Miracle-dz/UniFAR-FTUs huggingface dataset:
Surrogate-Assisted Evolutionary Q-Learning for Black-Box Dynamic Time-Linkage Optimization Problems

Dynamic time-linkage optimization problems (DTPs) are special dynamic optimization problems (DOPs) with the time-linkage property. The environment of DTPs changes not only over time but also depends on the previous applied solutions. DTPs are hardly solved by existing dynamic evolutionary algorithms because they ignore the time-linkage property. In fact, they can be viewed as multiple decision-making problems and solved by reinforcement learning (RL). However, only some discrete DTPs are solved by RL-based evolutionary optimization algorithms with the assumption of observable objective functions. In this work, we propose a dynamic evolutionary optimization algorithm using surrogate-assisted $Q$ -learning for continuous black-box DTPs. To observe the states of black-box DTPs, the state extraction and prediction methods are applied after the search process at each time step. Based on the learned information, a surrogate-assisted $Q$ -learning is introduced to evaluate and select candidate solutions in the continuous decision space in a long-term consideration. We evaluate the components of our proposed algorithm on various benchmark problems to study their behaviors. Results of comparative experiments indicate that the proposed algorithm outperforms other compared algorithms and performs robustly on DTPs with up to 30 decision variables and different dynamic changes.
Enzyme Encapsulation by Facile Self-Assembly Silica-Modified Magnetic Nanoparticles for Glucose Monitoring in Urine

Silica nanoparticles hold tremendous potential for the encapsulation of enzymes. However, aqueous alcohol solutions and catalysts are prerequisites for the production of silica nanoparticles, which are too harsh for maintaining the enzyme activity. Herein, a procedure without any organic solvents and catalysts (acidic or alkaline) is developed for the synthesis of silica-encapsulated glucose-oxidase-coated magnetic nanoparticles by a facile self-assembly route, avoiding damage of the enzyme structure in the reaction system. The encapsulated enzyme was characterized by scanning electron microscopy, transmission electron microscopy, energy-dispersive spectrometry, and a vibrating sample magnetometer. Finally, a colorimetric sensing method was developed for the detection of glucose in urine samples based on the encapsulated glucose oxidase and a hydrogen peroxide test strip. The method exhibited a good linear performance in the concentration range of 20~160 μg mL−1 and good recoveries ranging from 94.3 to 118.0%. This work proves that the self-assembly method could be employed to encapsulate glucose oxidase into silica-coated magnetic particles. The developed colorimetric sensing method shows high sensitivity, which will provide a promising tool for the detection of glucose and the monitoring of diabetes.
Working in hospitality and catering in Greece and the UK: Do trade union membership and collective bargaining still matter?

Existing literature suggests that terms and conditions of employment are universally poor in hospitality and catering. Based on fieldwork comprising more than 70 semi-structured in-depth interviews conducted in the UK and Greece in 2018 and 2019, this article maps the field in the two countries and discusses the key parameters that structure working conditions, pay determination, and the impact of trade unionism and absence thereof. Poor working conditions and precariousness are prevalent in both contexts as expected, but surprisingly wages and other terms and conditions at least for a significant section of the hospitality workforce are comparatively better in Greece. An explanation for these findings is suggested with reference to the tradition of collective bargaining in hotels in Greece which partially survived the 2008 crisis and the legal reforms that followed. Trade unionism, despite its weakening and discrediting, has left its mark as a tradition contributing to the emergence of formal labour-based initiatives and more broadly to maintaining an openness to collectivism and collective action, more so than in the UK context. This combination of the distinct institutional and sectoral features of the Greek case and worker agency has served to ameliorate the context and content of employment for substantial sections of the hospitality workforce.
Modelling enablers for building agri-food supply chain resilience: insights from a comparative analysis of Argentina and France

Smooth, efficient agri-food supply chain (AFSC) operations are becoming ever more difficult due to more intense and frequent natural disasters and man-made disruptions. Helping AFSCs to survive disturbances requires re-consideration of how to build their resilience. This study addresses this issue through a cross-country comparative analysis involving interviews with AFSC practitioners, thematic analysis to generate agri-food supply chain resilience (AFSCRes) capability factors, total interpretive structural modelling (TISM) to establish interrelationships among the factors, cross-impact matrix multiplication applied to classification (MICMAC) analysis to categorize the factors, and comparative analysis. The results reveal that contractual restraints regulating farmers’ opportunistic behaviour and regular interactions are key factors for building AFSCRes in France and Argentina, respectively. This study also confirms the critical role of farmers’ associations and coordinated activities amongst all AFSC stakeholders to build AFSCRes. For triggering AFSCRes, farmers’ resilience must be particularly prioritized, as they are the least resilient point in AFSCs.
INDOOR MAPPING WITH AN OMNIDIRECTIONAL CAMERA SYSTEM: PERFORMANCE ANALYSIS

