Search Results (574)

Driven by factors such as meteorology, topography, and industrial structure, the concentrations of volatile organic compounds (VOCs) exhibit significant spatial heterogeneity. Investigating the characteristics and sources of VOCs in different regions is therefore crucial for formulating targeted strategies to mitigate their contributions to fine particulate matter (PM_2.5) and ozone (O₃) pollution. This study comprehensively investigated—for the first time—the concentration characteristics, sources, and contributions to secondary organic aerosol (SOA) and O₃ formation of VOCs at an urban background site in Ganzhou, a southern Chinese city, based on hourly observations of VOCs during 2023. Analyses included ozone formation potential (OFP), secondary organic aerosol formation potential (SOAFP), and positive matrix factorization (PMF) source apportionment. The influence of photochemical loss was assessed using a photochemical age parameterization method. The results showed an annual average total VOC concentration of 22.6 ± 13.17 ppbv, with higher levels in winter and lower in summer. Alkanes were the dominant species (45.76%). After correcting for photochemical loss, the initial concentration of VOCs (IC-VOCs) was approximately 60% higher than the observed concentration of VOCs (OC-VOCs), with alkenes becoming the dominant group in IC-VOCs (≈72%). OFP analysis indicated that the OFP calculated using initial VOC concentrations (IC-OFP) was substantially higher (by 320 μg/m³) than the values calculated using observed VOC concentrations (OC-OFP), primarily due to the increased contribution of alkenes. SOAFP was higher in spring and winter, and lower in summer and autumn, with aromatic hydrocarbons being the dominant contributors (>85%). PMF results based on month-case studies identified combustion and industrial process sources as the major contributors (>20%) in August, while combustion and vehicle exhaust dominated in January. Photochemical loss significantly influenced source apportionment, particularly leading to an underestimation of biogenic emissions during a warm month (August). These findings underscore the necessity of accounting for photochemical aging and offer a scientific basis for refining targeted VOC control measures in Ganzhou and similar regions. Full article

Background/Objectives: The difficulties caused by breast cancer have been addressed in a number of ways. Since it is said to be the second most common cause of death from cancer among women, early intervention is crucial. Early detection is difficult because of the existing detection tools’ shortcomings in objectivity and accuracy. Quadra Sense, a fusion of deep learning (DL) classifiers for mitosis detection in breast cancer histopathology, is proposed to address the shortcomings of current approaches. It demonstrates a greater capacity to produce more accurate results. Methods: Initially, the raw dataset is preprocessed by using a normalization by means of color channel normalization (zero-mean normalization) and stain normalization (Macenko Stain Normalization), and the artifact can be removed via median filtering and contrast enhancement using histogram equalization; ROI identification is performed using modified Fully Convolutional Networks (FCNs) followed by the feature extraction (FE) with Modified InceptionV4 (M-IV4), by which the deep features are retrieved and the feature are selected by means of a Self-Improved Seagull Optimization Algorithm (SA-SOA), and finally, classification is performed using Mito-Quartet. Results: Ultimately, using a performance evaluation, the suggested approach achieved a higher accuracy of 99.2% in comparison with the current methods. Conclusions: From the outcomes, the recommended technique performs well. Full article

