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Engineering trustworthy software systems : 4th International School, SETSS 2018, Chongqing, China, April 7-12, 2018, Tutorial Lectures
This volume contains lectures on leading-edge research in methods and tools for use in computer system engineering; at the 4th International School on Engineering Trustworthy Software Systems, SETSS 2018, held in April 2018 at Southwest University in Chongqing, China. The five chapters in this volume provide an overview of research in the frontier of theories, methods, and tools for software modelling, design, and verification. The topics covered in these chapter include Software Verification with Whiley, Learning Büchi Automata and Its Applications, Security in IoT Applications, Programming in Z3, and The Impact of Alan Turing: Formal Methods and Beyond. The volume provides a useful resource for postgraduate students, resarchers, academics, and engineers in industry, who are interested in theory, methods, and tools for the development of trustworthy software.
Book
Chaos-Based Image Encryption: Review, Application, and Challenges
Chaos has been one of the most effective cryptographic sources since it was first used in image-encryption algorithms. This paper closely examines the development process of chaos-based image-encryption algorithms from various angles, including symmetric and asymmetric algorithms, block ciphers and stream ciphers, and integration with other technologies. The unique attributes of chaos, such as sensitivity to initial conditions, topological transitivity, and pseudo-randomness, are conducive to cross-referencing with other disciplines and improving image-encryption methods. Additionally, this paper covers practical application scenarios and current challenges of chaotic image encryption, thereby encouraging researchers to continue developing and complementing existing situations, and may also serve as a basis of future development prospects for chaos-based image encryption.
Journal Article
Therapeutic roles of mesenchymal stem cell-derived extracellular vesicles in cancer
Extracellular vesicles (EVs) are cell-derived membrane structures enclosing proteins, lipids, RNAs, metabolites, growth factors, and cytokines. EVs have emerged as essential intercellular communication regulators in multiple physiological and pathological processes. Previous studies revealed that mesenchymal stem cells (MSCs) could either support or suppress tumor progression in different cancers by paracrine signaling via MSC-derived EVs. Evidence suggested that MSC-derived EVs could mimic their parental cells, possessing pro-tumor and anti-tumor effects, and inherent tumor tropism. Therefore, MSC-derived EVs can be a cell-free cancer treatment alternative. This review discusses different insights regarding MSC-derived EVs' roles in cancer treatment and summarizes bioengineered MSC-derived EVs’ applications as safe and versatile anti-tumor agent delivery platforms. Meanwhile, current hurdles of moving MSC-derived EVs from bench to bedside are also discussed.
Journal Article
Overview of Propulsion Systems for Unmanned Aerial Vehicles
Unmanned Aerial Vehicle (UAV) propulsion technology is significantly related to the flight performance of UAVs, which has become one of the most important development directions of aviation. It should be noted that UAVs have three types of propulsion systems, namely the fuel, hybrid fuel-electric, and pure electric, respectively. This paper presents and discusses the classification, working principles, characteristics, and critical technologies of these three types of propulsion systems. It is helpful to establish the development framework of the UAV propulsion system and provide the essential information on electric propulsion UAVs. Additionally, future technologies and development, including the high-power density motors, converters, power supplies, are discussed for the electric propulsion UAVs. In the near future, the electric propulsion system would be widely used in UAVs. The high-power density system would become the development trend of electric UAVs. Thus, this review article provides comprehensive views and multiple comparisons of propulsion systems for UAVs.
Journal Article
Group Decision Making with Heterogeneous Preference Structures: An Automatic Mechanism to Support Consensus Reaching
In real-world decision problems, decision makers usually express their opinions with different preference structures. In order to deal with the heterogeneous preference information in group decision making, this paper presents an optimization-based consensus model for group decision making with heterogeneous preference structures (utility values, preference orderings, multiplicative preference relations and additive preference relations). This proposal seeks to minimize the information loss between decision makers’ heterogeneous preference information and individual preference vectors and also seeks the collective solution with a consensus. Meanwhile, in order to justify the consensus model, we discuss its internal aggregation operator between the obtained individual and group preference vectors, demonstrate that the proposed model satisfies the Pareto principle of social choice theory, and prove the uniqueness of the solution to the optimization model. Furthermore, based on the proposed optimization-based consensus model, we present an automatic mechanism to support consensus reaching in the group decision making with heterogeneous preference structures. In the consensus reaching process, the obtained individual and group preference vectors are considered as a decision aid which decision makers can use as a reference to adjust their preference opinions. Finally, detailed simulation experiments and comparison analysis are conducted to demonstrate the feasibility and effectiveness of our proposed model.
Journal Article
Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead
Advancements in audio synthesis and manipulation technologies have reshaped applications such as personalised virtual assistants, voice cloning for creative content, and language learning tools. However, the misuse of these technologies to create audio deepfakes has raised serious concerns about security, privacy, and trust. Studies reveal that human judgement of deepfake audio is not always reliable, highlighting the urgent need for robust detection technologies to mitigate these risks. This paper provides a comprehensive survey of recent advancements in audio deepfake detection, with a focus on cutting-edge developments in the past few years. It begins by exploring the foundational methods of audio deepfake generation, including text-to-speech (TTS) and voice conversion (VC), followed by a review of datasets driving progress in the field. The survey then delves into detection approaches, covering frontend feature extraction, backend classification models, and end-to-end systems. Additionally, emerging topics such as privacy-preserving detection, explainability, and fairness are discussed. Finally, this paper identifies key challenges and outlines future directions for developing robust and scalable audio deepfake detection systems.
