Search Results (76)

Changes in habitat suitability are critical indicators of the ecological impacts of climate change. Syndiclis Hook. f., a rare and endangered genus endemic to montane limestone and cloud forest ecosystems in China, holds considerable ecological and economic value. However, knowledge of its current distribution and the key environmental factors influencing its habitat suitability remains limited. In this study, we employed the MaxEnt model, integrated with geographic information systems (ArcGIS), to predict the potential distribution of Syndiclis under current and future climate scenarios, identify dominant bioclimatic drivers, and assess temporal and spatial shifts in habitat patterns. We also analyzed spatial displacement of habitat centroids to explore potential migration pathways. The model demonstrated excellent performance (AUC = 0.988), with current suitable habitats primarily located in Hainan, Taiwan, Southeastern Yunnan, and along the Yunnan–Guangxi border. Temperature seasonality (bio7) emerged as the most important predictor (67.00%), followed by precipitation of the driest quarter (bio17, 14.90%), while soil factors played a relatively minor role. Under future climate projections, Hainan and Taiwan are expected to serve as stable climatic refugia, whereas the overall suitable habitat area is projected to decline significantly. Combined with topographic constraints, population decline, and limited dispersal ability, these changes elevate the risk of extinction for Syndiclis in the wild. Landscape pattern analysis revealed increased habitat fragmentation under warming conditions, with only 4.08% of suitable areas currently under effective protection. We recommend prioritizing conservation efforts in regions with habitat contraction (e.g., Guangxi and Yunnan) and stable refugia (e.g., Hainan and Taiwan). Conservation strategies should integrate targeted in situ and ex situ actions, guided by dominant environmental variables and projected migration routes, to ensure the long-term persistence of Syndiclis populations and support evidence-based conservation planning. Full article

The increasing complexity of financial reporting has enabled the implementation of innovative accounting practices that often obscure a company’s actual performance. This project seeks to uncover manipulative behaviours by constructing an anomaly detection model that utilises unsupervised machine learning techniques. We examined a dataset of 149,566 Slovak firms from 2016 to 2023, which included 12 financial parameters. Utilising TwoSteps and K-means clustering in IBM SPSS, we discerned patterns of normative financial activity and computed an abnormality index for each firm. Entities with the most significant deviation from cluster centroids were identified as suspicious. The model attained a silhouette score of 1.0, signifying outstanding clustering quality. We discovered a total of 231 anomalous firms, predominantly concentrated in sectors C (32.47%), G (13.42%), and L (7.36%). Our research indicates that anomaly-based models can markedly enhance the precision of fraud detection, especially in scenarios with scarce labelled data. The model integrates intricate data processing and delivers an exhaustive study of the regional and sectoral distribution of anomalies, thereby increasing its relevance in practical applications. Full article

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

During the last few decades, several approximate, but useful, methods for including dynamical quantum effects in molecular simulations have been developed. These methods can be applied to systems with hundreds of degrees of freedom and with arbitrary potentials. Among these methods, we find the Feynman–Kleinert linearized path integral model, including its planetary versions, which are the focus of this review. The aim is to calculate quantum correlation functions for complex systems. Many important properties, e.g., transport coefficients, may thus be obtained. We summarize important applications of the method, and compare them to alternative ones, such as centroid molecular dynamics and ring polymer molecular dynamics. We finally discuss possible future improvements of the FK-LPI method. Full article

Concrete bridge cracks are critical indicators for maintenance planning. Traditional visual inspections are often subjective, labor-intensive, and time-consuming, requiring close-range access by inspectors. In contrast, UAV-based remote sensing, combined with advanced image processing, offers a more efficient and accurate solution. This study proposes an enhanced crack detection method combining Laplacian of Gaussian (LoG) filtering and Otsu thresholding to improve segmentation accuracy through background noise suppression. The proposed approach extracts key crack characteristics—including area, length, centroid, and main direction—enabling precise damage assessment. Experimental validation on a real bridge dataset demonstrates significant improvements in detection accuracy. The method provides a reliable tool for automated structural health monitoring, supporting data-driven maintenance decisions. Full article

