Search Results (1,181)

Active non-line-of-sight (NLOS) human detection aims to infer the presence of hidden individuals by analyzing indirectly reflected photons between a relay surface and occluded targets. In this study, a single-photon avalanche diode (SPAD) and time-correlated single-photon counting (TCSPC)-based acquisition system were used to measure time–photon waveforms in controlled NLOS environments designed to represent post-disaster rubble scenarios. Although the effective temporal resolution of the system is limited by the detector timing jitter and laser pulse width, the recorded transient signals retain distinguishable intensity and temporal delay patterns associated with the primary and secondary reflections. To construct a representative dataset, measurements were collected under varying subject poses, orientations, and surrounding object configurations. The recorded signals were processed using a unified preprocessing pipeline that included normalization, histogram shaping, and signal windowing. Three machine learning models, namely, Convolutional Neural Network, Gated Recurrent Unit, and Random Forest, were trained and evaluated for human presence classification. All models achieved full sensitivity in detecting human presence; however, notable differences emerged in the classification of human-absent scenarios. Among the tested approaches, random forest achieved the highest overall accuracy and specificity, demonstrating stronger robustness to statistical variations in time–photon histograms under limited photon conditions. These results suggest that tree-based classifiers capture amplitude distribution patterns and temporal dispersion characteristics more effectively than deep neural architectures under the present acquisition constraints. Overall, the findings indicate that low-cost SPAD-based NLOS sensing systems can provide reliable human detection in indirect-observation scenarios. Full article

Despite five years of hazard tree investigation and mitigation (Potential Hazard Tree monitoring and hazard tree removal) from 2020 to 2024, the incidence ratios of hazard trees (HTs) and Potential Hazard Trees (PHTs) along the Alishan Forest Railway corridor (spanning subtropical, warm temperate, and temperate zones) have not exhibited a significant downward trend. This study aims to investigate the impacts of climate zones, succession, and functional groups on hazard tree occurrence and to further predict the incidence ratios of hazard trees. We employed Generalized Linear Models (GLMs) and structural defect frequency to evaluate these interactions. The significance of the impacts is ranked as follows: functional group > succession regeneration > climate zone. Incidence was highest in subtropical (S) and warm temperate (W) zones (S ≈ W > T), and significantly greater for secondary succession (SS) areas compared to Planning Plantation (PP). Crucially, heliophilous species (H + P; small-to-medium pioneer and canopy heliophilous species) contributed significantly more to hazard incidence than non-heliophilous species (MT + T; mid-shade-tolerant and shade-tolerant species). Model predictions identified (H + P) + SS + S as the highest-incidence ecological combination, while (MT + T) + PP + T was the lowest. Structural defect relative frequency analysis revealed that the fast-growth strategy of H + P species fundamentally compromises their biomechanical stability, resulting in significantly higher defect frequencies compared to MT + T species. Furthermore, continuous corridor disturbances maintain a persistent light environment that perpetually recruits these H + P species via secondary succession. To effectively manage the incidence of HT and PHT, future mitigation measures must prioritize Planning Plantation (PP) using non-heliophilous (MT + T) species selected within their appropriate ecological amplitudes. Full article

Oyamel forest, Abies religiosa (Kunth) Schltdl. et Cham., is a high-mountain ecosystem that contains abundant biodiversity, contributes to supporting traditional medicine, and represents a reservoir of medicinal plants. Despite this medicinal relevance, the potential of the flora of the Mexican Oyamel forest from Santuario del Agua Presa Corral de Piedra (SAPCP), Mexico, has been scarcely studied. This review focused on identifying the flora of the SAPCP which has been reported as medicinal resource in the literature through the recovery of ethnomedicinal uses and their proven pharmacological effects. In addition, phytochemical reports of the SAPCP medicinal flora and their pharmacological activities were integrated and analyzed to estimate their medicinal potential. The results showed that the SAPCP forest represents an important source of medicinal plants, with 39% of the total species reporting at least one ethnomedicinal use belonging to different taxonomic families, but mainly included Asteraceae, Lamiaceae, Rosaceae, and Solanaceae. The most commonly observed ethnomedicinal uses among all the species were against inflammation, infections, diarrhea, and diabetes, while antioxidant, antidiabetic, and anti-inflammatory effects were predominantly proven as pharmacological effects. The phytochemical results revealed a great diversity of secondary metabolites, although flavonoids, phenolic acids, and triterpenes were observed in a major number of species, many of which have been proven to exert anti-inflammatory, antidiabetic, and antibacterial effects through several action mechanisms. In conclusion, these results highlight the importance of sustainable management and the conservation of forest species, as they provide a reservoir of medicinal species that produce bioactive metabolites. Full article

