory and applies the methodology to decrypting the secure communications of virtual machines. For Secure Shell, used for secure remote server management, file transfer, and tunnelling inter alia, MemDecrypt experiments rapidly yield AES-encrypted details for a live secure file transfer including remote user credentials, transmitted file name and file contents. Thus, MemDecrypt discovers cryptographic artefacts and quickly decrypts live SSH malicious communications including the detection and interception of data exfiltration of confidential data. [ABSTRACT FROM AUTHOR]

The Dirichlet multinomial mixtures mode was used to analyse illumina sequencing data to reveal both temporal and spatial variations of the fungi community present in the aerobic composting. Results showed that 670 operational taxonomic units (OTUs) were detected, and the dominant phylum was Ascomycota . There were four types of samples fungi communities during the composting process. Samples from the early composting stage were mainly grouped into type I and Saccharomycetales sp . was dominant. Fungi community in the medium composting stage were fallen into type II and III, Sordariales sp. and Acremonium alcalophilum , Saccharomycetales sp. and Scedosporium minutisporum were the dominant OTUs respectively. Samples from the late composting stage were mainly grouped into type IV and Scedosporium minutisporum was the dominant OTU; Scedosporium minutisporum was significantly affected by depth ( P < 0.05). Results indicate that time and depth both are factors that influence fungi distribution and variation in c waste during static aerobic composting. [ABSTRACT FROM AUTHOR]

The proliferation of malware has exhibited a substantial surge in both quantity and diversity, posing significant threats to the Internet and indispensable network applications. The accurate and effective classification makes a pivotal role in defending against malware. Numerous approaches employ supervised learning techniques, specifically Convolutional Neural Networks (CNNs), to train feature extractors. However, acquiring a substantial quantity of labeled samples incurs significant expenses, and relying solely on CNNs as feature extractors may result in restricted local receptive fields, consequently compromising the preservation of crucial features. In order to address these constraints, we propose an effective malware classification approach, denoted as MalSort, which leverages the masked self-supervised framework with Swin Transformer. Initially, each instance of malware is transformed into a color image. Furthermore, the Swin Transformer self-supervised framework is utilized to extract multi-scale key feature vectors from a randomly masked partial color image, while the prediction module is employed to predict the masked image. Ultimately, the pre-trained encoder is fine-tuned using the malware dataset to effectively carry out a malware classification task. Our MalSort exhibits a reduced reliance on labeled data samples during the training phase, thereby obviating the necessity for extensive amounts of labeled data. Consequently, the MalSort conserves hardware resources and improve its training efficiency. The experimental results indicate that the MalSort outperforms existing models by achieving a classification accuracy of 97.85%, a recall of 97.63%, a precision of 97.85%, and an F1-score of 97.85% on the BIG2015 dataset. Similarly, on the Malimg dataset, the model achieves percentages of 98.28%, 98.18%, 98.19%, and 98.28% for classification accuracy, recall, precision, and F1-score, respectively. • An asymmetric structure extracts features to handle labeled samples and feature loss. • The fine-tune incorporates class re-balancing to handle imbalance and overfitting. • A designed lightweight module improves convergence and decreases resource dependence. [ABSTRACT FROM AUTHOR]

• 5-Hydroxymethylfurfural(HMF) oxidation was carried out in aqueous phase. • Metal porphyrin as biomimetic oxidase catalyzed HMF conversion. • Excellent HMF conversion of 100% and FDCA yield of 94.94% were achieved. 2,5-Furandicarboxylic acid (FDCA) has the potential to replace terephthalic acid (TPA) as a building block in the preparation of bio-based and degradable polyester materials. However, the lack of efficient and stable catalysts limits the commercial viability of FDCA production. In this work, metalloporphyrins were immobilized on mesoporous SiO 2 and used as a novel oxidase to convert 5-hydroxymethylfurfural (HMF) to FDCA. As the catalytic active center of P450 monooxygenases and horseradish peroxidase, supported palladium, manganese, ferri, and cobalt porphyrins via ionic-liquid-functionalized SiO 2 (PdPOP-SiO 2 , MgPOP-SiO 2 , FePOP-SiO 2 and CoPOP-SiO 2) were able to simulate oxidase alone under mild conditions. 100% HMF (0.02 M) conversion and 94.94% FDCA yield were achieved using PdPOP-SiO 2 as a biomimetic oxidase after a 12 h reaction at 373 K in water at atmospheric pressure. This type of mimetic oxidase is stable, inexpensive, and readily available, and the method developed in this work can potentially be used to oxidize HMF to FDCA in an industrial setting. [ABSTRACT FROM AUTHOR]

