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.

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.

As the demand for liquefied petroleum gas (LPG) continues to increase worldwide, more LPG facilities are being constructed. However, the design and manufacture of pressure vessels used to store dangerous liquids or pressurised fluids, such as LPG, has been a major cause of hazards, including explosions and leakage. In this study, we aim to address this issue by designing and analysing a vertical pressure vessel with a capacity of 10 m3 of pressurised LPG in accordance with the American Society of Mechanical Engineers (ASME) code. Safety is the primary concern in designing pressure vessels due to the potential risks posed by accidents. Therefore, the main objective of this project is to design a pressure vessel that is safe from failure. The vessel is cylindrical with two elliptical heads, two nozzles, a manway and four leg supports, and we used Autodesk Inventor Professional 2023 for geometric modelling and Inventor Nastran for FEA analysis. To investigate the displacements, deflections and Von-Mises stresses in the pressure vessel, we used the finite element method, and Autodesk Inventor Nastran was employed for the analysis. Our results showed that changes to the structure of the pressure vessel were needed to reduce stress in the structure. Specifically, the displacement showed an inverse relationship with the tank section shell thickness. Moreover, we observed that the factor of safety increased linearly with shell thickness. We carefully considered permissible pressures and determined the required wall thickness to ensure acceptable maximum stresses. Our results indicate that the design is safe from failure, and the highest stresses are experienced by the manway followed by the shell, while the heads, nozzles and legs support experienced the lowest stresses. We also performed a theoretical calculation for the entire model and checked the results to ensure they were within the acceptable limits. In summary, our study highlights the importance of designing pressure vessels in accordance with ASME codes to ensure safety and prevent hazards caused by improper design and manufacturing. By using the finite element method, we identified potential stress points in the pressure vessel and were able to make modifications to improve its safety.

This paper investigates whether currency risk is priced differently in the different sectors (industrial, financial, and basic materials) of equity markets in a sample of developed United States of America (USA) and developing economies (Brazil, India, Poland, and South Africa). The paper makes use of the following techniques: (i) Univariate Autoregressive Fractionally Integrated Moving Average and Exponential General Autoregressive Conditional Heteroskedastic (ARFIMA-EGARCH), (ii) the Markov-Switching method (MS), and (iii) the Canonical Vine Copulas (C-Vine) techniques. Using a sample of daily data made of the foreign exchange rate against the domestic currency and equity market sectors; our findings show that there is an asymmetry effect between equities markets and the foreign exchange rate: there is a heterogeneous, strong, and positive dependence between the two. Higher equities prices are associated with depreciation of local currencies, according to US dollar (USD) exchange rates. In addition, we find that the selected emerging economies are pricing a positive and considerable currency risk. The pricing of currency risk has a varied effect in both regimes representing the states of the economy. In fact, when currency risk pricing has a beneficial impact on certain sectors of the economy, investors predict better returns.

A review is here provided on the thermal effects on the optical chirality. To this goal, chiral objects dispersed in an embedding fluid are examined for their magnetoelectric coupling. Archetypal twisted-Omega particles are examined with respect to electron transport, phonon dynamics, and temperature effects. Continuum-mechanical aspect of thermo-elasticity is reviewed along with transverse deformations. A transition temperature delineating a sign flip in the chirality parameter is identified as well.

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.

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

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

The 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.

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.