(1) Research Highlights: This study provides the first integrated assessment of Scots pine (Pinus sylvestris L.) growing in the urban forests of Karaganda, Kazakhstan, a city consistently ranked among the most air-polluted cities globally. We examined the adaptive phyto-chemical response of needles to extreme technogenic stress and evaluated their dual potential as biological filters and renewable sources of bioactive compounds. (2) Background and Objectives: Urban forests are critical for mitigating air pollution; however, the biochemical responses of trees in heavily industrialized environments remain poorly understood. Karaganda faces severe atmospheric pollution from mining, metallurgy, and energy sectors, with particulate matter (PM) levels exceeding permissible limits by up to 20-fold. This study aimed to evaluate the state of Pinus sylvestris, a key component of local protective plantations, by studying heavy metal accumulation, anatomical localization of secondary metabolites, and the phytochemical profile and biological activity of needle extracts obtained using different extraction techniques. (3) Materials and Methods: Needles were collected from 15 trees across three sites in Karaganda’s industrial green zones. Heavy metal content (Pb, Cd, As, and Hg) was determined using atomic absorption spectroscopy and voltammetry. Anatomical–histochemical analysis localizes major metabolite classes. Liquid extracts were prepared using four methods, percolation (PER), vortex-assisted (VAE), microwave-assisted (MAE), and ultrasound-assisted (UAE) extraction, and analyzed by GC-MS. Antimicrobial activity was tested against S. aureus, B. subtilis, E. coli, and C. albicans using the disk diffusion method. The antioxidant capacity (water- and fat-soluble) was measured amperometrically. Statistical analysis was performed using one-way ANOVA with Tukey’s HSD test (p < 0.05). Results: Despite extreme ambient pollution, heavy metal concentrations remained below pharmacopoeial limits (Pb < 0.1, Cd < 0.05, As < 0.01, Hg < 0.001 mg/kg), indicating effective biofiltration without toxic accumulation. Histochemistry confirmed the active synthesis of protective phenolics, flavonoids, and essential oils in the mesophyll, epidermis, and schizogenic cavities. GC-MS identified 72 compounds in the PER extract, 70 (the VAE), 72 in (MAE), and 46 in (UAE). The PER extract exhibited the highest relative abundance of bioactive terpenoids: α-cadinol (5.24%), α-muurolene (4.32%), and caryo-phyllene (2.20%). UAE extracts exhibited elevated 5-hydroxymethylfurfural (6.90%), indicating degradation. Antimicrobial testing revealed that PER produced the largest inhibition zone against S. aureus (15.0 ± 1.0 mm), significantly exceeding that of the other methods (p < 0.001). PER extract also demonstrated the highest water-soluble antioxidant capacity (3600 ± 0.40 mg quercetin equiv./dm³) and substantial fat-soluble activity (1633 ± 0.23 mg gallic acid equiv./dm³). (4) Conclusions: Pinus sylvestris in Karaganda exhibits remarkable adaptive resilience, maintaining safe heavy metal levels while accumulating a rich repertoire of stress-induced secondary metabolites. Classical percolation optimally preserves this native phytocomplex, yielding extracts with superior antimicrobial and antioxidant properties. These findings support a dual-use model wherein urban pine plantations simultaneously serve as living biofilters and renewable sources of standardized bioactive extracts, a concept with direct implications for circular bioeconomy strategies in industrial regions worldwide. This supports the strategic importance of coniferous plantations for bioremediation and sustainable resource use in industrial regions. Full article

