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In applications such as satellite remote sensing and aerial photography, imaging equipment must capture brightness information of different ground scenes within a restricted dynamic range. Due to camera sensor limitations, captured images can represent only a portion of such information, which results in lower resolution and lower dynamic range compared with real scenes. Image super resolution (SR) and multiple-exposure image fusion (MEF) are commonly employed technologies to address these issues. Nonetheless, these two problems are often researched in separate directions. In this paper, we propose MEFSR-GAN: an end-to-end framework based on generative adversarial networks that simultaneously combines super-resolution and multiple-exposure fusion. MEFSR-GAN includes a generator and two discriminators. The generator network consists of two parallel sub-networks for under-exposure and over-exposure, each containing a feature extraction block (FEB), a super-resolution block (SRB), and several multiple-exposure feedback blocks (MEFBs). It processes low-resolution under- and over-exposed images to produce high-resolution high dynamic range (HDR) images. These images are evaluated by two discriminator networks, driving the generator to generate realistic high-resolution HDR outputs through multi-goal training. Extensive qualitative and quantitative experiments were conducted on the SICE dataset, yielding a PSNR of 24.821 and an SSIM of 0.896 for 2× upscaling. These results demonstrate that MEFSR-GAN outperforms existing methods in terms of both visual effects and objective evaluation metrics, thereby establishing itself as a state-of-the-art technology.

The study of complex networks with topological indices has flourished in recent years. The aim of this paper is to study the limiting behaviors of Gutman and Schultz indices in random polygonal chains, whose graph-theoretic mathematical properties and their future applications have attracted the interest of scientists. By applying the concepts of symmetry and asymptotics as well as the knowledge of probability theory, we obtain explicit analytic expressions for the Gutman and Schultz indices of n random 2k-vertex chains and prove that they converge to a normal distribution, which contributes to a deeper understanding of the structural features of random polygonal chains and plays a crucial role in the study of the limiting behavior of topological indices and their applications.

Change detection (CD) in high-resolution remote sensing imagery remains challenging due to the complex nature of objects and varying spectral characteristics across different times and locations. Convolutional neural networks (CNNs) have shown promising performance in CD tasks by extracting meaningful semantic features. However, traditional 2D-CNNs may struggle to accurately integrate deep features from multi-temporal images, limiting their ability to improve CD accuracy. This study proposes a Multi-level Feature Cross-Fusion (MFCF) network with 3D-CNNs for remote sensing image change detection. The network aims to effectively extract and fuse deep features from multi-temporal images to identify surface changes. To bridge the semantic gap between high-level and low-level features, a MFCF module is introduced. A channel attention mechanism (CAM) is also integrated to enhance model performance, interpretability, and generalization capabilities. The proposed methodology is validated on the LEVIR construction dataset (LEVIR-CD). The experimental results demonstrate superior performance compared to the current state-of-the-art in evaluation metrics including recall, F1 score, and IOU. The MFCF network, which combines 3D-CNNs and a CAM, effectively utilizes multi-temporal information and deep feature fusion, resulting in precise and reliable change detection in remote sensing imagery. This study significantly contributes to the advancement of change detection methods, facilitating more efficient management and decision making across various domains such as urban planning, natural resource management, and environmental monitoring.

Conventional disinfection techniques, relying on a single disinfection step, often fail to directly eliminate microorganisms, instead causing them to enter a viable but nonculturable (VBNC) state. However, microorganisms in the VBNC state retain metabolic activity and can reactivate under suitable conditions, representing a “hidden source of contamination” that threatens drinking water safety. This study fundamentally assessed the feasibility of combined disinfection methods by integrating UV254 with disinfectant (NaClO, PAA, and PDS) for inactivating Pseudomonas aeruginosa (P. aeruginosa), an opportunistic pathogen that has been widely detected in water supply systems. The number of culturable cells was determined using the heterotrophic plate counting (HPC) method, and the number of VBNC cells was quantified using our recently developed qPCR approach. Quantitative analyses showed that combined disinfection methods can effectively reduce both culturable and VBNC cells by several orders of magnitude compared to a single disinfection step. Notably, VBNC P. aeruginosa, after 30 min of UV/NaCIO treatment, was below the detection limit (3.191 log CFU/mL) of PMA-qPCR. The reactivation experiment also confirmed that VBNC P. aeruginosa did not reactivate for 16 h after 30 min of UV/NaClO treatment under controlled laboratory conditions. The higher disinfection capacity of combined methods can be attributed to the generation of reactive radicals. This study highlighted combined disinfection as a promising approach for the inactivation of bacteria in the VBNC state, yet further studies are needed before an application can be considered for minimizing VBNC reactivation in city utility water processing or high-risk building water distribution systems.

Sequentially timed all-optical mapping photography is one of the main emerging ultra-fast detection technologies that can be widely applicable to ultra-fast detection at the picosecond level in fields such as materials and life sciences. We propose a new optical structure for an all-optical spatial mapping module that can control the optical field of two-dimensional imaging while improving spectral resolution and detector sensor utilization. The model of optical parameters based on geometrical optics theory for the given structure has been established, and the theoretical analysis of the inter-frame energy crosstalk caused by incident beam spot width, chromatic aberration, and main errors of the periscope array has been conducted. The optical design of the two-dimensional (2D) all-optical spatial mapping module was finally completed using ZEMAX OpticStudio 2018 software. The results show that our optical module can realize targets of 16 frames and 1.25 nm spectral resolution.

In harsh environments, bacteria often enter a viable but nonculturable (VBNC) state, which cannot be detected using heterotrophic plate counting (HPC). Importantly, VBNC bacteria can potentially resuscitate under favorable conditions, posing a risk to drinking water safety. This study introduces an innovative approach, combining improved quantitative polymerase chain reaction (qPCR) with propidium monoazide (PMA) dye and HPC to accurately quantify VBNC Pseudomonas aeruginosa (P. aeruginosa). The method was applied to assess the ability of various disinfection techniques to induce P. aeruginosa into the VBNC state. Different disinfection methods, including ultraviolet radiation (UV), sodium hypochlorite (NaClO), and peracetic acid (PAA), significantly reduced bacterial culturability (>99.9%), with the majority entering the VBNC state. Notably, under favorable conditions, UV-induced VBNC cells were resuscitated faster than those induced by NaClO. VBNC P. aeruginosa exhibited relatively high intracellular adenosine triphosphate (ATP) levels, indicating ongoing metabolic activity. Scanning electron microscopy (SEM) reveals that some bacteria maintained cellular integrity for UV and PAA treatment, while evident membrane disruption was observed after NaClO disinfection. This study represents a significant advancement in quantitatively detecting VBNC state P. aeruginosa, contributing to an accurate assessment of microbial inactivation during drinking water disinfection.