In this paper, we study the 2D Shape Equipartition Problem (2D-SEP) with minimal boundaries, and we propose an efficient method that solves the problem with a low computational cost. The goal of 2D-SEP is to obtain a segmentation into N equal-area segments (regions), where the number of segments (N) is given by the user under the constraint that the length of boundaries between the segments is minimized. We define the 2D-SEP, and we study problem solutions using basic geometric shapes. We propose a 2D Shape Equipartition algorithm based on a fast balanced clustering method (SEP-FBC) that efficiently solves the 2D-SEP problem under complex 2D shapes in O($N·\left|S\right|·log\left(\right|S\left|\right)$), where $\left|S\right|$ denotes the number of image pixels. The proposed SEP-FBC method initializes clustering using centroids provided by the k-means algorithm, which is executed first. During each iteration of the main SEP-FBC process, a region-growing procedure is applied, starting from the smallest region and expanding until regions of equal area are achieved. Additionally, a Particle Swarm Optimization (PSO) method that uses the SEP-FBC method under different initial centroids has also been proposed to explore better 2D-SEP solutions and to show how the selection of the initial centroids affect the performance of the proposed method. Finally, we present experimental results on more than 2800 2D shapes to evaluate the performance of the proposed methods and illustrate that their solutions outperform other methods from the literature. Full article

The development of beverages with functional potential and maximum sensory acceptability are priorities for the food industry. This study investigated the effect of orange juice (OJ), lemon juice (LJ) and ginger juice (GJ) concentrations on overall acceptability (OA), total phenic compounds (TPC) and vitamin C (VC). The experimental design used was simplex-centroid mixtures, and maximization was achieved using response surface methodology (RSM). The bioactive content of TPC and VC was measured using the Folin–Ciocalteu method and the dichlorophenol indophenol method, respectively; sensory evaluation was conducted with 100 panelists on a continuous scale acceptability primer (0–10 points). The RSM results indicated that the optimum formulation was obtained with: OJ = 79.8 mL, LJ = 5.7 mL and GJ = 4.37 mL, yielding maximum values of OA = 6.81 points, TPC = 14.64 mg GAE/100 g and VC = 32.3 mg/mL. The optimized beverage presented a free radical scavenging capacity of 65.72% (DPPH method). The regression models were validated and significantly (p < 0.05) predicted sensory acceptability, total phenolic compounds and vitamin C content. The development of this beverage and its potential application in the food industry is attractive due to its high sensory acceptability and bioactive content. Full article

This work describes the development of a method for the extraction of methylxanthines from cocoa bean shell (CBS) by employing the novel Energized Dispersive Guided Extraction (EDGE) system. The mixtures were composed of ethanol–methanol–water and the ratio was optimized using a simplex-centroid design. Doehlert design (DD) was used to optimize the variables of temperature and time while using methylxanthine content obtained by HPLC-DAD as an analytical response. The optimized mixture consisted of water–ethanol in a 3:2 ratio. The optimum operating conditions for extraction were achieved at a temperature of 148.5 °C and 382 s. Under optimal conditions, 20.14 mg g⁻¹ DM of theobromine and 3.53 mg g⁻¹ DM of caffeine were found in the CBS extract. Methylxanthines were quantified with good linearity, LOQs, LODs, precision, and accuracy. The EDGE system, a newly automated extraction instrument, has proven to be very efficient for the recovery of theobromine and caffeine, and is considered a green extraction procedure, as demonstrated by the analytical greenness metric for sample preparation. Full article

Net primary productivity (NPP) is a core ecological indicator within terrestrial ecosystems, representing the potential of vegetation growth to offset anthropogenic carbon emissions. Thus, assessing NPP in a given region is crucial for promoting regional ecological restoration and sustainable development. This study utilized the CASA model and GEE to calculate the annual average NPP in Shandong Province (2001–2020). Through trend analysis, Moran’s Index, and PLS−SEM, the spatiotemporal evolution and driving factors of NPP were explored. The results show that: (1) From 2001 to 2020, NPP in Shandong showed an overall increasing trend, rising from 254.96 to 322.49 g C·m⁻²/year. This shift was accompanied by a gradual eastward movement of the NPP centroid, indicating significant spatial changes in vegetation productivity. (2) Regionally, 47.9% of Shandong experienced significant NPP improvement, 27.6% saw slight improvement, and 20.1% exhibited slight degradation, highlighting notable spatial heterogeneity. (3) Driver analysis showed that climatic factors positively influenced NPP across all four periods (2005, 2010, 2015, 2020), with the strongest impact in 2015 (coefficient = 0.643). Topographic factors such as elevation and slope also had positive effects, peaking at 0.304 in 2015. In contrast, human activities, especially GDP and nighttime light intensity, negatively impacted NPP, with the strongest negative effect in 2010 (coefficient = −0.567). These findings provide valuable scientific evidence for ecosystem management in Shandong Province and offer key insights for ecological restoration and sustainable development strategies at the national level. Full article

