HIV-1 assembly is a complex mechanism taking place at the plasma membrane of the host cell. It requires nice spatial and temporal coordination to end up with a full immature virus. Researchers have extensively studied HIV-1 assembly molecular mechanism during the past decades, in order to dissect the respective roles of viral proteins, viral genome and host cell factors. Nevertheless, the time course of the process has been observed in living cells only a decade ago. The very recent revolution of optical microscopy, combining high speed and high spatial resolution now permit to study assemblies and their consequences at the single molecule level within (living) cells. In this review, after a short description of these new approaches, we will show how HIV-1 assembly in cells has been revisited using these advanced super resolution microscopy techniques and how much it could make a bridge in studying assembly from the single molecule to the host cell.

Optical tweezers offer a non-contact method for selecting single cells and translocating them from one microenvironment to another. We have characterized the optical tweezing of yeast S. cerevisiae and can manipulate single cells at velocities up to 0.77 mm/s using laser powers of 40 mW from a 785 nm diode laser. We have fabricated and tested three cell isolation devices; a micropipette, a PDMS chip and laser machined fused silica chip and we have isolated single bacteria, yeast and cyanobacteria cells. The most effective isolation was achieved in PDMS chip, where single yeast cells were grown and observed for 18 hours without contamination. The duration of budding in S. cerevisiae was not affected by the laser parameters used, but the time from tweezing until the first budding event began increased with increase laser energy (laser power x time). Cells tweezed using 25 mW for 1 minute were viable after isolation. We have constructed a micro-consortium of yeast cells, and a co-culture of yeast and bacteria, using optical tweezers in combination with the PDMS network of channels and isolation chambers, which may impact on both industrial biotechnology and understanding pathogen dynamics.

We conducted an in-situ crystal structure analysis of ferroselite at non-ambient conditions. The aim is to provide a solid ground to further the understanding of the properties of this material in a broad range of conditions. Ferroselite, marcasite-type FeSe2, was studied under high pressures up to 46 GPa and low temperatures, down to 50 K using single-crystal microdiffraction techniques. High pressure and low temperatures were generated using a diamond anvil cell and a cryostat. We found no evidences of structural instability in the explored P-T space. The deformation of the orthorhombic lattice is slightly anisotropic. As expected, the compressibility of the Se-Se dumbbell, the longer bond in the structure, is larger than that of the Fe-Se bonds. Less obvious is the behavior of the octahedral bonds, the shorter bond is the most compressible determining a small increase in the octahedron distortion with pressure. We also achieved a robust structural analysis of ferroselite at low temperature in the diamond anvil cell. Structural changes upon temperature decrease are small but qualitatively similar to those produced by pressure.

Layered superconductors are attractive because some of them show high critical temperatures. While their crystal structures are similar, those compounds are composed of many elements. Compounds with many elements tend to be incongruent melting compounds, thus, their single crystals cannot be grown via the melt-solidification process. Hence, these single crystals have to be grown below the decomposition temperature, and then the flux method, a very powerful tool for the growth of these single crystals with incongruent melting compounds, is used. This review shows the flux method for single-crystal growth technique by self-flux, chloride-based flux, and HPHT (high-pressure and high-temperature) flux method for many-layered superconductors: high-T_c cuprate, Fe-based and BiS₂-based compounds.

This paper describes RTMN 2.0 - an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. The reusability and intuitive robot programming allows people without programming expertise to plan and control robots. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modelling language, RTMN 2.0 concentrates on Human Robot Collaboration (HRC), which represents the current trend of the industry shift from ‘mass-production’ to ‘mass-customization’. While traditional robot systems provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity. Therefore, human-robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in industrial scenarios. HRC has made a great impact on the robotic industry: increased efficiency, reduced costs and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension in RTMN 2.0 includes the following notations: HRC tasks, requirement, Key Performance Indicator (KPI), condition check and decision making, join/split and data association. With all these additional elements RTMN 2.0 covers the full scale of agile manufacturing – light out and collaborative.

An algorithm is proposed for discriminating the fatigue state of air traffic controllers based on ap-plying multispeech feature fusion using an FSVM to voice data, and for extracting eye-fatigue-state discrimination features based on PERCLOS eye data. For the speech algorithm and an eye-fatigue index, a new controller fatigue-state evaluation index based on the entropy weight method is proposed based on decision-level fusion of fatigue discrimination results for speech and the eyes. Experimental results show that the fatigue-state recognition accuracy rate was 84.81% for the fatigue state evaluation index, which was 3.36% and 1.86% higher than those for speech and eye assessments, respectively. The comprehensive fatigue evaluation index provides important reference values for controller scheduling and mental-state evaluations.

The utilization of dual-potential mode of integrated array (IDA) electrodes and other two-electrode systems has revolutionized electrochemical detection by enabling the simultaneous and independent detection of two species, accompanied by the exhibition of unique characteristics. In contrast to conventional dual-potential electrodes, the rotating ring disk electrodes (RRDE), IDA electrodes demonstrate analogous yet vastly improved performance metrics, characterized by remarkable collection efficiency and sensitivity. Notably, due to the distinctive microscale structure of IDA electrode, the special “feedback” effect makes IDA a unique signal amplifier. In recent decades, the research surrounding IDA electrodes has garnered escalating interest due to their attractive attributes. This review centers its focus on the fabrication and applications of IDA electrodes. In the realm of fabrication, two critical breakthroughs are poised for realization: the achievement of reduced dimensions and the diversification of materials. Established fabrication methods for IDA electrodes encompass photolithography, inkjet printing, and direct laser writing, each affording distinct advantages in terms of size and precision in IDA construction. Predominantly employed materials for IDA electrodes include gold, platinum, and carbon, with the selection of fabrication methods guided by considerations such as material properties, desired dimensions, cost-efficiency, and specific application requisites. Within the domain of applications, IDA electrodes, capitalizing on their distinctive attributes, have found utility in diverse fields. This review summarizes the applications of IDA electrodes based on their fundamental working principles, encompassing redox cycling, resistance modulation, capacitance variations, and more. The potential for further development of this specialized tool to exhibit enhanced properties holds substantial promise.

Magnetorheological (MR) fluids have been known to react to magnetic fields of sufficient magnitudes. While in the presence of the field the material develops a yield stress. The tunable property has made it attractive in e.g. semi-active damper applications in vibration control domain in particular. Within the context of a given application the fluids can be exploited in at least one of the fundamental operating modes of which the so-called gradient pinch mode has been least explored. Contrary to the other operating modes, the MR fluid volume in the flow channel is exposed to a non-uniform magnetic field in such a way that the Venturi-like contraction is developed in a flow channel solely by means of a solidified material in the regions near the walls rather than mechanically driven changes of the channel’s geometry. The pinch mode rheology of the material has made it a potential candidate in developing on a new category of MR valves. By convention, a pinch mode valve features a single flow channel with poles over which non-uniform magnetic field is induced. In this study the authors examine ways of extending the dynamic range of pinch mode valves by employing a number of such arrangements (stages) in series. To accomplish this, the authors developed a prototype of a multi-stage (three-stage) valve, and then compared its performance against that of a single-stage valve across a wide range of hydraulic and magnetic stimuli. To summarize, improvements in the pinch mode valve dynamic range are evident, however, at the same time it is hampered by the presence of serial air gaps in the flow channel.

In this paper, a robust nonlinear dynamic controller is designed for a wind turbine with a permanent magnet synchronous generator. The wind turbine is subject to variations in all the parameters appearing in its mathematical model. Furthermore, the wind velocity is considered unavailable for direct measurement. This situation is of particular interest in practical applications, where only the nominal parameter values are known, and accurate wind velocity measurement is challenging due to perturbations caused by the turbine itself, even with appropriate sensors. The problem addressed in this work involves tracking the reference angular velocity corresponding to the wind velocity. To achieve accurate tracking, appropriate compensation of the perturbation terms resulting from parameter uncertainties and wind estimation errors is required. To address this problem, an estimator of the wind velocity is utilized, along with high–order sliding mode parameter estimators, to ensure high–performance operation of the turbine.

Target recognition mainly includes three approaches: optical image-based, echo detection-based, and passive signal analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noise environment. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signal, the recognition network is realized based on Multi-Scale Convolutional Attention Network (MSCANet). Firstly, the deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem that the conventional method is seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environments, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

Building on the study by Abdullah et al. (2020), this study aims to analyze the perception of travel, especially in this context of the pandemic, when the measures are alleviated, as well as the citizens' intention and preferences for travel methods during the COVID-19 pandemic. The purpose of the study is to investigate the impact of the COVID-19 pandemic on travel behavior and travel mode preferences in the example of the residents of Bosnia and Herzegovina. Five research questions were defined. A quantitative research approach was applied in this study. The data was collected through a questionnaire (online survey) distributed to respondents via e-mail and the social network Facebook. The convenience sample included 265 respondents. Descriptive analysis, as well as quantitative comparative analysis, were carried out on the collected data. The results show that the COVID-19 pandemic has influenced a change in travel behavior. The primary purpose of travel before and during the COVID-19 pandemic was social activities. The most significant change in the primary purpose of travel before and during the COVID-19 pandemic is reflected in a decrease in social activities and an increase in recreational sports activities. In contrast, other activities remained proportionally the same before and during the COVID-19 pandemic. The average distance traveled for primary outdoor trips before the COVID-19 pandemic is greater than that for primary outdoor trips during the COVID-19 pandemic. This article allows for better organization and planning for the future in the event of another pandemic.

Background: Malaria remains a serious public health concern worldwide, particularly in tropical and subtropical regions, and the leading causes of mortality and morbidity in children especially among those aged under five in developing countries. Malaria can be fatal if not promptly and accurately diagnosed, especially in children.This study was carried out to determine the level of malaria infection and its associated risk factors among febrile children.Methods: Blood samples were collected and analyzed from two- hundred (200) systematically selected febrile children aged 1-10 years old. assay of samples collected were carried out using standard methods.data were collected using a pretested structured questionnaire. Results: Results obtained from study subjects shows that, of the eighty-seven (87) infected subjects, 34 (39.1%) were aged 1-5 years, while 52 (59.8%) were within the range of 6 years and above.severity of infection showed 48 (55.2%) with scanty parasitic infection while 25 (28.7%) showed moderate infection, and 13 (14.9%) showed heavy infection.The association of age and level of parasitemia showed that 34 (58.8% ) subjects below 5 years, recorded scanty malaria compared to 14.7% with severe malaria within the age range.Considering gender, of the 33 infected female subjects, 45.5% had scanty malaria, 36.4% showed moderate malaria, while 18.2% had severe malaria, compared to the males subjects.Conclusion: This study reveals the prevalence of malaria infection with some level of severity among children at our study location.The need for prompt diagnosis and improved access to all malaria interventions becomes relevant,with a special focus on the high risk group.

