Scientific Reports最新文献

Accurate prediction of walking travel rates is central to wide-ranging applications, including modeling historical travel networks, simulating evacuation from hazards, evaluating military ground troop movements, and assessing risk to wildland firefighters. Most of the existing functions for estimating travel rates have focused on slope as the sole landscape impediment, while some have gone a step further in applying a limited set of multiplicative factors to account for broadly defined surface types (e.g., “on-path” vs. “off-path”). In this study, we introduce the Simulating Travel Rates In Diverse Environments (STRIDE) model, which accurately predicts travel rates using a suite of airborne lidar-derived metrics (slope, vegetation density, and surface roughness) that encompass a continuous spectrum of landscape structure. STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings. The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%. We demonstrate the use of STRIDE to map least-cost paths, highlighting its propensity for selecting logically consistent routes and producing more accurate yet considerably greater total travel time estimates than a slope-only model.

The global shift towards using biomass for biofuels and chemicals is accelerating due to increasing environmental concerns and geopolitical strategies. This study investigates a biorefinery model using citrus-processing-waste, specifically citrus pulp, to produce high-value products for various industries, including cosmetics, pharmaceuticals, flavours, fragrances, and food packaging. In Italy, particularly Sicily region, citrus processing generates significant amounts of waste, often improperly disposed of, contributing to environmental problems. Researchers have demonstrated that citrus waste can yield commercially valuable compounds. This study specifically focuses on orange peel waste (OPW), which constitutes about half of the fruit's weight, aiming to extract pectin and limonene through a combined process. The extraction process was carried out on a laboratory scale, and its sustainability was evaluated using a life cycle assessment (LCA) with SimaPro 8.1 software and the Impact 2002 + method. The functional unit adopted for this study is 300 g of OPW, obtained after the pre-treatment phase, from which 0.14 g of limonene and 8.22 g of pectin were extracted. The LCA results revealed that pectin extraction has a significantly higher environmental impact compared to limonene extraction, primarily due to the use of ethanol as a solvent, followed by electricity consumption. To mitigate this impact, the LCA assessed alternative, more sustainable solvents, resulting in a 73.4% reduction in the environmental footprint of the pectin extraction process. These findings underscore the critical role of LCA, even at the laboratory scale, in identifying environmental hotspots and providing insights for improving and optimizing processes for potential industrial-scale applications.

Analysis of amatoxins is of great importance as these cyclic peptides contribute to a high number of fatalities each year. Development of analytical approaches needs to focus on rapid, sensitive, and reliable methods. By establishing an affinity column chromatography-based assay using the monoclonal amanitin antibody AMA9G3 and liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS) for the trace detection of α-, β-, and γ-amanitin in human urine samples to confirm ingestion, we report the first approach that extents the current status of amatoxin analysis. The presented procedure allows detection of amatoxins in human urine down to 1 ng/mL. The method was successfully validated qualitatively for α- and γ-amanitin according to international recommendations. A proof of concept was performed by analyzing 37 urine samples after suspected amatoxin consumption submitted for regular clinical toxicological analysis. Using this antibody-based enrichment strategy, acute amatoxin intoxications can be determined within 90 min and due to the high sensitivity and selectivity, a comparable approach using target specific antibodies may also be used for other toxicological relevant peptides.

Accurate consumption forecasting is of great importance to grasp the energy consumption habits of consumers and promote the stable and efficient operation of integrated energy system (IES). To this end, this paper proposes an interactive multi-scale convolutional module-based short-term multi-energy consumption forecasting method for IES. Firstly, based on multi-scale feature fusion and multi-energy interactive learning, a novel interactive multi-scale convolutional module is proposed to extract and share the coupling information between energy consumption from different scales without increasing network parameters. Then, a short-term multi-energy consumption forecasting method is proposed, where different forecasting network structures are selected in different seasons to make full use of seasonal and coupling characteristics of the energy consumption, thus enhancing prediction performance. Furthermore, a Laplace distribution-based loss function weight optimization method is proposed to dynamically balance the loss magnitude and training speed of joint forecast tasks more robustly. Finally, the effectiveness and superiority of the proposed method are verified by comparative simulation experiments.

Hearing loss affects around 5% of the global population. Two preliminary studies have described genetic variants in sporadic individuals with hearing loss from Pakistan. Here we extend these studies to determine the spectrum of variants in a cohort of individuals with no previous history of hearing loss. Individuals with hearing loss born to consanguineous couples were identified from special schools. Audiograms were assessed. DNA from participants negative for GJB2 pathogenic variants was subjected to exome sequencing. Data were filtered to include variants with frequencies < 0.01 in the public databases. The effects of the missense variants on respective amino acids were analyzed by using PyMol software. Among the 44 participants, hearing loss was moderate for two individuals; 14 exhibited moderately-severe hearing loss while 25 had a severe degree of hearing loss. Hearing loss was reported to have been progressive in four participants and was currently profound in three participants. Variants were unambiguously identified in 17 genes, of which the majority affected SLC26A4. CDH23, MYO15A and OTOF were other significant contributors. Deleterious variants detected in two genes suggest new associations for hearing loss. Molecular characterization of hearing loss in our cohort revealed high genetic heterogeneity with a 75% diagnostic rate.

