Predicting the outcome of a future game between two National Basketball Association (NBA) teams poses a challenging problem of interest to statistical scientists as well as the general public. In this article, we formalize the problem of predicting the game results as a classification problem and apply the principle of maximum entropy to construct NBA maximum entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs by the NBAME model. The best NBAME model is able to correctly predict the winning team 74.4 percent of the time as compared to some other machine learning algorithms which is correct 69.3 percent of the time.

Huperzine A is an herbal reversible inhibitor of Acetylcholinesterase (AChE). A molecular docking analysis on Huperzine A molecule has been carried out to understand its structure, conformational flexibility, intermolecular interaction and the binding affinity in the active site of AChE enzyme. Further, the charge density distribution of huperzine A molecule (lifted from the active site of AChE) was determined from the high level quantum chemical calculations coupled with charge density analysis. The binding affinity of Huperzine A towards AChE was calculated from the molecular docking; the lowest docked energy is -8.46 kcal/mol. In the active site, huperzine A molecule interacts with acyl binding pocket-Phe330 of AChE, that is, the bicyclo ring group of huperzine A forms an intermolecular interaction with the oxygen atom of main chain of the amino acid residue Phe330 at the distances 3.02 and 3.25 Å respectively. On the other hand, a gas phase study on huperzine A molecule also performed using HF and DFT (B3LYP) methods with the basis set 6-311G**. The molecular structure, conformation, and the charge density distribution of huperzine A molecule in the gas phase have determined using quantum chemical calculations and the charge density analysis. The comparative studies between the gas phase and the active site forms of huperzine A molecule, explicitly reveals the degree of conformational modification and the charge density redistribution of huperzine A when present in the active site. The dipole moment of the molecule in the active site is 6.85 D, which is slightly higher than its gas phase value (5.91 D). The electrostatic potential (ESP) surface of active site molecule clearly shows the strong electronegative and positive ESP regions of the molecule, which are the expected strong reactive locations of the molecule.

Optimal control has emerged as an indispensable tool in the domain of mechanical robotic systems. The dynamic processes under consideration in this paper are characterized by differential equations with an unknown coefficient. The problem addressed is time-optimal and exhibits bilinear characteristics. To investigate this inverse optimal problem, the classical method has been employed alongside Pontryagin’s Maximum Principle (PMP). This article aims to provide an exact piecewise function for controlling trajectories, specifically accounting for viscous friction. The goal is to determine the reachability set and to find the minimal process time. Notably, no simplifying assumptions were made during the analytical transformations.

Nanoporous membranes promise energy-efficient water desalination. Hexagonal boron nitride (h-BN), like graphene, exhibits outstanding physical and chemical properties, making it a promising candidate for water treatment. We employed Car–Parrinello molecular dynamics simulations to establish accurate modeling of Na+ and Cl- permeation through hydrogen passivated nanopores in graphene and h-BN membranes. In these complex systems the classical potentials cannot properly account for the physics at play. We demonstrate that ion separation works well for the h-BN by imposing a barrier of 0.13 eV and 0.24 eV for Na+ and Cl- permeation respectively, compared to the graphene where the Cl- ion faces a negative barrier of 0.68 eV and is prone toward blockade and Na+ permeation is associated with a slightly negative barrier of 0.03 eV. From the change in the solvation shell, we get the following trend: Na/h-BN > Na/G > Cl/G > Cl/h-BN. We argue that the trend in the permeation barrier changes to Cl-h-BN > Na-h-BN > Na-G > Cl-G due to a combination of several interactions, including the distortion of the water network induced by the ions, the ion-water interaction, and the ion-nanopore interaction. Overall, the desalination performance of h-BN surpasses that of their graphene counterparts.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification tasks, predictive modelling, and variance analyses, such as those used in Geometric Morphometrics. Here different Generative Adversarial Network architectures are experimented with, testing the effects of sample size and domain dimensionality on model performance. For model evaluation, robust statistical methods were used. Each of the algorithms were observed to produce realistic data. Generative Adversarial Networks using different loss functions produced multidimensional synthetic data significantly equivalent to the original training data. Conditional Generative Adversarial Networks were not as successful. The methods proposed are likely to reduce the impact of sample size and bias on a number of statistical learning applications. While Generative Adversarial Networks are not the solution to all sample-size related issues, combined with other pre-processing steps these limitations may be overcome. This presents a valuable means of augmenting geometric morphometric datasets for greater predictive visualization.

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional manual inspection methods are time-consuming, labor-intensive, and often lack the spatial resolution required for detailed analysis. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary objective was to develop a methodology for precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This was achieved by integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. UAV imagery was ac-quired bi-weekly at altitudes of 40m and 70m, capturing the entire growth cycle of a corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. Local spatial analysis and image processing techniques were em-ployed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. For enhanced accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area esti-mates by eliminating interference from soil and shadows. The proposed methodology enables precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time, providing valuable insights for precision agriculture practices. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop mon-itoring at the individual plant level.

Tapping panel dryness (TPD) syndrome is a complex disease of Rubber tree (Hevea brasiliensis L.) which causes cessation of latex drainage upon tapping of rubber tree. Rubber tree virus (RTV1) was identified as a novel pathogen associated with rubber tree and a potential causal agent of TPD. RTV1 is a monopartite RNA virus that is linear, non-enveloped and has a single-stranded (ss) positive RNA genome of approximately 6081 nucleotides and is composed of two major open reading frames (ORFs), ORF1 (polyprotein), and ORF2 (movement protein. This study aimed to investigate the possibility of rubber genome encoded tree microRNAs (miRNAs) as novel therapeutic targets against RTV1 using in silico algorithms. Mature rubber tree miRNAs are retrieved from the miRBase database and are used for hybridization of RTV1 using five different five different computational algorithms including miRanda, RNA22, RNAhybrid and psRNATarget. A total of eleven common rubber tree miRNAs were identified based on consensus genomic positions. The consensus of four algorithms predicted the hybridization sites of hbr-miR396a and hbr-miR398 at common locus positions 6676, 1840 respectively. To validate the prediction, secondary structures of the consensual rubber tree miRNAs and free energy of duplex binding were calculated using the RNAfold and RNAcofold algorithms respectively. We created a plot between rubber tree miRNAs and RTV1 ORFs by using Circos algorithm. In this study, we predicted eleven consensual rubber tree miRNAs. Among these miRNAs, hbr-miR398 was identified as the most effectual miRNA that may target the ORF1 gene of the RTV1 genome. The predicted data will be important in the development of rubber trees resistant to RTV1.

In the world's first pig-to-human cardiac xenotransplant, the elevated levels of porcine cytomeg-alovirus (PCMV) are considered key factors contributing to patient mortality. This has renewed attention to PCMV, a virus widely prevalent in pigs. Currently, there are no effective drugs or vaccines targeting PCMV, and its high detection difficulty poses challenges for prevention and control research. In this study, antiviral small hairpin RNA (shRNA) was selected and inserted into the Rosa26 and miR-17-92 loci of pigs via a CRISPR/Cas9-mediated knock-in strategy. Further in vitro viral challenge experiments demonstrated that these genetically edited pig cells could effec-tively limit PCMV replication. Through this process, we constructed a PCMV-infected cell model, validated partial viral interference sites, enhanced gene knock-in efficiency, performed gene editing at two different gene loci, and ultimately demonstrated that RNA interference (RNAi) technology combined with CRISPR/Cas9 has the potential to generate pig cells with enhanced antiviral infec-tion capabilities. This opens up possibilities for the future production of pig populations with anti-viral functionalities.

Although Eulerian approaches are standard in computational acoustics, they are less effective for certain classes of problems like bubble acoustics and combustion noise. A different approach for solving acoustic problems is to compute with individual particles following particle motion. In this paper, a Lagrangian approach to model sound propagation in moving fluid is presented and implemented numerically, using three meshfree methods to solve the Lagrangian acoustic perturbation equations (LAPE) in the time domain. The LAPE split the fluid dynamic equations into a set of hydrodynamic equations for the motion of fluid particles and perturbation equations for the acoustic quantities corresponding to each fluid particle. Then, three meshfree methods, the smoothed particle hydrodynamics (SPH) method, the corrective smoothed particle (CSP) method, and the generalized finite difference (GFD) method, are introduced to solve the LAPE and the linearized LAPE (LLAPE). The SPH and CSP methods are widely used meshfree methods, while the GFD method based on the Taylor series expansion can be easily extended to higher orders. Applications to modeling sound propagation in steady or unsteady fluids in motion are outlined, treating a number of different cases in one and two space dimensions. A comparison of the LAPE and the LLAPE using the three meshfree methods is also presented. The Lagrangian approach shows good agreement with exact solutions. The comparison indicates that the CSP and GFD method exhibit convergence in cases with different background flow. The GFD method is more accurate, while the CSP method can handle higher Courant numbers.

Due to the vast conformational space proteins can adopt, accurate and efficient prediction of protein structure remains still a challenging task, coupled with the intricacies of interatomic interactions and the limitations of current computational models in effectively navigating this complex molecular landscape. Additionally, the lack of comprehensive experimental data for all protein structures further exacerbates the difficulty in reliable machine learning-based prediction of the three-dimensional conformations of the proteios building block of life. Geometrically, Cartesian coordinate system (CCS, X, Y and Z) and spherical coordinate system (SCS, ρ, θ and φ) are two interconvertible coordinate systems, and are like two sides of one coin. Since the beginning of Protein Data Bank (PDB) in 1971, CCS has been the default approach to specify atomic positions with X, Y and Z in PDB. In this manuscript, therefore, I present a novel method for the reversible spherical geometric conversion of protein backbone structure coordinate matrices to three independent vectors: ρ, θ and φ. This reversible conversion facilitates lossless extraction of essential structural features from protein backbone structural data, enabling the development of advanced novel algorithms for protein structure prediction in future. In short, this inter-atomic SCS approach offers a comprehensive yet efficient means of representing protein backbone geometry, leveraging spherical coordinates to capture spatial relationships in a compact and intuitive inter-atomic manner, and to provide a robust framework for reversible feature extraction for the ongoing efforts in advancing the field of protein structure prediction, the holy grail of computational structural biology.

