AI-based approaches for predicting buried pipeline stability in cohesive-frictional soil under inclined forces
Peer-Reviewed Publication
Updates every hour. Last Updated: 11-Jul-2025 04:11 ET (11-Jul-2025 08:11 GMT/UTC)
To address the limitations of conventional energy systems and optimize the energy conversion pathways and efficiency, a type of “five-in-one” multifunctional phase-change composite with magnetothermal, electrothermal, solar-thermal, and thermoelectric energy conversion and electromagnetic shielding functions is developed for multipurpose applications. Such a novel phase-change composite is fabricated by an innovative combination of paraffin wax (PW) as a phase-change material and a carbonized polyimide/Kevlar/graphene oxide@ZIF-67 complex aerogel as a supporting material. The carbonized complex aerogel exhibits a unique bidirectional porous structure with high porosity and robust skeleton to support the loading of PW. The reduced graphene oxide and CoNC resulting from high-temperature carbonization are anchored on the aerogel skeleton to generate high thermal conduction and magnetic effect, enhancing the phonon and electron transfer of the aerogel and improving its energy conversion efficiency. The phase-change composite not only exhibits excellent solar-thermal, thermoelectric, electrothermal, and magnetothermal energy conversion performance, but also achieves high electromagnetic interference shielding effectiveness of 66.2 dB in the X-band. The introduction of PW significantly improves the thermal energy-storage capacity during multi-energy conversion. The developed composite exhibits great application potential for efficient solar energy utilization, sustainable power generation, outdoor deicing, human thermal therapy, and electronic device protection.
High-performance Ti3C2Tx fibers have garnered significant potential for smart fibers enabled fabrics. Nonetheless, a major challenge hindering their widespread use is the lack of strong interlayer interactions between Ti3C2Tx nanosheets within fibers, which restricts their properties. Herein, a versatile strategy is proposed to construct wet-spun Ti3C2Tx fibers, in which trace amounts of borate form strong interlayer crosslinking between Ti3C2Tx nanosheets to significantly enhance interactions as supported by density functional theory calculations, thereby reducing interlayer spacing, diminishing microscopic voids and promoting orientation of the nanosheets. The resultant Ti3C2Tx fibers exhibit exceptional electrical conductivity of 7781 S cm-1 and mechanical properties, including tensile strength of 188.72 MPa and Young’s modulus of 52.42 GPa. Notably, employing equilibrium molecular dynamics simulations, finite element analysis, and cross-wire geometry method, it is revealed that such crosslinking also effectively lowers interfacial thermal resistance and ultimately elevates thermal conductivity of Ti3C2Tx fibers to 13 W m-1 K-1, marking the first systematic study on thermal conductivity of Ti3C2Tx fibers. The simple and efficient interlayer crosslinking enhancement strategy not only enables the construction of thermal conductivity Ti3C2Tx fibers with high electrical conductivity for smart textiles, but also offers a scalable approach for assembling other nanomaterials into multifunctional fibers.
Researchers at Harbin Institute of Technology and Singapore Management University have developed LR-GCN, an advanced AI method that significantly improves how artificial intelligence handles incomplete data. By learning to recognize hidden patterns and connections, LR-GCN increases AI accuracy by up to 17% in predicting missing information, helping AI systems make better decisions in real-world scenarios.
Dynamic-EC, developed by researchers at Shanghai Jiao Tong University, introduces a smarter approach to blockchain storage by using real-time risk assessment to drastically lower costs and improve performance.
Abstract
Purpose – This paper originally proposed the fuzzy option pricing method for green bonds. Based on the requirements of arbitrage equilibrium, this paper draws on Merton's corporate bond option pricing model.
Design/methodology/approach – Describing the asset value behavior of green bond issuing enterprises through diffusion-jump processes to reflect the uncertainty brought by carbon emission reduction policies and technologies, using approximation methods to get the analytical pricing formula and then, using a fuzzification technique of Choquet expectation under λ-additive fuzzy measures after considering fuzzy factors, the paper provides fuzzy intervals for the parity coupon rates of green bonds with different subjective levels for investors.
Findings – The paper proposes and argues the classical and fuzzy option pricing methods in turn for both corporate ordinary bonds and green bonds, considering carbon risk or climate risk. It implements the scenario analysis varying with industry emission standards and discusses the sensitiveness of the related key parameters of the option.