Long-term particulate matter (PM) chemical composition measurements were performed in Cyprus at two different sites (an urban/traffic site (“LIMTRA”) and a remote/background site (“AGM”)) in an effort to assess (i) the spatial and temporal variability of fine (PM2.5) particulate matter in the eastern Mediterranean; (ii) the main sources contributing to their levels and their relationship with the characteristics of the sampling location; and (iii) the enhancement effect of local anthropogenic and natural biogenic sources on PM levels. To this end, the simultaneous determination of 118 individual Secondary Organic Aerosol (SOA) components (carboxylic acids, organosulfates, and organonitrates) was performed. The “AGM” station showed average SOA yields more than three times higher than those at the “LIMTRA” station (15 ng∙m⁻³ and 4.4 ng∙m⁻³, respectively), whilst the organonitrate levels were higher at “LIMTRA” than at “AGM” (3.3 ng∙m⁻³ and 1.8 ng∙m⁻³, respectively). The most abundant SOA species were hydroxy-acetone sulfate, glycolic acid sulfate, and lactic acid sulfate (21 ng∙m⁻³ at “LIMTRA” and 84 ng∙m⁻³ at “AGM”). The highest SOA load was observed in spring at “AGM” (18 ng∙m⁻³) and in summer at “LIMTRA” (6.8 ng∙m⁻³). Two statistical factorization tools, Principal Component Analysis and Positive Matrix Factorization, were applied to extract common patterns and point to possible SOA sources and SOA formation pathways; the different categorization approaches produced similar results. Full article

VOCs are significant precursors for the formation of O₃ and SOA, directly impacting human health. This study employs multiple approaches to analyzing atmospheric VOCs by focusing on OVOCs including aldehydes, ketones, and phenols, with a case study in Beijing, China. We analyzed the concentration levels and compositions of VOCs and their atmospheric activities, offering a new perspective on VOCs. This analysis was conducted through offline measurements of volatile phenols and carbonyl compounds, complemented by online VOC observations during the summer period of high O₃ levels. The total atmospheric VOCs concentration was found to be 51.29 ± 10.01 ppbv, with phenols contributing the most (38.87 ± 11.57%), followed by carbonyls (34.91 ± 6.85%), and aromatics (2.70 ± 1.03%, each compound is assigned to only one category based on its primary functional group, with no double counting). Carbonyls were the largest contributors to the OFP at 59.03 ± 14.69%, followed by phenols (19.94 ± 4.27%). The contribution of phenols to the SOAFP (43.37 ± 9.53%) and the L_OH (67.74 ± 16.72%) is dominant. Among all quantified VOC species, phenol and formaldehyde exhibited the highest species-level contributions to atmospheric reactivity metrics, including L_OH, OFP and SOAFP, owing to their combination of elevated concentrations and large kinetic or MIR coefficients. Using the PMF model for source analysis, six main sources of volatile organic compounds were identified. Solvent use and organic chemicals production were found to be the primary contributors, accounting for 31.76% of the total VOCs emissions, followed by diesel vehicle exhaust (17.80%) and biogenic sources (15.51%). This study introduces important OVOCs such as phenols, re-evaluates the importance of OVOCs and their role in atmospheric chemical processes, and provides new insights into atmospheric VOCs. These findings are crucial for developing effective air pollution control strategies and improving air quality. This study emphasizes the importance of OVOCs, especially aldehydes and phenols, in the mechanism of summer O₃ generation. Full article

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

The rapid growth of Internet of Things (IoT) devices and the emergence of 5G/6G networks have created major challenges in secure and reliable data transmission. Traditional cryptographic algorithms, while robust, often suffer from high computational complexity and latency, making them less suitable for large-scale, real-time applications. This paper proposes a chaos-based encryption framework that uses the Sprott chaotic oscillator to generate secure and unpredictable signals for encryption. To achieve accurate synchronization between the transmitter and the receiver, two bio-inspired metaheuristic algorithms—the Pachycondyla Apicalis Algorithm (API) and the Penguin Search Optimization Algorithm (PeSOA)—are employed to identify the optimal control parameters of the Sprott system. This optimization improves synchronization accuracy and reduces computational overhead. Simulation results show that PeSOA-based synchronization outperforms API in convergence speed and Root Mean Square Error (RMSE). The proposed framework provides robust, scalable, and low-latency encryption for IoT and 5G/6G networks, where massive connectivity and real-time data protection are essential. Full article