Xanthium spinosum (X. spinosum) is a highly invasive weed native to South America and distributed in 17 provinces (municipalities) of China. It has severely negative influences on ecosystems, agriculture, and husbandry. However, few studies have reported on the impact of human activity and climate change on the future distribution and centroid shift of X. spinosum. This study aimed to investigate the potential geological distribution of X. spinosum in China, as well as the distribution pattern, centroid shift, and key environmental factors influencing its distribution, under four future climate scenarios (SSP1-26, SSP2-45, SSP3-70, and SSP5-85) based on the biomod2-integrated model. The results indicated that the suitable habitats for X. spinosum would expand in the future, mainly in Inner Mongolia, Northeast China, and the plateau regions (e.g., Xinjiang and Xizang). Under future climate scenarios, the centroid would shift toward the northwest or northeast part of China, with the SSP2-45-2050s scenario showing the maximum shift distance (161.990 km). Additionally, the key environmental variables influencing the distribution of X. spinosum, including human impact index, bio5, bio7, and bio12, were determined, revealing that most of them were related to human activities, temperature, and precipitation. This study enhances the understanding of the influence of human activity and climate change on the geographic range of X. spinosum. It provides references for early warning and management in the control of X. spinosum. Full article

Teas derived from Camellia sinensis are traditionally used in kombucha fermentation, a process in which bacteria and yeasts play key roles. This study aimed to evaluate the effects of green, black, and white teas, as well as their combinations, on the physicochemical properties, antioxidant capacity, and total phenolic compound content of kombucha. In addition, the production of kombucha flavored with fruits. Statistical analysis of simplex centroid mixture design indicated that green tea promoted a positive increase in total phenolic content and antioxidant activities against ABTS and DPPH free radicals, being observed at 5868.46 µmol/mL, 705.40 µmol/mL, and 380.77 mg GAE/100 mL in the kombucha prepared using this tea individually. Then, six kombucha beverages were prepared from green tea, flavored with grape, caja, cashew apple, genipap, passion fruit, and tamarind. The phenolic profile analysis revealed the presence of twenty-six compounds, including twenty-five phenolics and one caffeine derivative. Among the beverages, tamarind-flavored kombucha stood out, exhibiting the highest total flavonoid content (156.77 mg EQ/g), which highlights the positive influence of tamarind on the bioactive properties of kombucha beverages. These results provide valuable insights to optimize kombucha production and explore the potential beneficial effects of flavored non-alcoholic beverages. Full article

The introduction of sixth-generation mobile communication technology (6G) poses new requirements for the capacity, rate, latency, and reliability of communication systems. As a vital component of 6G technology, unmanned aerial vehicle (UAV) communications also face various challenges, such as noise interference and limited hardware resources. To meet the high demands of 6G, advanced channel coding techniques need to be adopted. Polar codes, due to their theoretically achievable Shannon limit performance, have potential applications in UAV communication systems. Constructing reliable polar decoding schemes is currently a research hotspot in the field of communications. The Belief Propagation List (BPL) decoding algorithm for polar codes can effectively enhance the accuracy of polar code BP decoding. However, existing BPL decoding algorithms for polar codes face issues such as high hardware resource consumption and unsatisfactory decoding accuracy. Addressing the aforementioned issues, this paper proposes a BPL decoding algorithm for polar codes based on information geometry. An information geometry framework is constructed, where the soft information output by the BP decoder is treated as points on a statistical manifold, and their geometric properties are calculated. By introducing the concept of the soft information centroid and a path selection criterion based on the soft information centroid, combined with geometric distance as a weight, the decoding performance is improved, and hardware overhead is reduced. Simulation results show that under the conditions of a maximum of 60 iterations and 5 decoders, the proposed algorithm reduces the bit error rate by 16.2–74.9% compared to the classic BPL algorithm, providing strong technical support for the application of polar codes in scenarios such as UAV communications. Full article

