Visible light communication (VLC) is an advanced, highly developed optical wireless communication (OWC) technology that can simultaneously provide lighting and high-speed wireless data transmission. A VLC system has several key advantages: ultra-high data rate, secure communication channels, and a lack of interference from electromagnetic (EM) waves, which enable a wide range of applications. Light-emitting diodes (LEDs) have been considered the optimal choice for VLC systems since they can provide excellent illumination performance. However, the quantum confinement Stark effect (QCSE), crystal orientation, carrier lifetime, and recombination factor will influence the modulation bandwidth, and the transmission performance is severely limited. To solve the insufficient modulation bandwidth, micro-LEDs (μ-LEDs) and laser diodes (LDs) are considered as new ideal light sources. Additionally, the development of modulation technology has dramatically increased the transmission capacity of the system. The performance of the VLC system is briefly discussed in this review article, as well as some of its prospective applications in the realms of the industrial Internet of Things (IoT), vehicle communications, and underwater wireless network applications.

The Health Insurance Portability and Accountability Act (HIPAA) privacy and security regulations are two crucial provisions in the protection of healthcare privacy. Privacy regulations create a principle to assure that patients have more control over their health information and set limits on the use and disclosure of health information. The security regulations stipulate the provisions implemented to guard data integrity, confidentiality, and availability. Undoubtedly, the cryptographic mechanisms are well defined to provide suitable solutions. In this paper, to comply with the HIPAA regulations, a flexible cryptographic key management solution is proposed to facilitate interoperations among the applied cryptographic mechanisms. In addition, case of consent exceptions intended to facilitate emergency applications and other possible exceptions can also be handled easily.

Review A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluation Methods Yihe Zhou 1, Tao Ni 1, Wei-Bin Lee 2,3 and Qingchuan Zhao 1,* 1 Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China 2 Information Security Center, Hon Hai Research Institute, New Taipei City 236, Taiwan 3 Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan * Correspondence: qizhao@cityu.edu.hk Received: 3 Feb 2025; Revised: 15 April 2025; Accepted: 18 April 2025; Published: 6 May 2025 Large Language Models (LLMs) have achieved significantly advanced capabilities in understanding and generating human language text, which have gained increasing popularity over recent years. Apart from their state-of-the-art natural language processing (NLP) performance, considering their widespread usage in many industries, including medicine, finance, education, etc., security concerns over their usage grow simultaneously. In recent years, the evolution of backdoor attacks has progressed with the advancement of defense mechanisms against them and more well-developed features in the LLMs. In this paper, we adapt the general taxonomy for classifying machine learning attacks on one of the subdivisions - training-time white-box backdoor attacks. Besides systematically classifying attack methods, we also consider the corresponding defense methods against backdoor attacks. By providing an extensive summary of existing works, we hope this survey can serve as a guideline for inspiring future research that further extends the attack scenarios and creates a stronger defense against them for more robust LLMs.

The increasing complexity of software systems has driven significant advancements in program analysis, as traditional methods unable to meet the demands of modern software development. To address these limitations, deep learning techniques, particularly Large Language Models (LLMs), have gained attention due to their context-aware capabilities in code comprehension. Recognizing the potential of LLMs, researchers have extensively explored their application in program analysis since their introduction. Despite existing surveys on LLM applications in cybersecurity, comprehensive reviews specifically addressing their role in program analysis remain scarce. In this survey, we systematically review the application of LLMs in program analysis, categorizing the existing work into static analysis, dynamic analysis, and hybrid approaches. Moreover, by examining and synthesizing recent studies, we identify future directions and challenges in the field. This survey aims to demonstrate the potential of LLMs in advancing program analysis practices and offer actionable insights for security researchers seeking to enhance detection frameworks or develop domain-specific models.

Photodissociation dynamics of ethyl iodide in the A-band is investigated at wavelengths between 294 and 308 nm using resonance-enhanced multiphoton ionization technique combined with velocity imaging detection. The I/I* branching, translational energy disposals, anisotropy parameters, and curve crossing probabilities of the dissociation channels are determined. The I(5p²P_3/2)-product channel is found to have hotter internal states of C₂H₅, and the I*(5p²P_1/2) channel is accompanied by colder C₂H₅. Anisotropy parameters (β) for the I* channel remain at 2.0, indicating that the I* production should originate from the ³Q₀ state. The β values are from 1.6 to 1.9 in the I-product channel, which comprises two components of direct excitation of ³Q₁ and nonadiabatic transition between the ³Q₀ and ¹Q₁ states. The curve crossing probability rises rapidly around the conical intersection but remains almost constant after passing through the curve crossing. The ¹Q₁ and ³Q₀ states in the exit region are thus expected to cross almost parallel along the dissociation coordinate. As compared to the case of CH₃I, the nonadiabatic transition probabilities are slightly enhanced by an ethyl-substituted group.