Coal fires are pervasive global issues that affect the environment continuously, particularly in coal-rich regions; these fires can cause significant environmental damage, safety hazards, and economic losses. While numerous studies have investigated coal fires using remote sensing techniques, research integrating multisource remote sensing data for comprehensive coal fire zone detection and monitoring remains relatively limited. Current methods often analyze different data sources independently, limiting our understanding of the complex relationships between various surface manifestations of coal fires. This study presents a novel comprehensive analysis method employing multisource remote sensing technology to identify and monitor coal fires. Using 29 Landsat-8 images from Sulabulak fire area, we derived fractional vegetation cover (FVC) and land surface temperature (LST) parameters to identify vegetation loss patterns and thermal anomalies. In addition, 135 dual-polarized Sentinel-1A images were analyzed using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) and persistent scatter interferometric synthetic aperture radar (PS-InSAR) techniques to obtain surface deformation data. The integration of these datasets, validated by field survey data, revealed a significant correlation between the identified coal fire zones and subsidence areas. Our results revealed an increase in sparse vegetation areas of 2.77 km2, an expansion of high-temperature anomalies of 0.75 km2, and a cumulative surface subsidence of -123.9mm in the study area. These findings indicate ongoing and intensified coal fire combustion as well as an expansion of coal fire zones. The effectiveness of this method in identifying coal fire areas highlights its potential for enhancing coal fire monitoring and management strategies.

Sustainable aquaculture production critically depends on continuous monitoring of water quality parameters such as pH, temperature, turbidity, and dissolved oxygen (DO). Conventional manual monitoring techniques are labor-intensive and incapable of providing real-time insights, often leading to delayed intervention and economic losses. This paper presents OxyTrack Pro, a low-cost, AI-enabled Internet of Things (IoT) framework designed for real-time water quality monitoring and intelligent fish suitability prediction. The proposed system integrates multi parameter sensing using an ESP32 microcontroller with cloud-based analytics and a Random Forest regression model for predictive analysis. Sensor data are transmitted via Wi-Fi to a cloud platform and visualized through a mobile dashboard. Experimental evaluation on 1,200 labeled samples demonstrates a prediction accuracy of 95.2%, with low latency (2.6–3.1 s) and reliable alert generation. The proposed framework offers an affordable and scalable solution for smart aquaculture management. Keywords— Artificial Intelligence, Aquaculture Monitoring, ESP32, Fish Prediction, Internet of Things, Water Quality

Streptococcus suis (S. suis) is a zoonotic pathogen that causes severe economic losses in the swine industry and life-threatening infections in humans. The high serotype variability and genomic diversity of S. suis have hindered the development of cross-protective vaccines. Although recent advances in in silico prediction and database-driven antigen discovery have accelerated the development of protein-based vaccines, discrepancies between predicted immunogenicity and experimentally verified protective efficacy in animal models emphasize the need to integrate computational design with empirical validation. Using an in silico-assisted design strategy, predicted T and B cell epitope-rich domains from five S. suis antigens (HP0197, Fnbp, Sao, ScpB, and SLY) were assembled into a multimeric vaccine construct, designated ATOMSSUISpenta, through optimization for predicted immunogenicity, solubility, and allergenicity. Vaccine immunogenicity and efficacy were evaluated in mice through antigen-specific antibody profiling, cellular immunity analysis, and in vivo assessment of protective and cross-serotype immunity. ATOMSSUISpenta elicited strong antigen-specific humoral immune responses against all five component antigens in a mouse model. The vaccine also induced robust Th1- and Th17-type cellular immune responses, which are critical for effective opsonic and mucosal defense against S. suis infection. In addition, we found that ATOMSSUISpenta conferred significant protection in a S. suis serotype 2 infection model and induced opsonic antibody activities against serotypes 4 and 9. These findings highlight the potential of ATOMSSUISpenta as a subunit vaccine strategy with potential for broader protection against S. suis and demonstrate the effectiveness of epitope-based multimeric design in targeting antigenically diverse Gram-positive pathogens.