Spinal and bulbar muscular atrophy (SBMA), also known as Kennedy’s disease, is a debilitating neuromuscular disease characterized by progressive muscular weakness and neuronal degeneration, affecting 1-2 individuals per 100,000 globally. While SBMA is relatively rare, recent studies have shown a significantly higher prevalence of the disease within the indigenous population of Western Canada compared to the general population. The disease is caused by a pathogenic expansion of polyglutamine residues in the androgen receptor protein, which acts as a key transcriptional regulator for numerous genes. SBMA has no cure, and current treatments are primarily supportive and focused on symptom management. Recently, a form of precision medicine known as antisense therapy has gained traction as a promising therapeutic option for numerous neuromuscular diseases. Antisense therapy uses small synthetic oligonucleotides to confer therapeutic benefit by acting on pathogenic mRNA molecules, serving to either degrade pathogenic mRNA transcripts or helping to modulate splicing. Recent studies have explored the suitability of antisense therapy for the treatment of SBMA, primarily focused on antisense-mediated mRNA knockdown approaches. Advancements in understanding the pathogenesis of SBMA and the development of targeted therapies offer hope for improved quality of life for individuals affected by this debilitating condition. Continued research is essential to optimize these genetic approaches, ensuring their safety and efficacy.

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.

Spontaneous, unwilled subjective imagery and symbols often emerge in psychotherapy that can appear baffling and confound interpretation. Moreover, early psychoanalytic theories seemed to diverge as often as they agreed on the meaning of such content. Nevertheless, reviewing key findings in the empirical science of spontaneous thought, as well as insights gleaned from neurosciences and especially embodied cognition, it is now possible to construct a more coherent theory of interpretation that is clinically useful. Given that thought is so thoroughly embodied, it is possible to demonstrate that universalities in human physiology yield universalities in thought. Such universalities can then be demonstrated to form a kind of biologically directed universal “code” for understanding spontaneous symbolic expressions that emerge in psychotherapy. An example is given that illustrates how this can be applied to clinical encounters.

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.

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

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

Guiding the visitor to appreciate the sights during an excursion is crucial to building a memorable experience. In the literature, we find many articles exploring the use of sophisticated technologies towards such a goal. The case of tourism proposals for inland areas exhibits a noteworthy aspect, since the presence of human artifacts and the signage may damage the experience, and introduce pollutants as well. This paper analyzes the signage for a natural trail putting into practice a holistic methodology that takes into account all aspects of the problem, starting from the touristic vocation of the area and the social fabric. The analysis indicates slow, community-involved tourism as the preferable target, with extreme requirements of sustainability in the social, economic, and environmental domains for the signage solution. Appropriate governance guidelines are given to implement the proposal. Footprint evaluations address the QR-code technology, which is used to design a real-scale solution finally deployed on the field.

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.

This article is devoted to the problem of genetically coding of inherited cyclic structures in biological bodies, whose life activity is based on a great inherited set of mutually coordinated cyclic processes. The author puts forward and arguments the idea that the genetic coding system is capable of encoding inherited cyclic processes because it itself is a system of cyclic codes connected with Boolean algebra of logic. In other words, the physiological processes in question are cyclical because they are genetically encoded by cyclic codes. In support of this idea, the author presents a set of his results on the connection of the genetic coding system with cyclic Gray codes, which are one of many known types of cyclic codes. This opens up the possibility of using for modeling inherited cyclic biostructures those algebraic and logical theories and constructions that are associated with Gray codes and have long been used in engineering technologies: Karnaugh maps, Hilbert curve, Hadamard matrices, Walsh functions, dyadic analysis, etc. Additionally, the connections of these constructions with statistical rules of genomic DNAs and binary-genomic numbers are considered.

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.

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.

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.

Transformer models have emerged as frontrunners in the field of natural language processing, primarily due to their adept use of self-attention mechanisms to grasp the semantic linkages between words in sequences. Despite their strengths, these models often face challenges in single-task learning scenarios, particularly when it comes to delivering top-notch performance and crafting strong latent feature representations. This challenge is more pronounced in the context of smaller datasets and is particularly acute for under-resourced languages such as Arabic. In light of these challenges, this study introduces a novel methodology for text classification of Arabic texts. This method harnesses the newly developed Reverse Positional Encoding (RPE) technique. It adopts inductive-transfer learning (ITL) framework combined with a switching self-attention shared encoder, thereby increasing the model's adaptability and improving its sentence representation accuracy. The integration of Mixture of Experts (MoE) and Reverse Positional Encoding (RPE) techniques empowers the model to process longer sequences more effectively. This enhancement is notably beneficial for Arabic text classification, adeptly supporting both the intricate five-point and the simpler ternary classification tasks. The empirical evidence points to its outstanding performance, achieving accuracy rates of 87.20% for the HARD dataset, 72.17% for the BRAD dataset, and 86.89% for the LABR dataset, as evidenced by the assessments conducted on these datasets.

