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Anilides of 3-oxo-4-phenylaminobutyric acid with Troc or COOEt protection at the phenylamino group undergo competing cyclization processes in neat polyphosphoric acid at 80 °C. Depending on the protecting group and the duration of the process, three main products in different ratios are formed. Along with the quinolin-2-ones, resulting from the classic Knorr cyclization, an indole derivative and a spirocyclic product have also been obtained from the COOEt-protected substrate. It has been demonstrated that the obtained indole derivative is capable of further dearomative spirocyclization under the studied conditions.

The unusual reactivity of key enamine intermediates led to the formation of 3-methylamino-2-(2-nitrobenzoyl)-4H-naphthalen-1-one as an unexpected product in an attempted synthesis of the P. aeruginosa metabolite 2-benzyl-4(1H)-quinolone. Although the synthesis of the natural product has not been successful, this methodology allows for the easy preparation of novel derivatives carrying a carboxamide moiety at the C3 position.

Marine debris poses a critical threat to environmental ecosystems, necessitating effective methods for its detection and localization. This study addresses the existing limitations in the literature by proposing an innovative approach that combines the instance segmentation capabilities of YOLOv7 with various attention mechanisms to enhance efficiency and broaden applicability. The primary contribution lies in the exploration and comparison of three attentional models: lightweight coordinate attention, combining spatial and channel focus (CBAM), and bottleneck transformer based on self-attention. Leveraging a meticulously labeled dataset of satellite images containing ocean debris, the study conducts a comprehensive assessment of box detection and mask evaluation. The results demonstrate that CBAM emerges as the standout performer, achieving the highest F1 score (77%) in box detection, surpassing coordinate attention (71%) and YOLOv7/bottleneck transformer (both around 66%). In mask evaluation, CBAM continues to lead with an F1 score of 73%, while coordinate attention and YOLOv7 exhibit comparable performances (around F1 scores of 68% and 69%), and bottleneck transformer lags behind at an F1 score of 56%. This compelling evidence underscores CBAM's superior suitability for detecting marine debris compared to existing methods. Notably, the study reveals an intriguing aspect of the bottleneck transformer, which, despite lower overall performance, successfully detected areas overlooked by manual annotation. Moreover, it demonstrated enhanced mask precision for larger debris pieces, hinting at potentially superior practical performance in certain scenarios. This nuanced finding underscores the importance of considering specific application requirements when selecting a detection model, as the bottleneck transformer may offer unique advantages in certain contexts.

β-Keto amides were used as convenient precursors to both 2-alkyl-4-quinolones and 2-alkyl-4-quinolone-3-carboxamides. The utility of this approach is demonstrated with the synthesis of fourteen novel and four known quinolone derivatives, including natural products of microbial origin such as HHQ and its C5-congener. Two compounds with high activity against S. aureus have been identified among the newly obtained quinolones, with MICs ≤ 3.12 and ≤ 6.25 µg/mL, respectively.

A four-step synthesis of the natural product pseudane IX, starting from 3-oxododecanoic acid phenylamide and including only one chromatographic purification, was accomplished with an overall yield of 52%. The same synthetic sequence, but with a controlled partial reduction of a nitro group in the penultimate intermediate, led to the N-oxide of pseudane IX (NQNO). A shortened three-step variation of the synthesis allowed for the preparation of novel carboxamide analogs of the natural product. An agar diffusion assay against six different bacterial strains revealed significant antibacterial activity of the novel analogs against S. aureus at a concentration of 100 µg/mL. One of the novel compounds showed a remarkably broad spectrum of antibacterial activity, comparable to that of the positive control NQNO.

2-Methyl-4-oxo-4,5-dihydro-1H-pyrrole-3-carboxylic acid phenylamide was obtained as a single product in an experiment on the cyclization modes of a glycine-derived enamino amide. High yield and operational simplicity are the main features of the presented synthetic procedure. Additionally, this result extends our previous observations on the cyclization reactions of similarly functionalized enamines, by revealing the preferred cyclization pathway under Boc-deprotection conditions.

3-(1-Ethylamino-ethylidene)-1-methyl-pyrrolidine-2,4-dione was obtained as an unexpected product in a three-step synthesis starting with o-nitrobenzoyl sarcosine, acetoacetanilide and ethylamine. The compound showed moderate antibacterial activity against S. aureus and E. coli.

3-Carbamoylmethyl-Indole-1-Carboxylic Acid Ethyl Ester (an ethoxycarbonyl derivative of indole-3-acetamide) is obtained by Friedel–Crafts type cyclocondensation of γ-functionalized acetoacetamide in neat polyphosphoric acid.

In a high-yielding and solvent-free procedure N-ethoxycarbonyl protected ω-amino-β-keto anilides undergo Knorr cyclisation in neat polyphosphoric acid to provide straightforward route to 4-aminoalkyl quinolin-2-one derivatives with variable length of the alkyl chain.

Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of trees died and they inevitably became a factor of forest fire. An important portent of the forest fire is the condition of forests. Aerial image-based forest analysis can give an early detection of dead trees and living trees. In this paper, we applied a synthetic method to enlarge imagery dataset and present a new framework for automated dead tree detection from aerial images using a re-trained Mask RCNN (Mask Region-based Convolutional Neural Network) approach, with a transfer learning scheme. We apply our framework to our aerial imagery datasets,and compare eight fine-tuned models. The mean average precision score (mAP) for the best of these models reaches 54%. Following the automated detection, we are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image, as an indicator of forest health that could be linked to the causal analysis of environmental changes and the predictive likelihood of forest fire.