Pseudomonas syringae functions as a model phytopathogen causing numerous crop diseases, resulting in substantial economic losses in global agriculture. Presently, management of P. syringae predominantly depends on chemical pesticides; however, their prolonged application has contributed to escalating resistance and environmental contamination, highlighting urgent requirement for sustainable biological control approaches. In this review, we examine recent advances in the utilization and mechanistic understanding of natural products derived from plants, animals, and microorganisms for the control of P. syringae. Plant-derived compounds—including flavonoids, terpenoids, and alkaloids—inhibit P. syringae infection by targeting the bacterial type III secretion system (T3SS), disrupting cell membrane integrity, promoting reactive oxygen species (ROS) accumulation, and activating plant immune signaling pathways such as salicylic acid (SA) and jasmonic acid (JA) cascades. Animal-derived substances, such as chitosan, propolis, and antimicrobial peptides, primarily exert antibacterial effects through membrane disruption and immune system stimulation. Microbial-derived natural products contribute to synergistic disease suppression by modulating host immunity and interfering with the pathogen’s quorum sensing mechanisms. Evidence indicates that these natural products possess multi-target antimicrobial properties, offering a rich repository of candidate molecules, such as baicalein, lignans, and carvacrol, for the development of eco-friendly antibacterial agents. Future investigations should focus on detailed characterization of these bioactive compounds and their specific disease targets, optimization of extraction methodologies to improve stability and bioavailability, and comprehensive assessment of environmental safety to advance the industrial implementation of sustainable biocontrol strategies

Abstract This paper describes the synoptic and mesoscale meteorology associated with the strong Santa Ana event of January 7-12, 2025, which resulted in a catastrophic wildfire with over $150 billion in economic loss and 31 deaths. Strong northerly and northeasterly low-level winds, reaching record levels at some locations, resulted from an unusually intense mid-tropospheric low to the south of the Los Angeles basin. With higher pressure/heights over the northeast Pacific, strong crest-level flow developed over the Southern California Transverse Ranges, with intense downslope flow on the southern, lee slopes. High-resolution model simulations produced a highly realistic wind evolution over the region. The strongest winds, some exceeding 80 kt, occurred on the southwestern slopes of the San Gabriel Mountains and were associated with high-amplitude mountain wave activity. The predictability of the event was high, with substantial skill within three days of the start of the strong, dry winds. The prior autumn and early winter months were the driest on record in the region, ensuring surface fuels would be dry and flammable. Furthermore, the two previous winter seasons were considerably wetter than normal, producing large fuel loads.

Climate change is increasing forest vulnerability, and extreme disturbances such as windstorms can cause major economic and social losses. Forest recovery after such events often relies on salvage logging and extensive planting of seedlings produced in nurseries to rapidly restore forest cover. While effective, these interventions, particularly when applied over large areas, may also produce environmental impacts that are largely absent under spontaneous regeneration. Following the Vaia windstorm in northern Italy in 2018, several reforestation interventions were implemented to restore forest cover. We focused on one intervention and conducted a life cycle assessment to quantify its environmental impacts, using the planting of 800 four-year-old Norway spruce (Picea abies (L.) H. Karst) seedlings as the functional unit, combined with chipping on the site of forest biomass residues. The largest contributions were to global warming potential (443.91 kg CO2 eq), human toxicity (167.72 kg 1,4-DCB eq), and freshwater ecotoxicity (142.43 kg 1,4-DCB eq). Seedling production and field establishment dominated these impact categories. Among field operations, manufacturing and transporting plastic shelters for seedling protection accounted for the highest share of global warming potential.

Traffic congestion is increasingly recognized not only as a transportation issue but also as a significant source of economic inefficiency, ‎particularly in developing and medium-sized cities. This study estimates congestion-related productivity losses at a major intersection in ‎Batangas City, Philippines, with emphasis on variations across days of the week. Using a value-of-time (VOT) framework, congestion ‎costs were estimated from observed travel time delays and additional fuel consumption during morning (AM) and afternoon (PM) peak ‎hours over seven consecutive days. Traffic data were obtained from closed-circuit television (CCTV) footage provided by the Batangas City Transportation Development Regulatory Office (TDRO) and supplemented by a brief key-informant interview to contextualize ‎the observed traffic conditions. Results show clear day-of-week variation in congestion costs, with weekdays, particularly Mondays, incurring the highest economic and productivity losses. Across all days, time-related productivity loss constituted the dominant share of total ‎congestion cost. The findings highlight how routine traffic delays translate into measurable economic losses and underscore the importance of integrating economic and productivity considerations into local traffic management and policy decisions in growing urban ‎areas‎.

Dairy spoilage enzymes: A review of impacts and diagnostic tools.