Root plasticity is vital for crop survival amid global warming. Yet, the molecular mechanisms governing wheat root thermotolerance remain largely unknown. In this study, we combined phenomics, transcriptomics, and metabolomics with machine learning to analyze the performance of heat-tolerant cultivar YM158 and heat-sensitive cultivar YM15 under varying heat stress. While high temperatures (35 °C) severely inhibited root growth and caused oxidative damage in YM15, YM158 maintained robust root architecture and redox balance. Using weighted gene co-expression network analysis (WGCNA) alongside the random forest feature selection algorithm, we identified the flavonoid biosynthesis pathway as central to thermotolerance. Protein–protein interaction network analysis revealed that wheat root adaptability to high temperatures involves maintaining protein homeostasis via the endoplasmic reticulum protein processing system, specifically activating the flavonoid biosynthesis pathway and enhancing the antioxidant enzyme system. Furthermore, we identified a potential regulatory hub involving the cell wall sensor FERONIA (FER) and heat shock factors (HSFs), highlighting a complex interaction between hormonal signaling and secondary metabolism. Our study offers a detailed map of root heat adaptation and positions the flavonoid-mediated antioxidant system as a promising target for breeding climate-resilient crops. Full article

Secondary succession in tropical forests is traditionally described as a linear process driven by time since disturbance. However, growing evidence suggests that recovery pathways depend strongly on historical and environmental contexts. We evaluated how disturbance legacies and edaphic constraints interact to shape successional trajectories in a tropical karst landscape of the Maya Forest, Mexico. We sampled 100 plots along a chronosequence, quantifying vegetation structure, floristic diversity, biomass (NDVI), disturbance legacies, and soil properties. Using unsupervised clustering (K-means) and multivariate ordination, we identified four contrasting ecological typologies that represent distinct successional states rather than transient stages. Our results show a pronounced dichotomy in vegetation dynamics following the abandonment of land-use practices: while some sites are experiencing diverse development due to positive forest legacies (Typology B), others remain stalled (Typology C), dominated by lianas, where biotic barriers inhibit tree regeneration despite decades of abandonment. Additionally, we documented an asynchronous recovery between floristic recovery and vertical development; in sites with edaphic constraints, forests reach high diversity and biomass but exhibit stunted growth (Typology D). This suggests that severe abiotic constraints—specifically high rockiness and shallow soils—limit the dominance of highly competitive species, thereby acting as a filter that maintains high levels of diversity despite structural limitations. Edaphic analysis confirmed that chemical fertility and physical constraints (rockiness and shallow depth) act as orthogonal filters. This explains the persistence of structurally constrained yet functionally mature forests as stable, edaphically determined outcomes. Overall, secondary succession in tropical karst is nonlinear and path-dependent, governed by a hierarchical filtering model where historical land use dictates community identity and physical substrate limits structural architecture. These findings highlight the need for trajectory-specific management and the abandonment of uniform expectations of forest recovery in karst landscapes. Full article

This study proposes an explainable machine learning framework for estimating the total project cost (TPC) of AI training-data construction, where cost information is difficult to structure due to heterogeneous workflows and quality requirements. Using 386 public AI training-data projects conducted between 2020 and 2022, we derive 24 numerical predictors from standardized final reports and construct three input tracks: a baseline feature set, a principal component analysis (PCA)-enhanced set, and a factor analysis (FA)–enhanced set capturing latent cost structures. Four regression models (Ridge, Random Forest, XGBoost, and LightGBM) are evaluated using nested cross-validation. XGBoost achieves the best overall performance across all three tracks (Baseline, PCA-enhanced, and FA-enhanced). Among them, PCA-enhanced XGBoost attains the highest predictive accuracy (R² = 0.868; RMSE = 1084.9; MAE = 746.9; MAPE = 0.358; pooled out-of-fold), while Baseline XGBoost yields the lowest MAE (731.4; R² = 0.863). To support transparent decision-making, Shapley Additive exPlanations (SHAP)-based attribution and scenario-based sensitivity analyses are conducted. Results show that project scale and process-level unit costs are dominant cost-drivers, while cloud usage, expert participation, and de-identification requirements exhibit secondary effects. The proposed framework provides an interpretable, data-driven approach to cost information management and decision support for data-intensive AI projects. Full article