As a key mediator of chronic pain, neurotrophin nerve growth factor (NGF) binds to two neurotrophin receptors: p75 neurotrophin receptor (p75NTR) and tyrosine kinase receptor A (TrkA). The formation of the p75NTR-NGF-TrkA complex is implicated in the potentiation of chronic pain signals, making it an attractive target for therapeutic explorations. NGF analogues and monoclonal antibodies (mAbs) targeting NGF represent two distinct approaches in modulating the intricate signaling pathways involved in pain transduction mediated by the p75NTR-NGF-TrkA complex. While NGF analogues offer the advantage of tailored design to fine-tune neurotrophic responses, monoclonal antibodies provide a more systemic and comprehensive blockade of NGF, inhibiting its interactions with both p75NTR and TrkA receptors. However, the use of mAbs may pose challenges related to potential side effects and interference with the physiological functions of NGF. As a result, balancing the benefits and drawbacks of the two approaches is critical for advancing therapeutic strategies towards the alleviation of p75NTR-NGF-TrkA-mediated pain. In this study, therefore, a novel structural and biophysical approach was employed for the design of NGF analogues to suppress p75NTR-NGF-TrkA-related chronic pain signaling. Employing high-throughput structural modeling and biophysics-based intermolecular binding affinity calculations, this article for the first time puts forward a set of NGF analogues, including in particular NGF analogues with four site-specific mutations, for whose dimerizations the K d at 37 ^◦C were reduced by three orders of magnitude (from 10^-9 M to 10^-6 M) compared to the K d at 37 ^◦C for the dimerization of native NGFs. Overall, the integration of structural and biophysical perspectives enhances our understanding of the rational design of NGF analogues as promising candidates for the development of NGF-targeted analgesic therapies, which balances the benefits and drawbacks of anti-NGF antibodies and NGF analogues for advancing therapeutic strategies towards the alleviation of p75NTR-NGF-TrkA-mediated pain.

Due to the significant price difference, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models exceeded 95%. Experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

Recent discovery of triangular boron Nanotubes makes it a competitor of carbon in many respects. Closed forms of M-polynomial of nanotubes produce closed forms of many degree-based topological indices which are numerical parameters of the structure and, in combination, determine properties of the concerned nanotubes. In this report, we give M-polynomials of triangular boron nanotubes and recover many important topological degree-based indices of these nanotubes. We also plot surfaces associated to these nanotubes.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

From a structural point of view, understanding interfacial electrostatic features of ligand-receptor complexes is crucial for biophysics-based design of therapeutics with improved efficacy and safety. Here, with a high-throughput structural feature extraction approach, this article reports a set of electrostatic structural features from glucagon-like peptide-1 (GLP-1) and GLP-1 receptor (GLP-1R) complex structures determined using experimental tools. Leveraging a set of computational structural biophysical analyses, this article systematically characterized the electrostatic interactions within the GLP-1-GLP-1R complexes with two sets of criteria to identify interfacial salt bridges and hydrogen bonds. Overall, the results here reveal intricate details of electrostatic interactions critical for GLP-1 binding to its receptor GLP-1R, providing biophysical insights into the stabilization of the GLP-1-GLP-1R complex structure and the molecular basis of GLP-1/GLP-1R signaling. Furthermore, the electrostatic structural features extracted from GLP-1 and GLP-1R complex structures are useful in the development of a machine learning-based ligand-receptor binding affinity calculating model, i.e., a GLP-1-GLP-1R-specific general intermolecular binding affinity calculator (GIBAC), paving the way for accelerated discovery and structure-based design of drug candidates targeting the GLP-1/GLP-1R system with improved efficacy and safety.

Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.

For a significant period, conventional breeding and genetic modification were the key techniques that were effective in managing biotic and abiotic stresses in crops and adding desirable traits. However, the recent appearance of novel diseases and unexpected climatic changes that have substantial implications for agriculture worldwide have urged scientists to look for alternative methods to quickly manage seasonal crises. The primary emphasis of this chapter is on the obstacles and diverse biotechnological methods employed to enhance crop resilience against a range of biotic and abiotic stressors in plants. Furthermore, we consider gene transformation, omics techniques, genome editing, and other sophisticated biotechnological tools that utilize transcriptomic, proteomic, metabolomic, phenomics, RNA interference, and epigenome modifications for enhancing plant resilience. Lastly, we examine the potential of merging these eco-friendly and innovative methods with conventional breeding to promote modern agriculture and aid in enhancing tolerance to different biotic, abiotic stresses and growth promotion.

RNAi is considered a major antiviral defense mechanism in insects but its relative importance compared to other antiviral pathways has not been evaluated comprehensively. Here, it is attempted to give an overview of the antiviral defense mechanisms in Drosophila that involve both RNAi and non-RNAi to acquire a sense of their relative importance. While RNAi is considered important in most viral infections, many other pathways can exist that confer antiviral resistance. It is noted that very few direct recognition mechanisms of virus infections have been identified in Drosophila and that the activation of immune pathways may be accomplished indirectly through cell damage incurred by viral replication. In several cases, protection against viral infection can be obtained in RNAi mutants by non-RNAi mechanisms, confirming the variability of the RNAi defense mechanism according to the type of infection and the physiological status of the host. This analysis invites to investigate more systematically the relative contribution of RNAi in the antiviral response and more specifically to ask whether RNAi efficiency is affected when other defense mechanisms predominate. While Drosophila can function as a useful model, this issue may be more critical for economically important insects that are either controlled (agricultural pests and vectors of diseases) or protected from parasite infection (beneficial insects as bees) by RNAi products.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

Every finite simple group P can be generated by two of its elements. Pairs of generators forP are available in the Atlas of finite group representations as (not necessarily minimal) permutation representations P . It is unusual but significant to recognize that a P is a Grothendieck’s dessin d’enfant D and that a wealth of standard graphs and finite geometries G - such as near polygons and their generalizations - are stabilized by a D. In our paper, tripods P − D − G of rank larger than two,corresponding to simple groups, are organized into classes, e.g. symplectic, unitary, sporadic, etc (as in the Atlas). An exhaustive search and characterization of non-trivial point-line configurationsdefined from small index representations of simple groups is performed, with the goal to recognize their quantum physical significance. All the defined geometries G's have a contextuality parameterclose to its maximal value 1.

The process of sexual reproduction in eukaryotes starts when gametes from two different sexes encounter each other. Paramecium, a unicellular eukaryote, undergoes conjugation and uses a gametic nucleus to enter the sexual reproductive process. The molecules responsible for recognizing mating partners, hypothetically called mating-type substances, are still unclear. We have identified an O3-type mating substance polypeptide and its gene sequence using protein chemistry, molecular genetics, immunofluorescence, RNA inter-ference, and microinjection. The O3-type substance is a polypeptide found in the ciliary membranes, located from the head to the ventral side of cells. The O3-type substance has a kinase-like domain in its N-terminal part located outside the cell and four EF-hand motifs that bind calcium ions in its C-terminal part located inside the cell. RNA interference and immunofluorescence revealed that this polypeptide positively correlated with the expression of mating reactivity. Microinjection of an expression vector incorporating the O3Pc-MSP gene induced additional O3 mating type in the recipient clones of different mating types or syngen. Phylogenetic analysis indicates this gene is widely present in eukaryotes and exhibits high homology among closely related species. The O3Pc-MSP gene had nine silent mutations compared to the complementary mating type of the E3 homologue gene.

Metagenomic sequencing has led to a recent and rapid expansion in the animal virome. It has uncovered a multitude of new virus lineages from under-sampled host lineages, including many that break up long branches among previously known clades, and many with genomes that display unexpected sizes and structures. Although there are challenges to inferring the existence of a virus from a virus-like sequence, the analysis of nucleic acid (including small RNAs) and sequence data can give us considerable confidence in the absence of an isolate. As a consequence, this period of ‘molecular natural history’ is helping to reshape our views of deep virus evolution. Nevertheless, there is a limit to what metagenomic discovery alone can tell us, and some open questions will require experimental isolates.

Mutual exclusion algorithms are at the heart of concurrent, distributed/parallel, real-time and operating systems. It is well-known that such algorithms are very difficult to analyze and in the literature different conjectures about starvation-freedom and the number of by-passes (also said the overtaking factor), which in turn affects the (hopefully) bounded waiting time that a process competing for entering the critical section has to suffer before accessing the shared resource, have been formulated for specific algorithms. This paper proposes a novel modelling approach based on Timed Automata and the Uppaal toolset, which proves effective for studying all the properties of a mutual exclusion algorithm for N≥2 processes, by exhaustive model checking. Although the approach, as already confirmed by similar experiments reported in the literature, is not scalable due to state explosion problems, and can be practically applied until N≤5, it is of great value for revealing true properties of analyzed algorithms. For dimensions N>5 the statistical model checker of Uppaal can be used which, although based on simulations, can confirm properties by estimations and probabilities. The paper describes the proposed modelling and verification method and applies it to several mutual exclusion algorithms, thus retrieving known properties but also showing new results about properties often studied only by peer-review and intuitive reasoning.

