Detection of subsurface archaeological remains using a range of remote sensing methods, poses several challenges until today. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near infrared part of the spectrum, were taken over a controlled environment in Cyprus during the 2011-2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, and in particular in the neighborhood of 570 nm, and (2) the red edge-near infrared part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on, in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors' knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

This review systematically examines the multifaceted relationship between the tumor microenvironment, stromal interactions, and colorectal cancer (CRC) progression and metastasis. The analysis begins by assessing the gut microbiome's influence on CRC development, emphasizing its association with gut-associated lymphoid tissue (GALT). The role of the Wnt signaling pathway in CRC tumor stroma is scrutinized, elucidating its impact on disease progression. Tumor budding, its effect on tumor stroma, and implications for patient prognosis are investigated. The review also identifies conserved oncogenic signatures (COS) within CRC stroma and explores their potential as therapeutic targets. Lastly, the seed and soil hypothesis is employed to contextualize metastasis, accentuating the significance of both tumor cells and the surrounding stroma in metastatic propensity. In summary, this review highlights the intricate interdependence between CRC cells and their microenvironment, providing valuable insights into prospective therapeutic approaches targeting tumor-stroma interactions.

The goal of our study was to identify signatures of selection in the Turopolje pigs and other commercial pig breeds. We conducted a comprehensive analysis of five datasets, including one local pig breed (Turopolje) and four commercial pig breeds (Large White, Landrace, Pietrain, and Duroc), using strict quality control measures. Our final dataset consisted of 485 individuals and 54,075 single nucleotide polymorphisms (SNPs). To detect selection signatures within these pig breeds, we utilized the XP-EHH and XP-nSL methodologies, which allowed us to identify candidate genes that have been subject to positive selection. Our analysis consistently highlighted the PTBP2 and DPYD genes as commonly targeted by selection across all studied breeds. Both of these genes are associated with muscular development in pigs and other species. Furthermore, in the Large White breed a number of genes were detected by the two methods, such as ATP1A1, CASQ2, CD2, IGSF3, MAB21L3, NHLH2, SLC22A15, VANGL1. In Duroc breed a different set of genes was detected, such as ARSB, BHMT, BHMT2, DMGDH, JMY. The function of these genes was related to body weight, production efficiency and meat quality, average daily gain and other similar traits. Overall, our results have identified a number of genomic regionsthat are under selective pressure between local and commercial pig breeds. This information can help to improve our understanding of the mechanisms underlying pig breeding, and ultimately contribute to the development of more efficient and sustainable pig production practices. Our study highlights the power of using multiple genomic methodologies to detect genetic signatures of selection, and provides important insights into the genetic diversity of pig breeds.

Understanding the hydrological behavior of watersheds and their driving factors is crucial for sustainable water resources management. However, at large scales, this task remains challenging due to the spatial heterogeneity in landscapes, topography, land use, geology, and soil properties. In this context, the aim of this study was to identify the key factors that influence the hydrological system of four watersheds: Ankavia (WS1: 55% forest cover), Ankaviabe (WS2: 77% forest cover), Sahafihitry (WS3: 41% forest cover), and Antsahovy (WS4: 48% forest cover), over a 10-month study period. These catchments are located within the SAVA region of northeastern Madagascar and have a humid-tropical climate. We investigated the relationship between selected catchment descriptors (CD) and hydrological signatures (HS) by using a Pearson coefficient-based correlation matrix. More specifically, CD extracted from topography/morphology (T), land use (LU), soil (S), and geological characteristics (G) were correlated with HS, including base flow index (BFI), runoff coefficient (rc), peak flow (Qp), runoff event time scales (ts), high flows (Q5), low flows (Q95), and mean discharge (q_mean). The analysis revealed that land use, soil properties, and geology seem to be the best predictors for BFI and Q95, while soil properties mainly govern rc, Qp, Q5, ts, and q_mean. These findings provide valuable insights into the key drivers of hydrologic behavior that can inform water resource management strategies. In particular, WS2 has better flood buffering capacity but suffers from lower base flows in the dry season potentially due to higher evapotranspiration. Conversely, WS3 and WS4 (and to a lesser extent WS1) have lower flood buffering capacity, but these watersheds experience less pronounced low flows in the dry season due to higher base flow index resulting from lower evapotranspiration. The results emphasize the importance of sustainable land use practices and conservation efforts, which are essential for the sustainable development of the region. By incorporating these practices into water management strategies, we can help ensure a more stable and reliable water supply for communities and ecosystems within the region.

