In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong com-pound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the Electromagnetic Interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, common mode rejection ratio , gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain Computer Interfaces and neurofeedback training systems.

The sustainable development of an area is highly dependable on reliable electrical energy supply. Microgrids are important in integrating distributed energy resources (DERs) using power electronic converters. However, microgrid control becomes challenging with the increasing number of distributed generators and loads. With the conventional droop control method, power contributions from DER converters cannot be accurately shared due to a mismatch of line impedances. In this paper, an augmented hierarchical control mechanism is proposed to solve the issues mentioned above. This hierarchical control mechanism consists of primary and secondary controllers. The primary stage utilized the droop controller to improve optimal power flow, mainly for the resistive network. The secondary stage is based on an improved methodology to compensate for the voltage and frequency variations during small and large signal disturbances. Moreover, the modelling and analysis for PMSG-based wind energy conversion systems are also presented. The response of the primary controller for the active and reactive power sharing is investigated. The analysis emphasizes the demonstration of optimal power-sharing under normal and abnormal conditions for the considered load. Finally, the suggested robust controller's performance is evaluated in MATLAB environment and simulation results show the proposed scheme's superiority under different operating conditions.

Regulatory cystathionine β-synthase (CBS) domains are widespread in proteins; however, difficulty in structure determination prevents a comprehensive understanding of the underlying regulation mechanism. Tetrameric microbial inorganic pyrophosphatase containing such domains (CBS-PPase) is allosterically inhibited by AMP and ADP and activated by ATP and cell alarmones diadenosine polyphosphates. Each CBS-PPase subunit contains a pair of CBS domains but binds cooperatively only one molecule of the mono-adenosine derivatives. We used site-directed mutagenesis of Desulfitobacterium hafniense CBS-PPase to identify the key elements determining the direction of the effect (activation or inhibition) and the “half-of-the-sites” ligand binding stoichiometry. Seven amino acid residues were selected in CBS1 domain based on the available X-ray structure of the regulatory domains and substituted by alanine and other residues. The interaction of 11 CBS-PPase variants with the regulating ligands was characterized by activity measurements and isothermal titration calorimetry. Lys100 replacement reversed the effect of ADP from inhibition to activation, whereas Lys95 and Gly118 replacements made ADP activator at low concentrations but inhibitor at high concentrations. Replacements of these residues for alanine increased the stoichiometry of mono-adenosine phosphate binding twofold. These findings identify several key protein residues and suggest “two non-interacting pairs of interacting regulatory sites” concept in CBS-PPase regulation.

Let BT4l denotes a subclass of bounded turning functions connected with a four-leaf-type domain. The goal of the study is to probe into coefficients of |b6|, |b7|, |b8|, the bounds of the logarithmic coefficients and the third-order determinants of H3,1, H3,2, H3,3 for functions in this class.

To achieve rapid post-earthquake repair of self-centering ribbed floor flat beam frame structures, a ductile hybrid joint consisting of dog bone weakened energy dissipating steel bars connected to the upper and lower column sections through high-strength threads based on the damage control design concept is proposed. By moving the ductile energy dissipating zone out to the locally weakened section of the energy dissipating steel bars and the locally unbonded prestressed steel bars in the core area, the residual deformation was limited and the seismic performance was improved. Based on the working principle of hybrid joints, low cycle loading tests were conducted on two joint specimens to analyze the influence of lateral prestress on the seismic performance of the hybrid joints. Numerical modeling methods were used to compare the position of the energy-consuming steel bars in the composite layer and the friction performance of joints. The research results indicated that the hybrid joint had stable load-bearing, deformation, and energy dissipation capabilities, with damage primarily concentrated on the energy-consuming steel bars. Even at an inter-story displacement angle of 5.5%, the upper and lower column segments remained elastic. After unloading, the connection seam at the joint was closed, and the self-centering performance was good. When the inter-story displacement angle reached 5.5%, the lateral prestress increased from 150 kN to 250 kN, the ultimate bearing capacity of the joint increased by 16.3%, and the cumulative energy consumption increased by 30.0%. The influence of the friction coefficient of the joint surface on the structural performance was set at a threshold of 0.7. When it was less than the threshold, the ultimate bearing capacity and initial stiffness of the joint increased with an increase in the friction coefficient. After reaching the threshold, the increase in the ultimate bearing capacity of the joint slowed down, and the rate of stiffness degradation gradually accelerated. This joint had excellent seismic performance and can also achieve post-earthquake repair of structures.

The field of domain adaptation, particularly in cross-domain sentiment classification, leverages labeled data from a source domain alongside unlabeled or sparsely labeled data from a target domain. The objective is to enhance performance in the target domain by mitigating the discrepancies in data distributions. Traditional methods in this area have focused on identifying and differentiating between pivotal sentiment words (shared across domains) and domain-specific sentiment words. In our work, we introduce a novel framework called Sentiment Domain Adapter (SDA), which incorporates a Category Attention Network (CAN) alongside a Convolutional Neural Network (CNN). This approach treats pivotal and domain-specific words as part of a collective category of attributes, which SDA learns to discern automatically, thereby enhancing domain adaptation. Additionally, SDA seeks to provide interpretative insights by learning these category attributes. Our model's optimization targets three main goals: (1) supervised classification accuracy, (2) minimizing the disparity in category feature distribution, and (3) maintaining domain invariance. Evaluations on three benchmark datasets for sentiment analysis affirm that SDA surpasses several established baselines.

AbstraIncorporating knowledge of indigenous forage legumes or associated nutritional attributes in cultivated pastures has the potential to improve grazing conditions, rangeland conditions, and cattle productivity. Climate change and other site-specific factors such as land sizes and other grazing management practices play a significant role in food security, job creation, and environmental health. Several research results have reported that the utilization of forage legumes in most farming systems has proved to increase herbage yield and soil fertility. The major aim of this review is to elucidate the potential benefits of intercropping indigenous forage legumes such as cowpea (Vigna unguiculacta L.Walp) and Dolichols (Lablab purpureaus) intercropped with Chloris gayana, this is to assesses the principal advantages, productivity, and overall performance of forage legumes grown in conjunction with grasses. Moreover, this review seeks to provide insights into the optimal management practices for intercropping forage legumes with grasses. The promising feature of intercropping South African pastoral systems must target the ability of forage legumes in terms of performance and utilization of agro-symbiotic relationships of N2 fixation symbiotic relationship in pastures. Moreover, this review aims also to address this knowledge gap by assessing the effects of defoliation frequency on the biomass production of selected forage legumes when intercropped with Chloris gayana in different growing seasons. By examining how varying defoliation frequencies influence biomass production, this review aimed to provide practical recommendations for optimizing forage management practices and promoting sustainable forage production systems. Through this investigation, we can gain insights into the complex dynamics between the defoliation frequency, intercropping systems, and biomass production.

A finite element model of 100m X-BOW polar exploration cruise is established. The entire ship frequency response analysis was carried out, comparing the simulated values with the test values, the maximum error is 22%, but only 0.24mm/s difference, the error can be ignored from the perspective of comfort, therefore, the simulation results meet the requirements of engineering accuracy and verify the effectiveness of the model. According to the solution method for global modal equivalent mass, combing the spatial distribution principle of local modal mass, the method for solving the equivalent mass of a single local mode in mixed mode is proposed, the equivalent mass of the local vibration region of 100m X-BOW polar exploration cruise ship is solved by this method, tuned mass dampers mass ratio parameters are calculated using the equivalent mass. According to the analysis, tuned mass dampers has 31dB vibration absorption effect at 13.4Hz when the mass ratio is 0.05, it also has certain control effect at 10Hz and 18.8Hz, the control effect is 3dB and 2dB respectively, the overall control frequency band is flat and robust.

The penetrability and directivity of ultrasound in different media is of interest in engineering and medical applications (imaging, nondestructive testing, sonochemistry, among others). The nonlinearity of a liquid can be used in the parametric antenna framework to generate low-frequency components with particular features from several ultrasonic signals at the source. Bubbly liquids are dispersive liquids in which a small amount of tiny gas bubbles leads to the increase of the nonlinear parameter of the media over certain frequency ranges. Parametric antenna applied to this huge nonlinear media gives rise to low-frequency components with relatively small intensity at the source. The evolution of a low-frequency component (difference-frequency component obtained from two primary signals) during its propagation in a bubbly liquid is somehow unknown. It is thus interesting to analyse its characteristics to establish whether this component can benefit from the quality of its own frequency and from the primary frequencies in terms of directivity and penetrability into the medium. It must be noted that no such study in bubbly liquids exists in the litterature, but only for homogeneous media. To this end, several numerical models developed previously are used here to analyse the difference-frequency component obtained from a parametric antenna emitting from two ultrasonic signals at the source in one and two-dimensional domains. These models allow us to observe the behavior of this frequency component. An angle that measures the directivity of a beam is also defined. Our results show a point hardly found in the literature: the high directivity and the huge penetrability of the secondary beam associated to the difference-frequency component into the bubbly liquid, compared to the same frequency signal excited directly from the source in the bubbly liquid and to the parametric acoustic array in homogeneous fluids.

Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.

In this essay, I revisit Neural Networks (NNs) through the lens of evolutionary dynamics. From the two most important features of NNs, I recover the two most general equations of evolutionary dynamics. These two equations may thus serve as a connection between NNs and a body of knowledge that has been built over multiple centuries and has found application across a vast range of disciplines. This connection may therefore also help explain why NNs with the two features in question are applicable across a much broader range of domains than initially envisioned.

Micro satellites must survive severe mechanical conditions during their launch phase. Therefore, structural design requirements are imposed on the micro satellites. These requirements are the strength requirement and the stiffness requirement. Usually, the structural design of a micro satellite is performed using the internal stress analysis and the natural frequency analysis, which are based on a Finite Element Method (FEM). The validity of this structural design is evaluated through vibration tests. In an early stage of development, which has a FEM model of a satellite in the process of creation, presumption of the natural frequency of a satellite may be difficult. In this study, a simple method for determining the longitudinal and lateral minimum natural frequencies of the micro satellites during the ascent phase was clarified. The structure of the micro satellites used in this research is made of aluminum alloy, and they have a monocoque structure. The Young's modulus and moment of inertia of area used to calculate the minimum natural frequencies were determined using the area ratio of the monocoque structure to the entire satellite. When this method is used, the estimated minimum natural frequencies of the longitudinal direction and the lateral direction are in agreement with the measured values acquired by the vibration tests. In order to shorten the process of micro satellites development, this paper describes a practical estimation method of the minimum natural frequency for micro satellites.

We investigate the impact of aging on exchangeable inter-arrival times in mixed renewal processes, exploring its implications for reliability and survival analysis. In this study, first, we revisit the definition of renewal point processes, where inter-event time intervals are considered as exchangeable non-negative random variables. Then, we define the concept of statistical aging as latency in the observational process of event counting. Latency affects event detection but preserves exchangeability. However, it may alter the statistical properties of inter-event time intervals. Our analytical and numerical assessments highlight the significance of aging in exchangeable lifetimes, offering insights into key metrics such as the failure survival function, renewal function, and hazard rate function. Through archetypal examples and empirical findings, we illustrate the implications of aging on renewal processes. In particular, employing a Bayesian perspective, we analyze high-frequency currency exchange rate data to assess the impact of aging on volatility risk evaluation. This study contributes novel insights to the literature of renewal theory and survival analysis, emphasizing the role of latency aging in stochastic point processes under exchangeability assumption.