Abstract. The use of BIM (Building Information Modeling), a component of the Digital Twin concept, is on the rise, and the need for indoor data is rapidly growing. BIM information is not only used for management purposes, but it is essential to support navigation indoors. Observing building interiors by optical sensors, such as cameras and laser scanners, has challenges as the image scale changes over a broad range in rooms and floors, and then complete coverage is required, requiring images taken from several locations with various camera orientations. Using 360° imaging sensors partially addresses the need for efficient wide FOV observations. In this study, we investigate the feasibility of using a 6-sensor omnidirectional/fisheye camera system and report about its performance.
Wideband circularly polarized SIW Cavity‐Backed slot antenna with sequential rotation feeding

We present a dual resonant substrate integrated waveguide cavity‐backed slot antenna with over 34% bandwidth at X‐band. A sequential rotation feeding scheme is employed to achieve circular polarization using two differential ports, resulting in excellent isolation. The antenna achieves wideband circularly polarized radiation with symmetric patterns in both principal planes. The full‐wave simulation results demonstrate a peak gain of 6.0 dB and a radiation efficiency >96% over the operating impedance bandwidth. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2566–2570, 2016
PERFORMANCE ANALYSIS OF SEMANTIC REFRESH INDOOR NAVIGATION FOR SMARTPHONE’S SENSORS USING INS/VINS INTEGRATION SCHEME

Abstract. Positioning and Orientation System (POS), which integrates Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS), is widely used to accomplish outdoor navigation missions for land vehicles. However, the positioning accuracy would become worse in GNSS-hostile environments (Chiang et al., 2013), which is quite challenging to accomplish indoor navigation environments. Nevertheless, smartphones are contained many embedded sensors, including GNSS, IMU, camera, which have the potential to be an ideal personal navigation device. In this research, we mainly propose an integrated scheme of INS/VINS/object detection refresh (ODR) for indoor challenging environments. The goal is to achieve indoor navigation for vehicular applications only using smartphones. The algorithm is developed based on the smartphone. By the conventional inertial navigation system, which is integrated with two designed processes to further improve the performance. First is assistance from the visual-inertial navigation system (VINS). The long-term drift caused by the INS could be decreased effectively, and complete the extended Kalman filter (EKF) composition. The second is to apply neural network, YOLO-v3 (Redmon et al.,2018), to detect objects and provide the object's describer information to refresh the proper position. Therefore, the proposed method uses visual estimation and recognition methods to assist the smartphone platform to obtain a more accurate solution.Finally, we use the navigation-grade IMU as the reference system for accuracy verification. The accuracy comparisons of the three integration solutions are analysed reasonably. The position accuracy is reasonable. Compared with the original smartphone INS integration method, the proposed integration scheme improves the accuracy from the horizontal direction by 78.5%.
The Rapid Emergence of Ceftazidime-Avibactam Resistance Mediated by KPC Variants in Carbapenem-Resistant Klebsiella pneumoniae in Zhejiang Province, China

Ceftazidime-avibactam (CAV) is a new treatment option against carbapenem-resistant Klebsiella pneumoniae (CRKP) infections. However, the rapid emergence of CAV resistance mediated by KPC variants has posed a severe threat to healthcare after its clinical application. The characteristics of CAV resistance in CRKP strains needs to be determined in China. A total of 477 CRKP isolates were collected from 46 hospitals in Zhejiang Province from 2018 to 2021. The results demonstrated that CAV had a potent activity against 94.5% of all CRKP (451/477, 95% CI: 93.0–96.1%) and 86.0% of CRKP strains carrying blaKPC genes (410/477, 95% CI: 83.5–88.4%). A total of 26 CAV-resistant strains were found. Among these strains, sixteen harbored metallo-β lactamases, and two carried KPC-2 carbapenemase and mutated ompK35 and ompK36. Eight CRKP strains encoded KPC-33 or KPC-93, belonging to ST11, among which seven strains were detected in patients hospitalized in 2021 after exposure to CAV and one strain was associated with intra-hospital spread. CAV is a potent agent in vitro against CRKP strains. The rapid development of CAV resistance mediated by various KPC variants after a short period of CAV treatment has increased and brought difficulties in treating infections caused by CRKP strains, especially those belonging to ST11. The surveillance of bacterial resistance against CAV is highly recommended due to the steep development of CAV resistance and rapid evolution of KPC enzymes.
Molecular Similarity Perception Based on Machine-Learning Models