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

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.

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.

Reliable motor control design is a critical part of the electric vehicle application. Control techniques and motor control devices play an important role in achieving system reliability. In this study, motor control devices have been proposed to implement control techniques in sensorless PMSM. Motor control devices including PWM signal generation by FPGA, gate driver circuit (GDC), and power inverter were developed to be integrated into single-stage motor control devices for sensorless motor control applications. Improvements in GDC design have been the focus to increase the performance of the motor control devices used. Switching losses due to the parasitic elements caused ringing switching behavior of the power inverter circuit, which reduces the performance when the system is integrated. Moreover, in the implementation of sensorless motor control using observer techniques, these conditions affect the accuracy of rotor position estimation, which uses the current or voltage information of the power converter as a variable in the observer design model. Therefore, a GDC design using IR2110 with additional ringing suppression circuit configuration was proposed for the application of motor device control topology to improve the performance of sensorless motor control system. Furthermore, the reduction of switching losses, reduction of current distortion, and accuracy of rotor position information were confirmed by experimental results.

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.

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.

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.

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.

A review is here provided about the thermal effects on the optical chirality. To this goal, chiral objects dispersed in an embedding fluid are examined for their magnetoelectric coupling. Thermal effects on several chiral meta-atoms and their ensembles are examined. To this goal, DNA-like helical structures are examined in detail. Mechanical aspect of thermo-elasticity is reviewed along with transverse deformations, while drawing analogies from condensed-matter physics. In this respect, chirality-induced spin selection is reviewed along with the temperature-mediated elec-tron-phonon interactions. A wide range of materials such as polymers and biological cells are also examined for temperature effects. A transition temperature delineating a sign flip in the chirality parameter is identified as well. Chirality-associated functionalities such as ratchet motions, switching, and modulations are investigated for their respective thermal effects. Issues of fabri-cating chiral meta-atoms are also discussed.

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.

The concept of ecosystem services (ESs) provides a valuable approach to communicate societies' dependence on natural ecosystems, assisting in the evaluation of measures to protect vegetation in watersheds that promote water-related ESs. Vegetation cover and land use are important factors related to the capacity of ecosystems to provide soil loss regulation and sediment retention services, which are highly relevant for sediment management in watersheds draining into reservoirs with multiple water uses. This study aims to assess the relationship between potential changes in land use and land cover and the sediment retention service provided by riparian vegetation in a watershed draining into a hydroelectric reservoir in the Brazilian Cerrado. Sediment load was selected as a physical variable that affects different water uses in reservoirs, with these uses being dependent on the quality or quantity of stored water. Scenarios were analyzed to represent landscape reconfigurations from a baseline condition in order to account for the recovery of riparian vegetation by the agricultural sector, as foreseen in the Brazilian Forest Code. To map and quantify variations in annual sediment retention, the InVEST model was used, which allows estimating an integrated response of the drainage basin to large-scale land use changes over time and space. The results show that for the drainage basin of the Corumbá IV Hydroelectric Power Plant (HPP), the recovery of riparian areas occupied by annual crops has the potential to generate greater benefits to water use in the reservoir. In the studied catchment area, 14% of the total vegetation deficit in riparian areas is occupied by crops, while 86% is occupied by pastures. This highlights the buffering role of riparian vegetation as an effective practice for managing sediment flows originating from sheet erosion in agricultural landscapes. The methodology employed in this study allows for the quantification and mapping of the effects of soil and water conservation measures in river basins. This enables the prioritization of sectoral efforts for the restoration of native vegetation, considering the highest returns in benefits perceived by water users affected by sediment input in reservoirs. The study's results reinforce the importance of conserving vegetation in riparian areas and their surroundings for sediment retention, highlighting the role of these areas as assets in providing water-related ecosystem services.