This paper is a brief review of our own work on the Planck quantized version of general relativity theory. It demonstrates several other straightforward methods to simply rewrite the same equations that we have already presented.

DNA Topoisomerase IIa (Top2A) is a nuclear enzyme that is a cancer drug target, and there is interest in identifying novel sites on the enzyme to inhibit cancer cells more selectively and to reduce off-target toxicity. The C-terminal domain (CTD) is one potential target, but it is an intrinsically disordered domain, which prevents structural analysis. Therefore, we set out to analyze the sequence of Top2A from 105 species using bioinformatic analysis including the PSICalc algorithm, entropy analysis, and other approaches. Our results demonstrate that large (10th-order) interdependent clusters are found including non-proximal positions across the major domains of Top2A. Further, CTD-specific clusters of 3rd, 4th, and 5th-order were identified that included positions that had been previously analyzed via mutation and biochemical assays. Some of these clusters coincided with positions that when mutated either increased or decreased relaxation activity. Finally, sites of low entropy (i.e., high information content) were identified and mapped as key positions in the CTD. Included in the low entropy sites are phosphorylation sites and charged positions. Together, these results help to build a clearer picture of the critical positions in the CTD and provide potential sites/regions for further analysis.

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.

The evolution of human societal dynamics over the course of decades and centuries has led to an ever-increasingly complex digital modern life, one of the hallmark of which is the need for multi-tasking, inclusive of that with media. Is there an empirical mapping between this macroscopic (digital) evolution of the human societal dynamics (in terms of multi-tasking) and the microscopic evolution of the functional molecular units within cells (i.e., proteins) and their manifold cross-talks? Multi-tasking requires a certain degree of intrinsic or adapted flexibility which is as true at the bio-molecular level. Evolutionary diversification of structure-function relationship in proteins emphasizes the functional importance of intrinsically disordered entities (IDPs/IDRs) which are highly dynamic biological soft matters. Multi-functionality is favorably supported by their fluid-like shapes and other new malleable members of the protein family. The new wealth of knowledge coming from recent research on protein intrinsic disorder, fold-switching, moonlighting, hub proteins etc. has given us new insights into protein structure – functional relationship. This has led to a paradigm shift in protein science, clearly diverging from the traditional 'one structure – one function' model applicable to enzyme classes to a more complex understanding of protein functionality. This paradigm shift has caused scientists to delve deeper into the subject, exploring various evolutionary toys and tools to fit the cumulative multi-functional demands in higher organisms. This commentary covers the different evolutionary arsenals to achieve the growing multi-functionality and argues in favor of protein intrinsic disorder as probably the sharpest weapon of all.

The ANSYS 2024R1 student edition was used to create a finite element model of a basketball rim and backboard. This finite element model included the use of steel for the rim and its mount, a tempered glass backboard, and an aluminum frame behind the backboard. After a mesh was created, fixed support boundary conditions were applied to the four corners of the aluminum frame. The subsequently calculated mode shapes and frequencies were compared to empirical modal analysis previously done at the United States Military Academy at West Point, New York. Four mode shapes and frequencies agreed rather well between the theoretical finite element analysis and previously published empirical modal analysis, specifically where the rim was vibrating in the vertical direction, which was the direction that the accelerometer was aligned for modal analysis. However, three mode shapes missed by the empirical modal analysis were found where the vibration of the rim was confined to the horizontal plane, which was orthogonal to the orientation of our accelerometer.

Recently, there has been considerable research on deepfake detection. However, most existing methods face challenges in adapting to the advancements in new generative models within unknown domains. In addition, the emergence of new generative models capable of producing and editing high-quality images, such as Diffusion, Consistency, and LCM, poses a challenge for traditional deepfake training models. These advancements highlight the need for adapting and evolving existing deepfake detection techniques to effectively counter the threats posed by sophisticated image manipulation technologies. In this paper, our objective is to detect deepfake videos in unknown domains using unlabeled data. Specifically, our proposed approach employs Meta Pseudo Labels (MPL) with Supervised Contrastive Learning, so-called SupCon-MPL, allowing the model to be trained on unlabeled images. MPL involves the simultaneous training of both a Teacher model and a Student model, where the Teacher model generates Pseudo Labels utilized to train the Student model. This method aims to enhance the adaptability and robustness of deepfake detection systems against emerging unknown domains. Supervised Contrastive Learning utilizes labels to compare samples within similar classes more intensively, while encouraging greater distinction from samples in dissimilar classes. This facilitates the learning of features in a diverse set of deepfake images by the model, consequently contributing to the performance of deepfake detection in unknown domains. When utilizing the ResNet50 model as the backbone, SupCon-MPL exhibited an improvement of 1.58% in accuracy compared to traditional MPL in known domain detection. Moreover, in the same generation of unknown domain detection, there was a 1.32% accuracy enhancement, while in the detection of post-generation unknown domains, there was an 8.74% increase in accuracy.

Quantum cryptography revolutionizes secure information transfer, providing defense against both quantum and classical computational attacks. The primary challenge in extending the reach of quantum communication comes from the exponential decay of signals over long distances. Addressing this challenge, we have developed Quantum-protected Control-based Key Distribution (QCKD), overcoming transmission range limitations by monitoring signal leakages and rendering leaked quantum states significantly non-orthogonal. This paper reports on the experiments with the QCKD, conducted over a 1,707 km fiber line. We demonstrate advancements in critical components of the protocol, showing its remarkable scalability and adaptability for extended distances and multi-user configurations.

Through the use of Smart Sensor Networks (SSN), Marine Observatories (MO) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of SSN devices’ hardware and software when combined with the Internet. Hence, SSN are regarded as complex distributed systems. As such, SSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed SSN. To this end, we describe in this article how we extended our previously proposed ArchiMO meta-model and design tool by extending an Enterprise Architecture Framework based on the ArchiMate Modeling Language. This extension proposes the incorporation of new Underwater Environmental Constraints (UEC) for SSN in ArchiMO which is coupled with the fusion of sensors with Artificial Intelligence. This serves as the basis for generating a new version of our ArchiMO design tool, which incorporates the new added constraints and invokes the AI created Database. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to handle and control the uncertainties and variances of the provided quality of service that may occur during the performance of the SSN, hence reducing the design activity’s complexity and time. It provides a way to share the different viewpoints of the designers in the domain of SSN.

It has been theorized that black holes are surrounded by firewalls, although there is not universal agreement concerning this. We first review basic concepts pertaining to Schwarzschild black holes and Hawking radiation. Then we discuss the anticipation of Hawking radiation—albeit from non-black holes—initially by R. C. Tolman and shortly thereafter with P. Ehrenfest. We compare evaporation into a vacuum at absolute zero (0 K) of black holes with that of non-black holes, and show that not only black holes but also non-black holes evaporate within a finite time. The times required for evaporation of black holes and non-black holes are compared. Next, we show that (i) if firewalls exist, they can originate via Hawking radiation at the minimum possible ruler distance (the Planck length) beyond the Schwarzschild horizon, where it has not suffered any gravitational redshift, or, alternatively, suffered maximal gravitational blueshift and (ii) the firewall temperature is on the order of the Planck temperature, independently of the mass and hence also of the Schwarzschild radius of a Schwarzschild black hole. We then explain the exponential nature of the gravitational frequency shift as a function of the gravitational potential. Next, we consider the firewall-mass problem, and provide an at least prima facie tentative resolution thereto based on: (i) the mass of a firewall being canceled by the negative gravitational mass = (negative gravitational energy)/c² accompanying its formation, (ii) the unchanged observations of a distant observer upon formation of a firewall, and (iii) Birkhoff's Theorem (actually first discovered by Jørg Tofte Jebsen). We then consider one aspect of thermodynamics in gravitational fields, showing that equilibrium relativistic gravitational temperature gradients cannot be exploited to violate the Second Law of Thermodynamics.

This paper investigates portfolio optimization methodologies and short-term investment strate-gies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from January 2020 to November 2023. We employ high frequency data and utilize the Kurtosis Mini-mization methodology, alongside other optimization strategies, to construct and evaluate port-folios under different rebalancing frequencies. The empirical analysis reveals that cryptocurren-cies exhibit significant volatility, skewness, and kurtosis, necessitating sophisticated portfolio management techniques. We find that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, delivering optimal returns to investors, particularly in shorter-term investment horizons. We demonstrate the diversification benefits of integrating cryptocurrencies into multi-asset portfolios and emphasize the importance of regular portfolio rebalancing in the volatile cryptocurrency market. Our findings offer insights for portfolio man-agers and investors seeking to optimize their investment outcomes in the cryptocurrency market, highlighting the importance of risk management, diversification, and dynamic portfolio man-agement strategies.

This paper revisits the analytical theory of fractional vibrations with the highlights in five aspects. First, we address the cases of structures with frequency dependent mass or damping or stiffness in Sections 2-4. Second, we introduce the theory based on the general second-order vibration motion equation with frequency dependent elements (mass, damping, stiffness) in Sections 5-7. Third, we present the analytical theory of seven specific classes of second-order vibration systems with frequency dependent mass or damping or stiffness in Sections 8 and 9. Fourth, we bring forward the analytical theory of seven classes of fractional vibration systems in Sections 10-12. Finally, as an application, we give the closed form expression of the forced response to multi-fractional Euler-Bernoulli beam in Section 13. The explanation of the nonlinearity of fractional vibrations is given in Section 14.

An optical frequency comb (OFC) generator based on an electro-optic Talbot laser and cross-phase modulation (XPM) in a high nonlinear fiber (HNLF) is designed and demonstrated. The Talbot laser is an electro-optic frequency shifting loop to produce repetition rate multiplied pulses, and these pulses working as a pump signal induce XPM process in the HNLF to modulate the phase of a probe signal. At the output of the HNLF, OFCs with a multiplied line-spacing can be generated. The effects of the pump power and the HNLF length on the performance of the generated OFCs are theoretically analyzed. In the experiments, the line-spacing of the generated OFCs is respectively multiplied to be 10 GHz, 15 GHz and 20 GHz with a factor of 2, 3 and 4. The center of the OFCs is tuned in a 4-nm range by adjusting the wavelength of the probe signal.

Zika virus (ZIKV) infection and pathogenesis are linked to the disruption of neurogenesis, congenital Zika syndrome and microcephaly by affecting neural progenitor cells. Nonstructural protein 5 (NS5) is the largest product encoded by ZIKV-RNA and is important for replication and immune evasion. Here, we studied the potential effects of NS5 on microtubules (MTs) and autophagy flux, together with the interplay of NS5 with histone deacetylase 6 (HDAC6). Fluorescence microscopy, biochemical cell-fractionation combined with the use of HDAC6 mutants, chemical inhibitors and RNA interference indicated that NS5 accumulates in nuclear structures and strongly promotes the acetylation of MTs that aberrantly reorganize in nested-like structures. Similarly, NS5 accumulates the p62 protein, an autophagic-flux marker. Therefore, NS5 alters events that are under the control of the autophagic tubulin-deacetylase HDAC6. HDAC6 appears to degrade NS5 by autophagy in a deacetylase- and BUZ domain-dependent manner and to control the cytoplasmic expression of NS5. Moreover, NS5 inhibits RNA-mediated RIG-I interferon (IFN) production, resulting in greater activity when autophagy is inhibited (i.e., effect correlated with NS5 stability). Therefore, it is conceivable that NS5 contributes to cell toxicity and pathogenesis, evading the IFN-immune response by overcoming HDAC6 functions. HDAC6 has emerged as an anti-ZIKV factor by targeting NS5.