RNA interference (RNAi) molecules have tremendous potential for cancer therapy but are limited by insufficient potency after i.v. administration. We previously found that polymer complexes (polyplexes) formed between 3’-cholesterol-modified siRNA (Chol-siRNA) or DsiRNA (Chol-DsiRNA) and the cationic diblock copolymer PLL[30]-PEG[5K] greatly increase RNAi potency against stably expressed LUC mRNA in primary syngeneic murine breast tumors after daily i.v. dosing. Chol-DsiRNA Polyplexes, however, maintain LUC mRNA suppression ~48 h longer after the final dose than Chol-siRNA Polyplexes, suggesting they are a better candidate formulation. Here, we directly compared the activities of Chol-siRNA and Chol-DsiRNA Polyplexes in primary murine 4T1 breast tumors against STAT3, a therapeutically relevant target gene overexpressed in many solid tumors including breast cancer. We found that Chol-siSTAT3 Polyplexes suppressed STAT3 mRNA in 4T1 tumors with similar potency (half-maximal ED₅₀ 0.3 mg/kg) and kinetics over 96 hours as Chol-DsiSTAT3 Polyplexes but with slightly lower activity against total Stat3 protein (29% vs. 42% suppression) and tumor growth (11.5% vs. 8.6% rate-based T/C ratio) after repeated i.v. administration of tumor-saturating doses every other day. Thus, both Chol-siRNA Polyplexes and Chol-DsiRNA Polyplexes may be suitable clinical candidates for RNAi therapy of breast cancer and other solid tumors.

Analysis of land use/land cover (LULC) in the catchment areas is the first action toward safeguarding the freshwater resources. The LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), such as Random Forests (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB) was investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs were based on the visual inspection of the analyst and the commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and kappa coefficient. The MLAs produced good results, where RF (OA= 97.02%, Kappa= 0.96), SVM (OA= 89.74 %, Kappa= 0.88), ANN (OA= 87%, Kappa= 0.86), and NB (OA= 68.64 Kappa= 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a small margin. While NB yielded satisfactory results, which could be primarily influenced by its sensitivity to limited training samples. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

The apicomplexan parasite, Sarcocystis neurona causes the degenerative neurological equine protozoal myeloencephalitis (EPM) disease of horses. Due to its host range expansion, S. neurona is an emerging threat that requires close monitoring. In apicomplexans, protein kinases (PKs) have been implicated in a myriad of critical functions such as host cell invasion, cell cycle progression and host immune responses evasion. Here, we used various bioinformatics methods to define the kinome of S. neurona and phylogenetic relatedness of its PKs to other apicomplexans. Further, three-dimensional (3D) homology models for selected S. neurona putative PKs were constructed and evaluated for inhibitor docking. We identified 92 putative PKs clustering within the AGC, CAMK, CK1, CMGC, STE, TKL, aPK and OPK groups. Although containing the universally conserved PKA (AGC group), S. neurona kinome was devoid of PKB and PKC, but contained the six apicomplexan conserved CDPKs (CAMK group). The OPK group was represented by ROPKs 19A, 27, 30, 33, 35 and 37, but was devoid of the virulence-associated ROPKs 5, 6, 18 and 38. Two out of the three S. neurona CK1 enzymes had high sequence similarities to T. gondii TgCK1-α and TgCK1-β and the Plasmodium PfCK1. Docking of four inhibitors onto homology models of putative ROP27 and PKA indicated that inhibition of S. neurona PKs is feasible, but needs to be experimentally tested. The essentiality of apicomplexan PKs makes the elucidation of S. neurona kinome a key milestone for development of novel therapeutics for EPM.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

In this paper, the synthesis of two novel luminescent Eu3+ ion complexes [Eu(TFT)3(phen) (1) and Eu(TFT)3(bpy)•Hex (2)] based on the combination of 2-(2,2,2-trifluoroethyl)-1-tetralone (TFT), containing n-donor ligands (1,10-phenanthroline or 2,2'-Bipyridine) and Eu3+ ion is reported herein, and their structural features are discussed on the basis of X-ray crystallographic and thermal analyses. We observed that the ligands transfer energy effectively to the metal center by fluorescence, IR and UV spectrograms studies. The structures and properties of complexes 1-2 were optimized by the DFT theoretical calculation, and the calculation results were consistent with the experimental X-ray structure data and spectral analysis.

This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.

Maritime data not only identify financial resources that can significantly enhance the efficiency of shipping companies but also play a pivotal role in promoting eco-friendly and sustainable shipping practices. However, active corporate strategies are required to include maritime data essential for the commercial operation of autonomous ships in the form of corporate assets by transparent evaluating the data. The aim of this study is to review previous studies on da-ta-valuation models, identify a valuation model suitable for maritime business, consider the characteristics of maritime data. The analytic hierarchy process (AHP) method is used to derive priorities for valuing maritime data assets, with emphasis placed on the characteristics of mari-time data, followed by the character of the future-oriented maritime-data market and the da-ta-valuation model. The results obtained that the most important factors in selecting a maritime data-valuation model by analyzing the AHP method were identified as the characteristics of the maritime data(M1). The next most important factors analyzed were the features of the mari-time-data market(M2) and the features of the maritime data-valuation model(M3) in order. In addition, the characteristics of maritime data were preferentially considered among the main criteria, and the market approach (A2) was selected as the most optimal valuation model. The potential impact of this implementation could contribute to the establishment of an intelligent technology market by estimating the value of data and developing a platform for maritime data trading, allowing for more efficient data sharing and utilization by maritime autonomous sur-face ship(MASS).

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

The susceptible-infected-recovered-vaccinated-deceased (SIRVD) epidemic compartment model extends the SIR model to include the effects of vaccination campaigns and time-dependent fatality rates on epidemic outbreaks. It encompasses the SIR, SIRV, SIRD, and SI models as special cases, with individual time-dependent rates governing transitions between different fractions. We investigate a special class of exact solutions and accurate analytical approximations for the SIRVD and SIRD compartment models. While the SIRVD and SIRD equations pose complex integro-differential equations for the rate of new infections and the fractions as a function of time, a simpler approach considers determining equations for the sum of ratios for given variations. This approach enables us to derive fully exact analytical solutions for the SIRVD and SIRD models. For nonlinear models with a high-dimensional parameter space, such as the SIRVD and SIRD models, analytical solutions, exact or accurately approximative, are of high importance and interest: not only as suitable benchmarks for numerical codes, but especially as they allow us to understand the critical behavior of epidemic outbursts as well as the decisive role of certain parameters. In the second part of our study, we apply a recently developed analytical approximation for the SIR and SIRV models to the more general SIRVD model. This approximation offers accurate analytical expressions for epidemic quantities, such as the rate of new infections and the fraction of infected persons, particularly when the cumulative fraction of infections is small. The distinction between recovered and deceased individuals in the SIRVD model affects the calculation of the death rate, which is proportional to the infected fraction in the SIRVD/SIRD cases but often proportional to the rate of new infections in many SIR models using a posteriori approach. We demonstrate that the temporal dependence of the infected fraction and the rate of new infections differs when considering the effects of vaccinations and when the real-time dependence of fatality and recovery rates diverge. These differences are highlighted for stationary ratios and gradually decreasing fatality rates. \Revised{The case of stationary ratios allows one to construct a new powerful diagnostics method to extract analytically all SIRVD model parameters from measured COVID-19 data of a completed pandemic wave.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Verticillium dahliae invades the roots of host plants and causes vascular wilt, which seriously diminishes the yield of cotton and other important crops. The protein AAC (ADP, ATP carrier) is responsible for transferring ATP from the mitochondria into the cytoplasm. When V. dahliae protoplasts were transformed with short interfering RNAs (siRNAs) targeting the VdAAC gene, fungal growth and sporulation were significantly inhibited. To further confirm a role for VdAAC in fungal development, we generated knockout mutants (ΔVdACC), which were hypersensitive to stresses such as UV light and high concentrations of NaCl or sorbitol. Compared with wild-type V. dahliae (Vd wt), ΔVdAAC was impaired in germination and virulence; these impairments were rescued in the complementary strains (ΔVdAAC-C). Moreover, when an RNAi construct of VdAAC under the control of the 35S promoter was used to transform Nicotiana benthamiana, the expression of VdAAC was downregulated in the transgenic seedlings, and they had elevated resistance against V. dahliae. The results of this study suggest that VdAAC contributes to fungal development, virulence and response to stresses and is a promising candidate gene to control V. dahliae. In addition, RNAi is a highly efficient way to silence fungal genes and provides a novel strategy to improve disease resistance in plants.

Caveolin-1 (Cav-1) is 22 kDa caveolae protein, acts as a scaffold within caveolar membranes. It interacts with alpha subunits of G-protein and thereby regulates their activity. Earlier studies reported elevated or up-regulated levels of caveolin-1 in the serum of prostate cancer patients. Secreted Cav-1 promotes angiogenesis, cell proliferation and anti-apoptotic activities in prostate cancer patients. Cav-1 upregulation is mainly related to prostate cancer metastasis. Keeping above facts in view, the present study was designed to explore Cav-1 as a target for prostate cancer therapy using computational approach. Molecular docking, structural base molecular modelling and molecular dynamics simulations were performed to investigate Cav-1 inhibitors. A predictive model was generated and validated to establish a stable structure. ZINC database of biogenic compounds was used for induced fit docking (IFD) and high throughput virtual screening. The H-bond interactions of the compounds with active site residues of Cav-1 were estimated by IFD and 100 ns long molecular dynamic simulations. The reported compounds showed significant binding and thus can be considered as potent therapeutic inhibitors of Cav-1. This study provides a valuable insight into biochemical interactions of Cav-1 for therapeutic applications and warrants for experimental validation of the predicted ‘active(s)’.

