Terms Of Input Research Articles

Prepare Linear Distributions with Quantum Arithmetic Units

Quantum arithmetic logic units (QALUs) perform essential arithmetic operations within a quantum framework, serving as the building blocks for more complex computations and algorithms in quantum computing. In this paper, we present an approach to prepare linear probability distributions with quantum full adders. There are three main steps. Firstly, Hadamard gates are applied to the two input terms, preparing them at quantum states corresponding to uniform distribution. Next, the two input terms are summed up by applying quantum full adder, and the output sum is treated as a signed integer under two’s complement representation. By the end, additional phase −1 is introduced to the negative components. Additionally, we can discard either the positive or negative components with the assistance of the Repeat-Until-Success process. Our work demonstrates a viable approach to prepare linear probability distributions with quantum adders. The resulting state can serve as an intermediate step for subsequent quantum operations.

Non-optimality of intellectual capital inputs: a new avenue for research

PurposeThis paper aims to initiate new avenues of research by examining optimal intellectual capital (IC) inputs, introducing three theories into the discussion: diminishing returns to scale, transaction costs economics and efficiency wage theory. In the second part, it advocates for demonstrating the existence of such non-optimality through empirical tests.Design/methodology/approachThis paper is divided into two parts. The first part provides a theoretical justification for the necessity of observing nonlinear relationships between IC inputs and firm performance. In the empirical section, the research design follows a four-step process, each progressively building on insights gained from the preceding phase: (1) establishing a baseline linear regression model; (2) introducing the logarithm of the IC inputs; (3) incorporating the square terms of the IC inputs and (4) investigating the phenomena of over- and under-input in IC.FindingsThe background theories and the obtained results highlight the necessity for firms to adopt a strategic approach to IC, acknowledging the diverse effects of IC components on different outcomes. They emphasize the nonlinear nature of IC returns, underscoring the importance of investing up to an optimal level to maximize benefits.Practical implicationsThe study’s discovery of optimal levels for the components of IC highlights the importance for practitioners to identify and invest up to these optimal levels. This ensures that IC initiatives are strategically aligned to maximize their positive impact on firm performance.Originality/valueThe integration of theories such as diminishing returns to scale, transaction costs economics and efficiency wage theory, alongside traditional frameworks like the resource-based view, the theory of dynamic capabilities and the knowledge-based theory of the firm, opens up new avenues for research on IC. The proposed methodology and measures – from financial reports – provide opportunities for replicating this type of study.

BLNN-based adaptive control for a class of spacecraft proximity systems with output constraints and unmodeled dynamics

In this work, a novel broad learning neural network based adaptive control (BLNNAC) scheme is designed for a class of spacecraft proximity systems with external perturbations, unmodeled dynamics and symmetrical time-varying constraints. Firstly, for avoiding the unacceptable complexity caused by conventional Barrier Lyapunov function, the constrained output signals are transformed into unconstrained ones by several nonlinear functions. Secondly, by virtue of fast response and insensitivity to external disturbance, the integral sliding-mode control (ISMC) scheme is incorporated into the presented control scheme. Then, to suppress the adverse impact of the dynamic uncertainties, a novel broad learning neural network (BLNN) is established to ameliorate the approximation performance. Based on that BLNN scheme, a nonlinear disturbance observer (NDO) is designed to approach the time-varying disturbances. Furthermore, a fractional power function, as a particular term of control input, is designed to guarantee the fast convergence rate. It is shown that all the signals are bounded, and the transient-states of the output signals satisfy the constraint conditions constantly. Finally, simulation results illustrate the effectiveness and advantages of the presented scheme.

Wind stress curl as a driving force of annual waves in the upper ocean for interpreting energetics at all latitudes

The dynamics of waves and eddies in the upper ocean plays an important role in the climate variation of tropical and subtropical regions. Previous diagnoses for annual Rossby waves in oceanic model outputs manifested zonally alternating signals (ZASs) in the time-averaged distributions of wind input as well as pressure-flux divergence terms in the budget equation of wave energy. This is the case when the annual mean of the wind input is estimated as the inner product of simulated velocity vector and wind stress vector in previous studies. The present study proposes a new mathematical expression for estimating the wind input that is analogous to one derived from the quasi-geostrophic potential vorticity equation. Namely, the wind input is estimated as the negative of the product of pseudo-streamfunction and wind stress curl, the latter of which is associated with the horizontal divergence of Ekman velocity. This can be interpreted as replacement of kinetic energy input with gravitational potential energy input. Pseudo-streamfunction in the present study is inverted from Ertel’s potential vorticity anomaly and is seamlessly available at all latitudes. This contrasts with the quasi-geostrophic streamfunction which is singular at the equator. The new expression enables reducing ZASs in the horizontal distributions of both wind input and pressure-flux divergence terms, without harming the qualitative advantage of energy flux vectors to indicate the group velocity of waves at all latitudes.

Anticipating Job Market Demands—A Deep Learning Approach to Determining the Future Readiness of Professional Skills

Anticipating the demand for professional job market skills needs to consider trends such as automation, offshoring, and the emerging Gig economy, as they significantly impact the future readiness of skills. This article draws on the scientific literature, expert assessments, and deep learning to estimate two indicators of high relevance for a skill’s future readiness: its automatability and offshorability. Based on gold standard data, we evaluate the performance of Support Vector Machines (SVMs), Transformers, Large Language Models (LLMs), and a deep learning ensemble classifier for propagating expert and literature assessments on these indicators of yet unseen skills. The presented approach uses short bipartite skill labels that contain a skill topic (e.g., “Java”) and a corresponding verb (e.g., “programming”) to describe the skill. Classifiers thus need to base their judgments solely on these two input terms. Comprehensive experiments on skewed and balanced datasets show that, in this low-token setting, classifiers benefit from pre-training and fine-tuning and that increased classifier complexity does not yield further improvements.

Evaluation Study of West Muna Kumite Karate Athlete Performance Coaching Program

Coaching achievements in karate athletes provides guidance to athletes starting from an early age, finding and achieving athlete talent in all sports in order to have maximum performance. This study aims to evaluate the implementation of the West Muna Kumite Karate achievement coaching program using the CIPP (Contexs, Input, Process, Product) evaluation model. This research is an evaluation research. The sample of this study was administrators, coaches and athletes of the West Muna Kumite Karate Dojo with a total of 27 people. Data was collected through questionnaires, interviews, observations and documentation. The data analysis techniques used in this study used quantitative and qualitative descriptive analysis. The results showed that the overall evaluation results of the West Muna Kumite Karate coaching program were still not good. In the evaluation of the Kumite Karate sports performance coaching program in West Muna, in terms of context, it has been going well. In terms of input, it is still not good because the facilities and infrastructure of the West Muna Kumite Karate sport are still very lacking. Likewise with funding that has not been distributed as a whole. In terms of input, there are coaches and athletes as expected. In terms of process, it is still very lacking because the implementation of the coaching and monev (monitoring and evaluation) program has not been carried out in accordance with the provisions, even though the implementation of the training program has been running well. In terms of overall product, the achievements that have been achieved have been good, it can be seen from the results of documentation of achievements that have been achieved by West Muna Kumite Karate athletes.

Supramolecular Logic Gates Based on the Conjugates of Calixarenes and Carbohydrates.

In the era of application-oriented research, laboratory to real life translation is highly regarded and in great demand. This could mean that molecular science developed for sensing and detecting a variety of chemical species awaits conversion to devices. In that, the molecular logic gates are the most promising ones where the information storage and/or data processing can be easily carried out in terms of molecular inputs and electrical response outputs. This would facilitate the simultaneous execution of a diverse array of molecular sensing functions. The recent progress in molecular logic gates based on supramolecular optical receptors, in particular, fluorescent ones, such as calixarene derivatives and carbohydrate conjugates will have a transformative impact on molecular devices and will encourage this science to yield technology. Therefore, this review provides a critical evaluation of recent publications on molecular logic gates based on the derivatives of calixarenes and glyco-conjugates, including several from our own research group, with the view that the corresponding applications are a beneficiary in laboratory-to-device translation. In addition, this review is also expected to assist young researchers in planning their research focus in the broad area of supramolecular-based logic gates targeting some specific applications.