The invasion of ship domain stands out as a significant factor contributing to the risk of collisions during vessel navigation. However, there is a lack of research on the mechanisms underlying collision risks specifically related to merchant and fishing vessels in coastal waters. This study proposes an assessment method for collision risks between merchant and fishing vessels in coastal waters and validated through a comparative analysis through visualization. First of all, the operational status of fishing vessels is identified. Collaboratively working fishing vessels are treated as a unified entity, expanding their ship domain during operation to assess collision risks. Secondly, to quantify the collision risk between ships, a collision risk index (CRI) is proposed and visualized based on the severity of collision risk. Finally, taking the high risk area of merchant and fishing vessels collision in the Minjiang River Estuary as an example, this paper conduct the analysis involves classifying ship collision scenarios, extracting risk data under different collision scenarios, and visually analyzing areas prone to danger. The results indicate that this method effectively evaluates the severity of collision risk, and the identified high-risk areas resulting from the analysis is verified by accidents occurred in the recent three years.

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

This paper focus on the modeling and time-domain analyzing method of piecewise-smooth fractional-order circuit systems. Firstly, an analytical framework is constructed based on a generalized piecewise-smooth two-port impedance network model, which takes the topological characteristics of Dickson type switched-capacitor converters (SCCs) as examples, and considers the fractional-order characteristics of class-2 X7R ceramic capacitors, which are widely used in SCCs. Secondly, the time-domain analyzing method for such a piecewise-smooth fractional-order circuit system is developed, numerical solutions are obtained, and the influence of the fractional-order capacitor on the power conversion efficiency of the system are revealed. Finally, in order to validate the analysis, both numerical simulation and experiments are provided in the work, the results of which confirm that, the proposed fractional-order analytical framework can capture the power loss mechanism of piecewise-smooth fractional-order circuit systems more effectively than the traditional integer-order based approach, thus benefiting the design of circuit systems.

In this paper, we propose and demonstrate a network analysis optical frequency domain reflectometer (NA-OFDR) for distributed temperature measurements at high spatial (down to 3 cm) and temperature resolution. The system makes use of a frequency-stepped, continuous-wave (cw) laser, whose output light is modulated using a vector network analyzer. The latter is also used to demodulate the amplitude of the beat signal obtained by coherently mixing the Rayleigh backscattered light with a local oscillator. The system is capable of high measurand resolution (50 mK at 3-cm spatial resolution), thanks to the high sensitivity of coherent Rayleigh scattering to temperature. Furthermore, compared to the conventional optical-frequency domain reflectometry (OFDR), the proposed system does not rely on the use of a tunable laser, therefore it is less prone to limitations related to the laser coherence or sweep nonlinearity. Two configurations are analyzed, both numerically and experimentally, based on either a double sideband or single sideband modulated probe light. The results confirm the validity of the proposed approach.

The manuscript presents the applicability of the Gumbel distribution in the frequency analysis of extreme events in hydrology. The advantages and disadvantages of using the distribution are highlighted, as well as recommendations regarding its proper use. A literature review was also carried out regarding the methods of estimating the parameters of the Gumbel distribution. Thus, for the verification of the methods, case studies are presented regarding the determination of the maximum annual flows and precipitations using nine methods for parameter estimation. The influence of the observed data lengths on the estimation of the statistical indicators, the parameters and the quantiles corresponding to the field of low annual exceedance probabilities (p <1%) is also highlighted. The results are compared to those obtained with the Generalized Extreme Value distribution, respectively the Burr and the Wakeby distributions with parameters estimated using the L-moments. The results highlight and reaffirm the statistical, mathematical and hydrological recommendation regarding the avoidance of using the Gumbel distribution in Flood Frequency Analysis and its use with reservations in the case of maximum precipitation, especially when the statistical indicators of the analyzed data are not close to the characteristic ones and unique to the distribution.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

This work introduces a new architecture for a time-domain feedback-based beamformer designed to meet real-time processing demands. The primary goal of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture is a unique aspect that refines localization precision. We conduct an in-depth analysis highlighting their respective benefits and limitations to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

In this study, the degradation characteristics of radio-frequency (RF) low-noise amplifier (LNA) based on silicon-germanium (SiGe) heterojunction bipolar transistors (HBTs) due to total ionizing dose (TID) are investigated. The small-signal equivalent model of a SiGe HBT is utilized to analyze circuit-level performance including the input and the output matching and noise figure (NF). As a target circuit, an LNA with a cascode common-emitter stage with emitter degeneration is studied and the equation of each performance parameter is derived. The RF LNA fabricated using commercial 350 nm SiGe technology was exposed to X-ray irradiation with the total dose up to 3 Mrad (SiO2). The experimental results exhibit that modeled device parameters estimate the degraded circuit performance. In addition, the relative impact of each parameter on the circuit metrics is revealed, which is expected from the derived design equations. The key device parameters for modeling TID-induced circuit degradations include the base resistance, the transconductance, and the base-to-emitter capacitance.

Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata), a nutraceutical fodder legume. An automated geospatial model for precision agriculture (PA) can identify suitable locations for its cultivation. Additionally, a novel approach of radio frequency identifier (RFID) supported telemetry technology can track animal movement, and the analyses of data using artificial intelligence can determine sickness of small ruminants. This RFID-based system is being connected to a smartphone app (under construction) to alert farmers of potential livestock health issues in real-time so they can take immediate corrective measures. An accompanying Decision Support System (DSS) site is being developed for R-P farmers to obtain all possible support on livestock production, including the designed PA and RFID-based DSS.

We delve into the axion-dilaton model within the supergravity framework, with a specific focus on the intricacies of domain wall construction and stability. Employing holographic vitrification, we unravel the dynamics of domain wall formation in gauge theories featuring periodic vacuum structures. Our model, incorporating a QCD-like axion term and a stabilizing dilaton, undergoes scrutiny for conductivity variations under weak disorder. The investigation reveals the model's resilience, manifesting near-perfect conductivity under mild disorder conditions. However, the rigorous mathematical motivation for the holographic setup demands further elucidation. The scattered nature of our results prompts the necessity for a more systematic interpretation of QCD phenomena and conductivity transitions. This study contributes to the mathematical understanding of the axion-dilaton model's behavior, highlighting the imperative for a refined holographic framework and a more coherent interpretation of observed phenomena.

A review on planar printed structures that are based on Matryoshka-like geometries is presented. These structures use the well-known principle of Matryoshka dolls that are successively nested inside each other. The well-known advantages of the planar printed technology and of the meandered nested Matryoshka geometries are combined to generate miniaturized, multi-resonance and/or wideband configurations. Both metal and complementary slot structures are considered. Closed and open configurations are analyzed. The working principles are explored do get physical insight of their behavior. Low cost and single layer applications as frequency selective surfaces, filters, antennas, and sensors, in the microwave frequency region, are reviewed. Potential future research perspectives and new applications are discussed.

This cross-sectional study evaluated the validity of three alternative methods compared to the gold standard 24-h urine collection for estimating dietary sodium intake, a modifiable risk factor for hypertension, among middle-aged and older adults with elevated blood pressure. These included spot urine collection (using Kawasaki, Tanaka, and INTERSALT equations), 24-h dietary recall, and food frequency questionnaire responses, compared against 24-h urine collection in a subset of 65 participants (aged 50–75 years, 58.5 % women, 61.6 % hypertensive) from the DePEC-Nutrition trial. The validity of the methods was assessed using bias, Spearman correlation coefficient (SCC), intraclass correlation coefficient (ICC), and Bland-Altman analysis. Among the alternative methods, spot urine collection using the Kawasaki equation showed the strongest correlation (SCC 0.238; ICC 0.119, 95 % CI -0.079 to 0.323) but it exhibited a significant bias (1414 mg/day, p-value < 0.001) relative to 24-h urine collection. Conversely, dietary surveys had a smaller bias but wider limits of agreement. These findings underscore the complexities of accurately estimating dietary sodium intake using spot urine collection or dietary surveys in this specific population, suggesting that a combination or refinement of existing methodologies might improve accuracy. Further research with larger samples is necessary to develop more reliable methods for assessing sodium intake in this high-risk group.

The aim of this paper is to investigate whether multifractal analysis can be applied to study choroidal blood vessels and help ophthalmologists for the early diagnosis of retinitis pigmentosa (RP). In a case study, we use Spectral Domain Optical Coherence Tomography (SDOCT), which is a non-invasive and highly sensitive imaging technique of the retina and choroid. The image of a choroidal branching pattern can be regarded as a multifractal. Therefore, we calculate generalized Renyi point-centered dimensions, which is considered a measure of inhomogeneity of data, to prove that it increases in the patients with RP with respect to the control group.

The origin of neurodegenerative diseases is linked to the abnormal folding of disease-associated proteins. The traditional amyloid hypothesis has been a focal point of research, but recent findings suggest a more complex picture. This review explores the role of physiological conformation of aggregation-prone domain (also known as prion-like low complexity domain) and multiple co-misfolded proteins in neurodegenerative diseases. We highlight the importance of targeting multi-heterotypic misfolded proteins for effective treatments and emphasize the irreversible neurodegeneration stemming from the loss of physiological folding in prion-like disease-causing proteins underscores the criticality of refolding. Additionally, we introduce the concept of ReproFold Therapeutics, a novel approach aimed at restoring the cellular prion-like folding of disease-causing proteins to rebuild normal cross-β interactomes and mitigate neurodegeneration. Baicalein, a potential drug candidate for this approach, shows promise in vitro and in animal models. Moreover, prion-like low complexity domains implicated in neurodegeneration are also associated with other disorders such as diabetes, cancers, and mental illnesses. Understanding the core pathology of neurodegenerative diseases and the broader implications of prion-like LC domains could revolutionize healthcare approaches and policies worldwide.

In this paper, the concept of absolute airspace is further elaborated, and the time transform effect, the frequency transform relation of clock timing, and the principle of Doppler effect of special relativistic light are analyzed and demonstrated. Based on the concept of absolute airspace, a new and accurate Doppler effect relation of light (electromagnetic wave) is proposed. It is proved that there is a synchronous relationship between the time shift effect and the frequency change of the timing inside the clock. When the light (electromagnetic wave) source is in motion, the Doppler effect includes two physical mechanisms: the frequency change caused by time transformation effect and the frequency change caused by relative motion. When the light source is in motion, the Doppler effect of the light there are some differences in different directions due to the rotation of the earth. The relation of Doppler effect of special relativistic light is only a highly approximate relation, and its theoretical basis, special relativity, is flawed in principle.

Domain generalization has led to remarkable achievements in Hyperspectral Image (HSI) classification. Inspired by contrastive language-image pre-training (CLIP), the language-aware domain generalization method has been explored to learn cross-domain-invariant representation. However, existing methods face some challenges: 1) The weak capacity to extract long-range contextual information and inter-class correlation. 2) Due to the inadequacies of the large-scale pre-training for HSI data, the spatial-spectral features of HSI and linguistic features can not be straightforwardly alignment. To address the above problems, a novel network has been proposed with a CLIP framework, which consists of an image encoder, based on an encoder-only transformer to obtain the global contextual information and inter-class correlation, a frozen text encoder, and a cross-attention mechanism, named Linguistic-Interact-with-Visual Engager (LIVE), enhances the interaction between two modalities. Extensive experiments demonstrating superior performance over state-of-the-art methods in HSI Domain Generalization with a CLIP framework.