The illegal wildlife trade is a significant threat to global biodiversity, often targeting already threatened species. In combating the trade, it is critical to know the provenance of the traded animal or part, to facilitate targeted conservation actions, such as education and enforcement. Here we present and compare two methods, portable X-ray fluorescence (pXRF) and stable isotope analysis (SIA), to determine both the geographic and source provenance (captive or wild) of traded animals and their parts. Using three critically endangered, frequently illegally traded Philippine species, the Palawan forest turtle (Siebenrockiella leytensis), the Philippine cockatoo (Cacatua haematuropygia), and the Philippine pangolin (Manis culionensisis) we demonstrate that using these methods we can more accurately assign provenance using pXRF data (x ̅ = 83%), than SIA data (x ̅ = 47%). Our results indicate that these methods provide a valuable forensic tool that can be used in combating the illegal wildlife trade.

Since the most important constituent of the cost of cryptocurrency production is energy bills, use of illegal electricity in cryptocurrency mining farms is very common. For this reason, many countries prohibit cryptocurrency production or restrict it with some legal regulations. This situation raises the problem of how to detect illicit cryptocurrency mining. Therefore, innovative approaches are needed to detect illicit cryptocurrency mining farms. In this study, it has been shown that unique noise and/or harmonic characteristics of machines producing cryptocurrency can be used to detect illicit cryptocurrency mining farms. The unique harmonic characteristics formed by data centers on neutral line have been detected by field measurements performed. Electricity distribution companies can make risk assessments with this approach by monitoring energy quality data. As a result, the necessary legal permissions can be obtained to investigate suspicious high-risk consumers. Illicit cryptocurrency mining farms can be detected by investigating at high-risk consumer facilities.

Whole genome pooled sequence data of 12 Pakistani Teddy goats is analyzed for positive selection signatures as their breed defining characteristics. Selection imprints left in the Teddy genome are unveiled by genomic differentiation after the successful paired-end alignment of 635,357,043 reads with (ARS1) reference genome assembly. Pooled-heterozygosity ( ) and Tajima’s D (TD) are applied for validation and getting better hits of selection signals, while pairwise F_ST statistics is conducted on Teddy vs. Bezoar (wild goat ancestor) for genomic differentiation. Annotation of regions under positive selection reveals 59 genes underlying production and adaptive traits. score ≥ 5 detected six windows having highest scores on Chr. 29, 9, 25, 15 and 14 that harbor HRASLS5, LACE1 and AXIN1 genes which are candidate for embryonic development, lactation and body height. Secondly, TD value of ≤ -2.2 showed 4 windows with very strong hits on Chr.5 & 9 harbor STIM1 and ADM genes related to body mass and weight. Lastly, F_ST analysis generated three strong signals with threshold ≤ 0.42 on Chr.12 & 5 harbor ITGB1 gene associated with milk production & lactation traits. Other significant selection signatures encompass genes associated with wool production, prolificacy, immunity and coat colors. In brief, this study identified the genes under selection in this Pakistani goat breed that will be helpful to refining future breeding policies and converging required productive traits within and across other goat breeds and to explore full genetic potential of this valued livestock species.

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, in south-central Chile by analyzing time series of Landsat-8 satellite images, specifically using the multispectral OLI sensor. The bio-optical parameters, i.e., chlorophyll-a (mg·m^-3) and turbidity (NTU) were also measured in-situ synchronized with the satellite passes to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in-situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process. Subsequently, we harnessed the reflectance data derived from the ACOLITE model to establish correlations between various spectral indices and the in-situ data. The empirical retrieval models (based on band combinations) that showed superior performance, as indicated by higher R² values, were subjected to rigorous statistical validation and optimization by applying a bootstrapping approach. Our analysis covered a spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014-2022) characterized by a decline in chlorophyll-a concentration in the lake possibly attributable to governmental measures in the region for the protection and conservation of the lake. The results of this initial study serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.

In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity to reach 90 % accuracy of the prediction.

The finding of important explanatory variables for the location parameter and the scale parameter of the generalized extreme value (GEV) distribution, when the latter is used for the modelling of annual streamflow maxima, is known to have reduced the uncertainties in inferences, as estimated through regional flood frequency analysis frameworks. However, important explanatory variables have not been found for the GEV shape parameter, despite its critical significance, which stems from the fact that it determines the behaviour of the upper tail of the distribution. Here we examine the nature of the shape parameter by revealing its relationships with basin attributes. We use a dataset that comprises information about daily streamflow and forcing, climatic indices, topographic, land cover, soil and geological characteristics of 591 basins with minimal human influence in the contiguous United States. We propose a framework that uses random forests and linear models to find (a) important predictor variables of the shape parameter and (b) an interpretable model with high predictive performance. The process of study comprises of assessing the predictive performance of the models, selecting a parsimonious predicting model and interpreting the results in an ad-hoc manner. The findings suggest that the shape parameter mostly depends on climatic indices, while the selected prediction model results in more than 20% higher accuracy in terms of RMSE compared to a naïve approach. The implications are important, since incorporating the regression model into regional flood frequency analysis frameworks can considerably reduce the predictive uncertainties.

