The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

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.

Ion channels contribute fundamental properties to all cell membranes. The ion channels are highly diverse in conductivity, structure, location and function. However, many of them can be regulated by common mechanisms, such as voltage or (de-)phosphorylation. Considering primarily pain-related ion channels, this review covers more novel and less known features of ion channel function, also with the aim to transfer knowledge between fields, which get inevitably more separate due to their size.

The Dph1•Dph2 heterodimer from yeast is a radical SAM (RS) enzyme that generates the 3-amino-3-carboxy-propyl (ACP) precursor for diphthamide, a clinically relevant modification on eukaryotic elongation factor 2 (eEF2). ACP formation requires SAM cleavage and atypical Cys-bound Fe-S clusters in each Dph1 and Dph2 subunit. Intriguingly, the first Cys residue in each motif locates next to another, ill-defined cysteine that we show in-here is conserved across eukaryotes. As judged from structural modeling, the orientation of these tandem cysteine motifs (TCM) suggests a candidate Fe-S cluster ligand role. Hence, we generated by site-directed DPH1 and DPH2 mutagenesis, Dph1•Dph2 variants with cysteines from each TCM replaced individually or in combination by serines. Assays diagnostic for diphthamide formation in vivo reveal that while single substitutions in the TCM of Dph2 cause mild defects, double mutations almost entirely inactivate the RS enzyme. Based on enhanced Dph1 and Dph2 subunit instability in response to cycloheximide chases, the variants with Cys substitutions in their cofactor motifs are particularly prone to protein degradation. In sum, we identify a fourth functionally cooperative Cys residue within the Fe-S motif of Dph2 and show that the Cys-based cofactor binding motifs in Dph1 and Dph2 are critical for the structural integrity of the dimeric RS enzyme in vivo.

Although cancers account for over 16% of all global deaths annually, at present, no reliable therapies exist for most types of the disease. As protein folding facilitators, heat shock proteins (Hsps) play an important role in cancer development. Not surprisingly, Hsps are among leading anticancer drug targets. Generally, Hsp70s are divided into two main subtypes: canonical Hsp70 (E. coli Hsp70/DnaK homologues) and the non-canonical (Hsp110 and Grp170) members. These two main Hsp70 groups are delineated from each other by distinct structural and functional specifications. Non-canonical Hsp70s are considered as holdase chaperones, while canonical Hsp70s are refoldases. This distinct characteristic feature is mirrored by the distinct structural features of these two groups of chaperones. Hsp110/Grp170 members are larger as they possess an extended acidic insertion in their substrate binding domains. While the role of canonical Hsp70s in cancer has received a fair share of attention, the roles of non-canonical Hsp70s in cancer development has received less attention in comparison. In the current review, we discuss the structure-function features of non-canonical Hsp70s members and how these features impact on their role in cancer development. We further mapped out their interactome and discussed the prospects of targeting these proteins in cancer therapy.

In the field of railroad safety, effective detection of surface cracks is critical, necessitating reliable, high-speed non-destructive testing (NDT) methods. This study introduces a hybrid Eddy Current Testing (ECT) probe, specifically engineered for railroad inspection, to address the common issue of ’lift-off noise’ due to varying distances between the probe and test material. Unlike traditional ECT methods, this probe integrates transmit and differential receiver coils, aiming to enhance detection sensitivity and minimize lift-off impact. The study optimizes ECT probes employing different driver coils, emphasizing three main objectives: a) quantitatively evaluating each probe using signal- to-noise ratio (SNR) and outlining a real-time data processing algorithm based on SNR methodology; b) exploring the frequency range proximal to the electrical resonance of the receiver coil; c) examining sensitivity variations across varying lift-off distances. The experimental outcomes indicate that the newly designed probe with figure 8-shape driver coil significantly improves sensitivity in detecting surface cracks on railroads. It achieves an impressive SNR exceeding 100 for defects with minimal dimensions of 1 mm in width and depth. Simulation results closely align with experimental findings, validating the investigation of optimal operational frequency and lift-off distance for selected probe performance, determined to be 0.3 MHz and 0.5 mm, respectively. The realization of this project would lead to notable advancements in enhancing railroad safety by improving crack detection efficiency.

Non-canonical NF-kB signalling plays important roles in development and function of the immune system but also is deregulated in a number of inflammatory diseases. Although, NF-kB and HIF crosstalk has been documented, this has only been described following canonical NF-kB stimulation, involving RelA/p50 and the HIF-1 dimer. Here we report that the non-canonical inducer TNFSF14/LIGHT leads to HIF induction and activation in cancer cells. We demonstrate that only HIF-2a is induced at the transcriptional level following non-canonical NF-kB activation, via a mechanism dependent on the p52 subunit. Furthermore, we demonstrate that p52 can bind to the HIF-2a gene in cells. These results indicate that non-canonical NF-kB can lead to HIF signalling implicating HIF-2a as one of the downstream effectors of this pathway in cells.

Inflammasomes are multiprotein complexes that activate inflammatory responses by inducing pyroptosis and secretion of pro-inflammatory cytokines. Along with many previous studies on inflammatory responses and diseases induced by canonical inflammasomes, an increasing number of studies have demonstrated that non-canonical inflammasomes, such as mouse caspase-11 and human caspase-4 inflammasomes, are emerging key players in inflammatory responses and various diseases. Flavonoids are natural bioactive compounds found in plants, fruits, vegetables, and teas and have pharmacological properties in a wide range of human diseases. Many studies have successfully demonstrated that flavonoids play an anti-inflammatory role and ameliorate many inflammatory diseases by inhibiting canonical inflammasomes. Others have demonstrated the anti-inflammatory roles of flavonoids in inflammatory responses and various diseases, with a new mechanism by which flavonoids inhibit non-canonical inflammasomes. This review discusses recent studies that have investigated the anti-inflammatory roles and pharmacological properties of flavonoids in inflammatory responses and diseases induced by non-canonical inflammasomes and further provides insight into developing flavonoid-based therapeutics as potential nutraceuticals against human inflammatory diseases.

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.

The nuclear architecture describes the organization of the various compartments in the nucleus of eukaryotic cells, where a plethora of processes such as nucleocytoplasmic transport, gene expression, and assembly of ribosomal subunits occur in a dynamic manner. During the different phases of the cell cycle, in post-mitotic cells and after oncogenic transformation, rearrangements of the nuclear architecture take place, and, among other things, these alterations result in reorganization of the chromatin and changes in gene expression. A member of the tubulin family, tubulin, was first identified as part of a multiprotein complex that allows nucleation of microtubules. However, more than a decade ago, γ tubulin was also characterized as a nuclear protein that modulates several crucial processes that affect the architecture of the nucleus. This review presents the latest knowledge regarding changes that arise in the nuclear architecture of healthy cells and under pathological conditions and, more specifically, considers the particular involvement of tubulin in the modulation of the biology of the nuclear compartment.

The emergence of non-canonical amino acids (ncAAs) has enriched the functional pool inherent in canonical amino acids (cAAs). ncAAs are important building blocks in the production of various pharmaceuticals. The biosynthesis of ncAAs has emerged as an alternative to traditional chemical synthesis because of its environmental friendliness and high efficiency. The breakthrough genetic code expansion (GCE) technique developed in recent years has allowed for the incorporation of ncAAs into target proteins, giving them special functions and biological activities. Biosynthesis of ncAAs and their incorporation into target proteins within a single microbe has become an enticing application of such molecules. Based on that, in this study we first review the biosynthesis methods for ncAAs and analyze the difficulties in biosynthesis. We then summarize the GCE methods and analyze their advantages and disadvantages. Further, we review the application progress of ncAAs and anticipate the challenges and future development directions of ncAAs.

The expanding use of devices emitting Pulsed Telecommunication Signals (PTS) has launched a serious debate over the possible effects of electromagnetic radiation (EMR) on living organisms. Our previous work has indicated that PTS exposure alters Amyloid Precursor Protein (APP) and alpha-synuclein (α-syn) metabolism in human cells of neural origin, providing a possible connection between exposure and neurodegeneration. This investigation aimed to reveal, in vitro in human non-neural cells (HEK293), the aftermath of the same exposure on the processing of APP and α-syn. Data presented here, indicate changes in APP metabolism, acquisition of different cellular topologies of the newly generated APP fragments, changes in monomeric α-syn accumulation and multimerization, indicating that APP and α-syn processing is possibly altered in the periphery by EMR. These effects are accompanied by a substantial increase in the levels of Reactive Oxygen Species (ROS). Further investigation is required in order to provide insights into the interaction of PTS with non-neural cells affecting the peripheral systemic functional stability. This is necessary because nowadays whole body human exposure from various EMR sources is a fact in normal life with the valid estimation that they may be increased in view of the forthcoming 5G telecommunications network implementation.

The Tumor Microenviroment (TME) is a complex milieu that is increasingly recognized as a key factor in multiple stages of disease progression and responses to therapy as well as escape from immune surveillance. However, the precise contribution of specific immune effector and immune suppressor components of the TME in Burkitt lymphoma (BL) remains poorly understood. In this paper, we applied the computational algorithm CIBERSORT to Gene Expression Profile (GEP) datasets of 40 BL samples to draw a map of immune and stromal components of TME. Furthermore, by VECTRA multispectral immunofluorescence (IF) and multiple immunohistochemistry (IHC), we investigated the TME of an additional series of 40 BL cases and evaluated the possible role of the PD-1/PD-L1 immune checkpoint axis. Our results indicated that M2 polarized macrophages are the most prominent TME component in BL. In addition, we investigated the correlation between PD-L1 and latent membrane protein-2A (LMP2A) expression on tumour cells, highlighting a subgroup of BL cases characterized by a non- canonical latency program of EBV with an activated PD-L1 pathway. In conclusion, our study analysed the TME in BL and identified a tolerogenic immune signature highlighting new potential therapeutic targets.

This work intends to present a study on relations between a Lie algebra called dispersion operators algebra, linear canonical transformation and a phase space representation of quantum mechanics that we have introduced and studied in previous works. The paper begins with a brief recall of our previous works followed by the description of the dispersion operators algebra which is performed in the framework of the phase space representation. Then, linear canonical transformations are introduced and linked with this algebra. A multidimensional generalization of the obtained results is given.