Plant viral nanoparticles (VNPs) are attractive to researchers in nanomedicine due to their safety, easy production, resistance, and straightforward functionalization. In this paper, we developed and successfully purified a VNP derived from turnip mosaic virus (TuMV), a well-known plant pathogen, that exhibits high affinity for immunoglobulins G (IgG) thanks to its functionalization with the Z domain from staphylococcal protein A via gene fusion. We selected cetuximab as a model IgG to prove the versatility of this novel TuMV VNP by developing a fluorescent nanoplatform to mark tumoral cells of the line Cal33, from tongue squamous cell carcinoma. We observed that the fluorescent VNP-cetuximab bound selectively to Cal33 and were internalized by confocal microscopy, revealing the potential of this nanotool in cancer research.

In recent years, researches on combining wavelet decomposition and convolutional neural network (CNN) together to classify hyperspectral images (HSI) have emerged, and some effective classification models have been proposed and achieved good classification results. However, there are two problems for most of the proposed models. One is the heavy training parameters and the other is that there is no distinction between the classification effectiveness of low frequency and high frequency features after wavelet decomposition. In this paper, a new light-weighted HSI classification model (LLFWCNN) is proposed, which performs multi-layer wavelet decomposition for HSI after dimensionality reduction, and only arranges the low frequency features in a specific stack mode, then classifies them through a well-designed convolutional neural network. Compared with other classification models, the number of parameters is only 2.5% of that of other models. And only the low frequency features after wavelet decomposition are used, while the high frequency features are abandoned. The results showed that compared with the state-of-the-art classification models, LLFWCNN could obtain the same or even better classification results with fewer network parameters and proved that the low frequency features after wavelet decomposition provide LLFWCNN with more favorable information for HSI classification as well.

Abstract: The worldwide spread of SARS-CoV-2 has led to a significant economic and social burden on a global scale. Even though the pandemic has concluded, there remains apprehension regarding the emergence of highly transmissible variants capable of evading immunity induced by either vaccination or prior infection. The success of viral penetration is the specific amino acid residues of the Receptor Binding Motif (RBM) involved in viral attachment. This region interacts with the cellular receptor ACE2, triggering a neutralizing antibody (nAb) response. In this study, we have evaluated the immunogenicity of serum from individuals who received either a single dose or a combination of different vaccines against the original SARS-CoV-2 strain and mutated linear RBM. Despite a modest antibody response to the wild-type SARS-CoV-2 RBM, the Omicron variants exhibit four mutations in the RBM (S477N, T478K, E484A, F486V) that result in even lower antibody titers. The primary immune responses observed were directed towards IgA and IgG. While nAbs typically target the RBD, our investigation has unveiled a reduced seroreactivity within the RBD's crucial subregion, the RBM. This deficiency may have implications for the generation of protective nAbs. Evaluation of S1WT and S2WT RBM peptides binding to nAbs using microscale thermophoresis revealed a higher affinity (35 nM) for the S2WT sequence (GSTPCNGVEGFNCYF), which includes the FNCY patch. Our findings suggest that the linear RBM of SARS-CoV-2 is not an immunodominant region in vaccinated individuals. Comprehending the intricate dynamics of the humoral response, its interplay with viral evolution, and host genetics is crucial for formulating effective vaccination strategies, not only targeting SARS-CoV-2 but also anticipating potential future coronaviruses.

In archaea, the interaction of HerA helicase and NurA nuclease is required for 3' end resection during DNA double-stranded break repair, and its disruption results in unfaithful DNA repair. Interactions transpire by the four conserved domains, including the N-terminal HAS domain (HerA-ATP synthase), which serves as a binding site for the exonuclease NurA. As a HerA hexamer between its two subunits, the conserved C-terminal brace extension functions as an adjacent brace. DBD serves as a location for DNA binding, which causes the DNA strands to be propelled into the HerA pocket to begin unwinding. The putative pore entrance is made up of four helix coils, which provide a suitable charged surface for guiding DNA into the pore. The crystal structure of the complex HerA-NurA allows for the identification of key residues for protein-protein interaction. This allows us to predict that most archaeal HerA orthologs will be able to interact with one another. Archaeal HerA is a homo-hexameric assembly featuring a DNA-binding domain that is conserved. It also features distinctive ATP and NurA binding domains. Following the structure and sequence conservation criteria, we find one HerA ortholog in Pyrobaculum calidifontis and multiple HerA paralogs, but the role of none is established yet.Interestingly, orthologous HerA is part of an Mre11-Rad50 gene neighborhood while paralogous HerA is not, which could be misleading. As a result, it sought to describe the criteria that may be used as a possible fingerprint for identifying orthologous genes, as well as the cloning procedures and tactics that would aid in the confirmation of the HerA helicase in P. calidifontis. Their confirmation is based on PCR cloning and characterization following HerA construct creation.

This paper reviews several techniques, including analytical theories and real frequency techniques, to design ultra-wideband impedance matching networks for low-power applications. The comparison of solutions obtained using the simplified real frequency technique to the published solution using Chebyshev filter theory is demonstrated.

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

GCN1 is recognized as a factor that is essential for the activation of GCN2, which is a sensor of amino acid starvation. This function is evolutionarily conserved from yeast to higher eukaryotes. However, recent studies have revealed non-canonical functions of GCN1 that are independent of GCN2, such as its participation in cell proliferation, apoptosis, and the immune response, beyond the borders of species. Although it is known that GCN1 and GCN2 interact with ribosomes to accomplish amino acid starvation sensing, recent studies have reported that GCN1 binds to disomes (i.e. ribosomes that collide each other), thereby regulating both the co-translational quality control and stress response. We propose that GCN1 regulates ribosome-mediated signaling by dynamically changing its partners among RWD domain-possessing proteins via unknown mechanisms. We recently demonstrated that GCN1 is essential for cell proliferation and whole-body energy regulation in mice. However, the manner in which ribosome-initiated signaling via GCN1 is related to various physiological functions warrants clarification. GCN1-mediated mechanisms and its interaction with other quality control and stress response signals should be important for proteostasis during aging and neurodegenerative diseases, and may be targeted for drug development.

This paper is a detailed study on radio frequency identification (RFID) technology focused on its application for warehouse traceability in the company FRUMECAR. It highlights the importance of controlling the traceability of items in warehouses or logistics companies, highlighting the need for reliable, real-time information to effectively manage inventory and resolve incidents quickly. The paper discusses in depth what RFID is, its types, main components such as tags, readers, and antennas, as well as discussing how it works, advantages, disadvantages, and specific applications. It also examines factors such as return on investment, cost feasibility, and environmental factors that influence the implementation of RFID systems. The paper presents a practical approach, proposing RFID-based solutions to improve traceability in FRUMECAR, analyzing the current state of the company and its warehouses, with the aim of implementing an efficient system that allows real-time tracking of products.

A parallel shallow water flow model is introduced in this paper. The explicit-time finite volume approach is adopted to solve the 2D shallow water equations on an unstructured triangular mesh. The proposed scheme is second-order accurate in temporal and spatial terms using the two-stage Runge-Kutta and the monotone upwind scheme for conservation law (MUSCL) methods, respectively. Based on Message Passing Interface (MPI) and OpenACC, a multi-GPU model is presented with the METIS library to produce the domain decomposition. A CUDA-aware MPI library through GPUDirect for peer-to-peer (P2P) transfer between two GPUs and overlapping computation and MPI communication are used to speed up MPI memory exchange and the performance of the code. A 2D circular dam break test with wet and dry downstream beds and grid resolutions of about 2 million cells is considered to verify the accuracy of the code, and good results were achieved compared to the numerical simulations of published studies. Compared with the multi-CPU version of the 6-core CPU, maximum speedups of 56.18 and 331.51 were obtained using the single-GPU and multi-GPU versions, respectively. Results indicate that acceleration performance improves as the mesh resolution increases.

Safety limits application has always been a traditional approach to ensure the safe operation of electro-mechanical systems within many industries including automated vehicles, robotics, aerospace, traditional automotive, railways, manufacturing, etc. In all of these applications, control and safety limits are usually hard-coded into the production firmware and are fixed throughout the entire product life cycle. Currently, due to the evolving needs of automated systems like automated vehicles and robots, this traditional approach does not address all the use cases and scenarios to ensure safe operation. Especially for data-driven machine learning applications that constantly evolve and learn over time, it is important to be able to adjust the safety limits application strategy to be more flexible and adaptable based on different Operational Design Domains (ODDs) and scenarios. Our ITSC conference paper ~\cite{4} introduced the concept of a new safety limits application strategy called the Dynamic Control Limits Application (DCLA) strategy that supports the flexible application of diverse limits profiles based on the parameters involved within the dynamic scenario at different layers of the Autonomy software stack.This paper extends the DCLA strategy to derive the complete methodology for safety limits application based on ODD elements, scenario identification and scenario classification using Decision Making Engines. It leverages the layered architecture introduced in the ITSC conference paper to implement the Decision Making (DM) algorithms. Another important extension is the use of cloud infrastructure that is based on the Vehicle-to-Infrastructure (V2I) technology to store the scenarios and the limits mapping to serve as a ground truth and/or a backup mechanism in case of errors or failures associated with the main Decision Making (DM) Engine. There is also a focus on providing a more comprehensive list of scenarios and a custom built experimental dataset to cover the maximum ODD elements, and multiple tables of safety limits to be chosen from, which eventually helps in creating different safety limits application profiles. These distinct safety limits application profiles are based on the scenario parameters that are perceived by the system upon which the Decision Making algorithms are applied or trained. This systematic approach can be used within the industry for any of the future automated vehicles and systems until Level 5 Autonomy.

The proliferation of radio frequency devices in today's society, especially in smart homes, Internet of Things devices, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the field of radio frequency fingerprinting to propose a Bluetooth device identification method based on the application of the Jensen-Shannon divergence to the statistical distribution of noise in Bluetooth signals. A detailed case study is performed to investigate the Bluetooth radio frequency noise recorded at 5 Gsps from different devices is investigated to define a statistical radio frequency fingerprint for each Bluetooth device. A noise model is used to extract a unique, universal, persistent, recoverable, and robust statistical radio frequency fingerprint that identifies each Bluetooth device. Then, using the Jensen-Shannon divergence, different noise signals provided by each Bluetooth device are compared with the statistical radio frequency fingerprint of all devices and a membership resolution is declared. The study shows that the proposed method can discriminate between devices of the same brand and model, achieving an identification effectiveness of 99.5 \%. By leveraging the statistical radio frequency fingerprint of Bluetooth devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of radio frequency identification techniques that could be useful in forensic processes.

Immune checkpoint receptors play a crucial role in steering tumor progression, prompting a focused investigation into the role of TIM3 in breast cancer. Analysis of TCGA BRCA data unveiled diverse correlations between TIM-3 expression and immune cell infiltration across subtypes. In our in-house cohort studied through IHC, significant changes in TIM3 expression with tumor grades were observed. Higher TIM3 expression correlated with increased disease-free survival in pan breast cancer, marking the first revelation of TIM3's positive role in pan BC prognosis. The study indicates that alluring TIM3 response, rather than suppression, may enhance breast cancer prognosis.

