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To realize a fast and efficient path planning for mobile robot in complex environment, an enhanced heuristic ant colony optimization (EH-ACO) algorithm is proposed. Four strategies are introduced to accelerate the ACO algorithm and optimize the final path. Firstly, the heuristic distance in the local visibility formula is improved by considering the heuristic distance from ant’s neighbor points to target. Secondly, a new pheromone diffusion gradient formula is designed, which emphasizes that pheromones left the path would spread into a region and the pheromone density would present a gradient distribution in the region. Thirdly, backtracking strategy is introduced to enable ants to find new path when their search is blocked. Finally, path merging strategy is designed to further obtain an optimal path. Simulations are carried out to verify each individual strategy, and comparisons are made with the state-of-the-art algorithms. The results show our proposed EH-ACO algorithm outperforms other algorithms in both optimality and efficiency, especially when the map is large and complex.

The differential diagnosis of psychiatric disorders is relatively challenging for several reasons. In this context, we believe that task-based magnetic resonance imaging (MRI) can serve as a tool for differential diagnosis. The aim of this study was to explore the commonalities in brain activities among individuals with psychiatric disorders and to identify the key brain regions that can distinguish between these disorders. The PubMed, MEDLINE, EMBASE, Web of Science, Scopus, PsycINFO, and Google Scholar databases were searched for whole-brain functional MRI studies that compared psychiatric patients and normal controls. The psychiatric disorders included schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder, attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Studies using go-nogo paradigms were selected, we then conducted activation likelihood estimation (ALE) meta-analysis, factor analysis, and regression analysis on these studies subsequently. A total of 152 studies (108 with patients) were selected and a consistent pattern was found, that is, decreased activities in the same brain regions across six disorders. Factor analysis clustered six disorders into three pairs: SCZ and ASD, MDD and BD, and ADHD and BD. Furthermore, the heterogeneity of SCZ and ASD was located in the left and right thalamus; and the heterogeneity of MDD and BD was located in the thalamus, insula, and superior frontal gyrus. The results can lead to a new classification method for psychiatric disorders, benefit the differential diagnosis at an early stage, and help to understand the biobasis of psychiatric disorders.

Background Infectious etiologies of deep neck space abscess (DNSA) by conventional culture tests can be challenging, which also leads to frequent irrational antibiotic usage. Metagenomic next-generation sequencing (mNGS), as a novel method for analyzing the complex microbial ecosystem from clinical samples, has been utilized in clinical research and practice of various infectious diseases but deep neck space abscess. We here aimed to explore the clinical value of mNGS for pathogen detection and treatment guidance in DNSA patients compared with conventional culture tests. Methods One hundred six patients diagnosed with DNSA were retrospectively enrolled and allocated into mNGS group and culture group according to whether mNGS was conducted. The pathogen detection effectiveness was of mNGS was compared with conventional culture. Effectiveness of mNGS-modified antimicrobial therapy was evaluated by comparing the treatment outcomes between two groups. Results mNGS showed a significantly higher detection rate than conventional culture ( p  < 0.05) with faster result acquisition. Treatment success rate of patients in the mNGS group was significantly higher than in the culture group (RR: 1.22, 95%CI: 1.07–1.82, p  = 0.033). Besides, patients in the mNGS group had shorter duration of irrational antimicrobial therapy, shorter hospital stay and less medical costs ( p  < 0.05). Conclusions mNGS is an effective technology for facilitating pathogen detection and improving treatment outcomes of DNSA patients.

Single-pixel imaging, which is also known as computational ghost imaging, can reconstruct an entire image using one non-spatially resolved detector. However, it often requires a large amount of sampling, severely limiting its application. In this paper, we discuss the implementation of secured regions of interest (SROIs) in single-pixel imaging and illustrate its application using two experiments. Under a limited number of sampling times, we improved the resolution and recovered spectral information of interest in the ROI. Meanwhile, this scheme has high information security with high encryption and has great potential for single-pixel video and compressive multi-spectral single-pixel imaging.