Eight lesions were analyzed using some algorithms of Intelligence Artificial: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma (MEL), actinic keratosis (AK), benign keratosis (BKL), dermatofibromas (DF), melanocytic nevi (NV), and vascular lesions (VASC). This manuscript presents the possibility of using concatenated signatures (instead of images) obtained from different integral transforms, such as Fourier, Mellin, and Hilbert, to classify skin lesions. Eleven other Artificial Intelligence models were applied so that eight skin lesions could be classified by analyzing the particular signatures of each lesion. The database was randomly divided into 80%–20% for the training and test datasets images, respectively. The metrics that are being reported are accuracy, sensitivity, specificity, and precision. Each case was repeated 30 times to avoid bias, according to the central limit theorem in this work, and the average and ±standard deviation were reported. Although all the results were very satisfactory, the best average mark for the eight lesions analyzed was obtained using the Subspace KNN model, where the metrics for the test were 99.98% accuracy, 99.96% sensitivity, 99.99% specificity, and 99.95% precision.

Ultraviolet (UV) radiation is a strong environmental carcinogen responsible for the pathogenesis of most skin cancers, such as malignant melanoma (MM) and non-melanoma (keratinocyte) skin cancers. The carcinogenic role of UV was firmly established based on epidemiological evidence and molecular findings of the characteristic mutation signatures which occur during excision repair of cyclobutane pyrimidine dimers and 6,4-photoproducts. The role of UV in the pathogenesis of mycosis fungoides (MF), the most common primary cutaneous T-cell lymphoma, remains controversial. Here, we performed whole exome sequencing of 61 samples of MF cells microdissected from the cutaneous lesions, and compared their mutational signatures to 340 MM. The vast majority of MM mutations had a typical UV mutational signature (SBS 7, SBS 38, DSB 1) underscoring the key role of ultraviolet as a mutagen. In contrast, the SBS 7 signature in MF comprised <5% of all mutations. SBS 7 was higher in the intraepidermal MF cells (when compared to the dermal cells) and in the cells from tumors as compared to early-stage plaques. In conclusion, our data do not support the pathogenic role of UV in the pathogenesis of MF and suggest that the UV mutations are the result of the cumulative, environmental ultraviolet exposure of cutaneous lesions, rather than an early mutagenic event.

The development of the ovarian antral follicle is a complex, highly regulated process. Oocytes orchestrate and coordinate the development of mammalian ovarian follicles, and the rate of follicular development is governed by a developmental program intrinsic to the oocyte. Characterizing oocyte signatures during this dynamic process is critical for understanding oocyte maturation and follicular development. Although the transcriptional signature of sheep oocytes matured in vitro and preovulatory oocytes have been previously described, the transcriptional changes of oocytes in antral follicles have not. Here, we used single-cell transcriptomics (SmartSeq2) to characterize sheep oocytes from small, medium, and large antral follicles. We characterized the transcriptomic landscape of sheep oocytes during antral follicle development, identifying unique features in the transcriptional atlas, stage-specific molecular signatures, oocyte-secreted factors, and transcription factor networks. Notably, we identified specific gene and signaling pathways in each antral follicle stage that may reflect oocyte quality and in vitro maturation competency. Additionally, we discovered key biological processes that drive the transition from small to large antral follicles, revealing hub genes involved in follicle recruitment and selection. Thus, our work provides a comprehensive characterization of the single-oocyte transcriptome, filling a gap in the mapping of the molecular landscape of sheep oogenesis. We also provide key insights into the transcriptional regulation of the critical stages of antral follicular development, which is essential for understanding how the oocyte orchestrates follicular development.