The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control tools. In statistical process control, control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. In the control chart, the mean value is taken as the center line, and 3σ limits are placed on either side of the center line. In CPV, manufactured lots are summarized, trended, and discussed at a cross-functional level at pre-defined intervals (either weekly, bi-weekly, or even monthly) based on the batch run rate. A cross-functional team assesses details regarding 1) violation frequency, 2) statistical strength of the signal, 3) violation magnitude and its impact on process or quality, and 4) any potential root causes for out-of-expectation or out-of-trend signals. The cross-functional team must analyze whether any signal identified by the control chart is reliable and must determine whether corrective action should be taken and justify the decision in the CPV meeting minutes based on statistical strength and reliability of the signal. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals.

As one of the typical deposits in Sichuan-Yunnan-Guizhou Pb-Zn metallogenic province, the Daliangzi Pb-Zn deposit has a close genetic relation with the structural system of “black/fracture zone” formed under the action of NWW-approx. EW strike-slip structures in the metallogenic province, but the mechanism of rock and ore-controlling action of the structural system remains unclear. Based on detailed analysis of tectonite-mineralized alteration lithofacies of R1 “black/fracture zone”, the tectonite-mineralized alteration lithofacies zones can be divided into 4 types in succession outward from the Pb-Zn mineralization center, i.e., ① brecciated and stockwork-like Pb-Zn mineralization-complex breccia facies zone, ② stockwork-like Pb-Zn mineralization-simple breccia and cataclasite facies zone, ③ veined pyrite-sulfide-dolomitic cataclasite facies zone, and ④ fine-veined calcite-black carbonized dolomite facies zone; Cl- and SO42--rich high-temperature oxidizing ore-bearing fluid mixes with reducing fluid in Zone ① while migration and precipitation occur in Zone ④. With the evolution of ore-forming fluid, the homogenization temperature decreases from Zone ① to Zone ④, the salinity increases from Zone ① to Zone ② and then decreases to Zones ③ and ④, and the fluid density has little change on the whole; the contents of Zn, Pb, Cu, Ga, Ge, Cd, Ag, and other metallogenic elements, the Zn/Pb ratio, and the CaO/MgO mole ratio decrease gradually from Zone ① to Zone ④; and REE fractionation, calcilization, silicification, and pyritization enhance gradually from Zone ① to Zone ④. This series of changes are the product of diapirism (cryptoexplosion) of strike-slip structures and “black/fracture zone”, among which the second-order structures (F5, F100, and other faults) derived from NWW-approx. EW striking dextral shear-tension faults F1 and F15 control the brecciated and stockwork-like Pb-Zn mineralized complex breccia facies zones and the stockwork-like Pb-Zn mineralized simple breccia and cataclasite facies zones. Therefore, this paper establishes the zoning mode of tectonite-mineralized alteration lithofacies of “black/fracture zone” and proposes that Zones ① and ② are important prospecting criteria. conclusions.

We report photon-phonon atomic coherence (cascade- and nested-dressing) interaction from various phase transitions of Eu3+: BiPO4 crystal. Such atomic coherence spectral interaction evolves from out of phase fluorescence to in-phase spontaneous four-wave mixing (SFWM) by changing the time gate. The dressing dip switch and three dressing dips of SFWM result from strong photon-phonon destructive cross- and self-interaction for hexagonal phase, respectively. The more phonon dressing result in destructive interaction, while less phonon dressing result in constructive interaction of atomic coherences. The experimental measurements of photon-phonon interaction agree with theoretical simulations. Based on our results, we proposed a model for an optical transistor (as an amplifier and switch).

The expression of polysialic acid (polySia) on the neuronal cell adhesion molecule (NCAM), is called NCAM-polysialylation, which is strongly related to the migration and invasion of tumor cells and with aggressive in the clinic. Thus, it’s important to select a proper drug to block tumor cell migration for clinic treatment. In this study, we proposed that lactoferrin (LFcinB11) may be a more ideal candidate for inhibition of NCAM polysialylation comparing with CMP and low molecular weight heparin (LMWH), which were determined based on our NMR studies. On the other hand, because neutrophil extracellular traps (NET) is the most dramatic stage in cell death process, and NETs’ release are related to pathogenesis of autoimmune and inflammatory disorders, with proposed involvement in glomerulonephritis, chronic lung disease, sepsis and vascular disorders. In this study, the molecular mechanism of inhibition of NETs’ release using LFcinB11 as inhibitor was also determined. Based on these results, LFcinB11 is proposed to be bifunctional inhibitor for inhibiting both NCAM polysialylation and NETs releases.

This paper presents a study to accurately evaluate defects in reinforced concrete decks using ultrasonic pulse-echo signals. Two validation specimens were designed and tested where reinforced concrete deck slab specimens included planned voids and defects. A commercial Ultrasonic Pulse-Echo (UPE) device obtains 2-D images of the void/defect locations of the reinforced concrete deck. The UPE is based on the ultrasonic shear-wave test method using dry-point-contact transmitting and receiving transducers and employing a synthetic aperture focusing technique. The authors analyzed the recorded UPE A-scan data to enhance the accuracy of estimating the defects' locations using the synthetic aperture focusing technique imaging method. The analysis benefitted from an advanced denoising approach and defect echo peak extraction based on empirical modal decomposition, Hurst exponent characterization, and Hilbert envelope estimation. While output from conventional UPC devices can only provide qualitative results, the new method provides quantitative information on the anomalies inside the reinforced concrete deck. The developed approach accurately assists with assessing the location and depth of the voids/defects in the reinforced concrete deck slabs.

It has long been known that phytohormone auxin plays a promoting role in tuber formation and stress tolerance in potatoes. Our study aimed to identify and characterize the complete sets of auxin-related genes that presumably constitute the entire auxin signaling system in potato (Solanum tuberosum L.). The corresponding genes were retrieved from sequenced genomes of the doubled monoploid S. tuberosum DM1-3-516-R44 (DM) of the Phureja group, the heterozygous diploid line RH89-039-16 (RH), and the autotetraploid cultivar Otava. Both canonical and noncanonical auxin signaling pathways were considered. Phylogenetic and domain analyzes of deduced proteins were supplemented by their expression profiling and 3D molecular modeling. The canonical and ABP1-mediated pathways of auxin signaling appeared to be well conserved. Total number of potato genes/proteins presumably involved in canonical auxin signaling is 46 and 108 in monoploid DM and tetraploid Otava, respectively. Among studied potatoes, spectra of expressed genes obviously associated with auxin signaling were partly cultivar-specific and quite different from analogous spectrum in Arabidopsis. Most of the noncanonical pathways found in Arabidopsis appeared to be low probable in potato. This was equally applied to all cultivars used irrespective of their ploidy. Thus, some important features of the (noncanonical) auxin signaling pathways may be variable and species-specific.

Byers Peninsula (62°34’-62°40’S-60°54’61°13’W), 60 km2 in size, is considered one of the largest ice-free areas in Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving effects of climate changes, limnology and microbiology, live cycles, but not albedo. Surface albedo is one of the key physical parameters in the surface energy budget of polar regions. Most of Antarctica is covered by ice sheets; only about 0.44% of the area is permanently ice-free. However, in maritime Antarctica, the ice-free areas, corresponding to small islands, peninsulas, and coastal beaches, account for about 3% of the territory. To incorporate the contribution of these areas into global albedo models, it is necessary to relate the soil properties of the ice-free areas to the surface albedo response. Also, mapping soil properties and albedo have and special interest in these ice-free areas. Image classification using machine learning methods trained with georeferenced soil data could be useful for mapping soil properties and albedo in multispectral optical satellite images. A shallow neural network implemented using the Keras Python module was used to define and train models of soil properties using 15 explanatory variables corresponding to bands and spectral indices of a Sentinel image and a population of 49 soil samples taken from the top 5 centimeters of the soil profile. The soil samples were analyzed in the laboratory and a spectral library in the Vis-Nir range (350-2500 nm) was created. The albedo of the samples was integrated from the ADS spectra. At the same time, a linear regression model of albedo using the soil properties as explanatory variables was performed. The R2 fit of this new model was about 0.82 and the error of estimation was 4.1. The model was extended to the entire Byers Peninsula using the ML soil property models as explanatory variables. The RMSE of the extended models increased up to 8.2. We think that the main cause of this increase (2.6 points) is related to the error propagation, phenomenon due to the use of models as explanatory variables. However, another important part of the error increase could be due, among other reasons, to the use of an image that, although corrected, is not completely free of clouds.

In recent experimental studies, we reported the aggravating effects of Asian sand dust (ASD) on male reproductive function in mice. However, the effects of fetal ASD exposure on male reproductive function have not been investigated. The present study investigated the effects of fetal ASD exposure on reproductive function in male offspring. Using pregnant CD-1 mice, ASD was administered intratracheally on days 7 and 14 of gestation, and the reproductive function of male offspring was determined at 5, 10, and 15 weeks after birth. The secondary sex ratio was significantly lower in the fetal ASD-exposed mice than in the controls. Histologic examination showed partial vacuolation of seminiferous tubules in immature mice. Moreover, daily sperm production (DSP) was significantly less in the fetal ASD-exposed mice than in the controls. DSP in the fetal ASD-exposed mice was approximately 10% less than the controls at both 5 and 10 weeks. However, both the histologic changes and the DSP decrease were reversed as the mice matured. These findings suggest that ASD exposure affects both the fetal development and the reproductive function of male offspring. In the future, it will be necessary to clarify the onset mechanisms of ASD-induced male fetus death and male reproductive disorders.

Non-isocyanate polyurethane synthesis by non-Sn catalysis is an essential challenge toward green polyurethane synthesis. Bismuth compounds are attractive candidates due to their low cost, low toxicity, and availability to urethane chemistry. We applied various Bi catalysts to the self-polycondensation of a bishydroxyurethane monomer and found BiCl3 to be an excellent catalyst through optimization. The catalytic activity and price of BiCl3 are comparable to those of Bu2SnO, while its toxicity is significantly low. BiCl3 is, therefore, a promising alternative to Sn-based catalysts in non-isocyanate polyurethane synthesis.

Non-Linear Simulation by Harmonic Balance Techniques of Load Modulated Power Amplifier Driven by Random Modulated Signals. Comparison with measured results.