The dissipative Kerr soliton (DKS) microcomb has been regarded as a highly promising multi-wavelength laser source for optical fiber communication, due to its excellent frequency and phase stability. However, in some specific application scenarios, such as direct modulation and direct detection (DM/DD), the relative intensity noise (RIN) performance of Kerr optical combs still fails to meet the requirements. Here, we systematically investigate the key factors that contribute to the power fluctuations in DKS combs. By exploiting the gain saturation effect of the semiconductor optical amplifier (SOA), the RIN of an on-chip DKS microcomb is effectively suppressed, achieving a maximum reduction of about 30 dB (@600 kHz offset frequency) for all comb lines. Moreover, such DKS comb RIN suppression technology based on an SOA chip can eliminate the need for additional complex feedback control circuits, showcasing the potential for further chip integration of the ultra-low-RIN DKS microcomb system. Full article

As optical networks continue to evolve toward higher speed and larger capacity, conventional security mechanisms relying on optoelectronic conversion are facing increasing limitations. The optical photonic firewall, as an emerging optical-layer security device, enables direct inspection in the optical domain, making its core technology—All-Optical Pattern Matching (AOPM)—a focal point of current research. This review provides a comprehensive survey of AOPM systems. It first introduces the main components of AOPM, namely symbol matching and system architectures, and analyzes their representative implementations. For low-order modulation formats such as OOK and BPSK, the review highlights matching schemes enabled by semiconductor optical amplifier (SOA) and highly nonlinear fiber (HNLF) logic gates, as well as their potential for reconfigurable extension. Building upon this foundation, the paper focuses on systems for high-order modulation formats including QPSK, 8PSK, and 16QAM, covering dimensionality-reduction-based approaches (e.g., PSA-based phase compression, squarer-based phase multiplication, constellation-mapping-based format conversion), direct symbol matching methods (e.g., phase interference, generalized XNOR, real-time Fourier transform correlation), and reconfigurable designs for multi-format adaptability. Furthermore, the review discusses optimization challenges under non-ideal conditions, such as noise accumulation, phase misalignment, and phase-locking-free operation. Finally, it outlines future directions in robust high-order modulation handling, photonic integration, and AI-driven intelligent matching, offering guidance for the development of optical-layer security technologies. Full article

Achieving sustainable air quality improvements in rapidly industrializing regions requires a clear understanding of the emission sources that drive the formation of PM_2.5 pollution. This study identified the sources of PM_2.5 and its organic carbon (OC) in Zibo, a typical industrial city in Northern China Plain, using the Positive Matrix Factorization (PMF) model during five pollution episodes (P1–P5) from 26 November 2022 to 9 February 2023. A high-temporal-resolution online observation of 61 organic molecular tracers was conducted using an Aerodyne TAG stand-alone system combined with a gas chromatograph–mass spectrometer (TAG-GC/MS) system. The results indicate that during pollution episodes, PM_2.5 was contributed by 32.4% from coal combustion and 27.1% from inorganic secondary sources. Moreover, fireworks contributed 13.1% of PM_2.5, primarily due to the extensive fireworks during the Gregorian and Lunar New Year celebrations. Similarly, coal combustion was the largest contributor to OC, followed by mobile sources and secondary organic aerosol (SOA) sources, accounting for 16.2% and 15.3%, respectively. Although fireworks contributed significantly to PM_2.5 concentrations (31.6% in P4 of 20–24 January 2023), their impact on OC was negligible. Overall, a combination of local and regional industrial combustion emissions, mobile sources, extensive residential heating during cold weather, and unfavorable meteorological conditions led to elevated secondary aerosol concentrations and the occurrence of this haze episode. The high-temporal-resolution measurements obtained using the TAG-GC/MS system, which provided more information on source-indicating organic molecules (tracers), significantly enhanced the source apportionment capability of PM_2.5 and OC. The findings provide science-based evidence for designing more sustainable emission control strategies, highlighting that the coordinated management of coal combustion, mobile emissions, and wintertime heating is essential for long-term air quality and public health benefits. Full article