Continuous water resources monitoring is needed for sustainable urban water supply. Remote sensing techniques have proven useful for monitoring some water qualitative parameters with optical characteristics. The study area was the Marateca reservoir in central inland Portugal. The aims were the following: (1) to explore the water quality parameters at the monitoring points of the Marateca reservoir that may explain the event; (2) to validate optical water quality parameters with the monitoring points data; and (3) to model the reservoir water characteristics regarding its deepness, trophic state, and turbidity. The parameters total phosphorus, total nitrogen, and chlorophyll-a were used to compute a trophic level index. The Sentinel-2 imagery was used to compute spectral indices and bands image ratio; to obtain spectral signatures for the monitoring points, and to model water characteristics. The water parameters were above the recommended values at the reservoir entry point from the Ocreza River. The reservoir trophic level was Hypereutrophic and Eutrophic. The spectral signatures confirmed a Hypereutrophic pattern in the entry point. The Marateca reservoir’s water characteristics modeling forecasted problematic zones by contamination. The methodological approach developed can be easily applied to other reservoirs and is a key support tool for decision-makers.

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of a cancer or transformed cell, the initiation and progression were shown to be driven by only a few mutational events viz. driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient’s cancer cell gained large attention recently for designing better targeting therapies towards paving way for the precision cancer medicine. With rolling advancements in high-throughput omics technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures, and their potential application in developing precision cancer therapies.Keywords:

Efficient member revocation and strong security against attacks are prominent requirements in group signature schemes. Among the revocation approaches Verifier-local revocation is the most flexible and efficient method since it requires to inform only the verifiers regarding the revoked members. The verifier-local revocation technique uses a token system to manage members’ status. However, the existing group signature schemes with verifier-local revocability rely on weaker security. On the other hand, existing static group signature schemes rely on a stronger security notion called, full-anonymity. Achieving the full-anonymity for group signature schemes with verifier-local revocation is a quite challenging task. This paper aims to obtain stronger security for the lattice-based group signature schemes with verifier-local revocability, which is closer to the full-anonymity. Moreover, this paper delivers a new key-generation method which outputs revocation tokens without deriving from the users’ signing keys. By applying the tracing algorithm given in group signature schemes for static groups, this paper also outputs an efficient tracing mechanism. Thus, we deliver a new group signature scheme with verifier-local revocation that satisfies a stronger security from lattices.

Shrubland and forestland covers are highly prone to fire. The Normalized Difference Vegetation Index (NDVI) has been widely used for biomass quantitative assessment. This study aims were the following: (1) to compute the NDVI annual curve for two types of land cover eucalypts and shrubland areas; (2) to collect field data in these two types of land cover to estimate aboveground biomass (AGB); and (3) to produce AGB maps for eucalypts and shrubland areas by modelling AGB with NDVI and validate them with other data sources. A study area in the central inland Portugal was considered. The Sentinel-2 MSI imagery for the year of 2022 and 2023 were used to compute the NDVI. The NDVI annual curve for 2022 showed a minimum observed between July and August, in accordance with the climatological data, and allowed differentiating eucalypts from shrubland areas. Spectral signatures also confirmed this differentiation. The fitted linear models for AGB prediction using the NDVI imagery showed good fitting performances (R2 of 0.76 and 0.69). The AGB maps provide a relevant decision support tool for forest management and for fire hazard and fire severity mitigation. Further research is needed using more robust data sets for models’ independent validation.

Purpose: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models’ performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). Methods: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods’ performance. Results: Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13), however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman’s rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. Conclusion: The IN method impacted the predictive power of survival models. Thus, performance is sequence-dependent.

In this communication, we present an innovative approach leveraging advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques, specifically the Non-Negative Matrix Factorization (NMF) method, to analyze downward and upward light spectra collected by Hyperspectral Ocean Color Radiometer (HyperOCR, HOCR) sensors in the water column. Our work focuses on the development of a robust and efficient tool for unraveling the structure and activities of natural microbial assemblages in the ocean. By applying the NMF method to HyperOCR data, we successfully extracted five spectral signatures, representing unique patterns in the data. These signatures were instrumental in predicting the abundances of various microbial components, including bacteria, heterotrophic nanoflagellates, and picoeukaryotes, showcasing the potential of ML and AI in advancing oceanographic studies. To validate the methods, the study area included a shallow coastal area under the influence of freshwater inflow and an open offshore area with a depth of 100 m. The study sites in coastal and offshore waters (Kaštela Bay and Stončica Vis, respectively) had significantly different hydrographic and microbiological characteristics. Kaštela Bay had lower temperatures and salinity than the site on Vis. We have proved by application of different AI and ML methods with specific HOCR sensors prediction of the structure of the microbial community.