Byers Peninsula is considered one of the largest ice-free areas in Maritime Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving effects of climate changes in lives cycles, limnology and microbiology. Soils from maritime Antarctica are generally weakly developed and have chemical, physical and morphological characteristics strongly influenced by the parent material. However, biological activity during the short Antarctic summer promotes intense transference of nutrients and organic matter in areas occupied by different species of birds and marine mammals. To mapping and monitoring those areas with high biological occupation, could be very useful to have models of edaphic properties prepared from satellite images. In this approach, Deep Learning and Linear Regression models of soil properties and spectral indexes as explicative variables were performed. We training models of soil properties closely related to biological activity such as Dissolved Organic Carbon (DOC) and the iron fraction associated with organic matter (Fe). We tested the best approach to model the spatial distribution of DOC, Fe and pH by training models of Linear Regression and Deep Learning over Sentinel2 and WorldView2 images. The most robust models were used to track possible areas with ornithogenic soils as well as areas of the Byers Peninsula that could support the highest biological development.

This paper gives both a general canonical quantum gravity theory and the general canonical quantum gravity theories of the Universe and general black hole, and discovers the relations reflecting symmetric properties of the standard nonlinear gravitational Lagrangian, which are not relevant to any concrete metric models. This paper concretely shows the general commutation relations of the general gravitational field operators and their zeroth, first, second and third style, respectively, of high order canonical momentum operators for the general nonlinear system of the standard gravitational Lagrangian, and then has finished all the four styles of the canonical quantization of the standard gravity.

Meat serves as a vital component of the human diet, offering a rich source of protein and essential trace elements. China stands as both a significant producer and consumer of meat on a global scale, leading the world in terms of total meat production and consumption. As the standard of living within the country continues to rise, there is a growing emphasis on the nutritional value and safety of meat consumption. The tenderness of meat is a critical factor influencing its overall quality. Traditionally, tenderness has been assessed through shear force testing, a method that is inherently destructive, inefficient, and results in considerable sample wastage. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into the application of five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations aimed at guiding the future direction of non-destructive, AI-enhanced meat tenderness detection, thereby contributing to the ongoing improvement of the meat industry in China and beyond.

A great deal of studies have assessed the expression of noncoding RNAs (ncRNAs) as well as their relevant molecular functions and biological mechanisms in the individuals of alcohol dependence. Alcohol dependence is one of the most prevalent neuropsychological disorders worldwide, and its pathogenesis is intricate and inadequately understood. There is considerable evidence demonstrating substantial links between multiple genetic factors and the development of alcohol dependence. Critical roles of ncRNAs have been emphasized in the pathophysiology of mental diseases including alcohol dependence. In the comprehension of ncRNAs action and their mechanisms of modification, they have emerged as therapeutic targets for a variety of psychiatric illness including alcohol dependence. Worth mentioning, dysregulated expression of ncRNAs has been regularly detected in individuals of alcohol dependence. An in-depth knowledge of roles of ncRNAs and m6A modification may be valuable for the development of a novel treatment against alcohol dependence. In general, a deeper comprehension of functional roles ncRNAs may make significant contributions to the precise diagnosis and/or actual treatment of alcohol dependence. Here in this review, we mostly focused on the up-to-date knowledge regarding alterations and/or modifications in the expression of ncRNAs in alcohol dependence, and then, presented their prospects for future research and therapeutic application with a novel concept of engram-system.

Pairs of pyrrolysyl-tRNA synthetase (PylRS) and tRNAPyl from Methanosarcina mazei and Methanosarcina barkeri are widely used for site-specific incorporations of non-canonical amino acids into proteins (genetic code expansion). Previously, we achieved full productivity of cell-free protein synthesis for bulky non-canonical amino acids, including Ne-((((E)-cyclooct-2-en-1-yl)oxy)carbonyl)-L-lysine (TCO*Lys), by using Methanomethylophilus alvus PylRS with structure-based mutations in and around the amino acid binding pocket (first-layer and second-layer mutations, respectively). Recently, the PylRS•tRNAPyl pair from a methanogenic archaeon ISO4-G1 was used for genetic code expansion. In the present study, we determined the crystal structure of the methanogenic archaeon ISO4-G1 PylRS (ISO4-G1 PylRS) and compared it with those of structure-known PylRSs. Based on the ISO4-G1 PylRS structure, we attempted the site-specific incorporation of Ne-(p-ethynylbenzyloxycarbonyl)-L-lysine (pEtZLys) into proteins, but it was much less efficient than that of TCO*Lys with M. alvus PylRS mutants. Thus, the first-layer mutations (Y125A and M128L) of ISO4-G1 PylRS, with no additional second-layer mutations, increased the protein productivity with pEtZLys up to 578% of that with TCO*Lys, at high enzyme concentrations in the cell-free protein synthesis.

Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it was unclear whether intentions relating to mental imagery of movement could be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.

In voice analysis, the electroglottographic (EGG) signal has long been recognized as a useful complement to the acoustic signal, if only when the vocal folds are actually contacting, such that this signal has an appreciable amplitude. However, phonation can occur also without vocal fold contacting, as in breathy voice, in which case the EGG amplitude is low, but not zero. It is of great interest to identify the transition from non-contacting to contacting, because this will substantially change the nature of the vocal fold oscillations; yet that transition is not in itself audible. The magnitude of the normalized peak derivative of the EGG signal is a convenient indicator of contacting, but no current EGG hardware has a sufficient signal-to-noise ratio of the derivative. The textbook techniques of spectral thresholding and static notch filtering are straightforward to implement, can run in real time, and can mitigate several noise problems in EGG hardware.

Lipid composition in the cellular membranes plays an important role in maintaining the struc-tural integrity of cells and in regulating cellular signaling that controls functions of both mem-brane-anchored and cytoplasmic proteins. ATP-dependent ABC and P4-ATPase lipid transport-ers, two integral membrane proteins, are known to contribute to lipid translocation across the li-pid bilayers on the cellular membranes. In this review, we will highlight current knowledge about the role of cholesterol and phospholipids of cellular membrane in regulating cell signaling and how lipid transporters participate this process.

A signal analysis of the genoma sequenced of coronavirus variants: B.1.1.7, B.1.135, B.1.429-B.1.427, B.1.525 and P1 is presented. We deal with a certain type of finite alternating sum series having independently distributed terms associated with binary (0,1) indicators for the nucleotide bases A,C,G,T. This method provides additional information to conventional Similarity comparisons and Power Spectrum approaches. It leads to uncover distinctive patterns regarding the intrinsic data organization of genomic sequences according to its progression along the nucleotide bases position. Hence, the method could be useful for survelliance of genoma variants.

Animal communication signals are regulated by multiple hormonal axes that ensure appropriate signal targeting, timing, and information content. The regulatory roles of steroid hormones and many peptide hormones are well understood and documented across a wide range of vertebrate taxa. Two recent studies have reported a novel function for leptin, a peptide hormone central to energy balance regulation: regulating communication signals of weakly electric fish and singing mice. With only limited evidence available at this time, a key question is just how widespread leptinergic regulation of communication signals is within and across taxa. A second important question is what features of communication signals are subject to leptinergic regulation. Here we consider the functional significance of leptinergic regulation of animal communication signals in the context of both direct and indirect signal metabolic costs. Direct costs arise from metabolic investment in signal production, while indirect costs arise from the predation and social conflict consequences of the signal’s information content. We propose a preliminary conceptual framework for predicting which species will exhibit leptinergic regulation of their communication signals and which signal features leptin will regulate. This framework suggests a number of directly testable predictions within and across taxa. Accounting for additional factors such as life history and reproductive strategies will likely require modification or elaboration of this model.

The search for periodic signals from blazars has become an actively pursued field of research in recent years. This is because periodic signals bring us information about the processes occurring near the innermost regions of blazars, which are mostly inaccessible to our direct view. Such signals provide insights into some of the extreme conditions that take place in the vicinity of supermassive black holes that lead to the launch of the relativistic jets. In addition, studies of characteristic timescales in blazar light curves shed light on some of the challenging issues in blazar physics that includes disk-jet connection, strong gravity near fast rotating supermassive black holes and release of gravitational waves from binary supermassive black hole systems. However, a number of issues associated with the search for quasi-periodic oscillations (QPOs) in blazars e.g., red-noise dominance, modest significance of the detection, periodic modulation lasting for only a couple of cycles and their transient nature, make it difficult to estimate the true significance of the detection. Consequently, it also becomes difficult to make meaningful inferences about the nature of the on-going processes. In this proceedings, results of study focused on searching for QPOs in a number of blazar multi-frequency light curves are summarized. The time series analyses of long term observations of the blazars revealed the presence of year-timescale QPOs in the sources including OJ 287 (optical), Mrk 501 (gamma-ray), J1043+2408 (radio) and PKS 0219 -164 (radio). As likely explanations, we discuss a number of scenarios including binary supermassive black hole systems, Lense–Thirring precession, and jet precession.

Ovarian cancer is the most frequent cause of deaths in gynecologic malignancies. Many possible mechanisms have been proposed via RNAseq and DNAseq technique recently. However, the driving factors are still obscure. The possible reasons are attributed to the incomplete human reference. This study integrated the canonical mapping-based and mapping-free protocols to extract reliable variations and novel events. We eventually obtained 450 reliable SNVs from the WES data and novel events from the RNAseq data, including 154 SNVs, 462 intron events, two repeats and six splice events. We identified six differentially expressed genes and six contigs that are significantly related to survival prognosis. The recurrent SNVs in significantly differentially expressed genes can be validated in an independent cohort of 20 Chinese ovarian cancer patients.

Image compression is a top priority today due to the need for faster encoding and decoding. To achieve this, the present study has proposed the use of Canonical Huffman Coding (CHC) as an entropy coder, which has a lower decoding time complexity than binary Huffman codes. For image compression, combining of the Discrete Wavelet Transform (DWT) and CHC with Principal Component Analysis (PCA) has been recommended. The lossy method has been introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed image has been found to have a better peak signal-to-noise ratio (PSNR) value. This study has developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for efficient use of storage space. The proposed technique has achieved up to 60% compression while maintaining high visual quality. This method has also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach has been tested on various color images, including Peppers 512×512 and Couple 256×256, showing improvement by 17 dB and 22 dB, respectively, while reducing bpp by 0.56 and 0.10, respectively. For grayscale images, i.e., Lena 512×512 and Boat 256×256, the proposed method has shown an improvement by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.