Nanoparticles (NPs) have various applications in medicine, cosmetics, optics, catalysis, environmental purification, and other areas nowadays. With an increasing annual production of NPs, the risks of their harmful influence to the environment and human health is rising. Currently, our knowledge about the mechanisms of interaction between NPs and living organisms is limited. Additionally, poor understanding of how physical and chemical characteristic and different conditions influence the toxicity of NPs restrict our attempts to develop the standards and regulations which might allow us to maintain the safe living conditions. The marine species and their habitat environment are under continuous stress due to anthropogenic activities which result in the appearance of NPs in the aquatic environment. Our study aimed to evaluate and compare biochemical effects caused by the influence of different types of carbon nanotubes, carbon nanofibers, and silica nanotubes on four marine microalgae species. We have evaluated the changes in growth-rate, esterase activity, membrane polarization, and size changes of microalgae cells using flow cytometry method. Our results demonstrated that toxic effects caused by the carbon nanotubes strongly correlated with the content of heavy metal impurities in the NPs. More hydrophobic carbon NPs with less ordered structure had a higher impact on the red microalgae P. purpureum because of higher adherence between the particles and mucous covering of the algae; silica NPs caused significant inhibition of microalgae growth-rate predominantly produced by mechanical influence.

In this paper, the fixed-time stabilization problem for a class of uncertain chained system is addressed by using a novel nonsingular recursive terminal sliding mode control approach. A fixed-time controller and an adaptive law are designed to guarantee the uncertain chained form system both Lyapunov stable and fixed-time convergent within the settling time. The advantage of the controller based on the sliding mode is that the settling time does not depend on the system initial state. Furthermore, we use RBF neural network to estimate the uncertainty of the system. Finally, the simulation results demonstrate the performance of the control laws.

Power converters with coupled inductors are very promising due to the high efficiency and high voltage gain. Apart from the aforementioned advantages, the boost-flyback converter reduces the voltage stress on the semiconductors. However, to obtain good performance with high voltage gains, the controller must include two control loops (current and voltage), and a compensation ramp. One of the most used control techniques for power converters is the peak current-mode control with compensation ramp. However, in the case of a boost-flyback converter there is no mathematical expression in the literature, to compute the slope of the compensation ramp. In this paper, a formula to compute the slope of the compensation ramp is proposed in such a way that a stable period-1 orbit is obtained. This formula is based on the values of the circuit parameters, such as inductances, capacitances, input voltage, switching frequency and includes some assumptions related to internal resistances, output voltages, and some other electrical properties related with the physical construction of the circuit. The formula is verified numerically using the saltation matrix and experimentally using a test circuit.

In this paper, we discuss voltage control method for buck converter operating in continuous conduction mode (CCM) using analog feedback system. The aim of this work is to control the output voltage of a buck converter during the variation in load current. This is obtained using analog feedback made with operational amplifier (Opamp). However, the same technique can be applied to other DC-DC converters (e.g boost, buck-boost, cuk converter, etc) in CCM mode, but for the purpose of analysis buck converter is chosen as an example.

The vibration signal of heavy gearbox presents non-stationary and nonlinear characteristics, which increases the difficulty to extract the fault feature. When the gear has a subtle fault, it may cause a perceptible change of local fluctuation rather than the large scale fluctuation. Therefore, the feature parameters extracted from local fluctuation can effectively improve the recognition performance of the gear fault. In this paper, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to identify the gear fault of heavy gearbox. Firstly, the raw vibration signal is decomposed several mode components by VMD, which is an adaptive and non-recursive signal decomposition method. Next, the sensitive mode component is selected by a maximal indicator, which is composed of kurtosis and correlation coefficient of relative higher frequency mode components corresponding to local fluctuation of raw vibration signal. Finally, the characteristics of the double-scales feature parameters of selected sensitive mode are extracted by DFA. In addition, the position of turning point of double scales is estimated by sliding windowing algorithm. The proposed method is evaluated through its application to gear fault classification using vibration signal. The results demonstrates that the recognization rate of gear faults condition have marked improvement by proposed method than the DFA of Small Time Scale (STS-DFA) method.

In this paper, the current technologies of the regenerative shock absorber systems have been categorized and evaluated. Three drive modes of the regenerative shock absorber systems, namely the direct drive mode, the indirect drive mode and hybrid drive mode are reviewed for their readiness to be implemented. The damping performances of the three different modes are listed and compared. Electrical circuit and control algorithms have also been evaluated to maximize the power output and to deliver the premium ride comfort and handling performance. Different types of parameterized road excitations have been applied to vehicle suspension systems to investigate the performance of the regenerative shock absorbers including that of the nonlinear regenerative shock absorber. The research gaps for comparison of the different drive modes and the nonlinearity analysis of the regenerative shock absorbers are identified and, the corresponding research questions have been proposed for future work.

The purpose of this work is to present an adaptive sliding mode luenberger state observer with improved disturbance rejection capability and better tracking performance under dynamic conditions. The sliding hyperplane is altered by incorporating the estimated disturbance torque with the stator currents. Also, the effects of parameter detuning on the speed convergence is observed and compared with the conventional disturbance rejection mechanism. The entire drive system is first built in simulink environment. Then, the simulink model is integrated with RT-Lab blocksets and implemented in a relatively new real-time environment using OP4500 real-time simulator. Real-time simulation and testing platforms have succeeded offline simulation and testing tools due to their reduced development time. The real-time results validate the improvement in the proposed state observer and also correspond to the performance of the actual physical model.

In the dynamic landscape of sustainable computing, use of edge devices is paramount for reducing the need for large-scale centralized data centers. By processing data locally, edge devices minimize the energy-intensive computing in data centers, improving the overall performance, cost-effectiveness whereas reducing the environmental impact. Edge devices may constitute edge clusters composed of resource frugal Single Board Computers (SBC) such as Raspberry Pi etc. The small form-factor and energy efficiency of these computers makes them ideal for processing large data on the edge. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the Yet Another Resource Negotiator (YARN) scheduling mechanism to address these challenges. Our proposed changes include the introduction of a Frugality Index and an adaptiveConfig policy. The Frugality Index categorizes SBC nodes based on their capabilities, enabling intelligent resource allocation. The adaptiveConfig policy dynamically adjusts resource allocation in response to workload and cluster conditions, enhancing system efficiency. Additionally, we introduce a fetch_threshold for reduce tasks to improve task prioritization based on locality and data processing efficiency. We evaluate our approach using a 13-node SBC cluster and conduct experiments with CPU-intensive and IO-intensive Hadoop benchmarks. The results demonstrate significant performance improvements compared to native YARN settings, with execution times 4.7 times faster than the worst_native and 1.9 times faster than the best_native scenarios. Furthermore, the proposed adaptiveConfig policy implementing the frugality index and a fetch_threshold outperforms the native YARN by 5.86 times and 1.79 times in Terasort and wordcount executions respectively. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations. The adaptive policies prove well-suited to the frugal SBC-cluster context, yielding enhanced outcomes and paving the way for sustainable big data processing initiatives.

The article proposes a mathematical model for creating playlists based on the emotional utility of the consumer, aiming to maintain high user interest in music streaming platforms (Lugos Abarca, 2024). The proposed method follows these steps: first, it calculates the MEEPE of Songs on the digital platform; subsequently, it quantitatively defines the Consumer's MEEPI before exposure to music; then, it operates the MEEPE of the Songs with the consumer's initial MEEPI using the known resolution process (Lugos Abarca, 2023); finally, it employs an emotional utility function to determine possible levels of emotional satisfaction and establish the playback order. This results in the creation of an efficient theoretical model. It is recommended to expand the computational study of the model in future research by using a broader set of songs and considering various initial MEEPI of the consumer. The article's originality lies in its ability to predict, quantitatively and theoretically, the level of satisfaction a music consumer could experience when listening to a playlist.

The objective of this research is to propose a utility function that calculates the possible level of satisfaction that a music consumer would achieve after listening to a song according to the emotional response obtained from a song. Assuming that the consumer's musical preferences are satisfied if the predominant emotions of his current emotional state increase; the probabilistic emotional-musical model (Lugos Abarca, 2023) is used to construct such a utility function. By posing a hypothetical scenario, realistic results were obtained, which guarantee, in principle, its use in theoretical practice. The originality of this research lies in proposing a mathematical tool to study, quantitatively, the decision making of a musical consumer influenced by his emotions. The main conclusion being its importance in the music industry, since the function could predict, theoretically, whether a song will be accepted or rejected by the consumer in comparison with the competition.

During the last few years, advancement in the area of biochemistry, science of the material world, engineering and computer-aided testing has directed towards advancement of high-throughput tools for the sake of profiling information encoded in a gene. Single-cell RNA-sequencing (scRNA-seq) tools capable to examine the sequence data from each individual cells and it shows within population variety and permit exploring of cell conditions and transformation by extreme resolution, possibly give out cell sub-types or gene expression fluctuations that are shaded in mass sequencing processes, which shows population-averaged evaluations. Yet, the major disadvantage for this tool is the lack of success to pick out location related details of the RNA transcriptome, as this needs tissue detachment and cell isolation. Location based transcript determination is among the advancements in the area of medical biotechnology as this can find out the molecules like RNA dataset in their intact physical placement in tissue segment with spatial context at the scale of a single-cell, which is very advantageous as compare to single-cell sequencing techniques. These approaches give key observation in the areas of biomedical field sub-disciplines like neurology, embryology, carcinoma study, immune cell investigation and histological activities. Generally, this review mainly focused towards single-cell sequencing methods, technology development, its challenges observed, different expression data analysis mechanisms and their applications in different areas, such as in cancer research, microbes, central nervous system, sex organs and immune-biology, intensifying the essentiality of sequencing tools at single-cell level for the characterization of extremely dynamic individual cells.