Rapid urbanization has seriously interfered with the carbon sink function of forests, and has even led to an increased risk of forest carbon imbalance. It is important to explore the regional carbon compensation mechanism and zoning optimization path based on forest carbon accounting to achieve the “dual carbon” goal and sustainable forest management in Fujian Province. Based on remote sensing and GIS technologies, this study measured forest carbon emissions and carbon sequestration of each county in Fujian Province, revealed spatial and temporal evolution of forest carbon budget during the period from 2000 to 2020, and calculated carbon compensation value of each county, so as to realize scientific accounting of forest carbon compensation, and then explored zoning optimization pathways of forest carbon compensation in Fujian Province. The results show the following: (1) From 2000 to 2020, the forest carbon budget in Fujian Province as a whole showed a spatial pattern of “coastal deficit, northwest surplus”, with obvious spatial imbalance characteristics, and showed a high growth trend of net carbon sequestration. (2) From 2000 to 2020, the average carbon compensation rate in Fujian Province was 7.92, and compensation zones were mainly concentrated in the economically developed southeast coastal regins such as Fuzhou, Quanzhou, Xiamen, Zhangzhou, and Putian, while compensation-receiving zones were mainly concentrated in northwestern mountainous areas such as Nanping, Ningde, and Longyan, which had a high forest coverage rate. (3) From 2000 to 2020, there was a significant difference in growth rates of compensation amounts and compensation-receiving amounts in Fujian Province. The cumulative increase in compensation amounts was 322.82%, while the cumulative increase in compensation-receiving amounts was only 17.5%. (4) Based on priority levels, the counties in Fujian Province are classified into six types of forest carbon compensation zones—potential compensation zones, secondary compensation zones, priority compensation zones, potential compensation-receiving zones, secondary compensation-receiving zones and priority compensation-receiving zones—and optimization paths of differentiated zones are explored. Full article

Soil fungi are pivotal drivers of biogeochemical cycling, mediating nutrient transformation, plant–soil feedbacks, and ecosystem stability. Understanding their responses to vegetation succession is essential for predicting ecosystem recovery in fragile volcanic landscapes. We investigated soil fungal communities across five successional stages on the Jingpo Lake lava plateau—grassland (GL), shrubland (SL), deciduous broad-leaved forest (DB), coniferous and broad-leaved mixed forest (CB), and coniferous forest (CF)—using high-throughput ITS sequencing and soil physicochemical analysis. Basidiomycota and Ascomycota dominated at the phylum level, with Sebacina, Cortinarius, and Mortierella as core genera. Alpha diversity (Shannon, Simpson, Chao1) was significantly higher in early-successional GL and SL than in DB (p < 0.05), while CB exhibited the lowest community evenness (Pielou-e). Co-occurrence networks revealed greater connectivity in GL, whereas forest types showed simplified topologies. LEfSe identified distinct fungal biomarkers for each vegetation type. PICRUSt2-based functional prediction indicated biosynthesis as the dominant pathway (>40%), with significant variation among vegetation types. Redundancy analysis (RDA) identified soil organic matter (SOM) as the primary predictor of fungal community composition. Our findings indicate that vegetation succession is associated with changes in fungal diversity and function primarily linked to variations in SOM, with moisture regimes as a secondary contextual factor. Notably, advanced forest stages exhibited reduced fungal diversity and simplified community structure—highlighting a trade-off between nutrient enrichment and microbial complexity in volcanic ecosystems. These insights advance our understanding of plant–soil–microbe coupling during ecosystem restoration on lava plateaus. Full article

Secondary broadleaf forests constitute a vital component of China’s forest resources, characterized by diverse ecological functions, strong regeneration capacity, and widespread distribution. They possess significant potential for carbon storage, yet their carbon sink capacity is influenced by multiple factors, including successional stage, tree species composition, soil conditions, and human disturbance levels. However, the response mechanism of carbon sequestration capacity in secondary broadleaf forest ecosystems to thinning intensity remains unclear. This study aims to elucidate the effects of different thinning intensities (0% (CK), 10% (LT), 25% (MT), and 35% (HT)) on soil greenhouse gas (GHG) emissions, vegetation, and soil organic carbon sinks. Results indicate that total GHG emissions increased by 1.9%, 31.86%, and 42.18% under LT, MT, and HT, respectively. Vegetation carbon sequestration decreased by 5.26% and 16.22% under LT and MT, respectively, while increasing by 13.17% under HT. Soil organic carbon sequestration increased by 37.33% under LT, but decreased by 5.89% and 61.41% under MT and HT, respectively. In summary, compared with the control, ecosystem carbon sequestration increased by 30.66% in LT, while decreasing by 32.06% and 71.73% in MT and HT, respectively. Our study indicates that light thinning intensity can enhance the carbon sequestration potential of ecosystems and effectively mitigate climate change. Full article