Solar energy plays a crucial role in mitigating greenhouse gas emissions in the context of global climate change. However, its deployment for green electricity generation can significantly influence regional climate and vegetation dynamics. While prior studies have examined the impacts of solar power plants on vegetation, the accuracy of these assessments has often been constrained by the availability of publicly accessible multispectral, high-resolution remotely sensed imagery. Given the abundant solar energy resources and the ecological significance of the Tibetan Plateau, a thorough evaluation of the vegetation effects associated with solar power installations is warranted. In this study, we utilize sub-meter resolution imagery from the GF-2 satellite to reconstruct the fractional vegetation cover (FVC) at the Gonghe solar thermal power plant through image classification, in situ sampling, and sliding window techniques. We then quantify the plant’s impact on FVC by comparing data from the pre-installation and post-installation periods. Our findings indicate that the Gonghe solar thermal power plant is associated with a 0.02 increase in FVC compared to a surrounding control region (p < 0.05), representing a 12.5% increase relative to the pre-installation period. Notably, the enhancement in FVC is more pronounced in the outer ring areas than near the central tower. The observed enhancement in vegetation growth at the Gonghe plant suggests potential ecological and carbon storage benefits resulting from solar power plant establishment on the Tibetan Plateau. These findings underscore the necessity of evaluating the climate and ecological impacts of renewable energy facilities during the planning and design phases to ensure a harmonious balance between clean energy development and local ecological integrity.

Plant seeds have long been promoted as a production platform for novel fatty acids such as the ω3 long-chain (≥ C20) polyunsaturated fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) commonly found in fish oil. In this article we describe the creation of a canola ( Brassica napus ) variety producing fish oil-like levels of DHA in the seed. This was achieved by the introduction of a microalgal/yeast transgenic pathway of seven consecutive enzymatic steps which converted the native substrate oleic acid to α-linolenic acid and, subsequently, to EPA, docosapentaenoic acid (DPA) and DHA. This paper describes construct design and evaluation, plant transformation, event selection, field testing in a wide range of environments, and oil profile stability of the transgenic seed. The stable, high-performing event NS-B50027-4 produced fish oil-like levels of DHA (9–11%) in open field trials of T3 to T7 generation plants in several locations in Australia and Canada. This study also describes the highest seed DHA levels reported thus far and is one of the first examples of a deregulated genetically modified crop with clear health benefits to the consumer.

Thin-walled parts exhibit high flexibility, rendering them susceptible to chatter during milling, which can significantly impact machining accuracy, surface quality, and productivity. Therefore, chatter detection plays a crucial role in thin-wall milling. In this study, a chatter detection method based on multi-sensor fusion and a dual-stream convolutional neural network (CNN) is proposed, which can effectively identify the machining status in thin-wall milling. Specifically, the acceleration signals and cutting force signals are first collected during the milling process and transformed into the frequency domain using fast Fourier transform (FFT). Secondly, a dual-stream CNN is designed to extract the hidden features from the spectrum of multi-sensor signals, thereby avoiding confusion when learning the features of each sensor signal. Then, considering that the characteristics of each sensor are of different importance for chatter detection, a joint attention mechanism based on residual connection is designed, and the feature weight coefficients are adaptively assigned to obtain the joint features. Finally, the joint features feed into a machining status classifier to identify chatter occurrences. To validate the feasibility and effectiveness of the proposed method, a series of milling tests are conducted. The results demonstrate that the proposed method can accurately distinguish between stable and chatter under various milling scenarios, achieving a detection accuracy of up to 98.68%.