A microfinance scheme has emerged as a crucial tool in reducing poverty gaps in many developing countries. A notable player in the arena is Amanah Ikhtiar Malaysia (AIM), a pioneer in microfinancing in Malaysia since 1987. AIM’s microfinance program injects small-scale businesses, particularly benefiting single mother entrepreneurs, contributing to capacity-building and empowerment. However, the advent of the COVID-19 pandemic, coupled with Movement Control Order (MCO), has adversely affected various business sectors and vulnerable groups, including single mothers engaged in microfinancing enterprises. This study employs primary data to assess the impact of AIM’s microfinancing programs on single-mother entrepreneurs during the crisis. Utilizing a quantitative approach with a two-stage sampling process involving 422 respondents in urban areas, the research evaluates the relationship between empowerment indicators (economic, social, digital, and psychological) and management and governance aspects of program effectiveness. The findings reveal that the microfinancing program’s overall effectiveness, economic, digital and psychological empowerment indicators, and program management are at a moderate level, while social empowerment is high. Economic and digital empowerment, along with effective management, positively and significantly influence program effectiveness, whereas social empowerment has a negative and significant impact. The study concludes with recommendations for enhancing AIM’s microfinance program, equipping single mother entrepreneurs to better navigate future challenges, and informing policy formulation related to their empowerment.

Electrocatalytic and photocatalytic hydrogen evolution reaction (HER) provides a promising approach to clean energy generation. Bimetallic single-atom catalysts raised and have been explored to be advanced catalysts for HER. It is urgent to review and summarize the recent advances in developing bimetallic single-atom HER catalysts. Firstly, the fundamentals of bimetallic single-atom catalysts are presented, highlighting their unique configuration of two isolated metal atoms on supports and resultant synergistic effects. Secondly, recent advances in bimetallic single-atom catalysts for electrocatalytic HER under acidic/alkaline conditions are then reviewed, including W-Mo, Ru-Bi, Ni-Fe, Co-Ag, and other dual-atom systems on graphene and transition metal dichalcogenides (TMDs) with enhanced HER activity versus monometallic analogs due to geometric and electronic synergies. Then, Photocatalytic bimetallic single-atom catalysts on semiconducting carbon nitrides for solar H2 production are also discussed. Finally, an outlook is provided on opportunities and challenges in precisely controlling bimetallic single-atom catalyst synthesis and gaining in-depth mechanistic insights into bimetallic interactions.

Purpose: Therapeutic targeting of RAF1 is promising, but it requires further investigation for the relation between clinical features and RAF1 aberrations regarding the MAPK signaling pathway in various solid tumors to realize the precision medicine. Methods: Between October 2019 and June 2023, Samsung Medical Center, 3,895 patients with metastatic cancer patients received a next-generation sequencing (NGS) using TruSight Oncology 500 (TSO500) assay as a routine clinical practice. We surveyed the incidence of RAF1 aberration including mutation (single-nucleotide variant [SNV]), amplification (copy-number variation), and fusion. Results: In 3,895 metastatic cancer patients, 77 (2.0%) had RAF1 aberrations in their tumor specimen. Of 77 patients, 44 (1.1%) had RAF1 mutations (SNV), 25 (0.6%) had RAF1 amplification and 10 (0.3%) had RAF1 fusions. Among 10 patients with RAF1 fusion, concurrent RAF1 amplification and RAF1 mutation were detected in one patient each. The most common tumor types were bladder cancer (11.5%), followed by ampulla of Vater (AoV) cancer (5.3%), melanoma (3.0%), gallbladder (GB) cancer (2.6%), and gastric cancer (2.3%). Microsatellite instability high (MSI-H) tumors were found in 5 out of 76 patients (6.6%) with RAF1 aberration, while MSI-H tumors were found only in 2.1% of patients with wild-type RAF1 cancer (p < 0.0001). Conclusion: We showed that when patients with metastatic solid cancer receive NGS test, approximately 2.0% have RAF1 aberrations in their tumor specimen.

Object detection is a popular image processing technique, widely used in numerous applications for detecting and locating objects in images or videos. While being one of the fastest algorithms for object detection, Single-Shot Multibox Detection (SSD) networks are also computationally very demanding, which limits their usage in real-time edge applications. Even though the SSD post-processing algorithm is not the most complex segment of the overall SSD object detection network, it is still computationally demanding and can become a bottleneck with respect to processing latency and power consumption, especially in edge applications with limited resources. When using hardware accelerators to accelerate backbone CNN processing, the SSD post-processing step implemented in software can become critical for high-end applications where high frame rates are required, as this paper shows. To overcome this problem, we propose Puppis, an architecture for hardware acceleration of SSD post-processing algorithm. As experiments will show, our solution will lead to an average SSD post-processing speedup of 34.42 when compared with a software implementation. Furthermore, execution of a complete SSD network will be on average 45.39 times faster than software implementation when the proposed Puppis SSD hardware accelerator is used together with some existing CNN accelerators.

To avoid the endless accumulation of QTL effects during pyramiding breeding, QTL epistasis plays a role of homeostasis. Four single-segment substitution lines with heading date QTLs were utilized to construct pyramiding materials with dual QTLs and triple QTLs, allowing for the estimation of QTL epistatic effects on heading date in rice. The epistatic components of dual QTL and triple QTL interactions were analyzed using data from three seasons of two years. All four QTLs can be considered as dominant loci, demonstrating superdominance. 75% of dual QTL interactions and 81.25% of triple QTL interactions obtained statistically significant results, reaffirming the prevalence of epistasis. Interactions of four QTLs with three positive effects and one negative effect generated 62.5% negative dual QTL epistatic effects and 57.7% positive triple QTL epistatic effects, forming the balance relationship “positive-negative-positive”. In fact, QTL effects were consistently partially neutralized by mixed QTL epistatic effects so as to control the infinite accumulation of QTL effects. QTL epistasis plays a role of homeostasis on heading date in rice. However, there were two exceptions. The directions of OsMADS50 and Hd3a-2 effects were consistently aligned with those of their mixed epistases, suggesting that the QTL or gene could be employed in pyramiding breeding with different objectives. This study elucidated the mechanism of epistatic interactions among four QTLs and provided valuable genetic resources for improving heading date in rice.

Understanding the catalytic performance of different materials is of crucial importance for further technological advancements. This especially relates to the behavior of different classes of catalysts under operating conditions. Here we analyze the effects of local coordination of metal centers (Mn, Fe, Co) in graphene-embedded Single-Atom Catalysts (SACs). We have started from well-known M@N4-graphene catalysts and systematically replaced nitrogen atoms with oxygen or sulfur atom to obtain M@OxNy-graphene and M@SxNy-graphene SACs (x+y=4). We show that local coordination strongly affects the electronic structure and the reactivity towards hydrogen and oxygen species. However, the stability is even more affected. Using the concept of Pourbaix plots, we show that the replacement of nitrogen atoms coordinating metal center with O or S destabilizes SACs towards the dissolution, while the metal centers get easily covered by O and OH acting as additional ligands at high anodic potentials and high pH values. Thus, not only should local coordination be considered in terms of the activity of SACs, but it is also necessary to consider its effects on the speciation of SACs' active centers under different potential and pH conditions.

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

The quest for accurate and more efficient methods for solving periodic/oscillatory problems is gaining more attention in recent time. This paper presents the construction and implementation of a family of exponentially–fitted Obrechkoff methods. A single–step Obrechkoff method involving terms up to the fourth derivatives was used as the base method. We also present the stability and convergence properties of the constructed family of methods. Two numerical examples were use to illustrate the performance of the constructed methods.

Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has gained a lot of interest in the Remote Sensing community, who sees it as an oportunity to compensate for satellite's ever-limited spatial resolution with respect to end users needs. While there has been a great amount of work on network architures in the latest years, deep learning based SISR in remote sensing is still limited by the availability of the large training sets it requires. The lack of publicly available large datasets with the required variability in terms of landscapes and seasons pushes researchers to simulate their own dataset by means of downsampling. This may impair the applicability of the trained model on real world data at the target input resolution. In this paper, we propose an open-data licenced dataset composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meter resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations on earth with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring the spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.

A Weyl semimetal is a novel crystal with low-energy electronic excitations that behave as Weyl fermions. It has received worldwide interest and was believed to have opened the next era of condensed matter physics after graphene and three-dimensional topological insulators. Howev-er, it is not easy to obtain a single large-size crystal because there are many nucleations in the preparation process. Here, a bottom-seed CVT growth method is proposed in this paper, and we acquired the large-size, high-quality NbAs single crystals up to 5x4x4 mm3 finally. X-ray diffrac-tion and STEM confirmed that they are tetragonal NbAs, which the key is to use seed crystal in vertical growth furnace. Notably, the photoelectric properties of the crystal are obtained under the existing conditions, which paves the way for the follow-up work.

The rise of data science in biology stimulates interdisciplinary collaborations to address fundamental questions. Here, we report the outcome of the first SINFONIA symposium focused on revealing the mechanisms governing plant reproductive development across biological scales. The intricate and dynamic target networks of known regulators of flower development remain poorly understood. To analyze development from the genome to the final floral organ morphology, high-resolution data that capture spatiotemporal regulatory activities are necessary and require advanced computational methods for analysis and modeling. Moreover, frameworks to share data, practices and approaches that facilitate the combination of varied expertise to advance the field are called for. Training young researchers in interdisciplinary approaches and science communication offers the opportunity to establish a collaborative mindset to shape future research.