Abstract: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with extensive genetic and aetiological heterogeneity. While the underlying molecular mechanisms involved remain unclear, significant progress has been facilitated by recent advances in high-throughput transcriptomic, epigenomic and proteomic technologies. Here, we review recently published ASD proteomic data and compare proteomic func-tional enrichment signatures to those of transcriptomic and epigenomic data. We iden-tify canonical pathways that are consistently implicated in ASD molecular data and find an enrichment of pathways involved in mitochondrial metabolism and neurogenesis. We identify a subset of differentially expressed proteins that are supported by ASD tran-scriptomic and DNA methylation data. Furthermore, these differentially expressed proteins are enriched for disease phenotype pathways associated with ASD aetiology. These proteins converge on protein-protein interaction networks that regulate cell pro-liferation and differentiation, metabolism and inflammation which demonstrates a link between canonical pathways, biological processes and the ASD phenotype. This review highlights how proteomics can uncover potential molecular mechanisms to explain a link between mitochondrial dysfunction and neurodevelopmental pathology.

There are currently around 78 Nuclear Power Plants (NPP) in the world based on Boiling Water Reactors (BWR). The current parameter to assess BWR instability issues is the linear Decay Ratio (DR). However, it is well known that BWRs are complex non-linear dynamical systems that may even exhibit chaotic dynamics that normally preclude the use of the DR when the BWR is working at a specific operating point during instability. In this work a novel methodology based on an adaptive Shannon Entropy estimator and on Noise Assisted Empirical Mode Decomposition variants is presented. This methodology was developed for real-time implementation of a stability monitor. This methodology was applied to a set of signals stemming from several NPPs reactors (Ringhals-Sweden, Forsmark-Sweden and Laguna Verde-Mexico) under commercial operating conditions, that experienced instabilities events, each one of a different nature

Europium is one of the most reactive lanthanides and humans use it in many different applica-tions, but we still know little about its potential toxicity and cellular response to its exposure. Two strains of the eukaryotic microorganism model Tetrahymena thermophila adapted to high concentrations of two Eu(III) compounds (EuCl3 or Eu2O3) were obtained and compared to a control strain and cultures treated with both compounds. In this ciliate, EuCl3 is more toxic than Eu2O3. LC50 values show that this microorganism is more resistant to these Eu(III) compounds than other microorganisms. Oxidative stress originated mainly by Eu2O3 is minimized by overexpression of genes encoding important antioxidant enzymes. The overexpression of metallothionein genes under treatment with Eu(III) compounds supports the possibility that this lanthanide may interact with the -SH groups of the cysteine residues from metallothi-oneins and/or displace essential cations of these proteins during their homeostasis function. Both lipid metabolism (lipid droplets that fuse with europium-containing vacuoles) and au-tophagy are involved in the cellular response to europium stress. Bioaccumulation, together with a possible biomineralization to europium phosphate, seems to be the main mechanism of Eu(III) detoxification in these cells.

The World Health Organization(WHO) in 2016 considered mHealth as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health” as an important resource for health services delivery and public health given their ease of use, broad reach and acceptance. WHO emphasizes the potential of this technology to increase access to health information, services and skills as well as promoting positive changes in health behaviors and management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring as well as verification of the patient the signal has become an important component of mHealth system. Most of the smartwatches could extract more than one bioelectrical signal therefore, therefore they provide suitable platform for extracting health data for e-monitoring. The existing approaches have not considered the integrity of data obtained from these smart devices. Therefore, it is important that the integrity of the collected data be verified continuously through user authentication. This could be done using any of the bioelectrical signals extracted and transmitted for e-monitoring. In this article, a smartwatch is used for extracting bioelectrical signal before decomposing the signal into sub-bands of Detail and Approximation Coefficient for user authentication. To select suitable features using biorthogonal wavelet decomposition of signal from a non-intrusive extraction, a detailed experiment is conducted extracting suitable statistical features from the bioelectrical signal from 30 subjects using different biorthogonal wavelet family. Ten features are extracted using Biorthogonal wavelet to decompose the signal into three levels of sub-band Detail and Approximation Coefficient and features extracted from each level the decomposed Detail and Approximation Coefficients. Comparison analysis is done after the classification of the extracted features based on the Equal Error Rate (EER). Using Neural Network (NN) classifier, Biorthogonal Wavelet Detail Coefficient Sub-band level 3 of bior1.1 achieved the best result of EER 13.80% with the fusion of the best sub-band three levels of bior1.1 achieving a better result of 12.42% EER.

Emotion recognition is becoming very relevant in educational scenarios, since previous research has proven the strong influence of emotions on the student's engagement and motivation. There is no standard method for stating student's affect, but physiological signals have been widely used in educational contexts. Physiological signals have been proved to offer high accuracy in detecting emotions because they reflect spontaneous affect-related information, and which is fresh and do not require an additional control or interpretation. However, most proposed works use measuring equipment that limit its applicability in real-world scenarios because of their high cost and their intrusiveness. Expensive material means an economic challenge for schools and reduce the scalability of the experiments. Intrusive equipment can be uncomfortable for the students which can lead to errors in the collected data. In this work, we analyse the feasibility of developing a low-cost non-intrusive device that integrates easy-to-capture signals that guarantee high detection accuracy. The advantage of the approach also lies in using user’s centred information sources (intra-subject) in real-world situations, which provide better detection accuracy, by offering models adapted to each subject. To this end, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We study the multi-fusion of every possible combination of these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. Results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task. This work concludes that the implementation of a low-cost wrist-worn device for recognising relevant emotions from each student is possible and open the way to a wide range of practical applications in the context of adaptive learning systems.

If "quantization is an art" then it can be greatly rened by adopting cyclic time formalism. In past papers we have proven the effectiveness of a formulation of physics based on cyclic relativistic time. Now we can demonstrate in a general way, by using theorems of Geometric Quantization, that the Poisson brackets of intrinsically cyclic time dynamics directly imply the ordinary canonical commutation relations and the other Dirac's rules of canonical Quantum Mechanics. In other words, according to our result, canonical quantization is an implicit way of imposing intrinsically cyclic time dynamics without explicitly saying that time is a cyclic dimension.

The paper analyzes the probability distribution of the occupancy numbers and the entropy of a system at the equilibrium composed by an arbitrary number of non-interacting bosons. The probability distribution is derived both by tracing out the environment from a bosonic eigenstate of the union of environment and system of interest (the empirical approach) and by tracing out the environment from the mixed state of the union of environment and system of interest (the Bayesian approach). In the thermodynamic limit, the two coincide and are equal to the multinomial distribution. Furthermore, the paper proposes to identify the physical entropy of the bosonic system with the Shannon entropy of the occupancy numbers, fixing certain contradictions that arise in the classical analysis of thermodynamic entropy. Finally, by leveraging an information-theoretic inequality between the entropy of the multinomial distribution and the entropy of the multivariate hypergeometric distribution, Bayesianism of information theory and empiricism of statistical mechanics are integrated into a common ''infomechanical'' framework.

We provide an axiomatic base for the set of natural numbers, that has been proposed as a canonical construction, and use this definition of $\mathbb N$ to find several results on finite group theory. Every finite group $G$, is well represented with a natural number $N_G$; if $N_G=N_H$ then $H,G$ are in the same isomorphism class. We have a linear order, on the quotient space of isomorphism classes of finite groups, that is well behaved with respect to cardinality. If $H,G$ are two finite groups such that $|H|=m<n=|G|$, then $H<\mathbb Z_n\leq G\leq\mathbb Z_{p_1}^{n_1}\oplus\mathbb Z_{p_2}^{n_2}\oplus\cdots\oplus\mathbb Z_{p_k}^{n_k}$ where $n=p_1^{n_1}p_2^{n_2}\cdots p_{k}^{n_k}$ is the prime factorization of $n$. We find a canonical order for the objects of $G$ and define equivalent objects of $G$, thus finding the automorphisms of $G$. The Cayley table of $G$ takes canonical block form, and we are provided with a minimal set of independent equations that define the group. We show how to find all groups of order $n$, and order them. We give examples using all groups with order smaller than $10$, and we find the canonical block form of the symmetry group $\Delta_4$. In the next section, we extend our results to the infinite case, which defines a real number as an infinite set of natural numbers. A real function is a set of real numbers, and a sequence of real functions $f_1,f_2,\ldots$ is well represented by a set of real numbers, as well. In general, we represent mathematical objects using the smallest possible data-type. In the last section, mathematical objects are well assigned to tree structures. We conclude with brief comments on type theory and future work on computational aspects of these representations.

Recent analyses of cosmic microwave background surveys have revealed hints that there may be a non-trivial running of the running of the spectral index. If future experiments were to confirm these hints, it would prove a powerful discriminator of inflationary models, ruling out simple single field models. We discuss how isocurvature perturbations in multi-field models can be invoked to generate large runnings in a non-standard hierarchy, and find that a minimal model capable of practically realising this would be a two-field model with a non-canonical kinetic structure. We also consider alternative scenarios such as variable speed of light models and canonical quantum gravity effects and their implications for runnings of the spectral index.

Insufficient mixing in large-scale bioreactors, provokes gradient zones of substrate, dissolved oxygen, pH and other parameters. E. coli responds to a high glucose, low oxygen feeding zone with the accumulation of mixed acid fermentation products, especially formate, but also with the synthesis of non-canonical amino acids, such as norvaline, norleucine and -methyl-norleucine. These amino acids can be mis-incorporated into recombinant products, which causes a problem for pharmaceutical production whose solution is not trivial. While these effects can also be observed in scale down bioreactor systems, these are challenging to operate. Especially the high-throughput screening of clone libraries is not easy, as fed-batch cultivations would need to be controlled via repeated glucose pulses with simultaneous oxygen limitation, as has been demonstrated in well controlled robotic systems. Here we show that not only glucose pulses in combination with oxygen limitation can provoke the synthesis of these non-canonical branched-chain amino acids, but also that pyruvate pulses produce the same effect. Therefore we combined the enzyme based glucose delivery method Enbase® in a PALL24 mini-bioreactor system and combined repeated pyruvate pulses with simultaneous reduction of the aeration rate. These cultivation conditions, produced an increase in the non-canonical branched chain amino acids norvaline and norleucine in both the intracellular soluble protein and inclusion body fractions with mini-proinsulin as an example product, and this effect was verified in a 15 L stirred tank bioreactor. To our opinion this cultivation strategy is easy to apply for the screening of strain libraries under standard laboratory conditions if no complex robotic and well controlled parallel cultivation devices are available.

