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Broadcast encryption ( BE ) allows a sender to encrypt a message to an arbitrary target set of legitimate users and to prevent non-legitimate users from recovering the broadcast information. BE has numerous practical applications such as satellite geolocation systems, file sharing systems, pay-TV systems, e-Health, social networks, cloud storage systems, etc. This paper presents two new decentralized BE schemes. Decentralization means that there is no single authority responsible for generating secret cryptographic keys for system users. Therefore, the scheme eliminates the concern of having a single point of failure as the central authority could be attacked, become malicious, or become unavailable. Recent attacks have shown that the centralized approach could lead to system malfunctioning or to leaking sensitive information. Another achievement of the proposed BE schemes is their performance characteristics that make them suitable for environments with light-weight clients, such as in Internet-of-Things (IoT) applications. The proposed approach improves the performance over existing decentralized BE schemes by simultaneously achieving constant size ciphertext, constant size secret key and fast decryption.

In modern applications, such as Electronic Voting, e-Health, e-Cash, there is a need that the validity of a signature should be verified by only one responsible person. This is opposite to the traditional digital signature scheme where anybody can verify a signature. There have been several solutions for this problem, the first one is we combine a signature scheme with an encryption scheme; the second one is to use the group signature; and the last one is to use the strong designated verifier signature scheme with the undeniable property. In this paper, we extend the traditional digital signature scheme to propose a new solution for the aforementioned problem. Our extension is in the sense that only a designated verifier (responsible person) can verify a signer’s signature, and if necessary (in case the signer refuses to admit his/her signature) the designated verifier without revealing his/her secret key is able to prove to anybody that the signer has actually generated the signature. The comparison between our proposed solution and the three existing solutions shows that our proposed solution is the best one in terms of both security and efficiency.

Attribute-based encryption () is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes. is widely applied in cloud storage, file sharing, e-Health, and digital rights management. schemes rely on hard cryptographic assumptions such as pairings and others (pairing-free) to ensure their security against external and internal attacks. Internal attacks are carried out by authorized users who misuse their access to compromise security with potentially malicious intent. One common internal attack is the attribute collusion attack, in which users with different attribute keys collaborate to decrypt data they could not individually access. This paper focuses on the ciphertext-policy (-), a type of where ciphertexts are produced with access policies. Our first work is to carry out the attribute collusion attack against several existing pairing-free - schemes. As a main contribution, we introduce a novel attack, termed the anonymous key-leakage attack, concerning the context in which users could anonymously publish their secret keys associated with certain attributes on public platforms without the risk of detection. This kind of internal attack has not been defined or investigated in the literature. We then show that several prominent pairing-based - schemes are vulnerable to this attack. We believe that this work will contribute to helping the community evaluate suitable - schemes for secure deployment in real-life applications.

Blind signatures play an important role in both electronic cash and electronic voting systems. Blind signatures should be secure against various attacks (such as signature forgeries). The work puts a special attention to secret key exposure attacks, which totally break digital signatures. Signatures that resist secret key exposure attacks are called forward secure in the sense that disclosure of a current secret key does not compromise past secret keys. This means that forward-secure signatures must include a mechanism for secret-key evolution over time periods. This paper gives a construction of the first blind signature that is forward secure. The construction is based on the SIS assumption in the lattice setting. The core techniques applied are the binary tree data structure for the time periods and the trapdoor delegation for the key-evolution mechanism.

Background: Neisseria meningitidis (N. meningitidis) is a leading cause of acute meningitis and is classified into 13 serogroups, six of which are predominantly associated with invasive meningococcal disease. This study aimed to investigate the genotype, subgenotype, and antigenic profiles of N. meningitidis serogroup B strains isolated in Vietnam. Methods: Genotyping was performed on 106 N. meningitidis strains isolated from clinical samples from Vietnamese patients and nasopharyngeal swabs of healthy adolescents between 2019 and 2024. The genetic profiles, including the porA, porB, fetA, fHbp, abcZ, adk, aroE, fumC, gdh, pdhC, and pgm genes, were analyzed using Sanger sequencing and bioinformatic methods. Results: We found that 84.9% of the strains carried VR3 families 36 or 35-1, with VR1, VR2, and VR3 families 22-25, 14, and 36 being the most prevalent. Among the 106 serogroup B isolates, 20 variants of the porB allele 3 were identified, with porB 3-1212 being the most frequent (30.2%). Dominant PorB variable loops included L1.6, L4.5, L5.7, L6.6, and L7.13. fHbp variant group 2 was predominant (104/106 strains), and 12 FetA allele variants were identified, with F1-7 being the most common (47.2%). Three clonal complexes were identified, and clonal complex ST-32 was the most predominant. Fifty-five strains (51.9%) belonged to sequence types that have not yet been assigned to any clonal complexes, and 15 strains (14.1%) with allelic profiles were not assigned to STs. The 3-253 and 3-1212 alleles of porB, the F1-7 variant of FetA, the ST-44 and ST-1576 sequence types, and the ST-41/44 complex were observed more frequently in patients compared to asymptomatic carriers, suggesting their association with more virulence. Conclusions: This study showed a high genetic and antigenic diversity of N. meningitidis serogroup B isolates in Vietnam, with VR3 family 36 most common and porB 3-1212 as the predominant allele. fHbp variant group 2 and FetA allele F1-7 were most frequent. ST-32 was the dominant clonal complex, though many strains remained unassigned, highlighting the need for ongoing molecular surveillance.