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ET_a) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ET_a over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ET_a values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ET_a is limited to water surplus areas only and in wet season, ET_a was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ET_a over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ET_a estimated for the whole estate were 107 Mm³, 140 Mm³, and 178 Mm³ using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

This study introduces a novel computational approach for testing conditional independence (BB CI test) within causal Directed Acyclic Graphs (DAGs), leveraging Bayesian non-parametric bootstrap and machine learning techniques. Our method offers an alternative for validating the assumptions underpinning causal DAGs. Through simulation studies and an industrial case analysis, we demonstrate the test procedure in accurately assessing conditional independence, comparing it with the Generalized Covariance Measure (GCM) test. Our findings suggest that the BB CI test is advantageous in scenarios where existing methods may falter due to violations of model assumptions. This research contributes to the causal inference literature by providing a computational tool for researchers and practitioners to validate causal models.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

Due to related uncertainties brought by changes in energy consumption and the integration of rooftop photovoltaic systems, the accuracy and availability of load profiling data are difficult to achieve. However, project managers and planners use a precise load profile as a crucial tool when determining whether the feeder must be updated or de-loaded. The paper aims to create a non-intrusive monitoring system that predicts the synthetic load profiles behavior of energy consumption using a Light Gradient Boosted and Support Vector Machine (SVM) machine learning technique. The most effective ML algorithm is chosen, and it has the potential to be assessed and verified using validation curves, residuals model generation, and prediction error metrics based on the following key statistical indicators: Mean Absolute Error, Mean Square Error, Root Mean Square Error, R-Square, Root Mean Squared Logarithmic Error, and Mean Absolute Percentage Error. The most effective ML has the potential to be assessed and verified using validation curves, residual model generation, and prediction error metrics based on the following key statistical indicators. The result shows that the estimation of the Irms avg -based on LightGBM is more accurate than the SVM because of its quick, economical, and difficult to overfit, particularly with high-dimensional data, speed, and efficiency of the MAE (3.0698), MSE (15.2757), RMSE (3.9020), RMSLE (0.1433), and MAPE (0.0049) respectively. Additionally, the machine learning model shows that, when compared to the SVM, the LightGBM model had the highest accurate prediction, with R-Square values of 89.8%, 90.3%, and 88.5% for Irms_A_avg, Irms_B_avg, and Irms_C_avg, respectively for Brakfontein Substation supply region, and best represents a diverse customer base.

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

Antisense oligonucleotide (ASO)-mediated exon skipping has emerged as a powerful tool for examining the function of genes and exons in basic research, as well as gene therapy. Computational methods, such as eSkip-Finder, have been developed to predict the efficacy of ASOs via exon skipping using machine learning. However, these methods can be computationally demanding and the prediction accuracy of the tool is not yet optimal. In this study, we propose an approach to reduce computational burden and improve prediction performance by utilizing feature selection within machine learning algorithms and employing ensemble learning techniques. The method was evaluated using a dataset of genes with experimentally validated exon skipping events. The dataset was divided into training and testing sets to assess the accuracy of the algorithm. Our results demonstrate that using a 3-way voting approach with random forest, gradient boosting, and XGBoost can significantly reduce computation time to under ten seconds while improving prediction performance, as measured by R2 for both 2’-O-methyl nucleotides (2OMe) and phosphorodiamidate morpholino oligomers (PMOs). Additionally, the feature importance ranking derived from our approach is in good agreement with previously published results. These findings suggest that this approach has the potential to enhance the efficiency and accuracy of predicting ASO efficacy via exon skipping, facilitating the development of novel therapeutic strategies.

Since the discovery of transposable elements (TEs) in Maize in the 1940s by Barbara McClintock [1] transposable elements have been described as junk, as selfish elements with no benefit to the host, and more recently as major determinants of genome structure and genome evolution. TEs are DNA sequences that are capable of moving to new sites in the genome and make additional copies while doing so. To limit the propagation of TEs, host silencing mechanisms are directed at transposon-encoded genes that are required for mobilization. The mutagenic properties of TEs, the potential of TEs to form new genes and affect gene expression, together with the host silencing mechanisms shape eukaryotic genomes and drive genome evolution. While TEs constitute more than half of the genome in many higher eukaryotes, transposable elements in the nematode C. ele-gans form a relatively small proportion of genome, about 15% [2]. Genetic studies of transposon silencing and the discovery of RNAi in C. elegans, propelled C. elegans to the forefront of studies of RNA-based mechanisms that silence TEs. Here, I will review the transposable elements that are present and active in the C. elegans genome, and the host defense mechanisms that silence these elements.

Insects possess an array of defense molecules allowing them to fight infections. They can also show a form of immune memory, named priming. However, the involvement of insect immune defense mechanisms in priming is unclear, since invertebrates lack the molecular machinery present in vertebrates to build an immune memory. In the red flour beetle Tribolium castaneum, larvae can be primed via the oral route with Bacillus thurigiensis var. tenebrionids (Btt). This results in changes in the expression of a large number of genes, among which some belong to families of ancient defense genes. In the present work, we tested whether three chosen candidate genes (a Thaumatin, a C-type Lectin and an Osiris-like gene) could be involved in the survival to a Btt exposure, as well as in the priming phenotype. We assessed changes in their expression over time and according to the priming treatment, knocked them down individually by RNA interference (RNAi), and observed how it affected survival upon challenge. The quantification of gene expression patterns in our larvae with RT-qPCR showed that up- and/or down-regulation of the genes, after the priming treatment, was quite volatile and time dependent. Upon knock-down, we did not observe the expected decrease in survival to Btt or the abolishment of the priming phenotype. We conclude that knocking down genes individually is probably insufficient to affect survival and priming in our system. This gives us insight into the complexity of the molecular processes underpinning priming.

The relationship between pangolin-CoV and SARS-CoV-2 has been a subject of debate. Further evidence of a special relationship between the two viruses can be found by the fact that all known COVID-19 viruses have abnormally hard outer shell (low M disorder; i.e., low content of intrinsically disordered residues in the membrane (M) protein) that so far was found in CoVs associated with burrowing animals, such as rabbits and pangolins, in which transmission involves virus remaining in buried feces for a long time. While a hard outer shell is necessary for viral survival, a harder inner shell could also help. For this reason, the N disorder range of pangolin-CoVs, not bat-CoVs, more closely matches that of SARS-CoV-2 especially when Omicron is included. The low N disorder (i.e., low content of intrinsically disordered residues in the nucleocapsid (N) protein), first observed in pangolin-CoV-2017 amd later in Omicron, is associated with attenuation according to the Shell-Disorder-Model. Our experimental study revealed that pangolin-CoV-2017 and SARS-CoV-2 Omicron (XBB.1.16 subvariant) show similar attenuations with respect to viral growth and plaque formation. Subtle differences have been observed that are consistent with disorder-centric computational analysis.

After the increasing number of SARS-CoV-2 infections all over the world, researchers and clinicians are struggling to find a vaccine or innovative therapeutic strategies to treat this viral infection. The SARS-CoV infection that occurred in 2002, MERS and other more common infectious diseases such as HCV, led to the discovery of many RNA-based drugs. Among them, siRNAs and antisense LNAs have been demonstrated to have effective antiviral effects both in animal models and humans. Owing to the high genomic homology of SARS-CoV-2 and SARS-CoV (80-82%) the use of these molecules could be employed successfully also to target this emerging coronavirus. Trying to translate this approach to treat COVID-19, we analyzed the common structural features of viral 5’UTR regions that can be targeted by non-coding RNAs and we also identified miRNAs binding sites suitable for designing RNA-based drugs to be employed successfully against SARS-CoV-2.

Antisense oligonucleotides (ASO), short single-stranded polymers based on DNA or RNA chemistries and synthesized in vitro, regulate gene expression by binding in a sequence-specific manner to an RNA target. The functional activity and selectivity in the action of ASOs largely depends on the combination of nitrogenous bases in a target sequence. This simple and natural property of nucleic acids provides an attractive route by which scientists can create different ASO-based techniques. Over the last 50 years, planned and realized applications in the field of antisense and nucleic acid nanotechnologies have produced astonishing results and posed new challenges for further developments, exemplifying the essence of the post-genomic era. Today the majority of ASOs are chemically modified and/or incorporated within nanoparticles to enhance their stability and cellular uptake. This review critically analyzes some successful cases using the antisense approach in medicine to address severe diseases, such as Duchenne muscular dystrophy and spinal muscular atrophy, and suggests some prospective directions for future research. We also examine in detail the elaboration of unmodified insect-specific DNA insecticides and RNA preparations in the areas of agriculture and forestry, a relatively new branch of ASO that allows circumvention of the use of non-selective chemical insecticides. When considering the variety of successful ASO modifications with an efficient signal-to-noise ratio of action, coupled with the affordability of in vitro oligonucleotide synthesis and post-synthesis procedures, we predict that the next half-century will produce a fruitful yield of tools created from effective ASO-based end products.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

Adamantinomatous Craniopharyngioma (ACP) is a rare epithelial tumor located in the craniopharyngeal duct, often associated with high morbidity and a high long-term recurrence rate. ACP is predominantly diagnosed in children below the age of 15 and accounts for a majority of Craniopharyngioma cases in the United States. It is most commonly caused by a mutation in exon 3 of the CTNNB1 gene, which promotes overaccumulation of β-Catenin in the WNT pathway. As β-Catenin accumulates, it over-activates the transcription factor, causing an over-transcription of WNT proteins and uncontrolled cell growth. Current methods of treatment for ACP are centered around surgical intervention and radiotherapy, including gross-total resection (GTR), subtotal resection (STR), and a combination of subtotal resection and adjuvant radiotherapy (STR+XTR). These methods of treatment are associated with high risk due to the proximity of ACP to critical structures near the craniopharyngeal duct. High recurrence rates and morbidity are closely associated with current methods of treatment and other methods have yet to be attempted in craniopharyngioma. This review will explore two possible methods of non-surgical and non-radiological therapeutic interventions. The use of RNAi therapy and CRISPR may provide insight and be a potential way to treat ACP with increased quality of life and decreased recurrence rates.

