As a behavior of bilingual individuals and an indispensable part of bilingual speech, code-switching has been investigated by many researchers. However, there are many variables influencing code-switching, and each variable has the potential to be a confounding variable. Among these variables is the gender; however, whether there are significant gender differences and what are the gender differences in code-switching remains unknown for Mandarin Mandarin-English child bilinguals, as previous literature diverse on the existence of gender differences. Therefore, this paper seeks potential code-switching and distribution of code-switching by quantitative analysis of speech data in Singapore Bilingual Corpus. The results indicate that gender differences are significant in the amount of intra code-switching. However, neither considerable gender difference is observed in the amount of inter nor the code-switching related environment.

In bilingual communities, social interactions take place in both single- and mixed-language con-texts. Some of the information shared in multilingual conversations is often required in conse-quent social encounters, like interlocutors’ personal information. In this study we explored whether the autobiographical information provided in a single-language context is better re-membered than in an equivalent mixed-language situation. More than 400 Basque-Spanish bilin-gual (pre-)teenagers were presented with new persons who introduced themselves by either using only Spanish or only Basque, or by inter-sententially mixing both languages. Different memory measures were collected immediately after the initial exposure to the new pieces of information (immediate recall and recognition) and on the day after (delayed recall and recognition). In none of the time points was the information provided in a mixed-language fashion worse remembered than that provided in a strict one-language context. Interestingly, the variability across partici-pants in their sociodemographic and linguistic variables had a negligible impact on the effects. These results are discussed considering their social and educational implications for bilingual communities.

We present improvements in n-best rescoring of code-switched speech achieved by n-gram augmentation as well as optimised pretraining of long short-term memory (LSTM) language models with larger corpora of out-of-domain monolingual text. In addition, we consider the application of large pretrained transformer-based architectures. Our experimental evaluation is performed on an under-resourced corpus of code-switched speech comprising four bilingual code-switched sub-corpora, each containing a Bantu language (isiZulu, isiXhosa, Sesotho, or Setswana) and English. We find in our experiments that, by combining n-gram augmentation with the optimised pretraining strategy, speech recognition errors are reduced for each individual bilingual pair by 3.51% absolute on average over the four corpora. Importantly, we find that even speech recognition at language boundaries improves by 1.14% even though the additional data is monolingual. Utilising the augmented n-grams for lattice generation, we then contrast these improvements with those achieved after fine-tuning pretrained transformer-based models such as distilled GPT-2 and M-BERT. We find that, even though these language models have not been trained on any of our target languages, they can improve speech recognition performance even in zero-shot settings. After fine-tuning on in-domain data, these large architectures offer further improvements, achieving a 4.45% absolute decrease in overall speech recognition errors and a 3.52% improvement over language boundaries. Finally, a combination of the optimised LSTM and fine-tuned BERT models achieves a further gain of 0.47% absolute on average for three of the four language pairs compared to M-BERT. We conclude that the careful optimisation of the pretraining strategy used for neural network language models can offer worthwhile improvements in speech recognition accuracy even at language switches, and that much larger state-of-the-art architectures such as GPT-2 and M-BERT promise even further gains.

We demonstrate a compact 4x4 wavelength selective switch with 50 % fewer electrical pads as compared with our previous generation. We report loss and crosstalk for different paths of the switch. We measure median loss of 5.32 dB and worst case crosstalk of -35 dB. The microring resonators tune by more than one free spectral range, which is an improvement over our previous generation of switches. This switch can support 8 channels at 400 GHz spacing. We conclude that it is not possible to drive both microring resonators with the same voltage and separate control is required because of fabrication variation of the current technology.

In this paper, we review devices used in silicon photonic switches. Devices in switches are divided into active and passive devices. Active devices consist of microring resonator, contra directional couplers, mach zhender switches. Passive devices consist of waveguide crossings and arrayed waveguide gratings. We also list the state of the art in devices in a comparison table.

This paper presents a comparative analysis of the ZVZCS soft switching technique with the ZVS and the ZCS counterpart. The generalization of the voltage-current crossover or the energy loss factor obtained from simulation of the prototype converter shows that the ZVZCS significantly reduces the loss and helps to improve the efficiency of the converter as compared to the ZVS or the ZCS. On the other hand, it is also found that the soft switching range of operation of the ZVS and the ZCS are largely affected by the maximum switch voltage and switch current respectively. In contrary, these factors have a negligible effect on the ZVZCS operation which results in an extended range of soft switching operation. Additionally, a detailed LTPICE simulation is performed for selected ZVS, ZCS and ZVSCS topologies from the recent literature and the switching losses in the main switches of the converters are measured. It is observed that the energy losses in the ZVZCS mode are reduced on average by approximately 26 % at turn on and 20 % at the turn off as compared to the ZVS and the ZCS. Besides, the low standard deviation in this mode confirms a stable low loss profile which renders extended soft switching range. An experimental test is also conducted by building the prototype converter to verify the simulation results. It is found that the switching losses are minimum while the converter is operated in the ZVZCS mode. Besides, the efficiency drop remains consistently low as compared to the ZVS and the ZCS in the whole operating range. Resultantly, the simulation and the experimental results are both found to be consistent.

Code summarization plays a vital role in aiding developers with program comprehension by generating corresponding textual descriptions for code snippets. While recent approaches have concentrated on encoding the textual and structural characteristics of source code, they often neglect the global hierarchical features causing limited code representation. Addressing this gap, our paper introduces the Statement-grained Hierarchy enhanced Transformer model (SHT), a novel framework that integrates global hierarchy, syntax, and token sequences to automatically generate summaries for code snippets. SHT is distinctively designed with two encoders to learn both hierarchical and sequential features of code. One relational attention encoder processes the statement-grained hierarchical graph, producing hierarchical embeddings. Subsequently, another sequence encoder integrates these hierarchical structures with token sequences. The resulting enriched representation is then fed into a vanilla Transformer decoder, which effectively generates concise and informative summarizations. Our extensive experiments demonstrate that SHT significantly outperforms state-of-the-art approaches on two widely used Java benchmarks. This underscores the effectiveness of incorporating global hierarchical information in enhancing the quality of code summarizations.

To solve the problem of smooth switching between car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver's behavior switching mechanism of normally following, generating intentions to change lanes, creating space and speed gains, and performing lane change. In the case of sufficient lane-changing space and speed gains, the ego vehicle's intention to change lane was considered to solve the switching boundary between car-following behavior and lane-changing behavior, which is also the IDM failure point. In the event that there is no lane-changing gains, the IDM was optimized by incorporating the constraint components of the target lane vehicles in conjunction with the actual motion state of the ego vehicle, and the Stepless Switching Intelligent Driver Model (SSIDM) was constructed. Drivers' natural driving information was collected and scenario mining was performed on structured roads. On the basis of the collected data, an elliptic equation was used to fit the behavior switching boundary, and the two component balance coefficients of the front and rear vehicles on the target lane were identified. According to the test set verification results, the Mean Square Error (MSE) of the SSIDM is 2.172, which is 57.98% less than that of the conventional single-lane IDM. The SSIDM can accomplish stepless switching comparable to driver's behavior between the car-following behavior and the lane-changing behavior, with greater precision than IDM.

The field of program classification, a critical aspect of software engineering, facilitates understanding and categorization of code across various applications, including anomaly detection, and code quality assessment. The advancement of cross-language program classification opens up avenues for efficient code translation among different programming languages, significantly aiding developers in rapid coding and reducing development cycles. Existing research primarily focuses on semantic code analysis, with limited emphasis on cross-linguistic challenges. To address this, we introduce an innovative neural network model, CodeSemanticsNN, which leverages a refined Syntax Structure (SS) approach. This model consists of two integral mechanisms: first, it adopts a novel SS representation that combines sequential and graph-based SS structures, enhancing semantic feature capture. Second, it employs a 'unified vocabulary' strategy to bridge the gap between diverse programming languages, facilitating efficient cross-language classification. Additionally, we have compiled a comprehensive dataset of 20,000 files spanning five programming languages, serving as a benchmark for cross-language classification. Our experiments on this dataset demonstrate that CodeSemanticsNN surpasses existing models in four key metrics: Precision, Recall, F1-score, and Accuracy.

Context: Code smells are associated with poor design and programming style that often degrades code quality and hampers code comprehensibility and maintainability. Goal: Identify reports from the literature that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We conducted a Systematic Literature Review (SLR) to reach the~stated goal. Results: The SLR includes selected studies from July 2007 to September 2017 which analyzed the source code for open source and proprietary projects, as well, as several code smells and anti-patterns. The results of this SLR show that 24 code smells are more influential in the occurrence of bugs according to 16 studies. In contrast, three studies reported that at least 6 code smells are less influential in such occurrences. Evidence from the selected studies also point out tools, techniques and procedures applied to analyze the influence. Conclusion: To the best of our knowledge, this is the first SLR to target this goal. This study provides an up-to-date and structured understanding of the influence of code smells on the occurrence of software bugs based on findings systematically collected from a list of relevant references in the latest decade.

The challenge of achieving ultrafast switching of electric polarization in ferroelectric materials remains unsolved, as there is no experimental evidence of such switching to date. In this study, we have developed an enhanced model that describes switching within a two-dimensional space of generalized coordinates. Our findings indicate that stable switching in barium titanate cannot be achieved through a single linearly polarized pulse. When the intensity of the linearly polarized pulse reaches a certain threshold, the sample experiences depolarization, but not stable switching. Our study also reveals that phonon friction plays a minor role in the switching dynamics and provides an estimate of the optimal parameters of the perturbing pulse with the lowest intensity that results in depolarization of an initially depolarized sample.