Infectious diseases have caused enormous disasters in human society, and asymptomatic carriers are an important challenge in our epidemic prevention and control process. Nucleic acid testing has played an important role in rapid testing for asymptomatic individuals. How to carry out nucleic acid testing in a scientific manner is a practical problem encountered in normal production and life. Based on the real COVID-19 epidemic data from Shanghai, we established a susceptible-exposed-infected-asymptomatic-recovered-death (SEIARD) dynamic model. The least squares method was used to fit the data and estimate the unknown parameters β and E(0) in the model, and MATLAB software was employed to simulate the development of the epidemic. The data fitting results indicated that the SEIARD model can better describe the early development patterns of the epidemic (R² = 0.98; MAPE = 2.54%). We calculated the basic reproduction number of the Shanghai epidemic as R₀ = 2.86. As the frequency of nucleic acid testing increased, the basic reproduction number R₀ continued to decrease. When there is one latent carrier and one asymptomatic carrier in the nucleic acid testing team, the number of queues is directly proportional to the number of infected individuals, the nucleic acid testing team increases by 50 people, and the number of new asymptomatic cases increases by approximately 4 people. If both susceptible individuals (S) and asymptomatic patients (A) are not wearing masks, the infection rate reaches approximately 7%; after wearing masks, the final infection rate is less than 1% at 1.5 m between two people. The queue spacing is inversely proportional to the number of infected individuals. With a distance of d = 1 m, a nucleic acid testing team of 100 people added 8% of the infected individuals; when d = 1.5 m, fewer than 2% of the newly infected individuals. The results confirmed that controlling the queue size for nucleic acid testing, strictly wearing masks, and maintaining a queue spacing of more than 1.5 m are safe and effective nucleic acid testing strategies. Our findings are also applicable to the prevention of other newly emerging infectious diseases.

Streptococcus pneumoniae, a medically important opportunistic bacterial pathogen of the upper respiratory tract, is a major public health concern, causing a wide range of pneumococcal illnesses, both invasive and noninvasive. It is associated with significant global morbidity and mortality, including pneumonia, meningitis, sepsis, and acute otitis media. The major purpose of this study was to determine the molecular epidemiology of Streptococcus pneumoniae strains that cause invasive and noninvasive infections in Ethiopia. A prospective study was undertaken in two regional hospitals between January 2018 and December 2019. Whole-genome sequencing was used to analyze all isolates. Serotypes and multilocus sequence types (MLST) were derived from genomic data. The E-test was used for antimicrobial susceptibility testing. Patient samples obtained 54 Streptococcus pneumoniae isolates, 33 from invasive and 21 from noninvasive specimens. Our findings identified 32 serotypes expressed by 25 Global Pneumococcal Sequence Clusters (GPSCs) and 42 sequence types (STs), including 21 new STs. The most common sequence types among the invasive isolates were ST3500, ST5368, ST11162, ST15425, ST15555, ST15559, and ST15561 (2/33, 6% each). These sequence types were linked to serotypes 8, 7 C, 15B/C, 16 F, 10 A, 15B, and 6 A, respectively. Among the noninvasive isolates, only ST15432, associated with serotype 23 A, had numerous isolates (4/21, 19%). Serotype 14 was revealed as the most resistant strain to penicillin G, whereas isolates from serotypes 3, 8, 7 C, and 10 A were resistant to erythromycin. Notably, all serotype 6 A isolates were resistant to both erythromycin and penicillin G. Our findings revealed an abnormally significant number of novel STs, as well as extremely diversified serotypes and sequence types, implying that Ethiopia may serve as a breeding ground for novel STs. Recombination can produce novel STs that cause capsular switching. This has the potential to influence how immunization campaigns affect the burden of invasive pneumococcal illness. The findings highlight the importance of continuous genetic surveillance of the pneumococcal population as a vital step toward enhancing future vaccine design.

Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.

Infrared thermal imaging (IRT) remotely and contactless maps the temperature on the examined surface, recording the distribution of infrared radiation emitted by each body whose temperature is higher than absolute zero. The aim of the study was to evaluate the usefulness of thermography in the assessment of asymptomatic infection foci in patients with high systemic infection. The 150 cases diagnosed based on roentgenograms, divided into 6 groups of diagnosed odontogenic lesions, along with a control group. Thermal imaging was performed with a FLIR Systems T1020 thermal camera. Thermal image analysis was performed using ThermaCAM Researcher Pro 2.10, MS Office Excel 2022 and Statistica 10. The periapical areas of selected dead teeth were selected as areas of interest. The Mann Whitney’s U test showed statistically significant (p < 0.001) differences in average temperature between each patient’s and healthy group. Depper’s analysis showed statistical significance also between the ZM and BZ groups (p = 0.004). Moreover, obtained results may also suggest that thermal imaging can be useful in identify odontogenic infection foci. The thermal asymmetry of periapical tissues of teeth differentiates dead from living teeth, as well as individual pathologies related to the process of gangrenous pulp decay. Thermographic mapping is a promising diagnostic technique that can detect asymptomatic inflammations that carry the risk of infection of the entire body.

Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect.