Simple Scheme Research Articles

Microwave synthesis of molybdenum disulfide quantum dots and the application in bilirubin sensing.

Molybdenum disulfide quantum dots (MoS2 QDs) is a new type of graphite like nanomaterial, which exhibited well chemical stability, unique fluorescence characteristics, and excellent biocompatibility. The conventional hydrothermal synthesis of MoS2 generally requires a long-term reaction at high temperature and high pressure. Herein, we have developed a simple and fast MoS2 QDs synthesis scheme using microwave heating, and further modified the surface of MoS2 QDs using 3-aminophenylboronic acid. The 3- aminophenylboronic acid modified MoS2 QDs (B-MoS2 QDs) were further coated by a zinc-based metal-organic backbone (ZIF-8) in a solution containing zinc ions and 2-methylimidazolium. The constructed nanohybrid B-MoS2@ZIF-8 were successfully applied to the visualization and rapid detection of bilirubin based on the ratiometric fluorescence changes. The linear range for bilirubin detection is 0.2-75 μmol•L-1, and detection limit is 0.017 μmol•L-1.

Long-term stable laser injection locking for quasi-CW applications

Generating high output powers while achieving narrow line single mode lasing are often mutual exclusive properties of commercial laser diodes. For this reason, efficient and scalable amplification of narrow line laser light is still a major driving point in modern laser system designs. Commonly, injection locking of high-power semiconductor laser diodes are used for this purpose. However, for many laser diodes it is very challenging to achieve stable operation of the injection locked state due to a complex interplay of non-linearities and thermal effects. Different approaches of active or passive stabilization usually require a large overhead of optical and electrical equipment and are not generally applicable. In our work we present an active, periodically applied stabilization scheme which is generally applicable, technically easy to implement and extremely cost-effective. It is based on the externally synchronized automatic acquisition of the optimal injection state. Central to our simple but powerful scheme is the management of thermalization effects during lock acquisition. By periodical relocking, spectrally pure amplified light is maintained in a quasi-CW manner over long timescales. We characterize the performance of our method for laser diodes amplifying 671 nm light and demonstrate the general applicability by confirming the method to work also for laser diodes at 401 nm, 461 nm and 689 nm. Our scheme enables the scaled operation of injection locks, even in cascaded setups, for the distributed amplification of single frequency laser light.

Tropical Intraseasonal Variability as a Leading Moisture Dynamic Mode of the Warm-Pool Background State

Abstract Tropical intraseasonal variability (ISV) is dominated by the Madden–Julian oscillation (MJO), and its spatiotemporal characteristics vary with the Indo-Pacific warm-pool background on seasonal and longer time scales. Previous works have suggested ISV dynamics in various frameworks, whereas a unifying view remains challenging. Motivated by the recent advance in moisture mode theory, we revisit the ISV as a leading moisture mode modulated by varying background states derived from a reanalysis, using a moist linear baroclinic model (mLBM) improved with a simple convective scheme relating convective precipitation to tropospheric and boundary layer moisture anomalies and simple cloud-radiative feedback representations. Under a boreal winter background state, this mLBM yielded a large-scale but local eastward-propagating mode with a phase speed of 3–5 m s−1 over the warm-pool region, resembling the MJO. Background lower-tropospheric winds and thermodynamic fields are important in determining the growth rate and periodicity of the leading mode, whose stability depends on cloud-radiative feedback and background state variations. We further demonstrate why the MJO is locally contained in the Indo-Pacific warm-pool region. The local thermal/moisture condition and Walker circulation greatly enhance its instability, but outside this region, this mode is heavily damped. Thus, the expansion/contraction of this warm-pool condition may enhance/reduce its instability and expand/reduce its domain of activity. Prescribing El Niño background causes eastward displacement of the wintertime ISV activity, reminiscent of the observed MJO modulations by El Niño. Under a summer background state, the eastward-propagating leading mode resembles the boreal summer ISV but is biased, requiring further model improvements.