Micro-blade design is an important factor in the cutting of single cells and other biological structures. This paper describes the fabrication process of three dimensional (3D) micro-blades for the cutting of single cells in a microfluidic “guillotine” intended for fundamental wound repair and regeneration studies. Our microfluidic guillotine consists of a fixed 3D micro-blade centered in a microchannel to bisect cells flowing through. We show that the Nanoscribe two-photon polymerization direct laser writing system is capable of fabricating complex 3D micro-blade geometries. However, structures made of the Nanoscribe IP-S resin have low adhesion to silicon, and they tend to peel off from the substrate after at most two times of replica molding in poly(dimethylsiloxane) (PDMS). Our work demonstrates that the use of a secondary mold replicates Nanoscribe printed features faithfully for at least 10 iterations. Finally, we show that complex micro-blade features can generate different degrees of cell wounding and cell survival rates compared with simple blades possessing a vertical cutting edge fabricated with conventional 2.5D photolithography. Our work lays the foundation for future applications in single cell analyses, wound repair and regeneration studies, as well as investigations of the physics of cutting and the interaction between the micro-blade and biological structures.

Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to-date we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

In this research paper, a high gain transformer less inverter modeling which is used for the smart grid technology or as the stand alone for home appliances is proposed. The proposed transformer less topology provides a higher voltage gain, low cost, small size and simple control stand alone transformer less inverter for home appliances. The above inverter is modeled by using software which is known as MATLAB/ Simulimk.

Two types of trenches cross-section in conventional vertical and brand new reverse-V-shape have fabricated on SCD substrate by micro-jet water-assist laser, the epitaxial lateral overgrowth technique has applied by microwave plasma chemical vapor deposition system in forming multiple micrometer-size channels. Raman and SEM techniques have applied in analyze both types growth layer characterization. Optical microscope has used to test microchannels hollowness. As a result, with the brand new reverse-V-shape trench, epitaxial lateral overgrowth layer reaches higher SCD surface morphology and crystal quality.

Quantum leaps advances in the single-molecule investigative science have been made possible over the past decades through the implication of nanopores, as versatile components on dedicated biosensors. Here, we employed the nanopore-tweezing technique to capture amino acid-functionalized, peptide-nucleic acids (PNA) with -hemolysin-based nanopores, and correlate the ensuing stochastic fluctuations of the ionic current through the nanopore with the composition and order of bases in the PNAs primary structure. We demonstrate that while the system enables detection of distinct bases on homopolymeric PNA or triplet bases on heteropolymeric strands, it also reveals rich insights into the conformational dynamics of the entrapped PNA within the nanopore, relevant for perfecting the recognition capability single-molecule sequencing.

Single cell RNA-seq (scRNA-seq) profiles conceal temporal and spatial tissue developmental information. De novo reconstruction of single cell temporal trajectory has been fairly addressed, but reverse engineering single cell 3D spatial tissue localization is hitherto landmark based, and de novo spatial reconstruction is a compelling computational open problem. Here we show that a new algorithm - named D-CE - for coalescent embedding of single cell transcriptomic networks can address this open problem. We rely merely on the spatial information encoded in the expression patterns of developmental signal transcription factor (DST) genes, and we find that D-CE of cell-cell association DST-transcriptomic networks reliably reconstructs the Geo-seq or single cell samples’ 3D spatial tissue distribution. Comparison to the novoSpaRC and CSOmap (recent and only available de novo 3D spatial reconstruction methods) on 16 datasets and 681 reconstructions, reveals a significantly distinctive superior performance of D-CE.

Single nanowires (NWs) are of great importance for optoelectronic applications, especially solar cells serving as powering nanoscale devices. However, weak off-resonant absorption can limit its light-harvesting capability. Here, we propose a single NW coated with the graded-index dual shells (DSNW). We demonstrate that with appropriate thickness and refractive index of the inner shell, the DSNW exhibits significantly enhanced light trapping compared with the bare NW (BNW), and the NW only coated with the outer shell (OSNW) and the inner shell (ISNW), which can be attributed to the optimal off-resonant absorption mode profiles due to the improved coupling between the reemitted light of the transition modes of the leak mode resonances of the Si core and the nanofocusing light from the dual shells with the graded refractive index. We found that the light absorption can be engineered via tuning the thickness and the refractive index of the inner shell, the photocurrent density is significantly enhanced by 134% (56%, 12%) in comparison with that of the BNW (OSNW, ISNW). This work advances our understanding of how to improve off-resonant absorption by applying graded dual-shell design and provides a new choice for designing high-efficiency single NW photovoltaic devices.

This paper investigated building data from multispectral and single-photon Lidar systems. The multispectral datasets from the individual channels and fused channels were explored. The multispectral and single-photon Lidar data were compared across multiple aspects: the data acquisition geometry, number of echoes, intensity, density, resolution, data defects, noise level, and the absolute and relative accuracy. In addition, we explored the performance of the multispectral and single-photon data for roof plane detection for eight complex/stylish buildings to investigate the suitability of these data for 3D building reconstruction. The building data from the single-photon and multispectral Lidar systems were evaluated with respect to the reference building vector data with an accuracy of better than 5 cm. The advantages and disadvantages of both technologies and their applications in the urban building environment are discussed.

Hydrogen evolution reaction (HER) is one of the most important reaction in electrochemistry. This is not only because it is the simplest way to produce high purity hydrogen and the fact that it is the side reaction in many other technologies. HER actually shaped current electrochemistry because it was in focus of active research for so many years (and it still is). The number of catalysts investigated for HER is immense, and it is impossible to overview them all. In fact, it seems that the complexity of the field overcomes the complexity of HER. The aim of this review is to point out some of the latest developments in HER catalysis, current directions and some of the missing links between a single crystal, nanosized supported catalysts and, recently emerging, single atom catalysts for HER

In this paper, the author obtains an analytic exact form of Heaviside function, which is also known as Unit Step function and constitutes a fundamental concept of the Operational Calculus.In particulat, this function is explicitly expressed in a very simple manner by the aid of purely algebraic representations. The novelty of this work is that the proposed explicit formula is not performed in terms of non – elementary special functions, e.g. Dirac delta function or Error function and also is neither the limit of a function, nor the limit of a sequence of functions with point wise or uniform convergence. Hence, it may be much more appropriate and useful in the computational procedures which are inserted into Operational Calculus techniques and other engineering practices.

Recent advances in single-atom catalysis resulted in readily accessible materials whose application in most catalytic reactions mediated by conventional nanoparticle-based catalysts often results in higher activity and selectivity. Can we expect catalysis by atomically dispersed atoms to find practical applications? Which are the hurdles to be overcome prior to widespread uptake of atomically dispersed metals in industrial synthetic processes and in hydrogen fuel cells?

The use of motion capture has increased from last decade in a varied spectrum of applications like film special effects, controlling games and robots, rehabilitation system, animations etc. The current human motion capture techniques use markers, structured environment, and high resolution cameras in a dedicated environment. Because of rapid movement, elbow angle estimation is observed as the most difficult problem in human motion capture system. In this paper, we take elbow angle estimation as our research subject and propose a novel, markerless and cost-effective solution that uses RGB camera for estimating elbow angle in real time using part affinity field. We have recruited five (5) participants of (height, 168 ± 8 cm; mass, 61 ± 17 kg) to perform cup to mouth movement and at the same time measured the angle by both RGB camera and Microsoft Kinect. The experimental results illustrate that markerless and cost-effective RGB camera has a median RMS errors of 3.06° and 0.95° in sagittal and coronal plane respectively as compared to Microsoft Kinect.

Solvent-free, single-ion conducting electrolytes are sought after for use in electrochemical energy storage devices. Here, we investigate the ionic conductivity and how this property is influenced by segmental mobility and conducting ion number in crosslinked single-ion conducting polyether-based electrolytes with varying tethered anion and counter-cation types. Crosslinked electrolytes are prepared by the polymerization of poly(ethylene glycol) diacrylate (PEGDA), poly(ethylene glycol) methyl ether acrylate, and ionic monomers. The ionic conductivity of the electrolytes is measured and interpreted in the context of differential scanning calorimetry and Raman spectroscopy measurements. A lithiated crosslinked electrolyte prepared with PEG₃₁DA and STFSI monomers is found to have a lithium ion conductivity of 3.2 × 10^-6 and 1.8 × 10⁻⁵ S/cm at 55 and 100 °C, respectively. The percentage of unpaired anions for this electrolyte was estimated at about 23% via Raman spectroscopy. Despite the large variances in metal cation – STFSI binding energies as predicted via DFT and large variations in ionic conductivity, STFSI-based crosslinked electrolytes with the same charge density and varying cations (Li, Na, K, Mg, and Ca) were estimated to all have unpaired anion populations in the range of 19 to 29%.

The past two decades has seen a growing demand for high-power, high-voltage utility scale inverters mostly fueled by the integration of large solar PV and wind farms. Multilevel inverters have emerged as the industry choice for these megawatt range inverters because their reduced voltage stress, capable of generating an almost sinusoidal voltage, in-built redundancy, among others. This paper present a new Switched-Source Multilevel Inverter (SS MLI) architecture. The new inverter show superior over existing topologies. It has reduced voltage stress on the semiconductor, uses less number of switches –reduced size/weight/cost and increased efficiency. The new SSMLI is comprised of two voltage sources (V₁, V₂) and 6 switches. It is capable of generating 5-level output voltage in symmetric modes (i.e., V₁ = V₂), and 7-level output voltage in asymmetric modes (i.e., V₁ ≠ V₂). To demonstrate the validity of the proposed inverter, simulations results using MATLAB^® /Simulink^® for 5- and 7-level output voltages are presented . The simulations are also verified experimentally using a laboratory prototype.

The rationalization of single molecule magnets’ (SMMs) magnetic properties by quantum mechanical approaches represents a major task in the field of the Molecular Magnetism. The fundamental interpretative key of molecular magnetism is the phenomenological Spin Hamiltonian and the understanding of the role of its different terms by electronic structure calculations is expected to steer the rational design of new and more performing SMMs. This paper deals with the ab initio calculation of isotropic and anisotropic exchange contributions in the Fe(III) dimer [Fe₂(OCH₃)₂(dbm)₄]. This system represents the building block of one of the most studied Single Molecule Magnets ([Fe₄RC(CH₂O)₃)₂(dpm)₆] where R can be an aliphatic chain or a phenyl group just to name the most common functionalization groups) and its relatively reduced size allows the use of a high computational level of theory. Calculations were performed using CASSCF and NEVPT2 approaches on the X-ray geometry as assessment of the computational protocol, which has then be used to evinced the importance of the outer coordination shell nature through organic ligand modelization. Magneto-structural correlations as function of internal degrees of freedom for isotropic and anisotropic exchange contributions are also presented, outlining for the first time the extremely rapidly changing nature of the anisotropic exchange coupling.

