Ultraviolet (UV) optical devices have plenteous applications in the fields of nanofabrication, military, medical, sterilization, and others. Traditional optical components utilize gradual phase accumulation phenomena to alter the wave-front of the light, making them bulky, expensive, and inefficient. A dielectric metasurface could provide an auspicious approach to precisely control the amplitude, phase, and polarization of the incident light by abrupt, discrete phase changing with high efficiency due to low absorption losses. Metalenses, being one of the most attainable applications of metasurfaces, can extremely reduce the size and complexity of the optical systems. We present the design of a high-efficiency transmissive UV metalens operating in a broadband range of UV light (250–400 nm) with outstanding focusing characteristics. The polarization conversion efficiency of the nano-rod unit and the focusing efficiency of the metasurface are optimized to be as high as 96% and 77%, respectively. The off-axis focusing characteristics at different incident angles are also investigated. The designed metalens that is composed of silicon nitride nanorods will significantly uphold the advancement of UV photonic devices and can provide opportunities for the miniaturization and integration of the UV nanophotonics and its applications.

Intrusion detection system (IDS) is a well-known and effective component of network security that provides transactions upon the network systems with security and safety. Most of earlier research has addressed difficulties such as overfitting, feature redundancy, high-dimensional features and a limited number of training samples but feature selection. We approach the problem of feature selection via sparse logistic regression (SPLR). In this paper, we propose a discriminative feature selection and intrusion classification based on SPLR for IDS. The SPLR is a recently developed technique for data analysis and processing via sparse regularized optimization that selects a small subset from the original feature variables to model the data for the purpose of classification. A linear SPLR model aims to select the discriminative features from the repository of datasets and learns the coefficients of the linear classifier. Compared with the feature selection approaches, like filter (ranking) and wrapper methods that separate the feature selection and classification problems, SPLR can combine feature selection and classification into a unified framework. The experiments in this correspondence demonstrate that the proposed method has better performance than most of the well-known techniques used for intrusion detection.

Smart fish farming faces critical challenges in achieving comprehensive automation, real-time decision-making, and adaptability to diverse environmental conditions and multi-species aquaculture. This study presents a novel Internet of Things (IoT)-driven intelligent decision-making system that dynamically monitors and optimizes water quality parameters to enhance fish survival rates across various regions and species setups. The system integrates advanced sensors connected to an ESP32 microcontroller, continuously monitoring key water parameters such as pH, temperature, and turbidity which are increasingly affected by climate-induced variability. A custom-built dataset comprising 43,459 records, covering ten distinct fish species across diverse pond environments, was meticulously curated. The data were stored as a comma-separated values (CSV) file on the IoT cloud platform ThingSpeak and synchronized with Firebase, enabling seamless remote access, control, and real-time updates. Advanced machine learning techniques, with feature transformation and balancing, were applied to preprocess the dataset, which includes water quality metrics and species-specific parameters. Multiple algorithms were trained and evaluated, with the Decision Tree classifier emerging as the optimal model, achieving remarkable performance metrics: 99.8% accuracy, precision, recall, and F1-score, a 99.6% Matthews Correlation Coefficient (MCC), and the highest Area Under the Curve (AUC) score for multi-class classification. Our framework’s capability to manage complex, multi-species fishpond environments was validated across diverse setups, showcasing its potential to transform fish farming practices by ensuring sustainable climate-adaptive management through real-time water quality optimization. This study marks a significant step forward in climate-smart aquaculture, contributing to enhanced fish health, survival, and yield while mitigating the risks posed by climate change on aquatic ecosystems.