In drilling small holes with diameters of 1 mm or less, minute clogging and twining of chips or adhesion of the workpiece material can become factors in causing breakages of the drill bit; moreover, it can be difficult to identify the machining state. Acoustic emission (AE) sensing is a nondestructive inspection technique that measures the elastic-stress waves that are generated when a material is deformed and fractured. AE sensing permits highly sensitive measurements to be made without changing the rigidity of the experimental system, unlike force sensing of cutting resistance, etc. In the present study, attempts were made to identify the machining state and tool wear, and to predict abnormalities in small-diameter drilling by using the change in the frequency of AE signal waveforms arising from deformation and fracture. It was shown that it is possible to predict the breakage of the drill bit by detecting the high-frequency AE signals caused by adhesion. In addition, a correlation map of the AE frequency spectrum for identifying the machining state in a drilling operation is suggested.

A facile and environmentally benign method for a single-phase barium titanate synthesis in water vapor medium was studied to reveal the mechanism of phase transformation of the initial simple oxides mixture and estimate the capability of the product to be used as a raw material for low-frequency dielectric ceramics. The composition and structure of the reaction mixture treated in vapor at 130…150 °C as well as 230 °C for various time were investigated by means of XRD, SEM, HRTEM, EDX, and FTIR methods. The kinetics of the occurring phase transformation was described by Johnson-Mehl-Avrami-Erofeev equation. The reaction between the initial oxides was considered as a topochemical process with an apparent activation energy of 74…80 kJ mol^-1. A crucial role in this process belonged to the water vapor medium, which facilitated the generation of the reaction zone and its spreading inward the solid particles. The synthesized tetragonal barium titanate powder (mean particle size of 135 ± 24 nm) was sintered by a conventional technique at 1250 °C to obtain ceramics with grains of about 2 μm. Capacitance measurements performed a dielectric constant and loss tangent of ceramics, which reached 3879 and 6.7 10^-3, respectively, at 1 kHz and room temperature.

Modern power systems are confronted with a spread concern on the frequency stability issue due to the widespread integration of randomly fluctuating renewable resources. To address the above concern, this work introduces a load frequency control (LFC) scheme based on a hydrogen based energy-storage unit and a parameter tuning strategy for fractional-order proportional-integral-derivative (FOPID) controller. Firstly, a two-area interconnected power system (IPS) model, including thermal, hydro, solar, wind, gas power generator, and hydrogen based energy-storage unit, is established. Then, a FOPID controller is designed for this IPS model, and an improved gradient-based optimizer (IGBO) is developed to adaptively regulate the parameters of the FOPID controllers. Finally, the effectiveness of the offered LFC scheme are tested through load disturbance and renewable energy fluctuations test scenarios, and provides comparison and robustness analysis among different schemes. The test results validated that the offered LFC scheme can effectively suppress the frequency fluctuations of IPS and has excellent robustness.

Controlling the collective behavior of micro/nanomotors with ultrasound may enable new functionality in robotic, medicine and other engineering disciplines. Currently, various collective behaviors of nanomotors, such as assembly, reconfiguration and disassembly, have been explored by using acoustic fields with a fixed frequency, while regulating their collective behaviors by varying the ultrasound frequency still remains challenging. In this work, we design an ultrasound manipulation methodology allowing nanomotors to exhibit different collective behaviors by regulating the applied ultrasound frequency. This work has important implications for design of artificial actuated nanomotors and optimize their performances.

In Korea, the underground silo structure for low- and intermediate-level radioactive waste disposal facilities has been constructed and operated since 2014. Large-scale earthquakes occurred in 2016 and 2017, respectively, in Gyeongju and Pohang areas near the underground silo structures, and interest in the stability of the underground silo increased significantly. In this paper, one-dimensional free field analyses have been carried out before the three-dimensional silo dynamic analyses subjected to earthquake loadings. As an additional study, a new form of finite element equilibrium equation is derived in terms of relative motions, which is essentially the same equation as that expressed in terms of total motions where the base shear force is applied for earthquake load. The accuracy of conventional finite element solutions is evaluated by directly comparing them with closed-form solutions by frequency domain analysis such as SHAKE91.

Generative models have recently gained popularity in Remote Sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and especially variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces a novel approach that harnesses the capabilities of Generative Adversarial Networks (GANs), augmented with Contrastive Learning, to generate target domain images that account for multi-spectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multi-spectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multi-spectral diversity.

Cooling fans are one of the critical components of air-forced (AF) dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Regular maintenance occurs periodically and issues with cooling fans may arise between maintenance periods, leading to prolonged operation under malfunctioning conditions and potential failures. In addition, utilities typically have online information about whether a fan works or not without providing information about cooling fan malfunctioning circumstances. To address these challenges, this study proposes learning-based online monitoring techniques to detect malfunctions in AF transformer cooling fans. Random forests (RFs) and convolutional neural networks (CNNs) are developed to classify the audio signals from cooling fans into normal and malfunctioning classes. Unlike RFs, which require separate feature extraction, CNNs are trained based on spectrogram images derived from audio signals. Thus, various time-frequency techniques are utilized for feature extraction in RFs. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. Simulations reveal that CNNs outperform RFs, whereas the latter provides superior interpretability of acoustic features compared to the former.

In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value at Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning from January 1st, 2017, to October 25th, 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.

Abstract: Earth’s Phanerozoic history is marked by about 100 major events (“golden spikes”) indicating the beginnings of new stages in the Geologic Time Scale (GTS). Stage boundaries signify major deterministic events that are relatively well-known and can be correlated worldwide, mostly on the basis of appearances or disappearances of fossil species. The latest stage (Anthropocene) is human-made and currently involves rapid (approximately linear) increase in average atmospheric temperature. The main cause of Earth’s past and current temperature increases is addition of CO2 to the atmosphere. There is continuous exchange of greenhouse gases between atmosphere and oceans, which (per unit of volume) contain 50 to 60 times as much CO2 as the atmosphere. Abundance of new very precise worldwide observations is allowing multifractal modeling of weather and climate during the latest Quaternary stages during which some variables like temperature of the atmosphere and frequency of forest fires can be described by using the Pareto-lognormal frequency distribution model with parameters that are subject to continuous (deterministic) changes, similar to those in the earlier Phanerozoic.

Clinical work conducted over the last seventeen years at the EEG Institute in Los Angeles and by other neurofeedback providers around the world has demonstrated the utility of extending frequency-based neurofeedback deep into the infra-low frequency (ILF) region, using the method of endogenous neuromodulation described herein. The method is characterized by the absence of any overt reinforcements, which makes it possible to extend the clinical reach to extremely low frequencies. As the training frequency is lowered, the signal becomes more difficult to discriminate, and ultimately it can only be discerned by the brain itself, in the process of endogenous neuromodulation. The method emulates how the brain does skill learning in general: It must observe itself performing the skill, with feedback on its performance. While the immediate target of ILF neurofeedback is enhanced self-regulatory competence--with symptomatic relief and functional recovery the secondary consequences, progressive lowering of the target frequencies has led to improved outcomes in application to challenging dysfunctions such as episodic suicidality, migraine, seizures, and bipolar mood swings. The work has also yielded insights into how the frequency domain is organized. The training proceeds best at frequencies that are specific to each individual, and these are referred to as optimal response frequencies (ORFs). These frequencies differ for various placements but stand in two fixed relationships to one another, one that holds over the EEG spectral range, and another that holds over the entire ILF range. Training in the ILF region engages the dynamics of the glial-neuronal networks, which govern tonic, resting state regulation. The collective clinical experience with ILF neuromodulation within a large practitioner network supports the case for making protocol-based, individualized ‘homeodynamic’ regulation a therapeutic priority, particularly for our most impacted clinical populations: addiction, trauma formations, traumatic brain injury, and the dementias. The case is made for further outcome studies and foundational research.

Urbanization in one of the more important phenomena affecting biodiversity in the Anthropocene. Some organisms can cope with urban challenges, and changes in birds’ acoustic communication have been widely studied. Although changes in the timing of the daily organization of acoustic communication have been previously reported, there is a significant gap regarding possible variations in song structure between dawn and dusk choruses. Considering that urbanization potentially imposes different soundscapes for the dawn and dusk choruses, we postulate two hypotheses: i) there are “natural” variations in song parameters between dawn and dusk choruses, and ii) such parameters within the city will vary in response to urban noise. We studied urban and extra-urban populations of Chiguanco Thrush in La Paz, Bolivia, measuring for their dawn and dusk choruses: song length, song sound pressure, minimum, maximum, range and dominant frequency, and the proportion of songs produced. The results support our two hypotheses: in natural and urban conditions dawn songs were louder and with larger bandwidths, and within the city, the frequency of the entire song rises along with increasing amplitude. Understanding structural variations between dawn and dusk choruses could allow for better interpretation of how some bird species cope with urban challenges.

Globally, weeds interfere with agricultural production activities and have a very serious impact on agriculture and animal husbandry. Identifying a safe and reliable weed control strategy might increase yield, production net income, and improve crop quality. A field experiment was carried out to explore the effects of weeds interference frequency on the yield and quality of Glycyrrhiza uralensis Fisch in an arid and semi-arid area of northwest China. The experiment consisted of seven treatments and were (1) no weeding, marked as WF0; (2)-(7) artificial weeding by hoe once every 1, 2, 4, 6, 8, 10 weeks after emergence, marked as WF1, WF2, WF4, WF6, WF8 and WF10, respectively. We found that the higher weeding frequency had a higher the plant height, photosynthesis, yield and quality. The highest yield was obtained in the WF1 treatment, while the cost of weeding was the highest among all treatments. The concentration of liquiritin and glycyrrhizic acid were increased by 53.24% and 36.57%, with the highest nitrogen metabolism enzymatic activities and quality in WF4 treatment. WF4 treatment exhibited the largest increases in the net income among all treatments in both growing seasons, with respective increases up to 71.39 % and 78.81%. These findings suggested that weeding once every 4 weeks would be an effective and sustainable measure to control weeds in an arid and semi-arid area.

Numerous experimental and theoretical studies undertaken to determine the effective-stress coefficient for seismic velocities in rocks stem from the importance of this geomechanical parameter both for monitoring changes in rock saturation and pore pressure distribution in connection with reservoir production, and for overpressure prediction in reservoirs and formations from seismic data. The present work pursues a task to determine, in the framework of a low-frequency laboratory study, the dependence of the elastic moduli of n-decane saturated sandstone on the relationship between pore and confining pressures. The study was conducted on a sandstone sample with high quartz and notable clay content in the quasi-static regime, when the 100 ml tank filled with n-decane was directly connected to the pore space of the sample. The measurements were carried out at a seismic frequency of 2 Hz and strains, controlled by semiconductor strain gauges, not exceeding 10-6. The study was performed using a forced-oscillation laboratory apparatus utilizing stress-strain relationship. The dynamic elastic moduli have been measured in two sets of experiments: at constant pore pressures of 0, 1 and 5 MPa and differential pressure (defined as a difference between confining and pore pressures) varied from 3 to 19 MPa; and at а constant confining pressure of 20 MPa and pore pressure varied from 1 to 17 MP. It was shown that the elastic moduli obtained in the measurements are in good agreement with the Gassmann moduli calculated for the range of differential pressures used in our experiments, which corresponds to the effective-stress coefficient equal to unity.