Under the development mode of deep blending of emerging technologies, major projects are important for enhancing China's comprehensive national strength and independent innovation capability. Based on the social benefits of major projects, and considering the influence of reputation factors, an evolutionary game model composed of a general contractor and subcontractor of major projects is established. This paper studies the influence of various factors including the income distribution coefficient of collaborative innovation, and MATLAB simulation software is analyzed the influence of factors on an evolutionary path. The results show that a reasonable income distribution coefficient can promote the game behavior of both parties to the direction of active collaborative innovation; The reduction of innovation cost and the improvement of spillover technology absorption capacity can make innovation subjects tend to choose active collaborative innovation strategies. In addition, reputation loss above a specific threshold can effectively inhibit free-riding behavior and encourage innovation subjects to choose active collaborative innovation strategies. The research results provide reference for promoting major projects' technology innovation.

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance the attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.009°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

With the increasing of China's international status and the acceleration of the process of global economic integration, the internationalization strategy of Chinese enterprises is getting deeper and deeper. This paper analyzes the problems and challenges faced by the international supply chain management of Chinese bamboo weaving and creative products, and puts forward a set of supply chain management model that ADAPTS to the international development. Firstly, the concept, connotation and types of supply chain management are sorted out through literature review. Secondly, this paper analyzes the current situation of international supply chain management of bamboo editing and creative products, and points out the existing problems. Thirdly, based on the theory and practice of international supply chain management, this paper puts forward a set of supply chain management model suitable for the international development of bamboo editing and creative products. Finally, taking a bamboo weaving and creative products enterprise as an example, the application effect of the supply chain management model is empirically analyzed. The results show that the supply chain management model can effectively reduce the cost and improve the efficiency, and is conducive to the international development of bamboo editing and creative products.

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from time series data. This paper proposes the combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves respectively an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.

This study aimed to determine the correlation of colour M-mode velocity propagation in the assessment of left ventricular diastolic function among acute decompensated heart failure patients with preserved ejection fraction (HFpEF). This research is an observational analytical study using a cross–sectional study design conducted from April to June 2022. The subjects of this study were patients diagnosed with acute HFpEF and treated at Dr. Soetomo General Academic Hospital, Surabaya. Patients with heart rhythm disturbances, moderate to severe valvular heart disease, and congenital heart defects were excluded from this study. Patients were measured for diastolic function including mitral valve early mitral inflow velocity (MV E Velocity), mitral peak velocity of early filling (E’) (E' Septal and E' Lateral), left atrial volume index (LAVI), The maximal tricuspid regurgitation velocity (TR V Max), Vp, the ratio of early diastolic mitral inflow velocity to early diastolic mitral annulus velocity (E/E'), the ratio between early and late ventricular filling velocity (E/A) and E/Vp Ratio. N-terminal-pro hormone B-type natriuretic peptide (NT–ProBNP) examination was also performed on study subjects as an additional diagnosis of acute heart failure. There were 33 patients who met the inclusion and exclusion criteria and were included in our study. There were 26 patients (78.8%) with grade I diastolic dysfunction, 4 patients (12.1%) with grade II diastolic dysfunction, and 3 patients (9.1%) with grade III diastolic dysfunction. There is a strong positive correlation between Vp with parameters E' Septal (p = &lt; 0.001; r = 0.636) and E' Lateral (p = &lt; 0.001; r = 0.650). Vp is negatively correlated with the ratio E/E' (p = 0.029; r = –0.37). The PCWP estimate with E/Vp ratio had a strong correlation with the NT–Pro BNP value &gt; 300 pg/ml in acute HFpEF patients (p = &lt; 0.001; r = 0.726) compared to the PCWP estimate with E/E' (p = &lt; 0.001; r = 0.549). There is no correlation between Vp value and degree of diastolic dysfunction in patients with acute HFpEF.

Spray technology is widely used in various industries, including medicine, food production, mechanical engineering, and nanopowder manufacturing. Achieving high dispersion and a narrow particle size distribution is crucial for many applications. Ultrasonic spraying is commonly used to achieve this. On other hand, hydraulic nozzles provide high atomization performance. Combining these two technologies promises to offer significant benefits, but the complex processes that occur simultaneously in such a device require careful study. This work proposes a fundamental design for an acoustic-hydraulic nozzle and investigates the physical processes when a liquid is sprayed using this nozzle, both theoretically and experimentally. The study identifies the critical modes of spraying and confirms that the simultaneous use of ultrasound and hydraulic pressure can produce a fine spray (droplet size less than 50 μm) with high productivity (5-10 ml/s). This approach has significant potential for modern industries and technologies.

This study presents an innovative approach named Optimal Adaptive Continuous Barrier Function Terminal Sliding Mode Control (OACBF-TSMC) approach with a novel switching manifold, to control mathematical model of nonlinear systems under model uncertainties and external disturbances. The proposed OACBF-TSMC approach combines the advantages of adaptive control, continuous barrier functions, and terminal sliding mode control to achieve robust stability of the mathematical model of nonlinear systems, and by using a genetic algorithm that tries to optimize the controller parameters as much as possible so that the proposed controller is the most to have efficiency and performance. The proposed method is designed to address the challenges of systems and provide superior performance in terms of stabilization accuracy, disturbance rejection, and robustness against uncertainties. However, the critical challenge facing the proposed controller is the complete elimination of the chattering phenomenon, which is the most important problem of ordinary sliding mode controllers. The simulation results show that the system trajectories in the proposed approach have converged to the origin with minimal overshoot and under-shoot in a finite time; In addition, the chattering problem in the control inputs has been well eliminated, which makes the proposed method suitable for various systems, including chaotic systems, energy sources, and distributed production systems. Furthermore, the OACBF-TSMC approach has the potential to enhance the stability and reliability of control systems, contributing to the integration of the system states.

Asphalt pavement temperature has several applications, including pavement structural design and evaluation, asphalt mixture design, asphalt binder grade determination and material aging characterization. However, available asphalt pavement temperature prediction models were mostly developed for temperate climatic conditions, outside of the tropics. Before such models are adopted for use in a tropical climate, their applicability must be evaluated. This study evaluated five empirical asphalt pavement temperature prediction models developed by Lukanen et al. [1], Park et al. [2], Diefenderfer et al. [3], Taamneh [4] all in the United States and Asefzadeh et al. [5] in Canada to ascertain their prediction accuracy in tropical climate, using Ghana as a case study. In-situ asphalt pavement temperature data were measured at two sites for eight months: Kumasi and Tamale in the Forest and Savannah climatic zones, respectively. The measured pavement temperature data were statistically compared with predicted temperatures, and it was found that the Park et al. [2] model provided a more accurate pavement temperature prediction in both climatic zones. The other models either over-predicted or under-predicted asphalt pavement temperature with significant error margins. However, there is a need to improve the prediction accuracy of the Park et al. [2] model or a local model developed.

This paper presents a case study on using Emacs and Org-mode for literate programming in undergraduate data science courses. Over three academic terms, the author mandated these tools across courses in R, Python, C++, SQL, and more. Onboarding relied on simplified Emacs tutorials and starter configurations. Students gained proficiency after initial practice. Live coding sessions demonstrated the flexible instruction enabled by literate notebooks. Assignments and projects required documentation alongside functional code. Student feedback showed enthusiasm for learning a versatile IDE, despite some frustration with the learning curve. Skilled students highlighted efficiency gains in a unified environment. However, uneven adoption of documentation practices pointed to a need for better incorporation into grading. Additionally, some students found Emacs unintuitive, desiring more accessible options. This highlights a need to match tools to skills levels, potentially starting novices with graphical IDEs before introducing Emacs. Key takeaways include literate programming aids comprehension but requires rigorous onboarding and reinforcement; Emacs excels for advanced workflows but has a steep initial curve. With proper support, these tools show promise for data science education.

The buildup dynamics of diode-pumped passively mode-locked solid-state laser is thoroughly explored by the real-time measurement with temporal sampling rate up to 40 GHz. A concise cavity is developed to ensure the transient dynamics purely arising from the gain medium and saturable absorber. Experimental results reveal that the laser output in the buildup process exhibits numerous passively Q-switched pulses followed with a damped relaxation oscillation prior to the stable mode locking. Furthermore, it is confirmed that the laser output has already displayed single clean mode-locked pulses inside the first several Q-switched envelopes before stepping into the stage of relaxation oscillation. The present real-time exploration is expected to provide important information for practical applications with temporal modulation of the pump intensity.

Worldwide, the cesarean section rate has steadily increased from 6.7% in 1990 to 21.1% in 2018 and is expected to rise even more. World Health Organization propose the adoption of the Robson classification system as a global standard for monitoring, evaluating, and comparing delivery rates. The purpose of the current study is to use the Robson classification system to investigate how, independently of medical factors, the day of the week and time of delivery may affect the mode of birth. In the sample analysis, we included the records of 8572 women giving birth in one private health facility in Greece. Over 60% of deliveries during the study period were performed by a cesarean section and 8.5% of deliveries were performed by operative vaginal delivery. The results of this study indicate that the lowest birth rates are observed on Monday, Saturday, and Sunday. In Robson category 1 women are 73% more likely to deliver by cesarean section between 08:00 a.m. - 03:59 p.m. compared to those who give birth between 12:00 a.m. - 07:59 a.m. Also, women in group 5.1 are 16.7 times more likely to deliver by cesarean section in the morning compared to overnight deliveries. These result point out two non-clinical variables that influences the CS rate. The Robson classification system was a useful tool for the above comparisons.

Loess is a special soil with high water sensitivity, which covers a large area in northwest China. Cracks are prone to generate in loess under the arid and semi-arid climate, which will provide the preferential channel for water and reduce the mechanical properties of soils. It is of great significance to understand the evolution characteristics and mechanisms of the cracks in loess areas. At present, the researches on cracks in soils are mainly concentrated in the characteristics of different cracking patterns. However, the spacing cracks are mostly discussed in materials like rock and concrete, rather than soils. The cracking characteristics and mechanisms of spacing cracks in loess is still inadequate. In this research, the drying tests of loess are carried out with different sizes of specimens. The parameters of spacing cracks and local strain distributions of the loess samples are obtained via PCAS and DIC methods during desiccation, respectively. The cracking modes, space cracking laws like insertion and saturation of spacing cracks are revealed. Finally, the size effect on the characteristics of spacing cracks is verified with a discrete element software MatDEM.