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

The diagnosis of Cardio-Vascular diseases (CVD) is highly dependent on analysis of ECG signals. ECG analysis can be helpful in estimating the underlying cause and condition of heart in cardiac abnormality. The effectiveness of ECG signal analysis in detection of CVDs is widely accepted by professional healthcare service provider. Many algorithms have been proposed but almost all of them have some kind of limitations, and these limitations largely influence the effectiveness of ECG analysis. The performed research work is dedicated for design of unique self-organizing maps (SOMs) based neural network for classification of arrhythmia according to a particular ECG signal, the generation of SOMs is based on the certain unique signatures of ECG signals and have potential to classify different cardiac conditions. For extraction of unique features from ECG signals, we have proposed to use Fast Discrete Stockwell Transform (FDST). Since the proposed technique is a result of combining two different techniques hence called as hybrid technology. The purpose of using FDST is to identify unique signatures of ECG signals in a more improved manner than existing one, the term improved is used because it has several advantages over existing techniques such as wavelet and Fourier Transform based methods. Results obtained from the implementation of the technique are capable in visualizing the ECG sinus rhythm and arrhythmia conditions in form of unique SOM for each associated arrhythmia condition. This unique SOM based classification makes them ideal for being used as a diagnostic tool. This ability of arrhythmia classification using FDST and SOMs makes the technique unique and useful providing valuable information about patient condition. Using proposed technology a portable diagnosis tool for monitoring of patient at their site may be facilitated later, that will improve the quality of life of the patient by diagnosing cardiac condition.

This article is the first part of a scientific project under the general title "Geometrized Vacuum Physics". This study is based on two main postulates: 1) independence of the propagation velocity of electromagnetic waves in vacuum from their frequency; 2) the constancy of the averaged zero vacuum balance, associated with the assertion that only mutually opposite formations are born from vacuum, so that on average they completely compensate for each other's manifestations. In this part of the "Geometrized Vacuum Physics" the foundations of the Algebra of Stignatures are laid, which is the mathematical and logical foundation of the entire project. In the next articles of this project it will be shown that the Algebra of Stignatures can be used for the development of "zero" (vacuum) technologies, algebraic genetics, vacuum cosmology, the vacuum standard model of "elementary particles", vacuum gravity and levitation, vacuum energy, ethics and aesthetics and many other branches of knowledge. At the end of this article, one of the many possible applications of the Algebra of Stignatures is given, in particular, the basics of the stignature-spectral analysis are outlined, with the help of which the possibilities of communication channels can be significantly expanded.

The Internet of Things (IoT) is a technological paradigm that has gained significant momentum that last decade and, among others, enables the development of intelligent and interoperable device networks. In this respect, it has triggered the creation and evolution of VANETs (Vehicular Ad Hoc Networks), which are initially implemented in order to guarantee the safety of the drivers and the avoidance of traffic accidents. The drawback is that this fast evolution flags serious concerns as far as the privacy of users is concerned, while the population of attackers or entities that try to eavesdrop and intercept information has significantly increased. This imposes a serious risk for drivers moving across a Smart City. The research presented in this paper aims to evaluate privacy protection mechanisms in VANET environments, based on their efficiency and security level ensured, considering the fact that VANETs provide limited resources to users/drivers. Moreover, the usage of the elliptic curve cryptography in reduced resources environments is discussed. Finally, this paper compares the performance of three cryptographic algorithms, i.e., Elliptic Curve Cryptography (ECC), Hyperelliptic Curve Cryptography genus 2 (HECC-2) and HECC genus 3 (HECC-3), employed for an efficient authentication and safe message transmission mechanism in VANETs, targeting to extract conclusions related to the implementation of each cryptographic scheme in this specific application area.

The current SARS- CoV-2 pandemic underscores the importance of understanding the evolution of RNA genomes. While RNA is subject to the formation of similar lesions as DNA, the evolutionary and physiological impacts RNA lesions have on viral genomes are yet to be characterized. Lesions that may drive the evolution of RNA genomes can induce breaks that are repaired by recombination or can cause base substitution mutagenesis, also known as base editing. Over the past decade or so, base editing mutagenesis of DNA genomes has been subject to many studies, revealing that exposure of ssDNA is subject to hypermutation that is involved in the etiology of cancer. However, base editing of RNA genomes has not been studied to the same extent. Recently hypermutation of single-stranded RNA viral genomes have also been documented though its role in evolution and population dynamics. Here, we will summarize the current knowledge of key mechanisms and causes of RNA genome instability covering areas from the RNA world theory to the SARS- CoV-2 pandemic of today. We will also highlight the key questions that remain as it pertains to RNA genome instability, mutations accumulation, and experimental strategies for addressing these questions.