In recent years, with the development of electronic products toward high frequency and high speed, Printed Circuit Board (PCB) routing technology has been continuously evolving to meet the requirements of complex signal transmission. Meanwhile, the increase in circuit frequency and device density has led to a sharp deterioration of simultaneous switching noise (SSN), which has escalated from a minor interference to a core bottleneck. SSN not only impairs signal integrity and increases bit error rate, but also interferes with circuit operation, causes device failure, and even leads to system collapse, becoming a “fatal obstacle” to the performance and reliability of high-frequency products. The SSN problem has become increasingly severe due to the rise in circuit operating frequency and device density, posing a key challenge in high-speed circuit design. To address the challenge of suppressing SSN at the PCB board level in high-speed digital circuits, this paper proposes a collaborative optimization scheme integrating simulation analysis and the Seagull Optimization Algorithm (SOA). In this study, a multi-physical field coupling model of SSN is established to reveal that SSN essentially arises from the electromagnetic interaction between the parasitic inductance of the power distribution network (PDN) and high-speed transient current. Based on the research on frequency-domain impedance analysis, time-domain response prediction, and decoupling capacitor suppression mechanism, the limitations of traditional capacitor placement in suppressing GHz-level high-frequency noise are overcome. This method enables precise power integrity (PI) design via simulation analysis frequency-domain parameter extraction and power–ground noise simulation quantify PDN impedance characteristics and the coprocessor switching current spectrum; resonance analysis locates key frequency points and establishes an SSN–planar resonance correlation model to guide decoupling design; finally, noise coupling analysis optimizes signal–power plane spacing, markedly reducing mutual inductance coupling. On this basis, the SOA is innovatively introduced to construct a multi-objective optimization model, with capacitor frequency, capacitance value, and package size as variables. A spiral search algorithm is used to balance noise-suppression performance and cost constraints. Simulation results show that this scheme can reduce the SSN amplitude by 37.5%, effectively suppressing the signal integrity degradation caused by SSN and providing a feasible solution for SSN suppression. Full article

An early and precise diagnosis is essential for successful intervention in Alzheimer’s disease (AD), a progressive neurological illness. In this study, we present a deep learning-based framework for multiclass classification of AD severity levels using MRI neuroimaging data. The framework integrates multiple convolutional and transformer-based architectures with a novel hybrid hyperparameter optimization strategy; Snake+EVO surpasses conventional optimizers like Genetic Algorithms and Particle Swarm Optimization by skillfully striking a balance between exploration and exploitation. A private clinical dataset yielded a classification accuracy of 99.81%for the optimized CNN model, while maintaining competitive performance on benchmark datasets such as OASIS and the Alzheimer’s Disease Multiclass Dataset. Ensemble learning further enhanced robustness by leveraging complementary model strengths, and Grad-CAM visualizations provided interpretable heatmaps highlighting clinically relevant brain regions. These findings confirm that hybrid optimization combined with ensemble learning substantially improves diagnostic accuracy, efficiency, and interpretability, establishing the proposed framework as a promising AI-assisted tool for AD staging. Future work will extend this approach to multimodal neuroimaging and longitudinal modeling to better capture disease progression and support clinical translation. Full article

Sentinel animals may play a key role in the surveillance of arbovirus circulation, particularly in developing countries. This study aimed to assess the relevance of using dogs as sentinel animals for Rift Valley fever virus (RVFV) surveillance in Madagascar. Serological surveys were conducted on 513 dogs and 135 cattle in the Ifanadiana district, southeastern Madagascar. In addition, 486 human dry blood samples available from the same area were used. Antibodies against RVFV were detected in 23 of 513 dogs, in 86 of 486 humans, and in 33 of 135 cattle. Serocatalytic models fitted to age-stratified serological data were developed to estimate the RVFV force of infection (FOI) under several hypotheses, ranging from no relationship to proportional RVFV FOIs between humans, cattle, and dogs. The best supported model indicated that RVFV FOI in humans and cattle was proportional to RVFV FOI in dogs. Proportionality parameters were estimated at 2.6 (95% credible interval: [1.4–5.1]) for humans and 3.5 (95% credible interval: [1.3–6.4]) for cattle. Our findings suggested that dog blood samples could be used to identify RVFV circulation in RVF endemic areas and infer the exposure of humans and cattle in these areas in Madagascar. Full article