Shading on photovoltaic (PV) modules due to shadows, covering, dust, etc., usually characterized as semi-transparent, will significantly affect the power generation capacity. No systematic study has considered the impact of semi-transparent coverings on the power generation capacity of PV modules. This paper covers a single cell in the PV module using a covering with a transmittance of 18.55% and systematically investigates its impact on the power generation capacity. The open-circuit voltage (Voc) of the PV module is nearly unaffected by semi-transparent coverings because the covered cell can be considered as working at a lower irradiance and thus can output a voltage close to that of the uncovered cell. The short-circuit current (Isc) is significantly affected by coverings because it is co-contributed by the photocurrent (evaluated based on the covering ratio R and transmittance) and the reverse bias current ΔIsc (the covered cell is in a reverse bias state). The ΔIsc increases with R because more charge accumulates at the bi-ends of the covered cell; but, it decreases at full covering, which implies that in a partially covered case the uncovered part contributes more to ΔIsc than the covered part. The fill factor (FF) of the PV module first increases and then decreases with R, as the equivalent resistance of the covered cell increases rapidly with R, which replaces the wire resistance in dominating the series resistance of the PV module when R > 0.6. This work is of great theoretical significance in analyzing the output characteristics of PV modules under real conditions.

Background Deep neck space abscess (DNSA) is a serious infection in the head and neck. Antibiotic therapy is an important treatment in patients with DNSA. However, the results of bacterial culture need at least 48 h, and the positive rate is only 30–50%, indicating that the use of empiric antibiotic treatment for most patients with DNSA should at least 48 h or even throughout the whole course of treatment. Thus, how to use empiric antibiotics has always been a problem for clinicians. This study analyzed the distribution of bacteria based on disease severity and clinical characteristics of DNSA patients, and provides bacteriological guidance for the empiric use of antibiotics. Methods We analyzed 433 patients with DNSA who were diagnosed and treated at nine medical centers in Guangdong Province between January 1, 2015, and December 31, 2020. A nomogram for disease severity (mild/severe) was constructed using least absolute shrinkage and selection operator–logistic regression analysis. Clinical characteristics for the Gram reaction of the strain were identified using multivariate analyses. Results 92 (21.2%) patients developed life-threatening complications. The nomogram for disease severity comprised of seven predictors. The area under the receiver operating characteristic curves of the nomogram in the training and validation cohorts were 0.951 and 0.931, respectively. In the mild cases, 43.2% (101/234) had positive culture results (49% for Gram-positive and 51% for Gram-negative strains). The positive rate of cultures in the patients with severe disease was 63% (58/92, 37.9% for Gram-positive, and 62.1% for Gram-negative strains). Diabetes mellitus was an independent predictor of Gram-negative strains in the mild disease group, whereas gas formation and trismus were independent predictors of Gram-positive strains in the severe disease group. The positivity rate of multidrug-resistant strains was higher in the severe disease group (12.1%) than in the mild disease group (1.0%) ( P  < 0.001). Metagenomic sequencing was helpful for the bacteriological diagnosis of DNSA by identifying anaerobic strains (83.3%). Conclusion We established a DNSA clinical severity prediction model and found some predictors for the type of Gram-staining strains in different disease severity cases. These results can help clinicians in effectively choosing an empiric antibiotic treatment.

In order to study the force and life of the key components in the gearbox of an existing double-rotor wind turbine, the design and structural parameters of the gearbox in the traditional National Renewable Energy Laboratory (NREL) 5 MW single-rotor wind turbine are adopted, and the fixed ring gear of the first planetary stage transmission is released to form a differential gearbox suitable for a double-rotor wind turbine with two inputs. The double input is used to connect the double rotor. Subsequently, the characteristics of the gearbox in a double-rotor wind turbine are discussed. On the basis of the constant rated power of the whole wind turbine, the total power is divided into two parts, which are allocated to the double rotors, then two rotational speeds of the two inputs are given according to different power ratios by complying with the matching principle of force and moment. Furthermore, the force acting on the pitch circle of the planet gear, as well as the force and life of the planet bearing of the two-stage planetary transmission are calculated and compared with a single-rotor wind turbine. The results show that the structural advantages of a double-rotor wind turbine can reduce the stress of key components of the gearbox and increase the life span of the planet bearing, thereby the life of the whole gearbox is improved and the downtime of the whole wind turbine is reduced.