Nuclear Magnetic Resonance (NMR) and its various forms are extensively utilized in both research and clinical settings to analyse molecules. NMR data pre-processing, while essential to NMR data analysis, can be uniquely complex. Despite the availability of software tools, understanding these processes can be challenging, complicating the selection of appropriate pre-processing steps. In this review, we elucidate pre-processing steps in the time domain from a mathematical and statistical perspective, explaining direct current offset removal, eddy current correction, shift and linear prediction, weighting, zero filling, and domain transformation using plain language and fundamental mathematical formulas. Our objective is to clarify these processes in simple terms and provide general guidance.

The Ames/quantitative structure-activity relationship (QSAR) International Challenge Projects held during 2014–2017 and 2020–2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the highest-performing prediction model, assuming a 100% coverage rate (COV) for all prediction target compounds and an estimated performance variation due to changes in COV. All models from both projects were evaluated using balance accuracy (BA), Matthews correlation coefficient (MCC), F1 Score (F1), and the first principal component (PC1). After normalizing the COV, a correlation analysis with these indicators was conducted, and the evaluation index for all prediction models in terms of the COV was estimated. In total, 109 models, the highest estimated BA (76.9) at 100% COV was MMI-VOTE1, reported by Meiji Pharmaceutical University (MPU). The best models for MCC, F1, and PC1 were all MMI-STK1, also reported by MPU. All models reported by MPU ranked in the top four. MMI-STK1 was estimated to have F1 scores of 59.2, 61.5, and 63.1 at COV levels of 90%, 60%, and 30%, respectively. These findings highlight the current state and potential of the Ames prediction technology.

A problem of efficacy of crystal growth methods for crystallization from solution or melt has been investigated. Axial vibrational control (AVC) technique was considered as a perspective method to manage both heat-mass transfer and chemical component composition of the melts in the case of crystallization of complex chemical compounds. Numerical modeling and the search for generalized dependencies made it possible to predict the AVC parameters that provide optimal heat and mass transfer modes for creating flat liquid-solid interfaces, as well as the component composition of dissociated melts of various chemical compounds – Ge, NaNO3, CdTe.

Artifact metrics is a technology for authenticating artifacts based on their unique characteristics. The artifact-metric system offers "clone resistance," i.e., it makes it highly improbable to create another object exhibiting the same measured values as a genuine artifact. However, determined adversaries may still attempt to create imitations or clones with close physical characteristics to those of registered products, even if they cannot perfectly replicate the genuine product. Such clones serve to deceive users into believing they are genuine rather than counterfeits. Thus, in this study, we consider a scenario in which we measure raster-scanning-generated clones via a non-raster scanning method. Further, we employ image processing techniques to generate image data representing the clones and theoretically assess the filtering effects on these images. Our findings reveal that applying filters to specific frequency components in the spatial frequency domain can effectively highlight differences between raster-scanning-created clones and the corresponding genuine artifacts. Thus, we demonstrate directional emphasis filtering in artifact metrics as an effective approach for rejecting raster-scanning-created clones.

Background: Oscillation and pulmonary expansion (OPE) therapy can decrease postoperative pulmonary complications in a general surgical population, but its effect after cardiac surgery has not been reported, to our knowledge. We hypothesized that using an OPE device after cardiac surgery before extubation would decrease pulmonary complications. Methods: This retrospective cohort study included adults undergoing elective open cardiac surgery at our institution from January 2018 through January 2019, who had an American Society of Anesthesiologists score of 3 or greater. For mechanically ventilated patients after cardiac surgery, a new OPE protocol was adopted July 1, 2019, comprising an initial 10-minute OPE treatment administered in-line with the ventilator circuit, then continued treatments for 48 hours after extubation. Severe postoperative respiratory complications (primary outcome measure) included need for antibiotics, increased supplemental oxygen use, and prolonged hospital length of stay (LOS). Demographic, clinical, and outcomes data were compared for patients before (usual care with postextubation hyperinflation) and after protocol implementation (OPE). Results: Of 104 patients, 54 patients received usual care, and 50 received OPE. Usual-care recipients had more men (74% vs 62%; P=.19) and were older (median, 70 vs 67 years; P=.009) than OPE recipients. The OPE group had a significantly shorter hospital LOS than the usual-care group (mean, 6.2 vs 7.4 days; P=.04). Other measures improved with OPE but did not reach significance: shorter ventilator duration (mean, 0.6 vs 1.1 days with usual care; P=.06) and shorter LOS in the intensive care unit (mean, 2.7 vs 3.4 days; P=.06). On multivariate analysis, intensive care unit LOS was significantly shorter for the OPE group (mean difference, 0.85 days; 95% CI, 1.65 to 0.06; P=.04). The OPE group had a lower percentage of postoperative complications (10% vs 20%). Conclusion: OPE therapy after cardiac surgery is associated with decreased ICU and hospital LOS.

Deep learning heavily relies on statistical correlations to drive artificial intelligence (AI) innovations, particularly in computer vision applications like autonomous driving and robotics. However, despite providing a solid foundation for deep learning, these statistical correlations can be vulnerable to unforeseen and uncontrolled factors. The lack of prior knowledge guidance can result in spurious correlations, introducing confounding factors and affecting the model's robustness. To address this challenge, recent research efforts have focused on integrating causal theory into deep learning methodologies. By modelling the inherent and unbiased causal structure, causal theory can potentially mitigate the impact of spurious correlations effectively. Hence, this paper explores the basics of causal methodologies in image classification.

In this study, we propose an efficient field-free line (FFL) generator for mechanically driven FFL-magnetic particle imaging (MPI) applications. The novel FFL generator comprises pairs of Halbach arrays and bar magnets. The proposed design generates high-gradient FFLs with low-mass permanent magnets, realizing fine spatial resolutions in MPI. We investigate the generated magnetic field using simulations and experiments. Our results show that the FFL generator yields a high gradient of 4.76 T/m at a cylindrical field of view of 30 mm diameter and a 70 mm open bore. A spatial resolution of less than 3.5 mm was obtained in the mechanically driven FFL-MPI.

Adjustable frequency converters are widely used in modern automated asynchronous drives due to their high efficiency and high responsiveness control strategies. Control algorithms for frequency converters are divided into two main groups: constant frequency commutation and random frequency commutation. Research results on various commutation frequencies, conducted using Matlab/Simulink software, show that the use of random frequency commutation provides better efficiency by significantly reducing the amplitude of harmonic oscillations at different frequencies, with a reduction in switching losses in transistors. Currently, a converter with random frequency commutation is being researched and applied in the direct torque control system. The traditional direct control system, based on a two-level inverter, is widely used due to its simplicity and ease of implementation. However, this method results in the output current of the two-level converter, supplied to the motor stator, having many harmonic distortions, leading to strong torque fluctuations regardless of the rotation speed and ultimately causing additional losses in the stator windings and reducing the motor's lifespan. To enhance the efficiency of automated asynchronous drives, this article presents an algorithm for a direct control system based on a three-level NPC inverter using a PI speed regulator. The proposed algorithm's research is validated in the Matlab/Simulink environment. The research results show that in the absence of harmonic filtering, the stator current harmonic distortion coefficient is approximately 11%, and torque fluctuations are less than 5% of the resistive torque. The fluctuations between the two capacitors remain almost unchanged throughout the entire process.

The Stepped Frequency Waveform (SFW) is commonly employed in radar technology to synthesize wideband signals through the aggregation of narrow-band pulses, thereby achieving high-resolution range profiles without the need to increase the radar's instantaneous bandwidth. However, the inherent large time-bandwidth product of SFW introduces substantial ranging errors and energy dispersion, which significantly hinders its efficacy in detecting high-speed objects. This paper introduces a pioneering velocity estimation technique utilizing the fractional Fourier transform (FrFT) to address these limitations. Leveraging the characteristic of the Doppler signal from a moving target, which manifests as a chirp signal with a rate proportional to the target's velocity, the FrFT is utilized for precise velocity estimation. Subsequent to this, velocity compensation is applied using the deduced metrics, followed by the application of the inverse fast Fourier transform (iFFT) to pinpoint the target's exact location. To optimize the computational efficiency of determining the FrFT's optimal order, we propose an iterative algorithm founded on the golden section search method. The effectiveness of the proposed approach is verified by simulation data, and the results demonstrate that the proposed approach can accurately estimate the velocity and the range of the high-speed targets with a relatively low computational complexity.

The reflection and transmission spectra of a metamaterial formed by omega-shaped elements with pre-calculated optimal parameters on a silicon substrate have been recorded in the terahertz range at oblique incidence for the s- and p- polarizations of the incident wave. The spectra were interpreted within the dipole radiation theory of electromagnetic waves. Both measurement results and analysis provide evidence sup-porting the presence of a pronounced polarization anisotropy impact in the reflection and transmission of the metamaterial. The potential of these materials to be utilized in the development of devices that control the polarization properties of THz radiation across a wide spectral range is examined.

The VEMoS AXL system is a new optical biometer based on spectral domain optical coherence tomography (SD-OCT) that has been tested in terms of intrasession repeatability and compared with a swept-source optical coherence tomography biometer (SS-OCT), which is considered the gold standard, for an agreement analysis. A biometric analysis was performed three consecutive times in 120 healthy eyes of 120 patients aged between 18 and 40 years with the SD-OCT system and afterwards a single measurement was obtained with the SS-OCT system. Within-subject standard deviations were 0.004 mm, 4.394 µm and 0.017 mm for axial length (AL), central corneal thickness (CCT), and anterior chamber depth (ACD) measures obtained with the SD-OCT biometer, re-spectively. The agreement between devices was good for AL (Limits of agreement, LoA: -0.04 to 0.03 mm) and CCT (LoA: -4.36 to 14.38 µm), whereas differences between devices were clinically relevant for ACD (LoA: 0.03 to 0.21 mm). In conclusion, the VEMoS AXL system provides consistent measures of anatomical parameters, being most of them interchangeable with those provided by the SS-OCT-based gold standard.

The effects of different pulse frequencies on the microstructure grain size and solid solubility of Al-9Si alloy were systematically investigated using OM, SEM, and EDS. The effects on the mechanical properties of the alloy were analyzed using micro-Vickers hardness tester and multifunctional friction tester. During the solidification process, the Al-9Si alloy is exposed to high-frequency electric current pulses with a current density of 300A/cm2 and frequencies of 0Hz, 500Hz, 1 000Hz, and 2 000Hz. The experimental results show that as the high-frequency pulse frequency increases, the Lorentz force also increases. The intense electromagnetic stirring leads to grain refinement. However, as the pulse frequency continues to increase, the combined effect of Joule heating and Lorentz force results in an enlargement of the melt zone and an increase in grain size. At a pulse frequency of 1 000Hz, the eutectic structure size of the Al-9Si alloy is optimal, with the average size reduced to 13.87μm and a dense distribution, effectively eliminating primary Si. The EDS results revealed that the high-frequency pulse led to a more uniform distribution of Si elements within the matrix, and the solid solubility of Si in the α-Al matrix increased to a maximum value of 1.99%, representing a 39.2% increase. At a pulse frequency of 1 000Hz, the sample demonstrates the most favorable mechanical properties, with the friction coefficient reaching a minimum value of 0.302, representing a 37.7% decrease in the average friction coefficient. The results demonstrate that high-frequency pulsing is an effective method for enhancing the mechanical properties of Al-9Si alloy.