The photoinitiation properties of two porphyrins were evaluated for the free-radical photopolymerization (FRP) of a bio-based acrylated monomer, i.e., soybean oil acrylate (SOA). Their combination with various co-initiators, such as a tertiary amine as electron donor (MDEA), an iodonium salt as electron acceptor (Iod), as well as two biosourced co-initiators used as H-donors (cysteamine and N-acetylcysteine), makes them highly efficient photoinitiating systems for FRP under visible light irradiation. Electron paramagnetic resonance spin trapping (EPR ST) demonstrated the formation of highly reactive radical species, and fluorescence and laser flash photolysis highlighted the chemical pathways followed by the porphyrin-based systems under light irradiation. High acrylate conversions up to 96% were obtained with these different systems at different irradiation wavelengths (LEDs@385 nm, 405 nm, 455 nm, and 530 nm), in laminate or under air. The final crosslinked and bio-based porphyrin-based materials were used for the full photo-oxidation in water of an azo-dye (acid red 14) and under UV irradiation. These materials have been involved in three successive depollution cycles without any reduction in their efficiency. Full article

In the field of remote sensing (RS) object detection, efficient and accurate target recognition is crucial for applications such as national defense and maritime monitoring. However, existing detection methods either have high computational complexity, making them unsuitable for real-time applications, or suffer from feature redundancy issues that affect detection accuracy. To address these challenges, this paper proposes a Feature-Differentiated Perception (FDP) lightweight remote sensing object detection method, which optimizes computational efficiency while maintaining high detection accuracy. The proposed method introduces two critical innovations: (1) Dynamic mixed convolution (DM-Conv), which uses linear mapping to efficiently generate redundant feature maps, reducing convolutional computation. It combines features from different intermediate layers through weighted fusion, effectively reducing the number of channels and improving feature utilization. Channel refers to a single feature map in the multi-dimensional feature representation, where each channel corresponds to a specific feature pattern (e.g., edges, textures, or semantic information) learned by the network. (2) The Spatial Orthogonal Attention (SOA) mechanism, which enhances the ability to model long-range dependencies between distant pixels, thereby improving feature representation capability. Experiments on public remote sensing object detection datasets, including DOTA, HRSC2016, and UCMerced-LandUse, demonstrate that the proposed model achieves a significant reduction in computational complexity while maintaining nearly lossless detection accuracy. On the DOTA dataset, the proposed method achieves an mAP (mean Average Precision) of 79.37%, outperforming existing lightweight models in terms of both speed and accuracy. This study provides new insights and practical solutions for efficient remote sensing object detection in embedded and edge computing environments. Full article

In this study, a numerical model consisting of high-resolution hydrodynamic and Lagrangian particle tracking modules based on the Finite-Volume Coastal Ocean Model framework was established to simulate the hydrodynamic conditions and characteristics of the sedimentation of aquaculture-derived organic matter (AOM) from cage aquaculture in Sansha Bay. The results showed that Sansha Bay was characterized by regular semidiurnal tides and large tidal ranges. Reciprocating currents with main currents directed northward and southward during the rising and falling tides, respectively, predominated the main channels of the bay. Residual feed had larger settling velocities than feces. The maximal dispersion distances of residual feed and feces during the spring tide were 217.1 and 1805.7 m, respectively, three times those during the neap tide (74.2 and 675.6 m, respectively). During the spring tide, the largest dispersion distance of AOM occurred at the rush moment. The AOM movement trajectories were mainly controlled by the main currents. Both the tidal structure and current characteristics affected the AOM sedimentation in Sansha Bay. The sedimentation characteristics of AOM were unrelated to feeding intensity. The results of simulations agreed with the field observations in this study, suggesting that the estimated model had a good accuracy and sensitivity. Full article