This research addresses the problem of automatic extraction of memorial objects from cultural heritage sites represented as scenes of 3D point clouds. Point clouds provide a fine spatial resolution and accurate proxy of the real world. However, how to use them directly is not always obvious. This is especially true for applications where extensive training data or computational resources are not available. In this paper, we present a methodology for automatic segmentation and labelling of cultural heritage objects from 3D point cloud scenes. The proposed methodology is based on machine learning techniques and, in particular, makes use of the concept of transfer learning. Memorial objects are segmented from the scene based on their geometric shape characteristic through a conditional multi-scale partitioning scheme. Then, high-level latent feature descriptors are extracted by a convolutional neural network pre-trained on different 3D object models from a standard dataset (e.g., ModelNet). Based on these descriptors, a classification model (multilayer perceptron) is trained and applied to obtain semantic labels. Experiments demonstrated that the proposed methodology is effective for the extraction and labelling of grave marker objects from cultural heritage sites.

A new approach for the synthesis of Santacruzamate A analogues is demonstrated. The method allows functionalization at position 3 of the gamma-aminobutyric fragment and carbon chain variation.

The knuckle creases present on the dorsal side of the human hand can play significant role in identifying the offenders of serious crime, especially when evidence images of more recognizable biometric traits, such as the face, are not available. These knuckle creases, if localized appropriately, can result in improved identification ability. This is attributed to ambient inclusion of the creases and minimal effect of background, which lead to quality and discerning feature extraction. This paper presents an ensemble approach, utilizing multiple object detector frameworks, to localize the knuckle regions in a functionally appropriate way. The approach leverages from the individual capabilities of the popular object detectors and provide a more comprehensive knuckle region localization. The investigations are completed with two large-scale public hand databases which consist of hand-dorsal images with varying backgrounds and finger positioning. In addition to that, effectiveness of the proposed approach is also tested with a novel proprietary unconstrained multi-ethnic hand dorsal dataset to evaluate its generalizability. Several novel performance metrics are tailored to evaluate the efficacy of the proposed knuckle localization approach. These metrics aim to measure the veracity of the detected knuckle regions in terms of their relation with the ground truth. The comparison of the proposed approach with individual object detectors and a state-of-the-art hand keypoint detector clearly establishes the outperforming nature of the proposed approach. The generalization of the proposed approach is also corroborated through the cross-dataset framework.

Ethylenediamine-derived β-enamino amides are used as equivalents of amide enolate synthons in C-acylation reactions with N-protected amino acids. Domino fragmentation of the obtained intermediates leads to functionalised β-keto amides, bearing a protected amino group in their side chain.

Digitalisation has opened a wealth of new data opportunities by revolutionizing how images are captured. Although the cost of data generation is no longer a major concern, the data management and processing have become a bottleneck. Any successful visual trait system requires automated data structuring and a data retrieval model to manage, search, and retrieve unstructured and complex image data. This paper investigates a highly scalable and computationally efficient image retrieval system for real-time content-based searching through large-scale image repositories in the domain of remote sensing and plant biology. Images are processed independently without considering any relevant context between sub-sets of images. We utilize a deep Convolutional Neural Network (CNN) model as a feature extractor to derive deep feature representations from the imaging data. In addition, we propose an effective scheme to optimize data structure that can facilitate faster querying at search time based on the hierarchically nested structure and recursive similarity measurements. A thorough series of tests were carried out for plant identification and high-resolution remote sensing data to evaluate the accuracy and the computational efficiency of the proposed approach against other content-based image retrieval (CBIR) techniques, such as the bag of visual words (BOVW) and multiple feature fusion techniques. The results demonstrate that the proposed scheme is effective and considerably faster than conventional indexing structures.

Presently, software tools for retrosynthetic analysis are widely used by organic, medicinal, and computational chemists. Rule-based systems extensively use collections of retro-reactions (transforms). While there are many public datasets with reactions in synthetic direction (usually non-generic reactions), there are no publicly-available databases with generic reactions in computer-readable format which can be used for the purposes of retrosynthetic analysis. Here we present RetroTransformDB—a dataset of transforms, compiled and coded in SMIRKS line notation by us. The collection is comprised of more than 100 records, with each one including the reaction name, SMIRKS linear notation, the functional group to be obtained, and the transform type classification. All SMIRKS transforms were tested syntactically, semantically, and from a chemical point of view in different software platforms. The overall dataset design and the retrosynthetic fitness were analyzed and curated by organic chemistry experts. The RetroTransformDB dataset may be used by open-source and commercial software packages, as well as chemoinformatics tools.

In the competitive telecommunications market, the information that the mobile telecom operators can obtain by regularly analysing their massive stored call logs, is of great interest. Although the data that can be extracted nowadays from mobile phones have been enriched with much information, the data solely from the call logs can give us vital information about the customers. This information is usually related with the calling behaviour of their customers and it can be used to manage them. However, the analysis of these data is normally very complex because of the vast data stream to analyse. Thus, efficient data mining techniques need to be used for this purpose. In this paper, a novel approach to analyse call detail records (CDR) is proposed, with the main goal to extract and cluster different calling patterns or behaviours, and to detect outliers. The main novelty of this approach is that it works in real-time using an evolving and recursive framework.

Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

This paper presents a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. To achieve this, the human silhouette is divided into regions in a radial fashion with the interval of a certain degree, and then region-based geometrical and Hu-moments features are obtained from each radial bin to articulate the feature descriptor. A multiclass support vector machine classifier is used for action classification. The proposed approach is quite simple and achieves state-of-the-art results without compromising the efficiency of the recognition process. Our contribution is two-fold. Firstly, our approach achieves high recognition accuracy with simple silhouette-based representation. Secondly, the average testing time for our approach is 34 frames per second, which is much higher than the existing methods and shows its suitability for real-time applications. The extensive experiments on a well-known multiview IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset confirmed the superior performance of our method as compared to similar state-of-the-art methods.