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.

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.

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 “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.

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.

The main objective of the paper is to provide a thorough analysis of currently used modulation control schemes for single phase bidirectional dual active bridge DC/DC converter. In this article, it will be shown that single phase shift, extended phase shift and Dual phase shift modulation schemes are special cases of triple phase shift (TPS) modulation scheme. The article aims to highlight six TPS switching modes and their complements with the operational constraints. Unlike previous studies that regarded TPS as a complex scheme, this paper simplifies the analysis of each mode and aims to standardize the understanding of TPS modulation in DAB converters. Power equations, range of power transferred and zero voltage switching (ZVS) are derived for all the modes under TPS without assuming fundamental component analysis. Additionally, a generic optimization algorithm is developed to show the advantages of TPS modulation and thus, detailed analysis presented in this paper provides valuable insights for single phase DAB converter designers in identifying a wide range of optimization algorithms to achieve higher efficiency under TPS modulation. This analysis contributes to the advancement of bidirectional DAB converter technology and facilitates its application in various power electronic systems.

Digital agricultural technology extension service and sustainable food production have attracted attention from academia. However, the association between digital agricultural technology extension service and sustainable food production has not yet been fully investigated. This research investigates the average and heterogeneous impacts of digital agricultural technology extension service use on eco-efficiency among 1302 maize producing farmers from Northeast China major maize producing area in 2022 to fill this knowledge gap. The slack-based measure model with undesirable outputs is applied to calculate the eco-efficiency of maize production. To obtain an unbiased estimation of the average effect, the self-selection problem generated by observable and unobservable factors is solved by the endogenous switching regression model. Quantile regression is utilized to analyze the heterogeneous effect. Notably, the mediated effects model is utilized to examine the potential mechanism between them. Our findings indicate that digital agricultural technology extension service use can increase maize production's eco-efficiency. Digital agricultural technology extension service users would reduce eco-efficiency by 0.148 (21.11%) if they had not used the digital agricultural technology extension service. Digital agricultural technology extension service nonusers would improve eco-efficiency by 0.214 (35.20%) if they had used it. The robustness check reconfirms the results. Moreover, digital agricultural technology extension service use is more helpful to maize farmers who have lower eco-efficiency than those who have higher eco-efficiency. Digital agricultural technology extension service use can improve eco-efficiency of maize production through the application of organic fertilizers, green pesticides and biodegradable agricultural films.

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 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.

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.

Neutral beam injection (NBI) is used for thermonuclear plasma heating, non-inductive current drive, rotation, operation scenario control, and for plasma diagnostics in nuclear fusion devices. NBI is the main source of fast particles in plasma which transmit energy to thermal plasma during their slow-down. Steady-state tokamak operation is only possible when the sub-thermal particles energy is absorbed and effectively confined in plasma volume. While NBI purposes and applications vary through different fusion systems, the engineering tasks performed during research and development (R&D) stage of neutral beamline design have much in common. The NBI studies, especially addressing long pulse and high-power operation typically include a beam transmission analysis and power loads distribution at the structural components of the beamline. Thermo-structural analysis is applied next to heat removal circuits design. ‘Beam Transmission’ (BTR) code is a dedicated tool, developed by a physicist and used by NBI physicists and engineers. BTR was created and 1^st applied during R&D phase of the world’s largest tokamak ITER. BTR code is intended for public usage and routinely applied for detailed simulations of neutral beam propagation through complex beamlines. BTR is flexible and intuitive in input, user-friendly and fully interactive. Thanks to visualization and high performance BTR looks and feels like a real NB flight simulator. Despite BTR ‘mature’ age, the code is still in evolution. Support is available to all new BTR users.

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.