Practical implications – The fuzzy option pricing for the green bonds provides the scope of the variable equilibrium values, operational theoretical supports and some policy implications of carbon reduction and promoting green funding.
Originality/value – The logic of introducing the fuzziness of the option pricing for the green bonds lies with considering the existence of fuzzy information about the project supported by the green bond and the subjectivity of investors and it also responds to changes in technological uncertainty and policy uncertainty in the process of “carbon peaking and carbon neutrality.”
Laboratory medicine is an essential part of the diagnostic process, supporting clinical decisions, guiding and addressing therapy. The recent COVID-19 pandemic illustrated well the key role of laboratory medicine in the diagnosis, management and prognosis of SARS-CoV-2 infected patients. Technological advances improved the laboratory diagnosis and patients’ management and others appear very promising as clustered regularly interspaced short palindromic repeats (CRISPR) or artificial intelligence (AI). This review describes the current diagnostic assays routinely used in laboratory as well as the novel technologies not in routine yet but that represent future directions and will probably dominate the laboratory in the next years. Serology is important for detecting antibodies and/or antigens of the infectious pathogens or for epidemiological purposes, while real-time PCR with its high sensitivity and specificity has a key role in pathogen detection in different biological matrices and in monitoring the therapy. Nanochip-based technologies make possible delivering a laboratory report at the patient’s bed or in settings where a laboratory-based hospital is not available. Next generation sequencing (NGS) is a massively high throughput parallel sequencing technology that allows the simultaneous sequence of billions of DNA fragments in a short time frame. This technology can be used to detect drug-associated mutations, minority species within an infected patient or for pathogen identification. CRISPR-based technology is a fast and accurate diagnostic method that can be applied to different human diseases including infectious diseases. Artificial intelligence is increasingly used in laboratory medicine. In clinical microbiology, it is used to build up diagnosis analyzing genomic information or mass spectra from isolated bacteria, for predicting antibiotic sensitivity or for processing in a short time a large number of images with meaningful results. Thus, the laboratory is becoming increasingly automated and interwoven with sophisticated software or algorithms that will increase the sensitivity and specificity of diagnoses, besides reducing time to results.
Mass spectrometry (MS) is an analytical technique for molecular identification and characterization, with applications spanning various scientific disciplines. Despite its significance, MS faces challenges in widespread adoption due to cost constraints, instrument portability issues, and complex sample handling requirements. In recent years, 3D printing has emerged as a technology across industries due to its cost-effectiveness, customization capabilities, and rapid prototyping features. This review explores the integration of 3D printing with MS technology to overcome existing limitations and enhance biomedical analysis capabilities. We first categorize mainstream 3D printing methods and assess their potential in MS applications. We also discuss their roles in different MS categories such as liquid chromatography mass spectrometry (LCMS), gas chromatography mass spectrometry (GCMS), ambient ionization mass spectrometry (AIMS), and matrix-assisted laser desorption/ionization MS (MALDI MS) in biomedical research. Additionally, we highlight the current challenges and future research directions for advancing 3D printing-assisted mass spectrometry, emphasizing its role in enabling portable, cost-effective, and customized MS solutions for biomedical analysis.
In a paper published in Molecular Biomedicine, researchers Xia Peng and Juan Du present a comprehensive review of lysine lactylation (Kla), a post-translational modification discovered in 2019. The study highlights Kla's role in linking cellular metabolism to epigenetic and signaling pathways by transferring a lactyl moiety to lysine residues, regulated by enzymes (writers/erasers) and metabolites like lactate, affecting both histone (e.g., H3K18la, H4K12la) and non-histone proteins (e.g., AARS1, ACSS2). In the review, the authors also elaborate on the mechanisms by which aberrant Kla triggers multiple disease processes. Meanwhile, the authors introduce the potential target sites of Kla.
A research collaboration between Shanghai Jiao Tong University, Shanghai Qi Zhi Institution, and Huawei Technologies has introduced “BAFT”, a cutting-edge auto-save system for AI training that minimizes downtime and optimizes efficiency. Designed to leverage idle moments in training workflows, BAFT significantly enhances fault tolerance while reducing computational overhead, setting a new industry benchmark for reliable AI model development.