Metasurfaces in the ultraviolet spectrum have stirred up prevalent research interest due to the increasing demand for ultra-compact and wearable UV optical systems. The limitations of conventional plasmonic metasurfaces operating in transmission mode can be overcome by using a suitable dielectric material. A metalens holds promising wavefront engineering for various applications. Metalenses have developed a breakthrough technology in the advancement of integrated and miniaturized optical devices. However, metalenses utilizing the Pancharatnam–Berry (PB) phase or resonance tuning methodology are restricted to polarization dependence and for various applications, polarization-insensitive metalenses are highly desirable. We propose the design of a high-efficiency dielectric polarization-insensitive UV metalens utilizing cylindrical nanopillars with strong focusing ability, providing full phase delay in a broadband range of Ultraviolet light (270–380 nm). The designed metalens comprises Silicon nitride cylindrical nanopillars with spatially varying radii and offers outstanding polarization-insensitive operation in the broadband UV spectrum. It will significantly promote and boost the integration and miniaturization of the UV photonic devices by overcoming the use of Plasmonics structures that are vulnerable to the absorption and ohmic losses of the metals. The focusing efficiency of the designed metalens is as high as 40%.

The Internet of things (IoT) and advancements of wireless technology have evolved intelligent transport systems to integrate billion of smart objects ready to connect to the Internet. The modern era of the Internet of things (IoT) has brought significant development in vehicular ad hoc networks (VANETs) which transformed the conventional VANET into the Internet of Vehicle (IoV) to improve road safety and diminished road congestion. However, security threats are increasing due to dependency on infrastructure, computing, dynamic nature, and control technologies of VANET. The security threats of VANETs could be addressed comprehensively by increasing trustworthiness on the message received and transmitting node. Conversely, the presence of dishonest vehicles, for instance, Man in the Middle (MiTM) attackers, in the network sharing malicious content could be posed as a severe threat to VANET. Thus, increasing trustworthiness among nodes can lead to increased authenticity, privacy, accuracy, security, and trusted information sharing in the VANET. In this paper, a lightweight trust model is proposed, presented model identifying dishonest nodes and revoking its credential in the MiTM attack scenario. Furthermore, addressing the privacy and security requirement, the pseudonym scheme is used. All nodes in the VANET established trust provided by initially RSU, which is a trusted source in the network. Extensive experiments are conducted based on a variety of network scenarios to evaluate the accuracy and performance of the presented lightweight trust model. In terms of recall, precision, and F-score, our presented model significantly outperformed compared to MARINE. The simulation results have validated that the proposed lightweight model realized a high trust level with 40 of MiTM attackers and in terms of F-score 95, whereas the MARINE model has 90, which leads to the model to attain high detection accuracy.

The conventional fixed acoustic sensors leak detection methods have been demonstrated to be very practical for locating leakages in water distribution pipelines. However, these methods demand proper installation of sensors, and therefore cannot be implemented on buried long water distribution pipelines for condition assessment, early leak detection, and the estimation of leak size effect. Due to these limitations, a free-swimming device is developed. The free-swimming device with the potential of high acoustic sensitivity is capable of detecting the small underwater leakages in the plastic water-filled pipes. Despite the fact that a number of factors influence the underwater acoustic signals, such as water flow noise. Therefore, the interpretation of the leakage and influence of leakage size is considerably challenging from the underwater measured signals. The new method is proposed for reliable leakage detection by tuning the wavelet transform to underwater water acoustic signals. In this method, firstly, Short-Time Fourier Transforms (STFT) of underwater acoustic signals over a relatively long time-interval is monitored to capture the leakage-signals signature. The captured signals efficiently lead in the selection of mother wavelet (tuned wavelet) for the excellent signal localization in the time-frequency domain. Finally, the acoustic signals are analyzed in the tuned wavelet transform to detect the events. In this paper, the practical application of the proposed method, the controlled experiments are designed, and acoustic signals are collected from an experimental setup by launching the free-swimming device. The measured acoustic signals are used to identify the leakage-signals signature from unwanted interfering signals (instantaneous pipe vibrations, water flow noise, pipe's natural frequencies, and background noise). The evaluation of results validated that the free-swimming device and the tuned wavelet transform together can efficiently lead to reliable underwater leakage detection, as well as the influence of the leakage size in plastic water-filled pipes.