Molecular similarity is an impressively broad topic with many implications in several areas of chemistry. Its roots lie in the paradigm that ‘similar molecules have similar properties’. For this reason, methods for determining molecular similarity find wide application in pharmaceutical companies, e.g., in the context of structure-activity relationships. The similarity evaluation is also used in the field of chemical legislation, specifically in the procedure to judge if a new molecule can obtain the status of orphan drug with the consequent financial benefits. For this procedure, the European Medicines Agency uses experts’ judgments. It is clear that the perception of the similarity depends on the observer, so the development of models to reproduce the human perception is useful. In this paper, we built models using both 2D fingerprints and 3D descriptors, i.e., molecular shape and pharmacophore descriptors. The proposed models were also evaluated by constructing a dataset of pairs of molecules which was submitted to a group of experts for the similarity judgment. The proposed machine-learning models can be useful to reduce or assist human efforts in future evaluations. For this reason, the new molecules dataset and an online tool for molecular similarity estimation have been made freely available.
A BUILDING CHANGE DETECTION METHOD BASED ON A SINGLE ALS POINT CLOUD AND A HRS IMAGE

Abstract. For common remote sensing image change detection based on different time phases, it is difficult to solve the problem of surface tilt caused by the different shooting angle and time, which makes it difficult to complete the accurate registration. Some scholars utilized three-dimensional data for change detection to avoid registration problem, however, it costs very high for using 3D data to detect changes. Aim at this problem, this paper proposes a method of combining a single phase of ALS (Airborne Laser Scanning) data and HRS (High Resolution Satellite) image. It is composed of the following four steps: (1) Extracting the shadow area of the new-phase optical image, and superimposing it with the classified old-time point cloud data, and then the disappeared building area and the approximate building area are obtained according to the associated relationship between the shadow area and buildings. (2) Determining the unchanged building areas as positive samples, and taking the remaining area and vegetation area after removing the approximate buildings from the image as negative samples, and then GrabCut algorithm is used to segment new-phase HRS images to obtain building areas. (3) Comparing between building areas obtained in the previous step and the registered old-time ALS data to obtain a new building area with noise. (4) Denoising the results of the previous step to obtain the final new building area. Two datasets are used to verify the method. The detection accuracy of the disappeared buildings is over 85%, and the detection accuracy of the newly added buildings is over 70%.
Hybrid method to calculate the spectral optical constants (n, k) of smooth and rough bulk samples

Spectra of the optical constants (n, k) of a substance are often obtained by comparing spectroscopic measurements of a bulk sample with a simulation model. The reflectance method requires a sample with a perfectly smooth surface to give unbiased values of n and k. The ellipsometric method generates n and k spectra which are accurate in general but which sometimes generate errors over limited spectral ranges when simulating polarized reflectance. We propose a new hybrid method to calculate reliable spectra of n and k. The proposed method uses both ellipsometric and s-polarized reflectance measurements and takes into account the potential roughness of the sample’s surface with the help of a specularity factor. The proposed method provides n and k that better simulate polarized reflectance measurements and applies to isotropic bulk samples with either smooth or rough flat surfaces. We provide demonstrations in the infrared spectral region with a smooth sample and a rough sample.
PhyloFunDB: A Pipeline to Create and Update Functional Gene Taxonomic Databases

The increase in sequencing capacity has amplified the number of taxonomically unclassified sequences in most databases. The classification of such sequences demands phylogenetic tree construction and comparison to currently classified sequences, a process that demands the processing of large amounts of data and use of several different software. Here, we present PhyloFunDB, a pipeline for extracting, processing, and inferring phylogenetic trees from specific functional genes. The goal of our work is to decrease processing time and facilitate the grouping of sequences that can be used for improved taxonomic classification of functional gene datasets.
Reactive power control in MMC HVDC system during ac fault

This paper discusses the reactive power control in multiple modular converter (MMC) HVDC system during ac faults. A control methodology is proposed to solve the overcurrent during and after ac faults. Based on the proposed method, a control strategy is added to the MMC HVDC test to enable the Low Voltage Ride Through (LVRT) function of the converter. The effectiveness of the proposed method in supressing the overcurrent caused by the ac system interruption is verified using electromagnetic (EMT) simulation. The simulation also shows with the proposed LVRT control method, the converter can inject the desired reactive current to the ac system within its ability during the symmetric and asymmetric ac faults.
AERIAL TRIANGULATION WITH LEARNING-BASED TIE POINTS

Abstract. Aerial triangulation (AT) has reached outstanding progress in the last decades, and now fully automated solutions for nadir and oblique images are available. Usually, image correspondences (tie points) are found using hand-crafted methods, such as SIFT or its variants. But in the last years, there were many investigations and developments to promote the use of machine and deep learning solutions within the photogrammetric processing pipeline. The paper explores learning-based methods for the extraction of tie points in aerial image blocks. Image correspondences are used to perform aerial triangulation (AT) and successively generate dense point clouds. Two different datasets are used to compare conventional hand-crafted detector/descriptor methods with respect to learning-based methods. Accuracy analyses are performed using GCPs as well as ground truth LiDAR point clouds. Results confirm the potential of learningbased methods in finding reliable image correspondences in the aerial block, still showing space for improvements due to camera rotations.
Reconstruction of the rRNA Sequences of LUCA, with Bioinformatic Implication of the Local Similarities Shared by Them