The Supersymmetry Genetic code (SSyGC) table is based on five physicochemical symmetries: 1) double mirror symmetry on the principle of the horizontal and vertical mirror symmetry axis between all bases (purines [A, G) and pyrimidines (U, C)], and 2) of bases in form of codons 3) direct – complement like codon/anticodon symmetry in the sixteen alternating boxes of the genetic code columns, 4) A+T rich and C+G rich alternate codons in the same row between both columns of the genetic code, 5) the same position between divided and undivided codon boxes in relation to horizontal mirror symmetry axes. The SSyGC table has a unique physicochemical purine – pyrimidine symmetry net which is as the core symmetry common for all, more than thirty different nuclear and mitochondrial genetic codes. This net is present in the SSyGC table of all RNA and DNA living species. None of these symmetries is present in the Standard Genetic Code (SGC) table which is constructed on the alphabetic horizontal and vertical U-C-A-G order of bases. Here we show that the free energy value of each codon incorporated as fundamentally mapping the “energy code” in the SSyGC table is compatible with mirror symmetry. On the other hand, in the SGC table the same free energy values of codons are dispersed and a mirror symmetry between them is not recognizable. At the same time the mirror symmetry of the SSyGC table and the DNA quadruplets together with our classification of codons/trinucleotides are perfectly imbedded in the mirror symmetry energy mapping of codons/trinucleotides and point out in favor of maintaining the integrity of the genetic code and DNA genome. We also argue that physicochemical symmetries of the SSyGC table in the manner of the purine – pyrimidine symmetry net, the quadruplet symmetry of DNA molecule and the free energy of codons have remined unchanged during all of evolution. The unchangeable and universal symmetry properties of the genetic code, DNA molecule and energy code are decreasing disorder between codons/trinucleotides and shed a new light on evolution. Diversity in all living species on Earth is broad, but the symmetries of the Supersymmetry Genetic Code as the code of life and the DNA quadruplets related to the” energy code” are unique, unchangeable and have the power of natural laws.

The paradigm of leveraging code as a dataset has recently gained traction, offering innovative solutions in domains such as automated commit message generation, pull request description automation, and program repair mechanisms. Consider the challenge in generating commit messages: traditional methods treat source code as a mere token sequence, applying neural machine translation models. This approach, however, overlooks the critical syntactic structures inherent in programming languages, which could offer deeper insights and improved accuracy. Building upon prior research, specifically the Code2Seq framework, which utilized Abstract Syntax Tree (AST) structural data for source code representation to automate method name generation, this paper extends and refines this concept. We introduce "CSR", a novel methodology adapted to represent code edits effectively. This paper investigates the impact of employing syntactic structure, focusing on the classification of code edits. Drawing inspiration from Code2Seq, "CSR" utilizes AST's structural properties, particularly the paths connecting leaf nodes, to enhance the task of code edit classification. This approach is rigorously evaluated on two distinct datasets, comprising fine-grained syntactic edits. Our comprehensive experiments reveal that incorporating syntactic structures does not significantly outperform simpler methodologies. While methods like Code2Seq and our proposed "CSR" show potential, our findings highlight that there is considerable scope for improvement and refinement before such techniques can be universally applied for learning representations of code edits. We anticipate that our findings will spark further research in this field, paving the way for more effective use of syntactic structures in code representation.

Security is a major topic in recent researchers, and finding new methods deliver was the motivations for this research to emphasize and as an awareness to browsers developers to handle images with more care and to enhance exploit detection technology in the browser, as this paper proves that hackers now can attack users with just an image.

To assist Java programming learning of novice students, we have developed the web-based Java programming learning assistant system (JPLAS). JPLAS provides several types of exercise problems to cultivate code reading/writing skills at various levels. In JPLAS, the code writing problem (CWP) asks a student to write a source code that will pass the test code given in the assignment where the correctness is verified by running them on JUnit. In this paper, to reduce the teacher load at marking process, we present the answer code validation program that verifies all the source codes from a lot of students for each assignment at a time and reports the number of passing tests for each source code in the CSV file. Besides, to test a source code with various input data, we implement the test data generation algorithm that identifies the data type, generates a new data, and replaces it for each test data in the test code. Furthermore, to verify the correctness of the implemented procedure in the source code, we introduce the intermediate state testing in the test code. For evaluations, we applied the proposal to source codes and test codes in a Java programming course in Okayama university, Japan, and confirmed the validity and effectiveness.

We consider a stochastic production planning problem with regime switching. There are k≥1 regimes corresponding to different economic cycles. The problem is to minimize the production costs and analyze the problem by the value function approach. Our main contribution is to show that the optimal production is characterized by an exact solution of an elliptic system of partial differential equations. A verification result is given for the determined solution.

The art of representing source code is pivotal in numerous programming analysis applications. Recent strides in neural networks have marked notable successes in this realm. However, the peculiar structural characteristics inherent in programming languages have not been fully exploited in existing models. While neural models based on abstract syntax trees (ASTs) adeptly manage the tree-like nature of source codes, they fall short in discerning the diverse substructural nuances within programs. This paper introduces the Dynamic Syntax Tree Model (DSTM), an innovative approach that fuses various neural network modules into tree architectures tailored to the specific AST of the input. Distinct from preceding tree-based neural models, DSTM adeptly discerns the semantic variances across different AST substructures. We validate DSTM through rigorous testing in program classification and code clone detection, outperforming contemporary methods and demonstrating the benefits of harnessing intricate source code structures.

The digital representation of various signals allows, at the subsequent stages of their transmission, to apply correction codes that provide protection against possible errors arising from the action of interference in the communication channel. At the same time, it is important that, with the required correcting ability, these codes have the maximum possible speed. The article presents the results of calculations for linear codes, showing their really achievable limiting capabilities.

Understanding the mechanism, how entirely new (EntNew) gene/protein or the first ancestral gene/protein of a family was created, should be one of the most important issues in the biological sciences. However, the mechanism is totally unknown still now. On the other hand, it is well known that mature protein is generally rigid and one catalytic center exists on the protein. Creation of such a mature EntNew gene/protein should be, of course, carried out through random process, because it cannot be designed in advance. However, the EntNew gene/protein never be created by random polymerization of the respective monomeric units, because of the extraordinary large sequence diversities of ~10¹⁸⁰ and ~10¹³⁰, respectively. Protein 0^th-order structure or a specific amino acid composition, in which immature but water-soluble protein can be produced even through random process, holds the key for solving the difficult problem. As it was fragmentally described in the previous papers how and where EntNew gene/protein was created, I describe in detail in this review three processes generating EntNew gene/protein with some flexibility under three genetic codes, the universal genetic code, SNS primitive code and GNC primeval code, and discuss why the mature gene/protein could be created through the processes.

In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We have developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We have also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms could address. Results showed that automatic machine learning platforms could provide a fast track for organizations seeking digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms could provide a viable option to many business cases and henceforth provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.

BiFeO3/La0.7Sr0.3MnO3 (BFO/LSMO) epitaxial heterostructures were successfully synthesized by pulsed laser deposition on (001)-oriented SrTiO3 single crystal substrates with Au top electrodes. Stable bipolar resistive switching characteristics regulated by ferroelectric polarization reversal was observed in the Au/BFO/LSMO heterostructures. The conduction mechanism was revealed to follow the Schottky emission model, and the Schottky barriers in high resistance and low resistance states were estimated based on temperature-dependent current–voltage curves. Further, the observed memristive behavior was interpreted via the modulation effect on the depletion region width and the Schottky barrier height caused by ferroelectric polarization reversal, combining with the oxygen vacancies migration near the BFO/LSMO interface.

This paper investigates a numerical code coupling technique to tackle multi-physics and multi-scale simulations using state-of-the-art software packages that typically address some specific modeling domain. The coupling considers the in-house FEM code, FEMuS, and the FVM code OpenFOAM, by exploiting the MED library from the SALOME platform. The present approach is tested on a buoyancy-driven fluid flow within a square cavity, where the buoyancy force constitutes the coupling term. In uncoupled scenarios, momentum and temperature equations are solved both in FEM and FVM codes without data exchange. In the coupled setting, only the OpenFOAM velocity and the FEMuS temperature fields are solved separately and shared at each time step (or vice versa). The MED library handles the coupling with the addition of suitable ad-hoc data structures that perform the field transfer between codes. Different Rayleigh numbers are investigated, comparing the outcomes of coupled and uncoupled cases with reference literature results. Additionally, a boundary data transfer application is presented to extend the capabilities of the coupling algorithm to coupled applications with separate domains. In this problem, the two domains share interfaces and boundary values on specific fields as fluxes are exchanged between the two numerical codes.

The “red code” (RD) represents the highest levels of emergency in the emergency department (ED). The study retrospectively analyzed RDs in the Regina Margherita Children’s Hospital ED, a regional referral center in north Italy, between 1 July 2020 and 30 June 2023. The aim was to describe RD characteristics and to identify significant correlations between presenting complaints and clinical management. The study includes 934 RDs (0.9% of overall ED admissions); 64% were assigned based on the Pediatric Assessment Triangle alteration. Most patients, 86.5%, followed the medical pathway, while 13.5% were surgical cases. Admission complaints were respiratory (46.9%), neuropsychiatric (26.7%), traumatic (11.8%), cardiologic (9.3%), metabolic (3.8%), and surgical (1.5%). Seventy-six per cent of patients received vascular access, and intraosseous access was obtained in 2.2% of them. In one-third of RDs, an urgent critical care evaluation was necessary, and 19% of cases required admission to the intensive care unit. The overall mortality rate was 3.4% (0.4% in ED setting). The study identified six distinct diagnostic pathways, each associated with specific characteristics in clinical presentation, management, therapeutic interventions, and outcomes: this underscored the significance of a systematic approach in improving outcomes in pediatric RD management.

In this paper, we propose a pre-configured error pattern ordered statistics decoding (PEPOSD) algorithm and discuss its application to short cyclic redundancy check (CRC)-polar codes. Unlike the traditional OSD that changes the most reliable independent symbols, we regard the decoding process as testing the error patterns, like guessing random additive noise decoding (GRAND). Also, the pre-configurator referred from ordered reliability bits (ORB) GRAND can better control the range and testing order of EPs. Offline-online structure can accelerate the decoding process. Additionally, we also introduce two orders to optimize the search order for testing EPs. Compared with CRC-aided OSD and list decoding, PEPOSD can achieve a better trade-off between accuracy and complexity.

The current framework of evolutionary theory postulates that evolution relies on random mutations generating a diversity of phenotypes on which natural selection acts. This framework was established using a top-down approach as it originated from Darwinism, which is based on observations made on complex multicellular organisms, and then modified to fit a DNA-centric view. In this article, I argue that, based on a bottom-up approach starting from the physicochemical properties of nucleic and amino acid polymers, we should reject the facts that: i) natural selection plays a dominant role in evolution, and ii) the probability of mutations is independent of the generated phenotype. I will show that the adaptation of a phenotype to an environment does not correspond to organism fitness but rather corresponds to maintaining the genome stability and integrity. In a stable environment, the phenotype maintains the stability of its originating genome, and both (genome and phenotype) are reproduced identically. In an unstable environment (i.e., corresponding to variations in physicochemical parameters above a physiological range), the phenotype no longer maintains the stability of its originating genome but instead influences its variations. Indeed, environment- and cellular-dependent physicochemical parameters define the probability of mutations in terms of frequency, nature and location in a genome. Evolution is non-deterministic because it relies on probabilistic physicochemical rules, and evolution is driven by a bidirectional interplay between genome and phenotype, the phenotype ensuring the stability of the genotype in a cellular and environment physicochemical parameter-depending manner.