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

In this observational study, we compared continuous physiological signals during an active stand test in adults aged 50 years and over, characterized as frail by three different criteria, using data from The Irish Longitudinal Study on Ageing (TILDA). Four independent groups were identified: those characterized as frail only by one of the frailty tools used (the physical Frailty Phenotype (FP), the 32-item Frailty Index (FI), or the Clinical Frailty Scale (CFS) classification tree), and a fourth group where participants were not characterized as frail by any of these tools. Continuous non-invasive physiological signals were collected during an active stand test: systolic (sBP) and diastolic (dBP) blood pressure, as well as heart rate (HR), using digital artery photoplethysmography. Additionally, frontal lobe cerebral oxygenation (Oxy), deoxygenation (Deoxy), and tissue saturation index (TSI) were also non-invasively measured using near-infrared spectroscopy (NIRS). The signals were visualized across frailty groups and statistically compared using one-dimensional statistical parametric mapping (SPM). A total of 1,124 participants (mean age 63.5 years, 50.2% women) were included: 23 were characterized as frail only by FP, 97 by FI, 38 by CFS, and 966 by none of these criteria. SPM analyses revealed that only the group characterized as frail by FI had significantly different signals (p<0.05) compared to the non-frail group. Specifically, they exhibited a lesser increase in HR between 5-15 seconds post-stand and larger deficits in sBP and dBP between 15-40 seconds post-stand, and possibly a lower Delta TSI at 20 seconds post-stand. In this comparison, the FI proved more adept at capturing distinct physiological responses to standing, likely due to its direct inclusion of cardiovascular morbidities in its definition.

Electroencephalography (EEG) is essential for tracking brain activity and identifying seizure effects. However, epileptic behaviour can only be detected after a specialist has carefully analysed all EEG recordings along with a proper history of the patient. A skilled physician is required for the right epilepsy diagnosis and therapy. But most of the time, patients visit the clinician in the interictal stage with no proper history documented. Therefore, it was essential to the automatic prediction of stages of seizure. K nearest neighbours (KNN) and random forest (RF) models using raw EEG signals, preictal, ictal, postictal, and interictal stages were identified in this study. The possibility of these characteristics is explored by examining how well time-domain signals work in the prediction of epileptic stages using intracranial EEG datasets from Freiburg Hospital (FH), Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT), and Temple University Hospital (TUHEEG). To test the viability of this approach, two different types of simulations were carried out on three binary classifications (interictal vs. preictal, interictal vs. ictal, preictal vs. postictal, and interictal vs. postictal), and one four-class problem (interictal vs. preictal vs. ictal vs. postictal) was performed for each model. The average accuracy when using time-domain signals in the FH database was 90.5% and 75.0%; CHB-MIT was 92.87% and 75.9%; and TUHEEG was 94.46% and 76.8%, respectively, for the KNN and RF models.

The cytoskeleton is a master organizer of cell cortex, organelles positioning and membrane trafficking. Therefore, playing a decisive role in establishment of apico-basal polarity. Septins are critical cytoskeleton components which function in apico-basal polarity remain weakly investigated. Here, using 3D culture system, we demonstrate that septin9 localizes at basolateral membrane (BM). Its depletion induces an inverted polarity phenotype, decreasing -catenin at BM and increasing the expression of the transforming growth factor (TGF) and the epithelial-to-mesenchymal transition (EMT) markers. Similar effects were observed upon deleting its two polybasic domains, and the mutant become cytoplasmic and apical. The cysts with an inverted polarity phenotype display an invasive phenotype, with src and cortactin accumulating at the peripheric membrane. Inhibition of TGF-receptor and RhoA rescues the polarized phenotype. Although the cysts from overexpressed-septin 9, overgrowth and present a filled lumen. Both phenotypes correspond to tumor features. This suggests that septin 9 expression and assembly through the two PB domains are essential for establishing and maintaining apico-basal polarity against tumor development through a mechanism involving TGF-dependent EMT and RhoA activity.

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4-6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

Keywords: pelvic ultrasound, follicle number per ovary, FNPO, ovarian volume, PCOM, upper normal limits, referral population

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets, based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structure in a broad range of data analytics problems including the study of cardiovascular signals. This review aims to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA has shown great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

Dental agenesis is one of the most common developmental anomaly in humans and it is frequently as-sociated with several other oral abnormalities. The present case describes nonfamilial agenesis of per-manent teeth in a twenty-one-year-old boy, with no apparent systemic abnormalities. The treatment in-cluded a personalized and interdisciplinary approach by the means of endodontics, orthodontics, im-plants supported restorations and prosthetic treatments. The treatment plan was thoroughly elaborated using photographic analysis, study models, orthopantomogram, CBCT and cephalograms. Virtual smile design, diagnostic waxing, and mock-up previsualized the treatment objectives. The edentulous spaces were reconstructed by inserting dental implants, and monolithic Zirconia implant supported restorations. The final results showed a highly aesthetic and functional rehabilitation. Periodic checkups have shown that the stability of the result is well maintained and that the implant supported restorations are an op-timal solution for patients with multiple anodontia.

Immune checkpoint inhibitors (ICIs), including anti-programmed cell death 1 ligand 1 (PD-L1) antibodies, are significantly changing treatment strategies for human malignant diseases including oral cancer. Cancer cells usually escape from immune system and acquire proliferative capacity and invasive/metastatic potential. We have focused on the two immune checkpoints, PD-1/PD-L1 and CD47/SIRPα in the tumor microenvironment of oral squamous cell carcinoma (OSCC), and performed a retrospective analysis of the expression of seven immune-related factors (PD-L1, PD-1, CD4, CD8, CD47, CD56 and CD11c), and examined their correlation with clinicopathological status. As a result, there were no significant findings relating seven immune-related factors and several clinicopathological statuses. However, the immune-checkpoint related factors (PD-1, PD-L1, CD47) were highly expressed in non-keratinized epithelium-originated tumors when compared to those in keratinized epithelium-originated tumors. It is of interest that immunoediting via immune checkpoint-related factors was facilitated in non-keratinized sites. Several researchers reported that keratinization of oral mucosal epithelia affected the immune response, but our present finding is the first study to show a difference of the tumor immunity in the originating-epithelium of OSCC, keratinized or non-keratinized. Tumor immunity, an immune escape status of OSCC might be different in the originating-epithelium, keratinized or non-keratinized.

EMG analysis is becoming increasingly important in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. Two main approaches are considered: (i) long-range correlation analysis using both the Hurst (H) and the Detrended Fluctuation Analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data collected from two application examples: a glass fiber reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis is also performed to reference the study of the other methods. Results indicate that the proposed methods yield a reliable indication of failure in the studied structures.

In this work, we explore the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behaviour of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behaviour. We centre this study on a small WF of five WTs. We use the fact that the same signals from different turbines in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, we use averaged signals from the SCADA system and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations and overlapings are explored. The capability to automatically identify WTs whose signals differ from the group’s behaviour can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.

Quorum sensing (QS) describes a process by which bacteria can sense the local cell density of their own species, thus enabling them to coordinate gene expression and physiological processes on a community-wide scale. Small molecules called autoinducers or QS signals, which act as intraspecies signals, mediate quorum sensing. As our knowledge of QS has progressed, so too has our understanding of the structural diversity of QS signals, along with the diversity of bacteria conducting QS and the range of ecosystems in which QS takes place. It is now also clear that QS signals are more than just intraspecies signals. QS signals mediate interactions between species of prokaryotes, and between prokaryotes and eukaryotes. In recent years, our understanding of QS signals as mediators of algae–bacteria interactions has advanced such that we are beginning to develop a mechanistic understanding of their effects. This review will summarize the recent efforts to understand how different classes of QS signals contribute to the interactions between planktonic microalgae and bacteria in our oceans, primarily N-acyl-homoserine lactones, their degradation products tetramic acids, and 2-alkyl-4-quinolones. In particular, this review will discuss the ways in which QS signals alter microalgae growth and metabolism, namely as direct effectors of photosynthesis, regulators of the cell cycle, and as modulators of other algicidal mechanisms. Furthermore, the contribution of QS signals to nutrient acquisition is discussed, and finally how microalgae can modulate these small molecules to dampen their effects.

The aim of this study was to examine brightness effect, which is the perceptual property of visual stimuli, on brain responses obtained during visual processing of these stimuli. For this purpose, brain responses of the brain to changes in brightness were explored comparatively using different emotional images (pleasant, unpleasant and neutral) with different luminance levels. Moreover, electroencephalography recordings from 12 different electrode sites of 31 healthy participants were used. The power spectra obtained from the analysis of the recordings using short time Fourier transform were analyzed, and a statistical analysis was performed on features extracted from these power spectra. Statistical findings obtained from electrophysiological data were compared with those obtained from behavioral data. The results showed that the brightness of visual stimuli affected the power of brain responses depending on frequency, time and location. According to the statistically verified findings, the distinctive effect of brightness occurred in the parietal and occipital regions for all the three types of stimuli. Accordingly, the increase in the brightness of pleasant and neutral images increased the average power of responses in the parietal and occipital regions whereas the increase in the brightness of unpleasant images decreased the average power of responses in these regions. However, the increase in brightness for all the three types of stimuli reduced the average power of frontal and central region responses (except for 100-300 ms time window for unpleasant stimuli). The statistical results obtained for unpleasant images were found to be in accordance with the behavioral data. The results also revealed that the brightness of visual stimuli could be represented by changing the activity power of the brain cortex. The main contribution of this research was to comprehensively examine brightness effect on brain activity for images with different emotional content and different frequency bands at different time windows of visual processing for different brain regions. The findings emphasized that the brightness of visual stimuli should be viewed as an important parameter in studies using emotional image techniques such as image classification, emotion evaluation and neuro-marketing.