In recent years, consumers have been increasingly concerned about their health. Therefore, the snack market is rapidly developing more innovative and functional products such as cereal bars. Quinoa (Chenopodium quinoa) and kiwicha (Amaranthus caudatus) are Andean pseudocereals with protein (10.90–11.35%) content and other functional components that reduce the risk of cardiovascular diseases and inflammatory illnesses. Peru is the world’s second largest exporter of Pota (Dosidicus gigas), with 476.5 million metric tons in 2023; however, only between 50% and 70% of it has been taken advantage of. Pota by-products such as skins, viscera, and necks have significant protein content (70%) and are discarded. In this investigation, cereal bar formulations with Pota by-products and Andean pseudocereals were optimized and characterized using a five-run simplex centroid mixture design. The effects of two independent variables were examined, namely collagen (2–8%) and binders (22–28%), on the sugar (%), protein (%), and antioxidant (µg Trolox/g dry weight, dw) content as response variables. The optimized cereal bar (M6) showed high protein (21.27 ± 1.51%) content, moisture (10.37 ± 0.04%), ash (2.57 ± 0.03%), fat (15.12 ± 0.15%), carbohydrates (53.67 ± 1.70%), total polyphenol (1570 ± 267 µg Gallic acid equivalent/g dw) content, antioxidant activity (1656 ± 77 µg Trolox/g dw), essential amino acid–leucine (15.65 ± 1.83 mg/g protein) content, and higher in vitro digestibility (78.78 ± 1.40%) than the control sample. The cereal bar had a positive sensory acceptability (88.89%) and complied with Peruvian standards. The functional bar emerges as a nutritious alternative in the food industry and proposes a sustainable solution using Pota by-products, fostering a circular economy. Full article

In Peru’s Andean region, a diversity of seeds and tubers with high nutritional value and health benefits are grown. Nevertheless, chronic malnutrition and obesity emphasize the need to take advantage of our agricultural wealth to improve public health and ensure sustainable development. The aim of this study was to develop an instant puree with native potatoes (Solanum andigenum) and black mashua (Tropaeolum tuberosum) fortified with black quinoa (Chenopodium quinoa). This study employed a simplex centroid mixture design. The proximal compositions of the three formulations developed were as follows: moisture content of 9.37 ± 0.13% to 9.45 ± 0.06%, ash content of 3.34 ± 0.02% to 3.79 ± 0.17%, protein content of 9.48 ± 0.25% to 11.16 ± 0.38% and total carbohydrate content of 72.81 ± 0.35% to 74.98 ± 0.22%. The samples showed significantly higher antioxidant (7280 ± 113.5 µg trolox/g powder to 12914 ± 604 µg trolox/g powder) and phenolic (3444 ± 241 µg gallic acid equivalent (GAE) /g powder to 7044 ± 322 µg GAE/g powder) content than the control sample. Also, the results of the techno-functional properties of the samples were as follows: water absorption capacity 3.56 ± 0.92 g H₂O/g to 3.95 ± 0.07 g H₂O/g, solubility 14.45 ± 0.07% to 17.88 ± 0.15% and in vitro protein digestibility 70.27 ± 0.05% to 71.61 ± 0.8%. The samples demonstrated an adequate balance of amino acids compared with the control sample. The sensory characteristics of rehydrated powders were determined. Therefore, in a fast-paced world where convenience food options are part of a continuously expanding market, a nutritionally improved instant puree from ancestral crops is not only more nutritious and tastier but also contributes to sustainability and promotes culinary diversity. Full article

Essential oils (EOs) from various medicinal and aromatic plants are known for their diverse biological activities, including their antimicrobial effects. Citrus aurantium EO is traditionally used for therapeutic benefits due to its high content of bioactive compounds. Therefore, this study focuses on its potential use as a food preservative by investigating the combined antibacterial properties of EOs from leaves (EO1), flowers (EO2), and small branches (EO3) of Citrus aurantium against six bacterial strains by the agar disk diffusion, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) methods. The chemical compositions of the EOs were analysed by gas chromatography–mass spectrometry (GC-MS) and revealed the presence of numerous compounds responsible for their antimicrobial properties. The MIC values for the EOs were 3.12 mg/mL, 4.23 mg/mL, and 1.89 mg/mL, for EO1, EO2 and EO3, respectively, while the MBC values were 12.5 mg/mL, 6.25 mg/mL, and 6.25 mg/mL, respectively. A simplex centroid design was created to analyse the effect of the individual and combined EOs against E. coli. The combined EOs showed enhanced antibacterial activity compared to the individual oils, suggesting a synergistic effect (e.g., trial 9 with an MIC of 0.21 mg/mL), allowing the use of lower EO concentrations and reducing potential negative effects on food flavour and aroma. Additionally, the practical application of investigated EOs (at concentrations twice the MIC) was investigated in raw chicken meat stored at 4 °C for 21 days. The EOs, individually and in combination, effectively extended the shelf life of the meat by inhibiting bacterial growth (total bacterial count of less than 1 × 10⁴ CFU/g in the treated samples compared to 7 × 10⁷ CFU/g in the control on day 21 of storage). The study underlines the potential of C. aurantium EOs as natural preservatives that represent a sustainable and effective alternative to synthetic chemicals in food preservation. Full article