A Generalized Design of On-Chip LTCC Balanced Filters Using Novel Hybrid Resonators with Intrinsic Ultra-Wideband Suppression for 5G Applications

In this paper, we examine an ultra-compact on-chip balanced filter based on novel hybrid resonators (NHRs) comprising short transmission line sections (STLSs) and series LC blocks using low-temperature co-fired ceramic (LTCC) technology. Based on a rigorous theoretical analysis, the proposed NHR demonstrates the potential for intrinsic ultra-wideband differential-mode (DM) and common-mode (CM) suppression without any additional suppressing structures. Furthermore, the resonance of NHRs was determined by four degrees of freedom, providing flexibility for miniaturization. Theoretical extensions of the Nth-order topology can be easily achieved by the simple coupling schemes that occur exclusively between STLSs. For verification, a balanced filter covering the 5G band n78 with an area of 0.065λg × 0.072λg was designed using the proposed optimization-based design procedure. An ultra-low insertion loss of 0.8 dB was obtained. The quasi-full CM stopband with a 20 dB rejection level ranged from 0 to 12.9 GHz. And the ultra-wide upper DM stopband with a 20 dB rejection level ranged from 4.4 to 11.5 GHz. Good agreement between the theoretical, simulated, and measured results indicate the validity of the proposed design principle.

Netromorph acritarchs and related morphotypes in the middle Palaeozoic: a proposal for classification

Acritarchs are organic-walled microfossils dominating the phytoplankton in Paleozoic seas. Current classification subdivides acritarchs into subgroups based on the outline of the main vesicle. Among them the netromorphs cover the fusiform morphotypes showing peak diversity during the middle Paleozoic. More than three hundred species and subspecies were reported in literature, many of which are probably synonyms or ecophenotypes. We review and emend the definition of netromorphs, restricting them to homopolar forms and reassigning related heteropolar morphotypes (Deunffia-Domasia complex) to another subgroup: the diacromorphs. We propose a simplified classification scheme as follows: i) Category 1 of elliptical forms; ii) reniform Category 2; iii) spindle-shaped Category 3 and iv) a Category 4 of crescent shaped genera. Similar-looking species within Leiofusa, Poikilofusa and Eupoikilofusa are merged into intrageneric morphotype groups. Several new combinations are proposed. The Deunffia-Domasia complex is also classified based on the presence of appendages, their number, and distal branches. Possible biological affinities of netromorphs to fungal spores, euglenids, and turbellarian (flatworm) oocytes are also discussed. However, clear biological affinities remain uncertain, and further investigation is needed to determine whether netromorphs are exclusively phytoplanktonic or belong in part to various kinds of animal, fungal or algal groups.

Computational Modeling of Biomass Fast Pyrolysis in Fluidized Beds with Eulerian Multifluid Approach

This study investigated the fast pyrolysis of biomass in fluidized-bed reactors using computational fluid dynamics (CFD) with an Eulerian multifluid approach. A detailed analysis was conducted on the influence of various modeling parameters, including hydrodynamic models, heat transfer correlations, and chemical kinetics, on the product yield. The simulation framework integrated 2D and 3D geometrical setups, with numerical experiments performed using OpenFOAM v11 and ANSYS Fluent v18.1 for cross-validation. While yield predictions exhibited limited sensitivity to drag and thermal models (with differences of less than 3% across configurations and computational codes), the results underline the paramount role of chemical kinetics in determining the distribution of bio-oil (TAR), biochar (CHAR), and syngas (GAS). Simplified kinetic schemes consistently underestimated TAR yields by up to 20% and overestimated CHAR and GAS yields compared to experimental data (which is shown for different biomass compositions and different operating conditions) and can be significantly improved by redefining the reaction scheme. Refined kinetic parameters improved TAR yield predictions to within 5% of experimental values while reducing discrepancies in GAS and CHAR outputs. These findings underscore the necessity of precise kinetic modeling to enhance the predictive accuracy of pyrolysis simulations.

Entanglement-Enabled Advantage for Learning a Bosonic Random Displacement Channel.

We show that quantum entanglement can provide an exponential advantage in learning properties of a bosonic continuous-variable (CV) system. The task we consider is estimating a probabilistic mixture of displacement operators acting on n bosonic modes, called a random displacement channel. We prove that if the n modes are not entangled with an ancillary quantum memory, then the channel must be sampled a number of times exponential in n in order to estimate its characteristic function to reasonable precision; this lower bound on sample complexity applies even if the channel inputs and measurements performed on channel outputs are chosen adaptively or have unrestricted energy. On the other hand, we present a simple entanglement-assisted scheme that only requires a number of samples independent of n in the large squeezing and noiseless limit. This establishes an exponential separation in sample complexity. We then analyze the effect of photon loss and show that the entanglement-assisted scheme is still significantly more efficient than any lossless entanglement-free scheme under mild experimental conditions. Our work illuminates the role of entanglement in learning CV systems and points toward experimentally feasible demonstrations of provable entanglement-enabled advantage using CV quantum platforms.

Output feedback control for non-strict feedback nonlinear systems: an adaptive fuzzy backstepping scheme

The adaptive fuzzy tracking control design issue is considered in this paper for a class of non-strict feedback nonlinear systems subject to external disturbances. A fuzzy high-gain state observer is constructed to reconstruct the unmeasurable states of the system by leveraging the universal approximation property of fuzzy logic systems for arbitrary unknown nonlinear functions. Within the framework of backstepping control design and in conjunction with adaptive techniques, an adaptive fuzzy control approach is presented. Subsequently, to work out the inherent ‘complexity explosion’ problem of the proposed control approach, dynamic surface control technique is introduced, leading to a simplified adaptive fuzzy output feedback control scheme. Stability analysis shows that the two proposed control schemes can ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, meanwhile the system tracking error can converge to a small neighborhood around the origin. Ultimately, a numerical simulation example is provided to validate the feasibility of the proposed control scheme.

PETAT — an ASIC for simple and efficient readout of large PET scanners

Modern PET scanners based on scintillating crystals use solid state photo detectors for light readout. The small area of these devices is beneficial for spatial resolution, but also leads to a large number of electronic channels to be read out, mostly by application specific integrated circuits (ASICs) containing amplification, noise reduction, hit finding, time stamping and amplitude measurement. Although each ASIC provides up to ≈ 64 channels, a large number of chips is required with the need for auxiliary electronic components like voltage regulators or FPGAs for control and data readout. The FPGAs in turn often require multiple supply voltages and configuration infrastructure, so that PCBs get complicated, cumbersome and power-hungry, in addition to the significant power requirement of the front-end ASICs. We address this issue in the latest generation of our PETA readout ASIC for SiPMs by a simplified control scheme and, in particular, by a hierarchical serial data readout which does not require any additional FPGA. In addition, it provides a time-sorted stream of hit data, allowing early on-detector data reduction and hit pre-processing like the removal of hits with no coincident partner. The simplicity of this readout facilitates a supply scheme where power/ground of multiple ASICs are connected in series instead of the standard parallel connection. This `serial-powering' approach can reduce supply current (while increasing overall supply voltage) so that voltage drop issues in the supply are alleviated.

Efficient Layer Optimizations for Deep Neural Networks

Deep neural networks (DNNs) have technical issues such as long training time as the network size increases. Parameters require significant memory, which may cause migration issues for embedded devices. DNNs applied various pruning techniques to reduce the network size in deep neural networks, but many problems still exist when applying the pruning techniques. Among neural networks, several applications applied autoencoders for reconstruction and dimension reduction. However, network size is a disadvantage of autoencoders since the architecture of the autoencoders has a double workload due to the encoding and decoding processes. In this research, we chose autoencoders and two deep neural networks AlexNet and VGG16 to apply out of order layer pruning. We perform the sensitivity analysis to explore the performance variations for the network architecture and network complexity through an out of order layer pruning mechanism. As a result of applying the proposed layer pruning scheme to the autoencoder, we developed the accordion autoencoder (A2E) and applied credit card fraud detection and MNIST classification. Our results show 4.9 Percent and 13.6 Percent performance drops, respectively, but we observe a significant reduction in network complexity, 85.1 Percent and 94.5 Percent for each application. We extend the out of order layer pruning to deeper learning networks. In our approach, we propose a simple yet efficient scheme, accuracy aware structured filter pruning based on the characterization of each convolutional layer combined with the quantization of fully connected layers. We investigate the accuracy and compression rate of each layer using a fixed pruning ratio, and then the pruning priority is rearranged depending on the accuracy of each layer. Our analysis of layer characterization shows that the pruning order of the layers does affect the final accuracy of the deep neural network. Based on our experiments using the proposed pruning scheme, the parameter size in the AlexNet can be up to 47.28x smaller than the original model. We also obtained comparable results for VGG16, achieving a maximum compression rate of 35.21x.

Developing a simple field triage scheme compatible with regional trauma situations and medical systems using machine learning: A nationwide trauma database study

Developing a simple field triage scheme compatible with regional trauma situations and medical systems using machine learning: A nationwide trauma database study

Influencia del liderazgo ético sobre el agotamiento emocional en el sector eléctrico colombiano. El papel moderador de un clima ético de principios

The objective of this study was to examine the effect of ethical leadership on emotional exhaustion in the Colombian electricity sector through the moderating role of a principled ethical climate, with a simple moderation scheme. The research design was transversal with a non-experimental scope. The sample was calculated probabilistically by conglomerates. A total of 175 women and 273 men from six organizations in the Colombian electricity sector participated. According to the results, ethical leadership and a principled climate are negatively related to emotional exhaustion. However, when ethical leadership transits through a highly moral principled climate, this relationship changes its meaning. In conclusion, ethical leadership and a principled ethical climate attenuate follower emotional exhaustion as long as there is a balance between the personal and managerial aspects conveyed by the leader.

Straightforward Phase I Dose-Finding Design for Healthy Volunteers Accounting for Surrogate Activity Biomarkers

Conventionally, a first-in-human phase I trial in healthy volunteers aims to confirm the safety of a drug in humans. In such situations, volunteers should not suffer from any safety issues and simple algorithm-based dose-escalation schemes are often used. However, to avoid too many clinical trials in the future, it might be appealing to design these trials to accumulate information on the link between dose and efficacy/activity under strict safety constraints. Furthermore, an increasing number of molecules for which the increasing dose–activity curve reaches a plateau are emerging. In a phase I dose-finding trial context, our objective is to determine, under safety constraints, among a set of doses, the lowest dose whose probability of activity is closest to a given target. For this purpose, we propose a two-stage dose-finding design. The first stage is a typical algorithm dose escalation phase that can both check the safety of the doses and accumulate activity information. The second stage is a model-based dose-finding phase that involves selecting the best dose–activity model according to the plateau location. Our simulation study shows that our proposed method performs better than the common Bayesian logistic regression model in selecting the optimal dose.

Structure-preserving exponential time differencing methods for modeling Josephson Junctions

Explicit, conformal symplectic, exponential time differencing (ETD) methods have numerous advantages over other well-known and commonly used methods, including structure-preservation, high stability, ease of implementation, and computational efficiency. Such methods are constructed with second and fourth order accuracy through composition techniques using a simple first order scheme. For modeling Josephson Junctions, these ETD schemes regularly exhibit the best balance of efficiency and accuracy when compared to other commonly used methods.

Bending analysis of viscoelastic plates according to the Reissner’s theory using meshless local Petrov–Galerkin method and hereditary integral formulation

Purpose The purpose of this study is to apply the Meshless Local Petrov–Galerkin (MLPG) method to solve the bending problems of linear viscoelastic plates, considering Reissner’s theory.Design/methodology/approachThe weak formulation for the set of equations that govern Reissner’s plate theory is implemented in conjunction with the integral formulation applied to viscoelastic constitutive expressions. A meshless method based on the Moving Least Squares (MLS) approximation is considered in the numerical implementation. The final equation system is assembled by adopting simple and efficient schemes for numerical integration, considering a simplified formulation through centralization of the local interpolation domains and Gaussian quadrature at the same field point. The results obtained are compared with available solutions to demonstrate the efficiency of the proposed formulation.FindingsThe hereditary integral approach proved to be the most general way to analyze the viscoelastic problem, especially when applied together with the modified scheme for numerical integration. In addition, the variable changing technique is demonstrated to be an efficient formulation for solving shear-locking effects in thin plate problems.Originality/valueThe differential of the present study is related to the manner in which the properties of linear viscoelastic materials are considered in the formulation. Although most authors consider this point through the application of the correspondence principle, the present study works with a hereditary integral formulation. In addition, the variable changing technique is applied to solve the shear-locking effects, and an alternative approximation technique is considered to speed up the numerical integration process.