Active front-end (AFE) rectifiers have been becoming widely employed in power systems to achieve unity power factor and harmonic mitigations. The typical modeling approaches applied for AFE rectifiers in the literature mostly relied on two baselines named the detailed model and the time average model. The former approach deals with the switching element model (SEM) leads to significant harmonics in currents with distorted waveforms. The latter approach uses the average-value model (AVM) to overcome the currents’ harmonics as well as provide fast responses. However, even the AVM baseline has contained problems during the starting stage (lack of control signals) and over the dead-time periods which causes serious issues in the implementation process. This paper presents an improved dynamic AVM for AFE rectifiers by precisely considering the issues mentioned above along with the practical starting procedure and desirable initialization. The studied AFE rectifier is developed using the voltage-oriented control (VOC) technique based on the different modeling methodologies including SEM, Conventional AVM, and the proposed AVM. The performance of all models is analyzed and compared using simulation results with MATLAB/Simulink R2023a Function blocks for all the algorithm parts and SimScape elements for the electrical circuit model. The simulation results illustrate that the performance of the proposed AVM approach can closely resemble the behavior of the SEM baseline with low harmonic distortion.

We consider a coded compressed sensing protocol for unsourced massive random access (URA) that concatenates a shared Patterned Reed-Muller (PRM) inner codebook to an outer error-correction code. In this paper, an iterative list PRM projection algorithm is proposed to supplant the signal detector associated with the inner PRM sequences. In particular, we first propose an enhanced paths-saving algorithm called list PRM projection detection for the single-user scenario that keeps multiple candidates for the first few layers to remedy the risk of spreading errors. On this basis, we further propose an iterative list PRM projection algorithm for the multi-user scenario. The vectors for PRM codes and channel coefficients are jointly detected in an iterative manner, which offers significant improvements regarding the convergence rate for signal recovery. Furthermore, the performances of the proposed algorithms are analyzed mathematically, and we verified that the theoretical simulations are consistent with the numerical simulations. Finally, we concatenate the inner PRM codes that employ the iterative list detection to two practical error-correction outer codes. According to the simulation results, we conclude that the packetized URA with the proposed iterative list projection detection works better than benchmarks in terms of the number of active users it can support in each slot and the amount of energy needed per bit to meet an expected error probability.

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.

We extend Ziv and Lempel's model of finite-state encoders to the realm of lossy compression of individual sequences. In particular, the model of the encoder includes a finite-state reconstruction codebook followed by an information lossless finite-state encoder that compresses the reconstruction codeword with no additional distortion. We first derive two different lower bounds to the compression ratio that depend on the number of states of the lossless encoder. Both bounds are asymptotically achievable by conceptually simple coding schemes. We then show that when the number of states of the lossless encoder is large enough in terms of the reconstruction block-length, the performance can be improved, sometimes significantly so. In particular, the improved performance is achievable using a random-coding ensemble that is universal, not only in terms of the source sequence, but also in terms of the distortion measure.

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.

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.

Alternative splicing is an essential regulatory mechanism for gene expression in mammalian cells contributing to protein, cellular, and species diversity. Isoform switching, the process where cells alter the expression of various splice forms (isoforms) of a gene, plays a critical role in cellular function and adaptation. Our study provides a comprehensive analysis of splice forms prevailing across ten of the most studied human cell lines using public RNAseq data. We analyzed the occurrence of splice forms and found substantial justification for revising the canonical sequences for 56 genes, suggesting that these alternative splice forms should be recognized as canonical. Additionally, distinct profiles of splice form prevalence across these cell lines offer new insights into their specific biological roles. Our findings reinforce and expand upon previous insights into the link between isoform expression and oncological processes. Notably, we identified three genes exhibiting statistically significant changes between cancerous and normal tissues. Moreover, a comparative analysis of prevailing isoforms across diverse cell lines with prior research unveiled 140 common splice variants associated with cancer, highlighting their importance for prognostication and understanding oncogenesis. We revealed unique patterns and variations in isoform switching of these lines that provide new insights into the importance of selecting specific ones for research.