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly in gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5,397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

In my paper, a divisibility rule named the Queen function is de- scribed, which has been generalized for every integer and encompasses all pre- viously established divisibility rules. Subsequently, the paper discusses other areas where this function is instrumental. These include simplifying ratios, generating prime numbers, and more. Towards the end of the paper, a set of hypotheses is also presented. Among these, the most significant is the novel approach involving the application of the Queen function for prime number generation.

History is often thought to be dull and boring – where large numbers of facts are memorized for passing exams. But the past informs the present and future, especially in delineating the context surrounding specific events that, in turn, help provide a deeper understanding of their causes and implications. Scientific progress (whether incremental or breakthroughs) is built upon prior work. Chronological examination of computational chemistry’s evolution reveals the existence of major “epochs” (e.g., transition from semi-empirical methods to first principles calculations), and the centrality of key ideas (e.g., Schrodinger equation and Born Oppenheimer approximation) in potentiating progress in the field. The longstanding question of whether computing power (both capacity and speed) or theoretical insights play a more important role in advancing computational chemistry was examined by taking into account the field’s development holistically. Specifically, availability of large amount of computing power at declining cost, and advent of graphics processing unit (GPU) powered parallel computing are enabling tools for solving hitherto intractable problems. On the other hand, this essay argues (using Born Oppenheimer approximation as an example) that theoretical insights’ role in unlocking problems through simple (but insightful) assumptions is often overlooked. Collectively, the essay should be useful as a primer for appreciating major development periods in computational chemistry, from which counterfactual questions illuminate the relative importance of theoretical insights and advances in computer science in moving the field forward.

RNA-guided genomic transcriptional regulation tools, namely Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) and CRISPR-mediated gene activation (CRISPRa), are a powerful technology for the field of functional genomics. Deriving from the CRISPR/Cas9 system, both systems comprise a catalytically dead Cas9 (dCas9) and a single guide RNA (sgRNA). This type of dCas9 is incapable of cleaving DNA but retains its ability to specifically bind to DNA. The binding of the dCas9/sgRNA complex to a target gene results in transcriptional interference. The CRISPR/dCas9 system has been explored as a tool for transcriptional modulation and genome imaging. Despite its potential applications and benefits, the challenges and limitations faced by the CRISPR/dCas9 system include the off-target effects, PAM sequence requirement, efficient delivery methods, and the CRISPR/dCas9-interfered crops being labeled as genetically modified organisms in several countries. This review highlights the progression of CRISPR/dCas9 technology as well as its applications and potential challenges in crop improvement.

Post-harvest handling and ripening techniques have an impact on peach quality and shelf life, which has a big impact on consumer satisfaction and market competitiveness. This review paper examines recent advancements in ripening techniques and post-harvest technologies with the goal of improving peach fruit quality and sustainability. The factors impacting fruit quality after harvest and the physiological changes that occur throughout peach ripening are fully explained. For maintaining peach freshness and reducing losses, novel handling methods like modified atmosphere packaging (MAP) and controlled atmosphere storage (CAS) have been investigated. The study explores the possibilities of nanotechnology applications and low-temperature storage for prolonging shelf life while maintaining texture, flavor, and aroma. Examining the effectiveness and waste reduction potential of automation and mechanization in post-harvest activities. The paper also discusses ethylene-based and non-ethylene-based ripening agents, as well as innovative techniques including gene editing and RNAi technology for controlled and delayed ripening. Analyses are done on how these technologies affect the sensory qualities and nutrient profiles of peaches. The study emphasizes the significance of sustainable practices in the peach industry by focusing on waste reduction, resource efficiency, and circular economy integration. Post-harvest technologies' potential environmental consequences have been taken into consideration and the paper encourages more study and cooperation to increase sustainability.

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

Decades ago, when computational was expensive and limited, the structural design was mostly performed by hand calculations using simple mathematical models. For example, it was a common practice to design a structure as complex as the wing of an aircraft by simple beam analysis. However, ever since the classic paper by Turner et al., due to a rapid increase in computational, more complex mathematical models are being used to simulate the physical behavior of complex structural components. To solve intractable problems unsolvable by hand calculations, numerical techniques like Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Finite Difference Method etc. are being employed. In fact, the availability of these methods has led to the development of an entirely new area of research known as Multidisciplinary Design Optimization (MDO) where various disciplines are considered in an optimization problem. The most important question while using a mathematical model to represent practical industrial problems is to what extent these models represent the real-life situation. Computational models are always built on upon assumptions. Simply at looking at the simulation outcomes i.e. the graphical and numerical results, it is often very difficult to ensure if the underlying assumption holds and that the results are reliable. This has led to the development of another field of research known as Verification and Validation (called V&V in short).

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Circular RNAs (circRNAs) are single-stranded RNA molecules often circularized by backsplicing. Growing evidence implicates circRNAs in the underlying mechanisms of various diseases, such as Alzheimer's and Parkinson's disease (PD) - the first and second most prevalent neurodegenerative disorders. Several circRNAs are associated with brain damage, including circSNCA, circHIPK2, circHIPK3, and circSLC8A1. Gain-of-function and loss-of-function studies on circRNAs have shed light on their roles in the pathobiology of various diseases. Gain-of-function approaches typically employ viral or non-viral vectors that hyperexpress RNA sequences capable of circularizing to form the specific circRNA under investigation. In contrast, loss-of-function studies utilize CRISPR/Cas systems, antisense oligonucleotides (ASOs), or RNAi techniques to knockdown the target circRNA. Given that aberrantly expressed circRNAs have been associated with brain pathologies, a critical question arises: could circRNAs serve as viable targets for neuroprotective treatments? Translating any oligonucleotide-based therapy, including those targeting circRNAs, involves developing adequate brain delivery systems, minimizing off-target effects, and addressing the high costs of treatment. Nonetheless, RNAi-based FDA-approved drugs have entered the market, and circRNAs have attracted significant attention and investment from major pharmaceutical companies. Spanning from bench to bedside, circRNAs present a vast opportunity in biotechnology for oligonucleotide-based therapies designed to slow or even halt the progression of neurodegenerative diseases.

Fast Field-Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry is a powerful non-destructive magnetic resonance technique that enables, among other things, the investigation of slow molecular dynamics at low magnetic field intensities. FFC-NMR relaxometry measurements provide insight into molecular motion across various timescales within a single experiment. This study focuses on a \textit{model-free} approach, representing the NMRD profile $R_1$ as a linear combination of Lorentzian functions, thereby addressing the challenges of fitting data within an ill-conditioned linear least-squares framework. Tackling this problem, we present a comprehensive review and experimental validation of three regularization approaches to implement the model-free approach to analysing NMRD profiles. These include: (1) \nameUpe, utilizing locally adapted $L_2$ regularization; (2) \nameL, based on $L_1$ penalties; and (3) a hybrid approach combining locally adapted $L_2$ and global $L_1$ penalties. Each method's regularization parameters are determined automatically, according to the Balancing and Uniform Penalty principles. % Our contributions include the implementation and experimental validation of the \nameUpe{} and \nameUp{} algorithms, and the development of a "dispersion analysis" technique to assess the existence range of the estimated parameters. The objective of this work is to delineate the variance in fit quality and correlation time distribution yielded by each algorithm, thus broadening the set of software tools for the analysis of sample structures in FFC-NMR studies. The findings underline the efficacy and applicability of these algorithms in the analysis of NMRD profiles from samples representing different potential scenarios. 1

Factorization reduces computational complexity and is therefore an important tool in statistical machine learning of high dimensional systems. Conventional molecular modeling, including molecular dynamics and Monte Carlo simulations of molecular systems, is a large research field based on approximate factorization of molecular interactions. Recently, the local distribution theory was proposed to factorize global joint distribution of a given molecular system into trainable local distributions. Belief propagation algorithms are a family of exact factorization algorithms for trees and are extended to approximate loopy belief propagation algorithms for graphs with loops. Despite the fact that factorization of probability distribution is their common foundation, computational research in molecular systems and machine learning studies utilizing belief propagation algorithms have been carried out independently with respective track of algorithm development. The connection and differences among these factorization algorithms are briefly presented in this perspective, with the hope to intrigue further development in factorization algorithms for physical modeling of complex molecular systems.

The properties of iterative splitting methods with serial versions have been analyzed since recent years, see [1] and [3]. We extend the iterative splitting methods to a class of parallel versions, which allow to reduce the computational time and keep the benet of the higher accuracy with each iterative step. Parallel splitting methods are nowadays important to solve large problems, which can be splitted in subproblems and computed independently with the dierent processors. We present a novel parallel iterative splitting method, which is based on the multi-splitting methods, see [2], [10] and [15]. Such a exibilisation with multisplitting methods allow to decompose large iterative splitting methods and recover the benet of their underlying waveform-relaxation (WR) methods. We discuss the convergence results of the parallel iterative splitting methods, while we could reformulate such an error to a summation of the individual WR methods. We discuss the numerical convergence of the serial and parallel iterative splitting methods and present dierent numerical applications to validate the benet of the parallel versions.