Due to nonlinear components in the charging piles of electric vehicles, harmonics and non-stationary signals in the electric vehicle charging load brings voltage and current distortion, seriously affecting the accuracy of the power-related calculation in non-sinusoidal environments. This paper proposed a new approach to calculate the active power and root mean square values from decomposed components using the adaptive chirp mode decomposition (ACMD) method on voltage and current. The advantage of the ACMD-based method is that it correctly provides the power-related quantities of harmonics or non-stationary components for the electric vehicle charging load. The performance of the proposed method was verified using synthetic signals and simulation tests. The experimental results presented better estimations for each quantity defined in IEEE Standard 1459-2010, compared with the discrete wavelet transform approach.

The purpose of this paper is to study the influence of diffusion and thermal loading due to pulsed laser heating on a thermoelastic solid with voids in the context of the model of three-phase-lag. The non-dimensional governing equations are formulated and then the exact expressions for the temperature field, the displacement components, the stress components, the concentration field and the change in the volume fraction field are obtained using the normal mode technique. The material is an isotropic, homogeneous elastic half-space and heated by a non-Gaussian laser beam with pulse duration of ps. The magnesium crystal element is used as an application to compare the predictions induced by diffusion and laser pulse on poro-thermoelastic medium of the model of three-phase-lag with those for the theory of Green–Naghdi of type III. The results obtained for thermal changes are verified by comparison with two theories of thermoelasticity, namely the model of three-phase lag (3PHL) and the Green and Nagdi type III theory. Plot the expressions to examine the effect of the laser pulse at two different values of time and the effect void parameter. This study also yields some interesting special cases.

The integrated DC-DC converter is appropriate for use in many domains, namely, display, cellular, and portable applications. This paper presents an integrated monolithic voltage-mode DC-DC boost converter with a low-power control circuit. The driver circuit requires an integrated converter to power up a digital logic circuit and converts the unregulated DC input to the controlled DC output at the desired voltage level. It is the integration of both power switches and control circuitry within the same CMOS technology to buck down and boost voltages using a switch mode regulator. In order to increase power efficiency in the DC-DC boost converter that provides low-power operation with a small chip size, a low-voltage operation is applied to the unique circuit characteristic. The operational transconductance amplifier(OTA), comparator, and oscillator in the control circuit are designed with the supply voltage of 3.3V and the operating frequency of 5.5 MHz. A compensator is used to create a pole that has sufficient phase margin for high stability. The DC- DC boost converter is measured in both experiment and simulation. Testing of the proposed circuit on the 0.35μm CMOS process shows that the output transient time of the amplifier can be controlled within of 7μsec and the output voltage is accurately controlled with a ripple ratio of 3%.

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: 1) as a single time series containing all measurements, and 2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 hours in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-Squares Support Vector Machines, Empirical Mode Decomposition, and the Wavelet Transform. Also, the traditional and simple Auto-Regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a MATLAB implementation showed that the Least-squares Support Vector Machine using data as a single time series outperformed the other combinations, obtaining the least mean square error.

This work mainly aims to identify and understand the factors influencing the switching of transportation modes among higher-education students in Joinville, Brazil when traveling to universities. Furthermore, this study evaluates the possibility of switching from individual vehicles to other modes of transport (i.e., bus, bicycle, and walking) by employing a multinomial logit model. The results indicate that students would be interested in switching from individual motor vehicles to other options. The scenarios for switching to buses presented the highest switching probability. The bus cost was the most important factor for switching. Meanwhile, the parking space reduction does not affect the student's choice, indicating that restricting available spaces should not be an isolated measure for decreasing the car mode attractiveness. Finally, the transport mode switch would occur only if alternative modes to the car or their infrastructure are improved; otherwise, students maintain their usual choices.

Diesel engines, as power equipment, are widely used in the fields of automobile industry, ship and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in time domain and angular domain, on which the current diagnosis methods based, are easily affected by working conditions or hard to extract accurate enough, as the diesel engine keeps running in transient and non-stationary process. This work arms at diagnosing this fault mainly based on frequency band features which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively，a decomposition technique based on improved variational mode decomposition is investigated in this work. As the connection between the features and the fault is fuzzy, the random forest algorithm is used to analyze the correspondence between features and faults. In addition, the feature dimension is reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition show that the method based on variational mode decomposition and random forest is capable to detect valve clearance fault effectively.

Extraction of high-resolution surface waves is essential in surface-wave survey. Because reflections usually interfere with surface waves on X component in a multicomponent seismic exploration, it is difficult to extract dispersion curves of surface waves. The situation goes more serious when the frequencies and velocities of higher-mode surface waves are close to those of PS-waves. A method for surface-wave extraction is proposed based on the morphological differences between reflections and surface waves. Frequency-domain high-resolution linear Radon transform (LRT) and time-domain high-resolution hyperbolic Radon transform (HRT) are used to represent surface waves and reflections respectively. Then, the sparse representation problem based on the morphological component analysis (MCA) is built and optimally solved to obtain high-fidelity surface waves. An advantage of our method is its ability to extract surface waves when their frequencies and velocities are close to those of reflections. Furthermore, results of synthetic and field examples confirm that the proposed method can attenuate the distortion of surface-wave dispersive energy caused by reflections, which contributes to extracting accurate dispersion curves.

In this study, a matched-mode autoregressive source depth estimation method (MMAR) based on autoregressive (AR) wavenumber estimation is proposed for a moving source in shallow water waveguides. The signal original frequency and the environmental parameters, namely, the sound speed profile and bottom properties are known as a prior knowledge. The mode wavenumbers are estimated by the AR modal wavenumber spectrum. On the basis of the mode wavenumber estimation, the mode amplitudes can be estimated by the wavenumber spectrum that is obtained by generalized Hankel transform. The source depth estimation is determined by the peak of source depth function wherein the data mode best matches the replica mode that is calculated using a propagation model. Compared with other methods of moving source depth estimation, the proposed method exhibits a better performance in source depth estimation under low signal-to-noise ratio or the small range span. The selection of horizontal line array depth is illustrated by simulation and normal mode theory in details.

The vibration signal of the engine contains strong background noise and many kinds of modulating components, which is difficult to diagnose. Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). VMD can effectively avoid endpoint effect and modal aliasing. However, VMD cannot effectively eliminate the random noise in the signal, so the random decrement technique is introduced to solve the problem. Based on the crankshaft bearing fault simulation experiment, the four kinds of wear state vibration signals are decomposed by VMD, and the modal components with smaller permutation entropy are selected as fault components. Then the fault component is processed by the random decrement technique, and the Hilbert envelope spectrum of the fault component is obtained. Compared with the fault feature extraction method based on EMD and EEMD, the feature extraction results of the proposed method are better than those of the above two methods. The simulation analysis and the simulation test of the crankshaft bearing fault verify the effectiveness of the proposed method.

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced.

The objective of this study is to evaluate the effect of constitutive equations on the prediction accuracy for springback in cold stamping with various deformation modes. In this study, two types of yield functions—Hill’48 and Yld2000-2d—were considered to describe yield behavior. Isotropic and kinematic hardening models based on the Yoshida–Uemori model were also adopted to describe hardening behavior. Various material tests (such as uniaxial tension, tension- compression, loading-unloading, and hydraulic bulging tests) were carried out to determine the material parameters of the models. The obtained parameters were implemented in the finite element (FE) simulation to predict springback, and the results were compared with experimental data. U-bending and T-shape drawing were employed to evaluate the springback prediction accuracy. Obviously, the springback prediction accuracy was greatly influenced by constitutive equations. Therefore, it is important to choose appropriate constitutive equations for accurate description of material behaviors in FE simulation.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

We use COMSOL Multiphysics software to model optical processes in media and the effects of light propagation in whispering gallery mode resonators (WGMRs). Although plane wave irradiating on resonator does not create resonances, it was shown that they can be obtained when focused tangentially on the perimeter of the resonator. We demonstrate that resonances can be excited in resonators by prism coupling when the incident angle of the light is larger than the angle of total internal reflection. We determined the optimal distance between the prism and the resonator to get the strongest resonances. Bio-sensing principle of WGMRs is shown by demonstrating that the shift of resonances increases with a larger number of "bio-molecules" attached to the surface of WGMR. Surface functionalization and surface roughness were explored.

When overhead lines are impacted by lightning, the traveling wave of the fault contains a wealth of fault information. The accurate extraction of feature quantities from transient components and their classification is fundamental to the identification of lightning faults. The extraction process may involve modal aliasing, optimal wavelet base issues, and inconsistencies between the lightning strike distance and the fault point, which have the potential to impact the effectiveness of recognition. This paper presents a method for identifying lightning strike faults by utilizing Kullback-Leibler (KL) divergence enhanced Variational Mode Decomposition (VMD) and Symmetric Geometry Mode Decomposition (SGMD) improved with Permutation Entropy (PE) to address the aforementioned issues. A model of a 220 kV overhead line is constructed using real faults to replicate scenarios of winding strike, counterstrike, and short-circuit. The three-phase voltage is chosen and then sub-jected to Karenbaren decoupling in order to transform it into zero mode, line mode 1, and line mode 2. The zero mode voltage is decomposed using KL-VMD and PE-SGMD methods, and lightning identification criteria are developed based on various transient energy ratios. The research findings demonstrate that the criteria effectively differentiate between winding strike, counterstrike, and short-circuit faults, thus confirming the accuracy and efficacy of the lightning fault identification criteria utilizing KL-VMD and PE-SGMD.