The extensive use of carrier-aided smoothing code (CSC) filter has led to reduce the noise level of raw code measurements in GNSS positioning and navigation applications. However, the existing CSC technique is sensitive to the changes of the integer ambiguity and then the smoothing proce-dure needs to be restarted in the presence of cycle-slips. As the Doppler shift is instantaneous ob-servation and immune to cycle-slips, Doppler-aided smoothing code (DSC) algorithm would be more promising in challenged environment. Based on the Hatch-filter, an optimal DSC approach is proposed with the principle of minimum variance. Meanwhile, to inhibit the effect of integral cumulative error of Doppler, a balance factor is adopted to adjust the contributions of raw code and DSC. The noise level of code observable is not only affected by thermal noise, but also limited by systematic bias. Satellite code bias (SCB) has been identified in the raw code observable on each frequency for each BDS-2 satellite. By minimizing the sum of absolute value of residuals, polyno-mial segment fitting algorithm as a function of elevation-angles is applied to establish the SCB cor-rection model based on epoch-differenced Multipath (MP) deviations. Finally, numerical experi-ments demonstrate the validity and efficiency of the refined DSC filter with SCB corrections on each available frequency for BDS-2 un-GEO satellites.

Aminoacyl-tRNA synthetases (aaRSs) originated from an ancestral bidirectional gene (mirror symmetry) and through the evolution of the genetic code the twenty aaRSs exhibit a symmetrical distribution in a 6-dimensional hypercube of the Standard Genetic Code. In this work, we assume a primeval RNY code, and the Extended Genetic RNA code type II which includes codons of the type YNY, YNR and RNR. Each of the 4 subsets of codons can be represented in a 4-dimensional hypercube. Altogether these 4 subcodes constitute the 6-dimensional representation of the SGC. We identify the aaRSs' symmetry groups in each of these hypercubes. We show that each of the four hypercubes contains the following sets of symmetries for the two known Classes of synthetases: RNY: dihedral group D2; YNY: binary group Z2; YNR: amplified octahedral group Oh; and RNR: binary group Z2. We demonstrate that for each hypercube, the group of symmetries of Class 1 is the same as the group of symmetries of Class 2. The biological implications of these findings are discussed.

Infection with human immunodeficiency virus (HIV) and induced acquired immune deficiency syndrome (AIDS) represent one of the greatest health burdens worldwide. The complex pathophysiological pathways that link highly active antiretroviral therapy (HAART) and HIV infection per se with dyslipidemia make the management of lipid disorders and the subsequent increase in cardiovascular risk essential for the treatment of people living with HIV (PLHIV). Amongst the HAART regimens, darunavir and atazanavir in terms of protease inhibitors, tenofovir disoproxil fumarate, nevirapine or rilpivirine in terms of nucleoside reverse transcriptase inhibitors and non-nucleoside reverse transcriptase inhibitors respectively, and especially integrase inhibitors, have demonstrated the most favorable lipid profile emerging as sustainable options in HAART substitution. To this day, statins remain the cornerstone pharmacotherapy for dyslipidemia in PLHIV, although important drug-drug interactions with different HAART agents should be taken into account upon treatment initiation. For those intolerant or not meeting therapeutic goals, the addition of ezetimibe, PCSK9, bempedoic acid, fibrates or fish oils should also be considered. This review summarizes the current literature on the multifactorial etiology and intricate pathophysiology of hyperlipidemia in PLHIV, with an emphasis on the role of different HAART agents, while also providing valuable insights into potential switching strategies and therapeutic options.

The paper first compares the turn-off performance of two package solutions (3-lead vs 4-lead) in Super-Junction MOSFETs. It is commonly assumed that the better performance (lower switching losses) of the 4-lead is obtained thanks to the decoupling of the power and driving loop. On the contrary, in this work, the experimental results, circuit models, and Kirchhoff laws have shown that the turn-off improvement (lower turn-off losses) obtained by adopting the Kelvin source is due to the lower inductance of the driver loop in comparison with the power loop inductance, instead of the decoupling between these loops.

We recently reported that refocusing attention between temporal and spatial tasks becomes more difficult with increasing age, which could impair daily activities such as driving (Callaghan et al., 2017). Here we investigated the extent to which difficulties in refocusing attention extend to naturalistic settings such as simulated driving. 118 participants in five age groups (18-30; 40-49; 50-59; 60-69; 70-91 years) were compared during simulated driving, where they switched from a spatially focal yet temporally complex task (braking due to traffic ahead) to a spatially more distributed task (reading a motorway road sign). Sequential-Task (switching) performance was compared to Single-Task performance (road sign only) to calculate age-related switch-costs. Electroencephalography was recorded in 34 participants (17 in the 18-30 and 17 in the 60+ years groups) to explore age-related changes in the neural oscillatory signatures of refocusing attention while driving. We indeed observed age-related impairments in attentional refocusing, evidenced by increased switch-costs in response times and by deficient modulation of theta and alpha frequencies. Our findings highlight virtual reality (VR) and Neuro-VR as important methodologies for future psychological and gerontological research.

Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimisation. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimisation models in order to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches like stochastic programming or Monte Carlo Simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.

Pairs of potential and flow variables have long been established for interfacing object-oriented component models of physical systems. Recently, however, the use of triplets has been discovered for the purpose of robust modeling. New and powerful Modelica libraries have been developed such as the DLR ThermoFluid Stream library or the introduction of the Dialectic Mechanics library. Their use of triplets is entangled with a special modeling approach that uses Linear Implicit Equilibrium Dynamics. In this paper, we study the basic motivation of this approach, its benefits and drawbacks before we finally demonstrate how it beneficially impacts the generation of corresponding simulation code.

This survey proposed an evaluation model to analyze and examine different approaches to generativity. In addition to problem domain concepts, the following concepts were used to define this model: Complexity and complexity management, and Systematics view to achieve unified and integrated insight into disparate evaluation criteria. The research's approach to the said concepts is first introduced. Then, the evaluation model is presented.

The physics–biology continuum relies on the fact that life emerged from prebiotic molecules. Here, I argue that life emerged from the physical coupling between the synthesis of nucleic acids and the synthesis of amino acid polymers. Owing to this physical coupling, amino acid polymers (or proto-phenotypes) maintained the physicochemical parameter equilibria (proto-homeostasis) in the immediate environment of their encoding nucleic acids (or proto-genomes). This protected the proto-genome physicochemical integrity (i.e., atomic composition) from environmental physicochemical stresses, and therefore increased the probability of reproducing the proto-genome without variation. From there, genomes evolved depending on the biological activities they generated in response to environmental fluctuations. Thus, a genome generating an internal environment whose physicochemical parameters guarantee homeostasis and genome integrity has a higher probability to be reproduced without variation and therefore to reproduce the same phenotype in offspring. Otherwise, the genome is modified by the imbalances of the internal physicochemical parameters it generates, until new emerging biological activities maintain homeostasis. In sum, evolution depends on feedforward and feedback loops between genome and phenotype, since the internal physicochemical conditions that a genome generates in response to environmental fluctuations in turn either guarantee the stability or direct the variation of the genome.

We present a method of effectively concealing data in two quantization levels representing blocks based on Absolute Moment Block Truncation Coding (AMBTC) using Hamming code. The reason for choosing AMBTC as the cover media is that AMBTC is not only easily compressible by simple arithmetic operations, but is also efficient for exchanging data in small portable terminals, such as smart devices, because compression efficiency and image quality are satisfactory. AMBTC is expressed in the form of a $trio (a, b, BM)$, where $a$ and $b$ are quantization levels and BM is a bitmap. The quantization level is a pixel value used as a representative value when dividing an image into $4 \times 4$ blocks. Bai \& Chang introduced a method of hiding data using Hamming code. However, this method presented a problem with efficiency compared to the existing methods. In this paper, in order to improve the problem of this method, we minimize the error that occurs when concealing the data using the optimized Hamming code lookup table. We verified that the performance of the proposed method is satisfactory in terms of embedding capacity and quality of the image through sufficient experimental results.

We analyze whether the central banks of a selected sample of emerging economies respond to exchange rate movements. We use economies that are inflation targters and exporters of a limited number of commodities. The latter renders them vulnerable to terms of trade shocks. The sample comprises Brazil, the Czech Republic, India, Indonesia, Russia and South Africa. We also investigate the role played by the terms of trade in explaining their business cycles. We estimate, for each economy separately and using Bayesian techniques, a regime switching open new Keynesian model. This model allows the parameters of the policy rule and the structural shocks to switch between periods of low and high regimes. We perform posterior simulation and find that, when compared to a constant parameters model, the regime switching fits the data better. The results indicate that, in some periods, the different economies respond strongly to exchange rate, while in others, some respond weakly. A terms of trade improvement leads to a currency appreciation. However, this is reversed for those economies that respond strongly to exchange rate movements. This leads to a rise in output and inflation even for economies that do not respond strongly but play a major role in international trade. This is the case of Russia. The terms of trade are found to play a major rule in explaining business cycles in emerging economies. Thus, although these economies follow an inflation targeting framework, they may set their respective policy rate in a manner to take into account exchange rate developments. This insulates themselves against adverse terms of trade shocks.

The effect of ferromagnetic CaMnO3 (CMO) addition on structural, magnetic, dielectric, and ferroelec-tric properties of BiFeO3 are presented. X ray diffraction and Raman investigation allowed the identifi-cation of single pseudo cubic perovskite structure. The magnetic measurement showed that the pre-pared films exhibit a ferromagnetic behavior at low temperature with both coercive field and remnant magnetization increased with increasing CMO content. However, a deterioration of magnetization was observed at room temperature. Ferroelectric study revealed an antiferroelectric like behavior with a pinched P-E hysteresis loop for 5%CMO doping BFO resulting in low remnant polarization and double hysteresis loops. Whereas, high remnant polarization and coercive field with a likely square hysteresis loop are obtained for 10 % CMO addition. Furthermore, a bipolar resistive switching behavior with a threshold voltage of about 1.8V is observed for high doped film that can be linked to the ferroelectric polarization switching.