Background: Vitamin D plays an important role in immune regulation, and vitamin D deficiency has been increasingly associated with susceptibility to infection and adverse outcomes in critically ill patients. Numerous systematic reviews and meta-analyses have examined the relationship between vitamin D status, vitamin D receptor (VDR) gene polymorphisms, and sepsis; however, the evidence remains fragmented. Objective: The aim of this work was to synthesize high-level evidence on the association between vitamin D deficiency, VDR gene polymorphisms, vitamin D supplementation, and sepsis-related outcomes through a PRISMA 2020-compliant umbrella review. Methods: An umbrella review of systematic reviews and meta-analyses published between 2014 and 2025 was conducted using PubMed, PubMed Central, and journal archives. Eligible studies included adult, pediatric, and neonatal populations and evaluated sepsis incidence, mortality, disease severity, secondary outcomes, and genetic associations. Data were synthesized qualitatively due to overlap of primary studies and heterogeneity. Conceptual forest plots and funnel plots were used to summarize evidence direction and potential publication bias. Results: Nineteen systematic reviews and meta-analyses encompassing over 300 primary studies were included. Vitamin D deficiency was consistently associated with an increased risk of sepsis, higher mortality, and greater disease severity across adult and pediatric populations. Stronger associations were observed in children and neonates, including higher PRISM III scores, increased need for mechanical ventilation, and longer hospital stays. VDR gene polymorphisms were modestly but consistently associated with increased sepsis susceptibility. In contrast, vitamin D supplementation did not demonstrate a consistent reduction in sepsis risk or mortality. Conclusions: Vitamin D deficiency is a robust marker of sepsis risk, severity, and poor prognosis, whereas current evidence does not support vitamin D supplementation as an effective treatment for established sepsis. Full article

Effectiveness of forest restoration efforts depends on the methods employed. Here, we compared tree species composition, niche characteristics, and mammal habitat use in primary, secondary, and artificial forests in Wanglang Nature Reserve. Results showed that primary forests were mainly indicated by Abies fargesii var. faxoniana (Af), Picea purpurea (Pp), and Juniperus saltuaria (Js); secondary forests were mainly indicated by Af and Betula albosinensis (Ba); and artificial forests were mainly indicated by Picea asperata (Pa) and Acer caesium (Ac). Af had the broadest niche breadth in natural forests, and Pa had the broadest niche breadth in artificial forests. Low niche overlap among common species was observed in natural forests, whereas high niche overlap between Pa and Ba occurred in artificial forests. Interspecific correlations showed that Af was negatively correlated with Pp in primary forests and Populus szechuanica (Ps) in secondary forests. In artificial forests, Af and Ac were positively correlated. Furthermore, no traces of the three National Class I protected species were found in artificial forests, while traces of two representative mammals were associated with Af. These findings highlight the differences among the three types of forests. Full article

The quality of wind power output data directly impacts the assessment of wind farm operational status and the accuracy of power forecasting models. However, due to factors such as sensor precision, communication interference, and the complex harbor environment, raw data collected from port-area wind turbines often contain noise, outliers, and missing values. Without effective cleaning, the resulting power curves can be distorted, reducing the generalization capability of predictive models. To overcome the limitations of traditional outlier detection methods in terms of adaptability and robustness, this study proposes a two-stage port-area wind power data cleaning approach based on dynamic interquartile range and an improved Sigmoid function fitting. In the first stage, an adaptive binning and density-weighting mechanism dynamically expands the interquartile range to identify and remove local outliers across different wind speed intervals. In the second stage, the cleaned wind speed–power data are subjected to secondary fitting and residual analysis using an improved Sigmoid model to detect hidden anomalies and boundary-type outliers. Using measured data from the #1 WT in the Chuanshan Port area as a case study, the experimental results demonstrate that the proposed method achieves high data retention while outperforming the conventional interquartile range, density-based spatial clustering of applications with noise and isolation forest algorithms in terms of the Pearson correlation coefficient (r = 0.93) and the coefficient of determination (R² = 0.89), with mean squared error and root mean squared error reduced to 446.39 kW and 545.58 kW, respectively. The findings verify the efficiency, stability, and practical feasibility of the method for port-area wind power data cleaning, providing a reliable data foundation for wind power forecasting and operational optimization in port environments. Full article