Predicting and evaluating the vitality level of public spaces of urban design is crucial before new construction and renewal, especially for developing countries, to avoid failures like "ghost neighborhood" and reduce resource waste. However, existing assessment methods for the urban vitality are either static and ignore changes in the time dimension, or rely on historical big data and are unable to predict unbuilt projects. This study combines previous methods and crowds’ characteristics observation in public space, puts forward the crowd-frequency hypothesis and constructs an algorithm, and establishes a time-dimensional urban vitality dynamic prediction model. Taking the Rundle Mall neighborhood in Adelaide, Australia, as an example, the effec-tiveness of the prediction model was proved through on-site observation sampling comparative verification method. In addition, the decision tree model was used to conduct machine learning on the case data, and a set of algorithm programs were obtained that can directly input some basic information to obtain the urban vitality level (high-medium-low) in a certain period. It can be used as a tool to support the design and decision-making processes of urban planners and government officials, providing a low-cost way to achieve sustainable urban vitality construction of late-developing cities directly.

SRC homology 3 (SH3) domains are critical interaction modules that orchestrate the assembly of protein complexes involved in diverse biological processes. They facilitate transient protein-protein interactions by selectively interacting with proline-rich motifs (PRMs). A database search revealed 298 SH3 domains in 221 human proteins. Multiple sequence alignment of human SH3 domains is useful for phylogenetic analysis and determination of their selectivity towards PRM-containing peptides (PRPs). However, a more precise functional classification of SH3 domains is achieved by constructing a phylogenetic tree only from PRM-binding residues and using existing SH3-PRP structures and biochemical data to determine the specificity within each of the 10 families for particular PRPs. In addition, the C-terminal proline-rich domain of the RAS activator SOS1 covers 13 of the 14 recognized proline-rich consensus sequence motifs, encompassing differential PRP pattern selectivity among all SH3 families. To evaluate binding capabilities and affinities, we conducted fluorescence dot blot and polarization experiments using 25 representative SH3 domains and various PRPs derived from SOS1. Our analysis has identified 45 interacting pairs with binding affinities ranging from 0.2 to 125 micromolar out of 300 tested and potential new interactors of SOS1. Furthermore, it establishes a framework to bridge the gap between SH3 and PRP interactions and provides predictive insights into potential interactions of SH3 domains with PRMs based on sequence specifications. This novel framework has the potential to enhance the understanding of protein networks mediated by SH3-PRM interactions and be utilized as a general approach for other domain-peptide interactions.

While machine learning (ML) has been quite successful in the field of structural health monitoring (SHM), its practical implementation has been limited. This is because ML model training requires data containing a variety of distinct damaged instances captured from a real structure and the experimental generation of such data is challenging. One way to tackle this issue is by generating training data through numerical simulations. However, simulated data cannot capture the bias and variance of experimental uncertainty. To overcome this problem, this work proposes a deep learning-based domain transformation method for transforming simulated data to the experimental domain. Use of this technique has been demonstrated for debonding location and size prediction of stiffened panels using a vibration-based method. The results are satisfactory for both, debonding location and size prediction. This domain transformation method can be used in any field where experimental data for training machine learning models is scarce.

In recent years, Synthetic Aperture Radar (SAR) has received attention in the Automatic Target Recognition (ATR) field due to its ability to acquire images both day and night, combined with the superior resolution offered by the latest sensors. However, despite advances, there remains a deficiency in dataset quality and quantity. This has necessitated the use of simulated data to overcome challenges associated with obtaining large quantities of high-quality images of targets and their subsequent labeling. In this study, we trained a Convolutional Neural Network (CNN) using a simulated dataset, subsequently employing it to classify real images from the MSTAR dataset. Traditional training methodologies often struggle with generalization when confronted with domain disparities between simulated and real data. To mitigate this, we exploited Margin Disparity Discrepancy (MDD), a domain adaptation technique not previously explored in SAR context. Findings from this study showcased a 13% increase in classification accuracy and improved generalization using MDD. Through Explainable AI (XAI) techniques such as t-SNE and other metrics like Kullback Leibler Divergence (KLD), we revealed a more distinct alignment of feature spaces between the two domains after domain adaptation, emphasizing MDD’s role in enhancing model accuracy and generalization.

This paper presents the design and measurement of an IEEE 802.11ad standard compatible RF transceiver for 60GHz wireless communication system. In addition to the traditional half-duplex (HD) mode, this work supports full-duplex (FD) operation to deliver better channel utilization and faster response time for the system. The isolation between the transmitter and receiver from the architecture design to system integration for FD operation has been fully considered. A digital self-interference cancellation (DSIC) is implemented in MATLAB to verify the FD performance. The super-heterodyne architecture with an intermediate frequency (IF) of 12GHz is designed to suppress the image frequencies without using extra filters. A flexible phase-locked loop (PLL) synthesizer provides a local oscillator (LO) frequency with a 2kHz resolution. Other than the time division duplex (TDD) mode used in the conventional 60GHz system, a wide bandwidth baseband digital variable gain amplifier (DVGA) with a 3dB bandwidth of more than 4GHz also supports frequency division duplex (FDD) operation. The transceiver chip is fabricated in Tower Jazz 0.18µm SiGe BiCMOS process. With on-board antenna, the transceiver covers all four channels in 802.11ad standard with MCS-12 (7.04Gbps under 1.76GSym/s and 16-QAM) under 1.5m. In the proposed system design, the RF frontend based self-interference (SI) suppression from the local transmitter to receiver LNA is around 54dB. To achieve a practical FD application, the SI is further suppressed with the help of digital SI compensation. The measured power consumption for transmitter and receiver configuration is 194mW and 231mW, respectively, in HD mode and 398mW for FDD or FD operation mode.

We are currently investigating the ultrasound imaging of a sensor consisting of a randomized encoding mask attached to a single lead zirconate titanate (PZT) oscillator for use in a puncture microscope. The proposed model was performed using a finite element method (FEM) simulator. To increase the number of measurements required by a single element system, the transducer was rotated at different angles. The image was constructed by solving a linear equation of the image model. In a previous study, the image resolution was improved by extracting phase information. In this study, we propose a strategy by integrating the weighted frequency subbands compound and a super resolution method (SCM) to improve the resolution in range and lateral resolution. We also evaluated the image performance by applying different methods to improve the image quality. The results show that better image resolution and speckle suppression were obtained by applying the proposed method.

ShK domain-containing proteins are peptides found in different parasitic and venomous organisms. From a previous transcriptomic dataset from Bursaphelenchus xylophilus, a plant-endoparasitic nematode that infects forest tree species, we identified 96 transcripts potentially as ShK domain-containing proteins with unknown function in the nematode genome. This study aimed to characterize and explore the functional role of genes encoding ShK domain-containing proteins in B. xylophilus biology. We selected and functionally analyzed nine candidate genes specific from B. xylophilus. In situ hybridization revealed expression of one B. xylophilus ShK in the pharyngeal gland cells, suggesting their delivery into host cells. Most of the transcripts are highly expressed during infection and showed a significant upregulation in response to peroxide products compared to the nematode catalase enzymes. We reported for the first time, the potential involvement of ShK domain genes in oxidative stress, suggesting that these proteins may have an important role protecting or modulating the reactive oxygen species (ROS) activity of the host plant during parasitism.

In this paper, a buck DC-DC converter with the proposed twin frequency control scheme (TFCS) and accurate load current sensing (ALCS) have been designed and implemented with 0.18µm CMOS technology for a supply voltage ranging from 2.0-3.0V which is compatible with state-of-the-art batteries (NiCd/NiMH: 1.1-2V, Li-Ion: 2.5-4.2V). The proposed converter yields a peak efficiency of about 92.7% with a load current of 30mA. Furthermore, it only occupies a silicon area of 1.3mm2. The proposed buck converter is dedicated for smartphone applications whereby it spends most of its time in idle, low load conditions.

To investigate the variation patterns of spectral characteristics of main branches in fruit trees due to uneven distribution of growth, pruning, and fruit quality, as well as to determine the optimal fruit vi-bration harvesting, a vibration differential model of tree branches was constructed using the concen-trated mass method. A three-dimensional model of the fruit tree was built using finite element software. The spectral characteristics of each branch were obtained using the linear sweep frequency method. The method of changing the mass of each branch was employed to simulate the variation in branch mass. The frequency of occurrence, mean acceleration, and coefficient of variation of the inherent frequencies were used as evaluation indicators. The results revealed that when the vibration frequency of the fruit tree was at the peak frequency in the tree trunk spectrum, the frequency of occurrence and mean accel-eration were optimal. And the coefficient of variation of branch acceleration was less than 1, indicating a moderate dispersion. Additionally, reducing the branch mass resulted in an increase in the peak fre-quency of the tree trunk spectrum. Therefore, the growth, pruning, and uneven distribution of the branches in fruit trees can alter the spectral characteristics. When harvesting fruit by vibration, selecting the peak frequency in the trunk vibration spectrum as the excitation force frequency yields the best fruit detachment.

Recently, more wind turbine systems are being installed in deep waters far from the coast. Several concepts of floating wind turbine systems (FWTSs) are developed, among which the semisubmersible platform, due to its applicability in different water depths, good hydrodynamic performance, and facility in the installation process, constitutes the most explored technology compared to the others. However, a significant obstacle to the industrialization of this technology is a design of a cost-effective FWTS, which can be achieved by optimizing the geometry, size and weight of the floating platform, along with the mooring system. This is only possible by selecting a method capable of accurately analyzing the FWTS coupled hydro-aero-structural dynamics at each design stage. Accordingly, this paper aims at providing a detailed overview of the most common coupled numerical and physical methods, including their basic assumptions, formulations, limitations, and costs, used for analyzing the dynamics of FWTSs, mainly those supported by a semisubmersible, to assist the choice of the most suitable method at each design phase of FWTSs. Finally, the article discusses possible future research to address challenges in modeling FWTSs dynamics that persist to date.

The effects of four cycles of recurrent selection on simple sequence repeat (SSR) marker allele frequencies and population structure were examined in the Maksimir 3 Synthetic (M3S) maize population (Zea mays L.). Genotyping of 32 plants from each cycle of selection at 38 SSR loci revealed that the mean number of alleles per locus and the mean expected heterozygosity were preserved over cycles of selection, indicating maintenance of sufficient genetic variability in the population needed for future genetic gain. Waples test of selective neutrality revealed that genetic drift was the main force in changing allele frequencies in the population. The proportion of selectively nonneutral loci in single cycles of selection varied between 16% and 37%. Some nonneutral loci shared the same genomic locations with previously published QTLs controlling important agronomic traits. Between 5% and 29% of loci were found to be in significant Hardy-Weinberg (HW) disequilibrium with the majority showing an excess of homozygosity. Excess of homozygosity at several loci was highly consistent across cycle populations suggesting positive assortative mating as the possible cause of the observed HW disequilibrium. Linkage disequilibrium (LD) tests revealed that the M3S population was essentially in linkage equilibrium. The proportion of pairs of loci in significant LD varied across cycles of selection between 0.1% and 1.8% probably due to the effects of genetic drift and epistatic selection.