• The 1D/2D MnO 2 /MXene heterostructures generate built-in electric fields that greatly accelerating the ionic and electronic diffusion/transport kinetics. • The synergistic mechanism generated by 1D/2D heterostructures achieve the balance between high energy storage activity and high active substance proportion of MnO 2 /MXene flexible electrode. • The quasi-solid-state FZIBs with MnO 2 /MXene paper-like film provide high total specific capacity and stability in flexible applications. Paper-like flexible electrode plays a critical importance role in booming the development of flexible energy storage devices. One-dimensional manganese dioxide (1D MnO 2) shows immense advantage in flexible zinc-ion batteries (FZIBs) on account of renowned tunnel crystal structure and high operating voltage. However, inherent inferior electrical conductivity and sluggish reaction kinetics seriously hinder the practical application of FZIBs, resulting in unsatisfactory rate capability and cycle performance. Herein, 1D/2D MnO 2 /MXene-5 heterostructure materials were constructed by electrostatic self-assembly, which were then prepared into paper-like membrane with high energy storage active substance ratio (95.2 %) towards high capacity and ultrastable FZIBs. The built-in electric field induced by the heterostructure accelerates the electron transfer, reduces the charge transfer resistance and promotes ion diffusion, enabling the 1D/2D phase collaboration to improve the zinc ion storage of the paper-like electrode. The design achieves a high total specific capacity (based on the total cathode mass) of 314.7 mAh g−1 at 0.1 A g−1 and ultrastable of 8000 cycles at 5 A g−1 with a retention of 81.1 %. Remarkably, quasi-solid-state FZIBs with MnO 2 /MXene-5 membrane can deliver an outstanding reversible capacity of 273.5 mAh g−1 at 0.1 A g−1 and maintain 500 cycles at 1 A g−1 across various bending angles without decay, demonstrating excellent stability. This work provides a novel approach for the targeted design of self-standing flexible electrodes. [Display omitted] [ABSTRACT FROM AUTHOR]

Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging to guarantee the global optimum. Besides, cumbersome structure and complex parameters setting-up make the existed algorithms hard for most users who are not specializing in NIC, to understand and use. To alleviate these limitations, this paper devises a succinct and efficient optimization algorithm called Nomad Algorithm (NA). It is inspired by the migratory behavior of nomadic tribes on the prairie. Extensive experiments are implemented with respects to accuracy, rate, stability, and cost of optimization. Mathematical proof is given to guarantee the global convergence, and the nonparametric tests are conducted to confirm the significance of experiment results. The statistical results of optimization accuracy denote NA outperforms its rivals for most cases (23/28) by orders of magnitude significantly. It is considered as a promising optimizer with excellent efficiency and adaptability. [ABSTRACT FROM AUTHOR]

A novel phage vB_VpP_DE17, which infects Vibrio parahaemolyticus , was isolated from the sewer of the Huangsha aquatic market in Guangzhou. Transmission electron microscopy indicated that DE17 had an icosahedral head (47 ± 2 nm diameter) and a short, non-contractile tail (17 ± 2 nm). The genome of DE17 was a double-stranded linear DNA with a length of 43,397 bp and GC content of 49.23%. In total, 49 putative open reading frames (ORFs) were predicted and could be divided into six modules: DNA metabolism, lysis, packaging, structure, additional function, and hypothetical proteins. Taxonomic analysis revealed that the phage belonging to the genus of Maculvirus, Autographivirinae subfamily , Podoviridae family. DE17 had a short latent period of 5 min with burst size of 80 pfu/cell. Its optimum temperature and pH ranges were 4 °C–50 °C and 5–10, respectively; it was completely inactivated after 20 min of ultraviolet irradiation. No transfer RNA (tRNA), virulence associated, or antibiotic resistance genes were identified. Bacterial challenge test revealed that DE17 had a certain inhibitory effect on V. parahaemolyticus within 6 h. Characterization, genomic analysis and in vitro antibacterial assays of DE17 will further enhance our understanding of phage biology and diversity. [ABSTRACT FROM AUTHOR]

There can be performance and vulnerability concerns with block ciphers, thus stream ciphers can used as an alternative. Although many symmetric key stream ciphers are fairly resistant to side-channel attacks, cryptographic artefacts may exist in memory. This paper identifies a significant vulnerability within OpenSSH and OpenSSL and which involves the discovery of cryptographic artefacts used within the ChaCha20 cipher. This can allow for the cracking of tunneled data using a single targeted memory extraction. With this, law enforcement agencies and/or malicious agents could use the vulnerability to take copies of the encryption keys used for each tunnelled connection. The user of a virtual machine would not be alerted to the capturing of the encryption key, as the method runs from an extraction of the running memory. Methods of mitigation include making cryptographic artefacts difficult to discover and limiting memory access. [ABSTRACT FROM AUTHOR]