Although wheat is used worldwide as a staple food, it can give rise to adverse reactions, for which the triggering factors have not been identified yet. These reactions can be caused mainly by kernel proteins, both gluten and non-gluten proteins. Among these latter, -amylase/trypsin inhibitors (ATI) are involved in baker’s asthma and realistically in Non Celiac Wheat Sensitivity (NCWS). In this paper, we report characterization of three transgenic lines obtained from the bread wheat cultivar Bobwhite silenced by RNAi in three ATI genes CM3, CM16 and 0.28. We have obtained transgenic lines showing an effective decrease of the activity of target genes that, although showing a higher trypsin inhibition as a pleiotropic effect, generate a lower reaction when tested with sera of patients allergic to wheat, accounting for the important role of the three target proteins in wheat allergies. Finally, these lines show unintended changes differences in high molecular weight glutenin subunits (HMW-GS) accumulation, involved in technological performances, but do not show differences in terms of yield. The development of new genotypes accumulating a lower amount of proteins potentially or effectively involved in such pathologies, not only offers the possibility to use them as a basis for the production of varieties with a lower impact on adverse reaction, but also to test if these proteins are actually implicated in those pathologies for which the triggering factor has not been established yet.

One of the central criteria for free will is “Could I have done otherwise?” But because of a temporal asymmetry in human choice, the question makes no sense. The question is backward-looking, while human choices are forward-looking. At the time when any choice is actually made, there is as of yet no action to do otherwise. Expectation is the only thing to contradict (do other than). So the ability to do something not expected by the ultimate expecter, Laplace’s demon, is a better criterion for free will. If human action is fundamentally unpredictable, then we have free will. Scientists have studied a form of fundamental unpredictability, known as undecidability. The features that make a system capable of undecidable dynamics have been identified: program-data duality; potential to access an infinite computational medium; and the ability to implement negation. Humans have all three of these features, so we very likely are fundamentally unpredictable, so we have free will.

RNA interference (RNAi) using small interfering RNAs (siRNAs) is a powerful tool to target any protein of interest and is becoming more implementable for in vivo applications due to recent developments in RNA delivery systems. To exploit RNAi for cancer treatment, it is expedient to increase its selectivity by e.g. a prodrug approach to activate the siRNAs upon external triggering, e.g. by using light. Red light is especially well suitable for in vivo applications due to its low toxicity and higher tissue penetration. Known molecular (not nanoparticle based) red light activatable siRNA prodrugs rely on singlet oxygen (1O2) mediated chemistry. 1O2 is highly cytotoxic. Additionally one of the side products in the activation of the known siRNA prodrugs is anthraquinone, which is also toxic. We here report on an improved red light activatable siRNA prodrug, which do not require 1O2 for their activation. The 5’ terminus of its antisense strand is protected with an electron-rich azobenzene promoiety. It gets reduced and cleaved upon red light exposure in the presence of Sn(IV)(pyropheophorbide a)dichloride acting as a catalyst and ascorbate required as a bulk reducing agent producing active siRNAs.

The proper regulation of RNA processing is critical for muscle development and the fine-tuning of contractile ability between muscle fiber-types. RNA binding proteins (RBPs) regulate the diverse steps in RNA processing including alternative splicing, which generates fiber-type specific isoforms of structural proteins that confer contractile sarcomeres with distinct biomechanical properties. Alternative splicing is disrupted in muscle diseases such as myotonic dystrophy and dilated cardiomyopathy, and is altered after intense exercise as well as with aging. It is therefore important to understand splicing and RBP function, but currently only a small fraction of the hundreds of annotated RBPs expressed in muscle have been characterized. Here we demonstrate the utility of Drosophila as a genetic model system to investigate basic developmental mechanisms of RBP function in myogenesis. We find that RBPs exhibit dynamic temporal and fiber-type specific expression patterns in mRNA-Seq data and display muscle-specific phenotypes. We performed knockdown with 105 RNAi hairpins targeting 35 RBPs and report associated lethality, flight, myofiber and sarcomere defects, including flight muscle phenotypes for Doa, Rm62, mub, mbl, sbr, and clu. Interestingly, knockdown phenotypes of spliceosome components, as highlighted by phenotypes for A-complex components SF1 and Hrb87F (hnRNPA1), revealed level- and temporal-dependent myofibril defects. We further show that splicing mediated by SF1 and Hrb87F is necessary for Z-disc stability and proper myofibril development, and strong knockdown of either gene results in impaired localization of Kettin to the Z-disc. Our results expand the number of RBPs with a described phenotype in muscle and underscore the diversity in myofibril and transcriptomic phenotypes associated with splicing defects. Drosophila is thus a useful model to gain disease-relevant insight into cellular and molecular phenotypes observed when expression levels of splicing factors, spliceosome components and splicing dynamics are altered.

Mucormycosis is a lethal disease caused by Mucorales, which are emerging as human pathogens with virulent behavior. Antifungal resistance and ineffective treatments are two major causes that explain the high mortality for this disease. Consequently, the research community is searching for virulence determinants that could be repurposed as targets to develop new treatments against mucormycosis. Our work explores an RNA interference (RNAi)-based approach to find targets involved in the virulence of Mucorales. A transcriptome-wide analysis compared sRNAs and their target mRNAs in two Mucor lusitanicus different pathotypes, virulent and avirulent, generating a list of 75 loci selected by their differential sRNA accumulation in these strains. As a proof of concept and validity, an experimental approach characterized two loci showing opposite behavior, confirming that RNAi activity causes their differential expression in the two pathotypes. We generated deletion mutants for two loci and a knockin-strain overexpressing for one these loci. Their functional analysis in murine virulence assays identified the gene wex1, a putative DEDDy exonuclease with RNase domains, as an essential factor for virulence. The identification of wex1 showed the potential of our approach to discover virulence factors not only in Mucorales but also in any other fungal model with an active RNAi machinery. But, more importantly, it adds a new layer to the biological processes controlled by RNAi in M. lusitanicus, confirming that the Dicer-dependent RNAi pathway can silence gene expression to promote virulence.

Mucorales are the causal agents for the lethal disease known as mucormycosis. Mortality rates of mucormycosis can reach up to 90%, due to the mucoralean antifungal drug resistance and the lack of effective therapies. A concerning urgency among the medical and scientific community claims to find targets for the development of new treatments. Here, we reviewed different studies de-scribing the role and machinery of a novel non-canonical RNAi pathway (NCRIP) only conserved in Mucorales. Its non-canonical features are the independence of Dicer and Argonautes proteins. Conversely, NCRIP relies on RNA-dependent RNA Polymerases and an atypical ribonuclease III (RNaseIII). NCRIP regulates the expression of mRNAs by degrading them in a specific manner. Its mechanism binds dsRNA but only cuts ssRNA. NCRIP exhibits a diversity of functional roles. It represses the epimutational pathway and the lack of NCRIP increases the generation of drug resistant strains. NCRIP also regulates the control of retrotransposons expression, playing an essential role in genome stability. Finally, NCRIP regulates the response during phagocytosis, affecting the multifactorial process of virulence. These critical NCRIP roles in virulence and antifungal drug resistance, along with its exclusive presence in Mucorales, mark this pathway as a promising target to fight against mucormycosis.

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteopancreatic neuroendocrine tumors (GEP-NETs), and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n=83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n=123 subjects). Set1, the data set from Scanner1 was reconstructed with standard clinical parameters (5 minutes; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-minutes). Set2, data from Scanner2, representing out-of-domain clinical scans, used standard iterative reconstruction (5 minutes; OSEM). Reconstructed datasets were divided into training, validation, and testing datasets in a 60%, 20%, 20% proportion, and a deep neural network was trained with each: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50%, and 75%) of Set1 for training. Scanner1, list-mode 2-minute reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1=0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1=0.755; p-value=0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1=0.478) to 100% training data (F1=0.713; p<0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets, and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

Data Extraction is a technique is called as clustering which is used to retrieve data either from the files or data bases or both. This paper focuses on the performance evaluation parameters of the clustering algorithms based on different parameters or conditions or constraints and parameters which are used to perform the clustering process to get the clusters on the data sets. Therefore best clusters are retrieved when best parameters or conditions or constraints or preferences which are applied on the data sources for the clustering process. These parameters or conditions or constraints are opted by the user called as user preferences.

There is increasing interest in automating train operations of mainline services, e.g. to increase network capacity. Automatic train operation (ATO) is already achieved by several pilot projects, but not implemented on a large scale. Before the general introduction of new or adapted technologies can have a transformative effect on the operation of such a complex system as train operation on mainlines, they have to pass functional, interoperability and performance tests. A virtual preliminary analysis is one way to ensure a smooth as well as safe introduction and implementation. This paper aims to present an approach that applies to the performance testing of ATO systems. Therefore, methods and test standards for technologies enabling automatic operation in other transport sectors are reviewed. The main findings have been adapted, transformed and combined to be used as a general strategy for virtual performance testing in the railway sector. Specifically, universal performance indicators, namely punctuality, accuracy, energy consumption, safety and comfort, are presented. A layer model for scenario description is adapted from the automotive sector, as well as the definition of different scenario types. Lastly, factors that can influence the performance of an ATO algorithm are identified. To demonstrate the developed approach, a straightforward investigation of a case study is conducted using a microscopic train simulator in combination with an ATO algorithm.