Journal Article
Catalytic inverse vulcanization
The discovery of inverse vulcanization has allowed stable polymers to be made from elemental sulfur, an unwanted by-product of the petrochemicals industry. However, further development of both the chemistry and applications is handicapped by the restricted choice of cross-linkers and the elevated temperatures required for polymerisation. Here we report the catalysis of inverse vulcanization reactions. This catalytic method is effective for a wide range of crosslinkers reduces the required reaction temperature and reaction time, prevents harmful H
2
S production, increases yield, improves properties, and allows crosslinkers that would be otherwise unreactive to be used. Thus, inverse vulcanization becomes more widely applicable, efficient, eco-friendly and productive than the previous routes, not only broadening the fundamental chemistry itself, but also opening the door for the industrialization and broad application of these fascinating materials.
Inverse vulcanization allows stable polymers to be made from elemental sulfur, but development is restricted by cross-linkers and the elevated temperatures required. Here the authors report a catalytic method for a wide range of cross-linkers and found a reduced reaction temperature and reaction time is required.
Journal Article
Prevalence and influencing factors of nonunion in patients with tibial fracture: systematic review and meta-analysis
Objective
The aim of this study is to assess the prevalence of nonunion in patients with tibia fracture and the association between influencing factors and tibia fracture nonunion.
Method
A database searches of PubMed, the Cochrane Library, EMBASE, China National Knowledge Infrastructure (CNKI), Weipu database, and Wanfang database from inception until June 2019 was conducted. The pooled prevalence, odds ratio (OR), and 95% confidence intervals (CI) were calculated with Stata software.
Results
In this study, 111 studies involving 41,429 subjects were included. In the study of the relationship between influencing factors and tibia fracture nonunion, 15 factors significantly influenced the fracture union, including > 60 years old, male, tobacco smoker, body mass index > 40, diabetes, nonsteroidal anti-inflammatory drugs (NSAIDs) user, opioids user, fracture of middle and distal tibia, high-energy fracture, open fracture, Gustilo-Anderson grade IIIB or IIIC, Müller AO Classification of Fractures C, open reduction, fixation model, and infection.
Conclusion
The prevalence of nonunion in patients with tibia fracture was 0.068 and 15 potential factors were associated with the prevalence. Closed reduction and minimally invasive percutaneous plate osteosynthesis (MIPPO) have the low risks of nonunion for the treatment of tibial fractures.
Journal Article
A Multimodal Large Language Model Framework for Intelligent Perception and Decision-Making in Smart Manufacturing
In modern manufacturing, making accurate and timely decisions requires the ability to effectively handle multiple types of data. This paper presents a multimodal system designed specifically for smart manufacturing applications. The system combines various data sources including images, sensor data, and production records, using advanced multimodal large language models. This approach addresses common limitations of traditional single-modal methods, such as isolated data analysis and poor integration between different data types. Key contributions include a unified method for representing different data types, dynamic semantic tokenization for better data processing, strong alignment strategies across modalities, and a practical two-stage training method involving initial large-scale pretraining and later fine-tuning for specific tasks. Additionally, a novel Transformer-based model is introduced for generating both images and text, significantly improving real-time decision-making capabilities. Experiments on relevant industrial datasets show that this method consistently performs better than current state-of-the-art approaches in tasks like image–text retrieval and visual question answering. The results demonstrate the effectiveness and versatility of the proposed methods, offering important insights and practical solutions to enhance intelligent manufacturing, predictive maintenance, and anomaly detection, thus supporting the development of more efficient and reliable industrial systems.
Journal Article
The Progress and Prospects of Immune Cell Therapy for the Treatment of Cancer
Immune cell therapy as a revolutionary treatment modality, significantly transformed cancer care. It is a specialized form of immunotherapy that utilizes living immune cells as therapeutic reagents for the treatment of cancer. Unlike traditional drugs, cell therapies are considered “living drugs,” and these products are currently customized and require advanced manufacturing techniques. Although chimeric antigen receptor (CAR)-T cell therapies have received tremendous attention in the industry regarding the treatment of hematologic malignancies, their effectiveness in treating solid tumors is often restricted, leading to the emergence of alternative immune cell therapies. Tumor-infiltrating lymphocytes (TIL) cell therapy, cytokine-induced killer (CIK) cell therapy, dendritic cell (DC) vaccines, and DC/CIK cell therapy are designed to use the body’s natural defense mechanisms to target and eliminate cancer cells, and usually have fewer side effects or risks. On the other hand, cell therapies, such as chimeric antigen receptor-T (CAR-T) cell, T cell receptor (TCR)-T, chimeric antigen receptor-natural killer (CAR-NK), or CAR-macrophages (CAR-M) typically utilize either autologous stem cells, allogeneic or xenogeneic cells, or genetically modified cells, which require higher levels of manipulation and are considered high risk. These high-risk cell therapies typically hold special characteristics in tumor targeting and signal transduction, triggering new anti-tumor immune responses. Recently, significant advances have been achieved in both basic and clinical researches on anti-tumor mechanisms, cell therapy product designs, and technological innovations. With swift technological integration and a high innovation landscape, key future development directions have emerged. To meet the demands of cell therapy technological advancements in treating cancer, we comprehensively and systematically investigate the technological innovation and clinical progress of immune cell therapies in this study. Based on the therapeutic mechanisms and methodological features of immune cell therapies, we analyzed the main technical advantages and clinical transformation risks associated with these therapies. We also analyzed and forecasted the application prospects, providing references for relevant enterprises with the necessary information to make informed decisions regarding their R&D direction selection.
Journal Article