B lymphocyte development has two DNA recombination processes: V(D)J recombination of the immunoglobulin (Igh) gene variable region and class switching of the Igh constant regions from IgM to IgG, IgA, or IgE. V(D)J recombination is required for successful maturation of B cells from pro-B to pre-B to immature-B and then to mature B cells in the bone marrow. CSR occurs outside of the bone marrow when mature B cells migrate to peripheral lymphoid organs, such as spleen and lymph nodes. Both V(D)J recombination and CSR depend on an “open chromatin” state that makes DNA accessible to specific enzymes, recombination activating gene (RAG), and activation-induced cytidine deaminase (AID). Acetyltransferases GCN5 and PCAF possess redundant functions acetylating histone H3 lysine 9 (H3K9). Here, we generated a mouse model that lacks both GCN5 and PCAF in B cells. We found that double-deficient mice possess low levels of mature B cells in the bone marrow and peripheral organs, an accumulation of pro-B cells in bone marrow, and reduced CSR levels. We conclude that both GCN5 and PCAF are required for B cell development in vivo.

The light response and resistance switching behavior in BaTiO3 (BTO) films are studied for a symmetric Pt/BTO/Pt structure. The resistance of films as a function of time with and without ultraviolet light has been studied. Furthermore, resistance switching behavior was clearly observed based on the application of 365 nm wavelength ultraviolet light. Consequently, the polarities of resistance switching can be controlled by ultraviolet light when the energy is larger than the band excitation energy. It is proposed that the polarity of the resistance switching is dictated by the competition of the ferroelectricity and oxygen vacancy migration. This provides a new mechanism for modulating the state of ferroelectric resistive memory devices.

In-plane electro-optical switching (IPS) is a natural feature of a conventional planar aligned display cell based on deformed helix ferroelectric liquid crystal effect (DHFLC-effect) with sub-wavelength helix pitch if the tilt angle close to 40 degrees.

The gene translation system possesses inherent complexity in distinguishing codons with degeneracy through repertoires of aminoacyl-tRNAs in the ribosome. Implementation of inosine nucleoside at the anticodon wobble-position in certain tRNAs, especially in eukaryotes, might be arisen to establish complex decoding systems. This report introduces the novel concept of “decoding tables”, which serve as concise summaries of codon-anticodon pairings provided in codon table-like format. Utilizing tRNA genome database information and a procedure based on compiled tRNA studies, the tRNA repertoire and the use of wobble pairings can be depicted for any organisms. The decoding table sheds light on the use of wobble pairings across diverse organisms.

Genetic code expansion has emerged as an enabling tool to provide insight into functions of understudied proteinogenic species such as small proteins and peptides, and to probe protein biophysics in the cellular context. Here we discuss recent technical advances and applications of genetic code expansion in cellular imaging of complex mammalian protein species, along with considerations and challenges upon using the method.

In some applications of microgrid and distributed generation, it is required to feed islanded or stand-alone loads with high-quality voltage to provide low total harmonic distortion (THD). To fulfil these demands, an LC filter is connected to the output terminals of power electronics converters. A cascaded voltage and current control loop with pulse-width modulation schemes are used to regulate the voltage and current in these systems. However, these strategies have some drawbacks, particularly when multiple-input multiple-output plants (MIMO) are controlled using single-input single-output (SISO) design methods. This methodology usually produces a sluggish transient response and cross-coupling between different control loops. In this paper, a model predictive control (MPC) strategy based on the concept of optimal switching sequences (OSS) is designed to control voltage and current in an LC filter connected to a three-level neutral-point clamped converter. The strategy solves two well-formulated optimisation problems to achieve control of the LC filter variables and the voltages of the DC-link capacitors. Hardware-in-the-Loop (HIL) results are obtained to validate the feasibility of the proposed strategy, using a PLECS-RT HIL platform and a DSP Microlab Box controller. In addition to the good dynamic performance of the proposed OSS-MPC, it is demonstrated by the HIL results that the control algorithm is capable of obtaining low total harmonic distortion (THD) in the output voltage for different conditions.

Domain-specific systems-on-chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, while hardware accelerators tailored to the target domain minimize task execution times and power consumption. Traditional operating system (OS) schedulers can diminish the potential of DSSoCs as their execution times can be orders of magnitude larger than the task execution time. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the advantages of a fast, low-overhead scheduler and a sophisticated, high-performance scheduler with a larger overhead. We present a novel runtime classifier that chooses the better scheduler type as a function of the system workload, leading to improved system performance and energy-delay product (EDP). Experiments with five real-world streaming applications indicate that DAS consistently outperforms fast, low-overhead, and slow, sophisticated schedulers. DAS achieves a 1.29x speedup and 45% lower EDP than the sophisticated scheduler under low data rates and a 1.28x speedup and 37% lower EDP than the fast scheduler when the workload complexity increases. Furthermore, we demonstrate that the superior performance of the DAS framework also applies to hardware platforms, with up to a 48% and 52% reduction in execution time and EDP, respectively.

We report a micro-ring resonator with adiabatic bends, non contact waveguide heaters and small bend radius. The ring has the lowest reported off resonance loss and can support 8 wavelength division multiplexed channels at 200 GHz spacing. We measure 0.49 nm/mW tuning efficiency and 0.085 dB off resonance loss.

Phenotypic switching in cancer cells has been found to be present across tumor types. Recent studies on Glioblastoma report a remarkably common architecture of four well-defined phenotypes coexisting within high levels of intra-tumour genetic heterogeneity. Tumours grown from any cell type recapitulate the original phenotypic composition. Similar dynamics have been shown to occur in breast cancer, melanoma and likely across further cancer types. Given the adaptive potential of phenotypic switching (PHS) strategies, understanding how it drives tumour evolution and how to break down these architectures is a major priority. Here we model the ecological dynamics behind PHS. The model is able to reproduce experimental results, and specific conditions for cancer progression and clearance reveal novel features of plastic tumours and its direct consequences on therapy resistance. Following our results we discuss \textit{transition} therapy as a novel scheme to target not only combined cytotoxicity but also the rates of phenotypic switching.

Evaluating mutation testing behavior can help decide whether refactoring successfully maintains the expected initial test results. Moreover, manually performing this analytical work is both time-consuming and prone to errors. This paper extends an approach to assess test code behavior and proposes a tool called MeteoR. This tool comprises an IDE plugin to detect issues that may arise during test code refactoring, reducing the effort required to perform evaluations. A preliminary assessment was conducted to validate the tool and ensure the proposed test code refactoring approach is adequate. By analyzing not only the mutation score but also the generated mutants in the pre- and post-refactoring process, results show that the approach is capable of checking whether the behavior of the mutants remains unchanged throughout the refactoring process. This proposal represents one more step toward the practice of test code refactoring. It can improve overall software quality, allowing developers and testers to safely refactor the test code in a scalable and automated way.

Due to the high solar irradiance or energy price, certain regions in the U.S. may reach 100% PV penetration and experience degradation of frequency response greater than the interconnection as a whole. Therefore, in this section, the 100% PV penetration region in each interconnection is simulated to study the local high PV penetration effects. The study was performed by quantifying RoCoF, frequency nadir, and settling frequency at different regional PV penetration levels. The impact of high regional PV penetration on the compliance of grid code on frequency response is also studied.

Software-based ultrasound imaging systems provide high flexibility that allows easy and fast adoption of newly developed algorithms. However, the extremely high data rate required for data transfer from sensors (e.g., transducers) to the ultrasound imaging systems is a major bottleneck in the software-based architecture, especially in the context of real-time imaging. To overcome this limitation, in this paper, we present a Binary cLuster (BL) code, which yields an improved compression ratio compared to the exponential Golomb code. Owing to the real-time encoding/decoding features without overheads, the universal code is a good solution to reduce the data transfer rate for software-based ultrasound imaging. The performance of the proposed method was evaluated using in vitro and in vivo data sets. It was demonstrated that the BL-beta code has a good stable lossless compression performance of 20 ~ 30% while requiring no auxiliary memory or storage.

Abstract—Hydraulic switching actuators are high efficiency, fast response, and low cost solutions for hydraulic control systems. One of the challenging problems is throttling losses during valve transitions. Previously the authors proposed a zero-flowrate switching method to reduce throttling energy loss of the switching valve, where a hydraulic resonator is applied to make the flowrates through the lines approaching zero before the valves are switched off. The major challenge of this approach is fast switching valves whose transition times are less than 2 ms. In this paper, an improved zero-flowrate switching method is presented. It utilizes the capacity with independent inlet/outlet ports to regulate flowrates through the lines. Models of capacity applied in a simple line with different pressure signals are developed to explore characteristics of the capacity, based on which a complete actuation system is developed. In the complete model, resistance and inductance are optimized to achieve desired flowrate response. The improved zero-flowrate switching method reduces throttling energy losses by 99.945% compared to a hard switching system. The simulation results demonstrated that the improved zero-flowrate switching method performs as expected in the design condition. A capacity with proper volume is able to regulate flowrates through all the lines to zero, with the help of appropriate resistance and inductance. Compared to previous zero-flowrate switching method, the novel strategy allows slower switching valves applied in hydraulic actuation systems and achieves better efficiency performance. This research paves a new avenue for reducing throttling energy losses and improves system efficiency in hydraulic switching actuators, as well as most of the hydraulic switch-mode circuits.

A three-phase multilevel inverter (MLI) synthesized with 31-levels in the output voltage is used to provide ac supply to a three-phase, squirrel-cage induction motor. The gating angles required for thirty power switches in MLI are optimized by both Genetic Algorithm (GA) and Gray Wolf Op-timization (GWO). In the optimization technique, the optimal angles are determined by solving the trigonometric equations from the Fourier analysis for targeting minimum total harmonic distortion (THD) at the MLI output. Simulation model and experimental prototype are developed for performance analysis and validation. The results obtained demonstrate that the MLI is effec-tively able to produce 31-levels of three-phase ac output voltages with THD not exceeding 5% when loaded with a resistive load and a three-phase induction motor. The voltages and currents are measured and recorded for different loads and operating conditions, including the amount of energy consumed by the loads, as well as frequency analysis, which shows the absence of most of the triple harmonics that have a negative impact on the efficiency of the inverter.

This paper investigates the dynamic tail dependence risk between BRICS economies and world energy market in the context of the COVID-19 financial crisis of 2020, to determine optimal investment decisions based on risk metrics. For this purpose, the study employs a combination of novel statistical techniques ranging from Markov Switching, GARCH and Vine copula. Using a dataset consisting of daily stock and world crude oil prices; we find high probability of transition between lower and higher volatility regimes. Furthermore, our results based on the C-Vine copula confirm the existence of two types of tail dependence: - symmetric tail dependence between South Africa and China; South Africa and Russia; and lower tail dependence between South Africa and India; South Africa and Brazil; South Africa and Oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using C-vine copula-based returns and optimize it using Particle Swarm algorithm with a rebalancing strategy. The results show an inverse relationship between the risk contribution and asset allocation of South Africa and oil market supporting the existence of lower tail dependence between them. This suggests that when South African stocks are in distress, investors tend to shift their holdings in oil market. Similar results are found between China and oil. In the upper tail, South African asset allocation is found to have an inverse relationship with that of Brazil, Russia and India suggesting that these three markets might be good investment destinations when things are not good in South Africa and vice-versa.