Abstract Introduction: In the last thirty years, brain neuromodulation techniques have been used as an alternative to pharmacological treatment of neurological disorders. Parkinson's disease (PD) is a neurodegenerative disorder leading to bradykinesia, rest tremor, postural changes, and non-motor symptoms such as depression, anxiety, sleep disorders, pain, and cognitive decline that compromises executive functions (EFs), responsible for the orderly execution of behaviors and tasks of daily life and intentional and directed actions. To this date, a few studies with transcranial direct current stimulation (tDCS) have shown beneficial effects in PD patients concerning specific motor and non-motor symptoms, targeting the motor cortex and/or prefrontal regions. Objective: The main objective of this study was to evaluate the effects of left prefrontal tDCS across a broad spectrum of motor and non-motor symptoms of PD using established validated scales. Method: Single-blind randomized clinical trial with 18 volunteers with PD, aged between 45 and 80 years (66.1 ± 9.65), who met inclusion and exclusion criteria. Participants were submitted to assessments of motor and non-motor functions employing psychometric scales and tests to evaluate EFs and were randomly divided into two groups: control (sham stimulation) and experimental (active stimulation). All participants were involved in three separate tDCS sessions. The anode was positioned over the left dorsolateral prefrontal cortex and the cathode over the right supraorbital region, with a direct current intensity of 2mA, lasting 20 minutes. At the end of the three sessions, all participants were reassessed. Results: Significant effects of tDCS on non-motor functions were observed for cognition (verbal fluency of actions, clock copy test, appointment by visual confrontation, and verbal memory with immediate free recall) and subjective assessment of sleep quality (overall restlessness and discomfort in the arms and legs at night, leg and arm cramps at night and distressing dreams). There was also an improvement in the rate of errors and successes for congruent and incongruent stimuli of the Stroop Test. The beneficial effects on motor function were decreased rigidity, improved gait, and greater agility in the finger-tapping test. Conclusion: Three tDCS sessions showed positive results for participants with PD, producing significant improvements in various motor and non-motor functions, including sleep quality, cognition, and EFs. Additionally, the present results indicate that tDCS neuromodulation of the left dorsolateral prefrontal cortex region is feasible, safe, and provides significant objective benefits for PD patients.

In this paper, we investigate the Green measure for a class of non-Gaussian processes in $\mathbb{R}^{d}$. These measures are associated with the family of generalized grey Brownian motions $B_{\beta,\alpha}$, $0

The thesis proposes a argument that a mass point moving in a horizontal circle on the Earth's surface can generate a radial centrifugal force of the Earth's center of mass, thus realizing an unconventional anti-gravity path. And through the theoretical analysis of the difference in the distribution of gravitational acceleration at different latitudes caused by the rotation of the earth, and the centrifugal force of a mass point moving in a horizontal circle on the earth's surface is analyzed, the validity of the argument is demonstrated. At the same time, the paper puts forward two possible basic principles, and makes theoretical analysis and demonstration. Conceive of the concrete experimental verification of principle two is proposed. Four technical schemes for antigravity ascent are proposed and basic analysis is made. On the premise of not fully considering the practical technical ability, it is theoretically proved that three of them have realistic operability and development potential. In theory, this paper opens up a different way of anti-gravity for human beings, which will bring new ideas and prospects for human beings to get rid of the bondage of gravity.

Day by day, the share of renewable energy-based power plants such as offshore wind farms and photovoltaic power plants, in the present-day power systems is escalating. Nevertheless, most of these power-producing facilities have been located quite far away from the load centers. At this stage, HVDC power systems provide satisfactory options due to qualities such as reduced line losses, substantial controllability, and environmental advantages. Nevertheless, unique converter designs are required to combine asynchronous HVDC networks with each other and with already existing high voltage alternating current (HVAC) networks. In this study, based on the above special cases, a unique DC-DC modular multilevel converter (MMC) architecture is presented that provides an excellent solution for both integrating modern asynchronous HVDC networks and integrating these modern HVDC power systems with existing asynchronous HVAC power systems. The mathematical analysis of the recommended DC-DC MMC design has been examined in detail and the power transfer between two asynchronous power grids has been realized in a simulation environment. Efficient bilateral power transmission has been made achievable by the control system. The recommended DC-DC MMC topology has shown to be a highly successful approach, in the interconnection of HVDC power systems, according to the simulation results.

Abstract: This study aims to analyze the physico-mechanical properties and dynamic models of tissue-simulating hydrogels, specifically the photoacoustic (PA) response signals, by varying the concentrations of polyvinyl alcohol (PVA) and molecular weight (MW). A state space model (SSM) is proposed to study the PVA hydrogels to retrieve the PA-related signal's damping ratio and nat-ural frequency. Nine box-shaped PVA hydrogels containing saline solution were used, with five concentrations of PVA (7, 9, 12, 15, 20%) for MW1 and four for MW2. The results indicated that the concentration of PVA and MW played an important role in the PA wave's amplitude, arrival time, and speed of sound over the hydrogels. The SSM parameters showed that increasing PVA and MW concentrations improved the hydrogels' ability to absorb and transfer energy under the PA effect. These parameters were also found to be correlated with density and modulus of elasticity. Addi-tionally, the concentrations of PVA and MW affected the absorption and optical scattering coeffi-cients. The physical-mechanical properties, including porosity, density, and modulus of elasticity, improved as the concentration of PVA and MW increased. The ultimate goal of this study is to develop hydrogels as phantoms that can be used for tissue simulation and imaging.

This addendum contains clarifications and a sharpening of some of the results on the VIT transform framework developed in [1]. The focus is on the right-coefficient and left- coefficient forms of the transform, the extraction of a first-order term from a left polynomial fraction, and the application to linear time-varying systems.

The segmentation and de-noising are basic operations, required in every signal processing and classification system. The classical segmentation and de-noising approaches are time-invariant. Consequently, it results in the post processing of an unnecessary information and causes an increase in the system processing activity and power consumption. In this context, an efficient event-driven segmentation and de-noising technique is proposed. It is founded on the principles of level crossing and activity selection. Therefore, it can adapt its sampling frequency, segmentation window length and position along with the filter order by analyzing the input signal local characteristics. As a result, the computational complexity and the power consumption of the proposed system is reduced compared to the counter ones. The suggested system performance is compared with the classical one. It is done for the case of a multi-channel Electroencephalogram (EEG) signals. Results show a noticeable compression gain with an effective adaptation of the de-noising filters order. It aptitudes a significant computational gain, transmission data rate reduction and power consumption reduction of the proposed technique, compared to the counter ones. It shows that the proposed solution is an attractive candidate to embed in the new generation EEG wearables.

A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is presented in this work, and it’s based on Schrödinger’s equation. In the classical world, it is named frequency in time (FIT), which is used here as a complement of the traditional frequency-dependent spectral analysis based on Fourier theory. Besides, FIT is a metric which assesses the impact of the flanks of a signal on its frequency spectrum - not taken into account by Fourier theory and let alone in real time. Even more, and unlike all derived tools from Fourier Theory (i.e., continuous, discrete, fast, short-time, fractional and quantum Fourier Transform, as well as, Gabor) FIT has the following advantages, among others: a) compact support with excellent energy output treatment, b) low computational cost, O(N) for signals and O(N2) for images, c) it does not have phase uncertainties (i.e., indeterminate phase for a magnitude = 0) as in the case of Discrete and Fast Fourier Transform (DFT, FFT, respectively). Finally, we can apply QSA to a quantum signal, that is, to a qubit stream in order to analyze it spectrally.

Non-coding RNAs (ncRNAs) comprise a diverse class of non-protein coding transcripts that regulate critical cellular processes associated with cancer. Advances in RNA-sequencing (RNA-Seq) have led to the characterization of non-coding RNA expression across different types of human cancers. Through comprehensive RNA-Seq profiling, a growing number of studies demonstrate that ncRNAs, including long non-coding RNA (lncRNAs) and microRNAs (miRNA), play central roles in progenitor B-cell Acute Lymphoblastic Leukemia (B-ALL) pathogenesis. Furthermore, due to their central roles in cellular homeostasis and their potential as biomarkers, the study of ncRNAs continues to provide new insight into the molecular mechanisms of B-ALL. This article reviews the ncRNA signatures reported for all B-ALL subtypes, focusing on technological developments in transcriptome profiling and recently discovered examples of ncRNAs with biologic and therapeutic relevance in B-ALL.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

The Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD) indices are used to assess the indoor environment in terms of human thermal comfort-discomfort. In this study, an experimental combined objective and subjective investigation of thermal comfort perception has been performed in students between 16-18 years old, in a non-air-conditioned school building. The objective approach included instrumentation measurements and data processing according to ISO 7730, whereas, the subjective one was based on answers collection following ISO 10551. The study is mainly devoted to the verification of Fanger’s approach in a building, in free running conditions, under a mild (moderate) climate.The comparison between instrumentation data and questionnaire results presented an underestimation of the mean vote, predicting a cooler sensation than the actual one.

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.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

Predicting change from multivariate time series has relevant applications ranging from medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aims to predict changes in behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a Temporal Convolutional Network, and the behavioral state was predicted through Bidirectional Long Short-Term Memory Auto-Encoder, operating jointly. From the comparison with the state-of-the-art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.

Building on recent directions in religion-related social and political science, our essay addresses a need for location-specific and religion-specific scientific research that might contribute directly to local and regional interreligious peacemaking. Over the past 11 years, our US-Pakistani research team has conducted research of this kind: a social scientific method for diagnosing the probable near-future behavior of religious stakeholder groups toward other groups. Integrating features of ethnography, linguistics, and semiotics, the method enables researchers to read a range of ethno-linguistic signals that appear uniquely in the discourses of religious groups. Examining the results, we observe, firstly, that our religion and location-specific science identifies features of religious group behavior that are inevident in broader, social scientific studies of religion and conflict; we observe, secondly, that our science integrates constative and performative elements: it seeks facts and it serves a purpose. We conclude that strictly constative, fact-driven sciences may fail to detect certain crucial features of religious stakeholder group behavior.

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contain rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Over this, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

Electromyographic (EMG) signals provide information about a person's muscle activity. For hand movements, in particular, the execution of each gesture involves the activation of different combinations of the forearm muscles, which generate distinct electrical patterns. Conversely, the analysis of these muscle activation patterns, represented by EMG signals, allows recognizing which gesture is being performed. In this study, we aimed to implement an automatic identification system of hand or wrist gestures based on supervised Machine Learning (ML) techniques. We trained different computational models and determined which of these showed the best capacity to identify six hand or wrist gestures and generalize between different subjects. We used an open access database containing recordings of EMG signals from 36 subjects. Among the results obtained, we highlight the performance of the Random Forest model, with an accuracy of 95.39%, and the performance of a convolutional neural network with an accuracy of 94.77%.