Clustering, or otherwise known as cluster analysis, is a learning problem that takes place without any human supervision. This technique has often been utilized, much efficiently, in data analysis, and serves for observing and identifying interesting, useful, or desired patterns in the said data. The clustering technique functions by performing a structured division of the data involved, in similar objects based on the characteristics that it identifies. This process results in the formation of groups, and each group that is formed, is called a cluster. A single said cluster consists of objects from the data, that have similarities among other objects found in the same cluster, and resemble differences when compared to objects identified from the data that now exist in other clusters. The process of clustering is very significant in various aspects of data analysis, as it determines and presents the intrinsic grouping of objects present in the data, based on their attributes, in a batch of unlabeled raw data. A textbook or otherwise said, good criteria, does not exist in this method of cluster analysis. That is because this process is so different and so customizable for every user, that needs it in his/her various and different needs. There is no outright best clustering algorithm, as it massively depends on the user’s scenario and needs. This paper is intended to compare and study two different clustering algorithms. The algorithms under investigation are k-mean and mean shift. These algorithms are compared according to the following factors: time complexity, training, prediction performance and accuracy of the clustering algorithms.

In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.

Deep learning techniques, such as convolutional neural networks (CNN), generative adversarial networks (GAN), and graph neural networks (GNN), have over the past decade changed the ac-curacy of prediction in many diverse fields. In recent years, the application of deep learning tech-niques in computer vision tasks in pathology demonstrated extraordinary potential in assisting clinicians, automating diagnosis, and reducing costs for patients. Formerly unknown pathologi-cal evidence, such as morphological features related to specific biomarkers, copy number varia-tions, and other molecular features, were also able to be captured by deep learning models. In this paper, we review popular deep learning methods and some recent publications about their appli-cations in pathology.

This work proposes a metaheuristic-based approach for hyperparameter selection in a multilayer perceptron to classify electromyographic signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and gray wolf optimizer significantly improves the performance of a multilayer perceptron for classifying EMG motion signals. The final model achieved an average classification rate of 93% for the validation phases. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications.

Helical (frontier) molecular orbitals [Chem. Sci. 2013, 4, 4278] have been reported almost a decade ago. Yet, helical molecular spin densities [Mol. Phys. 2022, e2157774] have been observed by us only very recently. In this work, we have observed that some chiral boron clusters (B–16, B–20, B–24, and B–28) can simultaneously have helical molecular orbitals and helical spin densities. To our best knowledge, it is the first time we have discovered that inorganic boron clusters assume such an unprecedented property. We have unambiguously unraveled that it is chiral Jahn-Teller distortion of quasi-planar boron clusters that drives the formation of helical molecular spin densities. More interestingly, we have shown that elongation/enhancement of helical molecular orbitals can be achieved by simply adding more building blocks via a linker. Aromaticity properties of these boron clusters are also discussed. Implication of this work is straightforward that boron clusters may find potential applications in spintronics, such as molecular magnets.

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

Since HIV was first identified, and in a relatively short period of time, AIDS has become one of the most devastating infectious diseases of the 21st century. Classical antiretroviral therapies were a major step forward in disease treatment options, significantly improving the survival rates of HIV-infected individuals. Even though these therapies have greatly improved HIV clinical outcomes, antiretrovirals (ARV) feature biopharmaceutic and pharmacokinetic problems such as poor aqueous solubility, short half-life and poor penetration into HIV reservoir sites, which contribute to the sub-optimal efficacy of these regimens. To overcome some of these issues, novel nanotechnology-based strategies for ARV delivery towards HIV viral reservoirs have been proposed. The current review focus on the benefits of using lipid-based nanocarriers for tuning the physicochemical properties of ARVs to overcome biological barriers upon administration. Furthermore, a correlation of these properties and the potential therapeutic outcomes has been established. Biotechnological advancements using lipid nanocarriers for RNA interference delivery for the treatment of HIV infections were also discussed.

In the pre era of synthetic antibodies, pharmaceutical companies depend on finding novel drugs from medicinal plants and other traditional resources; while in present, technological advances in biology, computer and robotics give the researchers the ability to rewrite and edit DNA in order to synthesize very large sets of drug candidates; these novel and improved candidates serves the basis for creating another library of drug candidates and so on until we find the right biomolecule for the disease of interest. all these technologies combined together to synthesize therapeutic antibodies for many types of cancer, autoimmune diseases, and infectious diseases, that can address diseases much more readily to very rapidly get therapeutics into patients so that we can potentially have an impact on disease. The antibodies mechanism is recognize and bind to disease cells and pinpoint the immune system to attack those cells effectively. Now a days, they dependent on computational approach to guide and accelerate the process of antibodies engineering by combination of selection system and use of high-throughput data acquisition and analysis to build and construct populations of next generation antibodies that are thermo-stable, non-immunogenic as possible, and to be administered to many humans as possible. In this review, I will discuss the latest in silico methods for antibodies engineering.

Coronavirus disease-2019 (COVID-19) was seen as a respiratory disease, however, an increasing number of reports indicated that the spike protein could also be the cause of the long-term post-infectious conditions known as Long-COVID characterized by a group of unresponsive idiopathic severe neuro, cardio-vascular disorders, including strokes, cardiopathies, neuralgias, fibromyalgia, and Parkinson's like-disease. Different lines of pieces of evidence confirmed that the spike protein that can be found on the surface of the SARS-CoV-2 virus latches onto angiotensin-converting enzyme 2 (ACE2) receptors located on target cells. The RNA genome of coronaviruses, which, has a median length of 29 kb and is the longest among all RNA viruses, is comprised of six to ten open reading frames (ORFs) that are responsible for encoding both the replicase and structural proteins for the virus. Each of the components of the viral genome is packaged into a helical nucleocapsid that is surrounded by a lipid bilayer. The viral envelope of coronaviruses is typically made up of three proteins that include the membrane protein (M), the envelope protein (E), and the spike protein (S). The spike protein not only facilitates the virus entry into healthy cells, which is the first step in infection but also promote profound damage to different organs and tissues leading to severe impairments and long-term disabilities. Here, we discussed the pervasive mechanism that spikes mRNA adopted to alter multipotent and pluripotent stem cell (SCs) genomes and the acquired disability of generating an infinite number of affected clonal cells. This stance is based on the molecular and evolutionary aspects obtained from retrotransposons-retrotransposition in mammalians and humans that documented the frequent integration of mRNA molecules into genomes and thus into DNA. Retrotransposition is the molecular process in which transcribed and spliced mRNAs are accidentally reverse-transcribed and inserted into new genomic positions to form a retrogene. Sequence-specific traits of mRNA clearly showed long interspersed element-1 (LINE-1 or L1) to confirm the retrotransposition, considered the most abundant autonomously active retrotransposons in the human genome. In mammals, L1 retrotransposons drive retrotransposition and are composed of long terminal repeats (LTRs) and non-LTR retrotransposons (mainly long interspersed nuclear elements or LINEs); specifically, the LTR-mediated retrocopies are immediately cotranscribed with their flanking LTR retrotransposons. In response to retrotransposons transposition, stem cells (SCs) employ a number of silencing mechanisms, such as DNA methylation and histone modification. This manuscript theorizes the expression patterns, functions, and regulation of mRNA Spike protein imprinted by SCs retrotransposons which generate unlimited lines of affected cell progenies and tissues as the main condition of untreatable Spike-related inflammatory conditions.

Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.

The Stochastic Optimisation Software (SOS) is a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. It reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including 1) customised implementations of statistical tests, such as the Wilcoxon Rank-Sum test and the Holm-Bonferroni procedure, for comparing performances of optimisation algorithms and automatically generate result tables in PDF and LaTeX formats; 2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; 3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation operator. each testbed function. Moreover, this article comments on the current state of the literature in stochastic optimisation and highlights similarities shared by modern metaheuristics inspired by nature. It is argued that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them.

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in 3D space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

I review previous attempts, including recent ones, to introduce technical aspects of digital information and computation into the discussion of ethics. I survey some limitations and advantages of these attempts to produce guiding principles at different scales. In particular, I briefly introduce and discuss questions, approaches, challenges, and limitations based on, or related to, simulation, information theory, integrated information, computer simulation, intractability, algorithmic complexity, and measures of computational organisation and sophistication. I discuss and propose a set of features that ethical frameworks must possess in order to be considered well-grounded, both in theoretical and methodological terms. I will show that while global ethical frameworks that are uncomputable are desirable because they provide non-teleological direction and open-ended meaning, constrained versions should be able to provide guidelines at more local and immediate time scales. In connection to the ethics of artificial intelligence, one point that must be underscored about computational approaches is that (General) AI should only embrace an ethical framework that we humans are willing to adopt. I think that such a framework is possible, taking the form of a general and universal (in the sense of computation) framework built from first computational principles.

This study investigates the stochastic dynamics of hepatitis B virus (HBV) infection using a newly proposed stochastic model. Contrary to deterministic models that fail to encapsulate the inherent randomness and fluctuations in biological processes, our stochastic model provides a more realistic representation of HBV infection dynamics. It incorporates random variability, thereby acknowledging the changes in viral and cellular populations and uncertainties in parameters such as infection rates and immune responses. We examine the solution's existence, uniqueness, and positivity for the proposed model, followed by a comprehensive stability analysis. We provide the necessary and sufficient conditions for local and global stability, offering deep insight into the infection dynamics. Furthermore, we utilize numerical simulations to corroborate our theoretical results. This research provides a robust tool for understanding the complex behavior of HBV dynamics, contributing significantly to the ongoing quest for more effective HBV control and prevention strategies.

ZnO was synthesized by Sol gel method using zinc nitrate as precursor at different calcination temperatures. Nucleation of ZnO crystallites and their growth with rise in temperature was observed. Exciton bands and lattice imperfections affected the absorption spectra. The decrease in band gap with temperature was due to the formation of defect energy levels. Phonon assisted non-radiative transitions caused broadening of the peaks. The possibility of tuning the band gap of ZnO by changing the temperature was explored. Simulation studies showed transition to single phase with rise in temperature.