This paper proposes a novel micro-grid structure, which can operate fully-autonomously with inherent seamless switching. It can operate independently in both grid-connected and islanded mode as a self-governed entity without relying on the utility grid. An AC/DC/AC converter is employed as the interface between the micro-grid and the utility grid, which enables the two entities to have different voltages in grid-connected mode. Seamless switching between operation modes can be achieved naturally. The micro-grid is regulated to exchange predefined amount of power with the utility grid in grid-connected mode. This will benefit the power dispatching algorithm of the power system. The predefined power is estimated based on power forecasting of local renewable generations and loads with consideration of the Sate of Charge (SOC) of the battery, and is updated and broadcasted every certain period. A small scale AC micro-grid with a rotating generator, battery storage and solar arrays etc. is built for investigation. Matlab/Simulink results are provided to validate the robustness and flexibility of proposed micro-grid and its operation strategy.

Symbolic semantic understanding of staff images is an important part in music information retrieval. Due to the complex composition of staff symbols and the strong semantic correlation between symbol spaces, it is difficult to understand the pitch and duration of each note during performances. In this paper, we design a semantic understanding system for optical staff symbols. The system uses the YOLOv5 to implement optical staff’s low-level semantic understanding stage, which understands the pitch and duration in natural scales and other symbols that affect the pitch and duration. The proposed note encoding reconstruction algorithm is used to implement high-level semantic understanding stage. Such algorithm understands the logical, spatial, and temporal relationships between natural scales and other symbols based on music theory, and outputs digital codes for the pitch and duration of main notes during performances. The model is trained with a self-constructed SUSN dataset. Experimental results of YOLOv5 show that the precision is 0.989 and the recall is 0.972. For the system, the error rate is 0.031 and the omission rate is 0.021. The paper concludes by analysing the causes of semantic understanding errors and offers recommendations for further research. The results of this paper provide a method for multimodal music artificial intelligence applications such as notation recognition through listening, intelligent score flipping and automatic performance.

The practice of the veterinary profession is regulated by public law and corporate regulations. The most fundamental corporate regulations of veterinary associations, to which membership is a condition of practising the profession, are the codes of veterinary ethics. The codes of ethics contain five basic rules for practising veterinary responsibilities to animals, clients, the public, the veterinary team, and the veterinarians themselves. Veterinarians are often faced with dilemmas about whose interests to protect; this is due to the tasks the profession carries out while protecting the animals of their owners and the public. Ethical principles are a guideline for veterinarians in difficult situations. Depending on the hierarchy of values imposed by codes of ethics, different decisions can be made. In this article, we present the importance that the Polish code of veterinary ethics gives to animal welfare as an obligation of veterinarians towards animals and compare how animal welfare is positioned in the European, American, British and New Zealand codes of veterinary ethics. In contrast to other codes, the Polish code of veterinary ethics does not sufficiently recognise animal welfare as a value to be protected in the practice of the profession.

In its Nationally Determined Contribution (NDC) to the United Nations Framework Convention on Climate Change, Brazil committed to reducing greenhouse gas emissions and restoring its forests. This study examines the challenges of fulfilling these commitments in Brazilian Amazonia. We carry out a detailed assessment of the current status of land tenure in the region and its relation to deforestation. After dealing with conflicts and overlaps between data from various sources, we produce a new map of public and private land tenure in Amazonia. Combining this map with Brazil's official data on deforestation, we find out how much natural vegetation has been preserved in each public or private area. The result is used to estimate how much deforestation is illegal. We also establish how much deforestation is associated with each land tenure type. Our results show that most deforestation inside rural properties is done by a few landowners, a finding that has important consequences for law enforcement. We then assess the challenges for reforestation in detail. To do so, we consider how much forest needs to be rehabilitated according to Brazil's Forest Code. Our analysis provides a comprehensive appraisal of the potential opportunity costs for forest restoration in the biome, considering farm size and land use. This analysis provides insights into targeted land use policies that can meet Brazil’s forest restoration goals.

In the present work we present a methodology for teaching the basis of the genetic code through music composition, with the aim to combine science and arts learning. The project was carried out by 155 students, the so-called MARGA Consortium, with ages comprised between 10 and 17 years from different public schools located in the Principality of Asturias, Spain. The different groups generated 8 different music works using a short genetic sequence obtained from the human notch1 gene, receptor of mutations leading to chronic lymphocytic leukemia.

We find that the degeneracies and many peculiarities of the DNA genetic code may be described thanks to two closely related (fivefold symmetric) finite groups. The first group has signature $G=\mathbb{Z}_5 \rtimes H$ where $H=\mathbb{Z}_2 . S_4\cong 2O$ is isomorphic to the binary octahedral group $2O$ and $S_4$ is the symmetric group on four letters/bases. The second group has signature $G=\mathbb{Z}_5 \rtimes GL(2,3)$ and points out a threefold symmetry of base pairings. For those groups, the representations for the $22$ conjugacy classes of $G$ are in one-to-one correspondence with the multiplets encoding the proteinogenic amino acids. Additionally, most of the $22$ characters of $G$ attached to those representations are informationally complete. The biological meaning of these coincidences is discussed.

The genetic code evolved around the reading of the tRNA anticodon on the primitive ribosome, and tRNA-34 wobble and tRNA-37 modifications coevolved with the code. We posit that EF-Tu, the closing mechanism of the 30S ribosomal subunit, methylation of wobble U34 at the 5-carbon and suppression of wobbling at the tRNA-36 position were partly redundant and overlapping functions that coevolved to establish the code. The genetic code devolved in evolution of mitochondria to reduce the size of the tRNAome (all of the tRNAs of an organism or organelle). “Superwobbling” or four-way wobbling describes a major mechanism for shrinking the mitochondrial tRNAome. In superwobbling, unmodified wobble tRNA-U34 can recognize all four codon wobble bases (A, G, C and U), allowing a single unmodified tRNA-U34 to read a 4-codon box. During code evolution, to suppress superwobbling in 2-codon sectors, U34 modification by methylation at the 5-carbon position appears essential. As expected, at the base of code evolution, tRNA-37 modifications mostly related to the identity of the adjacent tRNA-36 base. TRNA-37 modifications help maintain the translation frame during elongation.

The ubiquitous problem of electrical energy waste is driven by human error, in which people regularly forget to turn off appliances when they leave a room, resulting in excessive consumption. The paper presents an energy-efficient TRIZ-inspired (EETRIZ) paradigm to automate consumer electronic devices in order to address this problem. Employing motion detection at entry and exit points, the system discerns room occupancy, effectively managing electrical appliances like lamps, fans, and air conditioners. The integration of theory of inventive problem solving (TRIZ) principles guides the ideation and design of an efficient switching mechanism. The system demonstrates that it is flexible by controlling multiple electronic consumer devices simultaneously. This paradigm can be used in a variety of environments, such as homes, offices, schools, and universities, and its goal is to reduce excessive energy use that results from human negligence. With an emphasis on economic viability and environmental impact, the proposed EETRIZ paradigm not only offers enhanced energy efficiency but also lays the groundwork for long-term sustainability practices, aligning with global efforts towards greener and smarter energy management. The proposed EETRIZ is implemented on C platform with support of MPLAB, Nuvoton 8051 Serie MCU Programming, GMPLibrary (GMP-5.1.1) and, Miracl Library. The proposed EETRIZ outperforms current state-of-the-art methods in terms of energy efficiency, cost-effectiveness, precision, and user-friendliness, according to hardware testing.

Plasmonic nanocomposites have the ability to enhance light-matter interaction due to the 1 plasmonic effects leading to promising potential applications. In this paper, we theoretically study the 2 group index of the silver nanoparticle-gold nanoparticle-quantum dot plasmonic hybrid system in 3 the optical region of the electromagnetic spectrum using the density matrix approach. The quantum 4 dot is considered to be a three-level atomic system of a lambda configuration, interacting with strong 5 control and weak probe fields. We find that the plasmonic hybrid system exhibits at near resonance a 6 strong switching between slow and fast light, which can be controlled by the inclination angle of the 7 gold nanoparticle and the size of the metals nanoparticles, in addition to the strength and detuning 8 of the control field. Thus, the silver nanoparticle-gold nanoparticle-quantum dot could potentially be 9 used in optoelectronic devices.10

This work investigates the sliding mode control (SMC) problem for a class of uncertain switched systems subject to asynchronous switching and an assigned finite time constraint. Two important issues are how to ensure the reachability of state trajectories within the assigned time and the input-to-output finite-time stability (IO-FTS) of the closed-loop switched systems during the whole phase under asynchronous switching. To achieve these objectives, an asynchronous sliding mode controller with adjustable parameters is constructed to drive the state trajectories onto the sliding surface during the assigned finite-time interval. By means of the partitioning strategy, sufficient conditions for the IO-FTS of the closed-loop switched are derived during the whole phase [0,T] using the multiple Lyapunov function (MLF) approach. Additionally, the asynchronous characteristics are detailedly investigated while analyzing the reachability of a specified sliding surface. Finally, an illustrative example is given to illustrate the effectiveness of the proposed method.

For the first time the gain switching properties of an InAs-InP (113)B quantum dot laser are examined theoretically in detail to generate shorter pulses with the application of a Gaussian pulse beam to the laser excited state. The multi population rate equations considering nonlinear gain are solved by the Runge –Kutta method. The numerical results demonstrated that as the homogeneous and the inhomogeneous broadening increase, the differential gain, the gain compression factor and the threshold current of excited state decrease, while threshold current of ground state increases. It was also observed that the contribution of the excited state to gain-switched output pulses depends on not only the value of the inhomogeneous broadening but also the magnitude of the applied current. Finally it was shown that without an optical beam, output pulse has long pulse width due to ground state emission, whereas with an optical beam, narrow pulses having high peak power owing to the excited state emission are generated even though at low currents.

Rhenium Disulfide (ReS2) has evolved as a novel 2D transition-metal dichalcogenide (TMD) material which has promising applications in optoelectronics and photonics because of its distinctive anisotropic attributes. In this review, we emphasize on formulating saturable absorbers (SAs) based on ReS2 to produce Q-switched and mode-locked pulsed lasers of diverse operation wavelengths like 1 μm, 1.5 μm, 2 μm, and 3 μm. We outline ReS2 synthesis techniques and integration platforms concerning solid-state and fiber-type lasers. We discuss the laser performance based on SAs attributes. Lastly, we draw conclusions and outlook by recommending additional improvements for SA devices so as to advance the domain of ultrafast photonic technology.