SARS-CoV-2 Delta variant caused a large number of COVID-19 cases in many countries, including Vietnam. Understanding mortality risk factors is crucial for the clinical management of severe COVID-19. We conducted a retrospective study at an intensive care center in Ho Chi Minh City that urgently built by Bach Mai Hospital during the COVID-19 outbreak in Vietnam, when the Delta variant predominated. Participants were laboratory-confirmed patients with SARS-CoV-2 infection, admitted in August 2021. Data on patients' demographic and clinical characteristics, radiographic and laboratory findings, treatment, and clinical time course were compared between survivors and non-survivors. Risk factors to mortality were assessed using logistic regression. Among 504 eligible COVID-19 patients, case fatality was 52.2%. Unvaccinated patients accounted for 61.2% of non-survivors and 43.6% of survivors (p < 0.001). The time from onset to hospital admission was 8 days in non-survivors and 7 days in survivors (p = 0.004). Among non-survivors, 90.2% developed acute respiratory distress syndrome (ARDS). Oxygen therapy was administered for all patients, but antiviral agent was given to 51.7% of non-survivors. 54.2% of non-survivors tested positive for the bacterial infection using blood culture. The risk factors for mortality were diabetes mellitus, respiration rate, oxygen saturation, vaccination status, time from onset to admission, and older age. Critical patients with COVID-19 owing to the Delta variant in Vietnam had delayed hospital admission, leading to ARDS and death. Early availability of vaccines and preventing bacterial infections are crucial for reducing mortality of COVID-19, especially in low- and middle-income countries.

This is the preliminary calculation about the potential of solar power from second-floor+ houses along Hung Vuong Street, Hue city. Research team organized field trips to inventory the houses and areas, number of floors, sunshine direction of the house in accordance with its adjacent houses. We calculated the number of solar panels needed to install and the potential of solar power can be generated from each house from the second floor and more. Research team has chosen photovoltaic solar cell technology (PV) to calculate the actual solar power achieved. The calculated results of solar power reserves are 3GWh/year with an investment of 57 billion VND (not including inverter). This reserves can help to reduce 4.17 million tons of CO2, 19.08 million tons of nitrogen oxide, 0.06 million tons of sulfur dioxide, 3.12 million tons dust, 1.14 million tons of ash, 9.12 million m3 of water and 2.55 million tons of coal annually, if the lifetime of the solar system is 25 years, the emissions will decrease significantly. This research shows that the potential for solar power conversion is feasible to address the green growth needs of the economy and climate protection in Thua Thien Hue province.

Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset’s quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.

In this work, we investigate a numerical analysis of outdoor unit (ODU) configuration design by a three-dimensional steady Reynolds-averaged Navier-Stokes computational fluid dynamic simulation. The model was conducted from an actual building design with 10 floors and 5 ODUs each. The numerical results were used to trace back the ODUs in which the insufficient heat reject may happen. The velocity, temperature and pressure distribution were also analyzed. The importance of avoiding air recirculation around the OUDs is presented. Finally, by evaluating the airflow, this approach can significantly reduce the designing fault and help designers determine the ideal position of OUDs.

Abstract Unsupervised industrial anomaly detection trains models solely on anomaly-free images to detect unseen defects. While embedding-based methods have recently achieved state-of-the-art results, their use of memory banks substantially increases memory usage and inference times, limiting their practicality in industrial settings. In this work, we propose a lightweight and efficient framework for anomaly detection and localization using paired well-lit and low-light images. Our network learns to reconstruct well-lit features from low-light features on nominal (anomaly-free) samples, detecting anomalies by identifying inconsistencies between the reconstructed and extracted features. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches across multiple industrial datasets. Specifically, our model achieves an Image-level Area Under the Receiver Operating Characteristic (I-AUROC) of 0.854 and rea Under the Per-Region Overlap (AUPRO) of 0.823 on low-light industrial anomaly detection (LL-IAD), significantly surpassing existing methods. Furthermore, it attains I-AUROC scores of 0.864 and 0.858 on the Insulator and Clutch datasets, respectively, outperforming all prior approaches in these industrial settings. Notably, even when well-lit images are unavailable, our model maintains high performance using Retinexformer-enhanced low-light images, demonstrating its adaptability to real-world low-light scenarios. Additionally, we introduce a new industrial anomaly detection dataset featuring paired well-lit and low-light images. To our knowledge, this is the first dataset for LL-IAD dataset. Graphical Abstract Graphical Abstract Industrial anomaly detection using paired well-lit and low-light images.