NIRS uses the relative absorption of light at 850nm and 760nm, to determine skeletal muscle oxygen saturation. Previous studies have used the ratio of both signals to report muscle oxygen saturation. Purpose: To evaluate the different approaches used to represent muscle oxygen saturation, and to evaluate the pulsations of the O₂heme and Heme signal. Method: Twelve participants, ages 20-29years were tested on the forearm flexor muscles using continuous wave NIRS at rest. Measurements were taken during 2-3mins rest, during physiological calibration (5-minuts Ischemia) and during reperfusion. Results: There was a significant difference in pulse size between O₂heme and Heme signal at the three locations (p < 0.05). Resting oxygen saturation was 58.8+9.2%, 69.6+3.9%, and 89.2+6.9% when calibrated using O₂heme, TSI, and Heme, respectively. Conclusion: The difference in magnitude of O₂heme and Heme pulse with each heartbeat might suggest different anatomical locations of these signals, which propose calibrating with just one of the signals instead of the ratio of both. Calculations of physiological calibration must account for increased blood volume in the tissue, because of the changes in blood volume which appear to be primarily from the O₂heme signal. Resting oxygen levels calibrated with Heme agrees with theoretical oxygen saturation.

This paper addresses the analysis for the nonlinear vibration response of a rectangular tube with a flexible end and non-rigid acoustic boundaries. This is a further work of the linear structural acoustic problem in a well-known acoustic book. In fact, the acoustic boundaries of an enclosed space sometimes are non-rigid and the structural vibration responses are nonlinear. These two points are the focuses of this paper. The multi-level residue harmonic balance method is applied to this nonlinear structural acoustic problem. The results obtained from the multi-level residue harmonic balance method and numerical method are generally in good agreement. The effects of excitation magnitude, tube length, and phase shift parameter etc. are examined.

Two polyurethanes (PUs) were similarly synthesized by reacting a cycloaliphatic isocyanate with 1,4 butanediol and two polyols of different nature (polyester, polycarbonate diol) with molecular weights of 1000 Da. The PU made with polycarbonate diol polyol (YCD) was the only showing intrinsic self-healing at 20 °C. For assessing the mechanism of intrinsic self-healing of YCD, a structural characterization by molecular weights determination, infrared and X-ray photoelectronic spectroscopies, differential scanning calorimetry, X-ray diffraction, thermal gravimetric analysis and dynamic mechanical thermal analysis was carried out. It was concluded that the self-healing at 20 °C of YCD was due to non-covalent dynamic exchange interactions among the carbonate groups of the soft segments. Therefore, the chemical nature of the polyol played a key role in developing PUs with intrinsic self-healing at 20 °C.

This study was conducted to investigate the correlations between social-emotional competence (SEC) and academic achievement (AA) among nursing students and to compare students’ level of each core skill of SEC (critical thinking disposition, self-directed learning, creativity, emotional intelligence, problem-solving, and collaboration) and academic achievement (clinical performance and subjective academic achievement). A cross-sectional design was adapted. Data were collected from 195 nursing students in the junior and senior years who had participated in clinical practicum from four universities in South Korea. General characteristics, levels of critical thinking disposition, self-directed learning, creativity, emotional intelligence, problem-solving, collaboration, and academic achievement were collected via self-reported questionnaire. Canonical correlation analysis was performed to evaluate the relationship between SEC and AA. The canonical correlation coefficient between SEC and AA was .762. Critical thinking disposition (Rs = .89), problem-solving (Rs = .86), cooperation (Rs = .80) made the most important contributions to SEC. Clinical performance (Rs=.95) and subjective AA (Rs=.57) were correlated with AA. SEC should be addressed to improve the AA of nursing students. All core skills of SEC should be regularly promoted. It is particularly urgent for nursing students to improve their creativity.

In this paper, we compile the fractional power series method and the Laplace transform to design a new algorithm for solving the fractional Volterra integro-differential equation. For that, we assume the Laplace power series (LPS) solution in terms of power q=1m,m∈Z+, where the fractional derivative of order α=qγ for which γ∈Z+. This assumption will help us to write the integral, the kernel, and the nonhomogeneous terms as a LPS with the same power. The recurrence relations for finding the series coefficients can be constructed using this form. To demonstrate the algorithm's accuracy, the residual error is defined and calculated for several values of the fractional derivative. Two strongly nonlinear examples are discussed to provide the efficiency of the algorithm. The algorithm gains powerful results for this kind of problem. Under Caputo meaning the obtained results are illustrated numerically, and graphically. Geometrically, the behavior of the solution declares that the changing of the fractional derivative parameter values' in their domain alters the style of the attained solution in a symmetrical meaning and fully coinciding to the ordinary derivative value'. From these simulations, the results report that the recommended novel algorithm is a straightforward, accurate, and superb tool to generate analytic-approximate solutions for Integral, and integro-differental equations of fractional order.

The purpose of this study is to ​​explore the use of a custom Transformer model in brain-computer interfaces (BCIs) that translate the neural activity present when an individual with limited verbal and fine-motor skills attempts to handwrite. As found in previous studies, Transformers have performed better than recurrent neural networks (RNNs) in translation tasks which are similar to its usage here in decoding neural signals into intended handwritten text. Due to the known benefits of a Transformer, the hypothesis was that the Transformer would show promise in the context of a BCI through the recorded metrics. The neural signals of a tetraplegic individual, when they attempted to handwrite, were provided by existing research. Four trials were conducted using data with or without augmentation which the model used when training to separately minimize training loss and validation loss. When comparing the results of a BCI with the implementation of a Transformer model with the original RNN BCI (the original data source), the Transformer model performed less favorably across all four trials. Although the results do not indicate that the Transformer model currently outperforms an RNN BCI, it is important to note that further testing of the model's capabilities (such as training it with a larger and more preferable dataset and/or for longer, comparing training times between the RNN and Transformer, and/or seeing how the Transformer is improved with an offline autocorrect feature) is necessary before determining whether Transformers can enhance communication in this manner.

Inositol transporter (INT) is reputed as the pivotal transporter for vital metabolites like lipids, minerals, and sugars particularly. These transporters play important role in transitional metabolism and various signaling pathways in plants through regulating the transduction of messages from hormones, neurotransmitters, and immunologic and growth factors. Extensive studies have been conducted on animal INT with promising outcomes. However, few recent studies have highlighted the importance and the complexity of INT genes in the regulation of plant physiology stages including growth and tolerance to stress conditions. The present review sum-up the most recent findings on the role of INT or inositol genes in plant metabolisms and the responsive mechanisms that cope with external stressors. Moreover, we highlighted the emerging role of vacuoles and vacuolar inositol transporters in plant molecular transition and their related roles in plant growth and development. Inositol transporters are the essential mediator for the inositol uptake and its intracellular broadcasting for various metabolic pathways where they play crucial roles. Also, so far characterized only in animals, we reported evidence on Na+/inositol transporters H+/inositol symporters and suggested their roles and operating mode in plants. Thus, understanding the INT functioning system, the coordinated movement of inositol, and the relation between inositol generation and other important plant signaling pathways would be an excellent asset for advancement in researches on plant stress adaptation.

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) signals is a key signal processing problem. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new SWD method with a low computational complexity that can be easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the Morlet 1-D decomposition is applied. The generalized Gaussian distribution (GGD) statistical model is fitted to the resulting wavelet coefficients. A k-nearest neighbors (k-NN) self-supervised classifier is trained using the GGD parameters to detect the spike-and-wave pattern. Experiments were conducted using 106 spike-and-wave signals and 106 non-spike-and-wave signals for training and another 96 annotated EEG segments from six human subjects for testing. The proposed SWD classification methodology achieved 95 % sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These results set the path to new research to study causes underlying the so-called absence epilepsy in long-term EEG recordings.

In this article we present the design and development of T-Shirt prototypes that embed novel textile sensors for the capture of cardio and respiratory signals. The sensors are connected through textile interconnects to either an embedded custom designed data acquisition and transmission unit or to snap fastener terminals for connection to external monitoring devices. Prototypes with diverse approaches of integration are presented. The performance of the wearable systems is addressed in terms of signal-to-noise ratio amplitude and signal interference caused by baseline wander and motion artifacts, through laboratorial tests with subjects in standing and walking conditions. Performance tests were also conducted in Hospital environment using a T-Shirt prototype connected to a commercial 3-channel Holter monitoring device. The textile sensors and interconnects were realized with the assistance of an industrial 6-needle digital embroidery tool and their resistance to wear addressed with normalized tests of laundering and abrasion. The main aspects of the system´s design leading to major improvements and failure factors are discussed. Pathways and methods for the overall system´s optimization are highlighted.

The Wnt/β-catenin signaling deregulation is associated with the pathogenesis of many human diseases, including hypertension and heart disease. The aim of the study was to immunohisto-chemically evaluate and compare the expression of Fzd8, WNT1, GSK-3ß and β-catenin genes in the hearts of rats with spontaneous hypertension (SHR) and DOCA-salt- induced hypertension. The myocardial expression Fzd8, WNT1, GSK-3ß and β-catenin was detected by immunohisto-chemistry and the gene expression was assessed with real-time PCR method. In SHR immunore-activity of Fzd8, WNT1, GSK-3β and β-catenin was attenuated in comparison to normotensive animals. In DOCA-salt immunoreactivity of Fzd8, WNT1, GSK-3β and β-catenin was enhanced. In SHR, decrease in the expression of genes encoding Fzd8, WNT1, GSK-3ß and β-catenin was observed compared to the control group. Increased expression of genes encoding Fzd8, WNT1, GSK-3ß and β-catenin was demonstrated in the hearts of rats DOCA-salt. Wnt signaling may play an essential role in the pathogenesis of arterial hypertension and the accompanying heart damage. Obtained results may constitute the basis for further research aimed at better under-standing the role of the Wnt/β-catenin pathway in the functioning of the heart.