Accurate electrical power prediction is increasingly critical in industrial smart manufacturing environments, where energy fluctuations and demand variability pose significant operational challenges under the industry 4.0 paradigm. Existing approaches often rely on simulated or secondary data and lack integration with industrial-grade communication protocols, limiting their practical applicability. Incorporating machine learning with real-time data collection is essential for progressing industrial predictive monitoring. This research presents a framework to forecast electrical power usage by utilizing the RS-485 protocol to enhance smart manufacturing processes. The dataset used was obtained from a power meter, recorded over a period of 135 min, resulting in 3100 data. Three learning methods—Random Forest, Extra Trees, and XGBoost—were analyzed, with XGBoost being further refined through PSO for tuning hyperparameters. The models were trained on datasets that included voltage, current, frequency, and power factor, and their effectiveness was evaluated using time-based predictions, standard metrics, and error distributions through cross-validation. The findings illustrate that the PSO-XGBoost consistently surpasses the default XGBoost baseline R² of 0.5746, achieving MAE of 0.14 W, RMSE of 0.21 W, and R² of 0.8355, representing improvements of 41.67% in MAE, 38.24% in RMSE, and 45.40% in R². The RS-485 protocol enables seamless integration with existing industrial infrastructure, supporting anomaly detection and energy optimization aligned with Industry 4.0 interoperability objectives. Full article

Utilizing bamboo residues for the cultivation of Stropharia rugosoannulata is an ecological practice grounded in the concept of agricultural waste recycling, aiming to improve soil microecology and enhance nutrient cycling in bamboo forests. However, a comprehensive and systematic evaluation of the ecological effects of using bamboo residues as cultivation substrates is lacking. To evaluate soil responses following the cultivation of S. rugosoannulata, a field experiment was conducted using bamboo residues pre-fermented with 4% rapeseed cake. The results showed that cultivating S. rugosoannulata with rapeseed cake-fermented bamboo residues significantly enhanced soil nutrient levels and enzyme activities. Notable increases were observed in soil organic carbon, total nitrogen, available nitrogen, and total potassium, as well as in the activities of sucrase, urease, peroxidase, polyphenol oxidase, and neutral protease. Both bacterial and fungal α-diversity were significantly enhanced, and substantial shifts occurred in the community structure and composition of soil microbiota. Metabolomic analysis revealed that significantly differential metabolites were primarily enriched in five key pathways, including purine metabolism, glycerolipid metabolism, biosynthesis of plant secondary metabolites, and starch and sucrose metabolism. Correlation analyses further revealed that specific microbial taxa (four bacterial genera and seven fungal genera) exhibited strong correlations with soil nutrient indicators, whereas another group of taxa (six bacterial phyla and eight fungal genera) was closely linked to soil enzyme activities. Furthermore, bacterial communities were significantly correlated with metabolite variations after substrate addition. Specifically, Firmicutes showed strong positive correlations with multiple metabolites, whereas Planctomycetes exhibited negative correlations with some of the same metabolites, indicating potential competitive interactions. Based on these findings, this study proposes a preliminary “Microbe–Enzyme–Metabolite–Nutrient” coupling cycle, driven by the synergistic interplay among bamboo residues, hypha–microbiome complex, soil enzymes, and functional metabolites. This mechanism provides a scientific explanation for the soil health improvements observed during S. rugosoannulata cultivation and offers theoretical support for the efficient utilization of bamboo waste and maintenance of forest ecosystem stability. Full article