Individual users' payment behaviors have changed. The diffusion of mobile devices makes people suitable for proximity mobile payment (PMP) services without the need for traditional payment. Existing mobile payment literature mainly focuses on users' adoption and continuous usage behavior, nevertheless, switching behavior on payment received little attention, especially focusing on why users switch from traditional payment to PMP. Thus, the objective of the current research is to investigate factors influencing users’ traditional payment - PMP switching to comprehend how these factors shape users’ switching intentions. We developed a traditional payment - PMP transition model based on the push-pull-mooring framework derived from migration theory. This research adopted a structural equation modeling analysis on 311 valid data. The findings indicate that a push factor drives users away from traditional payment in terms of dissatisfaction. The pull factors, including perceived substitutability and perceived usefulness, attract users to PMP. Furthermore, in terms of perceived technical compatibility, a positive mooring factor facilitates users' switching to PMP. The negative mooring factor, in terms of perceived risk, hinder users' switching intention. However, another pull factor - perceived ease of use, failed to influence switching intention significantly. This study found some distinctions between mobile payment switching and mobile payment adoption. These findings provide pivotal insights for mobile payment service providers.

The novel coronavirus SARS-CoV-2 likely emerged from a wildlife source with transmission to humans followed by rapid geographic spread throughout the globe and severe impacts on both human health and the global economy. Since the onset of the pandemic, there have been many instances of human-to-animal transmission involving companion, farmed and zoo animals, and limited evidence for spread into free-living wildlife. The establishment of reservoirs of infection in wild animals would create significant challenges to infection control in humans and could pose a threat to the welfare and conservation status of wildlife. We discuss the potential for exposure, onward transmission and persistence of SARS-CoV-2 in an initial selection of wild mammals (bats, canids, felids, mustelids, great apes, rodents and cervids). Dynamic risk assessment and targeted surveillance are important tools for the early detection of infection in wildlife, and here we describe a framework for collating and synthesising emerging information to inform targeted surveillance for SARS-CoV-2 in wildlife. Surveillance efforts should be integrated with information from public and veterinary health initiatives to provide insights into the potential role of wild mammals in the epidemiology of SARS-CoV-2.

This paper presents the design of a high frequency zero voltage switching (ZVS) full-bridge converter with a phase-shifted driving signal for photovoltaic applications. The resonant power converter can provide high-power capacity under high-frequency operation. The proposed power converter can also reduce the size of the transformer under the same power rating. The high-frequency transformer was developed by using the resonant and switching frequencies of the power converter to reduce the switching loss and to improve the system efficiency. Phase-shifted modulation was selected to drive the switches of a full-bridge power converter based on the switching loss minimization method. The desired output voltage was controlled using a closed-loop controller under a loop gain stability margin. The simulation results showed that the output voltage can be controlled to the desired constant when the input voltage changes from 30 VDC to 60 VDC. The desired output voltage of power converter is constant at 400 VDC. The power converter can transfer the DC supply to a 220 VAC household via grid-connected inverter. Therefore, the proposed study showed the effectiveness of the phase-shift ZVS full-bridge power converter with high-frequency transformer. This power converter can control the operation of the desired voltage system and has a small sizing of power converter system, low switching loss, and high system efficiency.

Dynamic hedging has been adopted by many insurance companies to mitigate the financial risks associated with variable annuity guarantees. In order to simulate the performance of dynamic hedging for variable annuity products, insurance companies rely on nested stochastic projections, which is highly computationally intensive and often prohibitive for large variable annuity portfolios. Metamodeling techniques have recently been proposed to address the computational issues. However, it is difficult for researchers to obtain real datasets from insurance companies to test metamodeling techniques and publish the results in academic journals. In this paper, we create synthetic datasets that can be used for the purpose of addressing the computational issues associated with the nested stochastic valuation of large variable annuity portfolios. The runtime used to create these synthetic datasets would be about 3 years if a single CPU were used. These datasets are readily available to researchers and practitioners so that they can focus on testing metamodeling techniques.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

In this study, we once again use a set of Fibonacci-like sequences to examine the symmetries within the genetic code. This time, our focus is on the physiological state of the amino acids, considering them as charged, in contrast to our previous work where they were seen as neutral. In a pH environment around 7.4, there are four charged amino acids. We utilize the properties of our sequences to accurately describe the symmetries in the genetic code table. These include Rumer’s symmetry, the third-base symmetry and the "ideal" symmetry, along with the "supersymmetry" classification schemes. We also explore the special chemical structure of the amino acid proline, presenting two perspectives—shCherbak’s view and the Downes-Richardson view, which perspectives are included in the description of the above-mentioned symmetries. Our investigation employs also elementary modular arithmetic to precisely describe the chemical structure of proline, connecting the two views seamlessly. Finally, our Fibonacci-like sequences prove instrumental in quickly establishing the multiplet structure of non-standard versions of the genetic code. We illustrate this with an example, showcasing the efficiency of our method in unraveling the complex relationships within the genetic code.

Laser marking on polymer composite surfaces can be difficult to contrast and cause readability problems for electronic decoding equipment on production lines due to poor interaction between the laser and the fibres used to reinforce these materials. This problem can be solved with the right choice of marking parameters, resulting in savings for companies by avoiding production problems such as rejection, scrap and customer complaints. The present work uses the PBT/glass fibre composite used in the manufacture of instrument panels for motorcycles. The tests were carried out with different laser marking parameters using a Nd: YAG laser. Subsequently, the laser-marked DMC codes were analysed using a Verifier to evaluate the quality according to the ISO/IEC 29158:2020 standard. A detailed analysis of these surfaces was also carried out to observe some physical and chemical changes using SEM microscopy and EDS spectroscopy. The optical analysis showed that the lower the radiation power and pulse frequency and the higher the marking speed, the weaker the laser marking and therefore the poorer the DMC code quality, which was confirmed by the SEM. EDS spectroscopy showed that the laser marking process did not affect the chemical changes on the sample surface.

Recent years have witnessed a rapid growth of code-reuse attacks in advance persistent threats and cyberspace crimes. Carefully crafted code-reuse exploits circumvent modern protection mechanisms and hijack the execution flow of a program to perform expected functionalities by chaining together existing codes. The sophistication and intrication of code-reuse exploits hinder the scrutinization and dissection on them. Although the previous literature has introduced some feasible approaches, effectiveness and reliability in practical applications remain severe challenges. To address this issue, we propose Horus, a data-driven framework for effective and reliable detection on code-reuse exploits. In order to raise the effectiveness against underlying noises, we comprehensively leverage the strengths of time-series and frequency-domain analysis, and propose a learning-based detector that synthesizes the contemporary twofold features. Then we employ a lightweight interpreter to speculatively and tentatively translate the suspicious bytes to open the black box and enhance the reliability and interpretability. Additionally, a functionality-preserving data augmentation is adopted to increase the diversity of limited training data and raise the generality for real-world deployment. Comparative experiments and ablation studies are conducted on a dataset composed of real-world instances to verify and prove the prevalence of Horus. The experimental results illustrate that Horus outperform existing methods on the identification of code-reuse exploits from data stream with an acceptable overhead. Horus does not rely on any dynamic executions and can be easily integrated into existing defense systems. Moreover, Horus is able to provide tentative interpretations about attack semantics irrespective of target program, which further improve system's effectiveness and reliability.

The Virtual Blade Model (VBM) is the implementation of the Blade element model (BEM). This was done by coupling the Blade Element Momentum theory equations to simulate rotor operation with the Reynolds Averaged Navier-Stokes (RANS) equation to simulate rotor wake and the turbulent flow field around it. Exclusion of actual geometry of blades causes lower computational cost (about 10 to 100 times). Also, due to simplifications in the meshing procedure, VBM is easier to set up than the models that consider the actual geometry of blades. One of the main unaddressed limitations of the VBM code was the constraint of modeling up to ten rotor zones within one computational domain. This paper provides a detailed and well-documented general methodology to develop a virtual blade model for simulation of ten-plus turbines within one computational domain to remove the limitation of this widely used and robust code. It is strongly believed that the technical contribution of this paper, combined with the current sky-rocketing advancement of available computational resources and hardware, would open the gates to simulate various engineering problems in the field of aerospace, clean energy, and many more.

An adaptor signature can be viewed as a signature concealed with a secret value and, by design, any two of the trio yield the other. In a multiparty setting, an initial adaptor signature allows each party create additional adaptor signatures without the original secret. Adaptor signatures help address scalability and interoperabity issues in blockchain. They can also bring some important advantages to cryptocurrencies, such as low on-chain cost, improved transaction fungibility, and less limitations of a blockchain’s scripting language. In this paper, we propose a new two-party adaptor signature scheme that relies on quantum-safe hard problems in coding theory. The proposed scheme uses a hash-and-sign code-based signature scheme introduced by Debris-Alazard et al. and a code-based hard relation defined from the well-known syndrome decoding problem. To achieve all the basic properties of adaptor signatures formalized by Aumayr et al., we introduce further modifications to the aforementioned signature scheme. We also give a security analysis of our scheme and its application to the atomic swap. After providing a set of parameters for our scheme, we show that it has the smallest pre-signature size compared to existing post-quantum adaptor signatures.

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or in transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing imposes a major threat on the modern cryptosystems based on modular arithmetic whereby, even the computationally hard problems which constitute for the strength of the modular arithmetic ciphers could be solved in deterministic time. This threat triggered post-quantum cryptography research in order to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides a review of the various post-quantum cryptography and, in specific, code-based cryptography research dimensions. The research directions that are yet to be explored in code-based cryptography research is another key contribution of this paper.

We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.

Nano medicine seeks to deliver a valuable set of research tools and clinically useful devices in the near future. The current medical field is in dire need of new commercial applications in the pharmaceutical industry that may include advanced drug delivery systems, new therapies, and in vivo imaging. Here in this experimental study, the nano materials used in DNA biosensors like silver nano dots were incorporated as nano biosensors are used for both therapeutic and diagnostic applications. The most important step while preparing a DNA biosensor is the immobilization of DNA probe on the surface of a sensing device such as an electrode. The amount of immobilized DNA probe will influence the accuracy sensitivity, selectivity and life of a DNA biosensor directly. Because of the high surface to volume ratio and excellent biological compatibility, nano materials can enlarge the sensing surface area to increase the amount of immobilized DNA and the DNA mixed with nano materials can keep its biologically activity well.In this study,silver nano dots created in our lab were functionalized with thio nucleides and were used as nano sensor probes in bio bar code assays.