Simple Summary In order to explore the origin of 16S, 5S, and 23S ribosomal RNAs in novel views and methods, full lengths of the three rRNA sequences of the last universal common ancestor were reconstructed for the first time. Within these sequences, repeat short fragments or local self-similarities were shared. Moreover, these short fragments were conserved, clustered around the functional center of ribosome, and contained nearly all types of functional sites of ribosome. These results indicated a possibility that short fragments may act as component elements or parts of them in the origin of rRNAs, which can be practically tested by simulating experiments in the future. Abstract The theory of the RNA world, especially with the catalytic capability of RNA, provides a reasonable framework explaining the evolution of molecular genetics system before the scenario of the central dogma. However, it remains a challenge to deduce the origin mechanism of rRNAs. Here we reconstructed the phylogenetic relationships of archaea and bacteria with bootstrap values of most nodes, especially the deep ones, higher than 90%. Based on the well-resolved tree, the full lengths of 16S, 5S, and 23S rRNA sequences of the last universal common ancestor (LUCA) were reconstructed for the first time. The potential similarities shared by the three ancestral rRNA sequences were further explored by searching for repeat short fragments in the level of purine–pyrimidine (RY) with certain lengths and arrangements. With the lengths ranging from 2 to 14, functional short fragments could be found in the three RNAs. As a representative, a set with a total of 75 short fragments of 11 nucleotides in length can recover all types of the known functional sites of ribosomes in a most concise manner. The 75 short fragments cluster around the functional center of the ribosome, among which 18 of them are highly conserved across five or six kingdoms and still contain all types of known functional sites except one. Alternatively, according to the strategy using the level of AUGC instead of RY, a similar pattern can be recovered. Such results indicate the local similarities shared by 16S, 5S, and 23S rRNAs and thus suggest a possible general mechanism in the formation of the LUCA rRNAs.
HOW CHALLENGING IS THE DISCRIMINATION OF FLOATING MATERIALS ON THE SEA SURFACE USING HIGH RESOLUTION MULTISPECTRAL SATELLITE DATA?

Abstract. Developing a remote sensing-based monitoring system for detecting marine plastics requires a thorough investigation of their discrimination possibilities from other floating objects. To this end, this study aims to explore the ability to discriminate marine debris from other floating materials and sea features using high-resolution multispectral satellite data. To perform our analysis, we utilized the open-access Marine Debris Archive (MARIDA), which contains several marine classes on Sentinel-2 (S2) data and benchmarks machine learning frameworks. In particular, we investigated well-established spectral indices, GrayLevel Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) texture and other spatial features at multiple scales. A Random Forest (RF) classifier was also applied for the classification procedure, and the spectral and spatial features which contributed the most were underlined. The quantitative and qualitative assessment indicated that the spectral information alone is insufficient to distinguish marine plastic from other floating materials which exhibit similar spectral behavior, such as vessels. However, a strong potential even for challenging discrimination tasks is presented when combined with spatial information. By further evaluating our results qualitatively, significant insights are gained, and specific combinations are proposed for challenging floating materials discrimination.
Field weed recognition algorithm based on machine learning

Abstract. Currently, large-scale spraying of herbicides pollutes the environment, destroys the quality of cultivated land, and increases the cost of agricultural production. For this reason, “precision farming,” in which machine learning technology is used to detect weeds in crops and soil and then herbicides are sprayed at specific locations according to the distribution of weeds, is proposed. Machine learning is a multidisciplinary interdisciplinary major, covering knowledge of probability theory, statistics, approximate theory and complex algorithms. Machine learning uses computers as tools and is committed to simulating human learning methods in real time, and divides existing content into knowledge structures to effectively improve learning efficiency. We aim to use machine learning technology to study weed detection algorithms and provide feasible solutions for accurate weed classification and variable spraying of herbicides. Based on the research of four kinds of germinated corn and wheat weeds by Chinese and foreign experts, we use machine learning technology as the method of extracting and recognizing the combination of color features, shape features, and texture features. Plant development and software are used to process weeds to classify and identify wheat and corn. To better describe the gray distribution of each pixel and neighboring pixels, a more effective weed image classification function is required. The experimental results of this paper show that the machine learning-based weed image recognition classifier can describe the grayscale distribution of each pixel and adjacent pixels well, and it is beneficial to identify the characteristics of weeds, in which the correct rate of weed recognition exceeds 97.5%.