Identifying plant leaf diseases will be highly difficult due to the difficulties in gathering lesion characteristics from a quickly changing atmosphere, imbalanced illumination reflection of the incoming light source, and numerous other factors. A practical strategy for classifying plant leaf diseases is provided in this research. Using HSV, HU moments, and color histograms, we first created a leaf feature improvement framework that can enhance leaf characteristics in a complicated environment. Then, to increase feature classification capacity, a competent extreme boost method is modelled. Batch normalization is used to avoid network overfitting while also improving the model\'s resilience. The plant leaf disease feature improvement approach is favorable to boosting the efficiency of the XGBoost classification, as demonstrated in studies from various perspectives. For plant leaf disease photos obtained in the natural environment, our technique displays significant resilience, serving as a benchmark for the intelligent categorization of additional plant leaf diseases.

In this paper, the authors considered a new class for Pál type interpolation problems. They termed Pál type interpolation problems as PTIP. This new class for PTIP is defined by omitting a non-zero complex node from the set of value nodes and simultaneously adding another complex node to the set of derivative nodes.

Early screening of autism spectrum disorders (ASD) is a key area of research in healthcare. Currently artificial intelligence (AI)-driven approaches are used to improve the process of autism diagnosis using computer-aided diagnosis (CAD) systems. One of the issues related to autism diagnosis and screening data is the reliance of the predictions primarily on scores provided by medical screening methods which can be biased depending on how the scores are calculated. We attempt to reduce this bias by assessing the performance of the predictions related to the screening process using a new model that consists of a Self-Organizing Map (SOM) with classification algorithms. The SOM is employed prior to the diagnostic process to derive a new class label using clusters learnt from the independent features; these clusters are related to communication, repetitive traits, and social traits in the input dataset. Then, the new clusters are compared with existing class labels in the dataset to refine and eliminate any inconsistencies. Lastly, the refined dataset is utilised to derive classification systems for autism diagnosis. The new model was evaluated against a real-life autism dataset that consists of over 2000 instances of cases and controls. The results based on the refined dataset show that the proposed method achieves significantly higher accuracy, precision, and recall for the classification models derived when compared to models derived from the original dataset.

Parallel interference cancellation is considered as a simple yet effective multiuser detector for direct -sequence code-division multiple-access (DS-CDMA) systems. However, system performance be deteriorated due to unreliable interference cancellation in the early stages. Thus, a detector with the partial parallel interference cancellation in which the partial cancellation factors are introduced to control the interference cancellation level has been developed as a remedy. Although the partial cancellation factors are crucial, complete solutions for their optimal values are not available. In this paper, we consider a two-stage decoupled generalized receiver with the partial parallel interference cancella-tion. Using the minimum bit error rate (BER) criterion, we derive a complete set of optimal partial cancellation factors. This includes the optimal partial cancellation factors for periodic and aperiodic spreading codes in channels with the additive white Gaussian noise and multipath channels. Simulati-on results demonstrate that the considered theoretical optimal partial cancellation factors agree closely with empirical ones. The proposed two-stage generalized receiver with the partial parallel interference cancellation using the derived optimal partial cancellation factors outperforms not only a two-stage, but also a three-stage conventional generalized receiver with the full parallel interference cancellation.

Cryptology has experienced significant advancement in parallel with the development of computer systems, passing from an artisanal and confidential era to very high-tech systems requiring significant computing power. In this paper, we first review the Hill cipher so that its shortcomings are clear. Then, in order to make it harder to break, we come up with a new crypto-system that relies on the use of the Hill cipher surrounded by two different affine ciphers. As the secret key field of the proposed system consists of eight fields, the key space is of around 256. It is large enough to make a brute force attack hard; however, the key space can be of 291 or 2140 depending on the matrix dimensions preferred.

This paper highlights the results of the survey of Ukrainian scientists on the exchange of research data with other scientists without peer review, and their motivation to use and disseminate unreviewed research data. By “research data” we mean both processed (summarized in the form of text data, tables, figures, infographics, etc.) and unprocessed information collected by researchers due to experiments, observations, simulations, through surveys or interviews or by other ways, or created from available data. A questionnaire was distributed through Facebook groups “Ukrainian Scientific Journals” “Ukrainian Scientists Worldwide”, “Pseudoscience News in Ukraine”, “Scientific Conferences and Publications”, “Academic Virtue and Plagiarism”, “Higher School and Science of Ukraine: Disintegration or Blossoming?”, “Ukrainian cuisine of scientific publications”, and through university networks. Results from 736 respondents demonstrated awareness and attitudes toward data sharing, advantages and disadvantages of data sharing for scientists. Most of the respondents don’t trust the results of scientific research published in sources other than peer-reviewed scientific journals. Only 34.7 % of the respondents use to publish their unreviewed research data. The reasons that can stop scientists from sharing research data are the following: problems with copyright protection, luck of time, fear to lose publishing opportunities, contradictions with the requirements of the journals, risk of misinterpretation, risk of losing leadership in the field of research, ethical norms violations, prejudice. Researchers particularly those attached to universities highlighted lack of time for data sharing, because they are busy with other roles of teaching, supervising students, conducting research, administrative activities, and participating in community service. Among the reasons for data sharing the scientists noted cooperation, formation of reputation, increasing the likelihood of being quoted, feedback from the scientific community, development of science, saving results to use in the future, etc. 30.9 % of the respondents indicated that they do not find anything that could motivate them to share research data. Meanwhile about 78.0 % of respondents recognized the need for training in several areas of research data sharing.

Whereas T-cell activation through the blockade of PD-1/PD-L1 interactions by monoclonal antibodies has achieved remarkable therapeutic potential against multiple types of cancer, high production cost, instability, low tumor penetration of antibodies hamper their broader clinical application. This situation has awakened significant interest in design and discovery of small-molecule-based inhibitors of PD-1/PD-L1 interaction. At present, several small-molecule inhibitors (SMIs) of the PD-1/PD-L1 interactions have been identified. However, the structures of their complexes with PD-L1 have been resolved only for a few of them. Based on these studies, it has been suggested that these SMIs act through the formation of stable trimeric complexes of two PD-L1 molecules with one small-molecule ligand, making PD-L1 noncompetent for binding to PD-1. The physical basis of such small-molecule-induced PD-L1 complexing of high stability is not fully understood. In this work, the atomistic structure of the full-length PD-L1 dimer is predicted and a new protocol of enhanced protein conformational sampling combining Schrodinger and RosettaLigand Induced Fit docking with Molecular Dynamics simulations is used for determination of binding modes of four complexes of small-molecule inhibitors of PD-1/PD-L1 interaction with full-length PD-L1 dimers, possessing a high inhibiting activity towards PD-1/PD-L1 interaction in vitro. The molecular mechanisms of the high inhibiting activity of small-molecule inhibitors towards the PD-L1 dimer have been addressed taking into account the role of the membrane as well as the binding affinities of PD-L1 to its cis and trans protein partners