Arterial wall remodeling underlies increased pulmonary vascular resistance and right heart failure in pulmonary arterial hypertension (PAH). None of the established vasodilator drug therapies for PAH prevents or reverses established arterial wall thickening, stiffening and hypercontractility. Therefore, new approaches are needed to achieve long-acting prevention and reversal of occlusive pulmonary vascular remodeling. Several promising new drug classes are emerging from better understanding of pulmonary vascular gene expression programs. In this review potential epigenetic targets for small molecules and oligonucleotides will be described. Most are in preclinical studies aimed at modifying growth of vascular wall cells in vitro or normalizing vascular remodeling in PAH animal models. Initial success with lung-directed delivery of oligonucleotides targeting microRNAs suggests other epigenetic mechanisms might also be suitable drug targets. Those targets include DNA methylation, proteins of the chromatin remodeling machinery and long noncoding RNAs, all of which act as epigenetic regulators of vascular wall structure and function. Progress in testing small molecules and oligonucleotide-based drugs in PAH models is summarized.

The “tubulin code” combines different α/β-tubulin isotypes with several post-translational modifications (PTMs) to generate microtubule diversity in cells. During cell division, specific microtubule populations in the mitotic spindle are differentially modified, but only recently has the functional significance of these modifications started to be elucidated. In particular, α-tubulin detyrosination of stable microtubules in the spindle was shown to guide chromosomes during congression to the metaphase plate and allow the discrimination of mitotic errors, whose correction is required to prevent chromosomal instability (CIN), a hallmark of human cancers. Although alterations in certain tubulin PTMs have been reported in human cancers, it remains unclear whether and how tubulin PTMs have any functional implications for cancer cell properties. Here we review the role of the tubulin code in chromosome segregation during mitosis, together with the emerging cancer tubulin code and discuss possible links, as well as the respective diagnostic, prognostic and therapeutic implications for human cancers.

The scope of a lucrative career promoted by Google through its video distribution platform YouTube 1 has attracted a large number of users to become content creators. An important aspect of this line of work is the feedback received in the form of comments which show how well the content is being received by the audience. However, volume of comments coupled with spam and limited tools for comment classification makes it virtually impossible for a creator to go through each and every comment and gather constructive feedback. Automatic classification of comments is a challenge even for established classification models, since comments are often of variable lengths riddled with slang, symbols and abbreviations. This is a greater challenge where comments are multilingual as the messages are often rife with the respective vernacular. In this work, we have evaluated top-performing classification models and four different vectorizers, for classifying comments which are a mix of different combinations of English and Malayalam (only English, only Malayalam and Mix of English and Malayalam). The statistical analysis of results indicates that Multinomial Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest and Decision Trees offer similar level of accuracy in comment classification. Further, we have also evaluated 3 multilingual sub-types of the novel NLP language model, BERT and compared its’ performance to the conventional machine learning classification techniques. XLM was the top-performing BERT model with an accuracy of 67.31%. Random Forest with Term Frequency Vectorizer was the best the top-performing model out of all the traditional classification models with an accuracy of 63.59%.

Several studies have shown that changes in incoming solar radiation and variations of the diffuse fraction can significantly modify the vegetation carbon uptake. Hence, monitoring the incoming solar radiation at large scale and with high temporal frequency is crucial for this reason along with many others. The EUMETSAT Satellite Application Facility for Land Surface Analysis (LSA SAF) operationally disseminates in near real time estimates of the downwelling shortwave radiation at the surface since 2005. This product is derived from observations provided by the SEVIRI instrument onboard the Meteosat Second Generation series of geostationary satellites, which covers Europe, Africa, the Middle East, and part of South America. However, near real time generation of the diffuse fraction at the surface level has only recently been initiated. The main difficulty towards achieving this goal was the general lack of accurate information on the aerosol particles in the atmosphere. This limitation is nowadays less important thanks to the improvements in atmospheric numerical models. This study presents an upgrade of the LSA-SAF operational retrieval method, which provides the simultaneous estimation of the incoming solar radiation and its diffuse fraction from satellite every 15 minutes. The upgrade includes a comprehensive representation of the influence of aerosols based on physical approximations of the radiative transfer within an atmosphere-surface associated medium. This article explains the retrieval method, discusses its limitations and differences with the previous method, and details the characteristics of the output products. A companion article will focus on the evaluation of the products against independent measurements of solar radiation. Finally, the access to the source code is provided through an open access platform in order to share with the community the expertise on the satellite retrieval of this variable.

A new approach is presented to defining the amount of information, in which information is understood as the data about a finite set as a whole, whereas the average length of an integrative code of elements serves as a measure of information. We obtain a formula for the syntropy of reflection, that is, the information that two intersecting finite sets reflect about each other. The specificities of reflection of discrete systems through the combination of their parts are considered, and it is shown that the additive syntropy and entropy of reflection are measures of structural order and chaos. Three information laws have been established: the law of conservation of the sum of chaos and order; information law of reflection; the law of conservation and transformation of information. An assessment of the structural organization and the level of development of discrete systems is presented. It is shown that various measures of information are structural characteristics of integrative codes of elements of discrete systems. The conclusion is made that, from the informational-genetic positions, the synergetic theory of information is primary in relation to the Hartley-Shannon information theory. In the appendix we consider the asymmetry of the mutual reflection of finite sets and the proportionality of arbitrary quantities.

This work is devoted to consideration and analysis of the application of molecular dynamics simulation (MDS) methods to the study of nanosized polymer polyvinylidene fluoride (PVDF) thin ferroelectric films (two-dimensional ferroelectrics) and their composites with graphene layers: 1) to study and calculations of the polarization switching time depending on the electric field and PVDF film thickness; 2) to study and calculations of the polarization switching time depending changes of the PVDF on PVDF-TrFE film; 3) to study the polarization switching time in PVDF under influence of graphene layers. All calculations at each MDS step were carried out using quantum semi-empirical methods PM3. A comparison and analysis of the results of these calculations and the kinetics of polarization switching within the framework of the Landau-Ginzburg-Devonshire theory for homogeneous switching in ferroelectric polymer films is carried out. The study of the composite heterostructures of the “graphene-PVDF” type and calculations of their polarization switching times were presented too. It is shown that replacing PVDF with PVDF-TrFE significantly changes the polarization switching times in these thin polymer films, and that introducing various graphene layers into the PVDF layered structure leads to both an increase and a decrease in the polarization switching time. Here everything also depends on the position and displacement of the ferroelectric coercive field depending on the system damping parameters.

Objective: To perform a systematic review of real-world outcomes for anti-CGRP-mAbs. Methods: Following the PRISMA guidelines, we searched PubMed for real-world data of Erenumab, Galcanezumab, Fremanezumab, or Eptinezumab in patients with migraine. Results: We identified 104 publications (73 retrospective), comprising 8 pharmaco-epidemiologic and 63 clinic-based studies, 30 case reports and 3 other articles. None of the clinic-based studies provided follow-up data over more than one year in more than 200 patients. Findings suggest reductions in health insurance claims and days with sick-leave as well as better treatment adherence with anti-CGRP-mAbss. Effectiveness, reported in 59 clinic-based studies, was comparable to randomized controlled trials. A treatment pause was associated with an increase in migraine frequency and switching to another antibody resulted in a better response in some of the patients. Adverse events and safety issues were addressed in 70 papers including 22 single case reports. Conclusion: Real-world data on anti-CGRP-mAbs are limited by retrospective data collection, small patient numbers and short follow-up periods. The majority of papers seem to support good effectiveness and tolerability of anti-CGRP-mAbs in the real-world setting. There is an unmet need for large prospective real-world studies providing long-term follow-up of patients treated with anti-CGRP-mAbs.

Using optical characterization, it is evident that the spin state of the spin crossover molecular complex [Fe{H2B(pz)2}2(bipy)] (pz = tris (pyrazol-1-1y)-borohydride, bipy = 2,2’-bipyridine) depends on the ferroelectric polarization of an adjacent thin film of the polymer ferroelectric polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP). The UV-Vis spectroscopy reveals that room temperature switching of [Fe{H2B(pz)2}2(bipy)] molecules in bilayers of PVDF-HFP/[Fe{H2B(pz)2}2(bipy)] as a function of ferroelectric polar polarization. The electric polarity dependence of bilayers of PVDF-HFP/[Fe{H2B(pz)2}2(bipy)] shows a strong dependence on the thickness of the PVDF-HFP layer. The PVDF-HFP/[Fe{H2B(pz)2}2(bipy)] interface may affect polarization retention in the PVDF-HFP thin film limit.

Providing data on usage patterns is key to assessing the reduced-risk potential of novel tobacco and nicotine products at a population level. We performed a nationwide cross-sectional survey of the general population in Japan to assess usage patterns after the introduction of tobacco heating products (THPs). Eligible participants were Japanese residents, aged 20 years or older who consented to complete the survey. Individuals living in institutions were excluded. A three-stage probability sampling method was applied that was geographically stratified by street blocks proportionate to population density. Respondents self-reported patterns of product use and reasons for THP use. Complete responses were available from 5,306 individuals, of whom 933.5 (17.6%) were current users, 984.2 (18.5%) were former users and 3388.4 (63.9%) were never users of tobacco products. Cigarettes were used by 14.6% of current tobacco product users and THPs by 5.3%. Cigarettes and THPs were used exclusively by 64.5% and 12.2%, respectively, and both were used by 12.7%. The most common reasons reported for THP use were perceived reduction in harm to self and others compared to cigarettes. While the prevalence of cigarette use in Japan is decreasing, THPs seem to be increasingly used as long-term alternatives to cigarette smoking.

In the last years, the Distribution Grid Operators (DGOs) assumed transition strategies of the distribution grids towards an active area associated with the "Smart Grids" concept. They are considering the use of Artificial Intelligence techniques, combined with advanced technologies and real-time remote communication solutions of the enormous data amounts, to develop smart solutions into the small size distribution grids, also called microgrids (μGs). These solutions will provide support for the DGOs to ensure an optimal operation of the technical infrastructure of the μGs. In this context, a bi-level methodology for solving the phase load balancing problem in the μGs with complex topologies and a high number of single-phase consumers, considering a clustering-based selection criterion of the consumers for placement of the switching devices, was proposed in the paper. A real μG from a rural area, with 114 consumers integrated into the Smart Metering System (SMS), belonging to the DGO from Romania, was considered in testing the proposed methodology. An implementation degree of 17.5%, corresponding to the phase load balancing equipment installed to only 20 consumers from the μG, led to a faster computational time with 43% and reducing the number of switching operations by 92% than in the case of a full implementation degree (100%). The performance indicators related to the unbalance factor and energy-saving used in the evaluation of the technical benefits highlighted the efficiency of the proposed methodology.