Optical beam splitter is used for dividing an input optical beam into several separate beams with specific power ratio. Usually, conventional beam splitters have bulky dimensions and fixed dividing ratio which significantly limit the design of new miniaturized optical devices and integrated optical circuits. We propose and investigate a novel physical concept of a miniaturized planar optical splitter/coupler with a switching element in the form of a photonic molecule (PM) pair dispersing input optical fluxes along multiple ways with variable power proportions. The structural design of the proposed splitter is based on a silicon-on-insulator (SOI) platform and composed of high-quality resonators in the form of electromagnetically coupled submicron-sized microcylinders. The control on the power division ratio and the selection of optical beam directions is realized by tuning the photonic splitter structure to the corresponding resonance of PM supermode. Compared to known analogues, the proposed design is easy to fabricate, suitable for integration into a "System-on-a-chip" platform and can dynamically change the beam power division ratio by input wave-phase manipulation.

This paper presents a new low-voltage versatile mixed-mode filter which uses a multi-ple-input/output differential difference transconductance amplifier (MIMO-DDTA). The multi-ple-input of the DDTA is realized using a multiple-input bulk-driven MOS transistor (MI-BD-MOST) technique to maintain a single differential pair, thereby achieving simple structure with minimal power consumption. In a single topology, the proposed filter can provide five standard filtering functions (low-pass, high-pass, band-pass, band-stop, and all-pass) in four modes: voltage (VM), current (CM), transadmittance (TAM), and transimpedance (TIM). This provides the full capability of a mixed mode filter (i.e., twenty filter functions). Moreover, the VM filter offers high-input and low-output impedances and the CM filter offers high-output impedance; therefore, no buffer circuit is needed. The natural frequency of all filtering functions can be electronically controlled by a setting current. The voltage supply is 0.5 V and for a 4 nA setting current, the power consumption of the filter was 281 nW. The filter is suitable for low-frequency biomedical and sensor applications that require extremely low supply voltages and nano-watt power consumption. For the VM low-pass filter, the dynamic range was 58.23 dB @ 1 % total harmonic distortion. The proposed filter was designed and simulated in the Cadence Virtuoso System Design Platform using the 0.18 µm TSMC CMOS technology.

In recent years, the use of long-term seismic monitoring systems for the protection of structures is becoming increasingly widespread. This is because more and more awareness is gradually developing regarding the importance of keeping structures under observation so as to prevent any damage scenarios. Within the field of Structural Health Monitoring, data driven techniques allow to process and analyze data collected on site. This paper presents a Mode Tracking procedure to obtain the time-histories of the natural frequencies of a monumental structure. The procedure is applied on the Sanctuary of Vicoforte, an important monumental structure site in Piedmont known for its imposing oval dome and characterized by a permanent structural monitoring system. Through the procedure presented in the paper it has been possible to obtain and observe the time series of the first natural frequencies of the structure considering various aspects, including the temperature variation.

Knowing the reasons for mobility choices, how users evaluate the characteristics of public transport and how satisfied they are with the travel experience is essential to promote the use of LTP (Local Public Transport), especially in rural or suburban areas where the use of private car is often prevalent. The present study aimed to investigate the mobility experience and the reasons for use and non-use of the LPT in a suburban area by the people travelling to a large Hospital Centre in a Province of the North-east of Italy. An anonymous questionnaire was completed by people from different categories travelling to the hospital (students, employees, clients), user and non-users of the LPT. The results showed that insufficient hourly coverage and accessibility of the service are the primary reasons for opting not to use LPT and together with reliability and comfort, these factors contribute to user satisfaction with their travel experience. Perceived sustainability of LPT contributes to overall travel satisfaction. However perceived sustainability alone does not appear to influence the choice of LPT if the service lacks adequate accessibility and does not meet the needs of travelers in terms of hourly coverage

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.

As urban populations and transport demand expand, Pakistani metropolitan centers face traffic congestion and environmental challenges. This research aims to assess carpooling (CP) to alleviate traffic congestion, fuel demand, and pollution. This study was conducted in Islamabad, a fast-growing city with a high percentage of personal cars in Pakistan, to assess the travelers’ perceptions, and public acceptance of CP and determine the effects on traffic congestion, fuel consumption, and CO2 emissions. The investigation of travelers' perceptions towards CP was conducted through a stated preference questionnaire survey. A total of 700 responses from one million population of Islamabad were collected as per Sloven's Formula. SPSS and Smart PLS software were used for data analysis. Various factors influencing peoples’ travel perceptions, mode choice, and tendency to carpool, were categorized to evaluate the relationship between travelers’ perception and the tendency to carpool in the presence of mode choice as a mediating variable. Confirmatory factor analysis was conducted in SMART PLS to refine the number of scale items for each variable. One-way ANOVA, step-wise and mediated multiple regression analysis was conducted in SPSS to check the direct and mediating relationship between different variables. Moreover, the probability of reducing traffic congestion and assessing fuel reduction resulting from CP was also analyzed. The findings imply that travelers’ mode choice acts as a mediator between the relationship between travelers’ perception and tendency to carpool. Furthermore, results predicted that CP might reduce 0.25 million cars on the road, about 33.6% of the total private vehicles. Thereby, saving up to 546,810 barrels per year, about 3.42% of Pakistan's total petrol consumption. The findings of this study could be helpful for transport planners, environmentalists, and policymakers to implement carpool systems. Thus, reducing traffic congestion and harmful CO2 emissions and maintaining a sustainable environment.

A class of controlled plants, whose dynamics is governed by a vector system of ordinary differential equations with a partially known right-hand side, is considered. The state variables and their velocities are assumed to be measurable. The aim is to design a controller which minimizes a loss function under certain constraints which arguments is the current state of the controlled plant. The designed control action is admitted to be a function of the current sub-gradient only, which supposed to be measurable on-line. The control design is based on ASG (Average Sub-Gradient method) — version of Integral Sliding Mode (ISM) concept, aimed to minimize on average a given convex (not obligatory strongly convex) cost function of the current state under a set of given constraints. An optimization type algorithm is developed and analyzed using ideas of SDM technique. The main results consist in proving the reachability of the "desired regime" (nonstationary analogue of sliding surface) from the beginning of the process and obtaining an explicit upper bound for the averaged loss function decrement, that is, the averaged in time functional convergence is proven and the rate of such convergence is estimated.

Hydraulic fracturing for oil-gas and geothermal reservoir stimulation is closely related to the generation and propagation of Mode I crack. Nonlinear deformation due to rock heterogeneity occurs at such crack tip, which causes the fracture process zone (FPZ) to form before the crack propagates unsteadily. However, the relationship between the FPZ characteristics and rock heterogeneity still remain elusive. We used three rock types common in reservoir for experimental investigation, and each of them includes two subtypes with different heterogeneity due to grain size or microstructural characteristics. Drawing on the experiment results, we calculated the FPZ size in each cracked chevron notched Brazilian disk, and we reproduced the formation process of the FPZ in marble by discrete element method. We showed that strong heterogeneity is favorable to large FPZ size, can enhance the ability of crack generation and complicate crack morphology. Coupling the Weibull distribution with fracture mechanics, the dependence of the FPZ size on heterogeneity degree can be theoretically explained, which suggests that inherent heterogeneity of rocks set physical foundation for formation of FPZ. These findings can improve our recognition to formation mechanism of the Mode I crack and provide useful guidelines for evaluating reservoir fracability.

Smartphone-based mobility apps enable users to make informed transportation decisions, offering instant access to transport-related information. This development has created a smartphone-enabled ecosystem of mobility services in developed countries while it is slowly picking up pace in the global south, which can contribute towards the decarbonization of urban transport. Work on this has already started in India, and there is considerable evidence indicating the profound impact of these apps on the perceived utility and usage of transport modes, with far-reaching implications for sustainable development goals (SDGs). However, for most users, the use of smartphone apps is a novel trend, and the knowledge of the impacts of usage of existing apps on the usage pattern of transport modes by various user groups is essential for positioning new consolidated app-based services soon. Against this backdrop, the present study uses latent class cluster analysis to empirically investigate the impacts of mobility apps on transport mode usage patterns in Delhi by classifying users into latent classes based on socioeconomic characteristics, attitudes/preferences, smartphone app usage, and mode usage pattern. The characteristics of the latent class and factors affecting the individual’s probability of being classified to these cluster have been discussed, along with some measures to encourage app-based mobility for each cluster.

Training supervised machine learning models requires labeled examples. A judicious choice of examples is helpful when there is a significant cost associated with assigning labels. This article improves upon a promising extant method – Batch-mode Expected Model Change Maximization (B-EMCM) method – for selecting examples to be labeled for regression problems. Specifically, it develops and evaluates alternate strategies for adaptively selecting batch size in B-EMCM. By determining the cumulative error that occurs from the estimation of the stochastic gradient descent, a stop criteria for each iteration of the batch can be specified to ensure that selected candidates are the most beneficial to model learning. This new methodology is compared to B-EMCM via mean absolute error and root mean square error over ten iterations benchmarked against machine learning data sets. Using multiple data sets and metrics across all methods, one variation of AB-EMCM, the max bound of the accumulated error (AB-EMCM Max), showed the best results for an adaptive batch approach. It achieved better root mean squared error (RMSE) and mean absolute error (MAE) than the other adaptive and non-adaptive batch methods while reaching the result in nearly the same number of iterations as the non-adaptive batch methods.

This paper investigates the path following control problem for a unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.

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.

Since people spend most of their time in indoor environments, the objective of this work was to study indoor air quality perception and its effects on users’ thermal comfort. Based on previous data from a building with a central air-conditioning system and two mixed-mode buildings located in the humid subtropical climate of Florianópolis, southern Brazil, statistical analyses were performed. Each user subjective answer obtained through a questionnaire was combined with the corresponding environmental conditions – measured using microclimate stations, a portable thermo-anemometer and a CO2 analyser. Results showed that improvement in air quality was associated with the reduction of air temperature and humidity ratio. Also, there was a significant influence of thermal, air movement and humidity sensation and acceptability of air quality perception. Users felt more satisfied or neutral with air quality for being in thermal comfort, and not because of the CO2 level – which means that air quality perception is influenced by factors other than CO2. This study recommends the implementation of an air exchange device in split air-conditioners with air recirculation used in mixed-mode buildings in Brazil. It is important to provide suitable indoor ventilation to reduce pollutants concentration, ensure good air quality and prevent respiratory diseases.