A new set of alkoxy alcohols were synthesised by reaction of eugenol oxirane with aliphatic and aromatic alcohols. These eugenol derivatives were evaluated against their effect upon the viability of the insect cell line Sf9 (Spodoptera frugiperda).The most promising compounds, 4-(3-(tert-butoxy)-2-hydroxypropyl)-2-methoxyphenol and 4-(2-((4-fluorobenzyl)oxy)-3-hydroxypropyl)-2-methoxyphenol were submitted to in silico assays to predict possible targets. Thought an Inverted Virtual Screening approach, 23 common pesticide targets were screened and the top 2 targets predicted were further evaluated through molecular dynamics simulations and free energy calculations. In addition, these to eugenol derivatives were subjected to encapsulation and release assays using liposome-based nanosystems of egg phosphatidylcholine/cholesterol (7:3), with encapsulation efficiencies higher than 90% and release profiles well described by both Korsmeyer-Peppas and Weibull models.

Computational neuroscience combines mathematics, computer science models, and neurosciences for theorizing, investigating, and simulating neural systems involved in the development, structure, physiology, and cognitive abilities of the brain. Computational models constitute a major stake in translational neuroscience: the analytical understanding of these models seems fundamental to consider a translation towards clinical applications. Method: We propose a minimal typology of computational models, which allows distinguishing between more realistic models (e.g., mechanistic models) and pragmatic models (e.g., phenomenological models). Result: Understanding the translational aspects of computational models goes far beyond the intrinsic characteristics of models. First, we assume that a computational model is rarely uniquely mechanistic or phenomenological. Idealization seems necessary because of i) the researcher’s perspectives on the phenomena and the purposes of the study (i.e., by the relativity of the model); ii) The complexity of reality across different levels and therefore the nature and number of dimensions required to consider a phenomenon. Especially, the use of models goes far beyond their function, and requires considering external characteristics rooted in path dependence, interdisciplinarity, and pluralism in neurosciences. Conclusion: The unreasonable use of computational models, which are highly complex and subject to a shift in their initial function, could be limited by bringing to light such factors.

MicroRNAs (miRNAs) are small endogenous RNAs that regulate gene expression post-transcriptionally by targeting either the 3′ untranslated or coding regions of genes. They have been reported to play key roles in a wide range of biological processes. The recent remarkable developments of transcriptomics technologies, especially next-generation sequencing technologies and advanced bioinformatics tools, allow more in-depth exploration of messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs) including miRNAs. These technologies have offered great opportunities for a deeper exploration of miRNA involvement in farm animal diseases, as well as livestock productivity and welfare. In this review, we provide an overview of the current knowledge of miRNA roles in farm animal diseases with a particular focus on diseases of economic importance. In addition, we discuss the steps and future perspectives of using miRNAs as biomarkers and molecular therapy for livestock disease management as well as the challenges and opportunities for understanding the regulatory mechanisms of miRNAs related to disease pathogenesis.

Cellular Technology came into existence in Japan in 1979. From that time, we have seen many advances in technology. Currently, most of the person uses either 4G or 5G, among which 5G is a newer technology. 4G network was the first network to use LTE. LTE stands for Long Term Evaluation. Most people get confused between 4G and LTE, they assume both to be the same. In reality, LTE has evolved on the 4G technology. It was first introduced in 2008 to provide higher efficiency and data rates to the cellular network. It was designed to evolve the performance of the networks which includes the evolution of hardware components, optimization of software, power and cost efficiency. This work presents a study on LTE, LTE-A and VoLTE technology. We also discussed about the several packet scheduling algorithms that are being used for transmission of packets. It brings out new opportunities for the researchers to work on increasing the efficiency of those algorithms by modifications.

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision. Data pooling, a major component of CNNs, plays a crucial role for extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data, to 2-D image data, to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods. In our experiments, we utilized both noisy and noise-free quantum simulations on a state-of-the-art quantum simulator from IBM Quantum. We also show the efficiency of our proposed techniques for multidimensional data by reporting the fidelity of results.

Moving object detection plays a crucial role in various applications, particularly in traffic surveillance and collision warning systems. However, the high cost of training and slow detection pose significant challenges to the practicality and real-time performance of existing methods. These challenges hinder the practicality and real-time performance of existing methods. The need for a reliable and efficient moving object detection system prompts the search for cost-effective strategies. Enhancing detection speed without sacrificing accuracy is crucial for real-time applications. Overcoming these challenges is essential to advance the field of moving object detection and improve its applicability in various domains. This paper presents a comprehensive review of the literature on moving object detection, proposes novel strategies to overcome the challenges of high training costs and slow detection. The study emphasizes the importance of reliable speed monitoring systems and advancements in control systems to enhance driver assistance on urban highways. Each frame in the dataset undergoes YOLOv6 processing to detect and classify objects, generating bounding box predictions and class probabilities. Optical flow, specifically the Lucas-Kanade method, is then utilized to compute motion vectors between consecutive frames. These motion vectors facilitate object tracking across frames, enabling refined bounding box positions and improving overall object tracking accuracy. The results demonstrate the effectiveness of the proposed technique in reducing training costs, improving detection speed, and maintaining a high level of accuracy when compared to existing methods. The paper concludes by summarizing the findings and highlighting the potential of the proposed strategy for precise and reliable moving object recognition in real-world traffic scenarios

This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier, and Hoeffding Tree algorithms. Our results indicate that we achieved high accuracy results in our experiments in order to increase crop growth.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

Hyperspectral image (HSI) classification is a mechanism of analyzing differentiated land cover in remotely sensed hyperspectral images. In the last two decades, a number of different types of classification algorithms have been proposed for classifying hyperspectral data. These algorithms include supervised as well as unsupervised methods. Each of these algorithms has its own limitations. In this research, three different types of unsupervised classification methods are used to classify different datasets i-e Pavia Center, Pavia University, Cuprite, Moffett Field. The main objective is to assess the performance of all three classifiers K-Means, Spectral Matching, and Abundance Mapping, and observing their applicability on different datasets. This research also includes spectral feature extraction for hyperspectral datasets.

As the embedded device and internet of things (IoTs) concept prevalent in today's world, there is an increasing demand for the security and performance requirements on deploying these devices to private and public sectors. The crucial part of it is to protect privacy, confidentiality and integrity, meanwhile, maintain an adequate level of performance during transmission, storage and access of critical information. While the conventional cryptography methods, such as the Advanced Encryption Standard (AES), SHA$-$2 hashing method and RSA and Diffie Hellman for message authentication and identification, work well on systems which have reasonable processing power and memory capabilities. These do not scale well into a world with embedded systems and sensor networks, in conjunction with their nature of smaller size and lower cost. Thus, in the context of resource constrained device, lightweight cryptography methods are proposed to overcome many of the problems of conventional cryptography possessed. This includes constraints related to physical size, processing requirement, memory limitation, energy drain and production cost. This paper provides a survey of the architectures that are defined as replacements for conventional ciphers within an IoTs space and discuss some trends in the design of future lightweight algorithms. The performance metrics are carefully chosen to reflect and assess the suitability for embedded devices. The aim of this research is to identify the various performance metrics applied on different types of symmetric block ciphers especially lightweight ciphers and compare the results on versatile platforms with stream ciphers and public key methods. The comparative analysis on efficient LW cipher will be tested against other similar block ciphers on both MacBook Pro with Intel core and resource constrained device Raspberry Pi with ARM processor.

In the field of stochastic optimisation, the so-called structural bias constitutes an undesired behaviour of an algorithm that is unable to explore the search space to a uniform extent. In this paper, we investigate whether algorithms from a subclass of estimation of distribution algorithms, the compact algorithms, exhibit structural bias. Our approach, justified in our earlier publications, is based on conducting experiments on a test function whose values are uniformly distributed in its domain. For the experiment, 81 combinations of compact algorithms and strategies of dealing with infeasible solutions have been selected as test cases. We have applied two approaches for determining the presence and severity of structural bias, namely a visual and a statistical (Anderson-Darling) tests. Our results suggest that compact algorithms are more immune to structural bias than their counterparts maintaining explicit populations. Both tests indicate that strong structural bias is found only in one of the algorithms (cBFO) regardless of the choice of strategy of dealing with infeasible solutions and cPSO mirror. For other test cases, statistical and visual tests disagree on some cases classified as having mild or strong structural bias: the former one tends to make harsher decisions, thus needing further investigation.

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we looked at the duality of resource allocation problems. First, we chose a real world optimization problem and looked at its solution with linear programming. Then, we restudied this problem with a quantum algorithm in order to understand whether if there is a speedup of the solution. The improvement in computation is analysed and some interesting results are reported.

RNAi technology is a versatile, effective, safe, and eco-friendly alternative for crop protection. There is plenty of evidence of its use through host-induced gene silencing (HIGS) and spray-induced gene silencing (SIGS) techniques to control viruses, bacteria, fungi, insects, and nematodes. For SIGS, its most significant challenge is achieving stability and avoiding premature degradation of RNAi in the environment or during its absorption by the target organism. One alternative is encapsulation in liposomes, virus-like particles, polyplex nanoparticles, and bioclay, which can be obtained through the recombinant production of RNAi in vectors, transgenesis, and micro/nanoencapsulation. The materials must be safe, biodegradable, and stable in multiple chemical environments, favoring the controlled release of RNAi. Most of the current research on encapsulated RNAi focuses primarily on oral delivery to control insects by silencing essential genes. The regulation of RNAi technology focuses on risk assessment using different approaches; however, this technology has positive economic, environmental, and human health implications for its use in agriculture. The emergence of alternatives combining RNAi gene silencing with the induction of resistance in crops by elicitation and metabolic control is expected, as well as multiple silencing and biotechnological optimization of its large-scale production.