Thrown in at the deep end: a qualitative study with physicians on the purpose and challenges of discharge interviews

ABSTRACT Objectives Against the backdrop of poor discharge communication in hospitals, this study explores the purpose of discharge interviews from the physicians’ perspective and the challenges they are confronted with. Discharge interviews are legally required in Germany as part of the discharge management. Led by the ward physician, the discharge interview should summarize relevant information about the hospital stay, medication, lifestyle interventions and follow-up treatment. Methods Semi-structured interviews with n = 12 physicians were conducted at Heidelberg University Hospital between February and April 2020. Qualitative content analysis was carried out using MAXQDA. Results Physicians reported gaining information, providing information, and answering open-ended questions as the purpose of the discharge interview. Challenges in conducting discharge interviews were related to finding a common language, patient-related challenges, conditions of everyday ward life, and lack of training. Physicians reported receiving no explicit training on discharge interviews. While professional experience seems to mitigate the lack of training, some physicians expressed a prevailing sense of insecurity. Conclusion The lack of preparation for discharge interviews in medical school makes it particularly challenging for physicians to translate their theoretical knowledge into patient-centered discharge communication. Medical training on discharge interviews should be expanded in terms of theoretical input on the ideal content, its purpose and potential (e.g. in reducing readmissions), as well as practical exercises.

A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d'Urville, Adélie Land, East Antarctica

Abstract. Studying precipitation falling over Antarctica is crucial as snowfall represents the main water input term for the polar cap. However, precipitation observations still remain scarce – and, more particularly, in the atmospheric column – due to numerous experimental issues related to the white continent. This paper aims at helping to close this observation gap by presenting 7 years of Micro Rain Radar (Metek MRR-2) data at the Dumont d'Urville station in coastal Adélie Land, East Antarctica. Statistics are calculated on three radar variables (equivalent reflectivity, mean Doppler velocity and signal-to-noise ratio (SNR)) to outline the main characteristics of the radar dataset. Seasonal and interannual variabilities are also investigated, but no significant temporal trends are detected, except for the seasonal mean Doppler velocity, which is higher in summer and lower in winter. We then use the snowfall rate (S) data from a collocated snow gauge to estimate the MRR precipitation profile from the radar equivalent reflectivity (Ze) through a locally derived Ze–S relation. We find the relation Ze=43.3S0.88. The processing method used to obtain this relation, data quality and uncertainty considerations are discussed in the paper. In order to give an example of application of the dataset, a brief statistical comparison of the MRR precipitation rate along the vertical with model data from the ERA5 reanalysis and the LMDZ climate model is performed, which notably shows that models underestimate heavy precipitation events. All datasets are available on the PANGAEA database with the associated DOI: https://doi.org/10.1594/PANGAEA.962727 (Wiener et al., 2023).

Reinforcement learning‐based event‐triggered optimal control for unknown nonlinear systems with input delay

AbstractThe optimal control issue of discrete‐time nonlinear unknown systems with time‐delay control input is the focus of this work. In order to reduce communication costs, a reinforcement learning‐based event‐triggered controller is proposed. By applying the proposed control method, closed‐loop system's asymptotic stability is demonstrated, and a maximum upper bound for the infinite‐horizon performance index can be calculated beforehand. The event‐triggered condition requires the next time state information. In an effort to forecast the next state and achieve optimal control, three neural networks (NNs) are introduced and used to approximate system state, value function, and optimal control. Additionally, a M NN is utilized to cope with the time‐delay term of control input. Moreover, taking the estimation errors of NNs into account, the uniformly ultimately boundedness of state and NNs weight estimation errors can be guaranteed. Ultimately, the validity of proposed approach is illustrated by simulations.

Fault-Tolerant Fuzzy-Resilient Control for Fractional-Order Stochastic Underactuated System With Unmodeled Dynamics and Actuator Saturation.