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

Given the increase in consumers’ preferences for coffee, it is becoming important to understand their decision-making processes in the coffee chain context. To deepen the understanding of sustainable outcomes in this context, this study investigates the role of dedication- and constraint-based mechanisms in forming consumers’ repurchase and positive word-of-mouth (WOM) intentions, two critical sustainable outcomes. We examined the effects of coffee quality, the quality of the physical environment, and service quality in accelerating the formation of dedication-based factors. Moreover, this study offers an in-depth understanding of the enablers of perceived switching costs. Data collected from 238 university students that frequently visit coffee chains are empirically tested against the proposed theoretical framework by using structural equation modeling. The results confirm that both dedication- and constraint-based factors substantially predict consumers’ sustainable outcomes in the coffee chain context. Brand image and perceived switching costs play an important role in enhancing consumers’ repurchase and positive WOM intentions compared with customer satisfaction. Coffee quality is significantly associated with both customer satisfaction and brand image, whereas the quality of the physical environment and service quality are only significantly associated with brand image. Habit is found to be the key enabler of perceived switching costs, while loyalty programs have no significant impact on perceived switching costs.

This paper proposes a single-stage asymmetrical half-bridge fly-back (AHBF) converter with resonant mode using dual-mode control. The presented converter has an integrated boost converter and asymmetrical half-bridge fly-back converter and operates in resonant mode. The boost-cell always operates in discontinuous conduction mode (DCM) to achieve high power factor. The presented converter operates simultaneously using a variable-frequency-controller (VFC) and pulse-width-modulation (PWM) controller. Unlike the conventional single-stage design, the intermediate bus voltage of this controller can be regulated depending on the main power switch duty ratio. The asymmetrical half-bridge fly-back converter utilizes a variable switching frequency controller to achieve the output voltage regulation. The asymmetrical half-bridge fly-back converter can achieve zero-voltage-switching (ZVS) operation and significantly reduce the switching losses. Detailed analysis and design of this single-stage asymmetrical half-bridge fly-back converter with resonant mode is described. A wide AC input voltage ranging from 90 to 264 Vrms and output 19 V/ 120 W prototype converter was built to verify the theoretical analysis and performance of the presented converter.

The excited-state lifetimes of the anticoagulant drug warfarin (W) in water and in the absence and presence of methyl-β-cyclodextrins (Me-β-CD) were recorded using time-resolved fluorescence measurements. Selective excitation of the open and cyclic protonated isomers of W were acquired with laser emitting diodes (LED) producing 320 and 280 nm excitation pulses, respectively. Formation of the inclusion complex was checked by UV–visible absorption spectroscopy, and the values of binding constants (2.9 × 10³ M^–1 and 4.2 × 10² M^–1 for protonated and deprotonated forms, respectively) were extracted from the spectrophotometric data. Both absorption and time-resolved fluorescence results established that the interior of the macromolecular host binds preferentially the open protonated form, red shifts the maximum of its absorption of light at ~305 nm, extends its excited-state lifetime, and decreases its emission quantum yield (ФF). Collectively, sequestration of the open guest molecules decreases markedly their radiative rate constants (kr), likely due to formation of hydrogen-bonded complexes in both the ground and excited states. Due to lack of interactions, no change was observed in the excited-state lifetime of the cyclic form in the presence of Me-β-CD. The host also increases the excited-state lifetime and ФF of the drug deprotonated form (W¯). These later findings could be attributed to the increased rigidity inside the cavity of Me-β-CD. The pKa values extracted from the variations of the UV–visible absorption spectra of W versus the pH of aqueous solution showed that the open isomer is more acidic in both ground and excited states. The positive shifts in pKa values induced by three derivatives of cyclodextrins: HE-β-CD, Ac-β-CD, and Me-β-CD supported the preferential binding of these hosts to open isomers over cyclic.

The switched inertance hydraulic system (SIHS) is a novel high-bandwidth and energy-efficient digital device which can adjust or control flow and pressure by a means that does not rely on throttling the flow and dissipation of power. An SIHS can provide an efficient step-up or step-down of pressure or flow rate by using a digital control signal. In this article, analytical models of an SIHS in a four-port high-speed switching valve configuration are proposed, and the system dynamics and performance are investigated theoretically and experimentally. The flow responses, system characteristics and power consumption can be predicted effectively and accurately by using the proposed models, which were validated by comparing with experiments and with numerical simulation. The four-port configuration is compared with the three-port configuration, and it is concluded that the former one is less efficient for valves of the same size, but provides a bi-direction control capability. As bi-direction control is a common requirement, this constitutes an important contribution to the development of efficient digital hydraulics.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.

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

As the demands in reliability and high-degree of automation in industrial applications are increasing exponentially, the current Industry 4.0 trend lies in migrating towards smarter technologies that increase flexibility and provide autonomous operation. To tackle this, manufacturers shifted towards Real-Time Ethernet communication which supports autonomous operation, faster sensor/actuator configuration and high degree of determinism. Nevertheless, the uninterrupted operating principle of legacy industrial systems as well as the system complexity and heterogeneity constitutes the transition to Real-Time Ethernet very challenging. This article proposes a novel method allowing to automate the generation of executable code for Real-Time Ethernet architectures based on high-level programming models. The method is based on a programming model and modular code artifacts for the hardware architecture, employing Real-Time Ethernet communication. Through an tool-supported algorithm the high-level model is linked to the architecture's communication primitives and interfaces. The tool-support reduces significantly the development and debugging effort for Real-Time Ethernet applications and increases the application reliability, since the generated code is based on verified programming models. Additionally, the tool creates dedicated files, which allow automated industrial network configuration without any human intervention. Finally, by embedding firewall policies into the code generation process, the method guarantees cyber-resilience for the Real-Time Ethernet architecture. We demonstrate the proposed method in Programmable Logic Controllers (PLCs) of a Public Power Corporation's Hydroelectric Power Plant, which control the temperature and rotor speed of a power generator. The results demonstrate the method's ability to generate rapidly trustworthy and fault-tolerant code for the autonomous plant operation. The hurdles in time and effort for developing, configuring and debugging Real-Time Ethernet applications can be minimized through a high-level programming model that allows automated generation of executable code that is reusable for similar applications. Such time and effort reduction may pave the way towards the Industry 4.0 area.

Deep neural network (DNN) watermarking is a potential approach for protecting the intellectual property rights of DNN models. Similar to classical watermarking techniques for multimedia content, the requirements for DNN watermarking include capacity, robustness, and transparency, and other factors. Studies have focused on robustness against retraining and fine-tuning. However, less important neurons in the DNN model may be pruned. Moreover, although the encoding approach in renders DNN watermarking robust against pruning attacks, the watermark is assumed to be embedded only into the fully connected layer in the fine-tuning model. In this study, we extended the method such that the model can be applied to any convolution layer of the DNN model and designed a watermark detector based on a statistical analysis of the extracted weight parameters to evaluate whether the model is watermarked. Using a nonfungible token mitigates the overwriting of the watermark and enables checking when the DNN model with the watermark was created.

The genetic code conceals a ‘code within the codons’, which hints at biophysical interactions between amino acids and their cognate nucleotides. But research over decades has failed to corroborate systematic biophysical interactions across the code. Using molecular dynamics simulations and NMR, we have analysed interactions between the 20 standard proteinogenic amino acids and four RNA mononucleotides in three charge states. Our simulations show that 50% of amino acids bind best with their anticodonic middle base in the -1 charge state common to the backbone of RNA, while 95% of amino acids interact most strongly with at least one of their codonic or anticodonic bases. Preference for the cognate anticodonic middle base was greater than 99% of randomized assignments. We verify a selection of our results using NMR, and highlight challenges with both techniques for interrogating large numbers of weak interactions. Finally, we extend our simulations to a range of amino acids and dinucleotides, and corroborate similar preferences for cognate nucleotides. Despite some discrepancies between the predicted patterns and those observed in biology, the existence of weak stereochemical interactions means that random RNA sequences could template non-random peptides. This offers a compelling explanation for the emergence of genetic information in biology.

Nearly 50 years ago, Francis Crick propounded the frozen accident scenario for the evolution of the genetic code along with the hypothesis that the early translation system consisted primarily of RNA. Under the frozen accident perspective, the code is universal among modern life forms because any change in codon assignment would be highly deleterious. The frozen accident can be considered the default theory of code evolution because it does not imply any specific interactions between amino acids and the cognate codons or anticodons, or any particular properties of the code. The subsequent 49 years of code studies have elucidated notable features of the standard code, such as high robustness to errors, but failed to develop a compelling explanation for codon assignments. In particular, stereochemical affinity between amino acids and the cognate codons or anticodons does not seem to account for the origin and evolution of the code. Here I expand Crick’s hypothesis on RNA-only translation system by presenting evidence that this early translation already attained high fidelity that allowed protein evolution. I outline an experimentally testable scenario for the evolution of the code that combines a distinct version of the stereochemical hypothesis, in which amino acids are recognized via unique sites in the tertiary structure of proto-tRNAs, rather than by anticodons, expansion of the code via proto-tRNA duplication and the frozen accident.

Space instable target simulator (SITS) is a vital actuator for ground verification of on-orbital capture technology, the motion performance of which directly affects simulation credibility. Different delays reduce the stability of SITS and ultimately lead to its divergence. In order to achieve high fidelity simulation, the impact of force measurement delay, discrete control cycle, and simulator response delay on stability is analyzed firstly. Then, the dynamic equation and transfer function identification model of hybrid simulator is constructed, and the necessary and sufficient conditions of its stability and convergence are obtained using the Routh criterion. After that, a switching compensator with variable gain is proposed, the compensation principle diagram of which is build, and its mathematical model including the energy observer and tracking differentiator is also established. Finally, three sets of numerical simulations are conducted to validate the correctness of the stability analysis and effectiveness of proposed compensation method. Simulation results show that all three types of delay can cause SITS to lose stability under critical stable motion state, and the delay in force measurement has the greatest impact, followed by the influence of control cycle. Compared with the force applied to simulated target, velocity and its recovery coefficient of space instable target using fixed gain and linear gain compensation, the proposed compensator has significantly better performance.