Decoding the impact of extreme weather on aviation safety and economic losses using the SELENA-Net deep learning model

Twin births in dairy cattle are rare but present significant challenges for animal welfare, as both the health of the cow and the calves are affected. This causes economic losses, which prompts breeders to select against twin births and identify associated risk factors. This study examines the phenotypic relationship between milk yield, fertility traits and twin births in German Holstein cattle using a large, population-wide dataset. GEBV correlations for twin births, milk production and fertility traits were estimated. Genome-wide association studies (GWAS) were conducted for calving numbers 1-3 in order to explore the genetic background in more detail. The twin birth rate showed a strong phenotypic association with milk production and a moderate phenotypic association with the timing of successful insemination. However, GEBV correlations were low: 0.04 with milk yield and -0.10 to 0.01 with fertility traits. GWAS revealed two potential candidate genes on BTA11: LHCGR and FSHR, which encode receptors for LH and FSH, two hormones crucial to estrus. In contrast to the first calving, significantly associated regions on BTA5 and BTA25 were found in calving numbers 2 and 3. This study demonstrates the interaction between genotype and environment, concluding that a genetic predisposition for twin births, in combination with a favourable endocrine state (environment), increases the likelihood of twin births.

Use of hyperspectral imaging for the early detection of foodborne pathogens.

HAB species of the Bohai Sea detected through metabarcoding in three large-scale seasonal expeditions.

Gut microbiota dysbiosis and predicted metabolic functional shifts associated with fowl typhoid in layer hens.

Plant disease is a significant challenge for agriculture, leading to reduced yield, economic loss, and environmental impact. Leveraging digital photos of plant leaves, convolutional neural networks (CNNs) have emerged as promising tools for disease detection. The methodology involves several steps, including image pre-processing, segmentation, feature extraction using CNNs. Crucially, a diverse dataset comprising images of both healthy and diseased leaves under varying conditions is necessary for training accurate models. Transfer learning, particularly with pre-trained models like ImageNet, can further enhance accuracy, allowing for better performance with fewer training samples. The proposed method demonstrates impressive results, achieving over 95% accuracy, outperforming existing state-of-the-art techniques. This system could serve as a valuable tool for farmers, facilitating timely disease identification and treatment, ultimately leading to increased agricultural yields, reduced financial losses, and the adoption of more sustainable farming practices. Additionally, beyond its practical applications, the proposed system holds promise for advancing sustainable agriculture by promoting environmentally friendly farming methods and contributing to the overall resilience and productivity of agricultural systems.

The Siberian moth, Dendrolimus sibiricus Tschetverikov, is one of the most destructive conifer pests in Northern Asia, causing severe ecological and economic losses. In Russia, its range overlaps with that of the closely related pine-tree lappet Dendrolimus pini (L.), and this raises the potential for hybridization and complicates accurate identification, particularly in the context of the potential westward expansion of D. sibiricus. Here, we present the first comprehensive morphometric analysis of male genitalia aimed at distinguishing these two major forest pests and their hybrids. The study was based on D. sibiricus and D. pini specimens collected during the last 130 years (1894–2024) across Europe and Asia, including their hybrids reared indoors by crossing D. pini females with D. sibiricus males in 1956 and preserved in the collection of the Zoological Institute of the Russian Academy of Sciences (St. Petersburg, Russia). Overall, 70 permanent genitalia slides were prepared (33 D. sibiricus, 33 D. pini, and 4 hybrids), and the following genital structures were measured: valva and harpe length, aedeagus width and length, and cornuti length. Dendrolimus sibiricus had significantly larger genital structures compared to D. pini: 74% longer harpe, 32% longer valva, and a 28% wider and longer aedeagus. In contrast, in D. sibiricus cornuti were 21% shorter than in D. pini. Hybrids displayed intermediate values for valva, harpe, and aedeagus lengths, and for these parameters, they significantly differed from both parental species. The following diagnostic indices were suggested to distinguish between the two species and their hybrids: Harpe Length/Valva Length Index (HL/VL) and Cornuti Length/Aedeagus Length Index (CL/AL). Decision-tree analysis identified HL/VL as the strongest predictor for separating the parental species and the Combined Genital Proportion Index (CGPI), which integrates harpe, valva, aedeagus, and cornuti lengths, as the strongest predictor for identifying hybrids. The morphometric criteria developed here have practical applications for monitoring programs and quarantine diagnostics, particularly in sympatric zones and regions at risk of D. sibiricus expansion.

Protective behavior of bacterial biofilm at high temperatures: Corrosion control strategies for thermal system pipes.

Assessment of genetic diversity and pathogenicity of porcine rotavirus A, and immunogenicity of a bivalent inactivated vaccine in southern China.

High-throughput and rapid classification on harmful algal bloom species based on mega image database and artificial intelligence.

Development and evaluation of a live-attenuated novel duck reovirus vaccine strain E232-P100 conferring complete protection in ducklings.

Deep learning-based detection and viability assessment of Eimeria oocysts.