This article is considered on underactuated fractional-order stochastic systems (FOSSs) with actuator saturation and incrementally conic nonlinear terms, whose fractional-order α ∈ (0,1) . First, to bring FO dynamic signals, solving the unmodeled dynamics, in the meantime, the saturated nonlinear term of the control input is taken into account. At the time, to cope with the stability issue of FOSS under such situation, the fault tolerant resilient controller based on underactuated condition is designed. Then, according to the method of the Lyapunov and It∧ o differential formulation to design proper multiple Lyapunov-Krasovskii (L-K) functions, such that, a novel sufficient condition of the robustly asymptotically stability of fuzzy FOSS under underactuated conditions is rigorously proved in terms of linear matrix inequality (LMI). Furthermore, in order to research the mean square stability of the above-mentioned system, so the solution of FOSS is obtained to achieve this purpose. By applying the above method, which is proposed in this work that the controlled system can be obtained with faster response and higher control accuracy. At last, to display the superiority of the above-mentioned scheme is effective, tethered satellite system and numerical results are presented.

Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance

Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions. Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability.This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r2 = 0.76, RMSE = 3.13 %, validation r2 = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters.This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r2 = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r2 = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r2 = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved significantly. Notably, using raw low-cost sensor data to calibrate AquaCrop led to poorer performances than using the literature. Hence using literature values could save sensor costs without compromising model performance if sensor calibration was not possible. The results suggest the essentiality of calibrating low-cost soil moisture sensors for crop modeling calibration to improve crop water productivity.

Buck Converter with Cubic Static Conversion Ratio.

The paper introduces a step-down converter that exhibits a static conversion ratio of cubic nature, providing an output voltage which is much closer to the input voltage, and at the same duty cycle, compared to a wide class of one-transistor buck-type topologies. Although the proposed topology contains many components, its control is still simple, as it employs only one transistor. A dc analysis is performed, the semiconductor stresses are derived in terms of input and output voltages and output power, revealing that the semiconductor voltage stresses remain acceptable and anyway lower than in other cubic buck topology. All detailed design equations are provided. The state-space approach is used to analyze the converter in the presence of conduction losses and a procedure for calculating the individual power dissipation is provided. The feasibility of the proposed cubic buck topology is first validated by computer simulation and finally confirmed by an experimental 12 V-10 W prototype.

Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data.

Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable. We herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL. An implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch. dengmh@pku.edu.cn. Supplementary data are available at Journal Name online.

Impact of green finance on industrial structure upgrading: implications for environmental sustainability in Chinese regions.

Marking green finance to revolutionize industrial structure and achieve environmental sustainability is increasingly critical. We explore the impact of green finance (GF) on industrial structure advancement and industrial structure rationalization employing the panel data of 30 Chinese provinces from 2011 to 2020 in terms of technology inputs, environmental regulation, and green technology innovation. Results indicate that GF significantly stimulates China's industrial structure in both advanced and rationalization. The driving effect of GF on the industrial structure is mainly reflected in the advanced dimension of industrial structure in the eastern and economically developed area but mainly in the rationalization dimension of industrial structure in the economically less developed area. GF's role in facilitating industrial structure upgrading largely depends on technology inputs, environmental regulation, and green technology innovation.

An improved agricultural household margin insurance scheme with insured–insurer risk protection: a time-varying copula approach

Agricultural household margin insurance (AHMI) is a crop insurance scheme that considers a farmer's trading capability. The scheme examines farmers' losses in terms of both agricultural input and output using the farmer exchange rate index. In this article, we improve the previous design of the AHMI [A. Ahdika, D. Rosadi, A.R. Effendie, and Gunardi, Household margin insurance of agricultural sector in Indonesia using a farmer exchange rate index, Agric. Finance Rev. 81 (2020), pp. 169–188.] proposed to be implemented in Indonesia – which only provided risk protection for companies – by broadening the formula of losses so that risk protection covers both farmers and companies. We also provide premium payment scenarios, both seasonally and non-seasonally. We employ a time-varying Student-t copula with the extended dynamic parameter to identify the dynamic dependency between the indexes involved in determining the loss variable. We also determine the premium rate for the insurance scheme with various payment scenarios and investigate the implications for farmer survival and company management. Examining the farmer exchange rate index data of Indonesia, the empirical results show that the improved AHMI using a time-varying copula approach produces more reasonable loss estimates and various premium payment options with low premium rates for farmers, compared to either the previous AHMI design or the current crop insurance program in Indonesia.

Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence

The most important feature of load forecasting is enabling the building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has increased during the COVID-19 period, as compared to normal times. Therefore, the performance of forecasting methods is impacted, and further tuning of parameters is required to cope with energy consumption changes due to COVID-19. This paper proposes a new adaptive neuro-fuzzy 2 inference system (ANFIS2) for energy usage forecasting in residential buildings for both normal and COVID-19 periods. The particle swarm optimization (PSO) method has been implemented for parameter optimization, and subtractive clustering is used for data training for the proposed ANFIS2 system. Two modifications in terms of input and parameters of ANFIS2 are made to cope with the change in the consumption pattern and reduce the prediction errors during the COVID-19 period. Simulation results obtained by MATLAB software validate the efficacy of the proposed ANFIS2 in residential load forecasting during both normal and COVID-19 periods. Moreover, the performance of the proposed method is better than that of the existing adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), and random forest (RF) approaches.

Midpoint-Based Balanced Truncation for Switched Linear Systems With Known Switching Signal

We propose a novel model reduction approach for switched linear systems with known switching signal. The class of considered systems encompasses switched systems with mode-dependent state-dimension as well as impulsive systems. Our method is based on a suitable definition of (time-varying) reachability and observability Gramians and we show that these Gramians satisfy precise interpretations in terms of input and output energy. Based on balancing the midpoint Gramians, we propose a piecewise-constant projection based model reduction resulting in a switched linear system of smaller size.

DETERMINATION OF THE DROP SIZE AND DISTRIBUTIONS IN SWIRL INJECTION IN CROSS FLOWS, IMPINGING, AND EFFERVESCENT INJECTORS

We have extended the primary atomization analysis to swirl injection in cross flows, impinging, and effervescent injectors. Using the integral form of the conservation equations, the drop size can be expressed in terms of injection and fluid parameters, the main variable being the liquid and gas velocities. Using the measured velocities as inputs to this <i>D</i><sub>32</sub>-equation, good agreements with experimental data are found for the drop size in the three spray geometries. Underlying physical mechanisms for the drop formation are also revealed from the analysis. The aerodynamic interaction between the swirl spray and cross flow results in reduction in momentum, with a corresponding decrease in kinetic energy that appears as surface tension of energy of many small droplets. Similarly, cancellation of the lateral momentum in impinging jets and internal deceleration in effervescent injectors are the key primary atomization routes. The use of the analytical drop size-velocity correlation has also been demonstrated for swirl sprays in cross flows. Therefore, this approach can be used to predict the drop size and distributions in different spray geometries, with appropriate changes in the velocity input terms and fluid properties.

Evaluation of Local Supplementary Feeding Program in Toddler Nutrition Recovery House (Pelita) in Kedung Banteng District, Tegal Regency

<div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p><em>One approach taken to reduce the prevalence of stunting is by providing additional recovery food to malnourished toddlers. The Rumah Pelita supplementary food program has been running in Kedung Banteng District since 2020. In the implementation of the Rumah Pelita Program, there are still existing cases of stunting and the nutritional status of targets and there has never been an evaluation of the Rumah Pelita program. The aim of the research is to determine the evaluation of the supplementary feeding program made from local food in Kedungbanteng District, Tegal Regency in terms of input, process and output aspects. This research method is qualitative research which is descriptive and evaluative in nature. The research period is January-March 2023. The research was conducted in 5 villages in the Kedungbanteng Community Health Center area. The main informants were 1 nutrition officer and 5 village midwives, and triangulation informants were 5 Rumah Pelita cadres and 5 mothers of toddlers. Data collection used in-depth interviews, while the source triangulation method was used to validate the data. Research data analysis is data reduction, data presentation and drawing conclusions. The results of the research show that the number of cadres is still insufficient, and there are still villages that have not budgeted for equipment rental and transportation for cadres, there are deficiencies in reporting and recording and there are still toddlers who experience stunting. There needs to be integration of central and regional government policies regarding handling stunting in toddlers, so that there is a division of the budget for handling stunting. and it is recommended that health workers provide health education about stunting prevention and routinely monitor the development of toddlers. </em></p><p> </p></td></tr></tbody></table></div><strong><em>Keyword: Stunting, Policy, Nutrition, Provision of supplementary food, Toddler</em></strong>