CF-SOLT: Real-time and accurate traffic accident detection using correlation filter-based tracking

Traffic accident detection using video surveillance is valuable research work in intelligent transportation systems. It is useful for responding to traffic accidents promptly that can avoid traffic jam or prevent secondary accident. In traffic accident detection, tracking occluded vehicles in real-time and accurately is one of the major sticking points for practical applications. In order to improve the tracking of occluded vehicles for traffic accident detection, this paper proposes a simple online tracking scheme with correlation filters (CF-SOLT). The CF-SOLT method utilizes a correlation filter-based auxiliary tracker to assist the main tracker. This auxiliary tracker helps prevent target ID switching caused by occlusion, enabling accurate vehicle tracking in occluded scenes. Based on the tracking results, a precise traffic accident detection algorithm is developed by integrating behavior analysis of both vehicles and pedestrians. The improved accident detection algorithm with the correlation filter-based auxiliary tracker can provide shorter response time, enabling quick identification and detection of traffic accidents. The experiments are conducted on the VisDrone2019, MOT-Traffic and Dataset of accident to evaluate the performances metrics of MOTA, IDF1, FPS, precision, response time and others. The results show that CF-SOLT improves MOTA and IDF1 by 5.3% and 6.7%, accident detection precision by 25%, and reduces response time by 56 s.

A fast, simple and local protection scheme for fault detection and classification during power swings based on differential current component

Among the various challenges of transmission line distance protection, it has become increasingly difficult to deal with the symmetrical phenomenon of power swing (PS) and it can lead to maloperation of distance relays and power system blackout. Distance relays have challenges in distinguishing the symmetrical fault during power swing and would send a wrong trip command to the circuit breakers, leading to major blackouts. To solve this issue, the main objective of this study is to provide a fast, simple, and local protection scheme based on differential current component (DCC), which is extracted by the difference in predicted and actual samples of local current component. Autoregression technique is used to predict the future cycle current samples using available samples. Based on the DCC, a novel index named differential current component ratio (DCCR) is introduced to effectively detect the internal symmetrical faults from fast/slow power swing conditions. Several tests are run for different fault types, different power swing frequencies, different fault locations, different fault resistances and different fault inception instants. Finally, the results are compared with the available methods. Results demonstrate the high accuracy of the proposed method in fast and accurate detection of internal symmetrical faults during power swing in different frequency rates in less than one cycle.

Mechanochemical topological defects in an active nematic.

We propose a reaction-diffusion system that converts topological information of an active nematic into chemical signals. We show that a curvature-activated reaction dipole is sufficient for creating a system that dynamically senses topology by producing a concentration field possessing local extrema coinciding with ±1/2 defects. The enabling term is analogous to polarization charge density seen in dielectric materials. We demonstrate the ability of this system to identify defects in both passive and active nematics. Our results illustrate that a relatively simple feedback scheme, expressed as a system of partial differential equations, is capable of producing chemical signals in response to inherently nonlocal structures in anisotropic media. We posit that such coarse-grained systems can help generate testable hypotheses for regulated processes in biological systems, such as morphogenesis, and motivate the creation of bio-inspired materials that utilize dynamic coupling between nematic structure and biochemistry.

Dynamical system model of gentrification: Exploring a simple rent control strategy.

Motivated by the need to understand the factors driving gentrification, we introduce and analyze two simple dynamical systems that model the interplay between three potential drivers of the phenomenon. The constructed systems are based on the assumption that three canonical drivers exist: a subpopulation that increases the desirability of a neighborhood, the desirability of a neighborhood, and the average price of real estate in a neighborhood. The second model modifies the first and implements a simple rent control scheme. For both models, we investigate the linear stability of equilibria and numerically determine the characteristics of oscillatory solutions as a function of system parameters. Introducing a rent control scheme stabilizes the system, in the sense that the parameter regime under which solutions approach equilibrium is expanded. However, oscillatory time series generated by the rent control model are generally more disorganized than those generated by the nonrent control model; in fact, long-lived transient chaos was observed under certain conditions in the rent control case. Our results illustrate that even simple models of urban gentrification can lead to complex temporal behavior.

Efficient Multiplicative Calculus-Based Iterative Scheme for Nonlinear Engineering Applications

It is essential to solve nonlinear equations in engineering, where accuracy and precision are critical. In this paper, a novel family of iterative methods for finding the simple roots of nonlinear equations based on multiplicative calculus is introduced. Based on theoretical research, a novel family of simple root-finding schemes based on multiplicative calculus has been devised, with a convergence order of seven. The symmetry in the pie graph of the convergence–divergence areas demonstrates that the method is stable and consistent when dealing with nonlinear engineering problems. An extensive examination of the numerical results of the engineering applications is presented in order to assess the effectiveness, stability, and consistency of the recently established method in comparison to current methods. The analysis includes the total number of functions and derivative evaluations per iteration, elapsed time, residual errors, local computational order of convergence, and error graphs, which demonstrate our method’s better convergence behavior when compared to other approaches.