The power converter is the significant device in a wind power system. Wind turbine will be shut down and off grid immediately with the occurrence of the IGBT module open-circuit fault of power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies of power converter, there are few single and double IGBT modules open-circuit fault diagnosis methods producing negative results including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed. Above all, collecting the three-phase current varying with wind speed of 22 failure states including a normal state of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are used as the input of deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

In this paper, we analyze the measurements of the normalized radar cross-section(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, we verify the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. The Amazon rainforest experiment results show that the accuracy of obtained calibration constant meets the requirements. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it has an approximate cosine modulation with the wind direction when the wind speed is greater than 8m/s. The cross-polarized system noise floor is low enough to ignore it in wind retrieval. Furthermore, we also investigate the properties of the polarization ratio, denoted PR, and show that it is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the σ_VV^0 retrieved from new PR models as well as σ_HH^0 is in good agreement with σ_VV^0 extracted from SAR images directly. And it is also shown that considering the azimuth angle can improve polarization conversion accuracy.

This paper introduces a sliding mode control (SMC) based equivalent control method to a novel high output gain Cuk converter. An additional inductor and capacitor improves the efficiency and output gain of the classical Cuk converter. Classical PI controllers are widely used in DC-DC converters. However, it is a very challenging task to design a single PI controller operating in different load and disturbances. SMC based equivalent control method which achieves a robust operation in a wide operation range is also proposed. Switching frequency is kept constant in appropriate interval in different loading and disturbance conditions by implementing a dynamic hysteresis control method. Numerical simulations conducted on Matlab/Simulink confirm the accuracy of analytical analysis of high output gain modified Cuk converter. In addition, proposed equivalent control method is validated in different perturbations to demonstrate the robust operation in wide operation range.

Resistance against amitraz in varroa mite populations has become a subject of interest in recent years due to increasing reports of reduced field efficacy of amitraz treatments, especially from some beekeepers in France and the United States. The loss of amitraz as a reliable tool to effectively reduce varroa mite infestation in the field could severely worsen the position of beekeepers in the fight to keep varroa infestation rates in their colonies at low levels. In this publication, we present data from French apiaries, collected in the years 2020 and 2021. These data include field efficacy of an authorized amitraz-based varroa treatment (Apivar®) and results of laboratory sensitivity assays of varroa mites exposed towards the reference LC90 concentration of amitraz. In addition, a total of 240 varroa mites from Eastern, Central, and Southern regions in France that were previously classified as either “sensitive” or “resistant” towards amitraz in a laboratory sensitivity assay were genotyped. The genetic analyses of mite samples are focused on the β−adrenergic-like octopamine receptor, which is considered as the main target site for amitraz in varroa mites. Special attention was given to a single nucleotide polymorphism (SNP) at position 260 of the ORβ-2R-L gene that had previously been described by Hernández Rodríguez et al. (2021) as associated to the resistance to amitraz in French varroa mites. Our findings confirm that amitraz resistance occurs in patches or “islands of resistance” with a less severe reduction in treatment efficacy compared to pyrethroid resistance or coumaphos resistance in varroa mites. The results of our genetic analyses of varroa mites call into question the hypothesis of the SNP at position 260 of the ORβ-2R-L gene being directly responsible for amitraz resistance development.

With the rising cost of animal feed protein, finding affordable and effective substitutes is crucial. Walnut kernel cake, a polyphenols, fiber, protein and fat-rich byproduct of walnut oil extraction, has been underexplored as a potential protein replacement in pig feed. In this study, we found that feeding large Diqing Tibetan pigs walnut kernel cake promoted adipose deposition and improved pork quality during pig growth. Transcriptome analysis revealed the upregulation of genes ANGPTL8, CCNP, ETV4, and TRIB3, associated with adipose deposition. Pathway analysis highlighted enrichment in adipose-deposition-related pathways, including PPAR, Insulin, PI3K-Akt, Wnt, and MAPK signaling. Further analysis identified DEGs (differentially expressed genes) positively correlated with adipose-related traits, such as PER2 and PTGES. Single-cell transcriptome data pointed to the specific expression of CD248 and PTGES in adipocyte progenitor/stem cells (APSCs), pivotal for adipocyte differentiation and adipose deposition regulation. This study demonstrates walnut kernel cake's potential to substitute soybean cake in pig feed, providing high-quality protein and promoting adipose deposition. It offers insights into feed protein replacement, human functional food, fat metabolism, and related diseases, with marker genes and pathways supporting pig breeding and pork quality improvement.

We used a mixed-method single-case experimental research design to examine the effect of modelling (peer versus non-peer) on exercise self-efficacy in stroke survivors who participated in a community-based exercise program. Quantitative data were obtained using a ABCA design: (A1) no model/baseline 1 (3 weeks); (B) peer model (6 weeks); (C) non-peer model (6 weeks); and (A2) no model/baseline 2 (3 weeks). Four participants completed self-efficacy questionnaires after each weekly session. Qualitative data were obtained using researcher diaries and two semi-structured interviews: after B and A2. Based on quantitative and qualitative results, participants reported higher exercise self-efficacy in the model conditions, with ratings appearing highest for the non-peer model. This finding could be due to a lack of full integration of the peer model and low feelings of similarity. Modelling in general could help people recovering from a stroke increase their exercise self-efficacy, but non-peer models may not be most advantageous.

Recently, nanobodies have taken center stage in research on drug discovery and development. Several therapeutic possibilities using nanobodies are presently undergoing clinical trials and waiting for FDA approval. The goal of this study was to emphasize the potential of nanobodies as therapeutic agents by concentrating on the most recent published studies that examined their properties, manufacturing, and possible applications. This article demonstrates that the unique properties of nanobodies in comparison to conventional antibodies, which are based on their small size and offer a number of benefits, make them seem to have a promising future. These advantages include the ability to access complex or hidden target sites that may be elusive to their larger antibody counterparts, and increased resilience against extreme conditions like tempera-ture changes and pH variations. As a result, nanobodies seem to have a bright future as adaptable tools for imaging in cancer and non-malignant diseases, as well as for in vitro and in vivo diagnostic and therapeutic potential for a variety of conditions, including oncology, infectious, metabolic, neurological, and other conditions like ophthalmologic, immune-mediated, and genetic disorders. More research is required to determine their effectiveness and safety in clinical applications. The current analysis offers a thorough overview of the therapeutic uses for nanobody products that are either on the market or undergoing clinical trials.

Single parenting is a social phenomenon that has always been in existence and is as old as mankind, and today the number of single-parent families has been rampantly increasing. As a result, the researcher conducted a study in the Gambella region of Ethiopia to investigate the economic and educational implications of single parenting on female adolescent students. The study employed a qualitative research approach with a case study research design. The researchers used the purposive sampling technique to select the research area and research participants for in-depth interviews and focus group discussions, and eleven interviewees, seventeen participants in focus group discussions within two groups, and seven key informants participated. Interviews were audio-recorded, transcribed, translated, and thematically analyzed. Interviews with research participants revealed that adolescent girl students from single-parent homes experience financial difficulties. The study also explored that students with single parents are more likely to be absent from school, to come late to school, to score low marks, and to drop out of school. Therefore, having an understanding of the benefits of having intact parents for the well-being of adolescents and raising children together should be advocated.

In recent years, graphene has received so much attention because of its superlative properties and its potential to revolutionize electronics especially in VLSI. This study analyzes the effect of single event upset (SEU) in SRAM cell which employs metal-oxide semiconductor type graphene nano-ribbon field effect transistor (MOS-GNRFET) and compares the results respectively with another SRAM cell designed in PTM 10nm FinFET node. Our simulation show there is a change in data stored in the SRAM after heavy ion strike. However, it recovers from radiation effects after 0.46 ns for GNRFET and 0.51 ns for FinFET. Since the degradation observed in Q and Qb of GNRFET SRAM are 2.7X and 2.16X as compared to PTM nano-MOSFET, we can conclude that GNRFET is less robust to single effect upset. In addition, the stability of SRAM can be improved by increasing the supply voltage VDD.

Single-cell RNA sequencing data facilitates investigation of cell heterogeneity and subpopulations as well as differentially abundant states however modern single-cell RNA sequencing datasets are growing in size and complexity requiring advances in the bioinformatic methods that analyze them. Many methods exist for each step of analysis including read alignment, normalization, quality control, batch effect correction, imputation and dimensionality reduction. With so many options to choose from at each step of the analysis, benchmarking and a synthesis of the literature on the methods available is necessary to inform biological researchers on the most optimal workflow for their data. Here, recent key methods of analysis are highlighted with a focus on methods that facilitate identification of cell subpopulations and differentially abundant cell states. With a constantly expanding toolset for each step in single-cell RNA sequencing dataset analysis, biological researchers should stay informed to utilize the most applicable methods for their own analyses.

Two mononuclear Ru(III) complexes of formula trans-[RuCl4(Hgua)(dmso)]·2H2O (1) and trans-[RuCl4(Hgua)(gua)]·3H2O (2) [Hgua = protonated guanine (gua), dmso = dimethyl sulfoxide] have been synthesized and characterized magnetostructurally. Compounds 1 and 2 crystallize in the monoclinic system with space groups P21/n and Pc, respectively. Each Ru(III) ion in 1 and 2 is six-coordinate and bonded to four chloride ions and one (1) or two (2) nitrogen atoms from guanine molecules and one sulfur atom (1) of a dmso solvent molecule, generating quasi regular octahedral geometries in both cases. In their crystal packing, the Ru(III) complexes are self-assembled mainly through an extended network of N-H⋯Cl hydrogen bonds and π⋯Cl type intermolecular interactions, forming novel supramolecular structures based on this paramagnetic 4d metal ion. Variable-temperature dc magnetic susceptibility measurements performed on microcrystalline samples of 1 and 2 show a different magnetic behavior. While 1 is a ferromagnetic compound at low temperature, 2 exhibits a behavior typical of noninteracting mononuclear Ru(III) complexes with S = 1/2. Ac magnetic susceptibility measurements reveal slow relaxation of the magnetization in the presence of external dc fields only for 2, hence indicating the occurrence of field-induced single-ion magnet (SIM) phenomenon in this mononuclear guanine-based Ru(III) complex.