So far, scientific studies on high frequency welding (HF) have observed the three main parameters: anode current, welding time and pressure of upper electrode. Some authors have also observed and studied the welding frequency, the power, and the values of the dielectric constants of the materials or the angle of dielectric losses. Due to the complex effects that the parameters have on the quality of the welded joint, it is necessary to extend the research and study the mutual relationships between a large numbers of process parameters on the welded joint in order to achieve satisfactory strength of the welded joint. In this research, the number of considered parameters is increased to 17, each of which is relevant to the processes of HF welding, and their mathematical equations are presented. The research is carried out on a HF welding machine improved with a linear pneumatic actuator for lowering the upper electrode and regulating the compressive force, and a measuring machine with the appropriate measuring method for determining the breaking forces with a tension gage and performing the „T“ test is made. Numerous researches were carried out on the functional changes of the process parameters of the percentage share of the values of the coupling capacitor, the anode current and the welding time on the values of the breaking forces of the welded joints. Based on the conducted research, it was concluded that the optimization of the process parameters of HF machine must be performed before the welding process, which is determined by a certain amount of energy that must be introduced into the material. These parameters are the capacity of the output capacitor, i.e., the value of the electric current flowing through the material and the time for the development of the operating power to produce welded joints with a certain strength and quality of welded joints for a specific material used in the process.

The receptor-type protein tyrosine phosphatases (RPTPs) are involved in a wide variety of physiological functions which are mediated via their diverse extracellular regions. They play key roles in cell-cell contacts, bind various ligands and are regulated by dimerization and other processes. Depending on the subgroup they have been described as everything from ‘rigid rods’ to ‘floppy tentacles’. Here we review current experimental structural knowledge on the extracellular region of RPTPs and draw on AlphaFold structural predictions to provide further insights into structure and function of these cellular signalling molecules.

To search for novel thermostable esterase for optimized industrial applications, esterase from a thermophilic eubacterium species, Thermoanaerobacter tengcongensis MB4 was purified and characterised in this work. Sequence analysis of T. tengcongensis esterase with other homologous esterases of the same family revealed an apparent tail at the C-terminal that is not conserved across the esterase family. Hence, it was hypothesized that the tail is unlikely to have an essential structural or catalytic role. However, there is no documented report of any role for this tail region. We probed the role of the C-terminal domain on the catalytic activity and substrate preference of Thermoanaerobacter tengcongensis esterase EstA3 with a view to see how it could be engineered for enhanced properties. To achieve this, we cloned, expressed, and purified the wild-type and the truncated version of the enzyme. In addition, a naturally occurring member of the family (from B. brevis) that lacks the C-terminal tail was also made. In vitro characterization of the purified enzymes showed that the C-terminal domain contributes significantly to the catalytic activity and distinct substrate preference of T. tengcongensis esterase EstA3. All the three recombinant enzymes showed highest preference to paranitrophenyl butyrate (pNPC4), which suggests they are true esterases, not lipases. kinetic data revealed that truncation had a slight effect on the substrate-binding affinity. Thus, the drop in preference towards long-chain substrates might not be a result of substrate binding affinity alone.

This study compared cortical responses to speech in preschoolers with typical language development (TLD) and Developmental Language Disorder (DLD), and in adults. We investigated whether top-down language effects modulate speech perception in an adult-like manner. We compared cortical mismatch responses (MMR) during passive perception of speech differing in linguistic complexity in three groups of participants: preschoolers with TLD (n=11), preschoolers with DLD (n=16) and adults (n=20). We also measured children’s phonological skills and investigated if they are associated with the cortical responses. MMR results indicated top-down language effects in adults, with enhanced cortical discrimination of lexical stimuli but not of non-words. In preschoolers, the TLD and DLD groups did not differ on the MMR measures and no top-down effects were detected. Moreover, we found no association between MMRs and phonological awareness skills, even though the DLD group’s phonological skills were significantly lower. Our findings suggest that top-down language modulations on speech discrimination may not be present during early childhood, and that children with DLD may not exhibit cortical speech perception deficits. The lack of association between phonological and MMR measures indicates that further research is needed to understand the link between language skills and cortical activity in preschoolers.

Diversity-generating retroelements (DGRs) are prokaryotic systems providing rapid modification and adaptation of target proteins. In phages, the main targets of DGRs are receptor-binding proteins that are usually parts of tail structures and the variability of such host-recognizing structures enables phage adaptation to changes on the bacterial host surface. Sometimes, more than one target gene containing a hypermutated variable repeat (VR) can be found in phage DGRs. The role of mutagenesis of two functionally different genes is unclear. In this study, several phage genomes that contain DGRs with two target genes were found in the gut virome of healthy volunteer. Bioinformatics analysis of these genes indicated that they encode proteins with different topology; however, both proteins contain the C-type lectin (C-lec) domain with a hypermutated beta-hairpin on its surface. One of the target proteins belongs to a new family of proteins with a specific topology: N-terminal C-lec domain followed by one or more immunoglobulin domains. Proteins from the new family were named tentaclins after TENTACLe+proteIN. The genes encoding such proteins were found in the genomes of prophages and phages from the gut metagenomes. We hypothesized that tentaclins are involved in binding either to bacterial receptors or intestinal/immune cells.

This study explores the intricate relationship between the angle of vehicle-to-vehicle (V2V) conflicts and the severity of conflicts at signalized intersections during traffic signal failures, leveraging the advanced capabilities of LiDAR sensor technology. The research delves into the impact of conflict angles on collision outcomes and introduces LiDAR as a novel and precise tool for capturing spatial dynamics during unregulated traffic movements. Findings reveal that as the angle of V2V conflicts increases, indicating a wider approach between vehicles, the severity of conflicts tends to decrease. This reduction in severity is attributed to the detailed spatial data provided by LiDAR, enabling the identification of glancing impacts rather than direct head-on collisions. The mitigation of forces involved in collisions at increased angles contributes to a decreased likelihood of severe consequences, such as extensive vehicle damage and serious injuries to occupants.Conversely, as the angle of V2V conflicts decreases, signifying a narrower approach between vehicles, the potential for severe collisions tends to increase. LiDAR technology facilitates precise measurements of spatial coordinates and temporal dynamics, revealing that collisions occurring at smaller angles involve more direct and concentrated impact forces. This heightened risk of extensive damage to vehicles and increased injury severity is crucially captured by LiDAR, providing valuable insights into the relationship between conflict angles and collision outcomes during traffic signal failures. This study underscores the efficacy of LiDAR sensor technology in understanding the nuanced dynamics of V2V conflicts and highlights its role in developing targeted safety interventions. The integration of LiDAR data into traffic safety analyses offers a sophisticated approach to mitigating the impact of conflicts and enhancing overall safety at signalized intersections during signal failures. The research also highlighted that as the angle of V2P conflicts widens, PET experiences a proportional increase. A larger conflict angle signifies a more complex interaction between vehicles and pedestrians, leading to prolonged PET values. Conversely, a decrease in the angle of V2P conflicts is associated with a reduction in PET during traffic signal failures.The study commences with an examination of traffic characteristics at the E Cold Spring Ln – Hillen Rd intersection in Baltimore City, MD, utilizing a deployed LiDAR sensor. When the signal at this intersection experienced a temporary malfunction on a regular working day in September 2023, the LiDAR captured data related to vehicle and pedestrian volumes, as well as V2V and V2P conflicts. The primary objective of the research is to evaluate the repercussions of traffic signal failures on both traffic flow and safety, specifically V2V and V2P conflicts, occurring during periods of improper signal functionality. Additionally, this investigation delves into various factors that impact traffic signal performance, encompassing traffic demand, geometric layout, pedestrian interactions, and the incorporation of emerging technologies.

Ambient vibration energy is widely being harnessed as a source of electrical energy to drive low-power devices. The vibration energy harvester (VEH) of interest employs an electromagnetic transduction mechanism, whereby ambient mechanical vibration is converted to electrical energy. The limitations affecting the performance of VEHs, with an electromagnetic transduction structure, include its operational bandwidth as well as the enclosure-size constraint. In this study, an analysis and design of a nonlinear VEH system is conducted, using the Output Frequency Response Function (OFRF) representations of the actual system model. However, the OFRF representations are determined from the Generalised Associated Linear Equation (GALE) decompositions of the system of interest. The effect of both nonlinear damping and stiffness characteristics, to respectively extend the average power and operational bandwidth of the VEH device, is demonstrated.

This paper presents the design and experimental results of a long cavity length Nd: YAG laser with large stable zone for water jet guided laser (WJGL) applications. The design is based on the light transmission matrix and resonator stability conditions, aiming to achieve a large stable zone and a short cut-off thermal focal length (CTFL). A folded concave resonator is researched to enhance the cavity length, and the influence of the tun-able cavity arm length on the oscillating beam in the resonator and in the YAG crystal is theoretically studied. Moreover, the effects of the output mirror curvature and the cavity arm length on the range of the stable area and the cut-off thermal focal length are also investigated. Experimental results show that a stable green laser output is obtained after second harmonic generation (SHG), with a pulse width ranging from 43ns to 143ns within the laser operating frequency range of 5-20kHz. At an operation frequency of 10kHz, the output power is 21.33W, and the instability of the output power within 400 minutes is 0.88%. The laser source achieves a maximum power of 25.7W at 20kHz, and the maximum single pulse energy reaches 2.7mJ at 6kHz. Finally, this is used as the laser source to couple with a water jet with a diameter of 100 microns, achieving a lossless water conductivity transmission over 60mm length with a peak power density of 0.742GW/cm2. These results demonstrate the suitability of the designed laser source for WJGL technology research.

The processes of hydrogen reduction of silicon and germanium chlorides under the conditions of high-frequency (40.68 MHz) counteracted arc discharge stabilized between two rod electrodes are investigated. The main gas-phase and solid products of plasma-chemical transformations are determined. Thermodynamic analysis of SiCl4 + H2 and GeCl4 + H2 systems for optimal process parameters was carried out. Using the example of hydrogen reduction of SiCl4 by the method of numerical modeling, gas-dynamic and thermal processes for this type of discharge are investigated. The impurity composition of gas-phase and solid reaction products is investigated. The possibility of single-stage production of high-purity Si and Ge mainly in the form of compact ingots, as well as high-purity chlorosilanes and trichlorogermane, is shown.

Multiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity and hence improves coverage. If the GWs are within vicinity to each other, and they keep sending periodic beacons to be discovered, the result is interference and collisions. In this study, the impact of such intra-network interference is analyzed to determine the maximum number of GWs that can co-exist within a given urban neighborhood. The paper shows the relationship between beacon collisions and the resulting packet loss rates in a given urban environment. The expected collision rates are calculated for the Medium Access Control (MAC) layer. The eventual packet loss rates are calculated with the MAC and Physical (PHY) layers combined. For this purpose, a new co-channel interference model is presented. The model calculates the average Carrier to Interference ratio (C/I) averaged across all the colliding events. It takes into account the partial overlap durations and the relative power of each colliding signal. The model shows that a collision does not always cause a packet loss. Packet losses occur if there is a collision and if the C/I is below a threshold. The performance evaluation is presented using a combination of analytical and simulation methods. The analytical methods provide the theoretical foundation, and have been used to validate the simulation results. Furthermore, the system models are developed from experimental data obtained from measurements with real hardware. Numerical results are provided with a low bit rate Gaussian Frequency Shift Keying (GFSK) modulation at the PHY layer, which is derived from the IEEE 802.15.4 standard specification. This paper provides guidance on selecting the right GFSK modulation parameters for such low bit-rate and narrow bandwidth IoT applications. The analysis and simulation results show that larger beacon intervals help in reducing beacon loss rates, at the cost of larger beacon acquisition latency. On the flip side, the gateway discovery latency reduces with increasing GW density thanks to abundance of beacons.