The application of transformations of the state equations of continuous linear and bilinear systems to the canonical form of controllability allows one to simplify the computation of Gramians of these systems. In the paper we developed the method and obtain algorithms for computation of the controllability and observability Gramians of continuous linear and bilinear stationary systems with many inputs and one output, based on the method of spectral expansion of the Gramians and the iterative method for computing the bilinear systems Gramians. An important feature of the concept is the idea of separability of the Gramians expansion: sep333arate computation of the scalar and matrix parts of the spectral Gramian expansion reduces the sub-Gramian matrices computation to calculation of numerical sequences of their elements. For the continuous linear systems with one output the method and the algorithm of the spectral decomposition of the controllability Gramian are developed in the form of Xiao matrices. Analytical expressions for the diagonal elements of the Gramian matrices are obtained, making use of which the rest elements can be calculated. For continuous linear systems with many outputs the spectral decompositions of the Gramians in the form of generalized Xiao matrices are obtained, which allows to significantly reduce the number of calculations. The obtained results are generalized for continuous bilinear systems with one output. Iterative spectral algorithms for computation of elements of Gramians of these systems are proposed. Examples are given that illustrate theoretical results.

Beyond factors such as smoking and obesity, the risk of getting gastrointestinal cancer is passed down from parents to offspring, inherited by a gene mutation, predisposing them to develop cancer in their lifetime. Here we describe the clinical history of family members affected by gastrointestinal pathologies often leading to cancer. In the epidemiological study, the cohort of members of this family shared a high risk of developing gastrointestinal diseases. The subjects were monitored from May 2006 to December 2017 by collecting periodically clinical and endoscopic data, and determining both auto-modification of lymphocyte Poly(ADP-ribose)Polymerase as early signal of DNA damage, and erythrocyte membrane lipid composition (Fat Profile). Both biomarkers were demonstrated to be non invasive and easy tools, to monitor over time family members, first the oldest members (nine siblings), and thereafter their offspring, whose cell status evolved from physiological even to cancerous one. All subjects developed gastrointestinal pathologies of different kind and seriousness. Some diseases evolved to cancer, sometimes as a sudden and lethal event. The results of the two molecular approaches, agreed and even predicted the clinical and imaging paths. This cohort study supported the hypothesis that both noninvasive molecular analyses can predict altered cell states and support clinical and imaging tests.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

The quiet-time solar wind electrons feature non-thermal characteristics when viewed from the perspective of their velocity distribution functions. They typically have an appearance of being composed of a denser thermal “core” population plus a tenuous energetic “halo” population. At first, such a feature was empirically fitted with the Kappa velocity space distribution function, but ever since the ground-breaking work by Tsallis, the space physics community has embraced the potential implication of the Kappa distribution as reflecting the non-extensive nature of the space plasma. From the viewpoint of microscopic plasma theory, the formation of non-thermal electron velocity distribution function can be interpreted in terms of the plasma being in a state of turbulent quasi equilibrium. Such a finding brings forth the possible existence of a profound inter-relationship between the non-extensive statistical state and the turbulent quasi-equilibrium state. The present paper further develops the idea of solar wind electrons being in the turbulent equilibrium, but unlike the previous model, which involves the electrostatic turbulence near the plasma oscillation frequency (i.e., Langmuir turbulence), the present paper considers the impact of transverse electromagnetic turbulence, particularly, the turbulence in the whistler-mode frequency range. It is found that the coupling of spontaneously-emitted thermal fluctuations and the background turbulence leads to the formation of non-thermal electron velocity distribution function of the type observed in the solar wind during quiet times. This demonstrates that the whistler-range turbulence represents an alternative mechanism for producing the Kappa-like non-thermal distribution, especially close to the Sun and in the near-Earth space environment.

MIMO (multiple-input multiple-output) radar provides much more flexibility than the traditional radar for its ability to realize far more observation channels than the actual number of T/R (transmit and receive) elements. Designing the array of MIMO imaging radar, the commonly used virtual array theory generally assumes that all elements are placed on the same line. However, due to the physical size of the antennas and coupling effect between T/R elements, a certain height difference between T/R arrays is essential, resulting in the defocusing of edge points of the scene. On the other hand, the virtual array theory implies far-field approximation, leading to inevitable high grating lobes in the imaging result of near-field edge points of the scene observed by common MIMO array. To tackle these problems, this paper derives the relationship between target’s PSF (point spread function) and pattern of T/R arrays, by which the design criterion of near-field imaging MIMO array is presented. Firstly, the proper height between T/R arrays is designed to focus the near-field edge points well. Secondly, the far-field array is modified to suppress the grating lobes in the near-field area. Finally, the validity of the proposed methods is verified by simulations and an experiment.

Integrating systems acquired from third parties and legacies has become of great necessity in companies. This is mainly due to the need to exchange information between entities, such as banks, suppliers, customers, allies, etc. Therefore, it is essential to guarantee the integrity of the data and keep the integration up to date with the changes that can occur. Furthermore, reducing the transaction risk and avoiding losing information is necessary. Implementing this complex task involving technological and business challenges requires appropriate architecture implementation. This article presents an architecture named Dynamic Canonical Data Model through agnostic messages. The contribution involves treating the low link in integrating software units, also called the integration of loosely coupled software units. In particular, it focuses on internal data and external data integration. The proposal reduces their integration, maintenance time, and cost, maximizing their scalability and promoting reuse. To illustrate their components, a case study undertaken at the Mexican Logistics Company Paquetexpress is presented. The company has evaluated different enterprise application integration systems over 26 years to achieve a standardized integration. The article describes an architecture designed and implemented for this delivery company in a real-world case. The implementation’s source code was registered in the National Registry of Copyrights of Mexico.

Soil-borne Trichoderma spp. have been extensively studied for their biocontrol activities against pathogens and growth promotion ability with plants. However, the beneficial effect of Trichoderma on inducing resistance against insect herbivores has been considered only recently. Among diverse Trichoderma species, consistent with previous reports, we showed that root colonization by T. virens triggered induced systemic resistance (ISR) to leaf-infecting fungal pathogens. Whether T. virens induces ISR to insect pests has not been tested before. In this study, we investigated whether T. virens affects jasmonic acid (JA) biosynthesis and defense against fall armyworm (FAW) and western corn rootworm (WCR). Unexpectedly, the results showed that T. virens colonization of maize seedlings grown in autoclaved soil suppressed wound-induced production of JA, resulting in reduced resistance to FAW. Similarly, the bacterial endophyte Pseudomonas chlororaphis 30-84 suppressed systemic resistance to FAW due to reduced JA. Further comparative analyses of the systemic effects of these endophytes when applied into sterile or non-sterile field soil showed that both T. virens and P. chlororaphis 30-84 triggered ISR against hemibiotrophic fungal pathogen Colletotrichum graminicola in both soil conditions, but only suppressed resistance to FAW in sterile soil. In contrast to the effect on FAW defense, T. virens colonization of maize roots suppressed WCR larvae survival and weight gain. This is the first report suggesting the potential role of T. virens as a biocontrol agent against WCR.

Detecting human head movement during sleep is important as it can help doctor to access some physical or mental health problems like infantile eczema, calcium deficiency, insomnia, anxiety disorder and even Parkinson’s disease, and provide useful clues for scientific diagnosing. To obtain the information of sleep head movement, current solutions either use the camera or require the user to wear intrusive sensors to collect the image or motion data. However, the vision-based schemes depend on the environmental light conditions and also bring privacy concerns. Some people including the elderly and infant may be reluctant to wear wearable devices during sleep. In this paper, we propose a novel system called Wi-Senser to address the issues mentioned above. Our Wi-Senser directly reuses the existing WiFi infrastructure and exploits the fine-grained channel state information (CSI) of WiFi signals to capture the minute human head movement during sleep without attaching any sensors to the human body. We design and implement our system with just one commercial off-the-shelf (COTS) router and one laptop equipped with Intel 5300 network interface card (NIC). We evaluate the performance of Wi-Senser with 6 volunteers (4 adults and 2 children). Extensive experiments demonstrate that Wi-Senser can achieve a 98.5% accuracy for head movement detection during sleep. Wi-Senser provides a new solution for achieving noninvasive, continuous and accurate human minute movement detection without any additional cost.

Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life, in particular public and private health. This mental disorder is usually characterized by considerable changes in feelings, routines, or thoughts. With respect to the fact that early diagnosis of this illness would be of critical importance ineffective treatment, some development has occurred in the purpose of depression detection. EEG signals reflect the working status of the human brain by which are considered the most proper tools for a depression diagnosis. Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them. Owing to this significant characteristic of deep learning, recently, these methods have intensely utilized in the diverse field of researches, specifically medicine and healthcare. Thereby, in this article, we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data. Regarding the adopted search method, we finally evaluated 22 articles between 2016 and 2021. This article which is organized according to the systematic literature review (SLR) method, provides complete summaries of all exploited studies and compares the noticeable aspects of them. Moreover, some statistical analysis performs to gain a depth perception of the general ideas of the latest researches in this area. A pattern of a five-step procedure was also established by which almost all reviewed articles fulfilled the goal of depression detection. Finally, open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions discussed.

Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small data is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNN), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data, and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the UCI-KDD EGG dataset. This article explains the step-wise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques, and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset.

The next generation of wireless and mobile networks will have to handle a significant increase in traffic load compared to the actual one. This situation calls for novel ways to increase spectral efficiency. Therefore in this paper, we propose a wireless spectrum hypervisor architecture that abstracts a radio frequency (RF) front-end into a configurable number of virtual RF front-ends. The proposed architecture has the ability to enable flexible spectrum access in existing wireless and mobile networks, which is a challenging task due to the limited spectrum programmability, $i.e.$, the capability a system has to change the spectral properties of a given signal to fit an arbitrary frequency allocation. The main goal of the proposed approach is to improve spectral efficiency by efficiently using vacant gaps in congested spectrum-bandwidths or adopting network densification through infrastructure sharing. We demonstrate mathematically how our proposed approach works and present several simulation results proving its functionality and efficiency. Additionally, we designed and implemented an open-source and free proof of concept prototype of the proposed architecture, which can be used by researchers and developers to run experiments or extend the concept to other applications. We present several experimental results used to validate the proposed prototype. We demonstrate that the prototype can easily handle up to 12 concurrent physical layers.

This paper presents experimental identification and vibration suppression of a flexible manipulator with non-collocated piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model to compensate the non-collocation. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Besides, vibrations of the higher modes are also slightly suppressed. Accordingly, multi-mode vibrations of the manipulator are well attenuated, and the tip displacement converges quickly with the proposed method.